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. 2019 Mar 23;14(1):2.
doi: 10.1186/s13021-019-0117-9.

Forest degradation and biomass loss along the Chocó region of Colombia

Victoria Meyer  1 , Sassan Saatchi  2 , António Ferraz  2   3 , Liang Xu  2 , Alvaro Duque  4 , Mariano García  5 , Jérôme Chave  6
Affiliations

Affiliations

  • 1 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA. victoria.meyer@jpl.nasa.gov.
  • 2 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.
  • 3 Institute of the Environment and Sustainability, University of California, Los Angeles, CA, 90095, USA.
  • 4 Departamento de Ciencias Forestales, Universidad Nacional de Colombia, Calle 59A No. 63-20, Medellín, Colombia.
  • 5 Environmental Remote Sensing Research Group, Department of Geology, Geography and Environment, University of Alcalá, Alcalá de Henares, Spain.
  • 6 Laboratoire Evolution et Diversité Biologique, UMR 5174, CNRS Université Paul Sabatier, Toulouse, France.

Forest degradation and biomass loss along the Chocó region of Colombia

Victoria Meyer et al. Carbon Balance Manag. .
. 2019 Mar 23;14(1):2.
doi: 10.1186/s13021-019-0117-9.

Authors

Victoria Meyer  1 , Sassan Saatchi  2 , António Ferraz  2   3 , Liang Xu  2 , Alvaro Duque  4 , Mariano García  5 , Jérôme Chave  6

Affiliations

  • 1 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA. victoria.meyer@jpl.nasa.gov.
  • 2 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.
  • 3 Institute of the Environment and Sustainability, University of California, Los Angeles, CA, 90095, USA.
  • 4 Departamento de Ciencias Forestales, Universidad Nacional de Colombia, Calle 59A No. 63-20, Medellín, Colombia.
  • 5 Environmental Remote Sensing Research Group, Department of Geology, Geography and Environment, University of Alcalá, Alcalá de Henares, Spain.
  • 6 Laboratoire Evolution et Diversité Biologique, UMR 5174, CNRS Université Paul Sabatier, Toulouse, France.

Abstract

Background: Wet tropical forests of Chocó, along the Pacific Coast of Colombia, are known for their high plant diversity and endemic species. With increasing pressure of degradation and deforestation, these forests have been prioritized for conservation and carbon offset through Reducing Emissions from Deforestation and forest Degradation (REDD+) mechanisms. We provide the first regional assessment of forest structure and aboveground biomass using measurements from a combination of ground tree inventories and airborne Light Detection and Ranging (Lidar). More than 80,000 ha of lidar samples were collected based on a stratified random sampling to provide a regionally unbiased quantification of forest structure of Chocó across gradients of vegetation structure, disturbance and elevation. We developed a model to convert measurements of vertical structure of forests into aboveground biomass (AGB) for terra firme, wetlands, and mangrove forests. We used the Random Forest machine learning model and a formal uncertainty analysis to map forest height and AGB at 1-ha spatial resolution for the entire pacific coastal region using spaceborne data, extending from the coast to higher elevation of Andean forests.

Results: Upland Chocó forests have a mean canopy height of 21.8 m and AGB of 233.0 Mg/ha, while wetland forests are characterized by a lower height and AGB (13.5 m and 117.5 Mg/a). Mangroves have a lower mean height than upland forests (16.5 m), but have a similar AGB as upland forests (229.9 Mg/ha) due to their high wood density. Within the terra firme forest class, intact forests have the highest AGB (244.3 ± 34.8 Mg/ha) followed by degraded and secondary forests with 212.57 ± 62.40 Mg/ha of biomass. Forest degradation varies in biomass loss from small-scale selective logging and firewood harvesting to large-scale tree removals for gold mining, settlements, and illegal logging. Our findings suggest that the forest degradation has already caused the loss of more than 115 million tons of dry biomass, or 58 million tons of carbon.

Conclusions: Our assessment of carbon stocks and forest degradation can be used as a reference for reporting on the state of the Chocó forests to REDD+ projects and to encourage restoration efforts through conservation and climate mitigation policies.

Keywords: Biomass; Forest degradation; Forest height; Lidar; REDD+; Random forest; Remote sensing; Tropical forest; Wood density.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1

Fig. 1

Flowchart showing the steps leading…

Fig. 1

Flowchart showing the steps leading to the production of an AGB map. Data…

Fig. 1
Flowchart showing the steps leading to the production of an AGB map. Data inputs are shown in green, intermediate products are shown in grey, while operations are shown in pink and final products in yellow. Operations and products related to the uncertainty analysis are shown in a dashed box. AGB aboveground biomass, TCH mean top canopy height, LCA large trees canopy area
Fig. 2

Fig. 2

Location of lidar scenes and…

Fig. 2

Location of lidar scenes and ground plots over forest, wetland forest and mangrove…

Fig. 2
Location of lidar scenes and ground plots over forest, wetland forest and mangrove (a). Lidar scenes are not to scale for visualization purposes. Location of study area in South America (b). Examples of lidar canopy height models of terra firme forest, wetland forest and mangrove (c)
Fig. 3

Fig. 3

Lidar-derived AGB allometric model

Fig. 3

Lidar-derived AGB allometric model

Fig. 3
Lidar-derived AGB allometric model
Fig. 4

Fig. 4

Height map ( a )…

Fig. 4

Height map ( a ) and AGB map ( b ). Example 1…

Fig. 4
Height map (a) and AGB map (b). Example 1 shows how mangrove forest stands out in the AGB map because of its higher wood density. Example 2 displays the signature of fragmentation, as seen in ALOS and Landsat
Fig. 5

Fig. 5

Uncertainty of AGB map, taking…

Fig. 5

Uncertainty of AGB map, taking the error of the random forest mapping and…

Fig. 5
Uncertainty of AGB map, taking the error of the random forest mapping and the error of the lidar model. Example 1 shows how some lidar scenes show in the uncertainty map, because their prediction varies a lot depending on whether or not they were part of the training data for different iterations (a). Scatter plots of test samples based on the leave-one-scene-out cross validation: height (b), AGB form H (c)
See this image and copyright information in PMC

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