Effectiveness of survey points' density and distribution on vegetation coverage field measurement

Y. J. Yue; L. Gao; J. A. Wang; N. Li

doi:10.1117/12.813167

10 November 2008 Effectiveness of survey points' density and distribution on vegetation coverage field measurement

Y. J. Yue, L. Gao, J. A. Wang, N. Li

Proceedings Volume 7146, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Advanced Spatial Data Models and Analyses; 714622 (2008) https://doi.org/10.1117/12.813167
Event: Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Geo-Simulation and Virtual GIS Environments, 2008, Guangzhou, China

Abstract

Vegetation coverage is a widely used parameter to measure global and regional environment change. Evaluating the accuracy and efficiency of vegetation coverage using digital photography under various survey points' densities and distribution patterns has an important referential significance for providing an optimized field measurement method. The vegetation field measurement was carried out in a sample with Artemisia ordosica shrubs in Mu Us sandy land using vertical hoisting digital camera, with four densities and nine distribution patterns of survey points. The results showed that: different density of survey points led to a slight accuracy difference, and the precision improves as the density increases. The sample size had great impact on the precision. Different point distribution patterns led to significantly different results. "Diamond" pattern can get relatively higher degree of accuracy with least points and shortest walking distance in field survey. It's the best choice that could meet the requirements of the maximum precision and minimum workload in the vegetation field measurement.

Citation Download Citation

Y. J. Yue, L. Gao, J. A. Wang, and N. Li "Effectiveness of survey points' density and distribution on vegetation coverage field measurement", Proc. SPIE 7146, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Advanced Spatial Data Models and Analyses, 714622 (10 November 2008); https://doi.org/10.1117/12.813167

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KEYWORDS

Virtual colonoscopy

Vegetation

Digital photography

Digital cameras

Imaging systems

Remote sensing

Statistical analysis

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