Understanding changes to aboveground biomass (AGB) in forests undergoing degradation is crucial for accurately and completely quantifying carbon emissions from forest loss and for environmental monitoring in the context of climate change. Monitoring forest degradation as compared to deforestation presents technical challenges because degradation involves widespread, low-intensity AGB removal under varying temporal dynamics. Charcoal production is a key driver for forest degradation in Africa and is projected to increase in the future years. In Sub-Saharan Africa (SSA), where charcoal production drives widespread ABG removal, the utility of optical remote sensing for degradation quantification is challenged by the large inter-seasonal variation and high complexities in ecosystem structure. Limited field measurements on tree structure and aboveground biomass density (AGBD) in many parts of the SSA also impose constraints. In this study, we present a novel data fusion approach combining 3D forest structure from NASA's GEDI Lidar with optical time-series data from Landsat to quantify biomass losses associated with charcoal-related forest degradation over a 10-year time period. We used machine learning models with Landsat spectral indices from the time period of limited hydric stress (LHS) as predictor variables. By applying the best performing Random Forest (RF) model to LandTrendr-stabilized annual LHS Landsat composites, we produced annual forest AGBD maps from 2007 to 2019 over the Mabalane district in southern Mozambique where the dry forest ecosystem was under active charcoal-related degradation since 2008. The RF model achieved an RMSE value of 7.05 Mg/ha (RMSE% = 42%) and R2 value of 0.64 using a 10-fold cross-validation dataset. We quantified a total AGB loss of 2.12 ± 0.06 Megatons (Mt) over the 10-year period, which is only 6.35 ± 2.56% less than the total loss estimated using field-based data as previously published for the same area and time. In addition to quantifying biomass loss, we constructed annual AGBD maps that enabled the characterization of disturbance and recovery. Our framework demonstrates that fusing GEDI and Landsat data through predictive modeling can be used to quantify past forest AGBD dynamics in low biomass forests. This approach provides a satellite-based method to support REDD+ monitoring and evaluation activities in areas where field data is limited and has the potential to be extended to investigate a variety of different disturbance events.
Abstract Accurate quantification of forest carbon stocks is required for constraining the global carbon cycle and its impacts on climate. The accuracies of forest biomass maps are inherently dependent on the accuracy of the field biomass estimates used to calibrate models, which are generated with allometric equations. Here, we provide a quantitative assessment of the sensitivity of allometric parameters to sample size in temperate forests, focusing on the allometric relationship between tree height and crown radius. We use LiDAR remote sensing to isolate between 10,000 to more than 1,000,000 tree height and crown radius measurements per site in six U.S. forests. We find that fitted allometric parameters are highly sensitive to sample size, producing systematic overestimates of height. We extend our analysis to biomass through the application of empirical relationships from the literature and show that given the small sample sizes used in common allometric equations for biomass, the average site-level biomass bias is ~+70% with a standard deviation of 71%, ranging from −4% to +193%. These findings underscore the importance of increasing the sample sizes used for allometric equation generation.
Current and planned space missions will produce aboveground biomass density data products at varying spatial resolution. Calibration and validation of these data products is critically dependent on the existence of field estimates of aboveground biomass and coincident remote sensing data from airborne or terrestrial lidar. There are few places that meet these requirements, and they are mostly in the northern hemisphere and temperate zone. Here we summarize the potential for low-altitude drones to produce new observations in support of mission science. We describe technical requirements for producing high-quality measurements from autonomous platforms and highlight differences among commercially available laser scanners and drone aircraft. We then describe a case study using a heavy-lift autonomous helicopter in a temperate mountain forest in the southern Czech Republic in support of calibration and validation activities for the NASA Global Ecosystem Dynamics Investigation. Low-altitude flight using drones enables the collection of ultra-high-density point clouds using wider laser scan angles than have been possible from traditional airborne platforms. These measurements can be precise and accurate and can achieve measurement densities of thousands of points · m−2. Analysis of surface elevation measurements on a heterogeneous target observed 51 days apart indicates that the realized range accuracy is 2.4 cm. The single-date precision is 2.1–4.5 cm. These estimates are net of all processing artifacts and geolocation errors under fully autonomous flight. The 3D model produced by these data can clearly resolve branch and stem structure that is comparable to terrestrial laser scans and can be acquired rapidly over large landscapes at a fraction of the cost of traditional airborne laser scanning.
In order to help scientists in the above ground biomass community and to support the science behind the upcoming BIOMASS, NISAR, and GEDI satellite missions, ESA and NASA are collaborating on the Multi-Mission Algorithm and Analysis Platform (MAAP). The MAAP is a jointly developed and implemented platform that will include not only data (satellite, airborne, in situ data, and products), but also computing capabilities and sets of tools and algorithms developed to support this specific field of research. To best ensure that users are able to collaborate across the platform and to access needed resources, the MAAP requires all data, algorithms, and software to conform to open access and open source policies. In addition to aiding researchers, the MAAP exercise is establishing a collaboration framework between ESA and NASA that focuses on sharing data, science algorithms, and computable resources in order to foster and accelerate scientific research.
We introduce a multiscale superpixel approach that leverages repeat-pass interferometric coherence and sparse AGB estimates from a simulated spaceborne lidar in order to extend the NISAR mission’s applicable range of aboveground biomass (AGB) in tropical forests. Airborne and spaceborne L-band radar and full-waveform airborne lidar data are used to simulate the NISAR and GEDI mission, respectively. In addition to UAVSAR data, we use spaceborne ALOS-2/PALSAR-2 imagery with 14-day temporal baseline, which is comparable to NISAR’s 12-day baseline. Our reference AGB maps are derived from the airborne LVIS data during the AfriSAR campaign for three sites (Mondah, Ogooue, and Lope). Each tropical site has mean AGB of at least 125 Mg/ha in addition to areas with AGB exceeding 700 Mg/ha. Spatially sampling from these LVIS-derived AGB reference maps, we approximate GEDI AGB estimates. To evaluate our methodology, we perform several different analyses. First, we partition each study site into low (≤100 Mg/ha) and high (>100 Mg/ha) AGB areas, in conformity with the NISAR mission requirement to provide AGB estimates for forests between 0 and 100 Mg/ha with a RMSE below 20 Mg/ha. In the low AGB areas, this RMSE requirement is satisfied in Lope and Mondah and it fell short of the requirement in Ogooue by less 3 Mg/ha with UAVSAR and 6 Mg/ha with PALSAR-2. We note that our maps have finer spatial resolution (50 m) than NISAR requires (1 hectare). In the high AGB areas, the normalized RMSE increases to 51% (i.e., <90 Mg/ha), but with negligible bias for all three sites. Second, we train a single model to estimate AGB across both high and low AGB regimes simultaneously and obtain a normalized RMSE that is <60% (or <100 Mg/ha). Lastly, we show the use of both (a) multiscale superpixels and (b) interferometric coherence significantly improves the accuracy of the AGB estimates. The InSAR coherence improved the RMSE by approximately 8% at Mondah with both sensors, lowering the RMSE from 59 Mg/ha to 47.4 Mg/h with UAVSAR and from 57.1 Mg/ha to 46 Mg/ha. This work illustrates one of the numerous synergistic relationships between the spaceborne lidars, such as GEDI, with L-band SAR, such as PALSAR-2 and NISAR, in order to produce robust regional AGB in high biomass tropical regions.
Abstract The Global Ecosystem Dynamics Investigation (GEDI) lidar is a multibeam laser altimeter on the International Space Station (ISS). GEDI is the first spaceborne instrument designed to measure vegetation height and to quantify aboveground carbon stocks in temperate and tropical forests and woodlands. This document describes the algorithm theoretical basis underpinning the development of the GEDI Level‐4A (GEDI04_A) footprint aboveground biomass density (AGBD) data product. The GEDI04_A data product contains estimates of AGBD for individual GEDI footprints and associated prediction intervals. The algorithm uses GEDI02_A relative height metrics and 13 linear models to predict AGBD in 32 combinations of plant functional type and world region within the observation limits of the ISS. GEDI04_A models for the release 1 and release 2 data products were developed using 8,587 quality‐filtered simulated GEDI waveforms associated with field estimates of AGBD in 21 countries. Although this is the most geographically comprehensive data available for the development of AGBD models using lidar remote sensing, important regions are underrepresented, including the forests of continental Asia, deciduous broadleaf forests and savannas of the dry tropics, and evergreen broadleaf forests north of Australia. We describe the scientific and statistical assumptions required to develop globally representative estimates of AGBD using GEDI lidar, including generalization beyond training data, and exclusion of GEDI02_A observations that do not meet requirements of the GEDI04_A algorithm. The footprint‐level predictions generated by this process provide globally comprehensive estimates of AGBD. These footprint‐level predictions are a prerequisite for the GEDI04_B gridded AGBD data product.
Accurate tree-cover estimates are useful in deriving above-ground biomass density estimates from very high resolution (VHR) satellite imagery data. Numerous algorithms have been designed to perform tree-cover delineation in high-to-coarse-resolution satellite imagery, but most of them do not scale to terabytes of data, typical in these VHR data sets. In this paper, we present an automated probabilistic framework for the segmentation and classification of 1-m VHR data as obtained from the National Agriculture Imagery Program (NAIP) for deriving tree-cover estimates for the whole of Continental United States, using a high-performance computing architecture. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on conditional random field, which helps in capturing the higher order contextual dependence relations between neighboring pixels. Once the final probability maps are generated, the framework is updated and retrained by incorporating expert knowledge through the relabeling of misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates (FPRs). The tree-cover maps were generated for the state of California, which covers a total of 11 095 NAIP tiles and spans a total geographical area of 163 696 sq. miles. Our framework produced correct detection rates of around 88% for fragmented forests and 74% for urban tree-cover areas, with FPRs lower than 2% for both regions. Comparative studies with the National Land-Cover Data algorithm and the LiDAR high-resolution canopy height model showed the effectiveness of our algorithm for generating accurate high-resolution tree-cover maps.
Abstract Aim Understanding the drivers of forest structure, function and change is a fundamental problem in both theoretical ecology and applied forestry for carbon mapping and monitoring. An important component of forest ecology research often utilizes allometric equations to scale up local measurements to predict large‐scale forest and ecosystem‐level properties. However, both applied and theoretical allometries in forest ecology (such as metabolic scaling theory, MST ) assume that many scaling relationships are insensitive to broad‐scale climate gradients or species life histories. We aim to test these assumptions by mapping continental‐scale forest allometry across environmental gradients in the U nited S tates. Location U nited S tates. Methods We fit exponents to two allometric relationships in c . 100,000 F orest I nventory A nalysis ( FIA ) field plots: (1) the relationship between the height of an individual tree and its diameter, and (2) plot‐level tree size distributions. We compare fitted exponents to environmental and life‐history variables, such as climate, topography and forest structure, in an attempt to explain allometric variability and deviations from theoretically predicted allometries. Results We find that the structural allometry of forests varies strongly as a function of location in the U nited S tates. Allometric exponents appear to asymptote at approximately where MST theory predicts with increasing forest height, while deviations from MST are partially explained as a function of environmentally driven recruitment limitations and successional status. Main conclusions While we find support for invariant tree and stand allometric scaling relationships in forests that are in steady state with regard to demography and resources, we also find considerable spatial variability in forest allometric relationships when steady‐state conditions are violated. These findings suggest that extensions of metabolic scaling theory should incorporate variation in demographic dynamics in younger successional forests, and factors influencing recruitment limitation.
