Drones as a tool to monitor human impacts and vegetation changes in parks and protected areas

Abstract

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.

Assessment of karst rocky desertification from the local to regional scale based on unmanned aerial vehicle images: A case‐study of Shilin County, Yunnan Province, China

“…However, the highly heterogeneous landscapes in the karst region, as well as the coarse spatial resolution, fixed time intervals, cloud inference, and mixed pixel problem of satellite images, limit their application (Yue et al, 2011). UAVs have recently become able to acquire high‐resolution images because of their low cost and flexible control characteristics compared with other remote sensing tools (Ancin‐Murguzur et al, 2020; Lizuka et al, 2018; Messina et al, 2020; Sankey et al, 2021; Yao et al, 2019). UAVs can obtain orthophotos with extremely high spatial resolution and almost no shadows.…”

From a drone’s eye view: indicators of overtourism in a sea, sun, and sand destination

“…These approaches can improve the collection of use level data in many environments, but a continuing problem exists in areas such as beaches that possess porous boundaries or unspecified access points (Ziesler & Pettebone, 2018). UAV technology can potentially overcome this issue because it is now affordable, easy to operate, and can obtain imagery within a short period of time (Ancin-Murguzur et al, 2019;Provost et al, 2019).…”

“…Descriptive indicators define the directly observable and objective elements of a tourism or recreation system such as actual use levels, whereas evaluative indicators include subjective evaluations of visitors such as their perceived encounters, norms, and crowding. These evaluative indicators have been a focus of considerable research and testing (Manning, 2007(Manning, , 2011Needham et al, 2016), but integrating these with descriptive indicators has received less attention given the challenges associated with tracking and counting individuals over space and time (Ancin-Murguzur et al, 2019;D'Antonio et al, 2010).…”

Mask R-CNN and OBIA Fusion Improves the Segmentation of Scattered Vegetation in Very High-Resolution Optical Sensors

“…Remote sensing studies based on very high-resolution images have increased in the last years (e.g., [ 24 , 25 , 26 , 27 ]), partly because of the availability of Google Earth images worldwide [ 28 , 29 , 30 ] and the popularization of unmanned aerial vehicles (UAV). Although these images have shown a high potential for vegetation mapping and monitoring [ 31 , 32 , 33 ], two main problems arise when they are used. First, higher spatial resolution increases the spectral heterogeneity among and within vegetation types, resulting in a salt and pepper effect in their identification that does not correctly characterize the actual surface [ 34 ].…”