Browsing by Author "Gupta, Saurabh Kumar"

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    Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models
    (Multidisciplinary Digital Publishing Institute (MDPI), 2023-09-18T00:00:00) Rani, Ankush; Gupta, Saurabh Kumar; Singh, Suraj Kumar; Meraj, Gowhar; Kumar, Pankaj; Kanga, Shruti; ?urin, Bojan; Dogan?i?, Dragana
    The main aim of this study is to comprehensively analyze the dynamics of land use and land cover (LULC) changes in the Bathinda region of Punjab, India, encompassing historical, current, and future trends. To forecast future LULC, the Cellular Automaton�Markov Chain (CA) based on artificial neural network (ANN) concepts was used using cartographic variables such as environmental, economic, and cultural. For segmenting LULC, the study used a combination of ML models, such as support vector machine (SVM) and Maximum Likelihood Classifier (MLC). The study is empirical in nature, and it employs quantitative analyses to shed light on LULC variations through time. The result indicates that the barren land is expected to shrink from 55.2 km2 in 1990 to 5.6 km2 in 2050, signifying better land management or increasing human activity. Vegetative expanses, on the other hand, are expected to rise from 81.3 km2 in 1990 to 205.6 km2 in 2050, reflecting a balance between urbanization and ecological conservation. Agricultural fields are expected to increase from 2597.4 km2 in 1990 to 2859.6 km2 in 2020 before stabilizing at 2898.4 km2 in 2050. Water landscapes are expected to shrink from 13.4 km2 in 1990 to 5.6 km2 in 2050, providing possible issues for water resources. Wetland regions are expected to decrease, thus complicating irrigation and groundwater reservoir sustainability. These findings are confirmed by strong statistical indices, with this study�s high kappa coefficients of Kno (0.97), Kstandard (0.95), and Klocation (0.97) indicating a reasonable level of accuracy in CA prediction. From the result of the F1 score, a significant issue was found in MLC for segmenting vegetation, and the issue was resolved in SVM classification. The findings of this study can be used to inform land use policy and plans for sustainable development in the region and beyond. � 2023 by the authors.
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    Uncovering the hydro-meteorological drivers responsible for forest fires utilizing geospatial techniques
    (Springer, 2023-05-29T00:00:00) Gupta, Saurabh Kumar; Kanga, Shruti; Meraj, Gowhar; Kumar, Pankaj; Singh, Suraj Kumar
    Forest fires have become a growing concern worldwide, with climate change exacerbating their frequency and intensity. In the Simlipal region of India, forest fires are relatively rare; however, in 2021, significant damage occurred in the buffer area�s forests. Understanding the driving factors behind these events is essential for developing effective management strategies. This study investigates the impact of hydro-meteorological conditions on forest fire causes in the Simlipal region by analyzing Terra climatic data and geo-statistics for the period of 1984 to 2021. Long-term trends were determined using non-parametric tests on the Google Earth Engine (GEE) cloud computing platform. Our findings reveal that the maximum burned area location has a decreasing trend in Land Surface Temperature (LST), with a small portion (<10%) showing an increasing trend (0.02 �C/year) near burned locations. Wind speed is decreasing at a rate of ?0.006 m/s/year. The sudden forest fires are caused by the combined effect of increasing LST and decreasing wind speed in some areas (<10% of the region). However, the major factor contributing to forest fires in the entire area is the rising trend of annual potential water deficit and actual evapotranspiration, as well as an increasing trend of minimum temperature. The soil moisture deficit during the summer season, especially between 2012 and 2021, contributed to forest fires in the burned area. The soil moisture deficit during the summer season, particularly from 2012 to 2021, played a significant role in the occurrence of forest fires in the affected area. The study emphasized the need for increased attention to this region in order to preserve biodiversity, which was assessed through an analysis of burned severity mapping in GEE (Google Earth Engine). These findings have important implications for future forest management strategies in the Simlipal region. Climate variability is likely to exacerbate the frequency and intensity of forest fires in the region, necessitating effective management strategies to mitigate their impact. Such strategies could involve improving fire prevention and control measures, such as creating fire breaks and increasing the availability of fire-fighting equipment, as well as enhancing forest monitoring systems to detect potential fires early. Additionally, efforts to address climate change, proper management of land use practices, and reduce greenhouse gas emissions could help to mitigate the future impacts of forest fires in the Simlipal region and elsewhere. � 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.
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    Unveiling Nature�s Resilience: Exploring Vegetation Dynamics during the COVID-19 Era in Jharkhand, India, with the Google Earth Engine
    (Multidisciplinary Digital Publishing Institute (MDPI), 2023-09-08T00:00:00) Ahmad, Tauseef; Gupta, Saurabh Kumar; Singh, Suraj Kumar; Meraj, Gowhar; Kumar, Pankaj; Kanga, Shruti
    The Severe Acute Respiratory Syndrome Coronavirus Disease 2019 (COVID-19) pandemic has presented unprecedented challenges to global health and economic stability. Intriguingly, the necessary lockdown measures, while disruptive to human society, inadvertently led to environmental rejuvenation, particularly noticeable in decreased air pollution and improved vegetation health. This study investigates the lockdown�s impact on vegetation health in Jharkhand, India, employing the Google Earth Engine for cloud-based data analysis. MODIS-NDVI data were analyzed using spatio-temporal NDVI analyses and time-series models. These analyses revealed a notable increase in maximum vegetation greenery of 19% from April 2019 to 2020, with subsequent increases of 13% and 3% observed in March and May of the same year, respectively. A longer-term analysis from 2000 to 2020 displayed an overall 16.7% rise in vegetation greenness. While the maximum value remained relatively constant, it demonstrated a slight increment during the dry season. The Landsat data Mann�Kendall trend test reinforced these findings, displaying a significant shift from a negative NDVI trend (1984�2019) to a positive 17.7% trend (1984�2021) in Jharkhand�s north-west region. The precipitation (using NASA power and Merra2 data) and NDVI correlation were also studied during the pre- and lockdown periods. Maximum precipitation (350�400 mm) was observed in June, while July typically experienced around 300 mm precipitation, covering nearly 85% of Jharkhand. Interestingly, August 2020 saw up to 550 mm precipitation, primarily in Jharkhand�s southern region, compared to 400 mm in the same month in 2019. Peak changes in NDVI value during this period ranged between 0.6�0.76 and 0.76�1, observed throughout the state. Although the decrease in air pollution led to improved vegetation health, these benefits began to diminish post-lockdown. This observation underscores the need for immediate attention and intervention from scientists and researchers. Understanding lockdown-induced environmental changes and their impact on vegetation health can facilitate the development of proactive environmental management strategies, paving the way towards a sustainable and resilient future. � 2023 by the authors.