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The Global Ecosystem Dynamics Investigation (GEDI) is a waveform lidar instrument on the International Space Station used to estimate aboveground biomass density (AGBD) in temperate and tropical forests. Algorithms to predict footprint AGBD from GEDI relative height (RH) metrics were developed from simulated waveforms with leaf-on (growing season) conditions. Leaf-off GEDI data with lower canopy cover are expected to have shorter RH metrics, and are therefore excluded from GEDI’s gridded AGBD products. However, the effects of leaf phenology on RH metric heights, and implications for GEDI footprint AGBD models that can include multiple nonlinear RH predictors, have not been quantified. Here, we test the sensitivity of GEDI data and AGBD predictions to leaf phenology. We simulated GEDI data using high-density drone lidar collected in a temperate mountain forest in the Czech Republic under leaf-off and leaf-on conditions, 51 days apart. We compared simulated GEDI RH metrics and footprint-level AGBD predictions from GEDI Level 4A models from leaf-off and leaf-on datasets. Mean canopy cover increased by 31% from leaf-off to leaf-on conditions, from 57% to 88%. RH metrics < RH50 were more sensitive to changes in leaf phenology than RH metrics ≥ 50. Candidate AGBD models for the deciduous-broadleaf-trees prediction stratum in Europe that were trained using leaf-on measurements exhibited a systematic prediction difference of 0.6 – 19% when applied to leaf-off data, as compared to leaf-on predictions. Models with the least systematic prediction difference contained only the highest RH metrics, or contained multiple predictor terms that contained both positive and negative coefficients, such that the difference from systematically shorter leaf-off RH metrics was partially offset among the multiple terms. These results suggest that, with consideration of model choice, leaf-off GEDI data can be suitable for AGBD prediction, which could increase data availability and reduce sampling error in some forests.

© 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). 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 (~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.

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.

There are many indications that for a true understanding of aboveground canopy competition, the concept of symmetric trees is oversimplified and unsatisfactory; in spite of that, this concept is still commonly used in forest ecology research. In this study we analyzed and quantified the effect of tree/crown asymmetry on crown-to-crown interactions and canopy light availability with respect to tree size and species. Geometric crown models were used to represent the concept of symmetric trees, while data from terrestrial laser scanning were employed to constitute real crown shapes, positions and mutual crown-to-crown interactions. We developed an original approach for measuring three-dimensional crown asymmetry, separating the effect of positional crown shift and local crown plasticity, and analyzed their effect in aboveground competition for space and light. In comparison with reality, the models neglecting tree asymmetry were only poor predictors of trees mutually competing for space. Geometric models taking the positional crown shift into account were good predictors of ‘space competitors’ for Norway spruce, but were still insufficient for European beech. This is because for spruce crown shifting seems to be the major neighbor avoidance strategy, while beech in addition exhibited high local crown shape plasticity. Additionally, of the two species beech showed overall greater crown plasticity, which (in contrast to spruce) decreased only slowly with increasing tree size. Importantly, the concept of symmetric trees significantly underestimates the potential canopy light availability (and thus overestimates canopy competition for light), because asymmetric and the plastic ‘puzzle-like’ arrangement of real tree crowns is more effective than assumed symmetric organization. This most likely inserts a systematic bias into stand growth simulators that are based on the concept of symmetric trees.

Questions Recently there have been vital discussions about the validity of the European patch-mosaic conceptual model of forest dynamics – the traditional concept of a shifting patch-mosaic of developmental stages and phases, also known as the forest cycle concept. Here we try to answer the fundamental questions of this debate: How much do the forest dynamics proceed along a predictable path (in a chronological sequence: growth—optimum—breakdown)? Or vice versa, are the patches rather a result of disturbances and/or other stochastic growth and mortality patterns? Location The study was carried out at 5 long-term research plots located in 4 different study sites of central European natural temperate forests. Methods The long-term evolution of forest developmental phases was analyzed by a GIS based, spatially explicit, fully reproducible method enabling proper verification of the functionality of the model forest cycle. We analyzed long-term transitions among forest developmental phases from the 1970's through the 1990's to 2000's. Observed phase-to-phase transitions were compared to a random transition model. We identified preferential pathways within the forest cycle model as well as the proportion of cyclic/ acyclic transitions. Results In total, across all sites and observation periods about 65% of all observed phase-to-phase transitions were realized through preferential pathways, about 28% of observed transitions went along pathways of random frequency and only about 7% of observed transitions were realized through uncommon developmental pathways. On the other hand, only less than 40% of all observed transitions might be classified as cyclic (following the model cycle), and thus more than 60% of the transitions were acyclic (moving across or backward in the model cycle). The overall pattern of all observed transitions resembled a complex web rather than a simple repeating cycle. Conclusions Although in all sites we documented signs of the cyclic and predictable development anticipated by the forest cycle concept, the predominance and stochastic nature of multiple acyclic developmental pathways gave rise to reasonable doubts on the legitimacy and usability of the concept for descriptions of forest dynamics. On the other hand, the verification of the concept may contribute significantly to our understanding of the complexity of forest dynamics. This article is protected by copyright. All rights reserved.

We applied a supervised individual-tree segmentation algorithm to ultra-high-density drone lidar in a temperate mountain forest in the southern Czech Republic. We compared the number of trees correctly segmented, stem diameter at breast height (DBH), and tree height from drone-lidar segmentations to field-inventory measurements and segmentations from terrestrial laser scanning (TLS) data acquired within two days of the drone-lidar acquisition. Our analysis detected 51% of the stems >15 cm DBH, and 87% of stems >50 cm DBH. Errors of omission were much more common for smaller trees than for larger ones, and were caused by removal of points prior to segmentation using a low-intensity and morphological filter. Analysis of segmented trees indicates a strong linear relationship between DBH from drone-lidar segmentations and TLS data. The slope of this relationship is 0.93, the intercept is 4.28 cm, and the r2 is 0.98. However, drone lidar and TLS segmentations overestimated DBH for the smallest trees and underestimated DBH for the largest trees in comparison to field data. We evaluate the impact of random error in point locations and variation in footprint size, and demonstrate that random error in point locations is likely to cause an overestimation bias for small-DBH trees. A Random Forest classifier correctly identified broadleaf and needleleaf trees using stem and crown geometric properties with overall accuracy of 85.9%. We used these classifications and DBH estimates from drone-lidar segmentations to apply allometric scaling equations to segmented individual trees. The stand-level aboveground biomass (AGB) estimate using these data is 76% of the value obtained using a traditional field inventory. We demonstrate that 71% of the omitted AGB is due to segmentation errors of omission, and the remaining 29% is due to DBH estimation errors. Our analysis indicates that high-density measurements from low-altitude drone flight can produce DBH estimates for individual trees that are comparable to TLS. These data can be collected rapidly throughout areas large enough to produce landscape-scale estimates. With additional refinement, these estimates could augment or replace manual field inventories, and could support the calibration and validation of current and forthcoming space missions.

Terrestrial laser scanning is a powerful technology for capturing the three-dimensional structure of forests with a high level of detail and accuracy. Over the last decade, many algorithms have been developed to extract various tree parameters from terrestrial laser scanning data. Here we present 3D Forest, an open-source non-platform-specific software application with an easy-to-use graphical user interface with the compilation of algorithms focused on the forest environment and extraction of tree parameters. The current version (0.42) extracts important parameters of forest structure from the terrestrial laser scanning data, such as stem positions (X, Y, Z), tree heights, diameters at breast height (DBH), as well as more advanced parameters such as tree planar projections, stem profiles or detailed crown parameters including convex and concave crown surface and volume. Moreover, 3D Forest provides quantitative measures of between-crown interactions and their real arrangement in 3D space. 3D Forest also includes an original algorithm of automatic tree segmentation and crown segmentation. Comparison with field data measurements showed no significant difference in measuring DBH or tree height using 3D Forest, although for DBH only the Randomized Hough Transform algorithm proved to be sufficiently resistant to noise and provided results comparable to traditional field measurements.