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

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.

Optimization of a survey using spatially balanced sampling: a single-year application of clam monitoring in the Arcachon Bay (SW France)

“…For example, it has been used to determine bull trout (Salvelinus confluentus) population status through counts in basins of the Columbia River Plateau in the USA (Jacobs et al, 2009) and to develop ArcGIS tools via a forest biodiversity survey in a case study in Hunan Province, China (Li et al, 2012). Here, we illustrate the use of GRTS, and provide a comparison with StRS for bivalve surveys.…”