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  1. Home
  3. Visual Image Design Based On Multi-sensor Machine Learning For Monitoring Plateau Lake Dynamics And Pasture Change.
  1. Home
  3. Visual Image Design Based On Multi-sensor Machine Learning For Monitoring Plateau Lake Dynamics And Pasture Change.

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Visual Image Design Based on Multi-sensor Machine Learning for Monitoring Plateau Lake Dynamics and Pasture Change.

Yue Shen1, XiaoPeng Niu1, Pei Wang1

  • 1Hebei Vocational University of Technology and Engineering, Xingtai, 054000, Hebei, China.

Scientific Reports
|May 13, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Land use changes in Sahiwal show declining vegetation and forest cover, with a significant increase in built-up areas over 30 years. This impacts ecosystem service values and resource sustainability, necessitating better land and water management.

Keywords:
Google Earth EngineLULC changeMachine learningNDVIRemote sensingVisual image

Related Experiment Videos

Area of Science:

  • Environmental Science
  • Remote Sensing
  • Ecosystem Services Analysis

Background:

  • Land use/land cover (LULC) changes significantly impact ecosystem services (ES) and resource sustainability.
  • Monitoring these changes is crucial for effective environmental management, especially in semi-arid regions.

Purpose of the Study:

  • To analyze multi-decadal LULC dynamics in the Sahiwal area from 1994 to 2024.
  • To quantify the resulting changes in ecosystem service values (ESV).
  • To assess the sustainability of water and vegetation resources.

Main Methods:

  • Utilized Landsat time-series data for LULC classification.
  • Incorporated Random Forests (RF) algorithm within Google Earth Engine (GEE) for systematic analysis.
  • Calculated ESV based on LULC changes and assessed resource sustainability.

Main Results:

  • Observed a decline in forest (0.229%) and vegetation (3.9%) areas, alongside a substantial increase in built-up areas (from 7.83% to 16.53%) between 1994 and 2024.
  • Vegetation ESV initially increased then declined, while water body ESV showed variability.
  • Achieved high overall accuracy (OA) and kappa (OK) values for LULC classifications across the study period.

Conclusions:

  • Significant LULC changes necessitate urgent improvements in land and water management planning.
  • Ecosystem restoration and policy interventions are vital for promoting nature-based solutions.
  • Ensuring sustainable water resources in semi-arid regions requires integrated management strategies.