Development of Python-based ArcGIS Tools for Spatially Balanced Forest Sampling Design
Mingyang Li, Ting Xu, Qi Zhou
Available Online November 2012.
- https://doi.org/10.2991/citcs.2012.109How to use a DOI?
- spatial balanced sampling (SBS), Python; ArcGIS tool; forest survey
- 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.
- 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 -