SAYANTA GHOSH
@teriin.org
RESEARCH ASSOCIATE
The Energy and Resources Institute
EDUCATION
M.Sc.,
P.G. Diploma ( Remote Sensing and GIS)
RESEARCH INTERESTS
Remote Sensing and GIS Application,
Climate and Weather Modelling,
Aerosol Climatology,
Carbon Stock Assessment,
Disaster Management,
Ecological Assessment and modeling
Scopus Publications
Scholar Citations
Scholar h-index
Scopus Publications
Sayanta Ghosh, Aniruddh Soni and Jitendra Vir Sharma
Temporal variation in forest cover, the largest terrestrial ecosystem on Earth, influences the climate at both local, regional, and global scales through physical, chemical, and biological processes. At the same time, forests sequester and store more carbon dioxide than any other terrestrial ecosystem and act as a "natural brake" in climate variation. Here, we have made an attempt to assess the spaio-temporal variation in forest biomass combining field-based and remote sensing and machine learning approaches. For this purpose, Fractional Vegetation Cover (FVC) layers based on Linear Spectral Unmixing (LSU) Algorithm have been developed using cloud-free multi-temporal LANDSAT data for Dudhwa Tiger Reserve, Uttra Pradesh, India from the year 2001 to 2022. Linear Regression Model (LRM) have been developed between Field based forest biomass and FVC on the basis of field data collected from 60 sampling plots of 0.1 ha across three different forest strata, namely, Very Dense Forest (VDF), Moderate Dense Forest (MDF) and Open Forest (OF). LRM indicates strong positive correlation having R2 values 0.718 for VDF, 0.73 for MDF and 0.76 for OF forest strata. Also, the predicted biomass thus obtained shows strong positive correlation with observed biomass. Results highlights that in VDF, carbon stock shows a decreasing trend till 2018 (332 t/ ha) since the year 2001 (347 t/ha) before further increase during present year (339 t/ha). Simultaneously, temporal variation in FVC also suggests the same trend for the forest cover under VDF strata which is playing pivotal role in increasing trend of forest carbon stock from the 2018 onwards. Also, we have compared between best possible FVC model based on three vegetation index (NDVI, MSAVI and EVI) which highlights the FVC model based on NDVI shows highly significant correlation (R2=0.73, p<0.005) with the field-based forest biomass. Degradation matrix also developed using the temporal FVC layers for the delineation of degradation patches and trend analysis of forest degradation. Outcome of the paper will be helpful for the policy makers in visualizing proper development plan to regulate the Land-use and forest cover dynamics for achieving the higher carbon sequestration rate which would in turn helps to maintain the balance in the global climate scenerio.
K. C. Gouda, Sudhansu S. Rath, Nidhi Singh, Sayanta Ghosh, and Renu Lata
Springer Science and Business Media LLC
R Lata and S Ghosh
IOP Publishing
Abstract Here, the impact of spatio-temporal Land Use Land Cover Changes (LULC) on the variations of Land Surface Temperature (LST) has been estimated using Landsat 7 ETM+, Landsat 8 TIRS/OLI and Sentinel-2 data for Beas basin of Kullu district, Indian Himalayan Region (IHR). Also, the relationship of various remote sensing indices such as NDVI, NDSI, NDBI and NDMI with LST were established using coefficient of correlation to monitor the role of natural ecosystem alteration on LST. LST was calculated using Single Window algorithm (SW) for eight major land cover categories extracted from Landsat-7 ETM+ and Landsat-8 (Path-147 and Row-38) Thermal Infrared Sensor (TIRS) data for October, 2000 and October, 2020 respectively. LULC change detection revealed that there was a huge increase in agricultural land including orchard expansion of 123 % during the year 2020 than that of 2000. Also there’s a sharp increase of 40.63 % in settlement areas which includes the tourism activities. These are the significant factors for the changes in LST. Further, it shows that negative correlation with the strong correlation coefficients of R2 = 0.7072 and R2 = 0.5642 between NDSI and LST in Pre-winter season of 2000 and that of 2020 respectively. Whereas, the correlation between NDVI and LST showed positive correlation with the coefficient of R2 = 0.2577 in 2000, which increased to R2 = 0.5959 in 2020. This positive relationship highlighted the fact that an increase in LST provides favourable conditions for the vegetation growth in the valley during the pre-winter season 2000 and 2020 respectively. The main outcome of the paper, we believe, will be helpful in analysing the dynamics of land cover changes and sustainable environmental planning in the Beas Valley, which is crucial for livelihood sustainability of the people residing in the already fragile IHR.
Isha Thakur, Renu Lata, Jagdish Chandra Kuniyal, and Sayanta Ghosh
Springer Nature Singapore
Sayanta Ghosh, Renu Lata, Isha Thakur, K. C. Gouda, and J. C. Kuniyal
Springer Nature Singapore