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The information on land use/land cover (LULC) is indispensable in regional planning, policy formulation and tracking land use/land cover changes (LULCC). The rapid urbanization of hilly terrains, driven by population growth, has significant implications for landslide risk reduction. Recognizing the need for an innovative approach for extracting LULC information, the present study uses a random forest (RF) classifier to develop a novel, pre-trained and universal tool that automatically generates LULC classification maps based on natural colour satellite imagery without any training input from the end-user. The proposed framework with an overall accuracy of 0.75 and an area under the curve (AUC) score of 0.95 in the receiver operating characteristic curve (ROC) approach was used for mapping built-up area expansion in regions susceptible to rainfall-induced landslides in Idukki block panchayat (administrative division), Kerala, India. By comparing the LULC information for the years 2012 and 2022, it was understood that the built-up area in the location has increased from 12.76% of the total area in 2012 to 26.48% in 2022. It is important to consider the rapid increase in built-up area expansion in the 'very high' landslide susceptibility zones in the study area. This clearly demonstrates the need for hazard inclusive planning and tracking of LULCC, for disaster risk reduction. Research Highlights: A pre-trained Land Use/Land Cover (LULC) classification tool is developed using the Random Forest (RF) classifier. Based on natural colour satellite imagery, the tool automatically generates LULC maps for various landscapes worldwide. The tool demonstrates a satisfactory performance, achieving an overall accuracy of 0.75 and an overall ROC AUC score of 0.95. The tool was used to understand the LULC changes in Idukki block panchayat between 2012 and 2022. [ABSTRACT FROM AUTHOR]

This paper examines the significant agrarian transformations in India's Himalayan region of Kashmir, which is witnessing a rapid transition into commercial farming and, simultaneously, an increasing trend of deagrarianisation. The study uses a mixed-method approach to investigate why farmers adopt commercial farming in this region and how the processes of deagrarianisation, reconfiguration of land use, and deepening commodification have become inevitable for many small and medium-scale farmers as a result of this process. In the analysis, our empirically tested theoretical model confirms that institutional factors in terms of state support work as a significant driver toward commercialisation. We then argue that combined with the stimulus of the state, a vigorous process of neo-extractivism is being promoted, which renders the substantive changes in agrarian transitions by displacing the subsistence sector. The study highlights that this contemporary commercialisation is altering the social and physical landscape and driving broader forms of dispossession and consolidation of farmlands at micro-levels. Furthermore, it raises questions about the fundamental role of the Indian state's neo-extractivist policies in a politically contested region. [ABSTRACT FROM AUTHOR]

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]

Soil erosion is one of the major environmental concerns, causing a reduction in soil carbon stock and soil fertility. It is generally difficult to estimate soil erosion in hilly terrains. To overcome this, different types of erodibility indices are developed and used to describe the intensity of soil erosion. The present study was conducted in Nagaland state of northeast India, where clay ratio (CR), modified clay ratio (MCR), critical level of organic matter (CLOM) and erodibility factor (K) were used to assess the severity of soil erosion under different land uses. To study these, soil samples from forest, jhum, fallow jhum and tea gardens were collected from 0 to 30 cm soil depth. The results showed that sand and clay content were significantly different under different land uses, while soil organic carbon (SOC) and stocks followed the same trend, with maximum and minimum values under tea (60.55 t C ha–1 ) and jhum lands (40.74 t C ha–1 ) respectively. Among the different erodibility indices, only CR and MCR values were found to be significantly different from each other. Higher values of CR and MCR under jhum and fallow jhum lands indicate that these land uses are more prone to erosion. The present study provides a picture of severity of soil erosion under different land uses in Nagaland and recommends that CR and MCR can be used to evaluate soil erosion in hilly terrain areas. [ABSTRACT FROM AUTHOR]

This manuscript provides low-cost smart irrigation for agricultural land using IoT. For irrigation purposes, using a farmland manual system is time-consuming and risky. Agriculture is the most important occupation in India because villagers are primarily dependent on agriculture. We offer a solution to overcome traditional shortcomings. Farmland houses are Embedded by sensors through the node's microcontroller MCU. The statistics are handled with the microcontroller and sent to the cloud server. It acts as a bridge to send statistics through cell phones. The valve is activated using information acquired from signals sent to a cell phone. This increases performance and saves a lot of water. The hardware configuration becomes advanced and is examined within the farmland. The advantage is that the yields get enhanced for farmers. [ABSTRACT FROM AUTHOR]

The Ramsar Convention on Wetlands envisages the significance of various wetland types, including playas, attributing magnitude to their ecological value, biodiversity, and role in ecosystem services. However, both natural influences such as shifts in seasonal rainfall cycles, and anthropogenic factors like urbanization are determinantal in the sustenance of the wetlands. This study focuses on Kanjli wetland, a Ramsar site influenced by a mélange of natural and human-driven forces affecting its hydrological dynamics. Situated in the agricultural plains of Punjab, India, this research endeavors to gauge the impacts of changing land use in the surroundings of Kanjli wetland. A mixed-method approach is employed, Landsat satellite images from 1990 and 2022 are initially classified to assess landscape transformations. Subsequently, primary data is collected and analyzed through fieldwork conducted in villages neighboring the wetland area. The statistically processed results indicate that approximately 85 percent of encroachment in the study area stems from infrastructural developments, exhibiting both positive and negative implications for the wetland ecosystem. The ensuing discussion underscores the imperative of conserving and sustainably managing the unique ecosystems of Ramsar wetlands in alignment with the core objectives of the Ramsar Convention. [ABSTRACT FROM AUTHOR]

Limited information is available on the greenhouse gas (GHG) emission rates in the peri-urban agricultural systems. This information is necessary for the changing paradigm of climate-smart agriculture with less carbon footprint. The decreasing trends in water availability, agricultural area and productivity, and negative changes in agricultural land use patterns make agriculture, especially fruit and vegetable cultivation in peri-urban areas of Hyderabad city, in India, highly vulnerable to climatic variations. Hence a study was contemplated to develop a methodological framework to study the changes in land, water and energy use and their impact on GHG emissions and, thereby, on agricultural production and productivity. Variations in GHG emissions and carbon credit in the identified peri-urban sites were calculated using the accounting framework with respect to changes in land use and energy usage. [ABSTRACT FROM AUTHOR]

• Greening in China and India at administrative and built-up levels. • Greening status in relatively poor regions of China and India. • Comparing greening measures and their pros and cons in China and India. • China and India's greening experiences offer insights for sustainable development. Vegetation greening not only offers climate benefits but also enhances residents' well-being in urban areas. China and India lead the world in terms of global greening. However, systematic studies summarizing the green development levels of both countries are still lacking. Here, we explore the regional significant greening levels and spatial distribution characteristics of China and India across multiple spatial scales using the significant greening level as an indicator. Additionally, we analyze the diverse relationships between significant greening in China and India and factors such as land use, economy, and population. The results indicate that areas with significant greening in China are primarily located in some mountainous regions in the central and eastern parts, while in India, areas with significant greening are mainly distributed in plain and plateau regions. Within significantly greening areas, 55.57% in China are forests, and 95.67% in India are croplands. The greening level in built-up areas in China is mostly positively correlated with economic factors, but in economically disadvantaged areas, the greening level is not necessarily the lowest. In Indian built-up areas, the greening level is negatively correlated with the economy. Regarding the relationship with urbanization, the greening level in the surrounding areas of built-up areas in both China and India generally decreases with the increasing degree of urbanization's influence, but the damage caused by urban development to the greening level in India is much higher than in China. 83% of cities in China have medium-range per capita greening levels, while 60% of cities in India have per capita greening levels in the lower range. A systematic analysis and summary of China and India's greening development can provide strategic references for other developing countries to enhance greening initiatives and improve their level of sustainable development. [ABSTRACT FROM AUTHOR]

The understanding of spatio-temporal variation in land use and land cover (LULC) patterns is crucial for managing catchment land use planning, as it directly influences of tropical reservoir water quality and the subsequent Nutrient Contamination (NC) of unmonitored water bodies. The current research attempts to accurately measure the influence of LULC and its associated determinants on the quantities of NC loads by using Chl-a as a proxy, within tropical reservoirs, i.e. Bhadra and Tungabhadra, located in same river catchment. This Chl-a spread calculated by the Maximum Chlorophyll Index (MCI) derived from Sentinel 2 satellite data products covering the period from July 2016 to June 2021 were done using Google Earth Engine (GEE) platform. The validation analysis confirms the robustness of the methodology with a strong correlation between MCI-calculated values and EOMAP (Earth Observation and Environmental Services Mapping) Chl-a (μg/L) data points for both reservoirs, Bhadra (R2 = 0.64) and Tungabhadra (R2 = 0.68). The findings reveal that, Tungabhadra reservoir consistently exhibits an excessive spatial distribution of Chl-a spread area (17 km2 to 335 km2), reflecting nutrient-rich water inflows, particularly evident during the post-monsoon period. This notable rise could be linked to harvesting the Kharif crop, resulting in elevated nutrient concentrations. In contrast Bhadra reservoir, dominated by forested areas, maintains relatively lower Chl-a spread areas (<20 km2), highlighting its pivotal role in maintaining water cleanliness and serves as a riparian boundary. In addition, the changes in LULC classes show a strong relationship with variation in Chl-a during the studied period, for the Bhadra Reservoir R2 = 0.51 (F- statistics = 3.983, p = 0.021), and the Tungabhadra Reservoir R2 = 0.802 (F- statistics = 7.489, p = 0.0143). This highlights how changes in land use significantly shape contamination dynamics, deepening our understanding of nutrient inputs and contamination drivers in tropical reservoirs. • Sentinel 2 has potential for Chl-a estimation using Maximum Chlorophyll Index. • Demonstrates suitability for different trophic states of water bodies. • Heterogenous catchments LULC significantly influences water quality. [ABSTRACT FROM AUTHOR]

[Display omitted] Soils at 57 ancient Hindu temples in India are documented from field profiles as a basis for understanding livelihoods of the five cults of Puranic (Smarta) Hinduism during the first millennium CE. Similar soils were found at different temples of the same deity, but different kinds of soils at temples of different deities. Temples of Ganesha and Hanuman, for example, are alluvial Entisols and Vertisols, the principal cultivable soils of India. Temples of Vishnu and Lakshmi are well drained Alfisols and Aridisols suitable for grazing, housing and markets. Different soils supported different livelihoods, social classes and cults, such as servant (shudra) class for farmers Ganesha and Hanuman, and herder-merchant (vaishya) class for Vishnu and Lakshmi. This result matches accounts in the Matsya Purana (250–500 CE) specifying dark organic soils for shudras and red soil for vaishyas. Puranic Smarta polytheism may reflect amalgamation of different tribal traditions during expansion of Gupta and other Hindu empires. [ABSTRACT FROM AUTHOR]

[Display omitted] • Soil properties and enzymatic activities varied across land use types and villages. • Agroforestry establishment accelerated soil enzymatic activities by 39.41–90.17%. • Cultivation caused massive decline in level and variability of enzymatic activities. • Climate, vegetation, and management practices determined the enzymatic activities. Soil microbial enzymatic activities, often used as indicators of soil health and fertility, are vulnerable to changes in climate and anthropogenic activities. This study analyses the responses of specific soil enzymatic activities– dehydrogenase (DHA), acid phosphatase (APH), β-glucosidase (BG) and urease (URE)– in Nitosols/Luvisols of the subtropical zone of the Central Himalaya, India. We investigated how these enzymatic activities vary with different land uses: arable land (AL), kitchen garden (KG) and peach orchard (PO), across four villages– Niglat, Kainchidham, Majhera and Syalikhet. Considering associated management practices, we quantified cumulative average soil enzymatic activities for the rabi season (2017–2018). In general, catalytic activity of the selected enzymes was highest in Syalikhet and lowest in Majhera. However, for land use types, enzymatic activities were lower in AL and KG (75.56–90.17% and 39.41–61.72%, respectively) compared to PO values. MANOVA analysis evaluated significant spatial differences (P < 0.001) in enzymatic activities. Soil enzymatic mean values were highest in the surface layer (0–10 cm) and decreased gradually with increasing depth. Parameters such as soil moisture content (SMC) and pH, along with vegetation, management practices, and nutrients such as SOC, TN, K, AP, and N forms (NH 4 +–N and NO 3 ––N) showed significant and strong correlations with all the soil enzyme activities (r = 0.40–0.97; P < 0.05). TN, SOC, K, SMC, pH, AP, NH 4 +- N, and clay content were key determining factors of soil enzymatic activities, explaining approximately 59% of the variation. These results suggest that using soil microbial enzymatic activities as soil quality indicators is a feasible approach to assess the impacts of vegetation, management practices, and soil profile depths in soils under different land use types/systems in selected impoverished villages of the fragile Central Himalaya, India. [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]