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Mapped aboveground carbon stocks to advance forest conservation and recovery in Malaysian Borneo

Asner, Gregory P., Brodrick, Philip G., Philipson, Christopher, Vaughn, Nicolas R., Martin, Roberta E., Knapp, David E., Heckler, Joseph, Evans, Luke J., Jucker, Tommaso, Goossens, Benoit, Stark, Danica J., Reynolds, Glen, Ong, Robert, Renneboog, Nathan, Kugan, Fred, Coomes, David A.·2018·Biological Conservation
DOI 10.1016/j.biocon.2017.10.020
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Image 1
CC-BY · From the PDF
Fig. 1 (A) Aboveground carbon density (ACD) throughout the State of Sabah in Malaysian Borneo, at 30 m (0.09 ha) spatial resolution. Areas covered by mangrove and oil palm plantation are masked out. (B) Estimated absolute uncertainty in units of percent of mapped ACD at 30 m (0.09 ha) resolution.
Fig. 1 (A) Aboveground carbon density (ACD) throughout the State of Sabah in Malaysian Borneo, at 30 m (0.09 ha) spatial resolution. Areas covered by mangrove and oil palm plantation are masked out. (B) Estimated absolute uncertainty in units of percent of mapped ACD at 30 m (0.09 ha) resolution.CC-BY · From the PDF
Fig. 2 (A) Aboveground carbon density (ACD) statistics throughout the State of Sabah sorted by forest management class (see Fig. A1). (B) ACD statistics by land cover classifi cation of Gaveau et al. (2016). Red dots and lines are mean and median ACD values. Box plots indicate one standard deviation, and whiskers indicate data range.
Fig. 2 (A) Aboveground carbon density (ACD) statistics throughout the State of Sabah sorted by forest management class (see Fig. A1). (B) ACD statistics by land cover classifi cation of Gaveau et al. (2016). Red dots and lines are mean and median ACD values. Box plots indicate one standard deviation, and whiskers indicate data range.CC-BY · From the PDF
Fig. 3 Locations of the top 5% and top quartile of aboveground carbon densities (ACD) for natural forests of Sabah, Malaysia, not including mangroves. Thin black lines indicate the location of Sabah Forest ' Class I ' and ' Parks ' designated reserves, which are the most protected in the State. Lettering highlights areas of high carbon stock forest including (a) Crocker Range, (b) Mount Kinabalu area, (c) Maliau Basin and Imbak Canyon, (d) Danum Valley, (e) Tawau Hills area, (f) Tabin area, (g) Sepilok, and (h) southwestern Sabah near to the Sarawak, Malaysia and Kalimantan, Indonesian borders.
Fig. 3 Locations of the top 5% and top quartile of aboveground carbon densities (ACD) for natural forests of Sabah, Malaysia, not including mangroves. Thin black lines indicate the location of Sabah Forest ' Class I ' and ' Parks ' designated reserves, which are the most protected in the State. Lettering highlights areas of high carbon stock forest including (a) Crocker Range, (b) Mount Kinabalu area, (c) Maliau Basin and Imbak Canyon, (d) Danum Valley, (e) Tawau Hills area, (f) Tabin area, (g) Sepilok, and (h) southwestern Sabah near to the Sarawak, Malaysia and Kalimantan, Indonesian borders.CC-BY · From the PDF
Fig. 4 Forest top-of-canopy height (TCH) where unlogged forest (left side of river) abuts a previously logged forest within Danum Valley, Sabah, Malaysia.
Fig. 4 Forest top-of-canopy height (TCH) where unlogged forest (left side of river) abuts a previously logged forest within Danum Valley, Sabah, Malaysia.CC-BY · From the PDF
Image 6
CC-BY · From the PDF
Fig. 5 (A) Distribution of aboveground carbon density (ACD) for natural forests throughout Sabah. (B) Distribution for unlogged natural forests only.
Fig. 5 (A) Distribution of aboveground carbon density (ACD) for natural forests throughout Sabah. (B) Distribution for unlogged natural forests only.CC-BY · From the PDF
Fig. A1. Statewide distribution of o ffi cial forest reserves and other forest lands throughout Sabah, Malaysia. Map courtesy of Sabah Forestry Department. Inset map shows the location of Sabah outlined in red on the island of Borneo.
Fig. A1. Statewide distribution of o ffi cial forest reserves and other forest lands throughout Sabah, Malaysia. Map courtesy of Sabah Forestry Department. Inset map shows the location of Sabah outlined in red on the island of Borneo.CC-BY · From the PDF
Fig. A2. Distribution of data collection fl ightlines from the Carnegie Airborne Observatory sampling of Sabah. Each line is 1.5 -3.0 km wide (swath). Areas of particularly concentrated mapping coincide with landscapes containing fi eld calibration plots.
Fig. A2. Distribution of data collection fl ightlines from the Carnegie Airborne Observatory sampling of Sabah. Each line is 1.5 -3.0 km wide (swath). Areas of particularly concentrated mapping coincide with landscapes containing fi eld calibration plots.CC-BY · From the PDF
Fig. A3. (A) Resulting of near real-time modeling of Carnegie Airborne Observatory data density required to fully meet data sampling requirements. Green indicates 100% coverage, yellow indicates 95% coverage, and red indicates 90% coverage. Grid lines indicate mapping units of 25 km × 25 km. (B) The fraction each 25 km × 25 km grid cell mapping unit covered during CAO data collection fl ights. (For interpretation of the references to colour in this fi gure legend, the reader is referred to the web version of this article.)
Fig. A3. (A) Resulting of near real-time modeling of Carnegie Airborne Observatory data density required to fully meet data sampling requirements. Green indicates 100% coverage, yellow indicates 95% coverage, and red indicates 90% coverage. Grid lines indicate mapping units of 25 km × 25 km. (B) The fraction each 25 km × 25 km grid cell mapping unit covered during CAO data collection fl ights. (For interpretation of the references to colour in this fi gure legend, the reader is referred to the web version of this article.)CC-BY · From the PDF
Fig. A4. Work fl ow illustrating how airborne LiDAR point cloud data are used to estimate aboveground carbon density (ACD). First, tree canopy height (TCH) is calculated from the point cloud at 2 m ground-level resolution. Next, TCH is averaged to 30 m resolution, and the fraction of 2 m TCH pixels that exceed 20 m is used to determine Cover20 at 30 m resolution. Finally, we calculate aboveground carbon density (ACD) as a function of TCH and Cover20 (see main text).
Fig. A4. Work fl ow illustrating how airborne LiDAR point cloud data are used to estimate aboveground carbon density (ACD). First, tree canopy height (TCH) is calculated from the point cloud at 2 m ground-level resolution. Next, TCH is averaged to 30 m resolution, and the fraction of 2 m TCH pixels that exceed 20 m is used to determine Cover20 at 30 m resolution. Finally, we calculate aboveground carbon density (ACD) as a function of TCH and Cover20 (see main text).CC-BY · From the PDF
Fig. A5. Aboveground carbon density (ACD) as a function of top-of-canopy height (TCH) and the fraction of the canopy that exceeds 20 m (Cover20) for all CAO fl ight data (see main text for equation).
Fig. A5. Aboveground carbon density (ACD) as a function of top-of-canopy height (TCH) and the fraction of the canopy that exceeds 20 m (Cover20) for all CAO fl ight data (see main text for equation).CC-BY · From the PDF
Fig. A6. Six of the environmental dataset layers used in upscaling airborne LiDAR to the Sabah level. Topographic elevation, slope, aspect, and relative elevation were derived from NASA SRTM data, and Landsat-8 surface re fl ectance data were composited between 15 Oct 2015 and 15 Oct 2016 to generate a cloud-free mosaic.
Fig. A6. Six of the environmental dataset layers used in upscaling airborne LiDAR to the Sabah level. Topographic elevation, slope, aspect, and relative elevation were derived from NASA SRTM data, and Landsat-8 surface re fl ectance data were composited between 15 Oct 2015 and 15 Oct 2016 to generate a cloud-free mosaic.CC-BY · From the PDF
Fig. A7. Six of the environmental dataset layers used in upscaling airborne LiDAR to the Sabah level. Landsat-8 surface re fl ectance data were composited between 15 Oct 2015 and 15 Oct 2016 to generate a cloud-free mosaic. Bare substrate, photosynthetic, and non-photosynthetic vegetation were derived from Landsat-8 surface re fl ectance data.
Fig. A7. Six of the environmental dataset layers used in upscaling airborne LiDAR to the Sabah level. Landsat-8 surface re fl ectance data were composited between 15 Oct 2015 and 15 Oct 2016 to generate a cloud-free mosaic. Bare substrate, photosynthetic, and non-photosynthetic vegetation were derived from Landsat-8 surface re fl ectance data.CC-BY · From the PDF
Fig. A8. Two of the environmental dataset layers used in upscaling airborne LiDAR to the Sabah level. Sentinel synthetic aperture radar (SAR) data with VH and VV polarizations were collected using the interferometric wide swath mode were aggregated between 1 Feb 2016 and 1 July 2016 using the per-pixel median to account for view-angle e ff ects.
Fig. A8. Two of the environmental dataset layers used in upscaling airborne LiDAR to the Sabah level. Sentinel synthetic aperture radar (SAR) data with VH and VV polarizations were collected using the interferometric wide swath mode were aggregated between 1 Feb 2016 and 1 July 2016 using the per-pixel median to account for view-angle e ff ects.CC-BY · From the PDF
Fig. A9. Visualization of the deep learning model-based upscaling process. Data from each layer in the environmental dataset in a 5 × 5 pixel window (shown in green) around the target pixel (shown in red) are used in each model prediction. (For interpretation of the references to colour in this fi gure legend, the reader is referred to the web version of this article.)
Fig. A9. Visualization of the deep learning model-based upscaling process. Data from each layer in the environmental dataset in a 5 × 5 pixel window (shown in green) around the target pixel (shown in red) are used in each model prediction. (For interpretation of the references to colour in this fi gure legend, the reader is referred to the web version of this article.)CC-BY · From the PDF
Fig. A10. Veri fi cation that ACD estimated from airborne LiDAR data are represented in the upscaled Sabah-wide ACD map. The deviation is shown for both the (A) 30 m resolution data and (B) one-hectare resolution. Both plots show minimal bias, and the hectare average shows reduced variation compared with the 30 m resolution predictions.
Fig. A10. Veri fi cation that ACD estimated from airborne LiDAR data are represented in the upscaled Sabah-wide ACD map. The deviation is shown for both the (A) 30 m resolution data and (B) one-hectare resolution. Both plots show minimal bias, and the hectare average shows reduced variation compared with the 30 m resolution predictions.CC-BY · From the PDF
Fig. A11. The in fl uence of each mapped environmental factor (Figs. A6 -A8) on upscaling model performance. In order of contribution, the features are relative elevation, Landsat-8 surface re fl ectance (Surf. Re fl .) from the SWIR-1 band, geospatial location, elevation, Landsat-8 surface re fl ectance from the green band, radar data in the VV polarization, photosynthetic vegetation (PV), Landsat-8 surface re fl ectance from the red band, aspect, bare substrate, slope, Landsat-8 surface re fl ectance from the NIR band, Landsat-8 surface re fl ectance from the SWIR-2 band, radar data in the VH polarization, and non-photosynthetic vegetation (NPV). (For interpretation of the references to colour in this fi gure legend, the reader is referred to the web version of this article.)
Fig. A11. The in fl uence of each mapped environmental factor (Figs. A6 -A8) on upscaling model performance. In order of contribution, the features are relative elevation, Landsat-8 surface re fl ectance (Surf. Re fl .) from the SWIR-1 band, geospatial location, elevation, Landsat-8 surface re fl ectance from the green band, radar data in the VV polarization, photosynthetic vegetation (PV), Landsat-8 surface re fl ectance from the red band, aspect, bare substrate, slope, Landsat-8 surface re fl ectance from the NIR band, Landsat-8 surface re fl ectance from the SWIR-2 band, radar data in the VH polarization, and non-photosynthetic vegetation (NPV). (For interpretation of the references to colour in this fi gure legend, the reader is referred to the web version of this article.)CC-BY · From the PDF

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Metadata

Authors
Asner, G. P., Brodrick, P. G., Philipson, C., Vaughn, N. R., Martin, R. E., Knapp, D. E., Heckler, J., Evans, L. J., Jucker, T., Goossens, B., Stark, D. J., Reynolds, G., Ong, R., Renneboog, N., Kugan, F., & Coomes, D. A.
ORCID
Venue
Biological Conservation
Publisher
Elsevier BV
Published
2018-01
Volume
217
Pages
289-310
ISSN
0006-3207
DOI
10.1016/j.biocon.2017.10.020
Cited by
127
References
2
Language
en
Licence
Funders
  • W. M. Keck Foundation
  • Leverhulme Trust
  • Morgan Family Foundation
  • Grantham Foundation for the Protection of the Environment
  • National Eye Research Centre (NE/K016377/1)
  • Margaret A. Cargill Foundation
  • John D. and Catherine T. MacArthur Foundation
  • Avatar Alliance Foundation
  • David and Lucile Packard Foundation

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CoChat. (2026). Citation check: Mapped aboveground carbon stocks to advance forest conservation and recovery in Malaysian Borneo. https://app.cochat.ai/papers/10.1016/j.biocon.2017.10.020
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Asner, G. P., Brodrick, P. G., Philipson, C., Vaughn, N. R., Martin, R. E., Knapp, D. E., Heckler, J., Evans, L. J., Jucker, T., Goossens, B., Stark, D. J., Reynolds, G., Ong, R., Renneboog, N., Kugan, F., & Coomes, D. A. (2018). Mapped aboveground carbon stocks to advance forest conservation and recovery in Malaysian Borneo. Biological Conservation. https://doi.org/10.1016/j.biocon.2017.10.020