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Open AccessArticle

Exploring Digital Surface Models from Nine Different Sensors for Forest Monitoring and Change Detection

Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), D-82234 Wessling, Germany
Institute of Forest Management (IFM), Technical University of Munich (TUM), D-85354 Freising, Germany
Department of Information Technology, Bavarian State Institute of Forestry (LWF), D-85354 Freising, Germany
Microwaves and Radar Institute (HR), German Aerospace Center (DLR), D-82234 Wessling, Germany
Author to whom correspondence should be addressed.
Academic Editors: Guangxing Wang, Erkki Tomppo, Dengsheng Lu, Huaiqing Zhang, Qi Chen, Randolph H. Wynne and Prasad S. Thenkabail
Remote Sens. 2017, 9(3), 287;
Received: 31 August 2016 / Revised: 10 February 2017 / Accepted: 12 March 2017 / Published: 18 March 2017
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
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. View Full-Text
Keywords: DSM; stereo imagery; TanDEM-X; LiDAR; forest heights; change detection DSM; stereo imagery; TanDEM-X; LiDAR; forest heights; change detection
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MDPI and ACS Style

Tian, J.; Schneider, T.; Straub, C.; Kugler, F.; Reinartz, P. Exploring Digital Surface Models from Nine Different Sensors for Forest Monitoring and Change Detection. Remote Sens. 2017, 9, 287.

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