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Proceedings Paper

Effects of distribution density and cell dimension of 3D vegetation model on canopy NDVI simulation base on DART
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Paper Abstract

The 3D model is an important part of simulated remote sensing for earth observation. Regarding the small-scale spatial extent of DART software, both the details of the model itself and the number of models of the distribution have an important impact on the scene canopy Normalized Difference Vegetation Index (NDVI).Taking the phragmitesaustralis in the Yangtze Estuary as an example, this paper studied the effect of the P.australias model on the canopy NDVI, based on the previous studies of the model precision, mainly from the cell dimension of the DART software and the density distribution of the P.australias model in the scene, As well as the choice of the density of the P.australiass model under the cost of computer running time in the actual simulation. The DART Cell dimensions and the density of the scene model were set by using the optimal precision model from the existing research results. The simulation results of NDVI with different model densities under different cell dimensions were analyzed by error analysis. By studying the relationship between relative error, absolute error and time costs, we have mastered the density selection method of P.australias model in the simulation of small-scale spatial scale scene. Experiments showed that the number of P.australias in the simulated scene need not be the same as those in the real environment due to the difference between the 3D model and the real scenarios. The best simulation results could be obtained by keeping the density ratio of about 40 trees per square meter, simultaneously, of the visual effects.

Paper Details

Date Published: 1 September 2017
PDF: 8 pages
Proc. SPIE 10405, Remote Sensing and Modeling of Ecosystems for Sustainability XIV, 1040511 (1 September 2017); doi: 10.1117/12.2273308
Show Author Affiliations
Zhu Tao, East China Normal Univ. (China)
Joint Lab. for Enviornmental Remote Sensing and Data Assimilation (China)
Runhe Shi, East China Normal Univ. (China)
Joint Lab. for Enviornmental Remote Sensing and Data Assimilation (China)
Colorado State Univ. (United States)
Yuyan Zeng, East China Normal Univ. (China)
Wei Gao, East China Normal Univ. (United States)
Joint Lab. for Enviornmental Remote Sensing and Data Assimilation (China)
Colorado State Univ. (United States)


Published in SPIE Proceedings Vol. 10405:
Remote Sensing and Modeling of Ecosystems for Sustainability XIV
Wei Gao; Ni-Bin Chang; Jinnian Wang, Editor(s)

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