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Historical aerial surveys map long-term changes of forest cover and structure in the Central Congo basin

(2020) REMOTE SENSING. 12(4).
Author
Organization
Abstract
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 (similar to 93,431 ha) geo-referenced to similar 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.
Keywords
General Earth and Planetary Sciences, aerial survey, data recovery, CNN, deep learning, SfM, Congo Basin, TROPICAL DEFORESTATION, CARBON EMISSIONS, LAND-COVER, ANTHROPOGENIC DISTURBANCE, SELF-SIMILARITY, VEGETATION, CONSERVATION, PHOTOGRAPHS, RATES, CO2

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Citation

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MLA
Hufkens, Koen, et al. “Historical Aerial Surveys Map Long-Term Changes of Forest Cover and Structure in the Central Congo Basin.” REMOTE SENSING, vol. 12, no. 4, 2020, doi:10.3390/rs12040638.
APA
Hufkens, K., de Haulleville, T., Kearsley, E., Jacobsen, K., Beeckman, H., Stoffelen, P., … Wingate, L. (2020). Historical aerial surveys map long-term changes of forest cover and structure in the Central Congo basin. REMOTE SENSING, 12(4). https://doi.org/10.3390/rs12040638
Chicago author-date
Hufkens, Koen, Thalès de Haulleville, Elizabeth Kearsley, Kim Jacobsen, Hans Beeckman, Piet Stoffelen, Filip Vandelook, et al. 2020. “Historical Aerial Surveys Map Long-Term Changes of Forest Cover and Structure in the Central Congo Basin.” REMOTE SENSING 12 (4). https://doi.org/10.3390/rs12040638.
Chicago author-date (all authors)
Hufkens, Koen, 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, and Lisa Wingate. 2020. “Historical Aerial Surveys Map Long-Term Changes of Forest Cover and Structure in the Central Congo Basin.” REMOTE SENSING 12 (4). doi:10.3390/rs12040638.
Vancouver
1.
Hufkens K, de Haulleville T, Kearsley E, Jacobsen K, Beeckman H, Stoffelen P, et al. Historical aerial surveys map long-term changes of forest cover and structure in the Central Congo basin. REMOTE SENSING. 2020;12(4).
IEEE
[1]
K. Hufkens et al., “Historical aerial surveys map long-term changes of forest cover and structure in the Central Congo basin,” REMOTE SENSING, vol. 12, no. 4, 2020.
@article{8655405,
  abstract     = {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 (similar to 93,431 ha) geo-referenced to similar 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.},
  articleno    = {638},
  author       = {Hufkens, Koen and de Haulleville, Thalès and Kearsley, Elizabeth and Jacobsen, Kim and Beeckman, Hans and Stoffelen, Piet and Vandelook, Filip and Meeus, Sofie and Amara, Michael and Van Hirtum, Leen and Van den Bulcke, Jan and Verbeeck, Hans and Wingate, Lisa},
  issn         = {2072-4292},
  journal      = {REMOTE SENSING},
  keywords     = {General Earth and Planetary Sciences,aerial survey,data recovery,CNN,deep learning,SfM,Congo Basin,TROPICAL DEFORESTATION,CARBON EMISSIONS,LAND-COVER,ANTHROPOGENIC DISTURBANCE,SELF-SIMILARITY,VEGETATION,CONSERVATION,PHOTOGRAPHS,RATES,CO2},
  language     = {eng},
  number       = {4},
  pages        = {26},
  title        = {Historical aerial surveys map long-term changes of forest cover and structure in the Central Congo basin},
  url          = {http://dx.doi.org/10.3390/rs12040638},
  volume       = {12},
  year         = {2020},
}

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