Spatially Balanced Sampling for Monitoring Forest Resources and Ecological System

On the basis of introducing the fundamental work steps of spatially balanced sampling (SBS) and the algorithm of RRQRR (Reversed Randomized Quadrant Recursive Raster), taking Zijin Mount National Forest Park as a case study area, the evaluation of sampling design precision and scheme performance of three sampling methods, namely simple random, systematic sampling and spatially balanced sample (SBS) were conducted for the linear features of road and discrete distributed surface features (lakes, reservoirs) from two aspects of investigation cost and sampling accuracy by means of calculating the ratios of area variance of Voronoi polygon to assess the spatial balance of different sample methods. The studies showed that at the sampling scale from 100m to 1000m, the spatially balanced performance of spatially balanced sampling was superior to the simple random sampling, but inferior to systematic sampling. The property of spatial balance of SBS could be reflected only if the sampling volume was greater than 3000. In ecological monitoring of the discrete liner and surface features, the average elevation of sample points of SBS were lowest (68.72m,34.72m), the average slope were the smallest (8.20°, 2.63°), and the mean distance from sample points to the objects were the shortest (13.33m, 0.80m), the percentage of the sample points on objects to the total number were the highest (96%, 89%), the average points on the sampling objects were the largest (1.68, 2.70).