Abstract
Digital surface models (DSMs) derived from spaceborne and airborne sensors enable the monitoring of the vertical structures for forests in large areas. Nevertheless, due to the lack of an objective performance assessment for this task, it is difficult to select the most appropriate data source for DSM generation. In order to fill this gap, this paper performs change detection analysis including forest decrease and tree growth. The accuracy of the DSMs is evaluated by comparison with measured tree heights from inventory plots (field data). In addition, the DSMs are compared with LiDAR data to perform a pixel-wise quality assessment. DSMs from four different satellite stereo sensors (ALOS/PRISM, Cartosat-1, RapidEye and WorldView-2), one satellite InSAR sensor (TanDEM-X), two aerial stereo camera systems (HRSC and UltraCam) and two airborne laser scanning datasets with different point densities are adopted for the comparison. The case study is a complex central European temperate forest close to Traunstein in Bavaria, Germany. As a major experimental result, the quality of the DSM is found to be robust to variations in image resolution, especially when the forest density is high. The forest decrease results confirm that besides aerial photogrammetry data, very high resolution satellite data, such as WorldView-2, can deliver results with comparable quality as the ones derived from LiDAR, followed by TanDEM-X and Cartosat DSMs. The quality of the DSMs derived from ALOS and Rapid-Eye data is lower, but the main changes are still correctly highlighted. Moreover, the vertical tree growth and their relationship with tree height are analyzed. The major tree height in the study site is between 15 and 30 m and the periodic annual increments (PAIs) are in the range of 0.30–0.50 m.
Highlights
Height is one of the most important attributes to characterize forest stands
Four main remote sensing techniques are available to acquire three-dimensional data in forests: LiDAR (Light Detection And Ranging), aerial photogrammetry, satellite photogrammetry and Interferometric Synthetic Aperture Radar (InSAR) [2,3,4]
The trees measured within the concentric circles depended on their diameter at breast height (DBH): all trees with a DBH
Summary
Besides being indispensable for estimating forest timber volume, it is very helpful for forest structure analyses, classification, mapping and change detection [1]. New satellite sensors and advanced image processing techniques provide data and tools for an improved forest monitoring. The question is: can forest height and timber volume estimation be carried out efficiently using remote sensing data? 2017, 9, 287 this question, an extensive understanding about the quality and potential applicability of height information derived from the latest sensors and processing technologies is necessary. Surface Models (DSMs) and Canopy Height Models (CHMs) from remote sensing data have recently gained more attention. Four main remote sensing techniques are available to acquire three-dimensional data in forests: LiDAR (Light Detection And Ranging), aerial photogrammetry, satellite photogrammetry and Interferometric Synthetic Aperture Radar (InSAR) [2,3,4]
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