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    Utility of Landsat Data for Assessing Mangrove Degradation in Muthupet Lagoon, South India
    (Elsevier, 2018) Subbarayan, S.; Jegankumar, R.; Selvaraj, A.; Jacinth Jennifer, J.; Kulithalai Shiyam Sundar, K.S.S.
    Mangrove swamps and forests are an essential interface of the coastal zone that provide various ecological and economic services contributing to coastal protection and carbon credits. The ever-changing land use along the coastal tract, especially saltpans and agricultural activities in the mangrove habitats, contribute to the reduction of mangrove swamp sprawl and degrade the mangrove forest. Remote sensing techniques are routinely used to provide spatial-temporal information on mangrove ecosystem distribution, species identification, health status, and population. By adopting supervised classification techniques using the capabilities of indices such as the Normalized Difference Water Index (NDWI) and the Normalized Difference Vegetation Index (NDVI), we attempt to map the spatio-temporal variations of the Muthupet Lagoon regions. The increase of land use changes in the vicinity of the Muthupet Lagoon drastically decrease the freshwater flow and create significant impacts on the mangrove habitat. The study described herein documented degradation of 40.3. ha of dense mangroves from 2013 to 2016, 135.5. ha from 2008 to 2013, and 166. ha from 1999 to 2008 due to high salinity, coastal erosion, and the intrusion of saltpans, aquaculture farmlands, and other human activities. The area of sparse mangroves increased by 38.2. ha between 2013 and 2016, by 42.7. ha from 2008 to 2013, and by 191.3. ha from 1999 to 2008 due to prominent restoration activities. © 2019 Elsevier Inc. All rights reserved.
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    Utility of Landsat Data for Assessing Mangrove Degradation in Muthupet Lagoon, South India
    (Elsevier, 2019) Subbarayan, S.; Jegankumar, R.; Selvaraj, A.; Jennifer, J.; Kulithalai Shiyam Sundar, K.S.S.
    Mangrove swamps and forests are an essential interface of the coastal zone that provide various ecological and economic services contributing to coastal protection and carbon credits. The ever-changing land use along the coastal tract, especially saltpans and agricultural activities in the mangrove habitats, contribute to the reduction of mangrove swamp sprawl and degrade the mangrove forest. Remote sensing techniques are routinely used to provide spatial-temporal information on mangrove ecosystem distribution, species identification, health status, and population. By adopting supervised classification techniques using the capabilities of indices such as the Normalized Difference Water Index (NDWI) and the Normalized Difference Vegetation Index (NDVI), we attempt to map the spatio-temporal variations of the Muthupet Lagoon regions. The increase of land use changes in the vicinity of the Muthupet Lagoon drastically decrease the freshwater flow and create significant impacts on the mangrove habitat. The study described herein documented degradation of 40.3ha of dense mangroves from 2013 to 2016, 135.5ha from 2008 to 2013, and 166ha from 1999 to 2008 due to high salinity, coastal erosion, and the intrusion of saltpans, aquaculture farmlands, and other human activities. The area of sparse mangroves increased by 38.2ha between 2013 and 2016, by 42.7ha from 2008 to 2013, and by 191.3ha from 1999 to 2008 due to prominent restoration activities. © 2019 Elsevier Inc. All rights reserved.
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    Biomass Estimation Using Synergy of ALOS-PALSAR and Landsat Data in Tropical Forests of Brazil
    (Springer, 2020) Huggannavar, V.; Shetty, A.
    Satellite remote sensing technologies are currently tested and suggested as a tool in REDD+ (MRV, Measurement Reporting, and Verification). SAR (Synthetic Aperture Radar) has got an extensive application in the estimation of biomass due to its all-weather capabilities. L band radar signals penetrate the canopy more efficiently when compared to C band. Scientific biomass study using SAR has not been conducted in Para in spite of extensive field datasets being freely available under CMS (Carbon Monitoring System) project. This study aims in using various polarization combinations like HH + HV, HH − HV, HH + HV/HH − HV and vegetation index such as NDVI from the optical data. ALOS-PALSAR and Landsat 7 data acquired over Paragominas in Brazil, where field samples were collected in the form of transects. Regression analysis was performed using backscatter coefficients and field collected Above Ground Biomass (AGB). Semi-empirical model was developed to model AGB using various polarization combinations and NDVI as predictor variables. Combination gave higher R2 value of 0.657 for biomass prediction. Multiple linear regression using NDVI and HH + HV as variables yielded R2 of 0.73 during calibration and 0.363 during validation. There is future scope to use other vegetation indices such as RVI, EVI, etc., along with increased number of samples, which may yield more robust models with acceptable level of accuracy for practical application. © Springer Nature Singapore Pte Ltd. 2020.