@article{HolzwarthThonfeldAbdullahietal.2020,
  author    = {Holzwarth, Stefanie and Thonfeld, Frank and Abdullahi, Sahra and Asam, Sarah and Da Ponte Canova, Emmanuel and Gessner, Ursula and Huth, Juliane and Kraus, Tanja and Leutner, Benjamin and Kuenzer, Claudia},
  title     = {Earth Observation based monitoring of forests in Germany: a review},
  series = {Remote Sensing},
  volume    = {12},
  journal   = {Remote Sensing},
  number    = {21},
  issn      = {2072-4292},
  doi       = {10.3390/rs12213570},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-216334},
  year      = {2020},
  abstract  = {Forests in Germany cover around 11.4 million hectares and, thus, a share of 32\% of Germany's surface area. Therefore, forests shape the character of the country's cultural landscape. Germany's forests fulfil a variety of functions for nature and society, and also play an important role in the context of climate levelling. Climate change, manifested via rising temperatures and current weather extremes, has a negative impact on the health and development of forests. Within the last five years, severe storms, extreme drought, and heat waves, and the subsequent mass reproduction of bark beetles have all seriously affected Germany's forests. Facing the current dramatic extent of forest damage and the emerging long-term consequences, the effort to preserve forests in Germany, along with their diversity and productivity, is an indispensable task for the government. Several German ministries have and plan to initiate measures supporting forest health. Quantitative data is one means for sound decision-making to ensure the monitoring of the forest and to improve the monitoring of forest damage. In addition to existing forest monitoring systems, such as the federal forest inventory, the national crown condition survey, and the national forest soil inventory, systematic surveys of forest condition and vulnerability at the national scale can be expanded with the help of a satellite-based earth observation. In this review, we analysed and categorized all research studies published in the last 20 years that focus on the remote sensing of forests in Germany. For this study, 166 citation indexed research publications have been thoroughly analysed with respect to publication frequency, location of studies undertaken, spatial and temporal scale, coverage of the studies, satellite sensors employed, thematic foci of the studies, and overall outcomes, allowing us to identify major research and geoinformation product gaps.},
  language  = {en}
}
@article{HeinemannSiegmannThonfeldetal.2020,
  author    = {Heinemann, Sascha and Siegmann, Bastian and Thonfeld, Frank and Muro, Javier and Jedmowski, Christoph and Kemna, Andreas and Kraska, Thorsten and Muller, Onno and Schultz, Johannes and Udelhoven, Thomas and Wilke, Norman and Rascher, Uwe},
  title     = {Land surface temperature retrieval for agricultural areas using a novel UAV platform equipped with a thermal infrared and multispectral sensor},
  series = {Remote Sensing},
  volume    = {12},
  journal   = {Remote Sensing},
  number    = {7},
  issn      = {2072-4292},
  doi       = {10.3390/rs12071075},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-203557},
  year      = {2020},
  abstract  = {Land surface temperature (LST) is a fundamental parameter within the system of the Earth's surface and atmosphere, which can be used to describe the inherent physical processes of energy and water exchange. The need for LST has been increasingly recognised in agriculture, as it affects the growth phases of crops and crop yields. However, challenges in overcoming the large discrepancies between the retrieved LST and ground truth data still exist. Precise LST measurement depends mainly on accurately deriving the surface emissivity, which is very dynamic due to changing states of land cover and plant development. In this study, we present an LST retrieval algorithm for the combined use of multispectral optical and thermal UAV images, which has been optimised for operational applications in agriculture to map the heterogeneous and diverse agricultural crop systems of a research campus in Germany (April 2018). We constrain the emissivity using certain NDVI thresholds to distinguish different land surface types. The algorithm includes atmospheric corrections and environmental thermal emissions to minimise the uncertainties. In the analysis, we emphasise that the omission of crucial meteorological parameters and inaccurately determined emissivities can lead to a considerably underestimated LST; however, if the emissivity is underestimated, the LST can be overestimated. The retrieved LST is validated by reference temperatures from nearby ponds and weather stations. The validation of the thermal measurements indicates a mean absolute error of about 0.5 K. The novelty of the dual sensor system is that it simultaneously captures highly spatially resolved optical and thermal images, in order to construct the precise LST ortho-mosaics required to monitor plant diseases and drought stress and validate airborne and satellite data.},
  language  = {en}
}
@article{ThonfeldSteinbachMuroetal.2020,
  author    = {Thonfeld, Frank and Steinbach, Stefanie and Muro, Javier and Kirimi, Fridah},
  title     = {Long-term land use/land cover change assessment of the Kilombero catchment in Tanzania using random forest classification and robust change vector analysis},
  series = {Remote Sensing},
  volume    = {12},
  journal   = {Remote Sensing},
  number    = {7},
  issn      = {2072-4292},
  doi       = {10.3390/rs12071057},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-203513},
  year      = {2020},
  abstract  = {Information about land use/land cover (LULC) and their changes is useful for different stakeholders to assess future pathways of sustainable land use for food production as well as for nature conservation. In this study, we assess LULC changes in the Kilombero catchment in Tanzania, an important area of recent development in East Africa. LULC change is assessed in two ways: first, post-classification comparison (PCC) which allows us to directly assess changes from one LULC class to another, and second, spectral change detection. We perform LULC classification by applying random forests (RF) on sets of multitemporal metrics that account for seasonal within-class dynamics. For the spectral change detection, we make use of the robust change vector analysis (RCVA) and determine those changes that do not necessarily lead to another class. The combination of the two approaches enables us to distinguish areas that show (a) only PCC changes, (b) only spectral changes that do not affect the classification of a pixel, (c) both types of change, or (d) no changes at all. Our results reveal that only one-quarter of the catchment has not experienced any change. One-third shows both, spectral changes and LULC conversion. Changes detected with both methods predominantly occur in two major regions, one in the West of the catchment, one in the Kilombero floodplain. Both regions are important areas of food production and economic development in Tanzania. The Kilombero floodplain is a Ramsar protected area, half of which was converted to agricultural land in the past decades. Therefore, LULC monitoring is required to support sustainable land management. Relatively poor classification performances revealed several challenges during the classification process. The combined approach of PCC and RCVA allows us to detect spatial patterns of LULC change at distinct dimensions and intensities. With the assessment of additional classifier output, namely class-specific per-pixel classification probabilities and derived parameters, we account for classification uncertainty across space. We overlay the LULC change results and the spatial assessment of classification reliability to provide a thorough picture of the LULC changes taking place in the Kilombero catchment.},
  language  = {en}
}