Plantation forestry involves the management of man-made industrial forests for the
purpose of producing raw materials for the pulp and paper, saw milling and other
related wood products industries. Management of these forests is based on the cycle
of planting, tending and felling of forest stands such that a sustainable operation is
maintained. The monitoring and reporting of these forestry operations is critical to
the successful management of the forestry industry. The aim of this study was to test
whether the forestry operations of clear-felling, re-establishment and weed control
could be qualitatively and quantitatively monitored through the application of
classification and change detection techniques to multi-temporal medium (15-30 m)
and a combination of textural analysis and change detection techniques on high
resolution (0.6-2.4 m) satellite imagery.
For the medium resolution imagery, four Landsat 7 multi-spectral images covering
the period from March 2002 to April 2003 were obtained over the midlands of
KwaZulu-Natal, South Africa, and a supervised classification, based on the
Maximum Likelihood classifier, as well as two unsupervised classification routines
were applied to each of these images. The supervised classification routine used 12
classes identified from ground-truthing data, while the unsupervised classification
was done using 10 and 4 classes. NDVI was also calculated and used to estimate
vegetation status. Three change detection techniques were applied to the
unsupervised classification images, in order to determine where clear-felling,
planting and weed control operations had occurred. An Assisted "Classified" Image
change detection technique was applied to the Ten-Class Unsupervised
Classification images, while an Assisted "Quantified Classified" change detection
technique was applied to the Four-Class Unsupervised Classification images. An
Image differencing technique was applied to the NDVI images. For the high
resolution imagery, a series of QuickBird images of a plantation forestry site were
used and a combination of textural analysis and change detection techniques was
tested to quantify weed development in replanted forest stands less than 24 months
old. This was achieved by doing an unsupervised classification on the multi-spectral
bands, and an edge-enhancement on the panchromatic band. Both the resultant
datasets were then vectorised, unioned and a matrix derived to determine areas of
high weed.
It was found that clear-felling operations could be identified with accuracy in excess
of 95%. However, using medium resolution imagery, newly planted areas and the
weed status of forest stands were not definitively identified as the spatial resolution
was too coarse to separate weed growth from tree stands. Planted stands younger
than one year tended to be classified in the same class as bare ground or ground
covered with dead branches and leaves, even if weeds were present. Stands older
than one year tended to be classified together in the same class as weedy stands,
even where weeds were not present. The NDVI results indicated that further
research into this aspect could provide more useful information regarding the
identification of weed status in forest stands. Using the multi-spectral bands of the
high resolution imagery it was possible to identify areas of strong vegetation, while
crop rows were identifiable on the panchromatic band. By combining these two
attributes, areas of high weed growth could be identified. By applying a post-classification
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. A theoretical canopy model was also derived to test whether it
could identify thresholds from which weed infestations could be determined.
The conclusions of this study indicated that medium resolution imagery was
successful in accurately identifying clear-felled stands, but the high resolution
imagery was required to identify replanted stands, and the weed status of those
stands. However, in addition to identifying the status of these stands, it was also
possible to quantify the level of weed infestation. Only wattle (Acacia mearnsii)
stands were tested in this manner but it was recommended that in addition to
applying these procedures to wattle stands, they also are tested in Eucalyptus and
Pinus stands. The combination of textural analysis on the panchromatic band and
classification of multi-spectral bands was found to be a suitable process to achieve
the aims of this study, and as such were recommended as standard procedures that
could be applied in an operational plantation forest monitoring environment. / Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2006.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:ukzn/oai:http://researchspace.ukzn.ac.za:10413/5517 |
| Date | January 2006 |
| Creators | Norris-Rogers, Mark. |
| Contributors | Van Aardt, J., Ahmed, Fethi B., Coppin, P. R. |
| Source Sets | South African National ETD Portal |
| Language | en_ZA |
| Detected Language | English |
| Type | Thesis |
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