IntroductionEarly detection and reporting of plant Invasive Alien Spe-cies (IAS) is essential for their management: it is recog-nised that remote sensing (RS) and geographical infor-mation systems (GIS) can contribute to this.

RS techniques offer recognised advantages including: 1. a synoptic view; 2. multispectral (MS) data; 3. multi-temporal coverage and 4. clear benefits in terms of cost-effectiveness.

Aims1. Utilise Definiens Developer software, aerial photo-graph data, Phase 1 habitat survey and ground-valida-tion (field survey) data to develop an automated detec-tion algorithm for Japanese Knotweed s.l. taxa.2. Utilise the automated detection algorithm in order to examine the extent of Japanese Knotweed s.l. taxa in-vasion within Neath-Port Talbot CBC (South Wales, UK) (Figure 1).

Figure 1. Study site locations: Neath-Port Talbot CBC.

Figure 2. Pixel-based classification of Psidium guava on Isabel-la Island (Galápagos Islands) (Courtesy of Walsh 2007).

Typical ‘Salt and Pepper’ appearance of pixel-based spectral classification of VHSR imagery, obscuring the more defined pattern of land use/land cover evident in the QuickBird imagery.

Finding Fallopia: detection of Japanese Knotweed s.l. taxa in the

UK using remote sensing technologiesDaniel Jones1, Steve Pike2, Malcolm Thomas3 & Denis Murphy3

1) Department of Biosciences, Swansea University, Singleton Park, Swansea SA2 8PP2) Maplecroft, The Towers, St Stephen’s Road, Bath, BA1 5JZ3) Faculty of Health, Sport & Science (HESAS), University of Glamorgan, Pontypridd CF37 1DL

MethodsDefiniens Developer software, in combination with very high spatial resolution (VHSR) colour infra-red (CIR) and visible-band (RGB) aerial photography and ground-vali-dation data were used to detect knotweed. The Object-Based Image Analysis (OBIA) approach of Definiens was used in order to circumvent draw-backs associated with conventional pixel-based approaches (Figure 2).

Knotweed extraction OBIA extraction of Japanese Knotweed s.l. taxa from VHSR images included several steps, including: 1. data pre-processing; 2. multiresolution image segmentation; 3. definition of the characteristics used to delineate objects; 4. object extraction; 5. post-editing and 6. accuracy evaluation.

Following accurate classification of Japanese Knotweed s.l. taxa, a region merging process was applied to detected knotweed and a “best practice” 7 m buffer zone created around the detected knotweed. This buffer enables accurate estimation of volume of landfill material for on/off-site disposal and the likely ar-eal extent of knotweed where it is, for example, detected within woodland but is not canopy dominant.

Figure 3. Giants Grave initial study site, Briton Ferry (South Wales, UK) (51°64′43.0″N, 03°8′24.85″W).

ResultsThe results of the detec-tion algorithm were accu-rate, being confirmed at the initial study site (Gi-ant’s Grave) by purpose-ful field validation (with photographic image cap-ture) and desk-based studies (Figures 3—6). At subsequent sites, de-tection accuracy was as-sessed by desk-based studies alone.

At the Giant’s Grave study site, of the 94 field validation locations vis-ited, 4 were false posi-tives (Figure 3, points 91—94).

Further algorithm tri-als were conducted in a range of other locations in Neath-Port Talbot CBC. Please ask for more in-formation if required.

DiscussionAlgorithm classification results proved: (1) to be more accurate and of greater spatial ex-tent than specialist field survey (see Results); (2) able to detect Japanese Knotweed s.l. taxa in semi-natural habitats and urban environments and (3) effective in detection of knotweed stands, rang-ing from several (>0.8 m) to several hundreds of metres in extent.

The successful detection results developed within the Definiens software should enable greater man-agement and control efficacy; particularly given that they may be incorporated into GIS research as they are readily transferable as vector polygons (shape-files). Further to this, the basic principles of the de-tection process could enable detection of these taxa worldwide, given the (relatively) limited technical re-quirements necessary to conduct further analyses.

Further research (1) Incorporation of further ‘dynamic’ variables into the algorithm, that are less susceptible to differences in relative scene values;(2) Incorporation of modeling approaches with the enhanced automated detection algorithm, in order to develop a predictive tool that will be of utility to land managers and commercial inter-ests and(3) Using historic datasets, it may be possible to map the historic spread of these taxa though Wales and possibly, further afield.

ReferencesEA (2008). The Knotweed Code of Practice. (UK) Environment Agency (EA), pp. 10 - 14.

Joshi, C., de Leeuw, J. & van Duren, I. C. (2004). Remote Sensing and GIS Applications for Mapping and Spatial Modelling of Invasive Species. Pro-ceedings XX ISPRS Congress; Geo-imagery Bridging Contents.

Jiang, N., Zhang, J. X., Li, H. T. & Lin, X. G. (2008). Object-oriented building extraction by DSM and very high-resolution orthoimages. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII, Part B3b, Beijing 2008.

Kabat, T. (2008). Are Japanese knotweed (Fallopia japonica) control and eradication interventions effective? Centre for Evidence-Based Conservation (CEBC) Systematic Review No. 21 pp. 1 - 98.

Walsh, S. J. (2007). Remote Sensing of Invasive Plants in the Galapagos National Park and Archipelago, Ecuador: Merging Hyper-spatial and Hyper-spectral Data for Enhanced Mapping - Articles’, Directions Magazine 08, 9 - 13.

Wardlow, B. D., Egbert, S. L. & Kastens, J. H. (2007). Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Cen-tral Great Plains. Remote Sensing of Environment (2007) 108, 290 - 310.

FundingPart-funded by the European Social Fund (ESF) through the Welsh Government with Swansea University and P&T Ltd.

Figure 4. Points 1 and 2 - F. japonica var. japonica stand(s) detect-ed by algorithm, but not by specialist field survey.

Figure 5. Point 18 - F. japonica var. japonica stand(s) detected by algorithm, but not by specialist field survey.

Figure 6. Point 43 - F. japonica var. japonica stand(s) detected by algorithm, but not by specialist field survey.