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Natural resources are under tremendous amounts of threat as a result of the expanding human population, which over time intensifies changes in Land use and Land cover (LULC). Understanding how various machine learning classifiers function is crucial as the demand for an accurate estimate of LULC from satellite images. The purpose of this research was to classify the LULC in the entire Karnataka state, using three distinct methods on the Google Earth Engine (GEE) namely RF (Random Forest), SVM (Support Vector Machine) and CART (Classification Regression Trees), are examples of machine learning techniques. The LULC is classified by the training sets using supervised classification. The NDVI (Normalized difference vegetation index) was assessed and used to increase classification accuracy. The LULC classification for the years 2015 to 2021 utilizes multi-temporal images like Sentinel-2, Landsat-8, and MODIS data with spatial resolution of 10 m, 30 m, and 250 m. Agricultural land, Built-up land, Forest land, Fallow land, wasteland, water body and others, are major LULC classes, it lies on a level I classification. According to the findings, the change % of agricultural land is high from 2015 (64.03%) to 2021 (67.81%), this roughly increased about 3.78% during the study year. While water bodies increased by 5.25 to 6.3%. Based on the results, the largest LULC group is agricultural land (122,789.4 km2 or 64.03%), followed by forest land (37,678.56 km2 or 19.65%). Increased built-up land in the studied area indicates extraordinarily rapid urban growth in major cities of the state. This research offers a reliable approach for comprehensive, automated, and LULC classification in Karnataka State. [ABSTRACT FROM AUTHOR]

The availability of productive land is significantly impacted by the global phenomenon of urbanization. The amount of land available for food production and other essential activities decreases as cities grow because the urban perimeter encroaches on rural and natural areas. Conducting research on urban sprawl analysis and land use land cover (LULC) change assessment is essential in ensuring sustainable urban growth. Bengaluru, a rapidly expanding metropolitan city, has a significant impact on the area around it, making it a prime location for this kind of study. In this study, authors sought to assess how urban sprawl affected LULC in the Bengaluru rural district that surrounds the city of Bengaluru. The study evaluated changes in LULC over a two-decade period using remote sensing data and GIS tools. Five LULC classes were used to categorize the study area: settlement, waterbody, vegetation, agriculture, and barren land. The maximum likelihood technique was used to classify Landsat images from three different time periods using the supervised image classification method in the ERDAS software. Accuracy assessment was used to gauge the classified images' accuracy. The study's important findings showed how the LULC classes in the study area have been negatively impacted by the urban sprawl. The study emphasizes the significance of ongoing research in LULC change assessment and urban sprawl analysis to ensure sustainable urban growth and safeguard the availability of productive land. [ABSTRACT FROM AUTHOR]

Land degradation is a problem of grave concern and a major factor leading to it is soil erosion. Soil erosion and its rate are influenced by various factors intrinsic to the soil or related to the soil's external environment. These factors include rainfall, soil erodibility, topographic characteristics, crop cover, and conservation practices. The revised universal soil loss equation (RUSLE) is a well-renowned empirical formula used to compute a particular area's mean annual soil loss. Remote sensing (RS) and geographical information system (GIS) technologies make modeling and execution of RUSLE easy, reliable, and cost-effective. So, these were employed to compute the spatial distribution of soil erosion risk area in Nagavangala micro-watershed in Karnataka state, India. All the factors were generated using metrological data, CartoSat DEM, Quick bird imagery, and laboratory analysis data using GIS software and integrated in a GIS environment to estimate the soil loss rate. It was found that cropland (41.81%) was the dominant land use followed by agricultural plantation (41.5%) and scrubland (11.09%). The average annual soil loss of the watershed was estimated to be 9.80 t/ha/year. The average annual soil loss was highest in the area under scrubland (10.58 t/ha/year) followed by cropland (10.2 t/ha/year). The soil erosion map thus generated can serve as a basis for adopting suitable soil and water conservation measures in the watershed for sustainable management of the resources in it. [ABSTRACT FROM AUTHOR]

The Earth is frequently changed by natural occurrences and human actions that have threatened our environment to a certain extent. Therefore, accurate and timely monitoring of transformations at the surface of the Earth is crucial for precisely facing their harmful effects and consequences. This paper aims to perform a change detection (CD) analysis and assessment of the Dakshina Kannada region, being one of the coastal districts of Karnataka, India. The spatial and temporal variations in land use and land cover (LULC) are being monitored and examined from the data received as LULC maps from the National Remote Sensing Agency, Indian Space Research Organization, India. The time-series data from advanced wide-field sensor (AWiFS) Resourcesat2 satellite as LULC maps (1:250k) are analyzed using a deep learning approach with an encoder–decoder architecture with dual-attention modules for the change analysis. The model provides an overall accuracy and meanIOU(intersection over union) of 94.11% and 74.1%. The LULC maps from 2005 to 2018 (13 years) are utilized to decide the variations in the LULC, including urban development, agricultural variations, vegetation dynamics, forest areas, barren land, littoral swamp, and water bodies, current fallow, etc. The multiclass area-wise changes in terms of percentage show a decline in most LULC classes, which raises a point of concern for the environmental safety of the considered area, which is highly exposed to coastal flooding due to increased urbanization. [ABSTRACT FROM AUTHOR]

Historically marginalized foods, which occupy the social periphery, and often function as a bulwark in times of hunger, are increasingly being rediscovered and revalued as niche commodities. From açaí to quinoa, the move from marginal to miracle is often tied to larger narratives surrounding sustainable development, resilience to climate change, and traditional foodways. This article analyses the recent move towards millet production and consumption in the southern Indian state of Karnataka. Focusing upon one of the grain's chief proponents, I explore how narratives surrounding millets are grounded in conceptions of cultural authenticity and bioregionalism. Drawing upon human geographer's analyses of the turn towards the 'local' in food activism, I contribute to the development of critical bioregionalism, an emerging theoretical framework that explores how questions of value, identity, political economy, and histories of land use intersect to structure our understandings of marginal foods and their resurgence. [ABSTRACT FROM AUTHOR]

Background: Global climate change and its impact on crop water requirement have widely discussed in recent years. In the present century, climate change had become a significant concern and atmospheric temperature is the dominant climatic factor that indicates the changes in both regional and global scales. This study was undertaken to evaluate the trend and predict the changes in crop water requirement under various climate change scenarios. Methods: The statistical nonparametric Mann-Kendall test and Sen’s slope used to identify trend in the data series. In this study ArcGIS V. xx software used for investigating spatial patterns in data. CROPWAT-8.0 model used for calculation of crop water requirement under various climate change scenarios. Total six climate change scenarios were considered for assessment. Result: The crop water requirement (ETc ) of pigeonpea estimated and exhibited an increasing trend and a decreasing trend in study area in the past 35 years. The spatial distribution maps reveal that the distribution of ETc is found an increasing trend in all the scenarios to reference ETc . An increasing trend of ETc of pigeon pea was observed in all the places under various climate change scenarios. It was suggested to promote rainwater harvesting, soil and water conservation and increase ground water recharge in the study area to minimize the risk of yield reduction due to the availability of minimum water under changing climatic condition. [ABSTRACT FROM AUTHOR]

Zoning laws that restrict rural land to agricultural production pose an important institutional barrier to industrial development. We study the effects of the Industrial Areas (IA) program in Karnataka, India, which rezoned agricultural land for industrial use, but without the economic incentives common with other place-based policies. We find that the program caused a large increase in firm creation and employment in villages overlapping the IAs. Moreover, the surrounding areas experienced spillover effects, with workers shifting from agricultural to non-agricultural employment, and entrepreneurs establishing numerous small-scale service sector and agricultural firms. [ABSTRACT FROM AUTHOR]

Rivers are vital freshwater resources that cater to the needs of society. The burgeoning population and the consequent land-use changes have altered the hydrologic regime with biophysical and chemical integrity changes. This necessitates understanding the land-use dynamics, flow dynamics, hydrologic regime, and water quality of riverine ecosystems. An assessment of the land-use dynamics in the Aghanashini River basin reveals a decline in vegetation cover from 86.06% (1973) to 50.78% (2018). The computation of eco-hydrological indices (EHI) highlights that the sub-watersheds with native vegetation had higher infiltration (and storage) than water loss due to evapotranspiration and meeting the societal demand. The computation of water quality index helped to assess the overall water quality across seasons. The study provides insights into hydrology linkages with the catchment landscape dynamics to the hydrologists and land-use managers. These insights would aid in the prudent management of river basins to address water stress issues through watershed treatment involving afforestation with native species, appropriate cropping, and soil conservation measures. [ABSTRACT FROM AUTHOR]

This article presents a combined seismic risk assessment of Mangalore city, considering seismic hazard and Land Use (LU) patterns. Probabilistic seismic hazard analysis was carried out considering different source models and attenuation relations to predict Peak Ground Acceleration at every square kilometer. LU of the area was assessed using GIS tools with the built-in supervised Gaussian maximum likelihood algorithm on processed remote sensing data from temporal Landsat images. The risk map showed the concentration of built-up area South-West, toward the sea and the seismic hazard following an increasing trend from the coast to further inland. [ABSTRACT FROM AUTHOR]

Land Use Land Cover (LULC) change monitoring plays very significant role in planning, policy making, management programs required for development activities at regional levels of any country. This study is an attempt to monitor LULC change of Vijayapura taluk, Karnataka, India for the period of 25 years from 1995 to 2021 using Remote Sensing (RS) and Geographic Information System (GIS). Satellite Images from Sentinel-2A MSI (Multispectral Imager), Landsat-5TM (Thematic Mapper) are used to generate LULC maps. Vegetation Change in the study area is computed using Normalized Difference Vegetation Index (NDVI) and results show that vegetation rate is increased from 0.6% in 1995 to 27.5% in 2021. Supervised Classification is carried out by using Maximum Likelihood Classification (MLC). 5 major classes considered for classification are namely: Waterbodies, Cropland/Vegetation, Fallow Land, Built-up Area and Barren Land. ArcGIS software tool is used for implementing the proposed study. Google Earth Pro used for accuracy assessment which is done by taking ground truth values for corresponding Classifications. Results show that the proposed system is able to achieve 88.16% of overall accuracy. [ABSTRACT FROM AUTHOR]