Quantifying Long-Term Changes in Carbon Stocks and Forest Structure from Amazon Forest Degradation

Despite sustained declines in Amazon deforestation, forest degradation from logging and firecontinues to threaten carbon stocks, habitat, and biodiversity in frontier forests along the Amazon arcof deforestation. Limited data on the magnitude of carbon losses and rates of carbon recoveryfollowing forest degradation have hindered carbon accounting efforts and contributed to incompletenational reporting to reduce emissions from deforestation and forest degradation (REDD+). Wecombined annual time series of Landsat imagery and high-density airborne lidar data to characterizethe variability, magnitude, and persistence of Amazon forest degradation impacts on abovegroundcarbon density (ACD) and canopy structure. On average, degraded forests contained 45.1% of thecarbon stocks in intact forests, and differences persisted even after 15 years of regrowth. Incomparison to logging, understory fires resulted in the largest and longest-lasting differences in ACD.Heterogeneity in burned forest structure varied by fire severity and frequency. Forests with a historyof one, two, and three or more fires retained only 54.4%, 25.2%, and 7.6% of intact ACD,respectively, when measured after a year of regrowth. Unlike the additive impact of successive fires,selective logging before burning did not explain additional variability in modeled ACD loss andrecovery of burned forests. Airborne lidar also provides quantitative measures of habitat structure thatcan aid the estimation of co-benefits of avoided degradation. Notably, forest carbon stocks recoveredfaster than attributes of canopy structure that are critical for biodiversity in tropical forests, includingthe abundance of tall trees. We provide the first comprehensive look-up table of emissions factors forspecific degradation pathways at standard reporting intervals in the Amazon. Estimated carbon lossand recovery trajectories provide an important foundation for assessing the long-term contributionsfrom forest degradation to regional carbon cycling and advance our understanding of the currentstate of frontier forests

LANDSAT-D investigations in snow hydrology

Work undertaken during the contract and its results are described. Many of the results from this investigation are available in journal or conference proceedings literature - published, accepted for publication, or submitted for publication. For these the reference and the abstract are given. Those results that have not yet been submitted separately for publication are described in detail. Accomplishments during the contract period are summarized as follows: (1) analysis of the snow reflectance characteristics of the LANDSAT Thematic Mapper, including spectral suitability, dynamic range, and spectral resolution; (2) development of a variety of atmospheric models for use with LANDSAT Thematic Mapper data. These include a simple but fast two-stream approximation for inhomogeneous atmospheres over irregular surfaces, and a doubling model for calculation of the angular distribution of spectral radiance at any level in an plane-parallel atmosphere; (3) incorporation of digital elevation data into the atmospheric models and into the analysis of the satellite data; and (4) textural analysis of the spatial distribution of snow cover

Spaceborne Potential for Examining Taiga-Tundra Ecotone Form and Vulnerability

In the taiga-tundra ecotone (TTE), site-dependent forest structure characteristics can influence the subtle and heterogeneous structural changes that occur across the broad circumpolar extent. Such changes may be related to ecotone form, described by the horizontal and vertical patterns of forest structure (e.g., tree cover, density and height) within TTE forest patches, driven by local site conditions, and linked to ecotone dynamics. The unique circumstance of subtle, variable and widespread vegetation change warrants the application of spaceborne data including high-resolution (less than 5m) spaceborne imagery (HRSI) across broad scales for examining TTE form and predicting dynamics. This study analyzes forest structure at the patch-scale in the TTE to provide a means to examine both vertical and horizontal components of ecotone form. We demonstrate the potential of spaceborne data for integrating forest height and density to assess TTE form at the scale of forest patches across the circumpolar biome by (1) mapping forest patches in study sites along the TTE in northern Siberia with a multi-resolution suite of spaceborne data, and (2) examining the uncertainty of forest patch height from this suite of data across sites of primarily diffuse TTE forms. Results demonstrate the opportunities for improving patch-scale spaceborne estimates of forest height, the vertical component of TTE form, with HRSI. The distribution of relative maximum height uncertainty based on prediction intervals is centered at approximately 40%, constraining the use of height for discerning differences in forest patches. We discuss this uncertainty in light of a conceptual model of general ecotone forms, and highlight how the uncertainty of spaceborne estimates of height can contribute to the uncertainty in identifying TTE forms. A focus on reducing the uncertainty of height estimates in forest patches may improve depiction of TTE form, which may help explain variable forest responses in the TTE to climate change and the vulnerability of portions of the TTE to forest structure change. structural changes

GEDI and TanDEM-X Fusion for 3D Forest Structure Parameter Retrieval

GEDI: Global Ecosystem Dynamics Investigation. Selected in late 2014 for $94 M (Class C mission). Multi-beam waveform lidar instrument. Deployed on International Space Station. Launch on SpaceX-17: Nov. 2018. Nominal 2 year mission length

Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles

NASA's Global Ecosystem Dynamics Investigation (GEDI) is a key climate mission whose goal is to advance our understanding of the role of forests in the global carbon cycle. While GEDI is the first space-based LIDAR explicitly optimized to measure vertical forest structure predictive of aboveground biomass, the accurate interpretation of this vast amount of waveform data across the broad range of observational and environmental conditions is challenging. Here, we present a novel supervised machine learning approach to interpret GEDI waveforms and regress canopy top height globally. We propose a probabilistic deep learning approach based on an ensemble of deep convolutional neural networks (CNN) to avoid the explicit modelling of unknown effects, such as atmospheric noise. The model learns to extract robust features that generalize to unseen geographical regions and, in addition, yields reliable estimates of predictive uncertainty. Ultimately, the global canopy top height estimates produced by our model have an expected RMSE of 2.7 m with low bias

Use of waveform lidar and hyperspectral sensors to assess selected spatial and structural patterns associated with recent and repeat disturbance and the abundance of sugar maple (Acer saccharum Marsh.) in a temperate mixed hardwood and conifer forest.

Abstract Waveform lidar imagery was acquired on September 26, 1999 over the Bartlett Experimental Forest (BEF) in New Hampshire (USA) using NASA\u27s Laser Vegetation Imaging Sensor (LVIS). This flight occurred 20 months after an ice storm damaged millions of hectares of forestland in northeastern North America. Lidar measurements of the amplitude and intensity of ground energy returns appeared to readily detect areas of moderate to severe ice storm damage associated with the worst damage. Southern through eastern aspects on side slopes were particularly susceptible to higher levels of damage, in large part overlapping tracts of forest that had suffered the highest levels of wind damage from the 1938 hurricane and containing the highest levels of sugar maple basal area and biomass. The levels of sugar maple abundance were determined through analysis of the 1997 Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) high resolution spectral imagery and inventory of USFS Northern Research Station field plots. We found a relationship between field measurements of stem volume losses and the LVIS metric of mean canopy height (r2 = 0.66; root mean square errors = 5.7 m3/ha, p \u3c 0.0001) in areas that had been subjected to moderate-to-severe ice storm damage, accurately documenting the short-term outcome of a single disturbance event

Incorporating canopy structure from simulated GEDI lidar into bird species distribution models

The Global Ecosystem Dynamics Investigation (GEDI) lidar began data acquisition from the International Space Station in March 2019 and is expected to make over 10 billion measurements of canopy structure and topography over two years. Previously, airborne lidar data with limited spatial coverage have been used to examine relationships between forest canopy structure and faunal diversity, most commonly bird species. GEDI’s latitudinal coverage will permit these types of analyses at larger spatial extents, over the majority of the Earth’s forests, and most importantly in areas where canopy structure is complex and/or poorly understood. In this regional study, we examined the impact that GEDI-derived Canopy Structure variables have on the performance of bird species distribution models (SDMs) in Sonoma County, California. We simulated GEDI waveforms for a two-year period and then interpolated derived Canopy Structure variables to three grid sizes of analysis. In addition to these variables, we also included Phenology, Climate, and other Auxiliary variables to predict the probability of occurrence of 25 common bird species. We used a weighted average ensemble of seven individual machine learning models to make predictions for each species and calculated variable importance. We found that Canopy Structure variables were, on average at our finest resolution of 250 m, the second most important group (32.5%) of predictor variables after Climate variables (35.3%). Canopy Structure variables were most important for predicting probability of occurrence of birds associated with Conifer forest habitat. Regarding spatial analysis scale, we found that finer-scale models more frequently performed better than coarser-scale models, and the importance of Canopy Structure variables was greater at finer spatial resolutions. Overall, GEDI Canopy Structure variables improved SDM performance for at least one spatial resolution for 19 of 25 species and thus show promise for improving models of bird species occurrence and mapping potential habitat

The use of GEDI canopy structure for explaining variation in tree species richness in natural forests

Variables describing the abiotic environment (e.g. climate, topography or biogeographic history) have a long tradition of use as predictors of tree species richness patterns. However, these variables may capture variations in richness related to climate, but not those that are related to soil type or forest disturbance. Canopy structure has previously been shown to provide information on the variation of tree species richness, with richness generally increasing with larger canopy heights and denser foliage. The use of canopy structure is increasingly relevant with the availability of such data from the Global Ecosystem Dynamics Investigation (GEDI), a lidar mission onboard the International Space Station. In this analysis we show that GEDI canopy structure explains up to 66% of the variation in tree species richness in natural forests without a history of recent disturbance across the globe. However, this portion overlaps with the variation (up to 80%) explained by environmental and biogeographical variables. Our results show that relationships between tree species richness on one side and climate and canopy structure on the other side are not as straightforward as we initially expected, and should be further investigated across both natural and disturbed forests.Environmental Biolog