Unmanned aerial vehicles as innovative remote sensing platforms for high‐resolution infrared imagery to support restoration monitoring in cut‐over bogs. Issue 3 (25th January 2013)

• Record Type: Journal Article
• Authors: Knoth, Christian; Klein, Birte; Prinz, Torsten; Kleinebecker, Till; Goslee, Sarah
• Is Part Of: Applied vegetation science. Volume 16:Issue 3(2013:Jul.)
• Page Start: 509
• Page End: 517
• Publication Date: 2013-01-25
• Subjects: Plant ecology -- Periodicals; Plant communities -- Periodicals; Plant populations -- Periodicals; Nature -- Effect of human beings on -- Periodicals; 581.705
• DOI: 10.1111/avsc.12024
• Languages: English
• ISSNs: 1402-2001

Abstract

<abstract abstract-type="main" id="avsc12024-abs-0001"> <title>Abstract</title> <sec id="avsc12024-sec-0001" sec-type="section"> <title>Question</title> <p>Can UAV‐based NIR remote sensing support restoration monitoring of cut‐over bogs by providing valid information on species distribution and surface structure?</p> </sec> <sec id="avsc12024-sec-0002" sec-type="section"> <title>Location</title> <p>Restored polders of the Uchter Moor, a bog complex in NW Germany.</p> </sec> <sec id="avsc12024-sec-0003" sec-type="section"> <title>Methods</title> <p>We used autonomously flying quadrocopters, supplied with either a panchromatic or colour infrared calibrated small frame digital camera to generate high resolution images of the restored bog surface. We performed a two‐step classification process of automatic image segmentation and object‐based classification to distinguish between four pre‐defined classes (waterlogged bare peat, <italic>Sphagnum</italic> spp., <italic>Eriophorum vaginatum</italic> and <italic>Betula pubescens</italic>. An independent validation procedure was performed to evaluate the accuracy of the classification.</p> </sec> <sec id="avsc12024-sec-0004" sec-type="section"> <title>Results</title> <p>A set‐up composed of decision rules for reflectance, geometry and textural features was applied for identification of the four classes. The presented classification revealed an overall accuracy level of 91%. Most reliable attribution was obtained for waterlogged bare peat and <italic>Sphagnum</italic>‐covered surfaces, revealing producer accuracies of 95% and 91%, respectively. Lower but still feasible accuracy levels were obtained for <italic>Eriophorum vaginatum</italic> and <italic>Betula pubescens</italic> individuals (89% and 84%, respectively).</p> </sec> <sec id="avsc12024-sec-0005" sec-type="section"> <title>Conclusions</title> <p>UAV‐based NIR remote sensing is a promising tool for monitoring the restoration of cut‐over bogs and has the potential to significantly reduce laborious field surveys. UAVs may increasingly play a significant role in future ecological monitoring studies, since they are small in size, highly flexible, easy to handle, non‐emissive and available at a comparatively low cost.</p> </sec> </abstract>