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Research Article Open access CC BY 4.0

Detection of Land Use Changes in Forests Using Satellite Image Classification Based on Deep Learning: A Case Study of Sardasht Forests

Ziba Zarrin, Yousef Khajavigodellou, Mohammad Javad Valadan Zoej

Journal of Geography, Environment and Earth Science International · pp. 43–51 · Published 23 Apr 2024

10.9734/jgeesi/2024/v28i5769

Abstract

Awareness of changes in forest areas has always been one of the most crucial environmental considerations globally. Sardasht forests is located in the northern Zagros region and with an area of around 91,117 hectares constitute for 24% of Iran total forests. They play an important role in water supply, soil preservation, climate regulation and overall economic and social balance in the country. Nevertheless, these forests are currently perceived as degraded due to the primary reason of tree cutting for fuel and livestock feed. In this study, remote sensing data of the study area including Landsat 5 TM satellite images from 2002, Landsat 7 ETM images from 2012, Landsat 8 OLI images 2020 and classification algorithms, maximum likelihood and artificial neural network were utilized. To examine the accuracy of each classification, ground truth points were randomly collected using GPS devices and by implementing control points, statistical parameters of error accuracy including Kappa coefficient and overall accuracy were calculated. The results show that the map generated using the artificial neural network algorithm has an overall accuracy of 98.32% with a Kappa coefficient of 0.9781 while the map produced by the maximum likelihood algorithm has an overall accuracy of 92.45% with a Kappa coefficient of 0.901. As a result, the artificial neural network algorithm demonstrates to be a more suitable method for producing land cover maps compared to the maximum likelihood algorithm.

Forest land use changes satellite images deep learning artificial neural network maximum likelihood sardasht

References (17)

  1. 1 Interrater reliability: the kappa statistic [DOI]
  2. 2 Remote Sensing Digital Image Analysis [DOI]
  3. 3 Global Forest Resources Assessment 2015: how are the world's forests changing?
  4. 4 Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning [DOI]
  5. 5 Remote sensing of forest degradation: a review [DOI]
  6. 6 Modeling land use change using Cellular Automata and Artificial Neural Network: The case of Chunati Wildlife Sanctuary, Bangladesh [DOI]
  7. 7 Monitoring mangrove forests: Are we taking full advantage of technology? [DOI]
  8. 8 Automatic Detection of Pothole Distress in Asphalt Pavement Using Improved Convolutional Neural Networks [DOI]
  9. 9 Vulnerability of forest vegetation to anthropogenic climate change in China [DOI]
  10. 10 Deep Learning for Regular Change Detection in Ukrainian Forest Ecosystem With Sentinel-2 [DOI]
  11. 11 Amazon forest cover change mapping based on semantic segmentation by U-Nets [DOI]
  12. 12 Contour-aware semantic segmentation network with spatial attention mechanism for medical image [DOI]
  13. 13 Extracting and analyzing forest and woodland cover change in Eritrea based on landsat data using supervised classification [DOI]
  14. 14 Comparison of Different Convolutional Neural Network Architectures for Satellite Image Segmentation [DOI]
  15. 15 Self-Organizing Deep Learning (SO-UNet)—A Novel Framework to Classify Urban and Peri-Urban Forests [DOI]
  16. 16 Land Cover Mapping Using Remote Sensing Data
  17. 17 Learning transfer using deep convolutional features for remote sensing image retrieval

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