Details
| Original language | English |
|---|---|
| Article number | 114380 |
| Journal | Remote sensing of environment |
| Volume | 315 |
| Early online date | 16 Sept 2024 |
| Publication status | E-pub ahead of print - 16 Sept 2024 |
Abstract
Road development has affected many remote tropical forests around the world and has accelerated human-induced deforestation, forest degradation and biodiversity loss. The development of roads in tropical forests is largely driven by industrial selective logging, which can provide a sustainable source of revenue for developing countries while avoiding more detrimental forms of forest degradation or deforestation. Understanding the dynamics and impacts of road development is challenging, because road inventories in remote tropical forests have been largely incomplete or outdated. In this study, we present novel remote sensing-based methods for automated monitoring of road development and apply them across the Congo Basin forest region, an area characterized by increasing road development rates driven by logging activities. We trained a deep learning model with Sentinel-1 and -2 satellite imagery to map road development on a monthly basis at 10 m spatial scale, leveraging the complementary value of radar and optical imagery. Applying the model across the Congo Basin forest, we present a vectorized map of road development from January 2019 until December 2022, demonstrating an F1-score of 0.909, a false detection rate of 4.2% and a missed detection rate of 14.9%. In total, we mapped 35,944 km of road development in the Congo Basin forest during the four years, with at least 78% apparently related to logging activities, mainly located in the western part of the region. We estimate that 30% of the detected road openings were previously abandoned logging roads that were reopened. In addition, 23% of detected road development was located in areas considered to be intact forest landscapes. The road monitoring methods demonstrated in this study can facilitate several crucial forest management and conservation objectives in the tropics, such as assessing ecological and climate impacts related to selective logging, monitoring illegal or unsustainable activities, and providing a basis for improved understanding and evaluation of human impacts on forests at large scale. More information, including a full overview of the Congo Basin forest road map, can be found at: https://wur.eu/forest-roads.
Keywords
- Artificial intelligence, Central Africa, Deforestation, Forest degradation, Logging, Roads, Sentinel-1, Sentinel-2
ASJC Scopus subject areas
- Agricultural and Biological Sciences(all)
- Soil Science
- Earth and Planetary Sciences(all)
- Geology
- Earth and Planetary Sciences(all)
- Computers in Earth Sciences
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In: Remote sensing of environment, Vol. 315, 114380, 15.12.2024.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Monitoring road development in Congo Basin forests with multi-sensor satellite imagery and deep learning
AU - Slagter, Bart
AU - Fesenmyer, Kurt
AU - Hethcoat, Matthew
AU - Belair, Ethan
AU - Ellis, Peter
AU - Kleinschroth, Fritz
AU - Peña-Claros, Marielos
AU - Herold, Martin
AU - Reiche, Johannes
N1 - Publisher Copyright: © 2024 The Authors
PY - 2024/9/16
Y1 - 2024/9/16
N2 - Road development has affected many remote tropical forests around the world and has accelerated human-induced deforestation, forest degradation and biodiversity loss. The development of roads in tropical forests is largely driven by industrial selective logging, which can provide a sustainable source of revenue for developing countries while avoiding more detrimental forms of forest degradation or deforestation. Understanding the dynamics and impacts of road development is challenging, because road inventories in remote tropical forests have been largely incomplete or outdated. In this study, we present novel remote sensing-based methods for automated monitoring of road development and apply them across the Congo Basin forest region, an area characterized by increasing road development rates driven by logging activities. We trained a deep learning model with Sentinel-1 and -2 satellite imagery to map road development on a monthly basis at 10 m spatial scale, leveraging the complementary value of radar and optical imagery. Applying the model across the Congo Basin forest, we present a vectorized map of road development from January 2019 until December 2022, demonstrating an F1-score of 0.909, a false detection rate of 4.2% and a missed detection rate of 14.9%. In total, we mapped 35,944 km of road development in the Congo Basin forest during the four years, with at least 78% apparently related to logging activities, mainly located in the western part of the region. We estimate that 30% of the detected road openings were previously abandoned logging roads that were reopened. In addition, 23% of detected road development was located in areas considered to be intact forest landscapes. The road monitoring methods demonstrated in this study can facilitate several crucial forest management and conservation objectives in the tropics, such as assessing ecological and climate impacts related to selective logging, monitoring illegal or unsustainable activities, and providing a basis for improved understanding and evaluation of human impacts on forests at large scale. More information, including a full overview of the Congo Basin forest road map, can be found at: https://wur.eu/forest-roads.
AB - Road development has affected many remote tropical forests around the world and has accelerated human-induced deforestation, forest degradation and biodiversity loss. The development of roads in tropical forests is largely driven by industrial selective logging, which can provide a sustainable source of revenue for developing countries while avoiding more detrimental forms of forest degradation or deforestation. Understanding the dynamics and impacts of road development is challenging, because road inventories in remote tropical forests have been largely incomplete or outdated. In this study, we present novel remote sensing-based methods for automated monitoring of road development and apply them across the Congo Basin forest region, an area characterized by increasing road development rates driven by logging activities. We trained a deep learning model with Sentinel-1 and -2 satellite imagery to map road development on a monthly basis at 10 m spatial scale, leveraging the complementary value of radar and optical imagery. Applying the model across the Congo Basin forest, we present a vectorized map of road development from January 2019 until December 2022, demonstrating an F1-score of 0.909, a false detection rate of 4.2% and a missed detection rate of 14.9%. In total, we mapped 35,944 km of road development in the Congo Basin forest during the four years, with at least 78% apparently related to logging activities, mainly located in the western part of the region. We estimate that 30% of the detected road openings were previously abandoned logging roads that were reopened. In addition, 23% of detected road development was located in areas considered to be intact forest landscapes. The road monitoring methods demonstrated in this study can facilitate several crucial forest management and conservation objectives in the tropics, such as assessing ecological and climate impacts related to selective logging, monitoring illegal or unsustainable activities, and providing a basis for improved understanding and evaluation of human impacts on forests at large scale. More information, including a full overview of the Congo Basin forest road map, can be found at: https://wur.eu/forest-roads.
KW - Artificial intelligence
KW - Central Africa
KW - Deforestation
KW - Forest degradation
KW - Logging
KW - Roads
KW - Sentinel-1
KW - Sentinel-2
UR - http://www.scopus.com/inward/record.url?scp=85205668326&partnerID=8YFLogxK
U2 - 10.1016/j.rse.2024.114380
DO - 10.1016/j.rse.2024.114380
M3 - Article
AN - SCOPUS:85205668326
VL - 315
JO - Remote sensing of environment
JF - Remote sensing of environment
SN - 0034-4257
M1 - 114380
ER -