Abstract
The Shuttle Radar Topography Mission (SRTM) has produced the most accurate nearly global elevation dataset to date. Over vegetated areas, the measured SRTM elevations are the result of a complex interaction between radar waves and tree crowns. In this study, waveforms acquired by the Geoscience Laser Altimeter System (GLAS) were combined with SRTM elevations to discriminate the five forest landscape types (LTs) in French Guiana. Two differences were calculated: (1) penetration depth, defined as the GLAS highest elevations minus the SRTM elevations and (2) the GLAS centroid elevations minus the SRTM elevations. The results show that these differences were similar for the five LTs, and they increased as a function of the GLAS canopy height and of the SRTM roughness index. Next, a Random Forest (RF) classifier was used to analyze the coupling potential of GLAS and SRTM in the discrimination of forest landscape types in French Guiana. The parameters used in the RF classification were the GLAS canopy height, the SRTM roughness index, the difference between the GLAS highest elevations and the SRTM elevations and the difference between the GLAS centroid elevations and the SRTM elevations. Discrimination of the five forest landscape types in French Guiana was possible, with an overall classification accuracy of 81.3% and a kappa coefficient of 0.75. All forest LTs were well classified with an accuracy varying from 78.4% to 97.5%.Finally, differences of near coincident GLAS waveforms, one from the wet season and one from the dry season, were analyzed. The results showed that the open forest LT (LT12), in some locations, contains trees that lose leaves during the dry season. These trees allow LT12 to be easily discriminated from the other LTs that retain their leaves using the following three criteria: (1) difference between the GLAS centroid elevations and the SRTM elevations, (2) ratio of top energy in the wet season to top energy in the dry season, or (3) ratio of ground energy in the wet season to ground energy in the dry season.
Highlights
The assessment of forest landscape types and the monitoring of their dynamics are essential requirements for the sustainable management of forest resources, and the relevance of remote sensing in the creation of forest landscape databases is very apparent (e.g., Gond et al, 2011; Bartholomé et al, 2004; Mayaux et al, 2004)
A dataset of 12238 Geoscience Laser Altimeter System (GLAS) elevations calculated from GLAS waveforms, namely, the highest and centroid elevations was compared to Shuttle Radar Topography Mission (SRTM) elevations
Based on the VEGETATION-SPOT-derived forest landscape types from Gond et al (2011), GLAS footprints and their corresponding SRTM elevations were analyzed according to the five forest landscape types
Summary
The assessment of forest landscape types and the monitoring of their dynamics are essential requirements for the sustainable management of forest resources, and the relevance of remote sensing in the creation of forest landscape databases is very apparent (e.g., Gond et al, 2011; Bartholomé et al, 2004; Mayaux et al, 2004). Our study uses the interaction between the Shuttle Radar Topography Mission (SRTM) data and vegetation in the five forest landscape types in French Guiana to assess the potential of the SRTM to identify these five forest types This was accomplished by comparing SRTM elevations with elevations extracted from NASA’s Geoscience Laser Altimeter System (GLAS) full waveform data, namely, the highest (most likely canopy top) and centroid (distance-weighted average) elevations. The classification potential for the five forest landscape types (LTs) using the coupling of GLAS and SRTM was assessed using the Random Forest algorithm This classification was conducted using the penetration depth, the difference between the GLAS centroid elevations and the SRTM elevations, the GLAS canopy height and the SRTM roughness index.
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