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Tropical Forest Monitoring: Challenges and Recent Progress in Research
 
 
Article

Forest Conservation with Deep Learning: A Deeper Understanding of Human Geography around the Betampona Nature Reserve, Madagascar

1
Geospatial Institute, Saint Louis University, St. Louis, MO 63108, USA
2
Department of Earth and Atmospheric Sciences, Saint Louis University, St. Louis, MO 63108, USA
3
Madagascar Fauna and Flora Group, Kalinka, Lochearnhead FK19 8NZ, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Frédéric Achard, Martin Herold, Christophe Sannier and Sarah Carter
Remote Sens. 2021, 13(17), 3495; https://doi.org/10.3390/rs13173495
Received: 17 July 2021 / Revised: 27 August 2021 / Accepted: 30 August 2021 / Published: 3 September 2021
(This article belongs to the Special Issue National REDD+ Monitoring and Reporting)
Documenting the impacts of climate change and human activities on tropical rainforests is imperative for protecting tropical biodiversity and for better implementation of REDD+ and UN Sustainable Development Goals. Recent advances in very high-resolution satellite sensor systems (i.e., WorldView-3), computing power, and machine learning (ML) have provided improved mapping of fine-scale changes in the tropics. However, approaches so far focused on feature extraction or the extensive tuning of ML parameters, hindering the potential of ML in forest conservation mapping by not using textural information, which is found to be powerful for many applications. Additionally, the contribution of shortwave infrared (SWIR) bands in forest cover mapping is unknown. The objectives were to develop end-to-end mapping of the tropical forest using fully convolution neural networks (FCNNs) with WorldView-3 (WV-3) imagery and to evaluate human impact on the environment using the Betampona Nature Reserve (BNR) in Madagascar as the test site. FCNN (U-Net) using spatial/textural information was implemented and compared with feature-fed pixel-based methods including Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Network (DNN). Results show that the FCNN model outperformed other models with an accuracy of 90.9%, while SVM, RF, and DNN provided accuracies of 88.6%, 84.8%, and 86.6%, respectively. When SWIR bands were excluded from the input data, FCNN provided superior performance over other methods with a 1.87% decrease in accuracy, while the accuracies of other models—SVM, RF, and DNN—decreased by 5.42%, 3.18%, and 8.55%, respectively. Spatial–temporal analysis showed a 0.7% increase in Evergreen Forest within the BNR and a 32% increase in tree cover within residential areas likely due to forest regeneration and conservation efforts. Other effects of conservation efforts are also discussed. View Full-Text
Keywords: forest cover mapping; land cover mapping; FCNN; Betampona Nature Reserve (BNR); WorldView-3 SWIR; conservation efforts; Madagascar Flora and Fauna Group (MFG) forest cover mapping; land cover mapping; FCNN; Betampona Nature Reserve (BNR); WorldView-3 SWIR; conservation efforts; Madagascar Flora and Fauna Group (MFG)
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MDPI and ACS Style

Cota, G.; Sagan, V.; Maimaitijiang, M.; Freeman, K. Forest Conservation with Deep Learning: A Deeper Understanding of Human Geography around the Betampona Nature Reserve, Madagascar. Remote Sens. 2021, 13, 3495. https://doi.org/10.3390/rs13173495

AMA Style

Cota G, Sagan V, Maimaitijiang M, Freeman K. Forest Conservation with Deep Learning: A Deeper Understanding of Human Geography around the Betampona Nature Reserve, Madagascar. Remote Sensing. 2021; 13(17):3495. https://doi.org/10.3390/rs13173495

Chicago/Turabian Style

Cota, Gizelle, Vasit Sagan, Maitiniyazi Maimaitijiang, and Karen Freeman. 2021. "Forest Conservation with Deep Learning: A Deeper Understanding of Human Geography around the Betampona Nature Reserve, Madagascar" Remote Sensing 13, no. 17: 3495. https://doi.org/10.3390/rs13173495

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