Object-Based Image Analysis for Detection of Japanese Knotweed s.l. taxa (Polygonaceae) in Wales (UK)

Jones, Daniel; Pike, Stephen; Thomas, Moyer D.; Murphy, Denis J.

doi:10.3390/rs3020319

Cited by 45 publications

(29 citation statements)

References 28 publications

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“…Weed infestation has been recognized as a primary reason for loss in global biodiversity and even species extinction [8][9][10][11][12]. Therefore, non-native invasive plant species have long been of interest to natural resource managers, ecologists and biological conservationists [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Remote Distinction of A Noxious Weed (Musk Thistle: CarduusNutans) Using Airborne Hyperspectral Imagery and the Support Vector Machine Classifier

Mırık¹,

Ansley²,

Steddom³

et al. 2013

Remote Sensing

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Remote detection of non-native invasive plant species using geospatial imagery may significantly improve monitoring, planning and management practices by eliminating shortfalls, such as observer bias and accessibility involved in ground-based surveys. The use of remote sensing for accurate mapping invasion extent and pattern offers several advantages, including repeatability, large area coverage, complete instead of sub-sampled assessments and greater cost-effectiveness over ground-based methods. It is critical for locating, early mapping and controlling small infestations before they reach economically prohibitive or ecologically significant levels over larger land areas. This study was designed to explore the ability of hyperspectral imagery for mapping infestation of musk thistle (Carduus nutans) on a native grassland during the preflowering stage in mid-April and during the peak flowering stage in mid-June using the support vector machine classifier and to assess and compare the resulting mapping accuracy for these two OPEN ACCESSRemote Sens. 2013, 5 613 distinctive phenological stages. Accuracy assessment revealed that the overall accuracies were 79% and 91% for the classified images at preflowering and peak flowering stages, respectively. These results indicate that repeated detection of the infestation extent, as well as infestation severity or intensity, of this noxious weed in a spatial and temporal context is possible using hyperspectral remote sensing imagery.

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Section: Introductionmentioning

confidence: 99%

Remote Distinction of A Noxious Weed (Musk Thistle: CarduusNutans) Using Airborne Hyperspectral Imagery and the Support Vector Machine Classifier

Mırık¹,

Ansley²,

Steddom³

et al. 2013

Remote Sensing

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“…On such assumptions, OBIA is able to distinguish transformations from natural to rural land use on the basis of differences in spatial distribution and pattern of land cover forms [23]. Numerous empirical studies in many application fields [24][25][26] have provided sufficient evidence on the advantages of OBIA over PBC [27] but no significant research has been performed to evaluate the use of OBIA in mapping plasticulture.…”

Section: Plasticulture Extraction From Remote Imagerymentioning

confidence: 99%

Mapping Rural Areas with Widespread Plastic Covered Vineyards Using True Color Aerial Data

Tarantino

Figorito

2012

Remote Sensing

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Plastic covering is used worldwide to protect crops against damaging growing conditions. This agricultural practice raises some controversial issues. While it significantly impacts on local economic vitality, plasticulture also shows several environmental affects. In the Apulia Region (Italy) the wide-spreading of artificial plastic coverings for vineyard protection has showed negative consequences on the hydrogeological balance of soils as well as on the visual quality of rural landscape. In order to monitor and manage this phenomenon, a detailed site mapping has become essential. In this study an efficient object-based classification procedure from Very High Spatial Resolution (VHSR) true color aerial data was developed on eight test areas located in the Ionian area of the Apulia Region in order to support the updating of the existing land use database aimed at plastic covered vineyard monitoring.

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“…Whereas the RS detection of the tree of heaven was not previously investigated, for other three species the timing of the data acquisition showed to be important (exotic knotweeds: Dorigo et al, 2012;Jones et al, 2011;g. hogweed: Müllerová et al, 2005g.…”

Section: Study Speciesmentioning

confidence: 99%

“…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).…”

Section: Introductionmentioning

confidence: 99%

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

Bruna¹,

Dvořák²,

Bartaloš³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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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.

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Object-Based Image Analysis for Detection of Japanese Knotweed s.l. taxa (Polygonaceae) in Wales (UK)

Cited by 45 publications

References 28 publications

Remote Distinction of A Noxious Weed (Musk Thistle: CarduusNutans) Using Airborne Hyperspectral Imagery and the Support Vector Machine Classifier

Remote Distinction of A Noxious Weed (Musk Thistle: CarduusNutans) Using Airborne Hyperspectral Imagery and the Support Vector Machine Classifier

Mapping Rural Areas with Widespread Plastic Covered Vineyards Using True Color Aerial Data

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

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