The satellite-based nighttime lights (NTL) data from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS), available in the public domain from 1992 to 2013, are extensively used for socio-economic studies. The improved NTL products from the Visible Infrared Imaging Radiometer Suite’s Day/Night Band (VIIRS-DNB), on-board the Suomi National Polar-Orbiting Partnership spacecraft and National Oceanic and Atmospheric Administration – 20 (NOAA-20) spacecraft’s, are now available since April 2012. This study investigates the potential of machine-learning algorithms for inter-calibrating them (i.e., DMSP-OLS and VIIRS-DNB) to produce time-series annual VIIRS-DNB-like NTL datasets for the time when VIIRS-DNB data did not exist, for long-term studies. Uttar Pradesh, one of the most populous and largest States of India, is selected as the study area. Two machine-learning algorithms are utilized: (1) Multi-Layer Perceptron (MLP), having deep neural networks (DNN) architecture, and (2) Random Forest (RF), a widely used method. The DMSP-OLS and VIIRS-DNB data of 2013 (common year of data availability) and ancillary data pertaining to land cover, topography, and road network are used to train the models. The qualitative and quantitative analysis of annual VIIRS-DNB-like NTL images simulated from annual DMSP-OLS composites of 2004–2012 indicates that RF captures better spatial details at the local-scale and is able to efficiently handle the saturation problem at urban centers; while MLP is found to be superior at regional-scale. Both MLP and RF models significantly reduce the blooming effect around settlements, a common problem observed in DMSP-OLS data. It is inferred that depending on the research objectives, both RF and MLP algorithms can be appropriately utilized for producing VIIRS-DNB-like NTL images from DMSP-OLS annual NTL composites. The research can be further expanded by using other DNN architecture-based algorithms and improved spatio-temporal ancillary datasets over areas with different socio-economic, physiographic, and climatic settings.

The night-time light (NTL) data from Visible Infrared Imaging Radiometer Suite’s Day/Night Band (VIIRS-DNB) onboard Suomi National Polar-Orbiting Partnership (S-NPP) spacecraft available since April, 2012 have successfully addressed some of the limitations of Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS), owing to improved radiometric and spatial resolutions and onboard calibration. Earlier, DMSP-OLS NTL images majorly contributed for understanding the spatio-temporal dynamics of socio-economic factors such as Gross Domestic Product (GDP) and Electric Power Consumption (EPC), but the problem of saturated pixels and blooming effect affected the correlation with sum of lights (SOL). In the present study, we evaluate different methods to generate the annual VIIRS-DNB NTL composites from available monthly products for estimating EPC at different scales, and compare their performance with DMSP-OLS annual composites. The VIIRS-DNB annual NTL composites are prepared using various statistical measures, seasonal decomposition and principal component analysis (PCA). The SOL derived from these composites were analysed with respect to EPC at Zonal and Electricity Distribution Company (DISCOM) levels in the Uttar Pradesh State of India to identify the most appropriate annual representation of NTL from VIIRS-DNB. It is found that the annual composites prepared using PCA method provide the best results at both Zonal and DISCOM levels. Further, VIIRS-DNB annual NTL composites perform better than the DMSP-OLS composites. At the Zonal level, some of the highly populated and fully electrified Zones exhibit a distinct SOL-EPC relationship, attributable to enhanced power consumption by indoor equipment and industrial activities, which is not captured by satellite. Finally, empirical EPC prediction models are developed based on time-series VIIRS-DNB data from 2013 to 2017, which will help understanding the EPC patterns at regional level in the State of Uttar Pradesh.

This study investigates vegetation dynamics in boreal forests of Interior Alaska, focusing on topography, fire history, and climate influences. The study area includes Bonanza Creek Experimental Forest (BCEF) and surrounding region, categorized by topography (upland, floodplain, lowland) and fire history. Using Mann–Kendall trend and Theil–Sen slope analyses on Landsat-derived spectral metrics: Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR), we observed a shift from browning to greening trends, particularly in historically burned areas. The photosynthetic activity in burned upland converged with unburned areas ~30 years post-fire, coincident with a shift towards deciduous dominance during post-fire succession. Normalized Difference Moisture Index (NDMI) trends revealed a significant increase in vegetation moisture content across all topographies. We introduce Effective Seasonal Precipitation Index (ESPI), which combines prior-year annual precipitation with current-year spring snow depth. Its positive correlation with NDMI highlights its potential for monitoring vegetation moisture dynamics at the landscape scale. Furthermore, by correlating dendrochronology-based climate indices, we found strong correlation between NDMI and normalized Supplemental Precipitation Index (nSPI), across topographies. Overall, this research provides critical insights into how climate and fire influence interior boreal vegetation, highlighting the effects of increased precipitation, and topography on shaping differential vegetation responses across the landscape.