@article{UlloaTorrealbaStahlmannWegmannetal.2020,
  author    = {Ulloa-Torrealba, Yrneh and Stahlmann, Reinhold and Wegmann, Martin and Koellner, Thomas},
  title     = {Over 150 years of change: object-oriented analysis of historical land cover in the Main river catchment, Bavaria/Germany},
  series = {Remote Sensing},
  volume    = {12},
  journal   = {Remote Sensing},
  number    = {24},
  issn      = {2072-4292},
  doi       = {10.3390/rs12244048},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-220029},
  year      = {2020},
  abstract  = {The monitoring of land cover and land use change is critical for assessing the provision of ecosystem services. One of the sources for long-term land cover change quantification is through the classification of historical and/or current maps. Little research has been done on historical maps using Object-Based Image Analysis (OBIA). This study applied an object-based classification using eCognition tool for analyzing the land cover based on historical maps in the Main river catchment, Upper Franconia, Germany. This allowed land use change analysis between the 1850s and 2015, a time span which covers the phase of industrialization of landscapes in central Europe. The results show a strong increase in urban area by 2600\%, a severe loss of cropland (-24\%), a moderate reduction in meadows (-4\%), and a small gain in forests (+4\%). The method proved useful for the application on historical maps due to the ability of the software to create semantic objects. The confusion matrix shows an overall accuracy of 82\% for the automatic classification compared to manual reclassification considering all 17 sample tiles. The minimum overall accuracy was 65\% for historical maps of poor quality and the maximum was 91\% for very high-quality ones. Although accuracy is between high and moderate, coarse land cover patterns in the past and trends in land cover change can be analyzed. We conclude that such long-term analysis of land cover is a prerequisite for quantifying long-term changes in ecosystem services.},
  language  = {en}
}
@article{ConradFritschZeidleretal.2010,
  author    = {Conrad, Christopher and Fritsch, Sebastian and Zeidler, Julian and R{\"u}cker, Gerd and Dech, Stefan},
  title     = {Per-Field Irrigated Crop Classification in Arid Central Asia Using SPOT and ASTER Data},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-68630},
  year      = {2010},
  abstract  = {The overarching goal of this research was to explore accurate methods of mapping irrigated crops, where digital cadastre information is unavailable: (a) Boundary separation by object-oriented image segmentation using very high spatial resolution (2.5-5 m) data was followed by (b) identification of crops and crop rotations by means of phenology, tasselled cap, and rule-based classification using high resolution (15-30 m) bi-temporal data. The extensive irrigated cotton production system of the Khorezm province in Uzbekistan, Central Asia, was selected as a study region. Image segmentation was carried out on pan-sharpened SPOT data. Varying combinations of segmentation parameters (shape, compactness, and color) were tested for optimized boundary separation. The resulting geometry was validated against polygons digitized from the data and cadastre maps, analysing similarity (size, shape) and congruence. The parameters shape and compactness were decisive for segmentation accuracy. Differences between crop phenologies were analyzed at field level using bi-temporal ASTER data. A rule set based on the tasselled cap indices greenness and brightness allowed for classifying crop rotations of cotton, winter-wheat and rice, resulting in an overall accuracy of 80 \%. The proposed field-based crop classification method can be an important tool for use in water demand estimations, crop yield simulations, or economic models in agricultural systems similar to Khorezm.},
  subject      = {Geologie},
  language  = {en}
}