Does the Data Resolution/Origin Matter? Satellite, Airborne and Uav Imagery to Tackle Plant Invasions

Abstract

ABSTRACT:Invasive plant species represent a serious threat to biodiversity and landscape as well as human health and socio-economy. To successfully fight plant invasions, new methods enabling fast and efficient monitoring, such as remote sensing, are needed. In an ongoing project, optical remote sensing (RS) data of different origin (satellite, aerial and UAV), spectral (panchromatic, multispectral and color), spatial (very high to medium) and temporal resolution, and various technical approaches (object-, pixelbased and combined) are tested to choose the best strategies for monitoring of four invasive plant species (giant hogweed, black locust, tree of heaven and exotic knotweeds). In our study, we address trade-offs between spectral, spatial and temporal resolutions required for balance between the precision of detection and economic feasibility. For the best results, it is necessary to choose best combination of spatial and spectral resolution and phenological stage of the plant in focus. For species forming distinct inflorescences such as giant hogweed iterative semi-automated object-oriented approach was successfully applied even for low spectral resolution data (if pixel size was sufficient) whereas for lower spatial resolution satellite imagery or less distinct species with complicated architecture such as knotweed, combination of pixel and object based approaches was used. High accuracies achieved for very high resolution data indicate the possible application of described methodology for monitoring invasions and their long-term dynamics elsewhere, making management measures comparably precise, fast and efficient. This knowledge serves as a basis for prediction, monitoring and prioritization of management targets.

Radiometric calibration framework for ultra-high-resolution UAV-derived orthomosaics for large-scale mapping of invasive alien plants in semi-arid woodlands:Harrisia pomanensisas a case study

“…Recently, the UAV remote-sensing (UAV-RS) approach has been frequently reported as advantageous over spaceborne and traditional airborne remote-sensing approaches due to the resultant high temporal and spatial resolution data as well as the survey cost efficiency associated with this approach (Femondimo et al 2011;Müllerová et al 2017;Babapour et al, 2017). Applications of the UAV-RS approach include inter alia agriculture , precision agriculture (Primicerio et al 2012;Gómez-Candón, De Castro, and Lopez-Granados 2014;Rokhmana 2015;Bagheri 2017), land use (Akar 2017), forestry (Thiel and Schmullius 2017;Torrescan et al 2017), archaeology (Rinaudo et al 2012;Fernández-Hernandez González-Aguiler, Rodriguez-Gonzalvez, and Mancera-Taboada 2015), classification of native vegetation (Zhang 2014), and mapping of invasive alien plants (IAPs) (Dvořák et al 2015;Müllerová et al 2016;Hill et al 2017).…”

Optimizing the Timing of Unmanned Aerial Vehicle Image Acquisition for Applied Mapping of Woody Vegetation Species Using Feature Selection

“…In the context of cost-effectiveness [1], the presented methodology can be easily implemented within a reasonable overall cost by using accessible off-the-shelf products. For obtaining preliminary phenological information, a consumer-grade digital camera with no NIR capabilities is sufficient (see Figure 8) [61,70,118,119]; drones and other available fixed wing UAVs have been proven to be a suitable tool for collecting repetitive high-quality overhead imagery [6,34,35,120]; while open source free GIS software's (Geographic Information Systems; e.g., QGIS and R) includ sufficient image processing tools.…”

“…The success of vegetation mapping by remote sensing is derived directly from the mapping objectives and the properties of the sensor [6][7][8]. Precise identification of individual species usually requires state-of-the-art methods, such as hyperspectral optical sensors with high spatial resolution [9][10][11], preferably combined with morphological and structural data such as LiDAR (Light Detection and Ranging) [12,13].…”

“…UAVs, and to some extent also appropriate future satellite missions [28], hold great potential for phenological based mapping, due to flexibility in image acquisition timing and relatively low costs [6,29]. UAVs assist in producing high spatial resolution imagery and thus enable the use of object oriented image analysis (OBIA), which can improve species discrimination compared to traditional pixel-based classification [30,31].…”

“…On the basis of visual examination of various trials, the scale parameter was set to three; the smoothness and compactness ratio was set to 0.6:0.4 (respectively); and the shape and color parameter ratio was set to 0.1:0.9 (respectively) [6,29,105].…”