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Assessing fragmentation and disturbance of west Kenyan rainforests by means of remotely sensed time series data and landscape metrics
Author(s) -
Lung Tobias,
Schaab Gertrud
Publication year - 2006
Publication title -
african journal of ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.499
H-Index - 54
eISSN - 1365-2028
pISSN - 0141-6707
DOI - 10.1111/j.1365-2028.2006.00663.x
Subject(s) - geography , land cover , biodiversity , rainforest , fragmentation (computing) , forest fragmentation , forestry , land use , remote sensing , disturbance (geology) , physical geography , ecology , environmental science , cartography , geology , biology , paleontology
Biodiversity in tropical rainforests is heavily influenced by land use/cover change (LUCC), but so far there have been few LUCC studies conducted in Africa. We present several methods that make use of remotely sensed data and landscape metrics and allow for assessment of the development of land cover and thus forest fragmentation and disturbance over a substantial period of time. The study covers Kakamega Forest and its associated forest areas in western Kenya, over the last 30 years. The accuracy of a supervised multispectral classification of Landsat time series data encompassing seven time steps between 1972 and 2001 is numerically assessed using ground truth reference data considering the 2001 time step. Here, buffering the forest areas by 1 km, highest user's accuracies for the forest classes ‘near natural + old secondary forest’ (87.50%), ‘secondary forest’ (80.00%) and ‘bushland/shrubs’ (81.08%) are revealed. Images of a spatially distributed fragmentation index derived from the land cover time series by applying a three by 3 pixel‐sized moving window to determine forest pixels’ adjacency, highlight trends in forest fragmentation, e.g. the splitting into two separate forests along the Yala/Ikuywa corridor. Calculations of mean fragmentation indices for the Biodiversity Monitoring Transect Analysis in Eastern Africa (BIOTA‐East Africa) focus research areas are used to evaluate the fragmentation index and to demonstrate its potential to extrapolate (e.g. biological) field findings in space and time. Here we argue for a correlation of the fragmentation indices results not only with forest management regimes, but with population distribution and accessibility (e.g. by roads). A cluster analysis applying the isodata‐algorithm on the classification results of all seven times steps allows for a rapid visual assessment of the distinct pattern of typical land cover development trends since 1972. This reveals that parts of Kakamega Forest have experienced severe forest loss while others, especially in the north‐east, show signs of succession.