@article{AyanuConradJentschetal.2015,
  author    = {Ayanu, Yohannes and Conrad, Christopher and Jentsch, Anke and Koellner, Thomas},
  title     = {Unveiling undercover cropland inside forests using landscape variables: a supplement to remote sensing image classification},
  series = {PLoS ONE},
  volume    = {10},
  journal   = {PLoS ONE},
  number    = {6},
  doi       = {10.1371/journal.pone.0130079},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-151686},
  pages     = {e0130079},
  year      = {2015},
  abstract  = {The worldwide demand for food has been increasing due to the rapidly growing global population, and agricultural lands have increased in extent to produce more food crops. The pattern of cropland varies among different regions depending on the traditional knowledge of farmers and availability of uncultivated land. Satellite images can be used to map cropland in open areas but have limitations for detecting undergrowth inside forests. Classification results are often biased and need to be supplemented with field observations. Undercover cropland inside forests in the Bale Mountains of Ethiopia was assessed using field observed percentage cover of land use/land cover classes, and topographic and location parameters. The most influential factors were identified using Boosted Regression Trees and used to map undercover cropland area. Elevation, slope, easterly aspect, distance to settlements, and distance to national park were found to be the most influential factors determining undercover cropland area. When there is very high demand for growing food crops, constrained under restricted rights for clearing forest, cultivation could take place within forests as an undercover. Further research on the impact of undercover cropland on ecosystem services and challenges in sustainable management is thus essential.},
  language  = {en}
}
@article{AlavipanahWegmannQureshietal.2015,
  author    = {Alavipanah, Sadroddin and Wegmann, Martin and Qureshi, Salman and Weng, Qihao and Koellner, Thomas},
  title     = {The role of vegetation in mitigating urban land surface temperatures: a case study of Munich, Germany during the warm season},
  series = {Sustainability},
  volume    = {7},
  journal   = {Sustainability},
  doi       = {10.3390/su7044689},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-143447},
  pages     = {4689-4706},
  year      = {2015},
  abstract  = {The Urban Heat Island (UHI) is the phenomenon of altered increased temperatures in urban areas compared to their rural surroundings. UHIs grow and intensify under extreme hot periods, such as during heat waves, which can affect human health and also increase the demand for energy for cooling. This study applies remote sensing and land use/land cover (LULC) data to assess the cooling effect of varying urban vegetation cover, especially during extreme warm periods, in the city of Munich, Germany. To compute the relationship between Land Surface Temperature (LST) and Land Use Land Cover (LULC), MODIS eight-day interval LST data for the months of June, July and August from 2002 to 2012 and the Corine Land Cover (CLC) database were used. Due to similarities in the behavior of surface temperature of different CLCs, some classes were reclassified and combined to form two major, rather simplified, homogenized classes: one of built-up area and one of urban vegetation. The homogenized map was merged with the MODIS eight-day interval LST data to compute the relationship between them. The results revealed that (i) the cooling effect accrued from urban vegetation tended to be non-linear; and (ii) a remarkable and stronger cooling effect in terms of LST was identified in regions where the proportion of vegetation cover was between seventy and almost eighty percent per square kilometer. The results also demonstrated that LST within urban vegetation was affected by the temperature of the surrounding built-up and that during the well-known European 2003 heat wave, suburb areas were cooler from the core of the urbanized region. This study concluded that the optimum green space for obtaining the lowest temperature is a non-linear trend. This could support urban planning strategies to facilitate appropriate applications to mitigate heat-stress in urban area.},
  language  = {en}
}
@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{RedlichZhangBenjaminetal.2022,
  author    = {Redlich, Sarah and Zhang, Jie and Benjamin, Caryl and Dhillon, Maninder Singh and Englmeier, Jana and Ewald, J{\"o}rg and Fricke, Ute and Ganuza, Cristina and Haensel, Maria and Hovestadt, Thomas and Kollmann, Johannes and Koellner, Thomas and K{\"u}bert-Flock, Carina and Kunstmann, Harald and Menzel, Annette and Moning, Christoph and Peters, Wibke and Riebl, Rebekka and Rummler, Thomas and Rojas-Botero, Sandra and Tobisch, Cynthia and Uhler, Johannes and Uphus, Lars and M{\"u}ller, J{\"o}rg and Steffan-Dewenter, Ingolf},
  title     = {Disentangling effects of climate and land use on biodiversity and ecosystem services—A multi-scale experimental design},
  series = {Methods in Ecology and Evolution},
  volume    = {13},
  journal   = {Methods in Ecology and Evolution},
  number    = {2},
  doi       = {10.1111/2041-210X.13759},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-258270},
  pages     = {514-527},
  year      = {2022},
  abstract  = {Climate and land-use change are key drivers of environmental degradation in the Anthropocene, but too little is known about their interactive effects on biodiversity and ecosystem services. Long-term data on biodiversity trends are currently lacking. Furthermore, previous ecological studies have rarely considered climate and land use in a joint design, did not achieve variable independence or lost statistical power by not covering the full range of environmental gradients. Here, we introduce a multi-scale space-for-time study design to disentangle effects of climate and land use on biodiversity and ecosystem services. The site selection approach coupled extensive GIS-based exploration (i.e. using a Geographic information system) and correlation heatmaps with a crossed and nested design covering regional, landscape and local scales. Its implementation in Bavaria (Germany) resulted in a set of study plots that maximise the potential range and independence of environmental variables at different spatial scales. Stratifying the state of Bavaria into five climate zones (reference period 1981-2010) and three prevailing land-use types, that is, near-natural, agriculture and urban, resulted in 60 study regions (5.8 × 5.8 km quadrants) covering a mean annual temperature gradient of 5.6-9.8°C and a spatial extent of ~310 × 310 km. Within these regions, we nested 180 study plots located in contrasting local land-use types, that is, forests, grasslands, arable land or settlement (local climate gradient 4.5-10°C). This approach achieved low correlations between climate and land use (proportional cover) at the regional and landscape scale with |r ≤ 0.33| and |r ≤ 0.29| respectively. Furthermore, using correlation heatmaps for local plot selection reduced potentially confounding relationships between landscape composition and configuration for plots located in forests, arable land and settlements. The suggested design expands upon previous research in covering a significant range of environmental gradients and including a diversity of dominant land-use types at different scales within different climatic contexts. It allows independent assessment of the relative contribution of multi-scale climate and land use on biodiversity and ecosystem services. Understanding potential interdependencies among global change drivers is essential to develop effective restoration and mitigation strategies against biodiversity decline, especially in expectation of future climatic changes. Importantly, this study also provides a baseline for long-term ecological monitoring programs.},
  language  = {en}
}