Efficient sampling for ecosystem service supply assessment at a landscape scale

Abstract

Decision makers and stakeholders need high-quality data to manage ecosystem services (ES) efficiently. Landscape-level data on ES that are of sufficient quality to identify spatial tradeoffs, co-occurrence and hotspots of ES are costly to collect, and it is therefore important to increase the efficiency of sampling of primary data. We demonstrate how ES could be assessed more efficiently through image-based point intercept method and determine the tradeoff between the number of sample points (pins) used per image and the robustness of the measurements. We performed a permutation study to assess the reliability implications of reducing the number of pins per image. We present a flexible approach to optimize landscape-level assessments of ES that maximizes the information obtained from 1 m² digital images. Our results show that 30 pins are sufficient to measure ecosystem service indicators with a crown cover higher than 5% for landscape scale assessments. Reducing the number of pins from 100 to 30 reduces the processing time up to a 50% allowing to increase the number of sampled plots, resulting in more management-relevant ecosystem service maps. The three criteria presented here provide a flexible approach for optimal design of landscape-level assessments of ES.