Proceedings of the 2012 National Conference on Information Technology and Computer Science

Development of Python-based ArcGIS Tools for Spatially Balanced Forest Sampling Design

Authors
Mingyang Li, Ting Xu, Qi Zhou
Corresponding Author
Mingyang Li
Available Online November 2012.
DOI
https://doi.org/10.2991/citcs.2012.109How to use a DOI?
Keywords
spatial balanced sampling (SBS), Python; ArcGIS tool; forest survey
Abstract
The current forest survey sampling methods are based on classical statistics, can not solve the problems of close spatial autocorrelation and poor adaptability. General randomized tessellation stratified (GRTS), a commonly used algorithm to implement spatial balanced sampling (SBS) has gained popularity since 1997. In this paper, Python was used to make ArcGIS Tools for GRTS, followed by a case study of forest biodiversity computer simulation sampling in Hunan Province. To compare the performance of SBS with simple random sampling, systematic sampling, four index were calculated from three aspects of spatial autocorrelation, sampling efficiency, sampling precision. Research results show that, compared with simple random sampling and systematic sampling, SBS has obviously advantages in reducing spatial autocorrelation and improving sampling efficiency and precision.
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Proceedings
2012 National Conference on Information Technology and Computer Science
Part of series
Advances in Intelligent Systems Research
Publication Date
November 2012
ISBN
978-94-91216-39-8
ISSN
1951-6851
DOI
https://doi.org/10.2991/citcs.2012.109How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Mingyang Li
AU  - Ting Xu
AU  - Qi Zhou
PY  - 2012/11
DA  - 2012/11
TI  - Development of Python-based ArcGIS Tools for Spatially Balanced Forest Sampling Design
BT  - 2012 National Conference on Information Technology and Computer Science
PB  - Atlantis Press
SP  - 419
EP  - 422
SN  - 1951-6851
UR  - https://doi.org/10.2991/citcs.2012.109
DO  - https://doi.org/10.2991/citcs.2012.109
ID  - Li2012/11
ER  -