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1. Crowdsourced data can provide spatially explicit data on the contribution of nature to people. Spatial information is essential for effectively managing the diverse S U PP O RTI N G I N FO R M ATI O NAdditional supporting information may be found online in the Supporting Information section.

Increased visitation to protected areas could have adverse impacts on the conservation values in the protected areas, and therefore effective visitor monitoring methods are needed to meet the complex management challenges that arise. Collecting data on human impacts is highly time consuming, thus requiring more effective tools that allow for high‐quality and long‐term measurements. In this study, we show how unmanned aerial vehicles (i.e. UAV or drones) could be used to monitor tourism impacts in protected areas. Tourism has boomed in national parks in Norway in recent years, such as in Jotunheimen National Park for which this study applies. We test the use of drones on a site where new tourist facilities will be established to set a baseline to identify future changes. We demonstrate how drones could help protected area management by monitoring visitor use patterns and commonly associated impacts such as trail condition (width and depth), vegetation structure and disturbances, informal trail proliferation, trampling, and trash and other impacts along the trails. We assessed accuracy and reliability compared with intensive field measurements of impacts and found low‐cost drones to be effective in mapping the study area with a resolution of 0.5 cm/pixel: drone derived trail measurements were comparable to traditional measurements with a negligible divergence on trail width measurements and a consistent 1.05 cm divergence on trail depth measurements that can be corrected with a few validation points. In addition, we created a high‐resolution vegetation classification map that could be used as a baseline for monitoring impacts. We conclude that drones can effectively contribute to visitor monitoring by reducing time spent in the field and by providing high‐resolution time series that could be used as baseline to measure tourism impacts on conservation values in protected areas.

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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 m 2 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 landscapelevel assessments of ES.