Data underlying the publication: "Estimation of above‐ground biomass of large tropical trees with terrestrial LiDAR"

This dataset contains data underlying the publication: "Estimation of above‐ground biomass of large tropical trees with terrestrial LiDAR" and consists of the following data folders: 1_ReferenceMeasurementData (Destructive sampling tree AGB data): Destructive sampling measurement data of 29 large tropical trees from 3 sites (Indonesia, Peru and Guyana) for estimating tree wood volume and tree AGB. 2_AllometricEqInventoryData (Forest inventory data): Forest inventory data of 29 individual large tropical trees from 3 sites (Indonesia, Peru and Guyana) for estimating tree AGB with allometric equations. 3_QsmCylinderData (Quantitative Structure Models): Quantitative Structural Models (QSM) cylinder model outputs (3D tree architecture models), generated from the individual TLS point cloud data of 29 large tropical trees from 3 sites (Indonesia, Peru and Guyana). 4_LidarTreePoinCloudData (TLS tree point cloud data): TLS point cloud data for 29 large tropical trees in 3 study sites: Indonesia (peat swamp forest in Central Kalimantan, Borneo), Peru (lowland tropical moist forest in Madre de Dios) and Guyana (lowland tropical moist forest in Cayuni-Mazaruni).</p

Mean vertical profiles per vegetation type.

<p>Mean vertical profiles per vegetation type.</p

Soil characteristics, 0-30cm depth, per forest type, for several weatherable elements.

<p>Soil characteristics, 0-30cm depth, per forest type, for several weatherable elements.</p

Lopé National Park (LNP) and plots sampled in the north of the park characterised by a savanna-forest mosaic.

<p>Map showing the location of the field site within a landcover map for the year 2000 [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0156934#pone.0156934.ref078" target="_blank">78</a>] adapted from Mitchard et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0156934#pone.0156934.ref021" target="_blank">21</a>].</p

African Savanna-Forest Boundary Dynamics: A 20-Year Study - Fig 2

<p><b>Different forest types (left) and plot set up (right)</b>.</p

African Savanna-Forest Boundary Dynamics: A 20-Year Study - Fig 5

<p><b>Above ground biomass (AGB in Mg ha^-1), in relation to basal area (BA in m² ha^-1), stem density (SD in number stems ha^-1) and wood mass density weighted by BA (WMD) in 1993 (left) and 2013 (right).</b> Red: colonising forest (F1), green: monodominant Okoume forest (F2), blue: young Marantaceae forest (F3) and yellow: mixed Marantaceae forest (F4).</p

Changes in soil characteristics with increasing depth per forest type (mean plotted).

<p>Note that black dots refer to savanna, red dots: colonising forest (F1), green dots: monodominant Okoume forest (F2), blue dots: young Marantaceae forest (F3) and yellow dots: mixed Marantaceae forest (F4).</p

Table_1_Reliably mapping low-intensity forest disturbance using satellite radar data.XLSX

In the last decades tropical forests have experienced increased fragmentation due to a global growing demand for agricultural and forest commodities. Satellite remote sensing offers a valuable tool for monitoring forest loss, thanks to the global coverage and the temporal consistency of the acquisitions. In tropical regions, C-band Synthetic Aperture Radar (SAR) data from the Sentinel-1 mission provides cloud-free and open imagery on a 6- or 12-day repeat cycle, offering the unique opportunity to monitor forest disturbances in a timely and continuous manner. Despite recent advances, mapping subtle forest losses, such as those due to small-scale and irregular selective logging, remains problematic. A Cumulative Sum (CuSum) approach has been recently proposed for forest monitoring applications, with preliminary studies showing promising results. Unfortunately, the lack of accurate in-situ measurements of tropical forest loss has prevented a full validation of this approach, especially in the case of low-intensity logging. In this study, we used high-quality field measurements from the tropical Forest Degradation Experiment (FODEX), combining unoccupied aerial vehicle (UAV) LiDAR, Terrestrial Laser Scanning (TLS), and field-inventoried data of forest structural change collected in two logging concessions in Gabon and Peru. The CuSum algorithm was applied to VV-polarized Sentinel-1 ground range detected (GRD) time series to monitor a range of canopy loss events, from individual tree extraction to forest clear cuts. We developed a single change metric using the maximum of the CuSum distribution, retrieving location, time, and magnitude of the disturbance events. A comparison of the CuSum algorithm with the LiDAR reference map resulted in a 78% success rate for the test site in Gabon and 65% success rate for the test site in Peru, for disturbances as small as 0.01 ha in size and for canopy height losses as fine as 10 m. A correlation between the change metric and above ground biomass (AGB) change was found with R2 = 0.95, and R2 = 0.83 for canopy height loss. From the regression model we directly estimated local AGB loss maps for the year 2020, at 1 ha scale and in percentages of AGB loss. Comparison with the Global Forest Watch (GFW) Tree Cover Loss (TCL) product showed a 61% overlap between the two maps when considering only deforested pixels, with 504 ha of deforestation detected by CuSum vs. 348 ha detected by GFW. Low intensity disturbances captured by the CuSum method were largely undetected by GFW and by the SAR-based Radar for Detecting Deforestation (RADD) Alert System. The results of this study confirm this approach as a simple and reproducible change detection method for monitoring and quantifying fine-scale to high intensity forest disturbances, even in the case of multi-storied and high biomass forests.</p

Image_3_Reliably mapping low-intensity forest disturbance using satellite radar data.JPEG

In the last decades tropical forests have experienced increased fragmentation due to a global growing demand for agricultural and forest commodities. Satellite remote sensing offers a valuable tool for monitoring forest loss, thanks to the global coverage and the temporal consistency of the acquisitions. In tropical regions, C-band Synthetic Aperture Radar (SAR) data from the Sentinel-1 mission provides cloud-free and open imagery on a 6- or 12-day repeat cycle, offering the unique opportunity to monitor forest disturbances in a timely and continuous manner. Despite recent advances, mapping subtle forest losses, such as those due to small-scale and irregular selective logging, remains problematic. A Cumulative Sum (CuSum) approach has been recently proposed for forest monitoring applications, with preliminary studies showing promising results. Unfortunately, the lack of accurate in-situ measurements of tropical forest loss has prevented a full validation of this approach, especially in the case of low-intensity logging. In this study, we used high-quality field measurements from the tropical Forest Degradation Experiment (FODEX), combining unoccupied aerial vehicle (UAV) LiDAR, Terrestrial Laser Scanning (TLS), and field-inventoried data of forest structural change collected in two logging concessions in Gabon and Peru. The CuSum algorithm was applied to VV-polarized Sentinel-1 ground range detected (GRD) time series to monitor a range of canopy loss events, from individual tree extraction to forest clear cuts. We developed a single change metric using the maximum of the CuSum distribution, retrieving location, time, and magnitude of the disturbance events. A comparison of the CuSum algorithm with the LiDAR reference map resulted in a 78% success rate for the test site in Gabon and 65% success rate for the test site in Peru, for disturbances as small as 0.01 ha in size and for canopy height losses as fine as 10 m. A correlation between the change metric and above ground biomass (AGB) change was found with R2 = 0.95, and R2 = 0.83 for canopy height loss. From the regression model we directly estimated local AGB loss maps for the year 2020, at 1 ha scale and in percentages of AGB loss. Comparison with the Global Forest Watch (GFW) Tree Cover Loss (TCL) product showed a 61% overlap between the two maps when considering only deforested pixels, with 504 ha of deforestation detected by CuSum vs. 348 ha detected by GFW. Low intensity disturbances captured by the CuSum method were largely undetected by GFW and by the SAR-based Radar for Detecting Deforestation (RADD) Alert System. The results of this study confirm this approach as a simple and reproducible change detection method for monitoring and quantifying fine-scale to high intensity forest disturbances, even in the case of multi-storied and high biomass forests.</p

Journal officiel de la Guinée française

01 septembre 19231923/09/01 (A23,N537)