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Open AccessArticle

Historical Aerial Surveys Map Long-Term Changes of Forest Cover and Structure in the Central Congo Basin

1
Ghent University, Faculty of Bioscience Engineering, 0032 Ghent, Belgium
2
INRA, UMR ISPA, 75011 Villenave d’Ornon, France
3
Royal Museum for Central Africa, 3080 Tervuren, Belgium
4
Botanic Garden Meise, 1860 Meise, Belgium
5
National Archives of Belgium, 1080 Brussels, Belgium
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(4), 638; https://doi.org/10.3390/rs12040638
Received: 31 December 2019 / Revised: 9 February 2020 / Accepted: 10 February 2020 / Published: 14 February 2020
(This article belongs to the Section Forest Remote Sensing)
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. View Full-Text
Keywords: aerial survey; data recovery; CNN; deep learning; SfM; Congo Basin aerial survey; data recovery; CNN; deep learning; SfM; Congo Basin
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Hufkens, K.; de Haulleville, T.; Kearsley, E.; Jacobsen, K.; Beeckman, H.; Stoffelen, P.; Vandelook, F.; Meeus, S.; Amara, M.; Van Hirtum, L.; Van den Bulcke, J.; Verbeeck, H.; Wingate, L. Historical Aerial Surveys Map Long-Term Changes of Forest Cover and Structure in the Central Congo Basin. Remote Sens. 2020, 12, 638.

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