Synthesis of L-Band SAR and Forest Heights Derived from TanDEM-X DEM and 3 Digital Terrain Models for Biomass Mapping

Abstract

In this study, we compared the accuracies of above-ground biomass (AGB) estimated by integrating ALOS (Advanced Land Observing Satellite) PALSAR (Phased-Array-Type L-Band Synthetic Aperture Radar) data and TanDEM-X-derived forest heights (TDX heights) at four scales from 1/4 to 25 ha in a hemi-boreal forest in Japan. The TDX heights developed in this study included nine canopy height models (CHMs) and three model-based forest heights (ModelHs); the nine CHMs were derived from the three digital surface models (DSMs) of (I) TDX 12 m DEM (digital elevation model) product, (II) TDX 90 m DEM product and (III) TDX 5 m DSM, which we developed from two TDX–TSX (TerraSAR-X) image pairs for reference, and the three digital terrain models (DTMs) of (i) an airborne Light Detection and Ranging (LiDAR)-based DTM (LiDAR DTM), (ii) a topography-based DTM and (iii) the Shuttle Radar Topography Mission (SRTM) DEM; the three ModelHs were developed from the two TDX-TSX image pairs used in (III) and the three DTMs (i to iii) with the Sinc inversion model. In total, 12 AGB estimation models were developed for comparison. In this study, we included the C-band SRTM DEM as one of the DTMs. According to Walker et al. (2007), the SRTM DEM serves as a DTM for most of the Earth’s surface, except for the areas with extensive tree and/or shrub coverage, e.g., the boreal and Amazon regions. As our test site is located in a hemi-boreal zone with medium forest cover, we tested the ability of the SRTM DEM to serve as a DTM in our test site. This study especially aimed to analyze the capability of the two TDX DEM products (I and II) to estimate AGB in practice in the hemi-boreal region, and to examine how the different forest height creation methods (the simple DSM and DTM subtraction for the nine CHMs and the Sinc inversion model-based approach for the three ModelHs) and the different spatial resolutions of the three DSMs and three DTMs affected the AGB estimation results. We also conducted the slope-class analysis to see how the varying slopes influenced the AGB estimation accuracies. The results show that the combined use of the PALSAR data and the CHM derived from (I) TDX 12 m DEM and (i) LiDAR DTM achieved the highest AGB estimation accuracies across the scales (R2 ranged from 0.82 to 0.97), but the CHMs derived from (I) TDX 12 m DEM and another two DTMs, (ii) and (iii), showed low R2 values at any scales. In contrast, the two CHMs derived from (II) TDX 90 m DEM and both (i) LiDAR DTM and (iii) SRTM DEM showed high R2 values > 0.87 and 0.78, respectively, at the scales > 9.0 ha, but they yielded much lower R2 values at smaller scales. The three ModelHs gave the lowest R2 values across the scales (R2 ranged from 0.39 to 0.60). Analyzed by slope class at the 1.0 ha scale, however, all the 12 AGB estimation models yielded high R2 values > 0.66 at the lowest slope class (0° to 9.9°), including the three ModelHs (R2 ranged between 0.68 to 0.69). The two CHMs derived from (II) TDX 90 m DEM and both (i) LiDAR DTM and (iii) SRTM DEM showed R2 values of 0.80 and 0.71, respectively, at the lowest slope class, while the CHM derived from (I) TDX 12 m DEM and (i) LiDAR DTM showed high R2 values across the slope classes (R2 > 0.82). The results show that (I) TDX 12 m DEM had a high capability to estimate AGB, with a high accuracy across the scales and the slope classes in the form of CHM, but the use of (i) LiDAR DTM was required. On the other hand, (II) TDX 90 m DEM was able to achieve high AGB estimation accuracies not only with (i) LiDAR DTM, but also with (iii) SRTM DEM in the form of CHM, but it was limited to large scales > 9.0 ha; however, all the models developed in this study have the possibility to achieve higher AGB estimation accuracies at the 1.0 ha scale in flat terrains with slope < 10°. The analysis showed the strengths and limitations of each model, and it also indicates that the data creation methods, the spatial resolutions of datasets and topographic features affects the effective spatial scales for AGB mapping, and the optimal combinations of these features should be chosen to obtain high AGB estimation accuracies.

Monitoring Effect of Spatial Growth on Land Surface Temperature in Dhaka

“…The range and azimuth pixel spacing are approximately 0.91 m and 1.90 m, respectively [45]. Due to the bi-static mode of data collection, both satellite orbits can acquire data at the same location and at almost the same time with a short baseline [46]. Wessel et al [47] reported the absolute vertical mean error and Root Mean Square Error (RMSE) of TanDEM-X DEM was less than ±0.20 m, and 1.4 m, respectively, with an excellent absolute 90% linear height error below 2 m. Another study showed that the absolute height error was ±1.61 m and the relative height error was 1.05 m for TanDEM-X [48].…”

Use of TanDEM-X and SRTM-C Data for Detection of Deforestation Caused by Bark Beetle in Central European Mountains

“…While this study concentrated on negative differences associated with deforestation, other studies used DEM differencing to estimate both positive and negative changes in vegetation height (or biomass) due to both forest degradation and/or forest growth [5,21,22]. However, large errors in the vegetation biomass estimates are typically reported, along with numerous issues related to input DEMs [26], and the in-depth knowledge of the accuracy of SRTM and TanDEM-X is necessary for the accurate detection of vegetation changes.…”

“…The main aim of those missions was to create a global (or near-global in the case of SRTM) digital elevation model (DEM) of the Earth's surface. The resulting models (i.e., SRTM DEM, NASA DEM, TanDEM-X DEM and Copernicus DEM) were made freely available and have become an essential source of Earth's surface information, widely used, among others, in forestry [5], ecology [6,7], archaeology [8], and hydrology [9,10].…”

Estimating Forest Above-Ground Biomass in Central Amazonia Using Polarimetric Attributes of ALOS/PALSAR Images

“…Some studies have considered the variation in biomass due to different forest typologies [14], floristic and structural diversity [15,16], and local geomorphometric conditions [17,18]. Polarimetric analysis techniques have been used for biomass estimation [19,20], including interferometric approaches [21][22][23][24] employing interferometric height measurements and polarimetric SAR interferometry (PoLinSAR) [25,26], which have resulted in lower errors for biomass prediction [27] under different phytophysiognomic conditions [14], in addition to a reduced backscattering signal saturation [28], especially for tropical dense forests [29].…”