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

Abstract

Aim Optical satellite imagery has been used for mapping the spatial distribution of vegetation structure attributes; however, obtaining accurate estimates with optical imagery can be difficult in tropical forests due to their dense canopy and multi‐layered vegetation. Synthetic aperture radar imagery can be more suitable in this case, as the radar signal can penetrate the forest canopy and interact with stems, providing a better estimation of the vegetation structure. This study compared the accuracy of forest species richness, tree diameter, height, and basal area estimates obtained using Sentinel‐2 and Advanced Land Observing Satellite ‐1 (ALOS) Phased Array type L‐band Synthetic Aperture Radar (PALSAR) data, either combined or separately. Location The Yucatan Peninsula, Mexico. Methods Field data were collected in three 3600‐km2‐window areas with three different types of tropical dry forest. Three random forest regression models were fitted: one using explanatory variables derived from Sentinel‐2 data, a second using predictor variables derived from ALOS PALSAR, and the third using a combination of explanatory variables from both sensors. A variance partitioning analysis was carried out to examine the percent variability of each vegetation attribute that was explained by the models combining the explanatory variables of the two sensors (ALOS PALSAR and Sentinel‐2). Results Vegetation attribute estimation errors ranged from 13% to 38.5% when using ALOS PALSAR variables and from 11% to 33% when using Sentinel‐2 variables. Combining variables from both sensors provided more accurate estimates of vegetation attributes. A 5% reduction of the estimated error, and an increase from 0.50 to 0.63 of the percentage of variation explained by the models (R2) were achieved. Conclusions Our results suggest that both ALOS PALSAR and Sentinel‐2 data provide accurate estimates of vegetation structure and species richness in tropical dry forests. However, combining explanatory variables from the two sensors improved the estimation accuracy of vegetation attributes.

Modelling Species Richness and Functional Diversity in Tropical Dry Forests Using Multispectral Remotely Sensed and Topographic Data

“…However, the coefficients of determination and the estimation error in the validation with a separate dataset show the predictive power of the models (R 2 = 0.44 and %RMSE = 25.4 for species richness; R 2 = 0.50 and %RMSE = 48.8, for functional diversity). Estimates of the spatial distribution of species richness showed patterns similar to those reported by Hernández-Stefanoni et al [22] and Andres-Mauricio et al [55], who constructed maps of tree species richness in the Yucatan Peninsula. However, this study showed a higher predictive power than the previous investigations, which yielded lower coefficients of determination and higher estimation errors.…”

Carbon Stocks, Species Diversity and Their Spatial Relationships in the Yucatán Peninsula, Mexico

“…Conversely, texture metrics had a higher influence on species richness (7.3%) compared to backscatter (0.02%). SAR sensorssuch as ALOS PALSAR-with large wavelengths can easily penetrate the forest canopy, allowing accurate estimates of basal area, diameter and tree height, all which are related to biomass [63]. Meanwhile, texture metrics that measure spectral variability have been closely related to species richness [64], as these metrics are proxies of habitat heterogeneity.…”

Temporal Changes in Mediterranean Pine Forest Biomass Using Synergy Models of ALOS PALSAR-Sentinel 1-Landsat 8 Sensors

“…The early studies attempting to estimate biomass using satellite remote sensing variables utilized Landsat TM data with 30 m resolution [13][14][15] in structurally simple temperate and boreal forests. Vegetation indexes were the most frequently employed approach in optical remote sensing for biomass estimation [16]. Most indices rely on the connection between red and near-infrared wavelengths to maximize the spectral input from green vegetation while minimizing contributions from the soil, sun angle, sensor view angle, shaded vegetation, and atmosphere [17].…”