Canopy spectral biochemical analysis of forest species using Hyperspectral Remote Sensing – A case study from Eastern Ghats Forest ecosystems

Understanding the linkage between species biochemical properties and their spectral reflectance patterns is one of the core components in ecosystem related studies. The traditional methods of quantifying the biochemical analysis of the species is not only time consuming and expensive but also need more number of samples to capture the heterogeneity within the vegetation patterns of an ecosystem. However, the use of geospatial tools in assessing various components of ecosystem starting from vegetation area estimations at macro scale to account carbon and biomass at meso scale level and finally quantifying at micro level by analyzing species chemical properties using hyper spectral remote sensing is one of the major advancements in domain of technical nature. These tools are proved to be cost effective in estimating the plant biochemical components like chlorophyll, nitrogen, protein and other mineral nutrients based on their spectral data. In the context of a growing interest in remote sensing for vegetation applications, the possibilities of hyperspectral imaging for the extraction of information relevant to vegetation demand detailed understanding of spectral signatures in terms of position of feature specific absorption bands, shape of the spectrum, spectral variability and similarity of various types of vegetation species. In particular, credible information on correlation of field-collected spectral signatures of various forest species with spectral reflectance of space-borne hyperspectral sensors is important for the automatic identification and quantification of various vegetation types in an area. One possible way of integrating the in-situ spectral data with space-borne hyperspectral data is the availability of a spectral library of various surface features of interest. Objectives of the program Estimate the chlorophyll and nitrogen content of various species with integrated field and satellite-based methods. Correlating spectral variations with that of canopy biochemical patterns under stress conditions (factors leading to decrease in chlorophyll, nitrogen constituents). Stakeholders involved Project team from Lab for Spatial Informatics, International Institute of Information Technology, Hyderabad, Telangana and Indian Institute of Space Technology, Thiruvanthapuram, Kerala, India Implementation Plan Ground truth data generation Field work for data collection was carried out thrice: February 2017 (representing dry conditions), September 2017 (wet conditions) May 2018 (stress condition) to observe the behavior of spectrum of tree species with respect to seasonal conditions. Ten species were selected based on their economic importance as well as dominance distribution in the study area for spectra collection from leaf samples The field spectrum was corrected statistically for dropout and outliers in the reflectance values. The wavelength values collected during both the fields were different due to use of different instruments, so resampling technique was applied using Gaussian technique with 6 precision, to compare spectra of two seasons. The Spectral matching for the field collected data has been carried to check the discrimination between the species in different seasons. The parameters obtained from the field works were used for the calculation of up-scaling the leaf reflectance to canopy level. Field collected leaf samples were subjected to lab analysis for calculation of biochemical parameters like chlorophyll, nitrogen, cellulose and lignin. The biochemical parameters obtained for each sample was matched with the field collected spectra for three different seasons.