The Design and Building of Spectral Library of Tropical Rain Forest in Malaysia

The Design and Building of Spectral Library of Tropical Rain Forest in Malaysia Alvin M. S. LAU and Mazlan HASHIM Department of Remote Sensing, Faculty of Geoinformation Science and Engineering, Universiti Teknologi Malaysia 8131 UTM Skudai, Johore e-mails: alvin@fksg.utm.my, mazlan@fksg.utm.my Abstract: This paper reports the design and building of a spectral library of a selected tropical rainforest species in Malaysia. Several approaches to building the vegetation spectral library have been discussed. Spectral analysis were performed to the 37 vegetation species spectra and found that the vegetation spectra are very sensitive to environmental parameters such as leaf condition, vigorous and other physiological and biochemical parameters. The separability anlyses of each tree species in built spectral library was also conducted using three-best combination of narrow spectral window of 515.9 nm, 672 nm, 721.6 nm and 838.2 nm of all 512 bands generated. The implementation of a spectral library is crucial and varies on applications and the accuracy of the processing is much depends on the feature extraction techniques of hyperspectral data used. 1. INTRODUCTION The Spectral library is a set of class or endmember spectrum to be referred during feature extraction process from hyperspectral data. It can be further explained as a collection of spectral plots which is being compiled so that it can be used as a reference in information extraction from hyperspectral data. This library may consist of variable degree of details, i.e. the level of detail can range from very general (e.g. grass) to very specific sub classes (e.g. healthy grass, stressed grass, etc.). Spectral libraries for example, Jet Propulsion Laboratory (JPL) Spectral Library [1], ASTER Spectral library [2] and United States Geological Survey U.S.G.S. Spectral Library [3] are examples of spectral library. They were built specifically to focus on mineral exploration works and for recognition work of man-made features from hyperspectral data. Similarly for vegetation studies, a spectral library compiled from all the spectral responses from the targets of interest have to be created beforehand. The spectral libraries are usually built by compiling information from three sources; (1) spectra collected during field campaigns, (2) spectra built from laboratory analyses, and (3) spectra collected from image analyses. Field campaign is the most common and easiest way to collect object spectra of different land cover types, and this method produces results which can be easily compiled into spectral library. To carry out a field campaign, a spectroradiometer is needed to record the spectra received from the objects found in the study area. This method can be employed at anytime as long as proper calibration is done to the spectroradiometer before

it starts [6]. Spectra collected from laboratory analyses will give the best and accurate spectral profile of the target object but it requires a special and well equipped laboratory for calibration and processing. Another method to build a spectral library is to collect spectra from hyperspectral images. To perform the task, an image must be properly calibrated by undergoing pre-processing such as radiometric correction and normalization of topographic effects. In this study, the spectra were collected in a simulated laboratory analysis where the detailed of the methods used are discussed in the latter section. 2. MATERIALS AND METHODS 2.1 Study Area The study area is confined to Pasoh Forest Reserve (PFR) 5 ha plot, a lowland tropical rainforest in Peninsular Malaysia. The 5-ha plot contains 335,24 trees made of 814 species, 29 genera within tree78 families. On a per hectare basis, the species diversity of trees at Pasoh is comparable with those recorded anywhere in the world and roughly similar to many seasonal forests of Malaysia [4]. 2.2 Conceptual Design of Spectral Library System The study established a spectral library system of vegetation in PFR, consist of a multifunctional standard database which contains the spectral data of featured selected vegetation species. The spectral library also contains the normatively measured and processed circumstance parameters of background information of featured vegetation species. The spectral library system is composed of five parts: the knowledge database, measured spectral library, auxiliary library, spectral analysis and end-user application demonstration. In knowledge database, some information (background knowledge) of the vegetation species was identified. All the measured spectra of the various species were recorded in measured spectral library where the auxiliary library stored some of the ancillary data for examples physiological and biochemical parameters, geometry information, and etc of the species measured. In spectral analysis, the spectra collected will be pre-analyzed to ensure the accuracy of the library before being used by end-user in related applications [5]. Data manipulation and storage play an important role in a spectral library system. The organization of spectral data is described in data model framework of the spectral library in Fig 1. 2.3 Spectral Data Collection There are two main steps in spectral data collection task, namely (1) spectral sampling and (2) compilation of spectral library. In spectral sampling, spectra of the vegetation species obtain from field were recorded using a spectroradiometer under a simulated laboratory environment. After the spectra were collected successfully, the spectra of vegetation species were compiled into groups according to different vegetation types. To

ensure successful spectral sampling, which was done for vegetation from various species, a configuration as shown in Fig. 2 was used. POSITION LIBRARY Position_ID LONG and LAT Altitude AUXILIARY LIBRARY Physiological and biochemical parameters (chlorophyll, nitrogen etc) Geometry parameters (Leaf area index, density, dbh etc) Soil information SPECTRAL LIBRARY Spectral data (ranging from visible to Therma Infra Red) Spectral_data_ID Species_ID Measurement_position_ID Campaign_ID File format VEGETATION LIBRARY Species name Genera name Family name Species_ID Description figure Period of growth SENSOR LIBRARY Sensor name Sensor_ID Sensor description Spectral range Number of spectral bands Fig. 1: The data model of Spectral Library System of featured vegetation species Fig 2: Spectral sampling configuration used in the study.

To record the target s spectra, three types of radiant spectrum were recorded. These include: (1) Dark Current (DC), (2) Reference (White board) radiant spectrum and (3) Object s radiant spectrum. The purpose of recording DC and Reference radiant is to calibrate the spectra recorded for the target object. When recording the target object s spectra, some precautionary methods were taken in order to increase the accuracy of the spectral library. The leaves were placed on a dark cloth in order to reduce the reflected light from other nearby object(s) that might affect the target s spectrum. All measurements were done under stable light source (in this case, two tungsten lights, 5 watt each, were used) and the fore optics of the sensor was kept to look vertically to reduce the object s bireflectance effect. To minimize the shadow effect, operators were facing the light source and positioned themselves behind the object whilst measuring object s spectra. In order to build a good spectral library, several measurements were taken to the same object to reduce stochastic errors. Objects and reference were measured at intervals in order to avoid error resulting from the light source s instability. A total of 37 randomly selected vegetation species were collected from the study area for the building of spectral library. Four set of spectra were acquired for each species of vegetation, two reading for single leaf and two reading for mixture of few leaves of same species respectively. In single leaf reading, two set of data were acquired, i.e. with single leaf facing up and single leaf facing down. Further analyses were carried out to analyze the single leaf spectra in different leaf position. In mixture of leaves, one set of data recorded with leaves all facing up while another set of data recorded with mixture of leaves facing up and down of the same species. This was done by taking account in a natural condition of a vegetation community, vegetation leaves are consist with mixture of leaves facing up and down. This will be useful in analyzing the vegetation spectral for detailed species mapping. 3. RESULT, ANALYSES AND DISCUSSION A spectral library consisting of 37 spectra had been compiled into the spectral library according to type of vegetation species. Fig. 3 show a spectral plot for all the vegetation species created in the study. Some spectral analysis were perform to the spectral library acquired under four sampling condition, namely (1) leaves facing up, (2) leaves under mixed condition, (3) single leaf facing up and (4) single leaf facing down. From the total of 37 spectra collected, more than ~7% of the spectra over the 37 vegetation species will have the typical plots as shown in Fig 4. From Fig 4, the spectral obtain under single leaf facing down condition gives the highest reflectance compared to others sampling condition within the same species of vegetation. It follows by spectral recorded by single leaf facing up. Most of the leaves have some mossy condition at the reverse side and that is the cause why the spectral acquired with leaf facing down position having higher reflectance from visible to near infrared portion of the wavelength compared to other reading of the same vegetation species. This is proven with some mossy leaves with leaves facing up. The reflectance of the mossy leaf is higher if compared to same kind of leaf within same species of vegetation (see Fig 5). Hypothesis made earlier that supposed the mixed leaves would have the average reading of the single leaf facing up and single leaves

facing down but from what we observed, the mixed leaves always have lower reflectance compared to both single leaves reading. This may due to the spaces in between the leaves which affect the spectral reading. The separability of the spectral library built was analyzed by choosing the best combination of three or four wavebands from all 512 available bands. Fig. 6(a) and Fig. 6(b) show the separability of different species of vegetation under the genera shorea. analyzed by choosing three or four bands combination. The best combination of three bands was chosen as 55.64 nm, 672.86 nm, and 812.8 nm. The wavelength of 56 nm, 672 nm and 812.8 nm are located in blue-green, red edge, and near infrared regions, respectively. For choosing the best combination of four bands, 515.9 nm, 672 nm, 721.6 nm and 838.2 nm were chosen. Among the four best bands, the wavelength of 515.9 nm is in the blue-green spectral region, the 672 nm is in the red edge area, and the 721.6 nm and 838.2 nm wavelengths are within the near-infrared region. Therefore, it appears that the blue-green, red edge, and near infrared narrowband spectra are sensitive for the vegetation analysis. 4. CONCLUSION This study suggested a series of task to be completed in order to build a vegetation spectral library. The building of vegetation spectral library is vary from the building of other spectral library as it need to take into account some critical parameters like the suitable wavelength for vegetation identification, sensitivity of vegetation towards surrounding environment, the processing of spectral reading to match with other remotely sensed data. Feature extraction of vegetation studies in hyperspectral analysis need not only a good spectral library but with the aids of series of accurate feature extraction techniques designed for vegetation studies. 5. REFERENCES [1] http://speclib.jpl.nasa.gov/, 24 September 22 [2] http://asterweb.jpl.nasa.gov/, 7 September 24 [3] http://speclab.cr.usgs.gov/, 24 October 25 [4] Manokaran, N., and LaFrankie, J. V. (199) Stand Structure of Pasoh Forest Reserve, a Lowland Rain Forest in Peninsular Malaysia, Journal of Tropical Forest Science, Vol. 3, 14 24. [5] Chen, S. S. et al. (25) The Design and Development of Spectral Library of Featured Crops of South China, IEEE, Volume 2, 25-29 July 25. [6] Mazlan Hashim, M, Latif, I., Wahid Rasib and Wan Hazli Wan Kadir, (25). Foliar nutrient concentration mapping of oil palm plantation using remote sensing technique. Procs of the 4 th Malaysian Remote sensing and GIS conference, Aug 25, Kuala Lumpur, 15pp

1.9.8.7.6 Spectral Plot of 37 Vegetation Species in PFR Cynometra malaccrensis Ficus fistulosa Scrub Herbs Elateriospermum tapos Durize (J23) Ficus Variegata (J24) Macaranga Gigantea (J217) Uncaria sp Syzygium sp Macazanga gigantea Knema kunstleri Croton agyratus Shorea lepidota Shorea reprosula Shorea pauciflora Ganua spl Reflectance.5.4.3.2.1 38 48 58 68 78 88 98 18 Syzygium sp Payena lucida Xerespermum noronhianum Pimelodendzu griflithianum Shorea multiflora Endospermum malaccense Lithocarpus concarpus Porterandia anisophylla Archidendron clypeazia Uncaria sp Neonauclea Pallida var malacensis (J239) Endoma (J226) Artacarpuc Scortechinii (J221) Macaranga Coustricta (J215) Vitex Pinnata (J22) Glochidon wallichianum (small leaves) (J29) Glochidon Obscurum (J28) Ficus Variegata (J27) Ficus fistulosa (J21) Glochidon wallichianum (J199/2) Fig 3: Spectral plot of all 37 vegetation species of PFR.

Typical Spectra Plots of Vegetation Reflectance 1.9.8.7.6.5.4.3.2.1 38 58 78 98 118 Leaves facing up Mixed leaves Single Leaf facing up Single Leaf facing dow n Fig 4: Spectra Plots of vegetation under various sampling condition Reflectance Xerespernum noronhi 1.9.8.7.6.5.4.3.2.1 38 58 78 98 118 Leaves facing up Mixed leaves Single leaf facing up Singe leaf facing dow n Single mossy leaf facing up Fig 5: Mossy leaves shows higher reflectance

Reflectance 1.9.8.7.6.5.4.3.2.1 55.6473 672.8597 813.2 Shorea multiflora Shorea pauciflora Shorea reprosula Shorea lepidota (a) Reflectance 4 3.5 3 2.5 2 1.5 1.5 515.223 672.8597 72.6346 838.487 Shorea multiflora Shorea pauciflora Shorea reprosula Shorea lepidota (b) Fig 6: The separability of genera shorea analyzed by choosing (a) three and (b) four bands combination