Density-based Cluster Analysis Of Fire Hot Spots In Kenya's Wildlife Protected Areas
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Date
2016-04Author
Karanja, Stephen K
Type
ThesisLanguage
enMetadata
Show full item recordAbstract
Wild res occurring in Kenya's wildlife protected areas pose a signi cant risk to wildlife
conservation since they cause biodiversity loss and habitat degradation. There is a need
for the Kenya Wildlife Service (KWS) to identify the regions in the protected areas that
are prone to recurring wild re outbreaks during the re season.
This study identi ed regions that are re hot spots in Kenya's protected areas by
performing a density-based cluster analysis on the Moderate Resolution Imaging Spectroradiometer
(MODIS) MCD14ML active re data set for a 12 year period between 2003
and 2014. Feature subset selection was done using an AWK script written to extract the
latitude and longitude elds from the data set. QGIS was used to lter re points falling
outside protected area boundaries. The Environment for Developing Knowledge Discovery
in Databases Applications Supported by Index Structures (ELKI) implementation of
the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm
was used for the clustering. A sorted k-dist graph estimated the initial DBSCAN parameters.
25 trial runs of DBSCAN with di erent parameters were used to select the nal
values: MinPts = 7 re points; Eps = 700 meters. A web application with a Google
Maps interface was developed to provide an interactive visualization of the re hot spots.
4,968 re incidents were observed in 73% of the protected areas. The initial DBSCAN
parameters yielded 29 insigni cant re hot spot clusters from these incidents, while the
nal parameters yielded 43 signi cant clusters. The 43 clusters were identi ed in 31%
of the protected areas that recorded re activity. 60% of these clusters occurred in four
protected areas.
The ndings of this study indicate that density-based cluster analysis is a suitable
clustering method for identifying hot spots in geospatial data sets. For DBSCAN, the
performance of the sorted k-dist graph heuristic is in
uenced by the characteristics of a
data set. The results also indicate that Chyulu Hills, Dodori, Boni, and Ruma are the
protected areas most vulnerable to wild res in Kenya.
This study recommends the use of density-based cluster analysis for identifying hot
spots in geospatial data sets. Experimentation with a wide range of DBSCAN parameters
values is advisable. KWS should focus re management e orts on the identi ed re hot
spot regions. In addition, it should investigate the impact of wild re damage in the
ecological zones surrounding the hot spots.
Publisher
University of Nairobi
Rights
Attribution-NonCommercial-NoDerivs 3.0 United StatesUsage Rights
http://creativecommons.org/licenses/by-nc-nd/3.0/us/Collections
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