Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/70212
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Type: Journal article
Title: Use of airborne remote sensing to detect riverside Brassica rapa to aid in risk assessment of transgenic crops
Author: Elliott, L.
Mason, D.
Allainguillaume, J.
Wilkinson, M.
Citation: Journal of Applied Remote Sensing, 2009; 3(1):033562-1-033562-15
Publisher: SPIE - International Society for Optical Engineering
Issue Date: 2009
ISSN: 1931-3195
1931-3195
Statement of
Responsibility: 
Luisa M. Elliott, David C. Mason, Joel Allainguillaume and Mike J. Wilkinson
Abstract: High resolution descriptions of plant distribution have utility for many ecological applications but are especially useful for predictive modeling of gene flow from transgenic crops. Difficulty lies in the extrapolation errors that occur when limited ground survey data are scaled up to the landscape or national level. This problem is epitomized by the wide confidence limits generated in a previous attempt to describe the national abundance of riverside Brassica rapa (a wild relative of cultivated rapeseed) across the United Kingdom. Here, we assess the value of airborne remote sensing to locate B. rapa over large areas and so reduce the need for extrapolation. We describe results from flights over the river Nene in England acquired using Airborne Thematic Mapper (ATM) and Compact Airborne Spectrographic Imager (CASI) imagery, together with ground truth data. It proved possible to detect 97% of flowering B. rapa on the basis of spectral profiles. This included all stands of plants that occupied >2m square (>5 plants), which were detected using single-pixel classification. It also included very small populations (<5 flowering plants, 1-2m square) that generated mixed pixels, which were detected using spectral unmixing. The high detection accuracy for flowering B. rapa was coupled with a rather large false positive rate (43%). The latter could be reduced by using the image detections to target fieldwork to confirm species identity, or by acquiring additional remote sensing data such as laser altimetry or multitemporal imagery. © 2009 Society of Photo-Optical Instrumentation Engineers.
Keywords: agriculture
classification
ecology
sub-pixel.
Rights: © 2009 Society of Photo-Optical Instrumentation Engineers
DOI: 10.1117/1.3269615
Appears in Collections:Agriculture, Food and Wine publications
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