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

Despite the large amount of accessible spatial information, the issue of estimating aboveground biomass through remote sensing, especially radar, remains a challenge in complex ecosystems such as tropical forests. One of the advantages of radar sensors is that of "crossing clouds" (capacity that does not have optical images like Landsat), facilitating their use in areas with permanent cloud cover. This work defines, from several studies conducted in tropical forests using ALOS PALSAR, which are the factors with the most influence on the signal of the radar. This can be useful in the development and/or improvement of methodologies to estimate aboveground biomass in tropical forests, combining field data and satellite imagery of radar.

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

Forestry An International Journal of Forest Research ◽

10.1093/forestry/cpz037 ◽

2019 ◽

Vol 92 (5) ◽

pp. 599-615 ◽

Cited By ~ 1

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.

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Improving the accuracy of aboveground biomass estimations in secondary tropical dry forests

Forest Ecology and Management ◽

10.1016/j.foreco.2020.118384 ◽

2020 ◽

Vol 474 ◽

pp. 118384

Author(s):

Adrián Bojórquez ◽

Angelina Martínez-Yrízar ◽

Alberto Búrquez ◽

Víctor J. Jaramillo ◽

Francisco Mora ◽

...

Keyword(s):

Aboveground Biomass ◽

Tropical Dry Forests ◽

Dry Forests

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Factors influencing the estimation of aboveground biomass (AGB) in tropical forests using RADAR remote sensing.

10.7287/peerj.preprints.26534v1 ◽

2018 ◽

Author(s):

Victoria E Espinoza-Mendoza

Keyword(s):

Remote Sensing ◽

Tropical Forests ◽

Aboveground Biomass ◽

Field Data ◽

Spatial Information ◽

Alos Palsar ◽

Radar Remote Sensing ◽

Radar Sensors ◽

Factors Influencing ◽

Optical Images

Despite the large amount of accessible spatial information, the issue of estimating aboveground biomass through remote sensing, especially radar, remains a challenge in complex ecosystems such as tropical forests. One of the advantages of radar sensors is that of "crossing clouds" (capacity that does not have optical images like Landsat), facilitating their use in areas with permanent cloud cover. This work defines, from several studies conducted in tropical forests using ALOS PALSAR, which are the factors with the most influence on the signal of the radar. This can be useful in the development and/or improvement of methodologies to estimate aboveground biomass in tropical forests, combining field data and satellite imagery of radar.

Download Full-text