Remote sensing as a tool for monitoring plant invasions: Testing the effects of data resolution and image classification approach on the detection of a model plant species Heracleum mantegazzianum (giant hogweed)

Abstract

Plant invasions represent a threat not only to biodiversity and ecosystem functioning but also to the character of traditional landscapes. Despite the worldwide efforts to control and eradicate invasive species, their menace grows. New techniques enabling fast and precise monitoring and providing information on spatial structure of invasions are needed for efficient management strategies to be implemented. We present remote sensing assessment of a noxious invasive species Heracleum mantegazzianum (giant hogweed) that integrates different data sources, spatial and spectral resolutions, and image processing techniques. Panchromatic, multispectral and color very high spatial resolution (VHR) aerial photography , resolution 0.5 m), and medium spatial resolution satellite data (Rapid Eye 2010, resolution 5 m) were analyzed to assess their potential for hogweed monitoring by using pixel-(both supervised and unsupervised) and object-based image analysis (OBIA, automated hierarchical, iterative, and rule-based). Both point and grid based accuracy assessment was carried out. Described methods of object-based image analysis of VHR data enabled monitoring of hogweed at high classification accuracies measured by various means, regardless of the spectral resolution of the data provided that the data came from the species flowering period. Although the proposed automated processing of VHR data is relatively time-effective and standardized, application over large areas would be rather demanding due to the size of datasets, and multispectral satellite data of medium spatial resolution (lower than the size of individuals) was therefore tested. On such imagery, only larger stands could be identified but still the pixel-based supervised classification achieved moderate accuracy. Depending on the size of the area of interest and the detail needed the very high or medium spatial resolution data (acquired at the species flowering period) are to be used. High accuracies achieved for VHR data indicate the possible application of described methodology for monitoring invasions and their long-term dynamics elsewhere, making management measures comparable precise, fast and efficient.

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

“…The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B7, 2016XXIII ISPRS Congress, 12-19 July 2016 spectral resolution imagery such as historical panchromatic photography (see Laliberte et al, 2004, or Müllerová et al, 2013, less spectrally distinct species, and species with typical distinct shape pattern (Figure 8). Figure 8.…”

“…Multispectral optical data have been successfully used to study invasive plant species (for review see Huang and Asner, 2009), however, mostly for shrubs and trees (but see e.g. Müllerová et al, 2013;Jones et al, 2011). Detection of herb species is only possible if the data provide enough spectral and/or spatial details, the species are distinct from their neighborhood, form dense and uniform stands, and/or are large enough to be detected (Maheu-Giroux and de Blois, 2004;Müllerová et al, 2005).…”

Mapping Wild Leek through the Forest Canopy Using a UAV

“…Multispectral and hyperspectral data has also proven to be effective in classifying invasive species using objectbased classification methods [31][32][33]. An important disadvantage of using such data is the typically high cost associated with obtaining or producing the data.…”

“…In addition to providing a high spatial resolution, being light-weight, and easy to use, UAVs can avoid cloud contamination that often obscures features of interest in satellite imagery. Frequent and user-controlled revisit times offered by UAV-based systems are therefore important advantages that allow the capture of short phenological events for which cloud contamination is problematic [31,34].…”

Unmanned Aerial Vehicles for Alien Plant Species Detection and Monitoring

“…Due to these difficulties, hyperspectral data are often used to compensate for low differentiation of some invasive species in visible spectrum (for reviews see Huang and Asner, 2009;He et al, 2011). New possibilities of automated or semi-automated classification of invasive species arose with the development of the object-based image analysis (OBIA; Jones et al, 2011;Müllerová et al, 2013). In OBIA, the image is segmented into groups of contiguous pixels (image objects), in which features based on spectral variables, shape, texture, size, thematic data, and spatial relationship (contiguity) are assigned to each object (Blaschke et al, 2008).…”