Land cover change detection on the agriculture land

The main purpose of thesis is creation and evaluation of models for change detection of arable land to grassland by Hybrid-based Change Detection method, which combined approaches based on the Vegetation Indices, Image Differencing and Principal Component Analysis. Six locations with different seasonal configuration of images with high resolution and one locality covered by image with very high resolution were used. The areas were spread across the foothill areas of the Czech Republic. The selection of predictors and the most suitable model was supported by statistical calculation. Application selected models were carried out using a multi-temporal object classification and their accuracy were verified using reference data. The benefit of this thesis is finding generally applicable model useful to investigate the land cover change and evaluation of the potentially most appropriate seasonal configuration of images. Valuable is also methodology in this thesis which focus on selection of predictors and calculation the order of the most appropriate models, which is unique in the available literature. The thesis provides useful findings fitting to insufficiently explored issue of Change Detection arable land to grassland. Powered by TCPDF (www.tcpdf.org