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

DOI

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.

Copyright

© 2012, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - Proceedings of the 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  - 10.2991/citcs.2012.109
ID  - Li2012/11
ER  -