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- Title
Estimating Forest Canopy Height based on GEDI Lidar Data and Multi-source Remote Sensing Images.
- Authors
Lei, Yuqi; Wang, Yuanjia; Wang, Guilong; Song, Chengwen; Cao, Hui; Xiao, Wen
- Abstract
Estimating forest canopy height is crucial for assessing aboveground biomass and carbon sequestration. Light detection and ranging (lidar) is an important technology for its ability in capturing vertical structural information. However, due to instrument limitations and cost constraints, acquiring large-scale and continuous forest data solely through lidar is challenging. To compensate this, remote sensing images can be used to cover wide regions. Therefore, leveraging multi-source data for constructing canopy height models (CHMs) holds great promise in this field. The objective of this study is to evaluate and compare the contributions of multi-source remote sensing data and methods in estimating forest canopy height. In constructing the CHM, the commonly used random forest (RF) and fully convolutional network (FCN) are assessed. The canopy height obtained from GEDI was used as the reference data, and Landsat 8 and Sentinel-2 data were used for prediction. Multiple CHMs were constructed for the Dabie Mountains, Central China, in 2019 based on different data sources and methods, respectively, which are then comparatively analysed. The results showed that (1) the accuracy of the CHM using Sentinel-2 as input is marginally better than that using Landsat 8 based on RF, where the difference is insignificant; and (2) FCN is less accurate than RF despite domain-specific fine-tuning, although further improvement in accuracy is expected by weighing in more FCN models.
- Subjects
CHINA; REMOTE sensing; FOREST canopies; OPTICAL radar; LIDAR; LASER based sensors; AIRBORNE lasers; LANDSAT satellites
- Publication
International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 2024, Vol 48, Issue 1, p297
- ISSN
1682-1750
- Publication type
Article
- DOI
10.5194/isprs-archives-XLVIII-1-2024-297-2024