Abstract
Random sampling is an important approach to field vegetation surveys. However, sampling surveys in desert areas are difficult because determining an appropriate quadrat size that represent the sparse and unevenly distributed vegetation is challenging. In this study, we present a methodology for quadrat size optimization based on low-altitude high-precision unmanned aerial vehicle (UAV) images. Using the Daliyaboyi Oasis as our study area, we simulated random sampling and analyzed the frequency distribution and variation in the fractional vegetation cover (FVC) index of the samples. Our results show that quadrats of 50 m × 50 m size are the most representative for sampling surveys in this location. The method exploits UAV technology to rapidly acquire vegetation information and overcomes the shortcomings of traditional methods that rely on labor-intensive fieldwork to collect species-area relationship (SAR) data. Our method presents two major advantages: (1) speed and efficiency stemming from the application of UAV, which also effectively overcomes the difficulties posed in vegetation surveys by the challenging desert climate and terrain; (2) the large sample size enabled by the use of a sampling simulation. Our methodology is thus highly suitable for selecting the optimal quadrat size and making accurate estimates, and can improve the efficiency and accuracy of field vegetation sampling surveys.
Highlights
Random sampling survey is one of the most important methods for conducting ecological research; it is widely used in studies of plant species diversity, species association analysis, vegetation spatial pattern research, biomass estimation, and other areas [1,2,3]
The core area of the oasis is 324 km2 [25], the annual average precipitation is less than 10 mm [26], the weather is dominated by dust storms and floating dust, the vegetation composition is dominated by Populus euphratica and Tamarix chinensis [23]
The sampling results were not robust and had a large deviation from the true value of fractional vegetation cover (FVC) (FVCtrue = 0.05) in plot A. This implies that when a quadrat size of 20 m × 20 m is adopted for field vegetation surveys in areas that are similar to this plot, the quadrats were too small to cover both the sparse and dense vegetation areas
Summary
Random sampling survey is one of the most important methods for conducting ecological research; it is widely used in studies of plant species diversity, species association analysis, vegetation spatial pattern research, biomass estimation, and other areas [1,2,3]. The concept of spatial scale is among the most fundamental concepts in ecological research, making quadrat size a vital component of field survey design [6,7,8]. Sampling surveys can obtain detailed quantitative descriptions of vegetation characteristics, but an unsuitable sampling design can significantly limit the validity and usefulness of field survey data [11]. High-frequency and low-variance sampling approaches are considered the most representative in statistics [12]. Different quadrat sizes result in different sample characteristics and different population estimates [14]
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