Abstract
 Monitoring Weed Growth in Forest Stands Using QuickBird Imagery Track: Remote Sensing Imagery Author(s): Mark Norris-Rogers, Fethi Ahmed, Pol Coppin, Jan van Aardt High-resolution remotely sensed imagery offers the opportunity to quantitatively monitor plantation forestry operations. Using a series of QuickBird images of a plantation forestry site in KwaZulu-Natal, South Africa, a combination of textural analysis and change detection techniques was tested to quantify weed development in replanted forest stands less than 24 months old. While the multispectral bands could identify areas of strong vegetation, crop rows were identifiable on the panchromatic band. By combining these two attributes, areas of high weed growth could be identified. This was achieved by doing an unsupervised classification on the multispectral bands and an edge enhancement on the panchromatic band. Both the resultant datasets were then vectorized, unioned and a matrix derived to determine areas of high weed. By applying a postclassification change detection technique on the high weed growth classes, it was possible to identify and quantify areas of weed increase or decrease between consecutive images. Mark Norris-Rogers Mondi Business Paper SA Logistics & Procurement Box 39 Pietermaritzburg , null 3200 ZA Phone: +27 33897 4029 Fax: +27 33 345 6555 E-mail: mark.norris-rogers@mondibp.com Fethi Ahmed University of KwaZulu-Natal Environmental Science P.O.Box 17001 Congella DURBAN , KZN 4013 ZA Phone: +27726216476 Fax: +27312611216 E-mail: ahmed@ukzn.ac.za Pol Coppin Catholic University Leuven Faculty of Applied Bioscience Engineering Kasteelpark Arenberg 20 Heverlee Leuven , Flanders B-3001 BE Phone: +3216321619 Fax: +3216321999 E-mail: pol.coppin@biw.kuleuven.be Jan van Aardt Catholic University Leuven Land Management Vital Decosterstr 102 Leuven Leuven , Flanders B-3000 BE Phone: +3216329771 E-mail: jan.vanaardt@agr.kuleuven.ac.be |
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