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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Forest Conservation with Deep Learning: A Deeper Understanding of the Human Geography Around the Betampona Nature Reserve, Madagascar./
Author:	Cota, Gizelle Rebecca.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	70 p.
Notes:	Source: Masters Abstracts International, Volume: 83-05.
Contained By:	Masters Abstracts International83-05.
Subject:	Remote sensing. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28717337
ISBN:	9798480645897

Forest Conservation with Deep Learning: A Deeper Understanding of the Human Geography Around the Betampona Nature Reserve, Madagascar.
Cota, Gizelle Rebecca.

Forest Conservation with Deep Learning: A Deeper Understanding of the Human Geography Around the Betampona Nature Reserve, Madagascar. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 70 p.

Source: Masters Abstracts International, Volume: 83-05.

Thesis (M.Sc.)--Saint Louis University, 2021.

This item must not be sold to any third party vendors.

Documenting the coupled impact of climate change and human activities on tropical rainforest ecosystems is imperative, not only for protecting tropical biodiversity but also for better implementation of U.N. Sustainable Development Goals. Recent advances in very high-resolution satellite sensor systems (i.e., WorldView-3), computing power, and machine learning has provided improved mapping of fine scale changes in the tropics. However, approaches so far have been focused on feature extraction or pixel-based methods that require extensive manual tuning of machine learning parameters hindering the potential of machine learning in forest conservation mapping, not taking advantage of texture information that are found to be powerful for many applications. Additionally, the contribution of Shortwave Infrared (SWIR) bands in forest habitat mapping is unknown. The objectives of this study were to develop end-to-end, automated mapping of tropical forest habitat using fully convolution neural networks (FCNNs) with WorldView-3 (WV-3) imagery and evaluate human impact on the tropical environment using the Betampona Nature Reserve (BNR) in Madagascar as the test site. An FCNN model (U-Net) that can utilize spatial/textural information was implemented, and other feature-fed pixel-based machine learning methods including Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Network (DNN) were used for comparison. Our results show that FCNN model outperformed other pixel-based models with an accuracy of 90.9%, while SVM, RF and DNN provided accuracies of 88.6%, 84.8%, and 86.6%, respectively. (2) When SWIR bands were excluded from the input data, FCNN approach still provided superior performance over other methods with a negligible decrease (by 1.87%) in accuracies while accuracies of other pixel-based models SVM, RF, and DNN decreased significantly by 5.42%, 3.18%, and 8.55%, respectively. (3) Spatial-temporal analysis of land-cover and land-use types showed 0.7% increase in the Evergreen Forest class within BNR and 32% increase in tree cover within residential areas surrounding BNR from 2010 to 2019, likely due to forest regeneration and conservation efforts. The paper also discusses the effect of conservation efforts such as distribution of fuel-efficient stoves to mitigate firewood harvesting for cooking and agroforestry to improve food security.

ISBN: 9798480645897Subjects--Topical Terms:

535394
Remote sensing.
Subjects--Index Terms:

Conservation efforts`

Forest Conservation with Deep Learning: A Deeper Understanding of the Human Geography Around the Betampona Nature Reserve, Madagascar.
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500 $a Source: Masters Abstracts International, Volume: 83-05.
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506 $a This item must not be sold to any third party vendors.
520 $a Documenting the coupled impact of climate change and human activities on tropical rainforest ecosystems is imperative, not only for protecting tropical biodiversity but also for better implementation of U.N. Sustainable Development Goals. Recent advances in very high-resolution satellite sensor systems (i.e., WorldView-3), computing power, and machine learning has provided improved mapping of fine scale changes in the tropics. However, approaches so far have been focused on feature extraction or pixel-based methods that require extensive manual tuning of machine learning parameters hindering the potential of machine learning in forest conservation mapping, not taking advantage of texture information that are found to be powerful for many applications. Additionally, the contribution of Shortwave Infrared (SWIR) bands in forest habitat mapping is unknown. The objectives of this study were to develop end-to-end, automated mapping of tropical forest habitat using fully convolution neural networks (FCNNs) with WorldView-3 (WV-3) imagery and evaluate human impact on the tropical environment using the Betampona Nature Reserve (BNR) in Madagascar as the test site. An FCNN model (U-Net) that can utilize spatial/textural information was implemented, and other feature-fed pixel-based machine learning methods including Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Network (DNN) were used for comparison. Our results show that FCNN model outperformed other pixel-based models with an accuracy of 90.9%, while SVM, RF and DNN provided accuracies of 88.6%, 84.8%, and 86.6%, respectively. (2) When SWIR bands were excluded from the input data, FCNN approach still provided superior performance over other methods with a negligible decrease (by 1.87%) in accuracies while accuracies of other pixel-based models SVM, RF, and DNN decreased significantly by 5.42%, 3.18%, and 8.55%, respectively. (3) Spatial-temporal analysis of land-cover and land-use types showed 0.7% increase in the Evergreen Forest class within BNR and 32% increase in tree cover within residential areas surrounding BNR from 2010 to 2019, likely due to forest regeneration and conservation efforts. The paper also discusses the effect of conservation efforts such as distribution of fuel-efficient stoves to mitigate firewood harvesting for cooking and agroforestry to improve food security.
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710 2 $a Saint Louis University. $b Geoscience. $3 1677473
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856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28717337