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Understanding the hydrologic regime, and monitoring runoff at the micro-basin scale is critical for understanding water availability, and for managing water resources in strategic biomes such as the cerrado. The objective of this research is to evaluate morphometric characteristics and the hydrologic regime and determine their relationships with soil characteristics and typical land-use in the cerrado biome. To reach this objective morphometric characterization, runoff and precipitation monitoring, and determination of the water retention curve were conducted in three micro-basins in the municipality of Campo Verde, Mato Grosso. These micro-basins each have a different, but regionally typical land-use and were selected strategically to represent this area of cerrado: agriculture, pasture and native vegetation. The results obtained allow for the analysis of the hydrologic regime throughout the period measured. Micro-basins with native vegetation and pasture, with greater altimetric range and more sandy soils, show greater erosion. Despite this, the micro-basin with pasture cover showed greater regulation of runoff. The micro-basin with agriculture showed less regularization of water flux due to the physical characteristics of the local soils. O conhecimento e monitoramento da vazão, assim como o entendimento do regime hidrológico a nível de microbacias tem sido relevante para se determinar a disponibilidade hídrica, assim como promover o gerenciamento dos recursos hídricos em biomas considerados estratégicos como o cerrado. Este trabalho foi desenvolvido com o objetivo de avaliar as características morfométricas e o regime hidrológico e sua relação com as características do solo bem como do seu uso em três microbacias no bioma cerrado. Para alcançar o objetivo proposto, fez-se a caracterização morfométrica, o monitoramento da vazão e da precipitação diária e elaboração da curva de permanência de três microbacias hidrográficas localizadas no município de Campo Verde-MT contemplando os seguintes usos: agricultura, pastagem e vegetação nativa. Essas bacias foram selecionadas estrategicamente por serem representativas dos principais sistemas de uso do solo na região de cerrado. Os resultados obtidos permitem ponderar que há diferença no regime hidrológico das microbacias hidrográficas ao longo do período analisado. As microbacias com vegetação nativa e com pastagem, por terem maior amplitude altimétrica, além de apresentarem solos de textura arenosa, são mais propensas à erosão hídrica. A microbacia com pastagem apresentou maior regularidade na vazão e, a microbacia com agricultura uma menor regularização do fluxo hidrológico em função das características físicas dos solos que compõe a microbacia.

Tropical forest structural variation across heterogeneous landscapes may control above-ground carbon dynamics. We tested the hypothesis that canopy structure (leaf area and light availability) – remotely estimated from LiDAR – control variation in above-ground coarse wood production (biomass growth). Using a statistical model, these factors predicted biomass growth across tree size classes in forest near Manaus, Brazil. The same statistical model, with no parameterisation change but driven by different observed canopy structure, predicted the higher productivity of a site 500 km east. Gap fraction and a metric of vegetation vertical extent and evenness also predicted biomass gains and losses for one-hectare plots. Despite significant site differences in canopy structure and carbon dynamics, the relation between biomass growth and light fell on a unifying curve. This supported our hypothesis, suggesting that knowledge of canopy structure can explain variation in biomass growth over tropical landscapes and improve understanding of ecosystem function.

Exchange of carbon between forests and the atmosphere is a vital component of the global carbon cycle. Satellite laser altimetry has a unique capability for estimating forest canopy height, which has a direct and increasingly well understood relationship to aboveground carbon storage. While the Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud and land Elevation Satellite (ICESat) has collected an unparalleled dataset of lidar waveforms over terrestrial targets, processing of ICESat data to estimate forest height is complicated by the pulse broadening associated with large-footprint, waveform-sampling lidar. We combined ICESat waveforms and ancillary topography from the Shuttle Radar Topography Mission to estimate maximum forest height in three ecosystems; tropical broadleaf forests in Brazil, temperate broadleaf forests in Tennessee, and temperate needleleaf forests in Oregon. Final models for each site explained between 59% and 68% of variance in field-measured forest canopy height (RMSE between 4.85 and 12.66 m). In addition, ICESat-derived heights for the Brazilian plots were correlated with field-estimates of aboveground biomass (r(2) = 73%, RMSE = 58.3 Mgha(-1)).

The vertical extent of waveforms collected by the Geoscience Laser Altimeter System (onboard ICESat - the Ice, Cloud, and land Elevation Satellite) increases as a function of terrain slope and footprint size (the area on the ground that is illuminated by the laser). Over sloped terrain, returns from both canopy and ground surfaces can occur at the same elevation. As a result, the height of the waveform (waveform extent) is insufficient to make estimates of tree height on sloped terrain, and algorithms are needed that are capable of retrieving information about terrain slope from the waveform itself. Early work on this problem used a combination of waveform height indices and slope indices from a digital elevation model (DEM). A second generation algorithm was developed using datasets from diverse forests in which forest canopy height has been estimated in the field or by via airborne lidar. Forest types considered in this paper include evergreen needleleaf, deciduous broadleaf and mixed stands in temperate North America, and tropical evergreen broadleaf forests in Brazil. The algorithm described eliminates the need for a DEM, and estimates forest canopy height with an RMSE of 5 m (83% of variance in forest canopy height explained).