Water hyacinth (Eicchornia crassipes (Mart.) Solms) is an invasive aquatic macrophyte that has infested the lake Victoria, East Africa, since the late 1980s. It has been associated with major negative economic and ecological impact of this important water resource in East Africa. Remote sensing technology has significant potential in mapping this fast growing floating weed, in a mostly inaccessible area for field measurements.
Our study site is the Winam Gulf, on the Kenyan part of the Lake, which has had the highest reported infestation in recent years. The paper describes a study to evaluate the ability of ETM+ multispectral imagery in mapping water hyacinth and associated macrophytes in the hyacinth infested Winam Gulf. By applying hyperspectral techniques on multispectral data, a spectral mixture analysis was undertaken using image-derived endmembers. The study was also an evaluation of an alternative way of acquiring emergent macrophytic endmembers in cases where limitations like lack of hyperspectral data, spectrometric measurements and spectral libraries exist.
The results demonstrate that whereas it is possible to discriminate and map the different spectral constituents, a spectral library of the endmembers under investigation would be required for positive identification, especially for macrophytes that are closely related spectrally, fast growing, have varying concentrations (density) spatially, and are non-static in nature.
Understanding the dynamics of land cover change has increasingly been recognized as one of the key research imperatives in global environmental change research. Scientists have developed and applied various methods in order to find and propose solutions for many environmental world problems. From 1986-1995 changes in Kenya coastal zone landcover, derived from the post-classification TM images, were significant with arid areas growing from 3% to 10%, woody areas decreased from 4% to 2%, herbaceous areas decreased from 25% to 20%, developed land increased from 2% to 3%. In order to generate the change probability map as a continuous surface using geostatistical method-ArcGIS, we used as an input the Generalized Linear Model (GLM) probability result. The results reveal the efficiency of the Probability-of-Change map (POC), especially if reference data are lacking, in indicating the possibility of having a change and its type in a determined area, taking advantage of the layer transparency of the GIS systems. Thus, the derived information supplies a good tool for the interpretation of the magnitude of the land cover changes and guides the final user directly to the areas of changes to understand and derive the possible interactions of human or natural processes.
Desertification is reported to be intensifying and spreading in Kenya dry lands, threatening millions of inhabitants and severely reducing productivity of the land. Concern over desertification acceleration status in the country has been raised and measures to address the problem called upon. Among these measures is assessment of desertification using available data and technological tools. Vegetation cover was used as a land degradation indicator to determine land degradation and rate of change using spectral change detection technique based on pixel-wise operation. In combination with ancillary data, vegetation degradation occurrence and areas at risk of desertification were assessed. The study area is located in Northwestern Kenya, one of the dry land areas. Multi-spectral and multi-temporal analysis was applied to NOAA/AVHRR 1km and Landsat TM/ETM 30 meter resolution for periods covering wet and dry season of 1986 to 2001. Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) were used to detect change. The results show desertification is apparent and increased vegetation degradation. Arid areas were found to be increasingly degraded and at high risk of further degradation at a rate of 1.8% per year.
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