Remote Sensing for assessing vegetational dynamics and productivity of a peatland in southern Sweden

Transcription

1 Remote Sensing for assessing vegetational dynamics and productivity of a peatland in southern Sweden Preeti Rao March, 007

2 Course Title: Geo-Information Science and Earth Observation for Environmental Modelling and Management Level: Master of Science (Msc) Course Duration: September March 007 Consortium partners: University of Southampton (UK) Lund University (Sweden) University of Warsaw (Poland) International Institute for Geo-Information Science and Earth Observation (ITC) (The Netherlands) GEM thesis number:

3 Remote Sensing for assessing vegetational dynamics and productivity of a peatland in southern Sweden by Preeti Rao Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in Geo-information Science and Earth Observation, Specialisation: Environmental Modelling and Management. Thesis Assessment Board Professor Andrew Skidmore (Chair) Professor Peter Atkinson (External) Assistant Professor Iris van Duren/ Docent Andre Kooiman (Supervisor) Prof. Peter Pilesjo (Member ) International Institute for Geo-Information Science and Earth Observation, Enschede, The Netherlands

4 Disclaimer This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute.

5 To Aman, who probably might never want to read this report

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7 Abstract Carbon content of peatlands constitute nearly 33 percent of the global carbon sink. Remote sensing, which has been used extensively to study forests and other ecosystems, has not been used as often to assess peatlands. In this study, remote sensing methodologies have been applied to assess peatland vegetation dynamics and productivity of Fäjemyren peatland, an ombrotrophic bog which receives all it water and nutrients from the atmosphere. Vegetation spectra, collected in the growing season of 006 using field spectroradiometry, were analysed for spatial and temporal variations in the peatland vegetation and for characteristic features to differentiate between various peatland species. Results show that inter-species spectral differences vary from species to species and the differences range from small to insignificant. The field spectra were more effective in capturing variations in the vegetation than the apparent reflectance of SPOT-5 scene of the peatland. However, Hotelling s T tests on field spectra showed statistically insignificant differences between vascular and non-vascular peatland species. Comparison of two spectral indices, NDVI (Normalised Difference Vegetation Index) and PRI (Photochemical Reflectance Index), derived from field spectra showed that NDVI was more responsive to the temporal variations in peatland vegetation. Therefore, MODIS NDVI time series fitted with Savitsky Golay function was examined to understand peatland phenology from 000 to 006. The inter-annual variations in peatland phenology show that winter NDVI values, being difficult to estimate, can affect dependent variables such as length of growing season and its start and end dates. Hence, extracted seasonal parameters should be calibrated using ground phenological data and meteorological information. Gross Primary Productivity, GPP of the peatland and its controlling factors, air temperature and solar radiation intensity were assessed for the years 005 and 006. GPP, estimated from the net ecosystem exchange, was found to be correlated with the photosynthetic activity represented by satellite based NDVI, while light use efficiency was observed to follow the seasonal patterns of PAR, FAPAR and GPP. i

8 Acknowledgements I sincerely thank Lars Eklundh for being the perfect supervisor for his inspiration and guidance and for always being available for any discussion/doubts. Special thanks to Magnus Lund, Per Schubert, Jonas Akerman, Torbjorn Johansson and to people I met at the Abisko research centre for all their help during my field work. I enjoyed my time in the department of Physical Geography at Lund University my sincere thanks to everybody there. My other supervisors, Iris van Duren at ITC and Angela Harris at the University of Southampton, were enthusiastic about my work and extremely efficient in terms of feedback despite my late reports/drafts. I owe them for improving the quality of my thesis. And how can I ever forget Karin Larsson and Petter Pilesjo for their reassuring presence and support tack sa mycket. My thanks to everyone else who helped me in several different ways during my thesis; and also, my apologies for not being able to thank them individually. There are some people who deserve a special mention my parents who came all the way from India to look after my son Aman during my field work, for Manish s omnipresence and relentless support (through internet and his Christmas vacation in Lund) and definitely to Ritesh for helping me with Aman while I completed my report in Enschede. A very special thanks to International Pre-School Lund (Lisa, Melani and the whole IPSL community) for more than making up for my lack of attention to Aman I owe you my peace of mind during my thesis in Lund. Lastly, and most importantly, I would like to thank Aman for his resilience and support; and for making this thesis the most challenging piece of work in my life. ii

9 Table of contents. INTRODUCTION Background and rationale Thesis aims and objectives Research methodology Study area.... SPECTRAL CHARACTERISATION OF FÄJEMYREN PEATLAND USING FIELD SPECTRORADIOMETRY AND SPOT IMAGERY Specific objective Research questions Materials and methods Unsupervised classification of SPOT image Extraction of reflectance data from SPOT image Collection of field spectra Processing of field spectra Sampling and measurement errors of field spectra Statistical analyses of the spectral data Comparison of SPOT and field spectra Results Unsupervised classes of SPOT image Spatial variations in peatland Temporal variations in peatland SPOT and field spectra comparisons Discussion SPOT vegetation spectra Spectral features of vascular and non-vascular species SPECTRAL BANDS AND VEGETATION INDICES CHARACTERISING SPATIAL AND TEMPORAL VARIATIONS IN FÄJEMYREN PEATLAND VEGETATION Specific objective Research questions Materials and methods Statistical differentiation of species groups... 4 iii

10 3.3.. Spectral vegetation indices NDVI time series and TIMESAT Results Characteristic spectral bands Vegetation indices NDVI and interannual variation in peatland phenology Discussion Spectral bands characteristic of peatland species groups Temporal variations/phenology and vegetation indices POTENTIAL OF SPECTRAL INDICES FOR TRACKING GROSS PRIMARY PRODUCTIVITY OF FÄJEMYREN PEATLAND Specific objective Research questions Materials and methods Results Discussion NDVI and carbon uptake in peatland Annual growth cycle CONCLUSIONS AND FUTURE DIRECTIONS Spectral characterisation of Fäjemyren peatland using field spectroradiometry and SPOT imagery Spectral bands and vegetation indices characterising spatial and temporal variations in Fäjemyren peatland vegetation Potential of spectral indices for tracking gross primary productivity of Fäjemyren peatland iv

11 List of figures Figure : Fäjemyren peatland and study area... 3 Figure : Effect of diffuse light on field spectra measurement... 9 Figure 3: Unsupervised classification of SPOT scene using ISODATA... 6 Figure 4: Clustering of field spectra... 7 Figure 5: Spectra of common non-vascular species in September... 8 Figure 6: Spectra of common vascular species in September... 9 Figure 7: Common vascular species... 9 Figure 8: Ground vegetation under Pine/Birch trees Figure 9: Field spectra grouped according to the SPOT classes Figure 0: Difference between field spectra belonging to SPOT classes... 3 Figure : Entire peatland vegetation spectra over September-November.. 3 Figure : Sphagnum spp. groups: spectra for September November Figure 3: Spectra of common vascular spp. for September - November Figure 4: SPOT class-wise spectra for September November Figure 5: Variations in SPOT and field spectra for the same locations Figure 6: Band-wise corresponding variations in SPOT and field spectra 36 Figure 7: TIMESAT parameters for extracting NDVI time series data Figure 8: Sphagnum and Calluna species groups Figure 9: Common non-vascular and vascular species groups Figure 0: First derivative of reflectance of Sphagnum and Calluna Figure : Spectral range and medians of Sphagnum and Calluna spp Figure : Difference between Sphagnum and Vaccinium group spectra Figure 3: Vegetation indices PRI and NDVI Figure 4: Peatland NDVI time series raw and fitted curves... 5 Figure 5: Peatland seasonality during Figure 6: Peak values and seasonal amplitude of NDVI Figure 7: Length of growing season for the three land cover types Figure 8: NDVI time-series for peatland, forest and agriculture Figure 9: Annual cycle of primary productivity parameters Figure 30: NDVI and GPP plot for Figure 3: Scatterplot of GPP and NDVI v

12 List of tables Table : Species groups with spectral recordings... 0 Table : Peatland species and their relative abundance... Table 3: Month-wise spectra recorded for species groups... Table 4: Month-wise number of field spectra according to SPOT classes.. Table 5: SPOT classes identified from ISODATA unsupervised classification and field clusters generated using k-means clustering algorithm... 7 Table 6: Monthly averages of vegetation indices... 5 vi

13 . INTRODUCTION Global warming influences vegetation growth and phenology. Therefore, in order to accurately estimate future responses of vegetation to climatic variation, sound scientific understanding of vegetation dynamics is required. This research study on peatland remote sensing draws its inspiration from the fact that peatlands are a critical component of the global carbon cycle. Although, peatlands constitute only 3 percent of the total land surface area, their carbon content is approximately 33 percent of the global carbon sink. Understanding peatland processes are thus crucial to our understanding of carbon cycle and climate change. Since peatlands are small in size, have difficult terrain and conditions and are widely scattered in the northern latitudes; making ground assessments very difficult, developing appropriate remote sensing methods would contribute greatly towards evaluation of peatland productivity and its eventual role in carbon cycle... Background and rationale Definition and extent of peatlands Peatlands (the organic wetlands) are an unusual mix of terrestrial and aquatic ecosystems and form a unique habitat in the northern latitudes. Peatlands are highly productive ecosystems often characterised by their morphology, hydrology and plant communities. Most peatlands are currently carbon sinks, due to their ability to sequester carbon dioxide (a major greenhouse gas) from the atmosphere as peat. The carbon in peatlands is roughly estimated to be /3 of the global carbon sink (Gorham, 99). However, under changing climatic and hydrologic conditions, peatlands may be converted from carbon sinks to sources via the emission of large quantities of both carbon dioxide and methane. Methane is around times as radiatively effective as CO (Bubier et al., 993). Most peatlands are relatively small (around km ). The global aggregate of these environments is million square km, nearly 3% of total land surface (Charman, 00). Peatland-climate feedbacks Net ecosystem exchange is the net exchange of CO between terrestrial ecosystems and the atmosphere, that is, the balance between gross primary productivity and ecosystem respiration (autotrophic or plant respiration and heterotrophic or animal 7

14 plus microbial respiration). This balance in peatlands may be greatly affected by climatic warming (Post, 990 as quoted by Gorham, 99). This is especially so at high latitudes because of the largest prevalence of peatlands in boreal and subarctic regions. Alterations in water table depth, rise in temperature and increased length of the growing season, are all factors likely to affect the balance of carbon dioxide and methane in peatlands. It is also expected that climatic warming could renew peat accumulation in subarctic peatlands and even shift peat formation, like the tree line (Gorham, 99), to landscapes further north. Hence, a change in the peatland favourable conditions would result in a positive feedback to the global climate change by converting soil carbon to atmospheric CO. Remote sensing phenology Climatic conditions can hasten and/or delay phenological phases in vegetation, thus, influencing the annual carbon cycle. For instance, warmer temperatures have resulted in increased plant growth in the northern latitudes; indicated by longer growing seasons. The key annual phenological phases of vegetation dynamics are greenup, maturity, senescence and dormancy (Zhang et al., 003). Such phenological phases can be remotely observed as continuous changes in reflectance of vegetation, particularly through the calculation of spectral reflectance indices such as the normalised difference vegetation index (NDVI). A spectral reflectance index is, essentially, compressed spectral data; the most common being broad-band red/ nearinfrared indices. It is a measure of chlorophyll abundance and energy absorption (Myneni et al., 995). NDVI is a normalized ratio of the red and NIR bands, ρ NDVI = ρ NIR NIR ρ + ρ Re d Re d where, ρ = NIR reflectance, ρ Red = Red reflectance. NIR Inter-annual variations in NDVI, which is a measure of plant photosynthetic activity, can provide information such as changes in length of growing season and time of onset of photosynthesis. The Moderate Resolution Imaging Spectroradiometer (MODIS) onboard NASA s Terra spacecraft provides continuous data on NDVI, in addition to many other indices. MODIS-based NDVI has been used successfully to infer about the various phenological phases in vegetation. Myneni et al. (997) showed that remotely-sensed NDVI, along with carbon flux data, can indicate earlier and increased photosynthetic activity in the northern high latitudes due to higher temperatures. 8

15 Peatlands have phenological cycles which consist of annual cycles from both vascular and non-vascular plants. However, these cycles may differ significantly, hence it is important to evaluate the possibility of monitoring such temporal variations with satellite data by studying both the seasonal variations in the spectral signals and in the carbon fluxes from the peatland vegetation. GPP/ NPP from remote sensing and LUE Net primary production (NPP) is gross photosynthesis (also, Gross Primary Production, GPP) minus autotrophic respiration (R A ). NPP has been estimated for forests and other ecosystems using different methods such as from annual woody biomass increment (Gower et al., 00); on the basis of linear correlation of remotely sensed vegetation indices to the photosynthetically active radiation absorbed by plants, FAPAR (Goetz and Prince, 996; Goward & Huemmrich, 99; Seaquist et al., 003). Annual NPP of Scandinavian forests has been derived from temporal satellite-based Terra/MODIS vegetation indices and the model was found to have good accuracy when tested against field measurements (Olofsson & Eklundh, 005). However, the model has not been developed for peatlands. The most commonly used concept to estimate NPP/GPP is from the amount of absorbed photosynthetically active radiation (APAR) and a constant light use efficiency (LUE) (Monteith, 97) as given in the equation below. GPP = APAR LUE = ( FAPAR PAR) LUE FAPAR and LUE are functions of NDVI and PRI respectively, while PAR can be obtained from meteorological data. Initially, the value of LUE (ε) was considered as relatively constant but it has been found that factors such as ecosystem type, species age and composition, plant fertility and stress have a substantial effect on it. Therefore, it is crucial to estimate this parameter accurately (Goetz & Prince, 996; Gower et al., 00). The photochemical reflectance index (PRI) was developed as a remotely-sensed indicator of LUE (Gamon et al., 997). It has been demonstrated that PRI is a widely applicable index of photosynthetic function across multiple species, functional types and nutrient conditions (Gamon et al., 997; Penuelas et al., 998). In case of peatlands, PRI unlike other reflectance parameters has been found to be strongly influenced by the occurrence of moss with respect to the microtopography of the peatland (Lovelock & Robinson, 00). 9

16 There is a significant body of research on the productivity of peatlands (e.g. Bubier et al., 998; Moore et al., 00) but little research has been done on the use of remotely sensed data for determining their gross/net primary production (Connolly et al., 007). There are two challenges in doing this: () fraction of absorbed photosynthetically active radiation (FAPAR) is very difficult to measure for peat non-vascular vegetation. Moreover, the FAPAR-photosynthesis relationship for such vegetation is not the same as for vascular vegetation and () respiration is difficult, if not impossible, to estimate from remote sensing data. Therefore, it is important to know the difference in reflectance properties for vascular and non-vascular species; and then derive an appropriate vegetation index representative of non-vascular species. Such an index can be used to represent certain biophysical characteristics such as water content, chlorophyll and other pigments; based on which the aboveground net primary productivity of such ecosystems can be measured (Bubier et al., 997). Peatland vegetation and their productivity Peatlands have a relatively low capacity to fix CO in relation to forests and grasslands. This is in contrast to their high soil C accumulation and retention rates; which can be attributed to factors such as slow-decomposing Sphagnum, high water table and infrequent disturbances (Frolking, 998). Sphagnum mosses, which are non-vascular, dominate northern peatlands and hence influence peatland productivity. Sphagnum productivity is dependent on its photosynthesis and respiration, which in turn are affected by light intensity, nitrogen supply, temperature, atmospheric CO and waterlogged conditions. There is a need to remotely sense and interpret Sphagnum dynamics in relation to associated vascular species and other environmental controls (e.g. Gunnarsson et al., 00). This would provide vital information on peatland dynamics and productivity. Spectral characteristics of peatland vegetation While there is extensive literature on spectral properties of vascular plants (Gates, 970; Gausman et al., 978; Swain & Davis, 978 as quoted by Bubier et al., 997), less information is available on the spectral characteristics of non-vascular species (e.g. Vogelmann and Moss, 993; Bubier et al. 997; Bryant and Baird 003; Harris et al. 006). According to Bubier et al. (997), mosses and lichens in a peatland characterise different wetland and upland habitat conditions which makes it important to be able to remotely detect, discriminate and map the spatial patterns of these habitats. This would enable inferences regarding trace gas flux distribution, surface hydrology and carbon balance of northern ecosystems. 0

17 This research study could contribute to determining appropriate remote sensing methods for characterising peatland vegetation dynamics, interpreting peatland phenology and inferring their primary productivity... Thesis aims and objectives The primary aim of this research is to investigate the potential use of remote sensing for understanding peatland vegetation dynamics. It is expected that this would form a basis in determining peatland productivity from remotely sensed information. The specific objectives of the study are to: Characterise spectrally the spatial and temporal variations in peatland vegetation Identify phenological trends in vegetation over using remote sensing Investigate how spectral reflectance indices can be used to determine patterns of peatland gross primary production (the total amount of carbon fixed through plant photosynthesis)..3. Research methodology Details of the methods used in pursuing each of the research objectives are presented in chapters to 4. A brief overview of the general methodology used for this research study is outlined below. The research started with the initial stage of SPOT data processing and collection of field spectra. The next stage involved extraction of two spectral indices, NDVI and PRI from field spectra to compare their responsiveness to the temporal variations in peatland vegetation. Based on this, the phenology of the peatland was analysed for using MODIS NDVI time-series. The final stage assessed the correspondence between respiratory fluxes measured eddy covariance system and photosynthetic activity observed by satellite based spectral index (MODIS NDVI) for the annual cycles of 005 and 006. This thesis report is organised into 5 chapters. Chapter one introduces the research study and chapter 5 concludes it. Chapters -4 focus on the three specific objectives

18 of the research, corresponding research questions, materials and methods, results and discussions..4. Study area The Fäjemyren peatland, situated in Southern Sweden in the temperate climatic zone, was selected for the study. It is approximately 3km in length and around 0.5 km across. Fäjemyren is a Sphagnum-dominated, ombrotrophic bog which receives all its water and nutrients from the atmosphere and is therefore acidic and low in plant nutrients. It has a mean annual temperature of 6. C with the maximum reaching 5. C in July and minimum touching -.4 C in January. The average annual rainfall in Fäjemyren is 700mm. This is based on data from 96 onwards from the closest weather station of the Swedish Meteorological and Hydrological Institute. Vegetation at this site mainly consists of Sphagnum mosses, dwarf Ericaceous shrubs such as Calluna vulgaris (Heather) and Erica tetralix (Cross-leaved heath) and sedges such as Eriophorum vaginatum (Tussock cottongrass). Sedge with relatively lesser occurrence is Trichophorum caespitosum (Deer grass) that turns yellow in autumn. Sphagnum mosses occurring in the higher, drier parts (tussocks) are S. magellanicum (dominant moss), S. rubellum and S. fuscum; and those occurring in the lower, wetter parts (hollows) are S. balticum, S. tenellum and S. cuspidatum. These species occur throughout the peatland except for the drier parts and the edges, where shrubs and dwarf tree species dominate. These areas have more of pine, birch and spruce shrubs and dwarf trees, large proportions of Vaccinium vitis-idae (Lingonberry) and Vaccinium myrtillus (Bilberry), Empetrum nigrum (Crowberry) and mosses such as Polytrichium and Pleurozium (Feather moss). Shrubs and dwarf trees of pine and birch occur less frequently in small patches. The topography of the peatland is mostly comprised of hummocks, lawns and carpets, with a few hollows. The lawns and carpets are mainly dominated by dense growth of Calluna, Erica and Sphagnum interspersed with Eriophorum and Trichophorum sedges. The study area is the southern part of the Fäjemyren peatland. This area was chosen for collection of the vegetation spectra because of the presence of an eddy covariance mast thus enabling the comparison of spectral data with measurements of vegetation photosynthesis and net ecosystem exchange (NEE) of the same area. This southern part (the study area) is around.5km in length and almost km in breadth (Figure ).

19 Figure : Fäjemyren peatland and study area A SPOT false colour composite (R=3, G=, B=) of Fäjemyren peatland (A) and an orthophoto of this research study area (B) The location of eddy covariance mast and the sampling points for field spectra collection are marked on the orthophoto. The approximate centre coordinate for this southern part is 56 6 N 3 33 E. 3

20 4

21 . SPECTRAL CHARACTERISATION OF FÄJEMYREN PEATLAND USING FIELD SPECTRORADIOMETRY AND SPOT IMAGERY.. Specific objective The preliminary objective of this chapter is to characterise peatland vegetation in terms of spatial and temporal variations in its spectral properties... Research questions Is it possible to map peatland vegetation with medium resolution (0 30m) satellite data and how accurate is it? What are the characteristic spectral features of vascular and non-vascular peatland vegetation species?.3. Materials and methods SPOT scene of the study area was clustered using unsupervised classification algorithm. The spectral classes derived were used for stratified, random sampling of vegetation spectra. Collection of vegetation spectra at ground-level comprised the field-work component. The vegetation spectra were recorded with a handheld spectroradiometer during the months of September to November. These spectra were analysed for spatial and temporal variations in the peatland vegetation. Additionally, these field spectra were compared with the SPOT spectra for green, red and nearinfrared bands to observe the sensitivity of the SPOT sensor to variations in peatland vegetation..3.. Unsupervised classification of SPOT image A 0m resolution SPOT-5 scene, taken on June 9, 005 was used in this study. The acquired scene was already radiometrically corrected and geometrically orthorectified using a 50-m digital terrain model (DTM). It has 4 bands green ( µm), red ( µm), near infrared ( ) and mid infrared (.58.75µm). 5

22 Preliminary observations from the SPOT false colour composite of the Fäjemyren peatland, suggested that the peatland could be identified from the surrounding forested areas, but did not show spectral variations within the peatland. Consequently, an unsupervised classification, using Iterative Self-Organizing Data Analysis Technique (ISODATA), was performed (in the Erdas Imagine software). ISODATA clustering method uses spectral distance and assigns the pixels to the specified number of classes based on their distance from the class means (Erdas field guide). The number of classes for the unsupervised classification was decided upon as 5 after several trials with different number of classes from 4 to 0. Statistical distance measures such as transformed divergence did not show much difference between classes 5 and 6 (average and minimum values of 800, 40 and 790, 78 respectively). Practically also, 5 classes seemed to be more manageable in terms of selecting sampling locations. The five differentiable clusters (figure 3) obtained by unsupervised classification served as an initial approximate idea of the spectrally differentiable zones in the peatland and helped to plan for stratified random sampling of vegetation spectra (section.3.3)..3.. Extraction of reflectance data from SPOT image The DN values of the SPOT image were extracted for the 43 field sampling locations (section.3.3). These were converted into reflectance values using the following procedure (Tso and Mather, 00). Step : Apparent Radiance, L = A DN Bi app i + where, A i and B i are calibration gain and offset for band i Step : Apparent reflectance, where, ( d Lapp ) ρ = E (cosθs ) π d = ( cos( ( JD 4))) π θ s = cos(( ) 80 and JD = Julian Day, θ s = solar zenith angle, E = normal solar irradiance The final step of converting apparent reflectance to ground target reflectance requires the application of an atmospheric model such as LOWTRAN or 6S model to 6

23 account for atmospheric transmittance and absorption. This step has not been done for reasons of insufficient atmospheric data. The sensor calibration gain and offset and solar elevation angle were obtained from the SPOT header file and the normal solar irradiance from the technical specifications of SPOT data Collection of field spectra Clear days with direct sunlight, from September to November, were selected for spectral data collection. Transects were made from the central point of the southern bog, where the EC mast is located, to the bog edge in different directions, to collect vegetation spectra and record the vegetation type. In addition to random sampling points along the transects, specific locations were chosen for collecting spectral signatures over time. The transects and the sample points were decided on the basis of five classes obtained from the unsupervised classification of the SPOT scene for the study area (section.3.). The footprint of the EC mast was also considered when designing the sampling transects and specific locations for collection of vegetation spectra. Footprint refers to the sampling region in terms of the eddy covariance measurements. This footprint is dependent on wind direction, surface roughness and atmospheric stability; hence, the area and shape of the footprint keeps changing. Sampling of vegetation spectra was conducted in the area that contributes to more than 95% of the fluxes recorded by the EC system. The transects were chosen in a random manner however, their directions were decided in such a way that all five of SPOT classes could be covered. The transects generally started from the carbon flux tower and continued in a straight line till the edge of the peatland was reached. The footprint of the flux tower was also kept in mind while deciding upon the length of the transects. However, the transects could not proceed to their logical end each time either due to weather conditions or battery problems. It was attempted to maintain a constant distance between the samples and therefore the number of samples along a transect could vary with the length of the transect. But this requirement had to be balanced with the need to record the spectra of all species groups/associations, which did not always occur at constant distance. For recording temporal spectra, it was decided to take sample spectra from selected locations belonging to all the five SPOT classes. These locations were visited again in October and November for recording the spectra of the same species groups. 7

24 At each sampling location of vegetation spectra, the geographical coordinates were noted and photographs were taken of the different species types and associations. The description of the vegetation being recorded was noted down. The spectra were recorded using a Handheld Fieldspec spectroradiometer from Analytical Spectral Devices Inc. which can measure within the wavelength range of 79 to 087 nanometers (at a resolution of.58nm). The spectra were recorded from a height of around 70cm using the bare-head that has an FOV (field of view) of 5 degrees given a ground view of 3cm diameter. The spectroradiometer was configured to average 0 samples for each reading. A higher number of sampling average could not be taken because of frequent variations in light intensity. Over 700 spectra of the peatland vegetation were recorded over the months of September, October and November. samples were collected on species composition and their percentage cover. This was done using a m-by-m quadrat frame at random points along three initial transects, with the transects starting from the EC mast towards different directions to the peatland edge. This was done to ascertain the most commonly occurring species and the percent canopy cover that can be attributed to each of them. The percent canopy cover was averaged over the samples and has been presented in table. Ideally, these samples should have been taken according to the SPOT classes and averaged accordingly, so as to determine the composition of species in each class. It was difficult to take spectral samples and do these measurements simultaneously because the computer battery could work for only 4-5 hours when fully charged; which was why these canopy cover measurements were done in the initial three transects Processing of field spectra Spectra recorded during cloudy spells were weeded out because they produced inconsistent results. This inconsistency was evident from an experiment conducted to compare spectral recordings in direct and diffuse sunlight. The vegetation being measured was shaded by the researcher to get an effect of diffuse light conditions and a few measurements like these were taken in different spots within a time span of The weather conditions during August to mid-october were rather unpredictable with frequent rainy days and cloudy weather. However, maximum efforts were made to select clear sunny days for spectral data collection. 8

25 30 minutes. The results can be seen in figure. It maybe noted here that there is a large amount of noise in these spectra above 900nm, probably because of high atmospheric moisture content and low sun angle. Sphagnum spectra in direct and diffuse sunlight 0.5 Direct light Diffuse light Reflectance ofspectra Wavelength in nanometers Figure : Effect of diffuse light on field spectra measurement The sampling density of the spectra was high at some locations that is, -3 spectral readings were taken of the same vegetation type for a given spot. Generally, the first spectral reading was taken in case of similar readings. In case of doubt regarding the accuracy of the first spectrum compared to others of the same spot, the plots of these spectra were compared to select the most logically accurate one. In some cases, the two spectra were averaged or compared with a spectrum of similar vegetation type to select the one most similar to it. The field data, once analyzed for inconsistency, measurement errors, duplications and outliers, formed a final dataset of 544 observations with each observation measured on 380 wavelengths, from 400 to 000 nanometers. All vegetation spectra recorded were then classified into 8 groups plus a group called water, based on field notes about the association and structure of the plant species measured. For instance, a group was created for Sphagnum where it was measured when occurring alone. This species group of Sphagnum consists of all Sphagnum species except the wet 9

26 green Sphagnum. A class called Sphagnum+ was created where Sphagnum was the dominant species growing with other species occurring in significantly smaller proportions. Wet green Sphagnum (pool) is a species of Sphagnum which grows exclusively in wet pools and has therefore, been constituted as a group. The species groups defined are listed below in table. Spectral readings were also taken for Pine, Spruce and Birch shrubs about - m in height. Table : Species groups with spectral recordings Spp.id. Species group Spp.id. Species group Sphagnum spp. Empetrum & others Wet green Sphagnum (pool) Lichen (Cladina spp. & Cladonia spp.) 3 Sphagnum spp.& others 3 Lichen & others 4 Calluna 4 Vaccinium (Lingonberry, Blueberry) & others 5 Calluna & others 5 Pleurozium (Feathermoss)/ Polytrichium & others 6 Erica 6 Pinus spp. (Pine) 7 Erica & others 7 Picea spp. (Spruce) 8 Eriophorum, Trichophorum (Sedge) 8 Water 9 Sedge & others 9 Betula spp. (Birch) 0 Empetrum (Crowberry) The relative abundance of main species with respect to one another is given in table. The percent cover implies that if Sphagnum is growing under Erica and therefore occupying different strata in the same area, then the overlapping regions have been counted twice. However, since the idea is to see relative abundance of species the percent has been calculated over all the area these species occupy in different layers. Bare ground is hardly visible since there is a uniform layer of vegetation everywhere, except for bare patches of ground under the pine trees. 0

27 Table : Peatland species and their relative abundance Species group Percent canopy cover Sphagnum spp. 4 Calluna vulgaris 5 Eriophorum vaginatum 0 Erica tetralix 7 Empetrum nigrum 7 Wet green Sphagnum spp. 3 Water 4 Table 3 shows the number of spectra collected for each species group. Of the 8 species groups found in the peatland plus the water pools, only the following 9 most abundant species groups have been considered for comparison in temporal variations. For spatial patterns, only September spectra have been considered because the spectra have been collected within a time period of week and will not have temporal variations. For temporal patterns, spectra for each month were used to depict the group median. It may be noted that October and November spectra are not many in number, compared to September spectra. Table 3: Month-wise spectra recorded for species groups No. of spectra used for species group medians Species group September October November Sphagnum Sphagnum pool 9 Sphagnum Calluna Calluna Erica 8 5 Erica+ 5 Sedge Sedge

28 The field spectra, also grouped according to the SPOT classification outlined in section.3., can be seen in Table 4. The spectra collected during September have been compared with SPOT spectra for 43 specific geographic locations in section.3.7. Table 4: Month-wise number of field spectra according to SPOT classes SPOT-class No. of spectra used for group median spectra September October November Class 5 6 Class Class Class Class A metadata base was created for the field reflectance data containing information on the species group, date of collection, weather condition, GPS position and the unsupervised class (from the SPOT image) where the spectrum was measured (Appendix I). MATLAB, a high-performance technical computing language, was used for organizing the data and extracting required spectra and plotting them. It was also used for exploratory data analysis along-with the statistical package R. The field observations were grouped in different ways so as to examine spatial and temporal variations in peatland vegetation. Therefore, groups of spectra were extracted based on species type, SPOT class and date of collection (month). The median was taken to represent all the sample observations in a group so as to avoid the errors due to outliers and large standard deviations. Hence, only the medians of spectra groups have been used for visual analysis Sampling and measurement errors of field spectra Field spectrometry was used to understand the spectral properties of the pure and mixed occurrences of different peatland species groups and to see how these are detected using remote sensing (by the SPOT sensor). The sampling strategy of vegetation spectra was stratified random for spectra collected during September. Sample locations were randomly selected such that each spectral class of the SPOT scene was adequately sampled. The SPOT class-wise numbers of sample spectra are given in table 4. Of these, specific locations (SPOT

29 class-wise) were marked for temporal measurements (Appendix II). Spectra were recorded of the same species groups and their combinations at these select locations, in the months of October and November. These spectra, recorded for all the three months, were used to study the temporal variations in the vegetation. It would have been ideal to record temporal spectra by marking sampling points for each species group but for the time constraint. This way, some of the factors influencing spectral measurements such as microtopography, structure of vegetation, underlying vegetation and soil, surrounding objects would have been kept constant. Each vegetation spectrum was recorded in two stage process: first, calibrating measurement of white spectralon panel was taken followed by recording of the vegetation spectrum with the bare head of the handheld field spectroradiometer. The spectral sampling interval (interval between two wavelengths) is.58nm. The instrument was configured to average over 0 sample spectra before actually recording the spectrum. The calibration ensures that the spectrum is recorded in terms of reflectance values, and not the raw radiance values. However, variability in illumination characteristics between the time of measurement of the reference and target material can cause error in the spectra recorded. Target viewing and illumination geometry and atmospheric characteristics were kept in mind while recording the spectra. But frequent clouds and moisture in the atmosphere often hampered the measurements. Accuracy of and adequate number of samples were, therefore, affected by a wide range of factors such as water level, soil reflectance, time of day and year, cloud cover, humidity and temperature. Since the objective of the field spectrometry was also to look at the detectability of the peatland species by the satellite sensor, the measurements were taken so as to take into account all possible variations in terms of occurrence of the species Statistical analyses of the spectral data K-means/ISODATA clustering algorithm was used to cluster multivariate data (68 field spectral observations in this case) by partitioning the observations into k groups (5, in this case, to match the number of SPOT classes) so that the points have minimum squared distances from the assigned cluster centres. K-means cluster analysis was applied to the field spectra collected in September. The numbers of samples taken in November are inadequate because of rainy weather followed by an unexpected snowfall. 3

30 SPOT image of the study area was classified (unsupervised classification using the same algorithm) into 5 classes. The sampling locations for the field spectra were selected from each of the 5 SPOT classes. An attempt was then made to explore if there was a correspondence in the clusters discovered in the field data and in the SPOT image. A comparison matrix, table 5, was made to match the correspondence in the field data clusters and image data clusters. In order to reduce the dimensions (380 wavelength bands) of the field data, principal component analysis was done. Correlation matrix was preferred over covariance matrix for eigen analysis so that variables with high variance do not excessively dominate the transformation. First two principal components, which explained nearly 90% of the variance in the data, were plotted. Groups discovered through k-means clustering were used to label the data. This plot would help to confirm the separability/non-separability of the groups discovered. Separability of the data through this technique, plotting the first PCs, was examined for different values of k such as 3, 4, 6 and Comparison of SPOT and field spectra The vegetation spectra were compared with SPOT spectra to see their relationship for corresponding locations and to assess whether SPOT is able to differentiate between the peatland vegetation communities and with what accuracy. Only the spectra collected during September was considered here so as to ensure that comparison between SPOT and field spectra would deal only with spatial variations and not with temporality. It has been assumed here that since the peatland has not had much anthropogenic influences on it, the status of its vegetation might not have changed much from June of last year (SPOT image date is June 9, 005). The time of the SPOT image is 0:45 am while the field spectra have been recorded between 0 am and 3 pm (see discussion). There were a total of 68 spectra recorded in September at 43 different sampling locations. The spectra belonging to each of the sampling locations were averaged. The green, red and near infrared bands of the field spectra were averaged over the same wavelength range as that of the first three SPOT bands. SPOT pixels overlapping with the 43 sampling locations were extracted. The average field spectra at each of the sampling locations were compared with the apparent reflectance of corresponding SPOT pixels for these three bands. The fourth band of mid-infrared 4

31 has not been considered in the digital image analysis because it is out of the wavelength range of the field spectroradiometer..4. Results Unsupervised classes in the SPOT scene, which helped in strategising the sampling locations for collection of field spectra, are presented here. The spatial variations of the field spectra, in terms of different species and their associations and different regions in the peatland; and temporal variations examined at 3 different dates during the growing season are also shown. Finally, the correspondence in SPOT and field spectra are presented..4.. Unsupervised classes of SPOT image Figure 3 shows the five clusters derived from unsupervised classification of the SPOT image. These represent spectral classes. On ground, class corresponds to the transition zone between the peatland and the surrounding mixed coniferous forest and class belongs to the drier areas with shrubs and dwarf trees of Pine and Birch. The other 3 classes were not distinguishable on ground. 5

32 Figure 3: Unsupervised classification of SPOT scene using ISODATA The 86 spectra collected in the month of September were explored for clustering patterns using k-means/isodata. This was done to see if some well-defined clusters could be obtained in the field spectra like those identified in the SPOT imagery. The first two principal components of these wavelength bands were plotted against each other. The spectral clusters, other than the overlap in groups and, appear separable in figure 4. There is only one observation in cluster number 5 this single spectrum belongs to the species group of Sedge and is separate from other spectra of Sedge. Therefore, it can be considered an outlier and disregarded. 6

33 PC PC Figure 4: Clustering of field spectra Field spectral observations plotted, with the first two principal components of 380 wavelength bands on both axes; resulting in 5 clusters. Table 5 shows the 5 field clusters tabulated against the SPOT classes to see the correspondence between them. SPOT classes and are mostly in field cluster while SPOT classes 3, 4 and 5 are distributed in field clusters and. Field cluster 5 seems to have an outlier it could be a spectrum which went unnoticed when other outliers were weeded out. There does not seem to be any clear pattern between the SPOT and field clusters. Table 5: SPOT classes identified from ISODATA unsupervised classification and field clusters generated using k-means clustering algorithm Field cluster Field cluster Field cluster3 Field cluster4 Field cluster5 SPOT class SPOT class SPOT class SPOT class SPOT class

34 .4.. Spatial variations in peatland When the spectra of nonvascular species are compared (figure 5), Sphagnum and Sphagnum+ (Sphagnum with other species) exhibit a similar pattern while wet green Sphagnum (in pool) has a less steep red-edge and significantly lower reflectance in the near infra-red (NIR) region. Lichen+ (Lichens with other species) has an expected greater reflectance in the blue band and a rather flat pattern and therefore, much lower reflectance in the NIR region. All Sphagnum spectra seem to have a characteristic peak around 90nm Sphagnum Sphagnum pool Sphagnum+ Lichen+ Spectra of non-vascular species Reflectance of spectra X: Y: Wavelength in nanometers Figure 5: Spectra of common non-vascular species in September 8

35 Figure 6 shows the spectra of pure occurrence of Calluna, Erica and Sedge the most commonly occurring vascular species. There is no significant difference in their reflectance in the NIR region; however, they follow the same pattern in the visible bands but at different reflectance levels Calluna Erica Sedge Spectra of common vascular species Reflectance of spectra Wavelength in nanometers Figure 6: Spectra of common vascular species in September In figure 7, the spectra median of the pure occurrence of Calluna has been compared with that of Calluna s mixed occurrence (with Calluna as the dominating species). Similarly, the spectra of Erica and Sedge have been compared between pure and mixed occurrences Reflectance of spectra0.5 Spectra of common vascular species occurring alone and with other species Calluna Calluna Erica Erica Wavelength in nanometers Sedge Sedge Figure 7: Common vascular species Pure and mixed occurrences of a) Calluna, b) Erica and c) Sedge. Spectra for sedge and sedge+ are distinct but not distinct in case of Calluna and Erica. 9

36 Spectra of vegetation under Pine Lingon/Blueberry+ Feathermoss/Polytrichium+ Empetrum+ Reflectance of spectra Wavelength in nanometers Figure 8: Ground vegetation under Pine/Birch trees Vaccinium, Empetrum and mosses other than Sphagnum are found in the drier patches. The (non-vascular) mosses have a distinct spectrum from the vascular species - Vaccinium and Empetrum Reflectance of spectra Class Class Class 3 Class 4 Class 5 Field spectra corresponding to SPOT classes Wavelength in nanometers Figure 9: Field spectra grouped according to the SPOT classes Figure 8 depicts the spectra of vegetation found in the patches of Pine, Birch shrubs and dwarf trees. These species which grow close to the ground are Vaccinium vitis- 30

37 idae (Lingon), Vaccinium myrtillus (Blueberry), Empetrum nigrum, Polytrichium spp. and Pleurozium spp. Field spectra at all sampling locations were grouped according to the corresponding 5 unsupervised SPOT classes. Class is the transition zone between the peatland and the surrounding mixed coniferous forest and class belongs to the drier areas with shrubs and dwarf trees of Pine and Birch. Medians of these 5 groups of field spectra were plotted to see their correspondence with the SPOT classes. Figure 9 depicts these 5 spectra. Classes, 3 and 4 seem to be similar close to each other in reflectance values and almost similar patterns except that class is relatively flatter in NIR compared to classes 3 and 4. Spectra for classes and 5 seem to be separable in the visible region. Maximum separation between the 5 classes is at 550nm and 90nm. Figure 0 shows the differences in reflectance between class 4 spectrum and spectra of the remaining classes,, 3 and 5. Class 4 is the most commonly occurring spectral class and has therefore been taken as reference spectrum. Though the differences in reflectance between spectra of class 4 and other classes look discernible, the maximum difference is not large. The maximum difference is between spectra of classes and 4, which is A Hotelling s T test was applied to field spectra of SPOT classes and 4 to determine their statistical separability in feature space. The result showed that the statistical difference between these two classes is insignificant Temporal variations in peatland Spectra from all over the peatland were averaged for each month and have been plotted in figure. They show decreasing reflectance in visible and a corresponding increase in reflectance in the NIR region over the three months. Plots of spectra for the two Sphagnum classes and common vascular species (Calluna, Erica and Sedge) over the 3 months did not reveal a consistent pattern and this can be seen from figures and 3. These plots need to be interpreted keeping in mind the number of spectra used to calculate the median for each time period (September, October and November) given in table 3. Sphagnum and Sphagnum+ have conflicting temporal patterns as seen in figure. Sphagnum+ seems to follow the same trend as vascular species (figure 3). The species group of Sphagnum pool 3

38 did not have sufficient spectra for October and November and has therefore not been plotted. Reflectance of spectra Diff. wrt cl Diff. wrt cl Diff. wrt cl3 Diff. wrt cl5 Spectra difference for all classes wrt class 4 X: Y: Wavelength in nanometers Figure 0: Difference between field spectra belonging to SPOT classes Temporal spectra of the peatland 0.3 Reflectance of spectra Sep. spectra Oct. spectra Nov. spectra Wavelength in nanometers Figure : Entire peatland vegetation spectra over September-November 3

39 Reflectance of spectra Sphagnum Sep. Oct. Nov Sphagnum Wavelength in nanometers Figure : Sphagnum spp. groups: spectra for September November Calluna Calluna+ Reflectance of spectra Erica Sedge Erica Sedge Sep. Oct. Nov Wavelength in nanometers Figure 3: Spectra of common vascular spp. for September - November 33

40 In the case of vascular species, as in figure 3, except for Calluna+ and Sedge+ all other species groups show a relatively higher reflectance in NIR region for November than for September and October. Calluna+ and Sedge+ occurring with Sphagnum, could be causing such a different trend from vascular species. Figure 4 shows spectra according to the SPOT classes. All classes, except for class (transition zone), seem to be following a similar pattern of increasing reflection in NIR region over the three months. Class spectra have not been plotted because there are none for November SPOT and field spectra comparisons Figure 5 shows the SPOT spectra as compared with the field spectra for corresponding sampling locations. SPOT showed much lesser variation than field spectra in all the three bands. In the green band though, SPOT reflectance values are close to the median of field reflectance values. In red and NIR bands, SPOT reflectance values are closer to the minimum values of field reflectance values. Considering each band separately, the cluster plot in figure 6 shows that in the green band for a variation range of 0.07 in field spectra reflectance, there is a corresponding variation of 0.0 in the SPOT spectra reflectance. Similarly, in the red and NIR bands, field reflectance variations of 0.06 and 0.7 correspond to 0.0 and 0.05 variations respectively of SPOT reflectance. 34

41 Class 3 Class Sep Oct Nov Reflectance of spectra Class Wavelength in nanometers Class Figure 4: SPOT class-wise spectra for September November Corresponding field and SPOT spectra for GPS locations in G, R, NIR bands 0.35 Field spectra SPOT spectra 0.3 Reflectance of spectrum Green Red NIR Wavelength bands Figure 5: Variations in SPOT and field spectra for the same locations 35

42 SPOT spectra Field spectra Field vs. SPOT spectra for green, red and NIR bands Figure 6: Band-wise corresponding variations in SPOT and field spectra.5. Discussion The ability of SPOT sensor to distinguish between the peatland species groups is examined here. The spectral characteristics of peatland vegetation have been analysed for differences between vascular and non-vascular species..5.. SPOT vegetation spectra The SPOT sensor was chosen as an appropriate representative of medium resolution satellite sensor since it has a spatial resolution of 0m and four multispectral broad bands of green, red, near infrared and middle infrared. The SPOT scene of the peatland can be used to differentiate the peatland from its surrounding areas. The peatland has a uniform, low spectral signature and is a dull shade of grey in the false colour composite. This could be due to higher water content in the peatland and also because of a uniform layer of ground vegetation. Ideally it would have been better to have the SPOT scene for the same date as that of the field spectra. In this study, the date of the SPOT scene is June while that of the field spectra is September. But presumably, the reflectance variation in the SPOT spectra vis-à-vis the field spectra would not be affected much by the difference in their dates. When compared to field spectra of the same sampling location, SPOT has lower reflectance values and also has a lesser variation in reflectance values (figure 5). This lower reflectance can partly be explained by the fact that the DN values of the SPOT scene were converted to the apparent reflectance (reflectance at the top of the 36

43 atmosphere) and not ground target reflectance. The very low radiometric resolution of SPOT sensor could be due to atmospheric effects such as moisture. The field spectra are aggregated from different samples (sampling area of 3cm diameter) of a species group to derive the mean signal for that species group. The SPOT sensor generates a true mean over a sampling area of 0m by 0m. Therefore, each SPOT pixel represents an average of spectra belonging to more that one species groups found on the ground. It can be seen that averaging of spectra over more than one species groups leads to smaller spectral differences when compared to another averaged spectrum (figures 8, 9). Hence, the SPOT pixels show a rather uniform spectral signature throughout the peatland. Therefore, it can be concluded that a spatial resolution of 0m and spectral resolution of four broad bands of green, red, NIR and MIR are not sufficient to capture the variation in the peatland vegetation. SWIR bands would have been very beneficial because of the strong correlation between vegetation and moisture availability across a peatland. Of the five clusters derived for the peatland from unsupervised ISODATA classification (figure 3), the only distinguishable classes on ground are the classes and which represent the transition zone between the peatland and the surrounding mixed coniferous forest and the drier areas having shrubs and dwarf trees of Pine and Birch respectively. However, the other three classes do not seem to be as easily differentiated on ground. Perhaps, information on moisture status could have helped in determining the link between the spectral classes in SPOT and ground vegetation. The spectroradiometer used for measuring the peatland vegetation was capable of recording only upto.00µm which itself was a limitation. The spectral regions where Sphagnum can be differentiated from vascular plants occur at.5µm,.70µm and.0µm where it has relatively low reflectance and at.00µm and.0µm where its spectrum has water absorption features (Vogelmann and Moss, 993). Possibilities of using narrow hyperspectral bands of a finer spatial resolution in effective mapping of peatland vegetation and its variations through remote sensing, needs to be explored. Therefore, the unsupervised classification was only meant for investigating spectral clusters to be used for random stratified sampling of vegetation spectra. All the above-mentioned issues led to the conclusion that the SPOT scene cannot be used to 37

44 produce a vegetation map using supervised classification approach. More advanced classification techniques such as sub-pixel classification would need to be explored..5.. Spectral features of vascular and non-vascular species If all the 68 spectra collected in September are observed together, two common features that can be noticed in most of the spectra are () a peculiar dip at 760nm and () increased noise from 930nm. The first could be due to some error in the spectroradiometer. Perhaps, the spectroradiometer could have been configured to take a larger number of sample average 5 instead of 0 before finally recording a spectrum. But limited time due to unstable weather conditions and a faulty battery led to the decision of taking an average of 0 samples per reading. The noise after 930nm can perhaps be attributed to atmospheric effects including the high atmospheric moisture content because of frequent rains and cloudiness. Sphagnum has a characteristic reflectance peak around 90nm that is not there for any other species group even Sphagnum occurring with other species (Sphagnum+) has this characteristic. This reflectance peak is followed by a water absorption feature just before 000nm. These two features are in accordance with the laboratory-based spectral measurements of Sphagnum and other mosses by Bubier et al. (997) which conclude water absorption features at approximately and.µm resulting in three NIR peaks at around 0.85,.30 and.60µm. There is a slight shift in these two features when compared to Bubier s results that can be explained by the difference in methodologies of collecting spectral signatures; because collecting signatures in actual field conditions would have its inherent variances. A unique absorption feature at 850nm in the spectrum of Sphagnum has been reported by Vogelmann and Moss (993). This is not found in the spectra of other mosses and vascular plants. Similarly, in this study, a slight absorption feature has been found at 850nm in the spectra of Sphagnum and Sphagnum+. However, this absorption feature is not present in Sphagnum pool. Sphagnum pool has lower reflectance in NIR and visible regions than Sphagnum, which is similar to the results of Vogelmann et al. This is probably due to the presence of moisture which has the effect of reducing the overall reflectance spectrum. Bubier et al. (997) have also studied spectral signatures of other mosses such as feather mosses, lichens and different species within the genus of Sphagnum. The spectrum of feather moss in this study (figure 8) corresponds to their results, having 38

45 a broad, flat-topped green peak and slightly high reflectance in orange-red spectral region ( µm). Feather mosses are drier than Sphagnum and hence do not have similar water absorption features and instead, have a flat NIR plateau like vascular species. The lichens do not show very high reflectance in the visible region such as shown in their study. But then, there are not enough spectral signatures of lichens in this study to come to a reasonable conclusion. In this study, all the species of Sphagnum has been grouped together and therefore, the spectrum has the spectral properties of all Sphagnum species (except Sphagnum pool, which has been taken as a separate species group). 39

46 40

47 3. SPECTRAL BANDS AND VEGETATION INDICES CHARACTERISING SPATIAL AND TEMPORAL VARIATIONS IN FÄJEMYREN PEATLAND VEGETATION 3.. Specific objective Another objective of this thesis is to identify spectral bands and vegetation indices representative of spatial and temporal variations in peatland vegetation. 3.. Research questions Which spectral bands/ band combinations can be used to identify peatland species groups? Which spectral indices are representative of peatland vegetation temporal variations? Can they be used to infer peatland phenology? 3.3. Materials and methods MATLAB was used for visual analysis of the field spectra to identify spectral bands and plot vegetation indices. MATLAB and R were used for multivariate statistical tests to confirm whether the spectrum of one species group was significantly different from that of another species group. Time-series data of MODIS NDVI (product MOD3Q) for the years was used to study the phenological patterns of the peatland in conjunction with the phenology of a mixed coniferous forest and agricultural land Statistical differentiation of species groups Hotelling s T test is the multivariate equivalent of Student's-t test. It has been used here to determine if statistically significant difference existed between two groups of species. Hotelling s T tests for the equality of the means of the two groups under a pre-determined level of significance. By looking at the p value (given by the software package) we can also determine as to how close or far are we from the decided level of significance. The test requires normal distribution of the variables. The number of observations should also be at least two more than the number of 4

48 variables (wavelengths, in this case). This test has been applied to the following combinations of species groups. a) Sphagnum and Calluna. These species groups have been selected because they are the most commonly occurring non-vascular and vascular species in this peatland respectively. And, therefore, a significant result for these two groups could indicate that statistically significant difference exists between the spectra of vascular and nonvascular species. In order to perform the Hotelling s T test, principal component analysis was first done on each group consisting of all the 380 wavelengths. The first two principal components, which captured nearly 90 percent of the variance in each group, were then extracted and the final dataset was formed. The MANOVA test in MATLAB, which is a general version of Hotelling s T test, was applied to this extracted dataset. The null hypothesis for the test is H 0 : mean of species = mean of species Alternative hypothesis, Hα: mean of species mean of species. Reject H 0 if observed value of T > T at alpha,p,n+n- where alpha = chosen level of significance, p is the number of variables and n, n are sample sizes of species, species respectively. The test between Sphagnum and Calluna did not return statistically significant difference, at 0.0 level of significance, between the two species. This result of no significant difference at 0.0 level of significance, between the two groups of Sphagnum and Calluna, led to a further exploration of difference in a more restricted range of wavelength bands. It was hypothesized that perhaps the two groups are separable in a certain segments of their spectrum instead of all 380 wavelengths. The maximum difference in the reflectance of Sphagnum and Calluna was identified to be at the wavelengths 786.7nm and 930.5nm. So, only 4 wavelengths around 786nm and 930nm were selected to test for significance of difference between of the two species. As above, principal component analysis was done on the two groups. However, as the first principal component itself explained more than 90 percent variance in each group, only the first components were extracted from the each group and the final dataset was formed. The univariate t-test of MATLAB was then applied to this dataset. 4

49 b) Sphagnum and Vaccinium. Vaccinium s spectrum differs significantly from the spectra of Sphagnum. They also occur in mutually exclusive areas. Hotelling s test was applied to these two groups to check if their spectra were also statistically differentiable. c) Field spectra belonging to SPOT classes and 4. It was noticed that these two groups among the 5 had the maximum difference; therefore, it was assumed that a lack of statistical difference between the two would imply that none of the 5 classes are statistically distinguishable Spectral vegetation indices The spectral indices, Normalised Difference Vegetation Index (NDVI) and Photochemical Reflectance Index (PRI), were derived from field spectra to observe the sensitivity of these indices to the temporal changes in vegetation. These indices were extracted from field spectra collected in 8 days over 3 months (3 days each in September and October and days in November). The MODIS NDVI is also considered for these particular days so that the responses of satellite NDVI and ground NDVI can be tallied. NDVI and PRI were calculated from field spectra using the following equations: NDVI ( R ( R R R 645 ) ) = (USGS, 005) PRI ( R ( R 53 + R R 570 ) ) = (Gamon, 997) The bands chosen for NDVI were based on the MODIS bands used in their NDVI product MOD3Q. The reflectance values for these bands were obtained by averaging the values over 0nm (seven bands centred on the selected band). PRI values were scaled to obtain positive values by adding and dividing by (Rahman et al. quoted in Drolet et al., 005) NDVI time series and TIMESAT Of the two spectral indices extracted from field data, NDVI showed a greater sensitivity to temporal variations in the vegetation. Therefore, it was decided to use 43

50 MODIS NDVI to study the phenology of the peatland. Peatland phenology is compared with that of forest and agriculture so as to assess their differences in phenology and to also confirm whether the interpretations of peatland phenology are accurate. The NDVI values are composite values which imply that the maximum NDVI value over 6 days is taken as the value representing the 6 days. Hence, there are 3 values per year. By considering the maximum NDVI value over 6 days, under-estimation errors in NDVI values due to cloud cover and other atmospheric effects are taken into account. The MODLAND tile calculator was used to determine the MODIS pixels for particular geographic coordinates. Pixels were selected from the centre of each landcover type, taking care to keep a buffer of atleast - pixels on each side of the chosen pixel since the MODIS NDVI product MOD3Q has a spatial resolution of 50m. Seven years (000 to 006) of MODIS NDVI data was extracted for a single pixel of each of the three landcover types - peatland, forest and agriculture. The time-series were extracted in ASCII format using a program called curve4 (Eklundh, personal communication) and the NDVI converted back to the pixel DN value (range of 0-55). This DN value is further converted to real NDVI value, having a range of - to. This extracted data has 3 NDVI values per year for 9 years for each pixel. Dummy data for 999 and 007 was added to the extracted time-series by duplicating the adjacent year s data; so as to model better the limbs of the first and last years. This was input in the TIMESAT program (Eklundh and Jönsson, 004) to generate smooth model functions of the time series data and extract phenological parameters from it. Adaptive Savitsky Golay filter was chosen over Gaussian and double logistic functions, all of which are available in the program, because this function has a better fit over the upper envelope of the NDVI time series. The input parameters for the TIMESAT program were kept constant while fitting the Savitsky Golay function to each of the three time series and these are shown in figure 7. The fitted functions and phenological characteristics were thus obtained from the TIMESAT program for peatland, forest and agriculture. The beginning and start of the growing season can be set to a certain percentage of the seasonal amplitude in NDVI value and it is 0% by default. The default value has been considered for fitting the function to all the three time series. 44

51 Figure 7: TIMESAT parameters for extracting NDVI time series data 3.4. Results The spectral bands/ band combinations and indices characteristic of peatland vegetation have been presented here. These are only preliminary results and it is required to do a deeper analysis of the spectra to identify characteristic wavelength bands, band combinations and vegetation indices. MODIS NDVI seems to be representative of phenological cycles in peatland and other landuse categories Characteristic spectral bands Sphagnum and Calluna are the most commonly occurring species and have been compared with each other in figure 8. The spectra of Sphagnum and Calluna occurring alone are quite distinct from each other. Sphagnum has more pronounced peaks in all the three visible bands, a steeper slope in the NIR region and the characteristic peak (followed by a dip) at 90nm. However, Calluna, Sphagnum+ and Calluna+ have overlapping regions in their reflectance. 45

52 Sphagnum and Calluna species Reflectance of spectra Sphagnum Calluna Sphagnum+ Calluna Wavelength in nanometers Figure 8: Sphagnum and Calluna species groups Medians of the species groups indicate partial overlaps of mixed species spectra while pure species spectra are distinct. Though Sphagnum and Calluna look graphically distinguishable, Hotelling s T test did not find them statistically different. Initially, all the 380 wavelengths were included and the Hotelling s T test was done on the first two principal components for each species group. The observed T was considerably less than the T 0.0,,3 value at 0.0 level of significance. So, it was decided to test for separation between the two species groups by taking into account only the wavelength bands where they have maximum separation (4 bands around 786.7nm and 930.5nm). The first principal components extracted from this reduced dataset explained 98% and 99% of variance in species groups of Sphagnum and Calluna. So T-test was applied to these first PCs from each species group, but again the T-stat was insignificantly low. Hence, it can be concluded that the species groups of Sphagnum and Calluna are not distinguishable from each other. It can be seen in figure, that there is a large overlap in the spectra of Sphagnum and Calluna. It is probably because of this large overlapping zone that the statistical test of Hotelling s T is unable to differentiate between the two species. 46

53 Figure 9 compares the medians of one group of two non-vascular species (Sphagnum and Sphagnum pool) with that of a group of three vascular species (Calluna, Erica and Sedge) respectively. Compared to figure 8, there is a smaller difference in the reflectances (0.04 in 730nm 930nm) between the vascular and non-vascular species groups. This is in conformity with the result obtained between Sphagnum and Calluna that they are not statistically separable in feature space. Common non-vascular and vascular species groups 0.5 Sphagnum and Sphagnum pool Calluna, Erica and Sedge Reflectance of spectra Wavelength in nanometers Figure 9: Common non-vascular and vascular species groups Non-vascular species (Sphagnum and Sphagnum pool) and vascular species (Calluna, Erica and Sedge) 47

54 Figure 0 shows the wavelength bands where there is maximum difference in reflectance between Sphagnum and Calluna. The first derivative shows the change in reflectance for a corresponding change in wavelength and can identify zones of rapid and steep changes and can thus help locate zones of separability. The first derivatives of their spectra show differences in these spectra at nm, nm and nm First derivatives of Sphagnum and Calluna Sphagnum Calluna First derivative Wavelength in nanometers Figure 0: First derivative of reflectance of Sphagnum and Calluna Shows the wavelength bands having changes in reflectance in the spectra. Vaccinium, a low shrub under pine trees is quite distinct from Sphagnum. Their spectral plot (figure ) shows a maximum difference of 0.4 at 745nm between the medians of their group spectra and can perhaps be separated at this spectral band. A Hotelling s T test was applied to the species groups of Sphagnum and Vaccinium to test for separability. It was done taking into account the first three principal components of the 380 wavelengths which explained 99% and 98% of the total variance in Sphagnum and Vaccinium group spectra respectively. An insignificantly low observed T value of x 0-6 against the obtained T 0.0, 3, 4 value of implied that these two species groups cannot be statistically differentiated. 48

55 0.6 Spectra and medians of Sphagnum and Calluna species groups Reflectance of spectra Wavelength in nanometers Figure : Spectral range and medians of Sphagnum and Calluna spp. Sphagnum spectra in black and Calluna spectra in grey. The minimum and maximum spectra of each species group are shown, alongwith their medians (in dashed lines) Difference in spectra medians of Sphagnum and Vaccinium X: Y: X: Y: 0.75 Reflectance of spectra Wavelength in nanometers Figure : Difference between Sphagnum and Vaccinium group spectra 49

56 3.4.. Vegetation indices Vegetation indices, NDVI and PRI, calculated from field-data for the months of September to November were plotted together to identify interrelationships and temporal evolution. In figure 3, trends of field NDVI, MODIS NDVI and PRI (scaled values) have been plotted. Field PRI shows very little variation compared to field NDVI because of increasing NIR reflection of the field spectra. MODIS NDVI and field NDVI show different trends evident from the opposite slopes of their graphs Vegetation indices from field data and MODIS Sep -Sep 4-Sep 03-Oct 09-Oct -Oct 0-Nov 5-Nov Days of year 006 PRI NDVI MODIS NDVI Figure 3: Vegetation indices PRI and NDVI The Y-axis shows field estimates of PRI, NDVI and satellite NDVI (MODIS) for 3 days in September, 3 days in October and days in November. These days coincide with the days of field spectra collection. Field NDVI shows the greatest variation among the three indices. Looking at the monthly averages in table 6, field and satellite values of NDVI seem to match. Field PRI and NDVI show an increase while MODIS NDVI shows a decrease. 50

57 Table 6: Monthly averages of vegetation indices PRI NDVI MODIS NDVI September October November NDVI and interannual variation in peatland phenology The raw and the fitted curves of the peatland NDVI are shown in figure 4. The y- axis has scaled NDVI that has to be converted to an absolute NDVI value (between - and +). The x-axis has a time step of 6-days over , meaning unit equals 6 days. It should be noted here that one annual cycle has 3 values of NDVI with the highest value corresponding to the peak growing season. For instance, the growing season for 00 and 003 is around nineteen 6-day units, which is equal to about 0 months. The minimum NDVI (winter NDVI) values are highly varying unlike the peak NDVI values, therefore resulting in varying NDVI values for the beginnings and ends of the seasons. 50 Raw and TIMESAT-fitted curves for peatland 00 Scaled NDVI day of years Figure 4: Peatland NDVI time series raw and fitted curves. The grey curve represents the raw data and the black curve is the fitted function. The solid black circles denote the start and end of the growing season in each year. Each unit on the X-axis represents 6-day time step with the tic marks at, 36. representing peak growing season and those at 4, 48 coinciding with winter season. 5

58 Figure 5 depicts the time of occurrence of peak NDVI and the green-up and senescence rates of the peatland. The y-axis represents the change in scaled NDVI with time. The green-up rate shows a dip in the years 00 and 004 while the rate of senescence shows a consistent downward trend in the same period. However, both rise almost constantly from 004 onwards. The yearly peak time has been fluctuating between 9 and 56 days of the year. Peatland phenological parameters Change in NDVI with time Green-up rate Senescence Year rate yearly peak-time day of year Figure 5: Peatland seasonality during Trends in green-up rate and senescence rate are depicted along with the timing of peak NDVI values. 5

59 The NDVI time series has been studied for peatland in comparison with forest and agriculture so as to interpret its seasonality and interannual variations in relation to the other land cover types. Figure 6 shows the trend in peak values and seasonal amplitude of NDVI for peatland, forest and agriculture land cover types over The peatland had a mean peak NDVI value of 0.78 and its seasonal amplitude in NDVI was 0.50, starting from a mean base value of 0.9. In 003, the peak NDVI reached the highest value of 0.83 but has decreased to 0.73 in 006. However, its seasonal amplitude has been increasing since 004. In comparison to the peatland, the mixed coniferous forest and agricultural land had mean base to peak NDVI values of 0.35 to 0.87 and 0.3 to 0.8, with seasonal amplitude of 0.5 and 0.57 respectively. Peak NDVI values NDVI value Year Seasonal amplitude in NDVI NDVI value Year Peatland Forest Agriculture Figure 6: Peak values and seasonal amplitude of NDVI Peak NDVI values of peatland and agriculture show variations while forest peak NDVI remains constant and there is a cyclical trend in seasonal amplitude. 53

60 The variation in peak NDVI over these seven years has been more for peatland and agriculture (0. and 0.09 respectively) while that for forest has been In , the seasonal amplitude of peatland and forest has been increasing while that of agriculture has remained more or less constant. The same can be said for the length of their growing season, seen in figure 7. Also, in 004, peatland had the shortest amplitude in this time period but the longest growing season. 5 Growing season for peatland, forest & agriculture 0 6-day of year Peatland Forest Agriculture Year Figure 7: Length of growing season for the three land cover types Generally, the start and end dates for growing season for peatland, forest and agriculture seem to be quite close to each other. This is also evident from figure 8, which shows the NDVI times series for the three land cover types. On the X-axis, which shows days of the year in 6-day units, is equivalent to near mid-june and 4 is close to January. These coincide with the peak growing season and the dormant season respectively. The NDVI of the peatland is generally lower than that of the mixed coniferous forest and agriculture except for the year 003, when peak NDVI of the peatland was almost same as that of forest and greater than that of agriculture. The year

61 seems to be quite different from other years because the seasonal amplitude of peatland and forest is the smallest for this period of seven years. The off-season differences in NDVI values can be ignored since winter NDVIs are affected by clouds and low sun elevations. NDVI time-series for peatland and forest 0.8 NDVI value NDVI value Peatland P. season dates Forest F. season dates Diff. in NDVI NDVI time-series for peatland & agriculture day of years Peatland P. season dates Agril. A. season dates Diff. in NDVI Figure 8: NDVI time-series for peatland, forest and agriculture. X-axis represents a time step of 6 days. is equivalent to around mid-june (peak growing season) and 4 is almost January (winter/dormant season). Dashed line at bottom indicates the difference in NDVI values for peatland and forest and peatland and agriculture respectively Discussion Characteristic wavelength bands which characterise species groups such as Sphagnum, Calluna and Vaccinium have been discussed here. Vegetation indices, NDVI and PRI, calculated from the vegetation spectra have been observed for their correspondence with temporal variations in the peatland vegetation. MODIS NDVI has been applied to interpret peatland phenology. 55

62 3.5.. Spectral bands characteristic of peatland species groups Generally, when the pure species groups are compared to each other, the spectra are at different reflectance levels in the green, red and near infrared bands. For instance, the spectra of Sphagnum and Calluna appear different when compared together in figure 8. They show differences in reflectance of 0.09 in NIR and 0.05 in green and red bands. The derivative of vegetation reflectance shows greater amplitude than the primary spectrum for even a small change in reflectance. The first derivatives of the Sphagnum and Calluna spectra (figure 0) show differences in these spectra at nm, nm and nm, but the differences are relatively low. It should be noted that when the species groups of Sphagnum and Calluna are statistically tested for differences, the multivariate test of Hotelling s T results in an insignificant statistic, both when all the wavelengths were considered and when only the wavebands, where they show separability, were considered. This lack of statistical difference confirms what can be observed in figure which shows that the two spectra overlap each other and where they do not, the difference is not large. When spectra are averaged over two species groups (Sphagnum and Sphagnum+) and then compared with spectra averaged over three species groups (Calluna, Erica and Sedge), then there is no appreciable difference between these two larger groups of non-vascular and vascular species (figure 9). These groups show no difference in the visible region and a difference of only 0.04 in the NIR. This is due to the variance in spectra of each species group which increases when two species groups are combined. So there is a greater overlap in the spectra between the two larger groups being compared leading to insignificant statistical differences between the groups. It needs to be mentioned here that the vegetation in the peatland is so intermixed, that it is difficult to find a single species occurring alone. This implies that distinguishing spectrally between different species groups occurring together is quite difficult since the difference in reflectance decreases for averaged spectra. The most commonly occurring vascular species such as Calluna, Erica and Sedge have quite distinct spectra only in the visible region. However, the absolute difference in their reflectances in the green and red bands is less than In terms of structure and crown cover, they are quite different. Calluna has thick small needle-like leaves and a dense horizontal spread while Erica has a comparatively sparse distribution of branches and leaves however both grow to around the same height. Sedge - Eriophorum and Trichophorum - forms a hemisphere of long thin needle-like leaves radiating outwards (see photographs in appendix III). From figure 56

63 6, it can therefore be interpreted that there is not much difference between the common vascular species occurring alone and in combination with other species except for sedge (difference between sedge and sedge+ seems quite distinct). Calluna and Erica being quite dense in structure do not differentiate much between spectra of pure and mixed occurrence; whereas, sedge seems to have a large difference in the NIR region. This could be because sedge is quite thin and spreadout and any other vegetation occurring with it would be quite visible from above and therefore, have a greater influence on its spectrum. Therefore, it can be inferred from the spectra of these species groups that interspecies difference varies from species to species and the broad regions where the differences occur are the green, red and the NIR bands. Wavelength bands showing difference between two species can be determined for significant species. Isolating these wavelength bands or deriving a spectral index from these would therefore help in identifying different species groups. Specifically, for two species such as Sphagnum and Calluna, the exact wavelength bands would be as mentioned above. For instance, two visually distinct species groups are Sphagnum and Vaccinium (figure ). The low reflectance of Vaccinium (Lingon/Blueberry) can be seen in figure 8. This is because Vaccinium, Empetrum are small shrubs and the shadow underneath them might be causing the low reflectance in NIR as compared to Pleurozium/Polytrichium which grow much closer to the ground. And again, as in case of Sphagnum and Calluna, they are not statistically separated Temporal variations/phenology and vegetation indices Month-wise spectra of the peatland from September to November show a trend of gradual decrease in reflectance in the visible region accompanied with a corresponding increase in reflectance in the NIR region (figure ). This could be interpreted as the vegetation reaching its full canopy over the growing season between September and November (NASA, 985). However, considering that it is towards the end of the growing season, the vegetation should be showing evidence of senescence contrary to the increasing reflectance in NIR. The common vascular plants Calluna, Erica, Sedge are evergreen species and could be contributing to the increasing reflectance in NIR region even though the photosynthesis in them is decreasing. Hence, it can be concluded that the spectra during November is more due to other factors than due to senescence in the plants. 57

64 When the temporal changes in reflectance of different species groups are compared, a preliminary interpretation can be that different species are at varying stages of development and that some species are reaching senescence earlier than others. Looking at figures and 3, nothing definite can be said about the mixed species groups Sphagnum+, Calluna+, Erica+ and Sedge+. However, looking at the pure species groups, it can be seen in case of Sphagnum (figure ) that its canopy was developing in September-October but started senescing in November. In case of the vascular species (Calluna, Erica and Sedge in figure 3), September-October spectra show very little canopy development and surprisingly, the canopy increases only in November. Perhaps, this indicates a time lag between the senescence of Sphagnum and these three vascular species. This can be confirmed after observing the spectra of these species over one or more annual cycles of growth. It is also possible that this year, the growing season continued for a longer time because of the unusually wet and warm weather conditions. Therefore, the two major reasons affecting the temporal spectra of the vegetation are: (i) the increasing solar zenith angle over the months of September to November (ii) variations in shadow of the vegetation over the ground or upon underlying vegetation (such as Sphagnum) during the time of spectral measurement. The solar zenith angle is a major factor influencing the reflectance values of the vegetation because of variation in length of atmospheric path and the non- Lambertian properties of the vegetation and this is especially true for high latitude areas like the study area. There was a noticeable change in the altitude of the sun and its intensity while taking these measurements in November when compared to October. Though, all the spectral measurements were taken at nadir.5 to feet above the vegetation there can be varying effects on the reflectance due to shadow under vegetation and various factors such as microtopography, structure of the plant, time of measurement, underlying vegetation and soil. Since, the measurements have been taken in the natural environment, the causal factors attributing to the temporal spectra of the peatland vegetation could be one or more of these, in addition to the seasonal and daily change in solar zenith angle with its varying effects on reflectance of the vegetation. It is possible to identify vegetation indices which represent temporal variations in peatland vegetation. But this would require spectral information for the entire growing season and for more than one growing season to observe inter-annual 58

65 changes. Figure 3 shows the trend of the vegetation indices from field data NDVI and PRI and MODIS NDVI over September to November 006. It gives a good indication of how these indices represent the variation in the peatland vegetation over three months. PRI shows hardly any variation while NDVI shows a much larger variation in values. The peak in NDVI value around October 0, 006 seems to coincide with the peak value in PRI. The trend in MODIS NDVI does not seem to match that of the field NDVI while it is steadily decreasing, field PRI and NDVI seem to be increasing. A possible contributing factor is the vast difference in resolution of the remotely sensed information and also, the fact that MODIS data is a 6-day average. It should be noted here that field NDVI was calculated using the same wavelength bands as MODIS NDVI. Since NDVI seems to be a good representative of temporal variations in peatland vegetation, it has been used to study the phenology of peatland in terms of seasonality parameters such as length of growing season, the onset and rates of green-up and senescence; extracted from the NDVI time series data using TIMESAT software. MODIS NDVI 6-day composite values are the maximum NDVI values over a period of 6 days and therefore, aim to correct for errors and low values due to atmospheric effects, cloud cover and variations in illumination and viewing geometry. Zhang et al. (003) found this method, of fitting a logistic function to MODIS NDVI time-series data, to monitor vegetation phenology to be successful. Myneni et al. (997) found that photosynthetic activity of vegetation in the northern high latitudes in terms of increase in plant growth and growing season duration can be interpreted from NDVI time series. They also showed the association between surface air temperature and the amplitude and timing of the growing season, presumably caused by increase in length of growing period due to warmer temperatures. In this study, the interannual fluctuations observed in the length of the growing season for peatland and the other two landcover categories do not show a definite pattern of increasing duration of growing season. This could perhaps be attributed to fluctuations in the weather conditions over the years. It might also be possible to improve the accuracy of phenological parameters extracted from TIMESAT by inputting start-of-season data based on the historical, ground information on timings of the phenological phases of these landcover types. If we consider the definition of the start of the growing season as the air temperature being greater than 5 C for three days in a row and the reverse being true for the season-end, then it was April 6 to November 6, 006. But according to the fitted 59

66 function, it is April to mid-jan; thus implying an error in winter NDVI value and perhaps, the requirement for better adjustment of season beginning and end parameters (not the default 0% of seasonal amplitude). Since onset of growing season is calculated as a certain percentage of the seasonal amplitude which is dependent on winter NDVI values and winter NDVIs are difficult to estimate; therefore, the extracted phenological parameters should be interpreted alongwith ground information about phenology about a particular landcover in a given geographical area. There appears to be a cyclic trend of increase and decrease in the seasonal amplitude with the lowest value in 004 which coincides with the longest growing season in 004. An interesting feature is the very low rate of greenup and senescence in year 004 (figure 5). This can perhaps be explained by the meteorological conditions for this year in comparison to the other years. There is an inter-annual variation in the timing of maximum NDVI with the season in 000 having maximum NDVI - months earlier than the other years. Temperature and rainfall data for these years would have helped in inferring more from these phenological variables. Ideally, the GPP measured from an E.C. mast would have helped corroborate these observations on peatland phenology and to make coherent inferences. 60

67 4. POTENTIAL OF SPECTRAL INDICES FOR TRACKING GROSS PRIMARY PRODUCTIVITY OF FÄJEMYREN PEATLAND 4.. Specific objective The major objective of this thesis is to analyze if temporal variations in satellite observed vegetation indices can be used to track variations in gross primary production estimated from ground carbon flux data. 4.. Research questions Can NDVI be used to determine carbon uptake of peatland vegetation? What is the annual cycle of respiratory fluxes (observed with eddy covariance) as compared to the photosynthetic activity observed with spectral data? 4.3. Materials and methods The southern part of the Fäjemyren peatland has an eddy covariance (EC) system which has been taking carbon dioxide flux measurements since August 005. This 3.4m high mast measures among other variables, the Net Ecosystem Exchange (NEE), Photosynthetic Photon Flux Density (PPFD) and air temperature at 0Hz per second. NEE is the carbon dioxide flux (in mg CO m - s - ) and is measured using an infrared gas analyser and three-dimensional sonic anemometer. PPFD is the intensity of sunlight in the range of nm (in µmol m - s - ) and is measured with an upward facing sensor. These raw measurements are processed to half hourly data and further compiled to daily values. The data used for this study was compiled for daily values of NEE, PPFD and air temperature for (courtesy of Magnus Lund, Lund University). From satellite data, GPP can be estimated by a light-use efficiency model as specified by Monteith (97, 977). It can be represented by the following equation: GPP = APAR LUE = FAPAR PAR) LUE (.(i) GPP was calculated by deducting the night-time NEE (total respiration which includes autotrophic and heterotrophic respiration) from the day-time NEE 6

68 (photosynthetic uptake plus total respiration plus photorespiration). This may lead to an overestimation of GPP since the additional respiration component of photorespiration has not been corrected for (see discussion). Some large gaps in daily GPP values for the time period of were linearly interpolated. In equation (i), FAPAR is the fraction of APAR and it has been found to have a high linear correlation with MODIS NDVI by Myneni and Williams (994). NDVI was linearly scaled between its 5 th and 98 th percentile for corresponding FAPAR values of 0 and 0.95 respectively (Sellers et al., cited in Olofsson et al., 006). The linear regression equation derived from this (y =.578x 0.4) was applied to the NDVI values to compute FAPAR. Incident PAR was estimated from PPFD (in micromol m - s - ) measured by the EC system using the following equation by Oke (987): PAR hc ( PPFD NA) λ =... (ii) Here, N A is Avogadro s constant = 6.0 x 0 3 h, Planck s constant = 6.63 x 0-34 Js C, speed of light = 3 x 0 8 ms - λ is 0.55µm the median of µm, assuming that PAR is uniform in this wavelength range. LUE (a unitless constant) was modelled from equation (i) since all other parameters are known. All these parameters related to primary productivity of the peatland vegetation were evaluated. The GPP has also been observed in terms of daytime air temperature (in C) and approximate solar radiation intensity. The air temperature was measured by the EC system. Approximate solar radiation intensity cosine of solar zenith angle was calculated based on location on Earth (latitude longitude) and the Earth-Sun geometry and according to the Cosine law Results The gross primary productivity of the peatland was studied in relation to the other parameters in the Monteith equation and also other controlling factors such as the daytime air temperature and solar radiation intensity. Figure 9 shows the 6

69 relationship between the different variables for two annual cycles during GPP and PAR were estimated from NEE and PPFD measured by the EC system; FAPAR from MODIS NDVI and finally LUE from these three variables. LUE of the vegetation follows the seasonal patterns observed in PAR, FAPAR and GPP. LUE has positive non-zero values from April to November. PAR has a gradual increase and decrease in March and September respectively, whereas LUE has a very sharp increase and decrease based on a certain threshold level in PAR. LUE is also dependent on an optimum temperature of 0 0 C which occurs between April and November. deg C 0 0 a: Air temperature 0.5 b: Solar radiation intensity x 0-4 c: PAR d: FAPAR mg CO m- s- µmol m- s e: GPP Months of Figure 9: Annual cycle of primary productivity parameters f: LUE Months of GPP follows the annual cycles of solar radiation intensity and air temperature. GPP increases rapidly in April after the solar radiation intensity crosses a threshold of 0.6 and then reaches a peak in August-September after the solar radiation intensity reaches its peak in June. FAPAR is dependent on the air temperature and has peak values at the same time, in July-August while there is a time lag of one month between the peak in solar intensity and FAPAR; hence indicating the dependence of FAPAR on solar intensity and air temperature. 63

70 In figure 30, it can be observed that GPP and NDVI follow each other. They both increase at almost the same rate. While NDVI flattens out and remains constant for a longer time, GPP rises to its peak in mid-june and then starts to decrease. Hence, even though vegetation productivity decreases, the biomass continues to decrease with a time lag and at a much slower rate. NDVI and GPP NDVI GPP Day of year NDVI GPP Figure 30: NDVI and GPP plot for Scatterplot of 6-day GPP and NDVI values for NDVI GPP Figure 3: Scatterplot of GPP and NDVI There appears to be a significant correlation (although not linear) between the GPP measured by the E.C. mast and the satellite-based vegetation index, MODIS NDVI 64

71 (figure 3). There is a general increase in NDVI with increase in GPP. However, high values of NDVI also occur for low values of GPP in August to October, when the GPP decreases rapidly but the NDVI has a much slower rate of decrease Discussion NDVI has been found to be correlated to carbon uptake/photosynthetic activity in peatland vegetation and there is a considerable correspondence between respiratory fluxes measured by eddy covariance system and photosynthetic activity observed in MODIS NDVI NDVI and carbon uptake in peatland NDVI from the MODIS sensor shows fairly good correlation to the carbon uptake or the gross primary production (GPP) of the peatland vegetation estimated from the eddy covariance mast. The peak growing seasons coincide with high values of NDVI and the rate of increase in NDVI corresponds with the increase in the carbon uptake by the peatland vegetation in the months of April-May. GPP and NDVI maintain a plateau of high values for -3 months and 5-6 months respectively. But the GPP decreases quite rapidly from July-August onwards while the NDVI starts decreasing from October-November at a much slower rate. Hence, there is an expected lag of.5 months in their decrease from peak season to the end of the growing season implying that though carbon uptake by peatland vegetation decreases, the green biomass is still present for quite sometime (until winter). As observed by Connolly et al. (007), PAR decreases in wetter and cloudier conditions. The year 006 had unusual weather conditions there was a very dry summer followed by an equally intense rainy period in the months of August- September. This is evident from the lower PAR values during these particular months (figure 9c). Also, FAPAR and GPP have lower peak values in 006 because the peak growing season was drier than in 005. A study in the Arctic Tundra region by Hope et al. (995) found an almost linear relationship between field measurements of NDVI and photosynthetic fluxes (gross primary production, GPP normalised for variations in photosynthetically active radiation, PAR). This relationship was found to be stable over the growing season inspite of changes in solar elevation and vegetation conditions. 65

72 4.5.. Annual growth cycle Environmental factors such as temperature and solar irradiance affect photosynthesis. Photosynthetic activity increases with increase in temperature and then starts declining while the temperature is still high, with the decline in photosynthesis being faster than the decrease in temperature. The decline in photosynthesis is also due to higher respiration at warmer temperatures, which could be the reason for a faster decline in photosynthesis than its onset. Solar radiation intensity is another controlling factor in photosynthesis and therefore, there is a high correspondence in the annual cycles of GPP and solar intensity (see figure 9 b, e). Rough estimate of solar radiation intensity was determined using the cosine of solar zenith angle. As reported by Lund et al. (006), the average CO uptake in July 006 was significantly lower than in June. This must have been caused by a combination of warmer temperatures (increased respiration) and plant water stress induced by drier soils (decreased GPP). The two-year data on GPP and PAR from the EC system (figure 30) shows the annual cycles of respiration and photosynthesis for The active growing season of the peatland vegetation when there is high photosynthetic activity, starts around May-June and gets over by October-November. The period between November to May corresponds to the dormant season when plant respiration is dominant and there is little or no photosynthesis. NDVI from MODIS sensor shows a good correlation with the annual respiratory fluxes indicated by the EC-measured NEE. This can be observed from the correspondence of the time of maximum NDVI (peak photosynthetic activity) in mid-june, with that of peak carbon uptake shown by in-situ NEE measurements. Also, the timing of minimum NDVI is the same as that of minimum photosynthesis/ maximum respiration. The rates of increase and decrease of NDVI and photosynthetic activity are the same. In this study, LUE has been estimated as a residual of GPP and PAR derived from EC measurements and FAPAR from satellite-based NDVI; based on Monteith s equation (i) in section 4.3. Similar to the results of Connolly et al. (007), it had been found that LUE follows the seasonal variations of GPP, PAR and FAPAR. Therefore, LUE varies throughout the year according to the Earth-Sun geometry and has a predictable seasonal pattern. There is a need to determine a remotely sensed spectral index which is representative of the LUE variations for peatland vegetation. Since PRI has been found to be representative of LUE for boreal forests (Olofsson & 66

73 Eklundh, 005), it was originally planned to test this for the peatland; but could not be done because of lack of adequate data on PRI. GPP can be remotely sensed only when FAPAR, PAR and LUE can be interpreted using remotely sensed indices. It has been observed that NDVI has a substantial degree of correlation with FAPAR for peatland vegetation also; as has been concluded for forests and other ecosystems in other studies. Fuentes et al. (006) estimated LUE and FAPAR using AVIRIS-derived indices, PRI and NDVI respectively and obtained PAR from meteorological stations. They used these spectral indices with eddy covariance data, to map CO and water vapour fluxes in a chaparral ecosystem. Olofsson et al. (006) derived PAR from solar radiation data, FAPAR from MODIS NDVI and modelled LUE as a function of temperature and day of year (DOY) (Lagergren et al., 005) to calculate NPP of Scandinavian forests. Hence, it is possible to calculate peatland GPP from remotely-sensed information once appropriate spectral indices for estimating LUE, FAPAR and PAR have been identified. 67

74 68

75 5. CONCLUSIONS AND FUTURE DIRECTIONS 5.. Spectral characterisation of Fäjemyren peatland using field spectroradiometry and SPOT imagery The most common non-vascular species found at the Fäjemyren peatland was Sphagnum moss. Sphagnum was found to have characteristic spectral reflectance features which separate them from common vascular species Calluna, Erica, and the sedges, Eriophorum, Trichophorum. One major feature is that non-vascular species have a steeper slope in NIR region while vascular species have a flatter spectrum. Spectra of pure and mixed species of Sphagnum have a characteristic reflectance peak around 90nm followed by a water absorption feature just before 000nm. There is a large standard deviation in the spectra sampled for a single species group a major reason why it is statistically not possible to differentiate between different species groups. This large standard deviation is expected of spectra recorded in actual field conditions because of inherent variations such as the microtopography of the peatland, structure of the plant, underlying vegetation and soil, variable weather conditions (clouds and atmospheric moisture), and seasonal and daily change in solar zenith angle. A raw SPOT scene, with a spatial resolution of 0m and four multispectral broad bands of green, red, near infrared and middle infrared, can be used to differentiate the peatland from its surrounding areas because of its uniform, low spectral signature. For a particular location, SPOT exhibits only 4%, 33% and 9% of reflectance variation in green, red and near infrared wavelength bands respectively of spectra recorded by the handheld spectroradiometer. Therefore, it is not possible to use SPOT sensor to map the vegetation communities in the Fäjemyren peatland. It needs to be explored whether narrow hyperspectral bands, including the SWIR wavelengths, and of a finer spatial resolution might be effective in mapping peatland vegetation and its variations. 69

76 5.. Spectral bands and vegetation indices characterising spatial and temporal variations in Fäjemyren peatland vegetation Vaccinium is a low shrub which grows under pine trees. It is distinctly different from Sphagnum. It has a maximum difference of 0.4 at 745nm with the Sphagnum spectrum and can perhaps be separated at this spectral band. The two most commonly occurring species groups, Sphagnum and Calluna, are spectrally separable in visible and NIR regions. However, the two larger combined groups of two non-vascular species (Sphagnum and Sphagnum pool) and three vascular species (Calluna, Erica and Sedge) have maximum separability only in NIR region. Also, the difference in reflectance is smaller for vascular and non-vascular species groups than it is for Sphagnum and Calluna; implying that it is more difficult to separate out species groups which occur mixed/ together. It needs to be mentioned here that the vegetation in the peatland is so inter-mixed, that it is difficult to find a single species occurring alone. Hence, small/insignificant differences in reflectance between averaged spectra imply that spectrally distinguishing between different species groups occurring together is difficult. Spectral indices extracted from field data collected over the end of the growing season, indicated that NDVI was more sensitive to temporal variations in vegetation than PRI. The trend in MODIS NDVI (product MOD3Q) did not exactly match that of the field NDVI but the month-wise averages for field and satellite NDVI values were almost similar. Therefore, MODIS NDVI may be analysed to understand peatland phenology. The inter-annual variations in peatland phenology show that winter NDVI values, being difficult to estimate, can affect dependent variables such as length of growing season and its start and end dates. Hence, extracted seasonal parameters should be calibrated using ground phenological data of that particular landcover type. Weather information (annual and inter-annual changes) is also required to interpret the phenology Potential of spectral indices for tracking gross primary productivity of Fäjemyren peatland The annual variations in GPP and NDVI follow each other and there is a correlation between the two. Therefore, NDVI can be used to track changes in the carbon uptake (or the GPP) of peatland vegetation. GPP, PAR and FAPAR follow the annual cycles 70

77 of solar radiation intensity and air temperature in the years ; hence, environmental controlling factors have an important role to play. LUE of the vegetation follows the seasonal patterns observed in PAR, FAPAR and GPP. The carbon uptake observed from eddy covariance increases in April-May, maintains peak values for -3 months and then decreases rapidly from July-August. NDVI, indicating photosynthetic activity, increases at the same rate of carbon uptake; maintains a plateau of high values for 5-6 months and starts decreasing gradually over October-November. This expected lag of.5 months in their decrease from peak season to the end of the growing season implies that though carbon uptake by peatland vegetation decreases, the green biomass is still present for quite sometime (until winter). Peatlands are a unique ecosystem that plays an important role in the global carbon cycle. Given their crucial role in carbon cycle, one of the central scientific questions about peatlands is how they respond to climate change over different time scales. Scientific community s ability to accurately answer this question essentially rests on developing a sound understanding of peatland phenology and its responses in short and long term to environmental variables. Remote sensing has proved to be a valuable tool in studying ecosystem processes and phenology of forests and agriculture over different time and spatial scales. Similar progress has however not been made in the use of remote sensing to study peatlands and there is a need to attempt that. There are, however, challenges involved in developing remote sensing methods to study peatland processes. As this study demonstrated a medium resolution sensor, SPOT, could not be used for mapping the peatland vegetation because of its inability to differentiate between different vegetation types. Therefore, there is a need to explore the possibility of using hyperspectral sensor, especially with SWIR wavelength range, to distinguish between peatland vegetation species and map them effectively. Moreover, other factors need to be investigated such as trend of specific band combinations over time or a particular time of the year when there are maximal differences between vascular and non-vascular species. Further analysis is required for determination of spectral indices and wavelength bands characteristic of peatland vegetation variations. These can then be applied universally to other northern peatlands. In order to identify appropriate spectral indices, data throughout the growing and non-growing seasons needs to be collected and analysed. On the basis of long-term data, PRI should be evaluated to see if it can represent the LUE in peatland; indices such as Enhanced Vegetation Index (EVI) should be examined if 7

78 they can represent FAPAR in peatland better than NDVI and also, whether PAR can be represented by an appropriate spectral index. Such analyses would help in the remotely sensed estimation of peatland productivity. 7

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82 APPENDIX I: Sample of metadata index created in MATLAB s_no spp_id cloud day site_gps_id unsup_class orig_id A B D E F B D E F A

83 APPENDIX II: Sampling locations for temporal vegetation spectra recording Red and green stars indicate the status of sampling in the month of October. pt_id latitude longitude a b c d a b c d a b c d e f a b c d e f a b c d

84 APPENDIX III: photographs of common peatland species Sedge, Calluna and Erica Sphagnum 78

85 Empetrum (with very little Eriophorum) Wet green Sphagnum pool 79

86 Ground vegetation in Pine patches Lingon, blueberry (Vaccinium) 80