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<p>The rapidly worsening effects of climate change and human activity on the planet have made monitoring and predictive solutions to deforestation more critical than ever. This paper proposes an ecosystem-based approach that combines artificial intelligence (AI) technologies with remote sensing to directly monitor and assess the deforestation and reforestation processes occurring in a given territory over a set period, using the Béjaïa region of Algeria as a case study. This includes the pre and post processing of the Landsat and Sentinel satellite images through radiometric correction and cloud filtering. In this context, breaks within clouds on satellite images are filtered, and constant noise is radiometrically corrected. For more advanced image analyses, AI technologies such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks trained for land cover classification and temporal forecasting were employed. Real-time validation using sensor networks consisting of acoustic, environmental, and IoT devices were integrated with satellite data to provide enhanced validation capabilities. The combination of NDVI differencing with Kernel Density Estimation and Getis-Ord Gi* hotspot analysis has spatially and temporally captured specific clusters of deforestation that coincide with wildfire hotspots. Other datasets as well as time-series analysis validated the loss of forest coverage on a large scale, especially in years 2017, 2021, and 2023 when canopy cover reached a record low estimate according to Global Forest Watch. Accuracy assessments through field and drone-based ground truthing confirmed the high reliability of the model outcomes. The study demonstrates the forest situational awareness and proactive management level achievable using AI-based predictive models and ecosystem-wide sensor networks to intervene before critical thresholds of forest ecosystem damage are reached. The findings demonstrate the applicability of AI technologies working in distributed systems through integrated terrestrial and space networks to provide near real-time monitoring of deforestation with the precision required for proactive intervention in sustainable governance of the environment.</p>

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Spatial-Temporal Deforestation Forecasting via Remote Sensing and Artificial Intelligence Driven Sensor Networks

  • 期刊论文

  • 发表日期：

    2025年8月1日

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  • Bushra Haq,
  • Bakhtiar Kasi,
  • Mumraiz Kasi,
  • Syed Kamran Haider

摘要

The rapidly worsening effects of climate change and human activity on the planet have made monitoring and predictive solutions to deforestation more critical than ever. This paper proposes an ecosystem-based approach that combines artificial intelligence (AI) technologies with remote sensing to directly monitor and assess the deforestation and reforestation processes occurring in a given territory over a set period, using the Béjaïa region of Algeria as a case study. This includes the pre and post processing of the Landsat and Sentinel satellite images through radiometric correction and cloud filtering. In this context, breaks within clouds on satellite images are filtered, and constant noise is radiometrically corrected. For more advanced image analyses, AI technologies such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks trained for land cover classification and temporal forecasting were employed. Real-time validation using sensor networks consisting of acoustic, environmental, and IoT devices were integrated with satellite data to provide enhanced validation capabilities. The combination of NDVI differencing with Kernel Density Estimation and Getis-Ord Gi* hotspot analysis has spatially and temporally captured specific clusters of deforestation that coincide with wildfire hotspots. Other datasets as well as time-series analysis validated the loss of forest coverage on a large scale, especially in years 2017, 2021, and 2023 when canopy cover reached a record low estimate according to Global Forest Watch. Accuracy assessments through field and drone-based ground truthing confirmed the high reliability of the model outcomes. The study demonstrates the forest situational awareness and proactive management level achievable using AI-based predictive models and ecosystem-wide sensor networks to intervene before critical thresholds of forest ecosystem damage are reached. The findings demonstrate the applicability of AI technologies working in distributed systems through integrated terrestrial and space networks to provide near real-time monitoring of deforestation with the precision required for proactive intervention in sustainable governance of the environment.