The all-new NDUI+ dataset is a global, high-resolution (30-meter) remotely sensed urban dataset, covering the period from 1999 to the present. It solves key challenges in remote sensing, including gaps in resolution, coverage, and the continuity of urban data. This comprehensive dataset is valuable for a wide range of applications, such as urban growth analysis, microclimatic variability studies, and assessments of economic impacts, among others. NDUI+ data can be generated globally using the codes made available at https://github.com/manmeet3591/ndui_plus, and the trained weights available in Zenodo.
Read MoreCOVID-19 cases surged in late 2019, leading to worldwide lockdowns that closed non-essential places and activities, industries, and businesses to halt the spread of the virus. Many studies suggested improved air quality during lockdowns. However, these findings often focused on core city limits and did not account for heavy pollution sources outside cities (around the fringe areas), such as factories, power plants, and coal mines, which operated continuously for energy needs even during lockdowns. Therefore, this study quantified and re-analyzed the air quality data using a top-down approach.
Read MoreThis is a working paper. Keeping continuous, long-term data to examine changes in urban surroundings is crucial as cities expand and develop. The DMSP OLS nighttime lights data and the Landsat NDVI were used to create the Normalized Difference Urbanization Index (NDUI), which has proven to be an invaluable resource for studying urban areas. However, DMSP's reach and usefulness are constrained by the fact that data collecting ended in 2014 while VIIRS has continued to collect the nighttime lights data since 2012...
Read MoreThe increasing demand for tropical timber from natural forests has reduced the population sizes of native species such as Cedrela spp. because of their high economic value. To prevent the decline of population sizes of the species, all Cedrela species have been incorporated into Appendix II of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). The study presents information about the modeled distribution of the genus Cedrela in Peru that aims to...
Read MorePalmer Drought Severity Index (PDSI) is the most effective and well-acknowledged drought severity index that particularly determines the long-term drought conditions over the forest and other terrestrial ecosystems. However, the sensitivity of PDSI has not been explored yet based on productivity (i.e., Gross Primary Productivity (GPP)), biophysical parameters (i.e., biomass—Leaf Area Index (LAI) and Enhanced Vegetation Index (EVI) and greenness content—Normalized Difference Vegetation Index (NDVI)), and absorbed solar radiation by plants (i.e., fraction of Absorbed Solar Radiation (fAPAR)) over a humid-subtropical forest ecosystem...
Read MoreA proper Geomorphic study of a region can be useful in understanding past and present environmental circumstances and analyzing potential environmental risks. Careful analysis of morphodynamic processes and existing diagnostic land forms reveal several aspects about the origin, characteristics and possible pattern of morpho-climatic interactions on the landscape over temporal scale, which helps significantly in proper terrain evaluation from societal welfare and integrated management point of view, including environmental risk assessment and disaster management. This paper has made a thorough geomorphic investigation...
Read MoreHigh-resolution Forest biophysical parameter estimation is crucial to understand forest structural and functional variability. Moreover, mapping high-resolution biophysical products is significant to capture accurate forest carbon fluxes and understand seasonal variability. The existing biophysical products are coarse in spatial resolution and unable to capture intra-annual variability. In this study, we proposed a random forest machine learning approach embedded in Google Earth Engine to retrieve three major forest biophysical parameters. The training samples were distributed in a 70:30 ratio for model training and validation. The outcome of the work shows promising results that hold a good agreement with SNAP-derived biophysical variables, whereas the agreement is moderate-to-poor for MODIS and VIIRS biophysical products...
Read MoreResearch activity featured by COSAM Today news
Read MoreTeaching Assistant at the Department of Geosciences, Auburn University, USA. Courses Teaught: Advanced GIS, Drones and Geospatial Applications, Quantitative Methods and Spatial Analysis, Concepts of Science, Global Geography.
1/11/2024Working in a USGS-funded project - 'Earth as Art’ Interactive Global Museum. In this project, I am turning the complex remote-sensing images into simple, artistic photographs and working with a multidisciplinary team to transform the processed images into engaging virtual reality (VR) based interactive modules, ensuring alignment with educational objectives and target audience engagement to promote interest in Earth science and STEM disciplines.
Learn More10/1/2023Researching the impacts of global urbanization footprints and urban rainfall effect climatologies funded by National Aeronautics and Space Administration (NASA)At Department of Geosciences, Auburn University Duration: Aug 2021 to Present
Learn More8/16/2021Professionally worked and contributed to the project- “Space Based Information Support for Decentralized Planning (SIS-DP) Update”, sponsored by National Remote Sensing Centre (NRSC), Indian Space Research Organization (ISRO), Govt. of India. Duration: Mar 2021 - Jul 2021
Learn More3/1/2021Engaged as the Lead GIS Consultant to the office of West Bengal State Rural Development Agency - Purulia Division, Govt. of West Bengal for the project - ’Utility-Facility mapping, geo-database creation, and verification of road construction status sanctioned under Pradhan Mantri Gram Sadak Yojana (PMGSY) scheme' , funded by Government of India.
Learn More9/2/2019info@subhasisghosh.com