Sentinel-1 Shadows Used to Quantify Canopy Loss from Selective Logging in Gabon

Selective logging is a major cause of forest degradation in the tropics, but its precise scale, location and timing are not known as wide-area, automated remote sensing methods are not yet available at this scale. This limits the abilities of governments to police illegal logging, or monitor (and thus receive payments for) reductions in degradation. Sentinel-1, a C-band Synthetic Aperture Radar satellite mission with a 12-day repeat time across the tropics, is a promising tool for this due to the known appearance of shadows in images where canopy trees are removed. However, previous work has relied on optical satellite data for calibration and validation, which has inherent uncertainties, leaving unanswered questions about the minimum magnitude and area of canopy loss this method can detect. Here, we use a novel bi-temporal LiDAR dataset in a forest degradation experiment in Gabon to show that canopy gaps as small as 0.02 ha (two 10 m × 10 m pixels) can be detected by Sentinel-1. The accuracy of our algorithm was highest when using a timeseries of 50 images over 20 months and no multilooking. With these parameters, canopy gaps in our study site were detected with a false alarm rate of 6.2%, a missed detection rate of 12.2%, and were assigned disturbance dates that were a good qualitative match to logging records. The presence of geolocation errors and false alarms makes this method unsuitable for confirming individual disturbances. However, we found a linear relationship (r2=0.74) between the area of detected Sentinel-1 shadow and LiDAR-based canopy loss at a scale of 1 hectare. By applying our method to three years’ worth of imagery over Gabon, we produce the first national scale map of small-magnitude canopy cover loss. We estimate a total gross canopy cover loss of 0.31 Mha, or 1.3% of Gabon’s forested area, which is a far larger area of change than shown in currently available forest loss alert systems using Landsat (0.022 Mha) and Sentinel-1 (0.019 Mha). Our results, which are made accessible through Google Earth Engine, suggest that this approach could be used to quantify the magnitude and timing of degradation more widely across tropical forests

An Effective Method for InSAR Mapping of Tropical Forest Degradation in Hilly Areas

Current satellite remote sensing methods struggle to detect and map forest degradation, which is a critical issue as it is likely a major and growing source of carbon emissions and biodiveristy loss. TanDEM-X InSAR phase height is a promising variable for measuring forest disturbances, as it is closely related to the mean canopy height, and thus should decrease if canopy trees are removed. However, previous research has focused on relatively flat terrains, despite the fact that much of the world's remaining tropical forests are found in hilly areas, and this inevitably introduces artifacts in sideways imaging systems. In this paper, we find a relationship between InSAR phase height and aboveground biomass change in four selectively logged plots in a hilly region of central Gabon. We show that minimising multilooking prior to the calculation of InSAR phase height on a pixel-by-pixel basis. This shows that TanDEM-X InSAR can measure the magnitude of degradation, and that topographic effects can be mitigated if data from multiple SAR viewing geometries are available

Reliably Mapping Low-intensity Forest Disturbance Using Satellite Radar Data

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

Mangrove carbon stocks in Pongara National Park, Gabon

Mangroves are recognized for their valued ecosystem services to coastal areas, and the functional linkages between those services and ecosystem carbon stocks have been established. However, spatially explicit inventories are necessary to facilitate management and protection of mangroves, as well as providing a foundation for payment for ecosystem service programs such as REDD+. We conducted an inventory of carbon stocks in mangroves within Pongara National Park (PNP), Gabon using a stratified random sampling design based on forest canopy height derived from TanDEM-X remote sensing data. Ecosystem carbon pools, including aboveground and belowground biomass and necromass, and soil carbon to a depth of 2 m were assessed using measurements and samples from plots distributed among three canopy height classes within the park. There were two mangrove species within the inventory area in PNP, Rhizophora racemosa and R. harrisonii. R. harrisonii was predominant in the sparse, low-stature stands that dominated the west side of the park. In the east side of the park, both species occurred in tall-stature stands, with tree height often exceeding 30 m. Canopy height was an effective means to stratify the inventory area, as biomass was significantly different among the height classes. Despite those differences in aboveground biomass, the soil carbon density was not significantly different among height classes. Soils were the main component of the ecosystem carbon stock, accounting for over 84% of the total. The ecosystem carbon density ranged from 644 to 943 Mg C ha−1 among the three height classes. The ecosystem carbon stock within PNP is estimated to be 40,588 Gg C. The combination of pre-inventory information about stand conditions and their spatial distribution within the assessment area obtained from remote sensing data and a spatial decision support system were fundamental to implementing this relatively large-scale field inventory. This work exemplifies how mangrove carbon stocks can be quantified to augment national C reporting statistics, provide a baseline for projects involving monitoring, reporting and verification (i.e., MRV), and provide data on the forest composition and structure for sustainable management and conservation practices

Suivi des changements spatiaux et environnementaux dans les mangroves de la province de l'Estuaire du Gabon

International audienceLes mangroves du Gabon sont menacées par la dégradation et la déforestation. Dans ce contexte, a été étudiée la dynamique des mangroves de Libreville et sa région afin, d'une part, d’identifier les changements récents et prévoir les changements futurs et, d'autre part, de caractériser ces mangroves en termes de paramètres structuraux, physico-chimiques et floristiques. Couplant les méthodes de terrain et de télédétection, le projet « Modélisation de la dynamique des mangroves de Libreville et ses environs et des risques de leur dégradation » (MDMLERD) a recensé, dans l’ordre d’importance, Rhizophora harrisonii, Rhizophora racemosa, Avicennia germinans, Laguncularia racemosa, Conocarpus erectus et Phoenix reclinata comme espèce accompagnatrice. La distribution des diamètres des individus montre une courbe dominée par des jeunes, dont la hauteur varie entre 0 et 5 m, et qui se répartissent sur toutes les zones. Les mangroves les plus hautes sont situées dans la partie est de l’estuaire du Komo et atteignent jusqu’à 55 m (mesure par télédétection), et à un peu plus de 70 m (mesure terrain). L’ensemble des quatre espèces principales totalisent 150 512 tonnes de biomasse aérienne, et une biomasse souterraine de 3 tonnes environ. Les mangroves de Libreville et ses environs sont dominées par les arbres encore en bonne santé (83,13%), alors que les mangroves dégradées restent faibles (16,87%). Dans l’ensemble, la mangrove a enregistré des gains de surface totalisant 49,71 km2, contre des pertes de 86,01 km2. La simulation de 2028 annonce la poursuite d’une perte de superficie. Cette étude a permis de développer une nouvelle méthodologie d’évaluation de l’état de santé des mangroves, de cartographier les espèces et les hauteurs, et de mettre en relief les dégradations, selon que l’on soit en face de processus naturels ou anthropiques