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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLVIII-1/W2-2023
https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1831-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1831-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
14 Dec 2023
| 14 Dec 2023
A HIGH-PRECISION EXPLICIT FOREST CARBON STOCK MODEL BASED ON REMOTE SENSING
N. Zhu
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, China
B. Yang
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, China
W. Gong
Department of Geographical Sciences, University of Maryland, College Park, Maryland, USA
S. Ying
School of Resource and Environmental Sciences Sciences, Wuhan University, Wuhan, Hubei, China
W. Dai
School of Geography and Information Engineering, China University of Geosciences, Wuhan, Hubei, China
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, China
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Short summary
Short summary
This article proposes an optimized transformation for topographic datasets. The resulting topographic grid exhibits good surface approximation and quasi-uniform high-quality. Both features of the processed topography build a concrete base from which improved endogenous or exogenous parameters can be derived, and makes it suitable for Earth and environmental simulations.
Bisheng Yang, Yuan Liu, Fuxun Liang, and Zhen Dong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 433–439, https://doi.org/10.5194/isprs-archives-XLI-B3-433-2016, https://doi.org/10.5194/isprs-archives-XLI-B3-433-2016, 2016
C. Chen, B. S. Yang, and S. Song
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 169–174, https://doi.org/10.5194/isprs-archives-XLI-B1-169-2016, https://doi.org/10.5194/isprs-archives-XLI-B1-169-2016, 2016
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