Combining LiDAR data and airborne imagery of very high resolution to improve aboveground biomass estimates in tropical dry forests

2019 ◽  
Vol 92 (5) ◽  
pp. 599-615 ◽  
Author(s):  
Gabriela Reyes-Palomeque ◽  
Juan Manuel Dupuy ◽  
Kristofer D Johnson ◽  
Miguel Angel Castillo-Santiago ◽  
J Luis Hernández-Stefanoni
Keyword(s):  
High Resolution ◽  
Aboveground Biomass ◽  
Regression Models ◽  
Forest Canopy ◽  
Tropical Dry Forests ◽  
Accurate Estimation ◽  
Lidar Data ◽  
Dry Forests ◽  
Horizontal Structure ◽  
Very High

Abstract Knowledge of the spatial distribution of aboveground biomass (AGB) is crucial to guide forest conservation and management to maintain carbon stocks. LiDAR has been highly successful for this purpose, but has limited availability. Very-high resolution (<1 m) orthophotos can also be used to estimate AGB because they allow a fine distinction of forest canopy grain. We evaluated the separate and joint performance of orthophotos and LiDAR data to estimate AGB in two types of tropical dry forests in the Yucatan Peninsula. Woody plants were surveyed in twenty 0.1 ha plots in a semideciduous forest at Kaxil Kiuic Biocultural Reserve (RBKK) and 28 plots in a semievergreen forest at Felipe Carrillo Puerto (FCP). We fitted three regression models: one based on LiDAR data, another based on orthophoto variables calculated for forest canopy and canopy opening fractions, and a third model that combined both sets of variables. Variation in AGB was decomposed into LiDAR, orthophotos and joint components using variation-partitioning analyses. In FCP, regression models using LiDAR data only showed higher fit (R2 = 0.82) than orthophoto variables only (R2 = 0.70). In contrast, orthophotos had a slightly higher fit (R2 = 0.91) than LiDAR (R2 = 0.88) in RBKK, because orthophoto variables characterize very well the horizontal structure of canopies on this site. The model that combined both data sets showed a better fit (R2 = 0.85) only in FCP, which has a more complex forest structure. The largest percentage of AGB variation (88 per cent in RBKK and 67 per cent in FCP) was explained by the joint contribution of LiDAR and orthophotos. We conclude that both LiDAR and orthophotos provide accurate estimation of AGB, but their relative performance varies with forest type and structural complexity. Combining the two sets of variables can further improve the accuracy of AGB estimation, particularly in forests with complex vegetation structure.

scholarly journals Improving aboveground biomass maps of tropical dry forests by integrating LiDAR, ALOS PALSAR, climate and field data

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
J. Luis Hernández-Stefanoni ◽  
Miguel Ángel Castillo-Santiago ◽  
Jean Francois Mas ◽  
Charlotte E. Wheeler ◽  
Juan Andres-Mauricio ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Field Data ◽  
Tropical Dry Forests ◽  
Alos Palsar ◽  
Dry Forests

scholarly journals Stratification-Based Forest Aboveground Biomass Estimation in a Subtropical Region Using Airborne Lidar Data

2020 ◽  
Vol 12 (7) ◽  
pp. 1101 ◽  
Author(s):  
Xiandie Jiang ◽  
Guiying Li ◽  
Dengsheng Lu ◽  
Erxue Chen ◽  
Xinliang Wei
Keyword(s):  
Aboveground Biomass ◽  
Forest Type ◽  
Forest Stand ◽  
Airborne Lidar ◽  
Accurate Estimation ◽  
Biomass Estimation ◽  
Lidar Data ◽  
Forest Types ◽  
Estimation Model ◽  
Forest Aboveground Biomass

Species-rich subtropical forests have high carbon sequestration capacity and play important roles in regional and global carbon regulation and climate changes. A timely investigation of the spatial distribution characteristics of subtropical forest aboveground biomass (AGB) is essential to assess forest carbon stocks. Lidar (light detection and ranging) is regarded as the most reliable data source for accurate estimation of forest AGB. However, previous studies that have used lidar data have often beenbased on a single model developed from the relationships between lidar-derived variables and AGB, ignoring the variability of this relationship in different forest types. Although stratification of forest types has been proven to be effective for improving AGB estimation, how to stratify forest types and how many strata to use are still unclear. This research aims to improve forest AGB estimation through exploring suitable stratification approaches based on lidar and field survey data. Different stratification schemes including non-stratification and stratifications based on forest types and forest stand structures were examined. The AGB estimation models were developed using linear regression (LR) and random forest (RF) approaches. The results indicate the following: (1) Proper stratifications improved AGB estimation and reduced the effect of under- and overestimation problems; (2) the finer forest type strata generated higher accuracy of AGB estimation but required many more sample plots, which were often unavailable; (3) AGB estimation based on stratification of forest stand structures was similar to that based on five forest types, implying that proper stratification reduces the number of sample plots needed; (4) the optimal AGB estimation model and stratification scheme varied, depending on forest types; and (5) the RF algorithm provided better AGB estimation for non-stratification than the LR algorithm, but the LR approach provided better estimation with stratification. Results from this research provide new insights on how to properly conduct forest stratification for AGB estimation modeling, which is especially valuable in tropical and subtropical regions with complex forest types.

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