2019
DOI: 10.3390/f10020105
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Mapping Forest Canopy Height in Mountainous Areas Using ZiYuan-3 Stereo Images and Landsat Data

Abstract: Forest canopy height is an important parameter for studying biodiversity and the carbon cycle. A variety of techniques for mapping forest height using remote sensing data have been successfully developed in recent years. However, the demands for forest height mapping in practical applications are often not met, due to the lack of corresponding remote sensing data. In such cases, it would be useful to exploit the latest, cheaper datasets and combine them with free datasets for the mapping of forest canopy heigh… Show more

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Cited by 9 publications
(7 citation statements)
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“…The Shuttle Radar Topography Mission-Digital Elevation Model (SRTM-DEM) has previously been used for forest mapping and allows greater mapping accuracy when used in combination with other satellite images [38][39][40]. We downloaded SRTM-DEM images with a spatial resolution of 30 m for the mainland Vietnam, the images were obtained from the United States Geological Survey (USGS) website (https://earthexplorer.usgs.gov/).…”
Section: Classification Methodsmentioning
confidence: 99%
“…The Shuttle Radar Topography Mission-Digital Elevation Model (SRTM-DEM) has previously been used for forest mapping and allows greater mapping accuracy when used in combination with other satellite images [38][39][40]. We downloaded SRTM-DEM images with a spatial resolution of 30 m for the mainland Vietnam, the images were obtained from the United States Geological Survey (USGS) website (https://earthexplorer.usgs.gov/).…”
Section: Classification Methodsmentioning
confidence: 99%
“…In addition, we evaluated the new canopy height product [19,20] using a sample dataset from Fujian and found that the accuracy was only about 0.4. The main reason for this is that the GEDI footprint data in Fujian are too sparse, while the environmental gradients in the mountainous region vary greatly, and the change process is highly complex and uncertain [48,49]. Therefore, it is important to address how these problems can be solved while preserving the advantages of satellite-based LiDAR for fast and effective measurements of forest canopy height.…”
Section: Discussionmentioning
confidence: 99%
“…Thus, the Forests special issue "3D Remote Sensing Applications in Forest Ecology: Composition, Structure and Function" was conceptualized by the authors of this paper and finally hosted 10 peer-reviewed contributions in which 3D sources of remote sensing data were applied either as a preliminary or auxiliary sources of information to understand, classify, augment, model and predict forest ecological attributes. Geographically, the contributions published within this special issue were well distributed around the globe, including China (four contributions) [32][33][34][35], Canada [36], Germany [37], India [38], Iran [39], Panama [40] and the United States [41]. The geographical distribution of the countries in which the published contributions were carried out are summarized in Figure 1.…”
Section: Summary Of the Contributionsmentioning
confidence: 99%
“…In terms of global climatic regimes and ecological biomes, the temperate biome included the majority of works with seven studies [33][34][35][36][37]39,41], followed by sub-tropical [32,38] and tropical [40] biomes. The topics covered within the published contributions can be divided into multiple groups: There were studies with rather classical applications such as single tree-level prediction of forest structural attributes by terrestrial laser scanning or visual estimation from Google Street View [33,41] and area-based prediction of forest structural attributes by space-borne stereo imagery, laser scanning or combination of passive optical with multi-frequency SAR data [34,35,39]. As an example, Ataee et al [39] proved that a combination of space-borne SAR and optical data could improve performance and reduce uncertainties in the retrieval of tree volume.…”
Section: Summary Of the Contributionsmentioning
confidence: 99%
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