• Energy Research

The forest canopy is the functional interface between 90% of Earth's terrestrial biomass and the atmosphere. Multidisciplinary research in the canopy has expanded concepts of global species richness, physiological processes, and the provision of ecosystem services. Trees respond in a species-specific manner to elevated carbon dioxide levels, while climate change threatens plant-animal interactions in the canopy and will likely alter the production of biogenic aerosols that affect cloud formation and atmospheric chemistry.

AbstractThe 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.

The long-term viability of a forest industry in the Amazon region of Brazil depends on the maintenance of adequate timber volume and growth in healthy forests. Using extensive high-resolution satellite analyses, we studied the forest damage caused by recent logging operations and the likelihood that logged forests would be cleared within 4 years after timber harvest. Across 2,030,637 km 2 of the Brazilian Amazon from 1999 to 2004, at least 76% of all harvest practices resulted in high levels of canopy damage sufficient to leave forests susceptible to drought and fire. We found that 16 ± 1% of selectively logged areas were deforested within 1 year of logging, with a subsequent annual deforestation rate of 5.4% for 4 years after timber harvests. Nearly all logging occurred within 25 km of main roads, and within that area, the probability of deforestation for a logged forest was up to four times greater than for unlogged forests. In combination, our results show that logging in the Brazilian Amazon is dominated by highly damaging operations, often followed rapidly by deforestation decades before forests can recover sufficiently to produce timber for a second harvest. Under the management regimes in effect at the time of our study in the Brazilian Amazon, selective logging would not be sustained.

Agricultural expansion and climate variability have become important agents of disturbance in the Amazon basin. Recent studies have demonstrated considerable resilience of Amazonian forests to moderate annual drought, but they also show that interactions between deforestation, fire and drought potentially lead to losses of carbon storage and changes in regional precipitation patterns and river discharge. Although the basin-wide impacts of land use and drought may not yet surpass the magnitude of natural variability of hydrologic and biogeochemical cycles, there are some signs of a transition to a disturbance-dominated regime. These signs include changing energy and water cycles in the southern and eastern portions of the Amazon basin.

This letter reports the sensitivity of X-band interferometric synthetic aperture radar (InSAR) data from the first dual-spacecraft radar interferometer, TanDEM-X, to variations in tropical-forest aboveground biomass (AGB). It also reports the first tropical-forest AGB estimates from TanDEM-X data. Tropical forests account for about 50% of the world's forested biomass and play critical roles in the control of atmospheric carbon dioxide by emission through deforestation and uptake through forest growth. The TanDEM-X InSAR data used in this analysis were taken over the Tapajos National Forest, Para, Brazil, where field measurements from 30 stands were acquired. The magnitude of the InSAR normalized complex correlation, which is called coherence, decreases by about 25% as AGB increases from 2 to 430 Mg-ha -1 , suggesting more vertically distributed return-power profiles with increasing biomass. Comparison of InSAR coherences to those of small-spot (15 cm) lidar suggests that lidar penetrates deeper into the canopies than InSAR. Modeling InSAR profiles from InSAR coherence and lidar profiles yields an estimate of 0.29 dB/m for the X-band extinction coefficient relative to that of lidar. Forest AGB estimated from InSAR observations on 0.25-ha plots shows RMS scatters about the field-estimated AGB between 52 and 62 Mg-ha -1 , which is between 29% and 35% of the average AGB of 179 Mg-ha -1 , depending on the data analysis mode. The sensitivity and biomass-estimation performance suggest the potential of TanDEM-X observations to contribute to global tropical-forest biomass monitoring.

AbstractIn a recent paper (Mitchard et al. 2014, Global Ecology and Biogeography, 23, 935–946) a new map of forest biomass based on a geostatistical model of field data for the Amazon (and surrounding forests) was presented and contrasted with two earlier maps based on remote‐sensing data Saatchi et al. (2011; RS1) and Baccini et al. (2012; RS2). Mitchard et al. concluded that both the earlier remote‐sensing based maps were incorrect because they did not conform to Mitchard et al. interpretation of the field‐based results. In making their case, however, they misrepresented the fundamental nature of primary field and remote‐sensing data and committed critical errors in their assumptions about the accuracy of research plots, the interpolation methodology and the statistical analysis. By ignoring the large uncertainty associated with ground estimates of biomass and the significant under‐sampling and spatial bias of research plots, Mitchard et al. reported erroneous trends and artificial patterns of biomass over Amazonia. Because of these misrepresentations and methodological flaws, we find their critique of the satellite‐derived maps to be invalid.

AbstractThe future of tropical forests hinges on the balance between disturbance rates, which are expected to increase with climate change, and tree growth. Whereas tree growth is a slow process, disturbance events occur sporadically and tend to be short‐lived. This difference challenges forest monitoring to achieve sufficient resolution to capture tree growth, while covering the necessary scale to characterize disturbance rates. Airborne LiDAR time series can address this challenge by measuring landscape scale changes in canopy height at 1 m resolution. In this study, we present a robust framework for analysing disturbance and recovery processes in LiDAR time series data. We apply this framework to 8000 ha of old‐growth tropical forests over a 4–5‐year time frame, comparing growth and disturbance rates between Borneo, the eastern Amazon and the Guiana shield. Our findings reveal that disturbance was balanced by growth in eastern Amazonia and the Guiana shield, resulting in a relatively stable mean canopy height. In contrast, tall Bornean forests experienced a decrease in canopy height due to numerous small‐scale (<0.1 ha) disturbance events outweighing the gains due to growth. Within sites, we found that disturbance rates were weakly related to topography, but significantly increased with maximum canopy height. This could be because taller trees were particularly vulnerable to disturbance agents such as drought, wind and lightning. Consequently, we anticipate that tall forests, which contain substantial carbon stocks, will be disproportionately affected by the increasing severity of extreme weather events driven by climate change.