The Department of Arequipa, in Peru, is a region with limited water resources making freshwater management critical and requiring the development of water-demand models, which can be valuable tools for policymakers. This study developed a monthly agricultural water-demand mapping algorithm for the agricultural districts surrounding the city of Arequipa. It was accomplished by:(1) developing a ground-reference data collection method;(2) creating a crop mapping algorithm, which incorporates supervised classification methods, as well as spatial-and temporal-consistency correction methods to create crop maps out of high resolution (~3 m) PlanetScope satellite images; (3) integrating a crop growth-stage prediction algorithm for the crop maps and; (4) applying an algorithm for the estimation of the agricultural-water-demand maps using the results of steps 2 and 3, local climate data, and an irrigation demand estimation tool. The crop mapping algorithm was shown to create maps with acceptable accuracy, with 5 out of 6 months with available data having mean monthly classification accuracies of 69% to 77%for those classes which had available data. Growth stage predictions had mean absolute prediction errors of 0.55 to 0.69 months in 5 out of 6 months.The6th month (the first with ground reference data collection) had a mean absolute prediction error of 0.90 months because it lacked prior month information to correctly identify planting month. Water demand maps were produced with high spatial (3.0m) and temporal (monthly) resolution, allowing for a detailed look at local agricultural water demands. This study provides a framework for future large-scale agricultural-water demand mapping for the Department of Arequipa and similar regions around the world.

Polygon-based classification applied on the unitemporal SPOT4 XS, SPOT5 XS, IKONOS XS, QuickBird XS and QuickBird Pansharpaned (PS) images is described. The study site is an agricultural area located near Karacabey, Turkey covering an area of about 95 km2. The objective was to assess the effect of the spatial resolution on the polygon-based classification of agricultural crops. Both the post-polygon and pre-polygon classifications were carried out. In the post-polygon classification, first, the images were classified on per-pixel basis through a Maximum Likelihood classifier. Then, for each field, the model class was computed and the field was assigned the label of the model class. In the pre-polygon classification, first, the mean values were calculated for each field. Then, the per-pixel Maximum Likelihood Classification was carried out using the mean bands. The post-polygon classification of the SPOT4 XS and SPOT5 XS images produced an overall accuracy of 76,1% and 81,4%, respectively. The IKONOS XS image provided the highest overall accuracy of 88,6%. On the other hand, the QuickBird XS and QuickBird PS images provided the overall accuracies of 83,7% and 85,8%, respectively. For the pre-polygon classification, the overall accuracies of the SPOT4 XS and SPOT5 XS images were computed to be 65,2% and 69,8%, respectively. Similar to the post-polygon classification, the IKONOS image provided the highest overall accuracy of 81,8% while the SPOT5 XS image revealed slightly better results than the SPOT4 XS image. The overall accuracies of the QuickBird XS and PS images were found to be 78,6% and 82,1%, respectively.

ABSTRACT DECISION TREE CLASSIFICATION OF MULTI-TEMPORAL IMAGES FOR FIELD-BASED CROP MAPPING Sencan, Se&ccedil / il M. Sc., Department of Geodetic and Geographic Information Technologies Supervisor: Assist. Prof. Dr. Mustafa T&uuml / rker August 2004, 125 pages A decision tree (DT) classification approach was used to identify summer (August) crop types in an agricultural area near Karacabey (Bursa), Turkey from multi-temporal images. For the analysis, Landsat 7 ETM+ images acquired in May, July, and August 2000 were used. In addition to the original bands, NDVI, PCA, and Tasselled Cap Transformation bands were also generated and included in the classification procedure. Initially, the images were classified on a per-pixel basis using the multi-temporal masking technique together with the DT approach. Then, the classified outputs were applied a field-based analysis and the class labels of the fields were directly entered into the Geographical Information System (GIS) database. The results were compared with the classified outputs of the three dates of imagery generated using a traditional maximum likelihood (ML) algorithm. It was observed that the proposed approach provided significantly higher overall accuracies for the May and August images, for which the number of classes were low. In May and July, the DT approach produced the classification accuracies of 91.10% and 66.15% while the ML classifier produced 84.38% and 63.55%, respectively. However, in August nearly the similar overall accuracies were obtained for the ML (70.82%) and DT (69.14%) approaches. It was also observed that the use of additional bands for the proposed technique improved the separability of the sugar beet, tomato, pea, pepper, and rice classes.