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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]

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]

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 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]