allodb: An R package for biomass estimation at globally distributed extratropical forest plots

Allometric equations for calculation of tree above-ground biomass (AGB) form the basis for estimates of forest carbon storage and exchange with the atmosphere. While standard models exist to calculate forest biomass across the tropics, we lack a standardized tool for computing AGB across boreal and temperate regions that comprise the global extratropics. Here we present an integrated R package, allodb, containing systematically selected published allometric equations and proposed functions to compute AGB. The data component of the package is based on 701 woody species identified at 24 large Forest Global Earth Observatory (ForestGEO) forest dynamics plots representing a wide diversity of extratropical forests. A total of 570 parsed allometric equations to estimate individual tree biomass were retrieved, checked and combined using a weighting function designed to ensure optimal equation selection over the full tree size range with smooth transitions across equations. The equation dataset can be customized with built-in functions that subset the original dataset and add new equations. Although equations were curated based on a limited set of forest communities and number of species, this resource is appropriate for large portions of the global extratropics and can easily be expanded to cover novel forest types

Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (similar to 25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available

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Remote Sensing of Forests: Analyzing Biomass Stocks, Changes and Variability with Empirical Data and Simulations

Forests are an important component in the earth system. They cover nearly one third of the land surface, store about as much carbon as the entire atmosphere and host more than half of the planet’s biodiversity. Forests provide ecosystem services such as climate regulation and water cycling and they supply resources. However, forests are increasingly at risk worldwide, due to anthropogenic deforestation, degradation and climate change. Concepts for counteracting this development require abilities to monitor forests and predict possible future developments. Given the vast size of forest cover along with the variety of forest types, field measurements and experiments alone cannot provide the solution for this task. Remote sensing and forest modeling enable a broader and deeper understanding of the processes that shape our planet’s forests. Remote sensing from airborne and spaceborne platforms can provide detailed measurements of forest attributes ranging from landscape to global scale. The challenge is to interpret the measurements in an appropriate way and derive biophysical properties. This requires a good understanding of the interaction between radiation and the vegetation. Forest models are tools that synthesize our knowledge about processes, such as tree growth, competition, disturbances and mortality. They allow simulation experiments which go beyond the spatial and temporal scales of field experiments. In this thesis, several major challenges in forest ecology and remote sensing were addressed. The main variable of interest was forest biomass, as it is the most important variable for forest carbon mapping and for understanding the role of vegetation in the global carbon cycle. For the purpose of biomass estimation, remote sensing derived canopy height and structure measurements were combined with field data, forest simulations and remote sensing simulations. The goals were: 1) to integrate remote sensing measurements into a forest model; 2) to understand the effects of spatial scale and disturbances on biomass estimation using a variety of remote sensing metrics; 3) to develop approaches for quantifying biomass changes over time with remote sensing and 4) to overcome differences among forest types by considering several structural aspects in the biomass estimation function. In the first study, a light detection and ranging (lidar) simulator was developed and integrated in the forest model FORMIND. The model was parameterized for the tropical rainforest on Barro Colorado Island (BCI, Panama). The output of the lidar simulator was validated against real airborne lidar data from BCI. Undisturbed and disturbed forests were simulated with FORMIND to identify the most well suited lidar metric for biomass estimation. The objective hereby was to achieve a low normalized root mean squared error (nRMSE) over the entire range of forest structures caused by disturbances and succession. Results identified the mean top-of-canopy height (TCH) as the best lidar-derived predictor. The accuracy strongly depended on spatial scale and relative errors < 10% could be achieved if the spatial resolution of the produced biomass map was ≥ 100 m and the spatial resolution of the remote sensing input was ≤ 10 m. These results could provide guidance for biomass mapping efforts. In the second study, forest simulations were used to explore approaches for estimating changes in forest biomass over time based on observed changes in canopy height. In an ideal situation, remote sensing provides measurements of canopy height above ground which allows the estimation of biomass stocks and changes. However, this requires sensors which are able to detect canopy surface and terrain elevation, and some sensors can only detect the surface (e.g., X-band radar). In such cases, biomass change has to be estimated from height change using a direct relationship. Unfortunately, such a relationship is not constant for forests in different successional stages, which can lead to considerable biases in the estimates of biomass change. A solution to this problem was found, where missing information of canopy height was compensated by integrating metrics of canopy texture. Applying this improved approach enables estimations of biomass losses and gains after disturbances at 1-ha resolution. In mature forests with very small changes in height and biomass all tested approaches have limited capabilities, as was revealed by an application using TanDEM-X derived canopy height from BCI. In the third study, a general biomass estimation function, which links remote sensing-derived structure metrics to forest biomass, was developed. General in this context means that it can be applied in different forest types and different biomes. For this purpose a set of predictor metrics was explored, with each predictor representing one of the following structural aspects: mean canopy height, maximal possible canopy height, maximal possible stand density, vertical canopy structure and wood density. The derived general equation resulted in almost equally accurate biomass estimates across the five considered sites (nRMSE = 12.4%, R² = 0.74) as site-specific equations (nRMSE = 11.7%, R²= 0.77). The contributions of the predictors provide a better understanding of the variability in the height-to-biomass relationship observed across forest types. The thesis has laid foundations for a close link between remote sensing, forest modeling and forest inventories. Several ongoing projects carry this further, by 1) disentangling and quantifying the uncertainty in biomass remote sensing, 2) trying to predict forest productivity based on structure and 3) detecting single trees from lidar to be used as forest model input. These methods can in the future lead to an integrated forest monitoring and information system, which assimilates remote sensing measurements and produces predictions about forest development. Such tools are urgently needed to reduce the risks forests are facing worldwide

Tree Crowns Cause Border Effects in Area-Based Biomass Estimations from Remote Sensing

The estimation of forest biomass by remote sensing is constrained by different uncertainties. An important source of uncertainty is the border effect, as tree crowns are not constrained by plot borders. Lidar remote sensing systems record the canopy height within a certain area, while the ground-truth is commonly the aboveground biomass of inventory trees geolocated at their stem positions. Hence, tree crowns reaching out of or into the observed area are contributing to the uncertainty in canopy-height–based biomass estimation. In this study, forest inventory data and simulations of a tropical rainforest’s canopy were used to quantify the amount of incoming and outgoing canopy volume and surface at different plot sizes (10, 20, 50, and 100 m). This was performed with a bottom-up approach entirely based on forest inventory data and allometric relationships, from which idealized lidar canopy heights were simulated by representing the forest canopy as a 3D voxel space. In this voxel space, the position of each voxel is known, and it is also known to which tree each voxel belongs and where the stem of this tree is located. This knowledge was used to analyze the role of incoming and outgoing crowns. The contribution of the border effects to the biomass estimation uncertainty was quantified for the case of small-footprint lidar (a simulated canopy height model, CHM) and large-footprint lidar (simulated waveforms with footprint sizes of 23 and 65 m, corresponding to the GEDI and ICESat GLAS sensors). A strong effect of spatial scale was found: e.g., for 20-m plots, on average, 16% of the CHM surface belonged to trees located outside of the plots, while for 100-m plots this incoming CHM fraction was only 3%. The border effects accounted for 40% of the biomass estimation uncertainty at the 20-m scale, but had no contribution at the 100-m scale. For GEDI- and GLAS-based biomass estimates, the contributions of border effects were 23% and 6%, respectively. This study presents a novel approach for disentangling the sources of uncertainty in the remote sensing of forest structures using virtual canopy modeling

Mapping Amazon Forest Productivity by Fusing GEDI Lidar Waveforms with an Individual-Based Forest Model

The Amazon rainforest plays an important role in the global carbon cycle. However, due to its structural complexity, current estimates of its carbon dynamics are very imprecise. The aim of this study was to determine the forest productivity and carbon balance of the Amazon, particularly considering the role of canopy height complexity. Recent satellite missions have measured canopy height variability in great detail over large areas. Forest models are able to transform these measurements into carbon dynamics. For this purpose, about 110 million lidar waveforms from NASA’s GEDI mission (footprint diameters of ~25 m each) were analyzed over the entire Amazon ecoregion and then integrated into the forest model FORMIND. With this model–data fusion, we found that the total gross primary productivity (GPP) of the Amazon rainforest was 11.4 Pg C a−1 (average: 21.1 Mg C ha−1 a−1) with lowest values in the Arc of Deforestation region. For old-growth forests, the GPP varied between 15 and 45 Mg C ha−1 a−1. At the same time, we found a correlation between the canopy height complexity and GPP of old-growth forests. Forest productivity was found to be higher (between 25 and 45 Mg C ha−1 a−1) when canopy height complexity was low and lower (10–25 Mg C ha−1 a−1) when canopy height complexity was high. Furthermore, the net ecosystem exchange (NEE) of the Amazon rainforest was determined. The total carbon balance of the Amazon ecoregion was found to be −0.1 Pg C a−1, with the highest values in the Amazon Basin between both the Rio Negro and Solimões rivers. This model–data fusion reassessed the carbon uptake of the Amazon rainforest based on the latest canopy structure measurements provided by the GEDI mission in combination with a forest model and found a neutral carbon balance. This knowledge may be critical for the determination of global carbon emission limits to mitigate global warming

Structure Metrics to Generalize Biomass Estimation from Lidar across Forest Types from Different Continents

Forest aboveground biomass is a key variable in remote sensing based forest monitoring. Active sensor systems, such as lidar, can generate detailed canopy height products. Relationships between canopy height and biomass are commonly established via regression analysis using information from ground-truth plots. In this way, many site-specific height-biomass relationships have been proposed in the literature and applied for mapping in regional contexts. However, such relationships are only valid within the specific forest type for which they were calibrated. A generalized relationship would facilitate biomass estimation across forest types and regions. In this study, a combination of lidar-derived and ancillary structural descriptors is proposed as an approach for generalization between forest types. Each descriptor is supposed to quantify a different aspect of forest structure, i.e., mean canopy height, maximum canopy height, maximum stand density, vertical heterogeneity and wood density. Airborne discrete return lidar data covering 194 ha of forest inventory plots from five different sites including temperate and tropical forests from Africa, Europe, North, Central and South America was used. Biomass predictions using the best general model (nRMSE = 12.4%, R2 = 0.74) were found to be almost as accurate as predictions using five site-specific models (nRMSE = 11.6%, R2 = 0.78). The results further allow interpretation about the importance of the employed structure descriptors in the biomass estimation and the mechanisms behind the relationships. Understanding the relationship between canopy structure and aboveground biomass and being able to generalize it across forest types are important steps towards consistent large scale biomass mapping and monitoring using airborne and potentially also spaceborne platforms