scholarly journals Challenges to aboveground biomass prediction from waveform lidar

2021 ◽  
Author(s):  
Jamis M Bruening ◽  
Rico Fischer ◽  
Friedrich J. Bohn ◽  
John Armston ◽  
Amanda H. Armstrong ◽  
...  
Keyword(s):  
Forest Structure ◽  
Aboveground Biomass ◽  
Empirical Studies ◽  
Structural Complexity ◽  
Ecosystem Dynamics ◽  
Carbon Accounting ◽  
Forest Stands ◽  
Waveform Lidar ◽  
Structure Complexity

Abstract Accurate accounting of aboveground biomass density (AGBD) is crucial for carbon cycle, biodiversity, and climate change science. The Global Ecosystem Dynamics Investigation (GEDI), which maps global AGBD from waveform lidar, is the first of a new generation of Earth observation missions designed to improve carbon accounting. This paper explores the possibility that lidar waveforms may not be unique to AGBD —that forest stands with different AGBD may produce highly similar waveforms —and we hypothesize that non-uniqueness may contribute to the large uncertainties in AGBD predictions. Our analysis integrates simulated GEDI waveforms from 428 in situ stem maps with output from an individual-based forest gap model, which we use to generate a database of potential forest stands and simulate GEDI waveforms from those stands. We use this database to predict the AGBD of the 428 in situ stem maps via two different methods: a linear regression from waveform metrics, and a waveform-matching approach that accounts for waveform-AGBD non- uniqueness. We find that some in situ waveforms are more unique to AGBD than others, which notably impacts AGBD prediction uncertainty (7-411 Mg ha−1, average of 167 Mg ha−1). We also find that forest structure complexity may influence the non-uniqueness effect; stands with low structural complexity are more unique to AGBD than more mature stands with multiple cohorts and canopy layers. These findings suggest that the non-uniqueness phenomena may be introduced by the measuring characteristics of waveform lidar in combination with how forest structure manifests at small scales, and we discuss how this complexity may complicate uncertainty estimation in AGBD prediction. This analysis suggests a limit to the accuracy and precision of AGBD predictions from lidar waveforms seen in empirical studies, and underscores the need for further exploration of the relationships between lidar remote sensing measurements, forest structure, and AGBD.

scholarly journals Global patterns and climatic controls of forest structural complexity

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Martin Ehbrecht ◽  
Dominik Seidel ◽  
Peter Annighöfer ◽  
Holger Kreft ◽  
Michael Köhler ◽  
...  
Keyword(s):  
Forest Structure ◽  
Laser Scanning ◽  
Structural Complexity ◽  
Terrestrial Laser Scanning ◽  
Ecosystem Functions ◽  
The Earth ◽  
Precipitation Seasonality ◽  
Latitudinal Patterns ◽  
Global Patterns ◽  
Primary Forests

AbstractThe complexity of forest structures plays a crucial role in regulating forest ecosystem functions and strongly influences biodiversity. Yet, knowledge of the global patterns and determinants of forest structural complexity remains scarce. Using a stand structural complexity index based on terrestrial laser scanning, we quantify the structural complexity of boreal, temperate, subtropical and tropical primary forests. We find that the global variation of forest structural complexity is largely explained by annual precipitation and precipitation seasonality (R² = 0.89). Using the structural complexity of primary forests as benchmark, we model the potential structural complexity across biomes and present a global map of the potential structural complexity of the earth´s forest ecoregions. Our analyses reveal distinct latitudinal patterns of forest structure and show that hotspots of high structural complexity coincide with hotspots of plant diversity. Considering the mechanistic underpinnings of forest structural complexity, our results suggest spatially contrasting changes of forest structure with climate change within and across biomes.

scholarly journals Estimating Aboveground Biomass Using Sentinel-2 MSI Data and Ensemble Algorithms for Grassland in the Shengjin Lake Wetland, China

2021 ◽  
Vol 13 (8) ◽  
pp. 1595
Author(s):  
Chunhua Li ◽  
Lizhi Zhou ◽  
Wenbin Xu
Keyword(s):  
Aboveground Biomass ◽  
Carbon Accounting ◽  
Dynamic Monitoring ◽  
Gradient Boosting ◽  
Biomass Estimation ◽  
Floodplain Wetlands ◽  
Red Edge ◽  
Extreme Gradient Boosting ◽  
Ensemble Algorithms ◽  
Sentinel 2

Wetland vegetation aboveground biomass (AGB) directly indicates wetland ecosystem health and is critical for water purification, carbon cycle, and biodiversity conservation. Accurate AGB estimation is essential for the monitoring and supervision of ecosystems, especially in seasonal floodplain wetlands. This paper explored the capability of spectral and texture features from the Sentinel-2 Multispectral Instrument (MSI) for modeling grassland AGB using random forest (RF) and extreme gradient boosting (XGBoost) algorithms in Shengjin Lake wetland (a Ramsar site). We use five-fold cross-validation to verify the model effectiveness. The results indicated that the RF and XGBoost models had a robust and efficient performance (with root mean square error (RMSE) of 126.571 g·m−2 and R2 of 0.844 for RF, RMSE of 112.425 g·m−2 and R2 of 0.869 for XGBoost), and the XGBoost models, by contrast, performed better. Both traditional and red-edge vegetation indices (VIs) obtained satisfactory results of AGB estimation (RMSE = 127.936 g·m−2, RMSE = 125.879 g·m−2 in XGBoost models, respectively), with the red-edge VIs contributed more to the AGB models. Moreover, we selected eight gray-level co-occurrence matrix (GLCM) textures calculated by four processing window sizes using the mean value of four offsets, and further analyzed the results of three analysis sets. Textures derived from traditional and red-edge bands using a 7 × 7 window size performed better in biomass estimation. This finding suggested that textures derived from the traditional bands were as important as the red-edge bands. The introduction of textures moderately improved the accuracy of modeling AGB, whereas the use of textures alo ne was not satisfactory. This research demonstrated that using the Sentinel-2 MSI and the two ensemble algorithms is an effective method for long-term dynamic monitoring and assessment of grass AGB in seasonal floodplain wetlands, which can support sustainable management and carbon accounting of wetland ecosystems.

scholarly journals Deriving Stand Structural Complexity from Airborne Laser Scanning Data—What Does It Tell Us about a Forest?

2020 ◽  
Vol 12 (11) ◽  
pp. 1854
Author(s):  
Dominik Seidel ◽  
Peter Annighöfer ◽  
Martin Ehbrecht ◽  
Paul Magdon ◽  
Stephan Wöllauer ◽  
...  
Keyword(s):  
Forest Structure ◽  
Laser Scanning ◽  
Structural Complexity ◽  
Basal Area ◽  
Airborne Laser Scanning ◽  
Stand Age ◽  
Box Dimension ◽  
Forest Types ◽  
3D Point Clouds ◽  
Airborne Laser

The three-dimensional forest structure is an important driver of several ecosystem functions and services. Recent advancements in laser scanning technologies have set the path to measuring structural complexity directly from 3D point clouds. Here, we show that the box-dimension (Db) from fractal analysis, a measure of structural complexity, can be obtained from airborne laser scanning data. Based on 66 plots across different forest types in Germany, each 1 ha in size, we tested the performance of the Db by evaluating it against conventional ground-based measures of forest structure and commonly used stand characteristics. We found that the Db was related (0.34 < R < 0.51) to stand age, management intensity, microclimatic stability, and several measures characterizing the overall stand structural complexity. For the basal area, we could not find a significant relationship, indicating that structural complexity is not tied to the basal area of a forest. We also showed that Db derived from airborne data holds the potential to distinguish forest types, management types, and the developmental phases of forests. We conclude that the box-dimension is a promising measure to describe the structural complexity of forests in an ecologically meaningful way.

scholarly journals Highly Local Model Calibration with a New GEDI LiDAR Asset on Google Earth Engine Reduces Landsat Forest Height Signal Saturation

2020 ◽  
Vol 12 (17) ◽  
pp. 2840 ◽  
Author(s):  
Sean P. Healey ◽  
Zhiqiang Yang ◽  
Noel Gorelick ◽  
Simon Ilyushchenko
Keyword(s):  
Optical Sensors ◽  
Forest Structure ◽  
Model Calibration ◽  
Google Earth ◽  
Ecosystem Dynamics ◽  
Ecosystem Functions ◽  
Calibration Data ◽  
Local Models ◽  
Google Earth Engine ◽  
Signal Saturation

While Landsat has proved to be effective for monitoring many elements of forest condition and change, the platform has well-documented limitations in measuring forest structure, the vertical distribution of the canopy. This is important because structure determines several key ecosystem functions, including: carbon storage; habitat suitability; and timber volume. Canopy structure is directly measured by LiDAR, and it should be possible to train Landsat structure models at a highly local scale with the dense, global sample of full waveform LiDAR observations collected by NASA’s Global Ecosystem Dynamics Investigation (GEDI). Local models are expected to perform better because: (a) such models may take advantage of localized correlations between structure and canopy surface reflectance; and (b) to the extent that models revert to the mean of the calibration data due to a lack of discrimination, local models will revert to a more representative mean. We tested Landsat-based relative height predictions using a new GEDI asset on Google Earth Engine, described here. Mean prediction error declined by 23% and important prediction biases at the extremes of the range of canopy height dropped as model calibration became more local, minimizing forest structure signal saturation commonly associated with Landsat and other passive optical sensors. Our results suggest that Landsat-based maps of structural variables such as height and biomass may substantially benefit from the kind of local calibration that GEDI’s dense sample of LiDAR data supports.

Spectator Information Support

2005 ◽  
pp. 271-303 ◽  
Author(s):  
Andreas Nilsson ◽  
Urban Nulden ◽  
Daniel Olsson
Keyword(s):  
Empirical Studies ◽  
Information Support ◽  
Music Festivals ◽  
Limited Support ◽  
Conventional Technology ◽  
Geographical Locations ◽  
Distributed Events

In the context of temporary, distributed events such as music festivals and sports, the event is divided in several parts held at different geographical locations at the same time or in a sequence. Thus, the conventional technology used can only provide limited support at portions of the event. This research focuses on the challenges for design concerning information support in the context of distributed events. The chapter reports from three empirical studies and applies two perspectives on context as a background to the fieldwork findings. Within the results, three main contextual requirements are presented that need to be considered when designing information support for spectators in situ. The chapter contributes to existing research in terms of providing descriptions of the interplay between actors, context and the event itself. Among the conclusions regarding design, we find that technology should be shaped to behave and act according to how, where and with whom spectators are situated.

scholarly journals Energy potential of main forest-forming species of stands in the Northern Steppe, Ukraine

2018 ◽  
Vol 64 (No. 1) ◽  
pp. 25-32
Author(s):  
Lovynska Viktoriia ◽  
Sytnyk Svitlana ◽  
Gritsan Yurii
Keyword(s):  
Scots Pine ◽  
Aboveground Biomass ◽  
Carbon Allocation ◽  
Black Locust ◽  
Local Level ◽  
Age Groups ◽  
State Agency ◽  
Forest Stands ◽  
Energy Potential ◽  
Biomass Components

The study evaluated the energy potential of Scots pine and black locust stands within the Northern Steppe of Ukraine, in forest plantations subordinated to the State Agency of Forest Resources (Ukraine). This study defined general values of aboveground biomass components per age-class structure in the forest stands. Allocated carbon was calculated using the biomass components by age groups as follows: stem, branches and leaves (needles). Contribution of different age groups to carbon allocation was investigated. A key role of stem wood in the process of carbon allocation in the forest stands was shown. It was found that the maximum carbon budget was accumulated in stands of both forest-forming species aged 41–60 years. The models are made on a dependence of carbon allocation in the different components of aboveground biomass by age. Results of energy content in the aboveground biomass were presented in Scots pine and black locust stands within the surveyed area. The study has shown that the energy potential of carbon accumulated in the biomass of Scots pine stands amounted to 40.31 PJ, and that of black locust stands was 32.04 PJ. Development of forest ecosystems in the Steppe zone of Ukraine can result in the optimization of abiotic conditions on a local level under the influence of the global climate changes.

Structural complexity of layered-spinel composite electrodes for Li-ion batteries

2010 ◽  
Vol 25 (8) ◽  
pp. 1601-1616 ◽  
Author(s):  
Jordi Cabana ◽  
Christopher S. Johnson ◽  
Xiao-Qing Yang ◽  
Kyung-Yoon Chung ◽  
Won-Sub Yoon ◽  
...  
Keyword(s):  
Structural Complexity ◽  
Composite Electrodes ◽  
Li Ion Batteries ◽  
X Ray Diffraction ◽  
X Ray ◽  
Different Temperatures ◽  
Li Ion ◽  
Excess Phase ◽  
X Ray Absorption

The complexity of layered-spinel yLi2MnO3·(1 – y)Li1+xMn2–xO4 (Li:Mn = 1.2:1; 0 ≤ x ≤ 0.33; y ≥ 0.45) composites synthesized at different temperatures has been investigated by a combination of x-ray diffraction (XRD), x-ray absorption spectroscopy (XAS), and nuclear magnetic resonance (NMR). While the layered component does not change substantially between samples, an evolution of the spinel component from a high to a low lithium excess phase has been traced with temperature by comparing with data for pure Li1+xMn2–xO4. The changes that occur to the structure of the spinel component and to the average oxidation state of the manganese ions within the composite structure as lithium is electrochemically removed in a battery have been monitored using these techniques, in some cases in situ. Our 6Li NMR results constitute the first direct observation of lithium removal from Li2MnO3 and the formation of LiMnO2 upon lithium reinsertion.

scholarly journals Ground and Volume Decomposition as a Proxy for AGB from P-Band SAR Data

2020 ◽  
Vol 12 (2) ◽  
pp. 240 ◽  
Author(s):  
Francesco Banda ◽  
Mauro Mariotti d’Alessandro ◽  
Stefano Tebaldini
Keyword(s):  
Tropical Forests ◽  
Forest Structure ◽  
Synthetic Aperture ◽  
Above Ground Biomass ◽  
Volume Scattering ◽  
Interferometric Phase ◽  
Volume Decomposition ◽  
Sar Data

In this work, the role of volume scattering obtained from ground and volume decomposition of P-band synthetic aperture radar (SAR) data as a proxy for biomass is investigated. The analysis here presented originates from the BIOMASS L2 activities, part of which were focused on strengthening the physical foundations of the SAR-based retrieval of forest above-ground biomass (AGB). A critical analysis of the observed strong correlation between tomographic intensity and AGB is done in order to propose simplified AGB proxies to be used during the interferometric phase of BIOMASS. In particular, the aim is to discuss whether, and to what extent, volume scattering obtained from ground/volume decomposition can provide a reasonable alternative to tomography. To do this, both are tested on P-band data collected at Paracou during the TropiSAR campaign and cross-validated against in-situ AGB measurements. Results indicate that volume backscattered power as obtained by ground/volume decomposition is weakly correlated to AGB, notwithstanding different solutions for volume scattering are tested, and support the conclusion that forest structure actually plays a non-negligible role in AGB retrieval in dense tropical forests.

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