recent publications

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.

COVID-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.

Urbanization is advancing rapidly, covering less than 2% of Earth's surface yet profoundly influencing global environments and experiencing disproportionate impacts from extreme weather events. Effective urban management and planning require high-resolution, temporally consistent datasets that capture the complexity of urban growth and dynamics. This study presents NDUI+, a novel global urban dataset addressing critical gaps in urban data continuity and quality. NDUI+ integrates data from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS), VIIRS Nighttime Light, and Landsat 7 NDVI using advanced remote sensing and deep learning techniques. The dataset resolves sensor discontinuity challenges, offering a seamless 30-meter spatial and annual temporal resolution time series from 1999 to the present. NDUI+ demonstrates high precision and granularity, aligning closely with high-resolution satellite data and capturing urban dynamics effectively. The dataset provides valuable insights for urban climate studies, IPCC assessments, and urbanization research, complementing resources like UT-GLOBUS for urban modeling.

The 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...

Palmer 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...

A 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...

High-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...