Historical Aerial Surveys Map Long-Term Changes of Forest Cover and Structure in the Central Congo Basin
Given the impact of tropical forest disturbances on atmospheric carbon emissions, biodiversity, and ecosystem productivity, accurate long-term reporting of Land-Use and Land-Cover (LULC) change in the pre-satellite era (<1972) is an imperative. Here, we used a combination of historical (1958)...
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doaj-dc347d4e7124423492c31dc7ae6f6b6e2020-11-25T01:42:33ZengMDPI AGRemote Sensing2072-42922020-02-0112463810.3390/rs12040638rs12040638Historical Aerial Surveys Map Long-Term Changes of Forest Cover and Structure in the Central Congo BasinKoen Hufkens0Thalès de Haulleville1Elizabeth Kearsley2Kim Jacobsen3Hans Beeckman4Piet Stoffelen5Filip Vandelook6Sofie Meeus7Michael Amara8Leen Van Hirtum9Jan Van den Bulcke10Hans Verbeeck11Lisa Wingate12Ghent University, Faculty of Bioscience Engineering, 0032 Ghent, BelgiumRoyal Museum for Central Africa, 3080 Tervuren, BelgiumGhent University, Faculty of Bioscience Engineering, 0032 Ghent, BelgiumGhent University, Faculty of Bioscience Engineering, 0032 Ghent, BelgiumRoyal Museum for Central Africa, 3080 Tervuren, BelgiumBotanic Garden Meise, 1860 Meise, BelgiumBotanic Garden Meise, 1860 Meise, BelgiumBotanic Garden Meise, 1860 Meise, BelgiumNational Archives of Belgium, 1080 Brussels, BelgiumNational Archives of Belgium, 1080 Brussels, BelgiumGhent University, Faculty of Bioscience Engineering, 0032 Ghent, BelgiumGhent University, Faculty of Bioscience Engineering, 0032 Ghent, BelgiumINRA, UMR ISPA, 75011 Villenave d’Ornon, FranceGiven the impact of tropical forest disturbances on atmospheric carbon emissions, biodiversity, and ecosystem productivity, accurate long-term reporting of Land-Use and Land-Cover (LULC) change in the pre-satellite era (<1972) is an imperative. Here, we used a combination of historical (1958) aerial photography and contemporary remote sensing data to map long-term changes in the extent and structure of the tropical forest surrounding Yangambi (DR Congo) in the central Congo Basin. Our study leveraged structure-from-motion and a convolutional neural network-based LULC classifier, using synthetic landscape-based image augmentation to map historical forest cover across a large orthomosaic (~93,431 ha) geo-referenced to ~4.7 ± 4.3 m at submeter resolution. A comparison with contemporary LULC data showed a shift from previously highly regular industrial deforestation of large areas to discrete smallholder farming clearing, increasing landscape fragmentation and providing opportunties for substantial forest regrowth. We estimated aboveground carbon gains through reforestation to range from 811 to 1592 Gg C, partially offsetting historical deforestation (2416 Gg C), in our study area. Efforts to quantify long-term canopy texture changes and their link to aboveground carbon had limited to no success. Our analysis provides methods and insights into key spatial and temporal patterns of deforestation and reforestation at a multi-decadal scale, providing a historical context for past and ongoing forest research in the area.https://www.mdpi.com/2072-4292/12/4/638aerial surveydata recoverycnndeep learningsfmcongo basin |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Koen Hufkens Thalès de Haulleville Elizabeth Kearsley Kim Jacobsen Hans Beeckman Piet Stoffelen Filip Vandelook Sofie Meeus Michael Amara Leen Van Hirtum Jan Van den Bulcke Hans Verbeeck Lisa Wingate |
spellingShingle |
Koen Hufkens Thalès de Haulleville Elizabeth Kearsley Kim Jacobsen Hans Beeckman Piet Stoffelen Filip Vandelook Sofie Meeus Michael Amara Leen Van Hirtum Jan Van den Bulcke Hans Verbeeck Lisa Wingate Historical Aerial Surveys Map Long-Term Changes of Forest Cover and Structure in the Central Congo Basin Remote Sensing aerial survey data recovery cnn deep learning sfm congo basin |
author_facet |
Koen Hufkens Thalès de Haulleville Elizabeth Kearsley Kim Jacobsen Hans Beeckman Piet Stoffelen Filip Vandelook Sofie Meeus Michael Amara Leen Van Hirtum Jan Van den Bulcke Hans Verbeeck Lisa Wingate |
author_sort |
Koen Hufkens |
title |
Historical Aerial Surveys Map Long-Term Changes of Forest Cover and Structure in the Central Congo Basin |
title_short |
Historical Aerial Surveys Map Long-Term Changes of Forest Cover and Structure in the Central Congo Basin |
title_full |
Historical Aerial Surveys Map Long-Term Changes of Forest Cover and Structure in the Central Congo Basin |
title_fullStr |
Historical Aerial Surveys Map Long-Term Changes of Forest Cover and Structure in the Central Congo Basin |
title_full_unstemmed |
Historical Aerial Surveys Map Long-Term Changes of Forest Cover and Structure in the Central Congo Basin |
title_sort |
historical aerial surveys map long-term changes of forest cover and structure in the central congo basin |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2020-02-01 |
description |
Given the impact of tropical forest disturbances on atmospheric carbon emissions, biodiversity, and ecosystem productivity, accurate long-term reporting of Land-Use and Land-Cover (LULC) change in the pre-satellite era (<1972) is an imperative. Here, we used a combination of historical (1958) aerial photography and contemporary remote sensing data to map long-term changes in the extent and structure of the tropical forest surrounding Yangambi (DR Congo) in the central Congo Basin. Our study leveraged structure-from-motion and a convolutional neural network-based LULC classifier, using synthetic landscape-based image augmentation to map historical forest cover across a large orthomosaic (~93,431 ha) geo-referenced to ~4.7 ± 4.3 m at submeter resolution. A comparison with contemporary LULC data showed a shift from previously highly regular industrial deforestation of large areas to discrete smallholder farming clearing, increasing landscape fragmentation and providing opportunties for substantial forest regrowth. We estimated aboveground carbon gains through reforestation to range from 811 to 1592 Gg C, partially offsetting historical deforestation (2416 Gg C), in our study area. Efforts to quantify long-term canopy texture changes and their link to aboveground carbon had limited to no success. Our analysis provides methods and insights into key spatial and temporal patterns of deforestation and reforestation at a multi-decadal scale, providing a historical context for past and ongoing forest research in the area. |
topic |
aerial survey data recovery cnn deep learning sfm congo basin |
url |
https://www.mdpi.com/2072-4292/12/4/638 |
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