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The objective of this study was to identify the effects of local geomorphometry on the abundance, richness and floristic composition of tree species in the central Brazilian Amazon. Forty-six 0.25-ha plots in different phyto-ecologic sites were sampled, and their trees were inventoried. Geomorphometric data (elevation, slope, aspect, plan and profile curvatures) were derived from Shuttle Radar Topography Mission data. A detrended correspondence analysis (DCA) was used to examine the floristic distribution patterns among plots. In addition, geomorphometric variables were submitted to multiple regression analysis to identify the variables influencing floristic composition (represented by the first DCA component), abundance and species richness. Correlation analyses between the number of individuals from each species and the first DCA component were performed to evaluate the contribution of each species. Analysis of the results could not confirm an effect of geomorphometry alone on species richness and abundance, although floristic composition was significantly influenced by profile curvature and elevation. Despite the relatively low variation in altitude at the study site, species were found to be sensitive to terrain peculiarities such as elevation and profile curvature, which can constrain particular ecologic niches and contribute to the spatial distribution patterns of species. [ABSTRACT FROM AUTHOR]

• Anthropic land uses are replacing Brazilian Savanna and Seasonal Forest. • Negatives impacts on natural landscapes have grown over time. • Landscape indices could quantify the fragments environmental quality. Landscapes constitute the manifestation of natural and anthropic elements and its structure is the result of physical, biological, political, economic and social interactions, fragmented or connected for different land uses. Drawing on this, our study aims to analyze the environmental landscape quality using ecological indices in the Tietê-Jacaré Hydrographic Basin – SP, Brazil between 2007 and 2017. A set of Environmental Quality Indices was used to analyze the susceptibility of the ecological components to the effects of human activities. The Environmental Quality Index of Vegetation, the Environmental Quality Index of Water Resources and the Environmental Vulnerability Index were used to assess the condition of ecological sustainability of the landscape. It was identified a reduction in the areas with native vegetation with a loss of 2.72%, representing over 32,149 ha, an expansion of agricultural areas of 2.05% (24,507.53 ha) and consequently a reduction of the environmental quality in the landscape. These impacts compromise the ecosystem structure and ecosystem services, per example, through the impacts on soils that support vegetation cover as the major sources of energy for terrestrial life. The observed intensification of anthropogenic activities and the corresponding reduction of natural areas often lead to the loss of biodiversity and the benefits that its ecosystem services provided to people. [ABSTRACT FROM AUTHOR]

Remote sensing-radar was used to analyze forest mapping and biomass estimates on Brazilian territory. Two examples of SAR attributes for the modeling of the aboveground biomass of forest stands are presented: (1) full-polarimetric attributes of PALSAR/ALOS (Phased Array type L-band Synthetic Aperture Radar/ Advanced Land Observing Satellite) for modeling in the Amazonian tropical forest, considering the influence of the geomorphometric aspects on this radar response, and (2) polarimetric and interferometric airborne data (XHH and full-polarimetric of P-band) for modeling Eucalyptus sp. stands. In both cases, an analysis of forest structure variability through polarimetric signatures was conducted. A multivariate regression technique was used to integrate the variables from polarimetric and/or interferometric radar attributes and field inventory. Considering the terrain aspects where the tropical forest was located, the most significant variables for the biomass modeling were the Volumetric Scattering of Freeman-Durden target decomposition, Anisotropy, Relief Elevation, Slope, and the first and third helicity components of the Touzi model. For the Eucalyptus biomass model, the Interferometry Height and Canopy Scattering Index variables were significant. The statistical analysis based on field survey measures to validate each model, indicated a margin of error below 20% for the biomass estimations, showing the importance of SAR attributes for models of natural and planted forest stock density. [ABSTRACT FROM AUTHOR]

The monitoring of forest degradation in the Amazon through radar remote sensing methodologies has increased intensely in recent years. Synthetic aperture radar (SAR) sensors that operate in L-band have an interesting response for land use and land cover (LULC) as well as for aboveground biomass (AGB). Depending on the magnetic and solar activities and seasonality, plasma bubbles in the ionosphere appear in the equatorial and tropical regions; these factors can cause stripes across SAR images, which disturb the interpretation and the classification. Our article shows a methodology to filter these stripes using Fourier fast transform (FFT), in which a stop-band filter removes this noise. In order to make this possible, we used Environment for Visualizing Images (ENVI), Sentinel Application Platform (SNAP), and Interactive Data Language (IDL). The final filtered scenes were classified by random forest (RF), and the results of this classification showed superior performance compared to the original scenes, showing this methodology can help to recover historic series of L-band images. [ABSTRACT FROM AUTHOR]

The terrestrial forest carbon pool is poorly quantified, in particular in regions with low forest inventory capacity. By combining multiple satellite observations of synthetic aperture radar (SAR) backscatter around the year 2010, we generated a global, spatially explicit dataset of above-ground live biomass (AGB; dry mass) stored in forests with a spatial resolution of 1 ha. Using an extensive database of 110 897 AGB measurements from field inventory plots, we show that the spatial patterns and magnitude of AGB are well captured in our map with the exception of regional uncertainties in high-carbon-stock forests with AGB >250 Mgha-1 , where the retrieval was effectively based on a single radar observation. With a total global AGB of 522 Pg , our estimate of the terrestrial biomass pool in forests is lower than most estimates published in the literature (426–571 Pg). Nonetheless, our dataset increases knowledge on the spatial distribution of AGB compared to the Global Forest Resources Assessment (FRA) by the Food and Agriculture Organization (FAO) and highlights the impact of a country's national inventory capacity on the accuracy of the biomass statistics reported to the FRA. We also reassessed previous remote sensing AGB maps and identified major biases compared to inventory data, up to 120 % of the inventory value in dry tropical forests, in the subtropics and temperate zone. Because of the high level of detail and the overall reliability of the AGB spatial patterns, our global dataset of AGB is likely to have significant impacts on climate, carbon, and socio-economic modelling schemes and provides a crucial baseline in future carbon stock change estimates. The dataset is available at 10.1594/PANGAEA.894711 (Santoro, 2018). [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]

The high species richness of tropical forests has long been recognized, yet there remains substantial uncertainty regarding the actual number of tropical tree species. Using a pantropical tree inventory database from closed canopy forests, consisting of 657,630 trees belonging to 11,371 species, we use a fitted value of Fisher's alpha and an approximate pantropical stem total to estimate the minimum number of tropical forest tree species to fall between ∼40,000 and ∼53,000, i.e., at the high end of previous estimates. Contrary to common assumption, the Indo-Pacific region was found to be as species-rich as the Neotropics, with both regions having a minimum of ∼19,000-25,000 tree species. Continental Africa is relatively depauperate with a minimumof ∼4,500-6,000 tree species. Very few species are shared among the African, American, and the Indo-Pacific regions. We provide a methodological framework for estimating species richness in trees that may help refine species richness estimates of tree-dependent taxa. [ABSTRACT FROM AUTHOR]

Over the past decade, several global maps of above-ground biomass (AGB) have been produced, but they exhibit significant differences that reduce their value for climate and carbon cycle modelling, and also for national estimates of forest carbon stocks and their changes. The number of such maps is anticipated to increase because of new satellite missions dedicated to measuring AGB. Objective and consistent methods to estimate the accuracy and uncertainty of AGB maps are therefore urgently needed. This paper develops and demonstrates a framework aimed at achieving this. The framework provides a means to compare AGB maps with AGB estimates from a global collection of National Forest Inventories and research plots that accounts for the uncertainty of plot AGB errors. This uncertainty depends strongly on plot size, and is dominated by the combined errors from tree measurements and allometric models (inter-quartile range of their standard deviation (SD) = 30–151 Mg ha−1). Estimates of sampling errors are also important, especially in the most common case where plots are smaller than map pixels (SD = 16–44 Mg ha−1). Plot uncertainty estimates are used to calculate the minimum-variance linear unbiased estimates of the mean forest AGB when averaged to 0.1∘. These are used to assess four AGB maps: Baccini (2000), GEOCARBON (2008), GlobBiomass (2010) and CCI Biomass (2017). Map bias, estimated using the differences between the plot and 0.1∘ map averages, is modelled using random forest regression driven by variables shown to affect the map estimates. The bias model is particularly sensitive to the map estimate of AGB and tree cover, and exhibits strong regional biases. Variograms indicate that AGB map errors have map-specific spatial correlation up to a range of 50–104 km, which increases the variance of spatially aggregated AGB map estimates compared to when pixel errors are independent. After bias adjustment, total pantropical AGB and its associated SD are derived for the four map epochs. This total becomes closer to the value estimated by the Forest Resources Assessment after every epoch and shows a similar decrease. The framework is applicable to both local and global-scale analysis, and is available at https://github.com/arnanaraza/PlotToMap. Our study therefore constitutes a major step towards improved AGB map validation and improvement. • A framework to assess global above-ground biomass maps at 0.1° spatial scale is developed and demonstrated. • Uncertainty assessment of reference data is a prerequisite prior to map comparisons. • Spatial uncertainty modelling reveals regional map biases and map-specific spatial correlation of errors. • Map-based AGB totals become closer to the value estimated by the Forest Resource Assessment over time. [ABSTRACT FROM AUTHOR]