A Comparative Assessment of Machine-Learning Techniques for Forest Degradation Caused by Selective Logging in an Amazon Region Using Multitemporal X-Band SAR Images
| Autor: | Eraldo Aparecido Trondoli Matricardi, Polyanna da Conceição Bispo, Paulo Silva Filho, Edson Eyji Sano, Tahisa Neitzel Kuck, Elcio Hideiti Shiguemori |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Synthetic aperture radar
Computer science Science Point cloud Machine learning computer.software_genre Multilayer perceptron multilayer perceptron AdaBoost Artificial neural network business.industry Logging Random forest Lidar machine learning General Earth and Planetary Sciences Artificial intelligence synthetic aperture radar random forest business computer |
| Zdroj: | Remote Sensing, Vol 13, Iss 3341, p 3341 (2021) Kuck, T N, Sano, E E, Bispo, P D C, Shiguemori, E H, Filho, P F F S & Matricardi, E A T 2021, ' A Comparative Assessment of Machine-Learning Techniques for Forest Degradation Caused by Selective Logging in an Amazon Region Using Multitemporal X-Band SAR Images ', Remote Sensing, vol. 13, no. 17, 3341 . https://doi.org/10.3390/rs13173341 Remote Sensing; Volume 13; Issue 17; Pages: 3341 |
| ISSN: | 2072-4292 |
| Popis: | The near-real-time detection of selective logging in tropical forests is essential to support actions for reducing CO 2 emissions and for monitoring timber extraction from forest concessions in tropical regions. Current operating systems rely on optical data that are constrained by persistent cloud-cover conditions in tropical regions. Synthetic aperture radar data represent an alternative to this technical constraint. This study aimed to evaluate the performance of three machine learning algorithms applied to multitemporal pairs of COSMO-SkyMed images to detect timber exploitation in a forest concession located in the Jamari National Forest, Rondônia State, Brazilian Amazon. The studied algorithms included random forest (RF), AdaBoost (AB), and multilayer perceptron artificial neural network (MLP-ANN). The geographical coordinates (latitude and longitude) of logged trees and the LiDAR point clouds before and after selective logging were used as ground truths. The best results were obtained when the MLP-ANN was applied with 50 neurons in the hidden layer, using the ReLu activation function and SGD weight optimizer, presenting 88% accuracy both for the pair of images used for training (images acquired in June and October) of the network and in the generalization test, applied on a second dataset (images acquired in January and June). This study showed that X-band SAR images processed by applying machine learning techniques can be accurately used for detecting selective logging activities in the Brazilian Amazon. |
| Databáze: | OpenAIRE |
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