scholarly journals A Validated and Accurate Method for Quantifying and Extrapolating Mangrove Above-Ground Biomass Using LiDAR Data

2021 ◽  
Vol 13 (14) ◽  
pp. 2763
Author(s):  
Rafaela B. Salum ◽  
Sharon A. Robinson ◽  
Kerrylee Rogers
Keyword(s):  
Tree Height ◽  
Canopy Height ◽  
Lidar Data ◽  
Mangrove Forests ◽  
Above Ground Biomass ◽  
Rhizophora Stylosa ◽  
Ground Biomass ◽  
Mangrove Tree ◽  
Canopy Height Model ◽  
Height Model

LiDAR data and derived canopy height models can provide useful information about mangrove tree heights that assist with quantifying mangrove above-ground biomass. This study presents a validated method for quantifying mangrove heights using LiDAR data and calibrating this against plot-based estimates of above-ground biomass. This approach was initially validated for the mangroves of Darwin Harbour, in Northern Australia, which are structurally complex and have high species diversity. Established relationships were then extrapolated to the nearby West Alligator River, which provided the opportunity to quantify biomass at a remote location where intensive fieldwork was limited. Relationships between LiDAR-derived mangrove heights and mean tree height per plot were highly robust for Ceriops tagal, Rhizophora stylosa and Sonneratia alba (r2 = 0.84–0.94, RMSE = 0.03–0.91 m; RMSE% = 0.07%–11.27%), and validated well against an independent dataset. Additionally, relationships between the derived canopy height model and field-based estimates of above-ground biomass were also robust and validated (r2 = 0.73–0.90, RMSE = 141.4 kg–1098.58 kg, RMSE% of 22.94–39.31%). Species-specific estimates of tree density per plot were applied in order to align biomass of individual trees with the resolution of the canopy height model. The total above-ground biomass at Darwin Harbour was estimated at 120 t ha−1 and comparisons with prior estimates of mangrove above-ground biomass confirmed the accuracy of this assessment. To establish whether accurate and validated relationships could be extrapolated elsewhere, the established relationships were applied to a LiDAR-derived canopy height model at nearby West Alligator River. Above-ground biomass derived from extrapolated relationships was estimated at 206 t ha−1, which compared well with prior biomass estimates, confirming that this approach can be extrapolated to remote locations, providing the mangrove forests are biogeographically similar. The validated method presented in this study can be used for reporting mangrove carbon storage under national obligations, and is useful for quantifying carbon within various markets.

LiDAR remote sensing of forest structure

2003 ◽  
Vol 27 (1) ◽  
pp. 88-106 ◽  
Author(s):  
Kevin Lim ◽  
Paul Treitz ◽  
Michael Wulder ◽  
Benoît St-Onge ◽  
Martin Flood
Keyword(s):  
Remote Sensing ◽  
Forest Structure ◽  
Forest Ecosystems ◽  
Canopy Height ◽  
Forest Monitoring ◽  
Light Detection And Ranging ◽  
Lidar Data ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Canopy Volume

Light detection and ranging (LiDAR) technology provides horizontal and vertical information at high spatial resolutions and vertical accuracies. Forest attributes such as canopy height can be directly retrieved from LiDAR data. Direct retrieval of canopy height provides opportunities to model above-ground biomass and canopy volume. Access to the vertical nature of forest ecosystems also offers new opportunities for enhanced forest monitoring, management and planning.

scholarly journals A GIS-Based Algorithm to Generate a Lidar Pit-Free Canopy Height Model

2017 ◽  
Vol 11 (2) ◽  
pp. 89-95
Author(s):  
Casiana Marcu ◽  
Florian Stătescu ◽  
Nicoleta Iurist
Keyword(s):  
Canopy Height ◽  
Lidar Data ◽  
Forest Stands ◽  
Forest Development ◽  
Lidar Return ◽  
Canopy Height Model ◽  
Height Model

Abstract Lidar has provided significant benefits for forest development and engineering operations and provides a good means to collect information on forest stands. A common analysis using LiDAR data computes the CHM as a difference between DSM and DTM, create a DTM from the ground returns and a DSM from the first returns and subtract the two rasters, but how exactly are generated the DTM and the DSM. Irregular height variations, called data pits are present in the CHM and appear when the first Lidar return is far below the canopy. The purpose of this study is an approach that computes the CHM directly from height-normalized LiDAR points.

scholarly journals The Influence of Vegetation Characteristics on Individual Tree Segmentation Methods with Airborne LiDAR Data

2019 ◽  
Vol 11 (23) ◽  
pp. 2880 ◽  
Author(s):  
Qiuli Yang ◽  
Yanjun Su ◽  
Shichao Jin ◽  
Maggi Kelly ◽  
Tianyu Hu ◽  
...  
Keyword(s):  
Forest Type ◽  
The United States ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Lidar Data ◽  
Individual Tree ◽  
Segmentation Methods ◽  
Vegetation Characteristics ◽  
Canopy Height Model ◽  
Height Model

This study investigated the effects of forest type, leaf area index (LAI), canopy cover (CC), tree density (TD), and the coefficient of variation of tree height (CVTH) on the accuracy of different individual tree segmentation methods (i.e., canopy height model, pit-free canopy height model (PFCHM), point cloud, and layer stacking seed point) with LiDAR data. A total of 120 sites in the Sierra Nevada Forest (California) and Shavers Creek Watershed (Pennsylvania) of the United States, covering various vegetation types and characteristics, were used to analyze the performance of the four selected individual tree segmentation algorithms. The results showed that the PFCHM performed best in all forest types, especially in conifer forests. The main forest characteristics influencing segmentation methods were LAI and CC, LAI and TD, and CVTH in conifer, broadleaf, and mixed forests, respectively. Most of the vegetation characteristics (i.e., LAI, CC, and TD) negatively correlated with all segmentation methods, while the effect of CVTH varied with forest type. These results can help guide the selection of individual tree segmentation method given the influence of vegetation characteristics.

Dense Canopy Height Model from a low-cost photogrammetric platform and LiDAR data

Trees ◽  
2016 ◽  
Vol 30 (4) ◽  
pp. 1287-1301 ◽  
Author(s):  
Mónica Herrero-Huerta ◽  
Beatriz Felipe-García ◽  
Soledad Belmar-Lizarán ◽  
David Hernández-López ◽  
Pablo Rodríguez-Gonzálvez ◽  
...  
Keyword(s):  
Low Cost ◽  
Canopy Height ◽  
Lidar Data ◽  
Canopy Height Model ◽  
Height Model

scholarly journals Combining Satellite and UAV Imagery to Delineate Forest Cover and Basal Area after Mixed-Severity Fires

2018 ◽  
Vol 10 (7) ◽  
pp. 2227 ◽  
Author(s):  
Fernando Rossi ◽  
Andreas Fritz ◽  
Gero Becker
Keyword(s):  
Fire Severity ◽  
Canopy Cover ◽  
Basal Area ◽  
Tree Height ◽  
Canopy Height ◽  
Classification Error ◽  
Heterogeneous Structure ◽  
Partial Cover ◽  
Canopy Height Model ◽  
Height Model

In northern Argentina, the assessment of degraded forests is a big challenge for both science and practice, due to their heterogeneous structure. However, new technologies could contribute to mapping post-disturbance canopy cover and basal area in detail. Therefore, this research assesses whether or not the inclusion of partial cover unmanned aerial vehicle imagery could reduce the classification error of a SPOT6 image used in an area-based inventory. BA was calculated from 77 ground inventory plots over 3944 ha of a forest affected by mixed-severity fires in the Argentinian Yungas. In total, 74% of the area was covered with UAV flights, and canopy height models were calculated to estimate partial canopy cover at three tree height classes. Basal area and partial canopy cover were used to formulate the adjusted canopy cover index, and it was calculated for 70 ground plots and an additional 20 image plots. Four classes of fire severity were created based on basal area and adjusted canopy cover index, and were used to run two supervised classifications over a segmented (algorithm multiresolution) wall-to-wall SPOT6 image. The comparison of the Cohan’s Kappa coefficient of both classifications shows that they are not significantly different (p-value: 0.43). However, the approach based on the adjusted canopy cover index achieved more homogeneous strata (Welch t-test with 95% of confidence). Additionally, UAV-derived canopy height model estimates of tree height were compared with field measurements of 71 alive trees. The canopy height models underestimated tree height with an RMSE ranging from 2.8 to 8.3 m. The best accuracy of the canopy height model was achieved using a larger pixel size (10 m), and for lower stocked plots due to high fire severity.

scholarly journals Canopy Height and Above-Ground Biomass Retrieval in Tropical Forests Using Multi-Pass X- and C-Band Pol-InSAR Data

2019 ◽  
Vol 11 (18) ◽  
pp. 2105 ◽  
Author(s):  
Berninger ◽  
Lohberger ◽  
Zhang ◽  
Siegert
Keyword(s):  
High Resolution ◽  
Tropical Forests ◽  
Forest Degradation ◽  
Canopy Height ◽  
Carbon Accounting ◽  
Lidar Data ◽  
Above Ground Biomass ◽  
High Resolution Data ◽  
Ground Biomass ◽  
Resolution Data

Globally available high-resolution information about canopy height and AGB is important for carbon accounting. The present study showed that Pol-InSAR data from TS-X and RS-2 could be used together with field inventories and high-resolution data such as drone or LiDAR data to support the carbon accounting in the context of REDD+ (Reducing Emissions from Deforestation and Forest Degradation) projects.

scholarly journals Cloth simulation-based construction of pit-free canopy height models from airborne LiDAR data

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Wuming Zhang ◽  
Shangshu Cai ◽  
Xinlian Liang ◽  
Jie Shao ◽  
Ronghai Hu ◽  
...  
Keyword(s):  
Tree Height ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Lidar Data ◽  
Cloth Simulation ◽  
Simulation Based ◽  
Maximum Tree Height ◽  
Airborne Lidar Data ◽  
Better Than

Abstract Background The universal occurrence of randomly distributed dark holes (i.e., data pits appearing within the tree crown) in LiDAR-derived canopy height models (CHMs) negatively affects the accuracy of extracted forest inventory parameters. Methods We develop an algorithm based on cloth simulation for constructing a pit-free CHM. Results The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details. Our pit-free CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms, as evidenced by the lowest average root mean square error (0.4981 m) between the reference CHMs and the constructed pit-free CHMs. Moreover, our pit-free CHMs show the best performance overall in terms of maximum tree height estimation (average bias = 0.9674 m). Conclusion The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.

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