The Use Of Multitemporal Thematic Mapper Data And Principal Component Analysis For Landcover Classifications Over A Portion Of Fulton County Kentucky PDF Download

Land cover is described in other studies as the (bio)physical cover of the Earth’s surface and includes vegetated areas, artificial areas, bare areas and water bodies. Land cover is prone to changes due to anthropogenic activities and natural processes. These changes influence climate, e.g. by their effect on emissions of CO2 and other greenhouse gases and changes in carbon storage capacity. Therefore, accurate and continuous information on land cover is needed on a global scale. User requirements analysis conducted by the Climate Change Initiative Land Cover consortium (CCI-LC) proved that current land cover products derived from remotely sensed data are lacking accuracy and consistency. These issues often arise due to the inability of the input data to capture temporal dynamics by using a limited time span. Furthermore, land cover changes are often not taken into account in current classification approaches. This research aims to improve current classification approaches by investigating 1) how time series parameters, e.g. phenological metrics, can be extracted from multi-temporal MERIS data and 2) how these can be utilized for classification purposes. Furthermore, a comparison was made between classification results with and without these parameters in order 3) to determine to what extent these influence the classification result. In addition, given the fact that vegetation is highly dynamic, another goal of this study was to investigate 4) how temporally stable locations can be separated from unstable areas in order to ultimately limit classification to the stable period within a time series. The use of phenological metrics was emphasized during this study in order to include vegetation dynamics in the classification approach. During this study an operational method was developed to extract phenological metrics from MTCI and NDVI time series which were successfully used for land cover classification. The use of this method seems to increase the overall accuracy of the classification results and has the potential to be used on a large scale. In addition, an explorative study was conducted on the separation of temporary land cover change from permanent land cover change. This resulted in a fast method that may be effectively added to the classification process and applied on a larger scale.