Abstract
Abstract. The use of multispectral sensors on Unmanned Aerial Vehicles (UAVs) was until recently too heavy and bulky although this changed in recent times and they are now commercially available. The focus on the usage of these sensors is mostly directed towards the agricultural sector where the focus is on precision farming. Applications of these sensors for mapping of wetland ecosystems are rare. Here, we evaluate the performance of low altitude multispectral UAV imagery to determine the state of wetland vegetation in a localised spatial area. Specifically, NDVI derived from multispectral UAV imagery was used to inform the determination of the integrity of the wetland vegetation. Furthermore, we tested different software applications for the processing of the imagery. The advantages and disadvantages we experienced of these applications are also shortly presented in this paper. A JAG-M fixed-wing imaging system equipped with a MicaScene RedEdge multispectral camera were utilised for the survey. A single surveying campaign was undertaken in early autumn of a 17 ha study area at the Kameelzynkraal farm, Gauteng Province, South Africa. Structure-from-motion photogrammetry software was used to reconstruct the camera position’s and terrain features to derive a high resolution orthoretified mosaic. MicaSense Atlas cloud-based data platform, Pix4D and PhotoScan were utilised for the processing. The WET-Health level one methodology was followed for the vegetation assessment, where wetland health is a measure of the deviation of a wetland’s structure and function from its natural reference condition. An on-site evaluation of the vegetation integrity was first completed. Disturbance classes were then mapped using the high resolution multispectral orthoimages and NDVI. The WET-Health vegetation module completed with the aid of the multispectral UAV products indicated that the vegetation of the wetland is largely modified (“D” PES Category) and that the condition is expected to deteriorate (change score) in the future. However a lower impact score were determined utilising the multispectral UAV imagery and NDVI. The result is a more accurate estimation of the impacts in the wetland.
Highlights
The use of Unmanned Aerial Vehicles (UAVs) multispectral imagery for precision farming applications is recently receiving a lot of attention (Nebiker et al, 2016)
In previous studies we showed that UAV derived RGB imagery can inter alia significantly enhance wetland vegetation assessment through the extraction of relevant information from these imagery (Boon et al, 2016a)
In this study we focused on wetland vegetation integrity assessment (WET-Health) using multispectral UAV imagery
Summary
The use of UAV multispectral imagery for precision farming applications is recently receiving a lot of attention (Nebiker et al, 2016). This technology is used in agricultural planning for example to define management zones and create precise variable rate application maps. Adam et al (2010) compiled a review on multispectral and hyperspectral remote sensing for the identification and mapping of wetland vegetation They indicated that remote sensing of wetland vegetation has particular challenges. Key limitations included low spatial, spectral and temporal resolutions from commonly used digital multispectral imagery. These problems are being addressed in recent years. UAV multispectral sensors can discriminate spectral reflectance which is important when one needs to indicate vegetation health (Colmina and Molina, 2014). Marcaccio et al (2015) demonstrated that this technology provide the opportunity for researchers to obtain seasonally-relevant imagery themselves instead of using out-of-date commercial imagery
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
