@phdthesis{Rolf2020,
  author    = {Rolf, Werner},
  title     = {Peri-urban farmland included in green infrastructure strategies promotes transformation pathways towards sustainable urban development},
  doi       = {10.25932/publishup-47700},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-477002},
  school      = {Universit{\"a}t Potsdam},
  pages     = {IV, 116},
  year      = {2020},
  abstract  = {Urbanization and agricultural land use are two of the main drivers of global changes with effects on ecosystem functions and human wellbeing. Green Infrastructure is a new approach in spatial planning contributing to sustainable urban development, and to address urban challenges, such as biodiversity conservation, climate change adaptation, green economy development, and social cohesion. Because the research focus has been mainly on open green space structures, such as parks, urban forest, green building, street green, but neglected spatial and functional potentials of utilizable agricultural land, this thesis aims at fill this gap. This cumulative thesis addresses how agricultural land in urban and peri-urban landscapes can contribute to the development of urban green infrastructure as a strategy to promote sustainable urban development. Therefore, a number of different research approaches have been applied. First, a quantitative, GIS-based modeling approach looked at spatial potentials, addressing the heterogeneity of peri-urban landscape that defines agricultural potentials and constraints. Second, a participatory approach was applied, involving stakeholder opinions to evaluate multiple urban functions and benefits. Finally, an evidence synthesis was conducted to assess the current state of research on evidence to support future policy making at different levels. The results contribute to the conceptual understanding of urban green infrastructures as a strategic spatial planning approach that incorporates inner-urban utilizable agricultural land and the agriculturally dominated landscape at the outer urban fringe. It highlights the proposition that the linkage of peri-urban farmland with the green infrastructure concept can contribute to a network of multifunctional green spaces to provide multiple benefits to the urban system and to successfully address urban challenges. Four strategies are introduced for spatial planning with the contribution of peri-urban farmland to a strategically planned multifunctional network, namely the connecting, the productive, the integrated, and the adapted way. Finally, this thesis sheds light on the opportunities that arise from the integration of the peri- urban farmland in the green infrastructure concept to support transformation towards a more sustainable urban development. In particular, the inherent core planning principle of multifunctionality endorses the idea of co-benefits that are considered crucial to trigger transformative processes. This work concludes that the linkage of peri-urban farmland with the green infrastructure concept is a promising action field for the development of new pathways for urban transformation towards sustainable urban development. Along with these outcomes, attention is drawn to limitations that remain to be addressed by future research, especially the identification of further mechanisms required to support policy integration at all levels.},
  language  = {en}
}
@article{WehrhanSommer2021,
  author    = {Wehrhan, Marc and Sommer, Michael},
  title     = {A parsimonious approach to estimate soil organic carbon applying Unmanned Aerial System (UAS) multispectral imagery and the topographic position index in a heterogeneous soil landscape},
  series = {Remote sensing / Molecular Diversity Preservation International (MDPI)},
  volume    = {13},
  journal   = {Remote sensing / Molecular Diversity Preservation International (MDPI)},
  number    = {18},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2072-4292},
  doi       = {10.3390/rs13183557},
  pages     = {20},
  year      = {2021},
  abstract  = {Remote sensing plays an increasingly key role in the determination of soil organic carbon (SOC) stored in agriculturally managed topsoils at the regional and field scales. Contemporary Unmanned Aerial Systems (UAS) carrying low-cost and lightweight multispectral sensors provide high spatial resolution imagery (<10 cm). These capabilities allow integrate of UAS-derived soil data and maps into digitalized workflows for sustainable agriculture. However, the common situation of scarce soil data at field scale might be an obstacle for accurate digital soil mapping. In our case study we tested a fixed-wing UAS equipped with visible and near infrared (VIS-NIR) sensors to estimate topsoil SOC distribution at two fields under the constraint of limited sampling points, which were selected by pedological knowledge. They represent all releva nt soil types along an erosion-deposition gradient; hence, the full feature space in terms of topsoils' SOC status. We included the Topographic Position Index (TPI) as a co-variate for SOC prediction. Our study was performed in a soil landscape of hummocky ground moraines, which represent a significant of global arable land. Herein, small scale soil variability is mainly driven by tillage erosion which, in turn, is strongly dependent on topography. Relationships between SOC, TPI and spectral information were tested by Multiple Linear Regression (MLR) using: (i) single field data (local approach) and (ii) data from both fields (pooled approach). The highest prediction performance determined by a leave-one-out-cross-validation (LOOCV) was obtained for the models using the reflectance at 570 nm in conjunction with the TPI as explanatory variables for the local approach (coefficient of determination (R-2) = 0.91; root mean square error (RMSE) = 0.11\% and R-2 = 0.48; RMSE = 0.33, respectively). The local MLR models developed with both reflectance and TPI using values from all points showed high correlations and low prediction errors for SOC content (R-2 = 0.88, RMSE = 0.07\%; R-2 = 0.79, RMSE = 0.06\%, respectively). The comparison with an enlarged dataset consisting of all points from both fields (pooled approach) showed no improvement of the prediction accuracy but yielded decreased prediction errors. Lastly, the local MLR models were applied to the data of the respective other field to evaluate the cross-field prediction ability. The spatial SOC pattern generally remains unaffected on both fields; differences, however, occur concerning the predicted SOC level. Our results indicate a high potential of the combination of UAS-based remote sensing and environmental covariates, such as terrain attributes, for the prediction of topsoil SOC content at the field scale. The temporal flexibility of UAS offer the opportunity to optimize flight conditions including weather and soil surface status (plant cover or residuals, moisture and roughness) which, otherwise, might obscure the relationship between spectral data and SOC content. Pedologically targeted selection of soil samples for model development appears to be the key for an efficient and effective prediction even with a small dataset.},
  language  = {en}
}