Wetlands are the most important and valuable ecosystems on Earth. They are called “kidneys of the Earth”. Vegetation change detection is necessary to understand the condition of a wetland and to support ecosystem sustainable management and utilization. It has been a great challenge to estimate vegetation (including bald cypress trees) coverage of the wetland because it is difficult to access directly. Satellite remote sensing technology can be one important feasible method to map and monitor changes of wetland forest vegetation and land cover over large areas. Remote sensing mapping techniques have been applied to detect and map vegetation changes in wetlands. To address spectral mixture issues associated with moderate resolution remote sensing images, many spectral mixture methods have been developed and applied to unmix the mixed pixels in order to accurately map endmembers (e.g., different land cover types and different materials within pixels) fractions or abundance. Of them, Mixture Tuned Matched Filtering (MTMF) is an advanced spectral unmixing method that has attracted many researchers to test it for mapping land cover types including mapping tree species with medium or coarse remote sensing image data. MTMF is a partial unmixing method that suppresses background noise and estimates the subpixel abundance of a single target material. In this study, to understand impacts of anthropogenic (e.g., urbanization) and natural forces/climate change on the bald cypress tree dynamic change, the bald cypress trees cover change in University of South Florida Forest Preserve Area was mapped and analysed by using MTMF tool and multitemporal Landsat imagery over 30 years from 1984 to 2015. To evaluate the MTMF’s performance, a tradition spectral unmixing method, Linear Spectral Unmixing (LSU), was also tested. The experimental results indicate that (1) the bald cypress tree cover percentage in the study area has generally increased during the 30 years from 1984 to 2015, but over the time period from 1994 to 2005, the bald cypress tree cover percentage reduced; (2) MTMF tool outperformed the LSU method in mapping the change of the bald cypress trees over the 30 years to demonstrate its powerful capability; and (3) there potentially exists an impact of human activities on the change of the bald cypress trees although a further quantitative analysis is needed in the future research.

Remote sensing has the potential to monitor spatial variation in water quality over large areas. While ocean colour work has developed analytical bio-optical water quality retrieval algorithms for medium spatial resolution platforms, remote sensing of lake water is often limited to high spatial resolution satellites such as Landsat, which have limited spectral resolution. This thesis presents the results of an investigation into satellite monitoring of lake water quality. The aim of this investigation was to ascertain the feasibility of estimating water quality and its spatial distribution using Landsat 7 ETM+ imagery combined with in situ data from Rotorua and Waikato lakes. For the comparatively deep Rotorua lakes, r² values of 0.91 (January 2002) and 0.83 (March 2002) were found between in situ chlorophyll (chl) a and the Band1/Band3 ratio. This technique proved useful for analysing the spatial distribution of phytoplankton, especially in lakes Rotoiti and Rotoehu. For the more bio-optically complex shallow lakes of the Waikato, a linear spectral unmixing (LSU) approach was investigated where the water surface reflectance spectrum is defined by the contribution from pure pixels or endmembers. The model estimates the percentage of the endmember within the pixel, which is then used in a final regression with in situ data to map water quality in all pixels. This approach was used to estimate the concentration of chl a (r² = 0.84). Total suspended solid (TSS) concentration was mapped using the traditional Band 3 regression with in situ data, which combined atmospherically corrected reflectance for both images into a single relationship (r² = 0.98). The time difference between in situ data collection and satellite data capture is a potential source of error. Other potential sources of error include sample location accuracy, the influence of dissolved organic matter, and masking of chl a signatures by high concentrations of TSS. The results from this investigation suggest that remote sensing of water quality provides meaningful and useful information with a range of applications and could provide information on temporal spatial variability in water quality.

The master thesis deals with subpixel classification of hyperspectral data from senzor APEX. In the first part there is research from the literature describing algorithms of the subpixel classifications and spectral characteristics of the vegetation. In the practical part there is a work focusing on the classification of the areas with the cover of Norway Spruce trees at eight areas in the Krkonoše national park. Three methods of supervised classification were used - Linear Spectral Unmixing, Support Vector Machine and Spectral Angle Mapper. Field data, spectral curves for exact trees from the eight areas obtained by the contact probe ASD FieldSpec 4 Wide-Res, were used for the extraction of endmembers of the spruces. For each research area maps of land cover were produced by means of the classification methods described above and the accuracies of the classifications were evaluated. Powered by TCPDF (www.tcpdf.org)

MENVSC (Geography) / Department of Geography and Geo - Information Sciences / Detailed geomorphological mapping is important for monitoring environmental phenomena, it is therefore crucial that the methods employed for mapping are accurate. The basis of remote sensing for geomorphological work is moving from the consideration of whether satellite data are accurate for landform mapping to how surfaces of interest can be defined from remote sensing data, since earlier approaches of mapping are deemed costly and tedious. The aim of this study is to assess the suitability of ASTER and SRTM DEMs, and satellite imagery in detailed geomorphological mapping. Field survey and aerial photo interpretation were used to prepare a reference geomorphological map for comparisons. A similar approach of demarcating landform boundaries from aerial photographs was implemented to segment the DEMs into landform classes. The software packages that were used for processing the satellite data to create detailed geomorphological maps are QGIS with GRASS and SAGA plugins, and ENVI. The resultant geomorphological units’ maps from the DEMs when compared with the reference geomorphological map, show that the automated classification technique has advantages in terms of its efficiency and reproducibility. Nevertheless, distinct limitations of the technique are apparent and the technique is not suitable for detailed geomorphological mapping in the proposed study area. / NRF