@phdthesis{Cord2012,
  author    = {Cord, Anna},
  title     = {Potential of multi-temporal remote sensing data for modeling tree species distributions and species richness in Mexico},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-71021},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2012},
  abstract  = {Current changes of biodiversity result almost exclusively from human activities. This anthropogenic conversion of natural ecosystems during the last decades has led to the so-called 'biodiversity crisis', which comprises the loss of species as well as changes in the global distribution patterns of organisms. Species richness is unevenly distributed worldwide. Altogether, 17 so-called 'megadiverse' nations cover less than 10\% of the earth's land surface but support nearly 70\% of global species richness. Mexico, the study area of this thesis, is one of those countries. However, due to Mexico's large extent and geographical complexity, it is impossible to conduct reliable and spatially explicit assessments of species distribution ranges based on these collection data and field work alone. In the last two decades, Species distribution models (SDMs) have been established as important tools for extrapolating such in situ observations. SDMs analyze empirical correlations between geo-referenced species occurrence data and environmental variables to obtain spatially explicit surfaces indicating the probability of species occurrence. Remote sensing can provide such variables which describe biophysical land surface characteristics with high effective spatial resolutions. Especially during the last three to five years, the number of studies making use of remote sensing data for modeling species distributions has therefore multiplied. Due to the novelty of this field of research, the published literature consists mostly of selective case studies. A systematic framework for modeling species distributions by means of remote sensing is still missing. This research gap was taken up by this thesis and specific studies were designed which addressed the combination of climate and remote sensing data in SDMs, the suitability of continuous remote sensing variables in comparison with categorical land cover classification data, the criteria for selecting appropriate remote sensing data depending on species characteristics, and the effects of inter-annual variability in remotely sensed time series on the performance of species distribution models. The corresponding novel analyses were conducted with the Maximum Entropy algorithm developed by Phillips et al. (2004). In this thesis, a more comprehensive set of remote sensing predictors than in the existing literature was utilized for species distribution modeling. The products were selected based on their ecological relevance for characterizing species distributions. Two 1 km Terra-MODIS Land 16-day composite standard products including the Enhanced Vegetation Index (EVI), Reflectance Data, and Land Surface Temperature (LST) were assembled into enhanced time series for the time period of 2001 to 2009. These high-dimensional time series data were then transformed into 18 phenological and 35 statistical metrics that were selected based on an extensive literature review. Spatial distributions of twelve tree species were modeled in a hierarchical framework which integrated climate (WorldClim) and MODIS remote sensing data. The species are representative of the major Mexican forest types and cover a variety of ecological traits, such as range size and biotope specificity. Trees were selected because they have a high probability of detection in the field and since mapping vegetation has a long tradition in remote sensing. The result of this thesis showed that the integration of remote sensing data into species distribution models has a significant potential for improving and both spatial detail and accuracy of the model predictions.},
  subject      = {Fernerkundung},
  language  = {en}
}
@article{ForkuorHounkpatinWelpetal.2017,
  author    = {Forkuor, Gerald and Hounkpatin, Ozias K.L. and Welp, Gerhard and Thiel, Michael},
  title     = {High resolution mapping of soil properties using remote sensing variables in south-western Burkina Faso: a comparison of machine learning and multiple linear regression models},
  series = {PLOS One},
  volume    = {12},
  journal   = {PLOS One},
  number    = {1},
  doi       = {10.1371/journal.pone.0170478},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-180978},
  pages     = {21},
  year      = {2017},
  abstract  = {Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties-sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen-in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models-multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)-were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources.},
  language  = {en}
}
@phdthesis{Babu2021,
  author    = {Babu, Dinesh Kumar},
  title     = {Efficient Data Fusion Approaches for Remote Sensing Time Series Generation},
  doi       = {10.25972/OPUS-25180},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-251808},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2021},
  abstract  = {Fernerkundungszeitreihen beschreiben die Erfassung von zeitlich gleichm{\"a}ßig verteilten Fernerkundungsdaten in einem festgelegten Zeitraum entweder global oder f{\"u}r ein vordefiniertes Gebiet. F{\"u}r die {\"U}berwachung der Landwirtschaft, die Erkennung von Ver{\"a}nderungen der Ph{\"a}nologie oder f{\"u}r das Umwelt-Monitoring werden nahezu t{\"a}gliche Daten mit hoher r{\"a}umlicher Aufl{\"o}sung ben{\"o}tigt. Bei vielen verschiedenen fernerkundlichen Anwendungen h{\"a}ngt die Genauigkeit von der dichte und der Verl{\"a}sslichkeit der fernerkundlichen Datenreihe ab. Die verschiedenen Fernerkundungssatellitenkonstellationen sind immer noch nicht in der Lage, fast t{\"a}glich oder t{\"a}glich Bilder mit hoher r{\"a}umlicher Aufl{\"o}sung zu liefern, um die Bed{\"u}rfnisse der oben erw{\"a}hnten Fernerkundungsanwendungen zu erf{\"u}llen. Einschr{\"a}nkungen bei den Sensoren, hohe Entwicklungskosten, hohe Betriebskosten der Satelliten und das Vorhandensein von Wolken, die die Sicht auf das Beobachtungsgebiet blockieren, sind einige der Gr{\"u}nde, die es sehr schwierig machen, fast t{\"a}gliche oder t{\"a}gliche optische Fernerkundungsdaten mit hoher r{\"a}umlicher Aufl{\"o}sung zu erhalten. Mit Entwicklungen bei den optischen Sensorsystemen und gut geplanten Fernerkundungssatellitenkonstellationen kann dieser Zustand verbessert werden, doch ist dies mit Kosten verbunden. Selbst dann wird das Problem nicht vollst{\"a}ndig gel{\"o}st sein, so dass der wachsende Bedarf an zeitlich und r{\"a}umlich hochaufl{\"o}senden Daten nicht vollst{\"a}ndig gedeckt werden kann. Da der Datenerfassungsprozess sich auf Satelliten st{\"u}tzt, die physische Systeme sind, k{\"o}nnen diese aus verschiedenen Gr{\"u}nden unvorhersehbar ausfallen und einen vollst{\"a}ndigen Verlust der Beobachtung f{\"u}r einen bestimmten Zeitraum verursachen, wodurch eine L{\"u}cke in der Zeitreihe entsteht. Um den langfristigen Trend der ph{\"a}nologischen Ver{\"a}nderungen aufgrund der sich schnell {\"a}ndernden Umweltbedingungen zu beobachten, sind die Fernerkundungsdaten aus der gegenw{\"a}rtig nicht ausreichend. Hierzu werden auch Daten aus der Vergangenheit ben{\"o}tigt. Eine bessere Alternativl{\"o}sung f{\"u}r dieses Problem kann die Erstellung von Fernerkundungszeitreihen durch die Fusion von Daten mehrerer Fernerkundungssatelliten mit unterschiedlichen r{\"a}umlichen und zeitlichen Aufl{\"o}sungen sein. Dieser Ansatz soll effektiv und effizient sein. Bei dieser Methode kann ein zeitlich und r{\"a}umlich hoch aufgel{\"o}stes Bild von einem Satelliten, wie Sentinel-2 mit einem zeitlich und r{\"a}umlich niedrig aufgel{\"o}sten Bild von einem Satelliten, wie Sentinel-3 fusioniert werden, um synthetische Daten mit hoher zeitlicher und r{\"a}umlicher Aufl{\"o}sung zu erzeugen. Die Erzeugung von Fernerkundungszeitreihen durch Datenfusionsmethoden kann sowohl auf die gegenw{\"a}rtig erfassten Satellitenbilder als auch auf die in der Vergangenheit von den Satelliten aufgenommenen Bilder angewandt werden. Dies wird die dringend ben{\"o}tigten zeitlich und r{\"a}umlich hochaufl{\"o}senden Bilder f{\"u}r Fernerkundungsanwendungen liefern. Dieser vereinfachte Ansatz ist kosteneffektiv und bietet den Forschern die M{\"o}glichkeit, aus der begrenzten Datenquelle, die ihnen zur Verf{\"u}gung steht, die f{\"u}r ihre Anwendung ben{\"o}tigten Daten selbst zu generieren. Ein effizienter Datenfusionsansatz in Kombination mit einer gut geplanten Satellitenkonstellation kann ein L{\"o}sungsansatz sein, um eine nahezu t{\"a}gliche Zeitreihen von Fernerkundungsdaten l{\"u}ckenlos gew{\"a}hrleistet. Ziel dieser Forschungsarbeit ist die Entwicklung eines effizienten Datenfusionsansatzes, um dichte Fernerkundungszeitreihen zu erhalten.},
  language  = {en}
}
@phdthesis{Forkuor2014,
  author    = {Forkuor, Gerald},
  title     = {Agricultural Land Use Mapping in West Africa Using Multi-sensor Satellite Imagery},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-108687},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2014},
  abstract  = {Rapid population growth in West Africa has led to expansion in croplands due to the need to grow more food to meet the rising food demand of the burgeoning population. These expansions negatively impact the sub-region's ecosystem, with implications for water and soil quality, biodiversity and climate. In order to appropriately monitor the changes in croplands and assess its impact on the ecosystem and other environmental processes, accurate and up-to-date information on agricultural land use is required. But agricultural land use mapping (i.e. mapping the spatial distribution of crops and croplands) in West Africa has been challenging due to the unavailability of adequate satellite images (as a result of excessive cloud cover), small agricultural fields and a heterogeneous landscape. This study, therefore, investigated the possibilities of improving agricultural land use mapping by utilizing optical satellite images with higher spatial and temporal resolution as well as images from Synthetic Aperture Radar (SAR) systems which are near-independent of weather conditions. The study was conducted at both watershed and regional scales. At watershed scale, classification of different crop types in three watersheds in Ghana, Burkina Faso and Benin was conducted using multi-temporal: (1) only optical images (RapidEye) and (2) optical plus dual polarimetric (VV/VH) SAR images (TerraSAR-X). In addition, inter-annual or short term (2-3 years) changes in cropland area in the past ten years were investigated using historical Landsat images. Results obtained indicate that the use of only optical images to map different crop types in West Africa can achieve moderate classification accuracies (57\% to 71\%). Overlaps between the cropping calendars of most crops types and certain inter-croppings pose a challenge to optical images in achieving an adequate separation between those crop classes. Integration of SAR images, however, can improve classification accuracies by between 8 and 15\%, depending on the number of available images and their acquisition dates. The sensitivity of SAR systems to different crop canopy architectures and land surface characteristics improved the separation between certain crop types. The VV polarization of TerraSAR-X was found to better discrimination between crop types than the VH. Images acquired between August and October were found to be very useful for crop mapping in the sub-region due to structural differences in some crop types during this period. At the regional scale, inter-annual or short term changes in cropland area in the Sudanian Savanna agro-ecological zone in West Africa were assessed by upscaling historical cropland information derived at the watershed scale (using Landsat imagery) unto a coarse spatial resolution, but geographically large, satellite imagery (MODIS) using regression based modeling. The possibility of using such regional scale cropland information to improve government-derived agricultural statistics was investigated by comparing extracted cropland area from the fractional cover maps with district-level agricultural statistics from Ghana The accuracy of the fractional cover maps (MAE between 14.2\% and 19.1\%) indicate that the heterogeneous agricultural landscape of West Africa can be suitably represented at the regional or continental scales by estimating fractional cropland cover on low resolution Analysis of the results revealed that cropland area in the Sudanian Savanna zone has experienced inter-annual or short term fluctuations in the past ten years due to a variety of factors including climate factors (e.g. floods and droughts), declining soil fertility, population increases and agricultural policies such as fertilizer subsidies. Comparison of extracted cropland area from the fractional cover maps with government's agricultural statistics (MoFA) for seventeen districts (second administrative units) in Ghana revealed high inconsistencies in the government statistics, and highlighted the potential of satellite derived cropland information at regional scales to improve national/sub-national agricultural statistics in West Africa. The results obtained in this study is promising for West Africa, considering the recent launch of optical (Landsat 8) and SAR sensors (Sentinel-1) that will provide free data for crop mapping in the sub-region. This will improve chances of obtaining adequate satellite images acquired during the cropping season for agricultural land use mapping and bolster opportunities of operationalizing agricultural land use mapping in West Africa. This can benefit a wide range of biophysical and economic models and improve decision making based on their results.},
  subject      = {Westafrika},
  language  = {en}
}