Jordan Land Cover Mapping Data Preparation Emanuela De Leo, John Latham, Lorenzo De Simone (DDNS) FOOD AND AGRICULTURE ORGANIZATION OF THE UNITED NATIONS Rome 2016
2 FAO/Jordan Land Cover and Land Cover change Analysis and Mapping
Table of Contents BACKGROUND... 4 METHODOLOGY AND STEPS TO PRODUCE THE LAND COVER DATABASE OF JORDAN... 4 Module 1 Sentinel 2 imagery selection, acquisition... 4 Module 2 Preliminary analysis of area of interest... 14 Module 3 Land cover database generation: Segmentation... 17 DATA SEGMENTATION PROCEDURE... 19 Definiens E-Cognition Software... 20 Chessboard Segmentation... 20 First segmentation cycle... 20 Classification... 21 Second segmentation cycle... 21 Spectral difference segmentation... 21 Export results... 22 PRO and CO of merging... 23 Vector and raster tiles... 24 Edge matching procedure... 24 SIMPLIFYING PROCEDURE... 25 CALCULATION OF NDVI AND RVI FROM MULTI TEMPORAL IMAGERY... 27 Calculating the Vegetation Indices... 27 RVI... 27 NDVI... 27 Zonal Statistic... 27 Consolidating results... 28 LAND COVER MAPPING/CHANGE MONITORING... 33 EcoNet approach... 33 BIOGRAPHY OF FAO GEOSPATIAL TEAM... 35 Annex I: simplifying tolerance... 37 Annex II: FAO s procedure for producing land cover and land cover change map... 38 3
BACKGROUND The project describes the vegetation status and its dynamics in Jordan, including crops and natural vegetation, using the new generation satellite imagery from Sentinel 2 (ESA) and applying innovative methodology that integrates segmentation and NDVI analysis. The work has been carried out through three main phases: 1. Sentinel 2 Image selection, acquisition and processing 2. Preliminary analysis of the area of interest, agro ecological and physiographic characterization 3. Analysis of NDVI series within segmentation Final deliverables of the work are a full report on data and methods, 8 Sentinel 2 mosaic covering Jordan territory, segmentation vector layer defining the boundaries of major land cover classes to be finalised with supervised classification and visual interpretation. In the context of the workshop in Amman, FAO is presenting a short version of the report and the results of the third phase of the work Analysis of NDVI series with segmentation and presents the Sentinel 2 mosaics with high quality printouts. METHODOLOGY AND STEPS TO PRODUCE THE LAND COVER DATABASE OF JORDAN The project is presented in three main modules as follows. MODULE 1 Sentinel 2 Image selection, acquisition and processing The Area of Interest (AOI) was set to the national borders of Jordan. Sentinel-2 imagery covering the AOI were downloaded from the Sentinel 2 Hub, and mosaicked in false colour composite 843 (NIR-Red -Green) from April 2016 to November 2016. The Sentinel-2 Mission is a European earth polar-orbiting satellite constellation (Sentinel-2A and 2B) designed to feed the GMES system with continuous and operational high-resolution imagery for the global and sustained monitoring of Earth land and coastal areas. The Sentinel-2 system is based on the concurrent operations of two identical satellites flying on a single orbit plane but phased at 180º, each hosting a Multi-Spectral Instrument (MSI) covering from the visible to the shortwave infrared spectral range and delivering high spatial resolution imagery at global scale and with a high revisit frequency. The MSI aims at measuring the earth reflected radiance through the atmosphere in 13 spectral bands spanning from the Visible and Near Infra-Red (VNIR) to the Short Wave Infra-Red (SWIR): 4 bands at 10m: blue (490nm), green (560nm), and red (665nm) and near infrared (842nm). 6 bands at 20m: 4 narrow bands for vegetation characterisation (705nm, 740nm, 783nm and 865nm) and 2 larger SWIR bands (1610nm and 2190nm) for applications such as snow/ice/cloud detection or vegetation moisture stress assessment. 3 bands at 60m mainly for cloud screening and atmospheric corrections (443nm for aerosols, 945 for water vapour and 1375nm for cirrus detection). 4
The Sentinel-2 mission objectives include the operational supply of optical data, with high revisit frequency, coverage, timeliness and reliability, for services such as: Risk Management (floods and forest fires, subsidence and landslides) European Land Use/Land Cover State and Changes Forest Monitoring Food Security/Early Warning Systems Water Management and Soil Protection Urban Mapping Natural Hazards Terrestrial Mapping for Humanitarian Aid and Development Sentinel-2 mission presents a new challenge requiring large storage space and ground segment resources in terms of: Temporal coverage, which translated into the need for a short orbit repeat cycle (10-days) and for a dual spacecraft operations in twin configuration providing a 5-days revisit frequency (currently only one satellite is operational); Large spatial coverage and high coverage frequency, which translated into the need for a wide swath coverage (290 km) with capabilities of global land masses acquisitions; High operation time during the daylight portion of the orbit; Wide spectrum optical range (visible to short-wave infrared) including Figure 1- Number of Sentinel-2 scenes intersecting Jordan boundary The following figures show the Sentinel-2 mosaics covering Jordan from April 2016 to November 2016. 5
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MODULE 2 Preliminary analysis of area of interest The AOI has been characterized in terms of agro ecological zones and physiographic regions analysing ancillary datasets (existing maps, databases, climate, crop calendars, and publications) and processing recent datasets (imagery, DEM). Western Jordan has essentially a Mediterranean climate with: a hot dry summer; a cool, wet winter; two short transitional seasons. However, about 75% of the country is covered by bare area 1 having a desert climate. More than 90 percent of the country receives less than 200 mm annual precipitation. Figure 2- Land Cover of Jordan 2006 from The Royal Jordanian Geographic Centre (RJGC) 1 The Royal Jordanian Geographic Centre (RJGC) has differentiated and classified six types of bare classes (total coverage 75%) in the latest released land cover maps of Jordan: bare rocks, chert plans, basaltic rocks, granite rocks, bare soil and sand. 14
There are four main physiographic regions in Jordan (Jordan Country Study On Biological Diversity, 1998): Jordan Rift Valley: it extends from Lake Tiberius in the north to the Red Sea. This zone is divided into three areas: 1) The Jordan Valley (the northern Ghor): it lies between 200-400 m below sea level, extending from Lake Tiberius in the North to the Dead Sea, with a length of 104 km and a width of between 4-16 km; it is surrounded in the east and west by high mountains. I 2) The southern Ghor: lies below sea level to the lower end of the Dead Sea 3) The Wadi araba: this area extends between the Southern Ghor and Aqaba on the Red Sea. It is extremely dry, with limited cultivated areas using underground water. The highlands: These extend from the Yarmouk River in the north passing through the Ajloun Mountains, the hills of Ammon and Moab, and the Edom mountains. Many creeks and wadi drain from the east to the Jordan River, Dead Sea, and Wadi Araba. The rainfall exceeds 300 mm. The average altitude ranges from 600 m to 1600 m above sea level. The Arid Zone (plains): also called steppe region, it comprises the plains between the Badia (semi desert) and the Highlands. The rainfall rate is between 300 mm and 500 mm. More than 50 percent of the arable land is in this zone. The rainfed crops are mainly barley, (areas of 200-300 mm of rainfall) wheat and fruit trees (where rainfall ranges between 300 Figure 3 - Physiographic regions (Data Source: GCEP, Jordan Country Study on Biological Diversity, 1998) 15
Figure 4- Agro Ecological Zones of Jordan Al-Jaloudy in 2001 classified Jordan into five agro ecological zones, which are: Badia (desert and steppe): used for pastoralism and agriculture with underground water. Marginal Zone: used for barley growing and grazing around cultivation. Semi- arid zone: used for cultivation divided according to the slope (see Arid zone described above) Semi-humid zone: used for wheat cultivation on flat areas; olives and fruit trees on land with slope ranges between 9-25 percent, forest trees in areas with slopes over 25 percent. ZONE AREA (million ha) PERCENT OF TOTAL AREA Average rainfall (mm) BADIA DESERT GRAZING 7.12 79.9 % < 100 BADIA STEPPE GRAZING 1 11.2 % 100-200 MARGINAL ZONE 0.53 5.9 % 200-300 SEMI-ARID ZONE 0.17 1.9 % 300-500 SEMI-HUMID ZONE 0.1 1.1 % > 500 8. 92 100 % Tab. 1- Agro Ecological Zones of Jordan SOURCE DATA: http://www.fao.org/ag/agp/agpc/doc/counprof/jordan.htm#3 16
MODULE 3 Land cover data base generation: SEGMENTATION The segmentation procedure generates automatically vector layers whose polygon s number is reduced by the interpretation exercise according to the specific land cover characteristics. Automatic classification by pixel has been considered the best method to assure a high level of thematic detail. The definition of the specific parameters and the best acquisition date to be used in the segmentation procedure is a critical step that must be evaluated carefully in preliminary tests to set the more appropriate. The first step is the identification of the best acquisition date of images considering the cloud cover and crop calendar. For the AOI of Jordan Sentinel-2 were downloaded and mosaicked from April 2016 to November 2016. Jordanian agriculture follows three growing seasons: one over the summer, one over the autumn and one over winter. The first cycle starts at half February when potatoes and wheat are sown. The harvesting of these crops starts then at the end of May. The sowing of barley starts in May. This crop is then harvested in October/November. The sowing of Sorghum takes place at the end of September and harvest starts in June. Considering the crop calendar the best month of acquisition for generating the segmentation layer is April. The reason is clear in the following figures that analyse the imagery from April to August. According to the variation of NDVI, April is the month in which most of the crops are in their peak of growing. It means to segment polygons with high values of NDVI. These polygons can be easily differentiated during the interpretation exercise from the brown/green floor colour composed of harvested crop or not yet sowed crops. April 2016 May 2016 June 2016 July 2016 August 2016 Sept. 2016 Octob. 2016 Nov. 2016 Figure 5 - Crops from April to August (from growing to harvesting) 17
Therefore, running the segmentation on the April dataset provides the best date to distinguish more types of crops. April, unlike the month August, which is the peak of dry season, facilitates the process of discrimination of features such as: Seasonal lake April 2016 August 2016 Seasonal vegetation and/or wetland along Wadi April 2016 August 2016 Active Crops in the Badia Region April 2016 August 2016 18
DATA SEGMENTATION PROCEDURE Imagery segmentation has been performed with Definies-eCognition software, the objects oriented software utilized to generate the polygon layer. The calendar analysis of major crops and imagery of Sentinel-2 from April 2016 to November 2016 has led to the choice of April as the best acquisition imagery date to be used for this procedure. The second important step has been the assessment of the size and the shape of polygons that must represent the features present in the raster, our source of data. The final segmented layer should have the correct balance between number of polygons and the level of detail represented. Segmentation is carried out following three fundamental principles: 1. Experimentation and qualitative assessment are extremely important strategies 2. Always have Object(s) Of Interest in mind (OOI s) 3. As large as possible as small as necessary. Several tests has been performed in order to: Evaluate, the required level of detail for major land cover classes Define an adequate scale factor to be used per major land cover classes. Badia Region or Jordanian steppe (semi desert zone) covers more than 80 percent of Jordan. A very sparse vegetation cover characterizes this area, providing pasture for livestock. It means that a lower level of detail can be applied in more than 80 percent of Jordan, enlarging the segmentation tangle. Figure 6 - Segmentation at scale 200 in agricultural area Figure 7 - Segmentation at scale 200 in bare area Following the third principle of segmentation as large as possible as small as necessary a more structured segmentation procedure has been implemented to decrease the number of polygons/detail in bare areas but maintaining high level of detail in both agricultural area and sparse vegetated areas. The advantage of this practice is the selective reduction of non-relevant and redundant information, facilitating the subsequent analytical steps. 19
Definiens E-Cognition Software FAO/Jordan Land Cover and Land Cover change Analysis and Mapping Definies-eCognition software has been used to generate the polygon layer. The work has been carried out following a structured procedure according to the following steps: Chessboard segmentation First segmentation cycle Classification Second segmentation cycle Spectral difference segmentation Export Chessboard segmentation Chessboard segmentation has been utilised to divide the scene into equal square objects with a predefined size (details in the full report to be released). Such segmentation has been carried out within the border of Jordan, with a buffer of 5 km to resolve potential issues with pixels located along the border line of Jordan. First segmentation cycle The first cycle of segmentation has been run with the following parameters: Segmentation level 200 (shape = 0.1 Compactness = 0.5) on Sentinel 2 mosaicked image (April) In this first cycle, the system indifferently applies the same segmentation value to all objects. The result is a very detailed polygon layer even where the spectral difference between adjacent polygons is very slight. The cycle generates the polygon layer one. Figure 8- Segmentation at scale 200 20
Classification This rule uses a simple classification algorithm, which allows the assignment of a class to the image object with certain features based on a threshold condition. Many parameters have been tested as threshold condition but only the ratio vegetation index or Simple Ratio (RVI or SR) has given good results in discriminating, quickly, macro classes. The ratio vegetation index (RVI) or Simple Ratio (SR) is the simplest vegetation index: RVI or SR = NIR RED Since vegetation has a high NIR reflectance but low red reflectance, vegetated areas have higher RVI values compared to non-vegetated areas. The three macro classes identified with RVI are: WATER = RVI 0.2 and RVI 0.95 It contains waterbodies without any distinction between natural waterbodies (small or large), artificial waterbodies (small and large), seasonal waterbodies (small and large), seasonal flowing water BARE AREA = RVI 1.29 It contains areas not vegetated without any distinction between bare soil, different bare soil types, urban areas, wadi. UNCLASSIFIED = RVI > 1.29 It contains all vegetated areas without any distinction between agricultural areas and natural vegetation. Second segmentation cycle The second segmentation cycle focuses on objects merging and decreasing the number of polygons in bare areas maintaining unchanged the other macro classes. The multiresolution segmentation region grow algorithm has been applied in a new segmentation level two. The new level two contains only polygons classified as bare area. Segments are aggregated using the region grow up to scale 400. Spectral difference segmentation The system applies the spectral difference segmentation to the second level of segmentation containing exclusively polygons classified as bare areas. It merges neighbouring image objects if the difference between their layer mean intensities is below the value given by the maximum spectral difference. It is designed to refine existing segmentation results, by merging spectrally similar image objects produced by previous segmentation level one and therefore is a bottom-up segmentation. Increasing the value of maximum spectral difference the system merges neighbouring polygons as shown in the images below. 21
Figure 9 - Spectral difference segmentation in Bare Area at scale 200. Number of Polygons = 2016 Figure 10 - Spectral difference segmentation in Bare Area at scale 250. Number of Polygons = 1543 Export results The system provides output results in the form of ESRI.shp vector file containing objects outline and the features predefined by user. The attribute table of the final exported shapefile contains the following attributes: - Class_name: Classes predefined by user in the procedure (water, barea, unclassified) - NDVI_S2: Normalized Difference Vegetation Index (NDVI) calculated from Sentinel -2 imagery - RVI: Ratio Vegetation Index calculated from Sentinel -2 imagery. 22
PRO and CO of merging FAO/Jordan Land Cover and Land Cover change Analysis and Mapping The immediate advantages obtainable from the second cycle of segmentation and spectral merging are: Maintain adequate level of detail on the areas of interest such as agricultural areas Eliminate redundant segments automatically Maintain the level of aggregation Reduction of manual process (interpretation and post-processing time-saving) The only disadvantage that could be encountered is the editing (splitting) of bigger polygons to isolate features merged to the bare soil class such as small urban villages whose spectral value is often similar to bare area. Considering the high reduction of number of polygons and file size (more than 50% in both cases) that have made the tiles easy to be managed, the editing work becomes a feasible compromise that will be included in the production chain. Figure 11 - Evident differences in level of segmentation between agricultural and vegetated area (red zone), bare area and waterbody (respectively grey zone and blue zone). 23
Vector and raster tiles The segmentation layer, as well as the raster monthly Sentinel-2 mosaic that covers the whole country, has been divided into 15 tiles as shown in the figure below. The smallest squares are 100 kilometers on each side with an overlap area of 10 kilometers. Some squares have been merged together generating larger rectangular areas to reduce the number of tiles and matching. 100 km Figure 12 - Tile index Figure 13 Segmentation at border Edge matching procedure The edge matching is a procedure that assures continuity among adjacent tiles. It consists of running the segmentation into specific frames that fit perfectly with the adjacent ones in order to avoid gaps and overlapped polygons. For that reason, specific editing steps have been implemented before and after the segmentation process in order to: Assure continuity in segmentation layers Avoid double polygons in the overlap areas Avoid irregularity at the edge of 15 tiles Simplify the final matching. 24
SIMPLIFYING PROCEDURE Imagery segmentation generates very detailed polygon layers with a high number of vertexes that increase greatly the size of the shapefiles. Therefore, in order to have a final easily-manageable database, a generalization procedure has been implemented to reduce the number of vertex in the final shapefiles. Generalizing simplifies the shapes of features. It is an important technique for: Reducing the vertex count in features that were captured in too much detail Standardizing features that were created at different scales Simplifying procedure has been performed in 15 tiles with the following parameters: Figure 14 - Detail of simplification test (tolerance at 40 meters). The yellow colour represents the final shapefile smoothed while the original segmentation and nodes that are cut off and/or smoothed, are in black colour. 25
The tables hereafter show the size reduction of tiles before and after the simplifying procedure (see ANNEX I for comparing different levels of simplifying tolerance). The total size reduction is 49.72 % (about twice the estimation done in preliminary study) while the total decrease of polygons is close to one third. It means that the original sizes of shapefiles resulted from the segmentation procedure have been halved eliminating redundant segments, nodes, vertex and thousands of small polygons. TILE ORIGINAL SEGMENTATION number of polygons Size on disk (MB) number of polygons Size on disk (MB) SIMPLIFIED SEGMENTATION water (km²) bare area (km²) unclassified (km²) Total area (km²) 1 28,544 67.40 17,288 37.50 13.85 305.53 1,891.85 2,211.23 2 52,784 119.00 13,962 59.30 5.58 7,735.43 118.49 7,859.49 3 37,847 94.80 12,598 48.30 2.99 5,591.81 747.52 6,342.32 4 26,334 81.40 17,735 45.30 257.94 968.65 1,790.60 3,017.19 5 64,463 167.00 23,629 83.50 7.49 6,256.32 1,355.27 7,619.08 6 78,173 161.00 16,464 76.80 22.51 7,536.10 387.83 7,946.45 7 52,903 117.00 11,952 57.30 35.71 6,673.32 482.65 7,191.68 8 30,391 84.20 14,496 44.70 556.86 1,933.43 1,048.75 3,539.04 9 60,210 167.00 17,864 83.60 6.08 7,765.38 209.19 7,980.65 10 51,384 122.00 10,638 58.10 0.26 5,614.53 58.39 5,673.19 11 52,346 140.00 18,979 71.80 1.02 4,716.76 1,342.61 6,060.39 12 70,945 166.00 14,526 79.20 16.72 8,460.83 33.31 8,510.86 13 53,868 180.00 14,379 90.60 0.04 8,955.14 31.40 8,986.59 14 45,989 139.00 13,164 69.50 134.69 7,140.47 69.39 7,344.55 15 64,743 110.00 6,795 47.00 1.32 7,243.68 43.48 7,288.48 770,924 1,915.80 224,469 952.50 1,063.07 86,897.38 9,610.73 97,571.18* Tab. 2- Comparison between original segmentation and simplified segmentation. * The total area includes 5 km buffering from national boundary FILE SIZE REDUCTION TILE Decrease (MB) file size reduction % 1 29.90 44.36 2 59.70 50.17 3 46.50 49.05 4 36.10 44.35 5 83.50 50.00 6 84.20 52.30 7 59.70 51.03 8 39.50 46.91 9 83.40 49.94 10 63.90 52.38 11 68.20 48.71 12 86.80 52.29 13 89.40 49.67 14 69.50 50.00 15 63.00 57.27 Tot 963.30 49.72 Tab. 3- File size reduction in each tile after the simplifying procedure 26
CALCULATION OF NDVI AND RVI FROM MULTI TEMPORAL IMAGERY The average NDVI (Normalized Difference Vegetation Index) and the average RVI (Ratio Vegetation Index) have been computed from Sentinel-2 monthly mosaic. The average vegetation indices have been calculated for each object (polygons resulted from the process of segmentation) from April 2016 to August 2016 through a Python routine in Arcgis. The routine performs the following steps: Calculating monthly NDVI and RVI from mosaic images Applying a zonal statistic as table Consolidating results by month Calculating the vegetation indices Photosynthetically active vegetation absorbs most of the red band while reflecting much of the near infrared band. Vegetation that is dead or stressed reflects more in the red band and less in the near infrared band. Likewise, non-vegetated surfaces have a much more even reflectance across the light spectrum. Therefore, chlorophyll absorption in Red band and relatively high reflectance of vegetation in Near Infrared band (NIR) are used for calculating vegetation indices. RVI The ratio vegetation index (RVI) or Simple Ratio (SR) is the simplest vegetation index: RVI or SR = NIR RED RVI may take the values in the range from zero to infinity, depending on the reflection. Vegetated areas have higher RVI values compared to non-vegetated areas. NDVI The Normalized Differential Vegetation Index (NDVI) is an improvement in the ratio vegetation index and is calculated by the relationship: Zonal statistics NDVI = NIR RED NIR + RED Zonal statistic is an ArcGis geoprocessing tool that calculates statistics on values of a raster within the zones of another dataset (vector or raster layer predefined by user) and reports the results to a table. The average NDVI and RVI have been extracted from monthly Sentinel-2 images (raster dataset) from April 2016 to August 2016. The zone within which the computing of vegetation indices has been done is defined by polygons of the segmentation layer. A unique ID number (UNIQUEID) identifies each object. 27
Consolidating results FAO/Jordan Land Cover and Land Cover change Analysis and Mapping The resulting Excel table has been joined with the segmentation tiles through the UNIQUEID field. Each tile has the following attribute table: Each polygon has its Normalized Differential Vegetation Index and Ratio Vegetation Index named NDVI_XX and VI_XX respectively, where XX stands for the month expressed by number ( 04 = April; 05 = May and so on). The following line chart shows the NDVI profile of some sample areas throughout the months considered. 0.80 NDVI PROFILE 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00-0.10 APR MAY JUN JUL AUG SEPT OCT NOV BROADLEAVED TREES FRUIT TRESS (ORCHARD) NEEDLE LEAVED TREES SAND RAINFED CROP III SEASONAL LAKE SPARSE NATURAL VEGETATION URBAN AREA IRRIGATED CROP RAINFED CROP I BARE SOIL RAINFED CROP II Figure 15 - NDVI profile As expected some classes can be identified easier than others and can be classified automatically (i.e. irrigated crops or trees), while objects that have the same low spectral reflectance in the NIR like bare soil and urban area are harder to be extracted automatically. The complex classes need to be enriched with a new procedure that classifies objects through sequential GIS queries. For that reason the deep analysis of NDVI pattern throughout the months considered is still ongoing for generating a second classification procedure or guidelines for photo interpreters. The following graphs show the NDVI profile for some classes. 28
Figure 16 Irrigated agricultural fields are areas that receive full or partial application of water to the soil to offset periods of rainfall shortfalls under dryland conditions. Irrigation practices lead to a strong mismatch between the greenness cycle of rain-fed crop and irrigated crops particularly in arid and semi-arid locations. The greenness associated with non-irrigated crops in arid/semi-arid landscapes is often a direct result of rainfall events while greenness associated with irrigated sites is generally independent of rainfall. As a consequence irrigated crops have a higher peak NDVI and maintain a higher NDVI during each crop s growth cycle compared to non-irrigated crop. Figure 17 - Some rainfed crops (type I and II) are characterized by monoculture. The different phases of vegetation cycle are clearly detectable from satellite imagery. The NDVI values strictly depend on rainfall pattern over the year with a peak in April/May followed by a progressive reduction because of the dryness of the climate. 29
Figure 18 - Some other non-irrigated cultivation exhibit a bimodal seasonal pattern with two NDVI peaks: the first follows planting in the fall and the second just before harvesting in late spring/early summer, closely connected with the moisture availability. Figure 19 - Areas covered by orchards are characterized by a seasonal trajectory very similar to the one shown by the broadleaved trees (fig. 20) and high values of NDVI due also to the constant water supply with very little variations. 30
Figure 20 - The NDVI in broad-leaved trees is generally high and changes little through the year. It first increases in June, it stabilizes in summer season and then it slowly decreases in fall. Needle-leaved trees show a similar general trend but in this case the NDVI values are generally lower compared to those of broadleaved trees and never above 0.4. Figure 21 - The sparse natural vegetation shows a NDVI profile very similar to the rain fed crop or pasture. It is correlated to the annual precipitation and the values are low because of the very little difference in intensity of NIR and visible of wavelengths reflected. 31
Figure 22 - Non-vegetated areas are characterized by values close to zero. Indeed bare soil generally shows similar reflectance in the near-infrared and visible generating positive but lower NDVI values. Similar NDVI values for different types of bare areas make difficult to distinguish urban surfaces from other categories. Figure 23 The red or visible reflectance of water is larger than its near-infrared reflectance, so negative NDVIs are generated (April-June). However, in seasonal lake the negative NDVI values increase in the summer time due to the progressive reduction of water surface that leaves space to the natural vegetation growth (July-October). 32
LAND COVER MAPPING/CHANGE MONITORING A standardized and accurate land cover and land cover change database is an essential input for many environmental studies and for monitoring the progression of changes in response to local changes in climate, environment, local natural resources and land degradation. FAO-DDN Geospatial Unit has devoted considerable attention to the development of techniques for land cover mapping using enhanced methodologies and tools, underpinned by standards (e.g. LCML, ISO TC-211). We have also considered different approaches to measure and monitor the land cover changes of the landscape exploiting the repeatability of the remotely sensed products with different satellite time series, with processing locally or on a cloud based platform. Different approaches including: Wall-to-wall: a common approach for producing land cover and land cover change, in particular when a benchmark land cover map, at the country level (using the LCML Legend ISO standard), is needed; Sample Based: it refers to the FAO EcoNet approach for a statistical assessment of the LC changes. EcoNet approach The acronyms EcoNet stands for Earth COver NETwork and refers to a specific FAO-GLCN programme implemented for Africa but applicable to every country to address the main question of land cover monitoring. ECONET approach aims to produce information on land cover using a sample grid to ensure a statistical basis for the validity of the results, at regional, sub regional and country levels. The EcoNet approach is articulated in three phases. 1. Preparation of sample grid The size and distribution of the samples follows basic zonation criteria. The basic sample design (10 km 10 km sample areas) is distributed at each ½ ½ intersection over the region. Deviation from the base grid will occur: in desert areas where the grid density of samples can be reduced to 1 1 ; in small countries where the grid density can be intensified to ¼ ¼. Jordan ECONET grid includes: - 38 tiles located at ½ confluence - 9 tiles located at 1 confluence - 149 tiles located at ¼ confluence 2. Interpretation It is a production phase, where an international team of experts interprets the very high resolution images of each sample for the selected time series locally, or on the cloud with tools such as Google Earth Engine. The interpretation is undertaken using a standard protocol. 3. Generalization of results After reviewing and updating of interpretation according the knowledge of local experts and verifications, the final results obtained from sample areas are statistically analysed and generalized. The LC/LCC information extracted from the samples is upscaled at country level. 33
The following figures show the distribution of tiles at different intervals all over the Jordan country and an example of differential sampling method: EcoNet sampling grid of 10 km x 10 km squares at different intervals applied to Kenya. Figure 24 Sample grid at half degree Figure 25 - Sample grid at quarter degree Figure 26 Sample grid at one degree Figure 27 Differential sampling in artificial surface and cropland 34
Biography of FAO geospatial team Assessment of land cover and land cover change, are essential requirements for the sustainable management of natural resources, environmental protection, food security and humanitarian programmes, as well as core data for monitoring and modelling. Land cover data are therefore fundamental to fulfilling United Nations mandates, international and national institutions and many programmes. However, current users of land cover data still lack access to sufficient reliable or comparable baseline land cover data. As the result of a common efforts of partners and sponsors to address the needs expressed by the international community of standardized and interoperable land cover products, FAO developed the Land Cover (LC Change) databases, methods and toolbox. The Geospatial Unit of FAO, sitting in the Office of Deputy Director General for Natural Resources, has extensive experience in national capacity building of in land cover mapping and land cover change assessments and uses enhanced methodologies and tools underpinned by FAO developed ISO standards. As a result of this FAO is leading globally on the harmonization of the high resolution country dataset into a unique global land cover layer, the GLC-SHARE which is FAO s flagship LC product. Such initiative that integrated national and global LC capacity, has been launched at the conference "Strategies for Global Land Cover Mapping and Monitoring" held in Florence, 6-8 May 2002 with the aims to develop a global collaboration for developing a fully harmonized to ensure national datasets are reliable and comparable, to ensure land cover data land cover change data are accessible to local, national and international initiatives. In particular, the creation of the Global Land Cover Network (GLCN - www.glcn.org) was intended to support the stakeholder community in developing countries which have difficulty to produce and make accessible reliable, consistent and updated information. Main activities in the framework of the GLCN approach include: Harmonize land cover definitions, classification systems, mapping and monitoring specifications; Develop standards for global mapping; Building of a global database; Promote outreach initiatives on development methodologies and applications of land cover data; Provide advisory services; Provision of database structure and maintenance guidelines; Supply of, and access to, methodology and software; Awareness, training and capacity building at the local and national level; Development of institutional technical networks and promotion of national and local collaboration; Development of effective communication links among relevant organizations; Function as an international, politically neutral and not-for-profit clearinghouse for land cover information at global and regional levels; National and Global Land Suitability assessment based on the FAO Agro-Ecological Zoning (AEZ) methodology. The AFRICOVER Eastern Africa module was the first important project implemented in the framework of the GLCN activities. It was operational in the period 1995-2002 and it was signed by ten countries: Burundi, Democratic Republic of Congo, Egypt, Eritrea, Kenya, Rwanda, Somalia, Sudan, Tanzania and Uganda. FAO geospatial unit has continued this important experience developing updated tools and methodologies for land cover/ land cover change as well many other important studies and integrated analysis (e.g. land cover /land cover change analysis of the Himalaya Region, Kenya, Cambodia, Afghanistan, Lesotho, Nile delta, Uruguay, main Deltas of the World). In additions, the creation of the land form database of the Africover countries, and many other applications about irrigation, soil, climate and land suitability mapping. 35
Additional information about the FAO Land Cover/LC Change methodologies, approaches, standards, tools, applications is available at www.glcn.org and www.fao.org/nr/. It is also interesting explore www.fao.org/nr/gaez where an important and innovative application concerning the Agro-Ecological Zoning / Land Suitability methodology, model and database is available. Another interesting and useful application is the Pakistan s Crop Information Portal; It aims to support data and information dissemination on Pakistan s major crops (wheat, maize, rice, cotton and sugarcane) and agro-meteorological conditions (temperature, precipitations, etc...) affecting crop growth. The portal website is available at: http://dwms.fao.org/~test/home_en.asp. FAO geospatial unit has moreover recognized the value for the land cover mapping with an operative and effective classification system. It has fully supported activity concerning the definition of the standards. It started with the Land Cover Classification System (LCCS2) in 1995 and evolved into the ISO standard on Geographic Information ISO TC-211, the Land Cover Meta-Language (LCML) with a final approval in July, 2012. The new classification method now offers important innovations in respect to the other classification system previously adopted (e.g. CORINE Land Cover, USGS Anderson Classification, etc.). The application of the LCML assists to map the real elements of the land cover in the ground and the changes rather than changes in semantics and definitions as it happens by using any other predefined classification system which often has a lot of overlapping classes and sometimes even gaps. The land cover activity proposed by FAO-DDNS has multiple benefits. It contributes to provide an accurate assessment of the land cover types that can be applied and exploited in many environmental and planning activities; meanwhile, its creation triggers several connected action as develop awareness and contribute to the development of capacities, facilitate contribute to the exchange of knowledge and technologies among stakeholders for the sustainable management and use of natural resources, as well as develop basic guidelines for a new methodology to be re-applied for different scope or areas. 36
ANNEX I: Simplifying tolerance FAO/Jordan Land Cover and Land Cover change Analysis and Mapping Original segmentation Size on disk: 236 KB (100%) - File size reduction: 0 % Simplification Tolerance 20m Size on disk: 196 KB (83%) - File size reduction: 17 % Simplification Tolerance 40m Size on disk: 148 KB (63%) - File size reduction: 37 % Simplification Tolerance 60m Size on disk: 124 KB (52%) - File size reduction: 48 % Simplification Tolerance 80m Size on disk: 108 KB (46%) - File size reduction: 54 % Simplification Tolerance 100m Size on disk: 104 KB (44%) - File size reduction: 56 % 37
Annex II: FAO s procedure for producing land cover and land cover change map The FAO s procedure developed for the production of a land cover database using remote sensing products requires a systematic approach. Its implementation can diverge according with the specific needs and the local conditions of the project, but the main steps follow a precise schema outlined below: Selection, procurement and pre-processing of the most appropriate remote sensing imagery; Legend creation in LCCS classification system; when possible, the preliminary version is prepared in close collaboration with the counterparts considering their familiarity with the environment; Segmentation of the selected images for the automatic creation of the baseline for interpretation; Land cover interpretation using semi-automatic/visual procedure; preparation of the land cover database based on the multi-date and multi-sensor segmentation and semi-automated procedure for pre-labelling; Fieldwork, if feasible, carried out by the national experts in collaboration with the local Departments/Institutions). Alternatively, a validation phase is carrying out using other available remote sensing and ancillary products; Integration and ingestion of field data for correction of misinterpretation and enrichment of the database; Creation of the land cover change database; Comparative interpretation analysis with previous images dataset to highlight the changes occurred; Creation of Land cover / Land cover change statistics; Training of national experts on image interpretation, data collection and data integration; the aim of the training is to give a practical overview of all the steps needed for the realization of a Land Cover map using the FAO methodological approach. Final Workshop for dissemination and preparation of information materials. The final products are in a digital format and they indicate: The distribution of the land cover types and their changes over time; The semantic and direction of the changes; The creation of the legend is a crucial step. The details that are included are very important because they will be used during the interpretation. The Land Cover Meta Languages (LCML) is based on the utilization of the classifiers as class elements that are defined unambiguously worldwide and it provides the classification rules to describe land cover types. However, some specific details are not possible to be discriminate using only RS, even at very high resolution. For example, it is feasible distinguish between tree and herbaceous crops but difficult differentiate different types of herbaceous crops. To overcome these limitations, the integration with fieldwork and/or ancillary data is very important to generate a consistent database. This activity is strongly suggested if the supplementary and additional information are available. 38