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Woody encroachment and forest progression are widespread in forest-savanna transitional areas in Central Africa. Quantifying these dynamics and understanding their drivers at relevant spatial scales has long been a challenge. Recent progress in open access imagery sources with improved spatial, spectral and temporal resolution combined with cloud computing resources, and the advent of relatively cheap solutions to deploy laser sensors in the field, have transformed this domain of study. We present a study case in the Mpem & Djim National Park (MDNP), a 1,000 km2 protected area in the Centre region of Cameroon. Using open source algorithms in Google Earth Engine (GEE), we characterized vegetation dynamics and the fire regime based on Landsat multispectral imagery archive (1975–2020). Current species assemblages were estimated from Sentinel 2 imagery and the open source biodivMapR package, using spectral dissimilarity. Vegetation structure (aboveground biomass; AGB) was characterized using Unmanned Aerial vehicle (UAV) LiDAR scanning data sampled over the study area. Savanna vegetation, which was initially dominant in the MDNP, lost about 50% of its initial cover in <50 years in favor of forest at an average rate of ca. 0.63%.year−1 (6 km2.year−1). Species assemblage computed from spectral dissimilarity in forest vegetation followed a successional gradient consistent with forest age. AGB accumulation rate was 3.2 Mg.ha−1.year−1 after 42 years of forest encroachment. In savannas, two modes could be identified along the gradient of spectral species assemblage, corresponding to distinct AGB levels, where woody savannas with low fire frequency store 40% more AGB than open grassy savannas with high fire frequency. A fire occurrence every five year was found to be the fire regime threshold below which woody savannas start to dominate over grassy ones. A fire frequency below that threshold opens the way to young forest transitions. These results have implications for carbon sequestration and biodiversity conservation policies. Maintaining savanna ecosystems in the region would require active management actions to limit woody encroachment and forest progression, in contradiction with global reforestation goals. [ABSTRACT FROM AUTHOR]

With the improvement of remote sensing techniques for forest inventory application such as terrestrial LiDAR, tree volume can now be measured directly, without resorting to allometric equations. However, wood specific gravity ( WSG ) remains a crucial factor for converting these precise volume measurements into unbiased biomass estimates. In addition to this WSG values obtained from samples collected at the base of the tree ( WSG Base ) or from global repositories such as Dryad ( WSG Dryad ) can be substantially biased relative to the overall tree value. Our aim was to assess and mitigate error propagation at tree and stand level using a pragmatic approach that could be generalized to National Forest Inventories or other carbon assessment efforts based on measured volumetric data. In the semi-deciduous forests of Eastern Cameroon, we destructively sampled 130 trees belonging to 15 species mostly represented by large trees (up to 45 Mg). We also used stand-level dendrometric parameters from 21 1-ha plots inventoried in the same area to propagate the tree-level bias at the plot level. A new descriptor, volume average-weighted WSG ( WWSG ) of the tree was computed by weighting the WSG of tree compartments by their relative volume prior to summing at tree level. As WWSG cannot be assessed non-destructively, linear models were adjusted to predict field WWSG and revealed that a combination of WSG Dryad , diameter at breast height ( DBH ) and species stem morphology ( S m ) were significant predictors explaining together 72% of WWSG variation. At tree level, estimating tree aboveground biomass using WSG Base and WSG Dryad yielded overestimations of 10% and 7% respectively whereas predicted WWSG only produced an underestimation of less than 1%. At stand-level, WSG Base and WSG Dryad gave an average simulated bias of 9% (S.D. = ±7) and 3% (S.D. = ±7) respectively whereas predicted WWSG reduced the bias by up to 0.1% (S.D. = ±8). We also observed that the stand-level bias obtained with WSG Base and WSG Dryad decreased with total plot size and plot area. The systematic bias induced by WSG Base and WSG Dryad for biomass estimations using measured volumes are clearly not negligible but yet generally overlooked. A simple corrective approach such as the one proposed with our predictive WWSG model is liable to improve the precision of remote sensing-based approaches for broader scale biomass estimations. [ABSTRACT FROM AUTHOR]

Tropical rainforests from the Brazilian Amazon are frequently degraded by logging, fire, edge effects and minor unpaved roads. However, mapping the extent of degradation remains challenging because of the lack of frequent high-spatial resolution satellite observations, occlusion of understory disturbances, quick recovery of leafy vegetation, and limitations of conventional reflectance-based remote sensing techniques. Here, we introduce a new approach to map forest degradation caused by logging, fire, and road construction based on deep learning (DL), henceforth called DL-DEGRAD, using very high spatial (4.77 m) and bi-annual to monthly temporal resolution of the Planet NICFI imagery. We applied DL-DEGRAD model over forests of the state of Mato Grosso in Brazil to map forest degradation with attributions from 2016 to 2021 at six-month intervals. A total of 73,744 images (256 × 256 pixels in size) were visually interpreted and manually labeled with three semantic classes (logging, fire, and roads) to train/validate a U-Net model. We predicted the three classes over the study area for all dates, producing accumulated degradation maps biannually. Estimates of accuracy and areas of degradation were performed using a probability design-based stratified random sampling approach (n = 2678 samples) and compared it with existing operational data products at the state level. DL-DEGRAD performed significantly better than all other data products in mapping logging activities (F 1 -score = 68.9) and forest fire (F 1 -score = 75.6) when compared with the Brazil's national maps (SIMEX, DETER, MapBiomas Fire) and global products (UMD-GFC, TMF, FireCCI, FireGFL, GABAM, MCD64). Pixel-based spatial comparison of degradation areas showed the highest agreement with DETER and SIMEX as Brazil official data products derived from visual interpretation of Landsat imagery. The U-Net model applied to NICFI data performed as closely to a trained human delineation of logged and burned forests, suggesting the methodology can readily scale up the mapping and monitoring of degraded forests at national to regional scales. Over the state of Mato Grosso, the combined effects of logging and fire are degrading the remaining intact forests at an average rate of 8443 km2 year−1 from 2017 to 2021. In 2020, a record degradation area of 13,294 km2 was estimated from DL-DEGRAD, which was two times the areas of deforestation. • Mapping forest degradation is a major gap for carbon emissions in tropical forests. • Here we train a deep learning model using Planet NICFI data to map degradation. • The U-Net model segments degraded forests due to logging, fire, and roads. • Our maps show the highest overlap with official Brazilian data from DETER and SIMEX. • This novel approach provides mapping and attribution of tropical forest degradation. [ABSTRACT FROM AUTHOR]