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Land use land cover (LULC) assessment is essential for monitoring naturally available land resources through spatio-temporal changing pattern to protect land resources sustainably. The primary objective is to identify the changing pattern in LULC over last 10 years in Imphal-Iril catchment. Multi-spectral datasets of Landsat 7 and 8 were used for five different time periods (2011, 2013, 2015, 2017 and 2019). Supervised classification method was employed to generate the classified LULC maps using the maximum likelihood classifier approach in ArcGIS 10.3. In this assessment, five major land use classes have been classified as vegetation, agriculture, settlement, barren land and water body, and the dominant land cover is vegetation. The classified images obtained from this technique were checked for their accuracy using statistical kappa co-efficient and overall accuracy. According to this study, settlement and barren areas have been significantly increased by 72.25% (39.03 km2) and 22.60% (118.29 km2) during the last 10 years, whereas areas assigned to agriculture, vegetation and water bodies have decreased by 27.26% (72.16 km2), 7.59% (73.22 km2) and 60.39% (11.94 km2), respectively. LULC map for the year 2030 was also predicted using MOLUSCE model in QGIS. The results of this assessment will be significant to the policy implicates for sustainable LULC management in the region. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has posed unprecedented challenges to the livelihoods of rural households, especially in developing countries. The reverse migrated workers faced the most economic crisis. They have created additional pressure on local resources and opportunities to maintain their livelihood. In this connection the agriculture-livestock-forestry nexus plays a significant role by providing them livelihood opportunities. This paper attempts to understand the resource use patterns during the post-COVID period, and analyse the impact of agriculture-livestock-forest nexus on household income diversification and poverty reduction. The present study uses a novel approach to fulfil the objectives of the study. A well-designed survey was conducted to collect the necessary primary data from sixteen sample villages of West Bengal, India. The scientific design of the survey and data collection procedure is maintained. Moreover, geospatial techniques are used to examine the land use and land cover patterns of the study areas. The econometric models are estimated to inspect the role of agriculture-livestock-forest nexus and household characteristics on household level income diversification and the reduction of poverty. Our analysis reveals that a significant number of the sample households depend on agriculture, livestock and forestry to maintain their livelihoods. The reverse migration of workforce has created additional pressure on these activities. This is mainly due to non-availability of other non-farm employment opportunities locally. The analysis of econometric models shows that while engagement in different activities of the primary sector makes income diversified, it is more so when the households possess necessary technologies, skills and financial supports. All of these are crucial given that income diversification helps households to overcome of poverty. The intricate web of the agriculture-livestock-forestry nexus serves as a critical source of livelihood for a substantial number of households, yet the challenges and opportunities it presents necessitate targeted policy interventions. Enabling technology transfer, fostering skill development, and extending financial support emerge as key measures to diversify rural livelihoods, thereby fostering sustainable rural development and poverty reduction in the post-pandemic landscape. The integration of socioeconomic data and geospatial satellite information offers an invaluable basis for formulating effective policy strategies in this evolving era. [Display omitted] • Examines the agriculture-livestock-forest nexus and its impact on income diversification during the post-COVID period. • Analysing resource use and livelihoods with primary data, geospatial and econometric methods. • Technology transfer, skill development, and financial aid boost rural income diversification. • Socio-economic and satellite data integration aids in crafting impactful policies. [ABSTRACT FROM AUTHOR]

Rainwater Management (RWM) interventions were implemented to address the scarcity of water and to restrict the degradation of land resources in the Parasai-Sindh watershed covering an areal extent of 1246 ha located in Jhansi district of Bundelkhand region, Central India. The region is constrained by several factors, the most significant of which is its high vulnerability to natural disasters and poor infrastructure development, resulting in low agricultural production and precarious livelihoods. This study's goal was to evaluate the impact of RWM intervention on groundwater dynamics and the changes in LULC that it has brought about. A total of 380 dug wells were monitored to investigate the ground water dynamics. Integration of Remote Sensing (RS) and Geographic Information Systems (GIS) were used to prepare LULC maps using Landsat images. The changes in LULC were studied before and after the implementation of RWM interventions. The study revealed enhanced groundwater availability for agricultural and domestic applications. Due to increased water availability, a significant portion of fallow land was converted into cropland. Built-up land coverings have also increased in the watershed. A decrease in the areal extent of barren rocky and scrubland was witnessed over the study period. The area under plantation was also increased.The study underlines the relevance of RWM interventions in restoring watershed land cover, assisting in poverty alleviation, and providing long-term livelihood. [ABSTRACT FROM AUTHOR]