Abstract

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Paper
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