Student Number : 0310612G -
MSc research report -
School of Geography, Archaeology and Environmental Studies -
Faculty of Science / Wildfire prediction and management is an issue of safety and security for many rural
communities in South Africa. Wildfire prediction and early warning systems can
assist in saving lives, infrastructure and valuable resources in these communities.
Timely and accurate data are required for accurate wildfire prediction on both weather
conditions and the availability of fuels (vegetation) for wildfires. Wildfires take place
in large remote areas in which land use practices and alterations to land cover cannot
easily be modelled. Remote sensing offers the opportunity to monitor the extent and
changes of land use practices and land cover in these areas.
In order for effective fire prediction and management, data on the quantity and state of
fuels is required. Traditional methods for detecting vegetation rely on the chlorophyll
content and moisture of vegetation for vegetation mapping techniques. Fuels that burn
in wildfires are however predominantly dry, and by implication are low in chlorophyll
and moisture contents. As a result, these fuels cannot be detected using traditional
indices. Other model based methods for determining above ground vegetation
biomass using satellite data have been devised. These however require ancillary data,
which are unavailable in many rural areas in South Africa. A method is therefore
required for the detection and quantification of dry fuels that pose a fire risk.
ASTER and MAS (MODIS Airborne Simulator) imagery were obtained for a study
area within the Lowveld region of the Limpopo Province, South Africa. Two of the
ASTER and two of the MAS images were dated towards the end of the dry season
(winter) when the quantity of fuel (dry vegetation) is at its highest. The remaining
ASTER image was obtained during the middle of the wet season (summer), against
which the results could be tested. In situ measurements of above ground biomass were
obtained from a large number of collection points within the image footprints.
Normalised Difference Vegetation Index and Transformed Vegetation Index
vegetation indices were calculated and tested against the above ground biomass for
the dry and wet season images. Spectral response signatures of dry vegetation were
evaluated to select wavelengths, which may be effective at detecting dry vegetation as
opposed to green vegetation. Ratios were calculated using the respective bandwidths
of the ASTER and MAS sensors and tested against above ground biomass to detect
dry vegetation.
The findings of this study are that it is not feasible, using ASTER and MAS remote
sensing data, to estimate brown and green vegetation biomass for wildfire prediction
purposes using the datasets and research methodology applied in this study.
Correlations between traditional vegetation indices and above ground biomass were
weak. Visual trends were noted, however no conclusive evidence could be established
from this relationship. The dry vegetation ratios indicated a weak correlation between
the values. The removal of background noise, in particular soil reflectance, may result
in more effective detection of dry vegetation.
Time series analysis of the green vegetation indices might prove a more effective
predictor of biomass fuel loads. The issues preventing the frequent and quick
transmission of the large data sets required are being solved with the improvements in
internet connectivity to many remote areas and will probably be a more viable path to
solving this problem in the near future.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:wits/oai:wiredspace.wits.ac.za:10539/2139 |
| Date | 26 February 2007 |
| Creators | Goslar, Anthony |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 3656102 bytes, application/pdf, application/pdf |
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