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<p>Appraisal of land use (LU) dynamics through time-series remote sensing data offers valuable insights into the extent and condition of a landscape, which is essential for the sustainable management of natural resources. Integrated spatial analyses of LU with social, ecological, hydrological, bio-geo-climatic, and environmental variables would aid in the prioritization of natural resources-rich regions (NRRRs). The current study assesses the LU changes in an agrarian district using temporal remote sensing data through a supervised machine learning technique- Random Forest (RF) in the Google Earth Engine platform (GEE) with access to a multi-petabyte catalog of satellite imagery and geospatial datasets, enabling efficient processing and analysis. Paddy cultivation spatial extent has increased from 0.74% (1973) to 18.41% (2024), with the increase in the extent of water bodies due to the Krishna and Tungabhadra Rivers. The simulated LU using Cellular Automata reveals that the area under agriculture will decrease to 1159.33 sq. km, and a significant increase in road network and industrial area is expected by 2038. The consequence of LU changes on soil health, evident from declining nutrients, necessitates the identification of natural resources-rich regions (NRRRs) for formulating effective policies for prudent management of natural resources to achieve sustainable development goals (SDGs especially SDG 1, 2, 6, 11, 12, 13, and 15) by exploring all feasible dimensions and analyzing the patterns and dynamics across various interdisciplinary themes such as social, hydrological, ecological and bio-geo-climatic. The study reveals that 15% of the total geographical area of the district is rich in natural resources (NRRR 1 and 2), which requires prudent management to sustain natural resources.</p> Graphical Abstract <p></p>

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Soil environmental health linkages with the landscape structure in Raichur district, Karnataka, India

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月15日

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  • T. V. Ramachandra,
  • Paras Negi

摘要

Appraisal of land use (LU) dynamics through time-series remote sensing data offers valuable insights into the extent and condition of a landscape, which is essential for the sustainable management of natural resources. Integrated spatial analyses of LU with social, ecological, hydrological, bio-geo-climatic, and environmental variables would aid in the prioritization of natural resources-rich regions (NRRRs). The current study assesses the LU changes in an agrarian district using temporal remote sensing data through a supervised machine learning technique- Random Forest (RF) in the Google Earth Engine platform (GEE) with access to a multi-petabyte catalog of satellite imagery and geospatial datasets, enabling efficient processing and analysis. Paddy cultivation spatial extent has increased from 0.74% (1973) to 18.41% (2024), with the increase in the extent of water bodies due to the Krishna and Tungabhadra Rivers. The simulated LU using Cellular Automata reveals that the area under agriculture will decrease to 1159.33 sq. km, and a significant increase in road network and industrial area is expected by 2038. The consequence of LU changes on soil health, evident from declining nutrients, necessitates the identification of natural resources-rich regions (NRRRs) for formulating effective policies for prudent management of natural resources to achieve sustainable development goals (SDGs especially SDG 1, 2, 6, 11, 12, 13, and 15) by exploring all feasible dimensions and analyzing the patterns and dynamics across various interdisciplinary themes such as social, hydrological, ecological and bio-geo-climatic. The study reveals that 15% of the total geographical area of the district is rich in natural resources (NRRR 1 and 2), which requires prudent management to sustain natural resources.

Graphical Abstract