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

Abstract

Background: Reliable information about the spatial distribution of aboveground biomass (AGB) in tropical forests is fundamental for climate change mitigation and for maintaining carbon stocks. Recent AGB maps at continental and national scales have shown large uncertainties, particularly in tropical areas with high AGB values. Errors in AGB maps are linked to the quality of plot data used to calibrate remote sensing products, and the ability of radar data to map high AGB forest. Here we suggest an approach to improve the accuracy of AGB maps and test this approach with a case study of the tropical forests of the Yucatan peninsula, where the accuracy of AGB mapping is lower than other forest types in Mexico. To reduce the errors in field data, National Forest Inventory (NFI) plots were corrected to consider small trees. Temporal differences between NFI plots and imagery acquisition were addressed by considering biomass changes over time. To overcome issues related to saturation of radar backscatter, we incorporate radar texture metrics and climate data to improve the accuracy of AGB maps. Finally, we increased the number of sampling plots using biomass estimates derived from LiDAR data to assess if increasing sample size could improve the accuracy of AGB estimates. Results: Correcting NFI plot data for both small trees and temporal differences between field and remotely sensed measurements reduced the relative error of biomass estimates by 12.2%. Using a machine learning algorithm, Random Forest, with corrected field plot data, backscatter and surface texture from the L-band synthetic aperture radar (PALSAR) installed on the on the Advanced Land Observing Satellite-1 (ALOS), and climatic water deficit data improved the accuracy of the maps obtained in this study as compared to previous studies (R 2 = 0.44 vs R 2 = 0.32). However, using sample plots derived from LiDAR data to increase sample size did not improve accuracy of AGB maps (R 2 = 0.26). Conclusions: This study reveals that the suggested approach has the potential to improve AGB maps of tropical dry forests and shows predictors of AGB that should be considered in future studies. Our results highlight the importance of using ecological knowledge to correct errors associated with both the plot-level biomass estimates and the mismatch between field and remotely sensed data.

Mapping structural attributes of tropical dry forests by combining Synthetic Aperture Radar and high‐resolution satellite imagery data

“…Finally, the maps of vegetation attributes showed clear differences between forest types. Species richness, tree height, diameter, and basal area showed lower values in the deciduous TDF, while the highest values were found in the semi‐evergreen TDF (Figure 7, Appendixes –), following a precipitation gradient similar to that found by Hernández‐Stefanoni et al (2020) for the distribution of above‐ground biomass. The estimation errors of the vegetation attributes varied along with the vegetation attribute and the type of tropical forest.…”

Identifying Variables to Discriminate between Conserved and Degraded Forest and to Quantify the Differences in Biomass

“…Since the data from L-band and P-band SAR become saturated in forest with a medium to high biomass level at approximately 60-100 and 100-150 Mg ha −1 , respectively, they are not suitable for mapping forest biomass in all conditions [55,59], although they should function well at the densities we found in degraded forests in Ayuquila River watershed.. The application of texture data derived from L-band SAR has been reported to be able to reduce the saturation at high biomass values, since texture data capture variation in horizontal forest structure attributes, such as tree height and crown diameter [32]. The best result could probably be obtained with a combination of optical, LiDAR, and SAR data [60].…”

“…LiDAR estimates of AGB have been shown to be highly correlated with field-measured AGB data. For example, Hernandez-Stefanoni et al [32] show a high association between AGB and LiDAR data with an R 2 = 0.87 using a linear regression analysis. An advantage of LiDAR data is that they do not get saturated in areas of high biomass; however, the high cost of acquisition could be a limitation [6].…”

“…It is composed of deciduous and semi-deciduous woodlands with low biomass density, canopy height, and cover, and has low AGB compared to other areas of TDFs in the Neotropics [31]. TDFs in Mexico are widespread and they suffer higher rates of forest loss compared to the humid tropical forests due to higher population densities [32]. TDF in Ayuquila watershed is heavily used for shifting cultivation, fuelwood extraction, cattle grazing, extraction of poles for constructing fences, and for mushrooms and medicinal plants [33].…”

Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach

“…Different types of sensors, such as satellite-based multispectral imagers and Synthetic Aperture RADAR (SAR) systems, as well as airborne light detection and ranging (LiDAR), have been successfully applied to estimate AGB in the tropics [21][22][23][24][25][26]. SAR and LiDAR have been increasingly used to estimate AGB in the last eight years [27][28][29][30][31][32][33][34][35][36]. The microwave pulses transmitted by a SAR system, especially at longer wavelengths such as the L-or P-band, interact with the branches and trunks, providing information about the forest structure, which is highly correlated to AGB [37][38][39].…”