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Dissertation
Geospatial crop yield modeling for climate change risk transfer, impact assessment, and adaptation strategies
(2023)
Changing climate and weather patterns increase the pressure on food production systems worldwide, significantly impacting livelihoods and economies at various scales. Understanding and adapting/mitigating climate change are essential to cope with the adverse effects of climate change. Biophysical crop modeling offers a unique way to assess the pros and cons of different adaptation strategies because they integrate physical and biological principles to simulate the use and allocation of captured resources mechanistically. ...
Dissertation
Using Optical Satellite Imagery to Monitor and Understand Agricultural Land-cover Change
(2020-06)
The activities of humans have transformed the earth over the last decades. The transformation is estimated to increase further in response to the growing world population. This has caused many countries to devise means to utilize resources sustainably. Bio-economy is seen as a means to reduce the high reliance on fossil materials and ensure the provision of raw materials, energy and food to the increasing world population in a sustainable manner. However, the rising demands for biomass to produce bioenergy have relied ...
Dissertation
Robustness of portable and laboratory-based visible/near- and mid-infrared spectroscopy for optimized determination of temporally and spatially variable soil properties
(2021)
Application of visible/near- (VISNIRS) and mid-infrared (MIRS) spectroscopy to the field of soil science is promising, as it allows information about a range of properties to be collected simultaneously and rapidly, thereby providing high spatial and temporal resolution data for use in, e.g., soil mapping, precision agriculture, and long-term monitoring. However, investigation of the conditions under which visNIRS and MIRS can replace traditional lab methods is required, as the accuracy, robustness, and efficiency ...
Dissertation
Fine-scale grassland monitoring using unmanned aerial vehicle borne remote sensing
(2020-04)
RS is widely employed as a non-destructive methodology to monitor grasslands. Usage of UAV as the RS platform benefits to provide high spatial (fine-grain) and temporal resolution, unclouded data over small grassland extent. Therefore, this thesis evaluated the applications of UAV-borne RS for fine-scale grassland monitoring along with practical farm condition. Grass biomass estimation, predicting forage quality of grass and mapping invasive lupine in grasslands are considered as applications of UAVborne RS.