RESUMEN
The Amazon Forest, the largest contiguous tropical forest in the world, stores a significant fraction of the carbon on land. Changes in climate and land use affect total carbon stocks, making it critical to continuously update and revise the best estimates for the region, particularly considering changes in forest dynamics. Forest inventory data cover only a tiny fraction of the Amazon region, and the coverage is not sufficient to ensure reliable data interpolation and validation. This paper presents a new forest above-ground biomass map for the Brazilian Amazon and the associated uncertainty both with a resolution of 250 meters and baseline for the satellite dataset the year of 2016 (i.e., the year of the satellite observation). A significant increase in data availability from forest inventories and remote sensing has enabled progress towards high-resolution biomass estimates. This work uses the largest airborne LiDAR database ever collected in the Amazon, mapping 360,000 km2 through transects distributed in all vegetation categories in the region. The map uses airborne laser scanning (ALS) data calibrated by field forest inventories that are extrapolated to the region using a machine learning approach with inputs from Synthetic Aperture Radar (PALSAR), vegetation indices obtained from the Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite, and precipitation information from the Tropical Rainfall Measuring Mission (TRMM). A total of 174 field inventories geolocated using a Differential Global Positioning System (DGPS) were used to validate the biomass estimations. The experimental design allowed for a comprehensive representation of several vegetation types, producing an above-ground biomass map varying from a maximum value of 518 Mg ha-1, a mean of 174 Mg ha-1, and a standard deviation of 102 Mg ha-1. This unique dataset enabled a better representation of the regional distribution of the forest biomass and structure, providing further studies and critical information for decision-making concerning forest conservation, planning, carbon emissions estimate, and mechanisms for supporting carbon emissions reductions.
Asunto(s)
Biomasa , Bosques , Tecnología de Sensores Remotos , Brasil , Carbono/análisis , Tecnología de Sensores Remotos/métodos , Clima TropicalRESUMEN
Very few studies have been devoted to understanding the digital terrain model (DTM) creation for Amazon forests. DTM has a special and important role when airborne laser scanning is used to estimate vegetation biomass. We examined the influence of pulse density, spatial resolution, filter algorithms, vegetation density and slope on the DTM quality. Three Amazonian forested areas were surveyed with airborne laser scanning, and each original point cloud was reduced targeting to 20, 15, 10, 8, 6, 4, 2, 1, 0.75, 0.5 and 0.25 pulses per square meter based on a random resampling process. The DTM from resampled clouds was compared with the reference DTM produced from the original LiDAR data by calculating the deviation pixel by pixel and summarizing it through the root mean square error (RMSE). The DTM from resampled clouds were also evaluated considering the level of agreement with the reference DTM. Our study showed a clear trade-off between the return density and the horizontal resolution. Higher forest canopy density demanded higher return density or lower DTM resolution.
São poucos os estudos dedicados a entender a criação de modelo digital de terreno (MDT) para florestas amazônicas. O MDT tem uma importante função quando o escaneamento laser aerotransportado é usado para estimar a biomassa da vegetação. Examinamos a relação da densidade de pulsos, resolução espacial, algoritmos de filtragem, densidade da vegetação e inclinação do terreno com a qualidade do MDT. Três áreas de floresta amazônica foram sobrevoadas usando LiDAR aerotransportado. Cada nuvem de dados original teve sua densidade reduzida objetivando 20; 15; 10; 8; 6; 4; 2; 1; 0,75; 0,5 e 0,25 pulsos por metro quadrado, utilizando um processo de reamostragem aleatória. Os MDTs das nuvens reamostradas foram comparados com o MDT de referência, produzido a partir da nuvem original, calculando o desvio pixel a pixel e resumindo-o por meio do erro padrão da estimativa (RMSE). Os MDTs das nuvens reamostradas também foram avaliados quanto ao nível de correspondência com o MDT de referência. Houve uma clara compensação entre densidade de pontos e resolução horizontal. Dosséis mais densos exigem uma maior densidade de retornos, ou MDT com menor resolução.
Asunto(s)
Análisis del Suelo , Mapeo Geográfico , Sistemas de Información Geográfica , Brasil , Ecosistema AmazónicoRESUMEN
Exotic species are used to trigger facilitation in restoration plantings, but this positive effect may not be permanent and these species may have negative effects later on. Since such species can provide a marketable product (firewood), their harvest may represent an advantageous strategy to achieve both ecological and economic benefits. In this study, we looked at the effect of removal of a non-native tree species (Mimosa caesalpiniifolia) on the understory of a semideciduous forest undergoing restoration. We assessed two 14-year-old plantation systems (modified "taungya" agroforestry system; and mixed plantation using commercial timber and firewood tree species) established at two sites with contrasting soil properties in São Paulo state, Brazil. The experimental design included randomized blocks with split plots. The natural regeneration of woody species (height ≥0.2 m) was compared between managed (all M. caesalpiniifolia trees removed) and unmanaged plots during the first year after the intervention. The removal of M. caesalpiniifolia increased species diversity but decreased stand basal area. Nevertheless, the basal area loss was recovered after 1 year. The management treatment affected tree species regeneration differently between species groups. The results of this study suggest that removal of M. caesalpiniifolia benefited the understory and possibly accelerated the succession process. Further monitoring studies are needed to evaluate the longer term effects on stand structure and composition. The lack of negative effects of tree removal on the natural regeneration indicates that such interventions can be recommended, especially considering the expectations of economic revenues from tree harvesting in restoration plantings.