Use of Hyperion for Mangrove Forest Carbon Stock Assessment in Bhitarkanika Forest Reserve: A Contribution Towards Blue Carbon Initiative

Abstract

Mangrove forest coastal ecosystems contain significant amount of carbon stocks and contribute to approximately 15% of the total carbon sequestered in ocean sediments. The present study aims at exploring the ability of Earth Observation EO-1 Hyperion hyperspectral sensor in estimating aboveground carbon stocks in mangrove forests. Bhitarkanika mangrove forest has been used as case study, where field measurements of the biomass and carbon were acquired simultaneously with the satellite data. The spatial distribution of most dominant mangrove species was identified using the Spectral Angle Mapper (SAM) classifier, which was implemented using the spectral profiles extracted from the hyperspectral data. SAM performed well, identifying the total area that each of the major species covers (overall kappa = 0.81). From the hyperspectral images, the NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) were applied to assess the carbon stocks of the various species using machine learning (Linear, Polynomial, Logarithmic, Radial Basis Function (RBF), and Sigmoidal Function) models. NDVI and EVI is generated using covariance matrix based band selection algorithm. All the five machine learning models were tested between the carbon measured in the field sampling and the carbon estimated by the vegetation indices NDVI and EVI was satisfactory (Pearson correlation coefficient, R, of 86.98% for EVI and of 84.1% for NDVI), with the RBF model showing the best results in comparison to other models. As such, the aboveground carbon stocks for species-wise mangrove for the study area was estimated. Our study findings confirm that hyperspectral images such as those from Hyperion can be used to perform species-wise mangrove analysis and assess the carbon stocks with satisfactory accuracy.

Integrating Multi-Sensors Data for Species Distribution Mapping Using Deep Learning and Envelope Models

“…The spatial species distribution is highly associated with regional environmental conditions, climatic variability, and land use [68,69]. The species distribution is simulated using the correlation models between the dependent as well as independent parameters.…”

Research on the Temporal and Spatial Distributions of Standing Wood Carbon Storage Based on Remote Sensing Images and Local Models

“…The ground survey method is highly credible, but it is cumbersome and can cause certain damage to the forest. Moreover, traditional surveys are not ideal for dynamically monitoring forest carbon storage over a large area [5]. In particular, large-scale surveys of natural forests in the Liangshui Nature Reserve are bound to cause damage to the local ecology.…”

Applicability of Smoothing Techniques in Generation of Phenological Metrics of Tectona grandis L. Using NDVI Time Series Data

“…Specifically, time series normalized difference vegetation index (NDVI) data derived from moderate resolution imaging spectroradiometer (MODIS) [32][33][34][35][36][37][38] and advanced very high resolution radiometer (AVHRR) [39][40][41][42][43][44] are largely used for the generation of phenological metrics. This satellite-based vegetation index is recognized as an effective tool in the quantification of vegetation greenness [45][46][47], canopy carbon [48][49][50][51][52], and water fluxes [53][54][55]. It has proved its utility in monitoring the phenological changes in vegetation across large spatial scales and over long time periods.…”