abstract: Droughts are a common phenomenon of the arid South-west USA climate. Despite water limitations, the region has been substantially transformed by agriculture and urbanization. The water requirements to support these human activities along with the projected increase in droughts intensity and frequency challenge long term sustainability and water security, thus the need to spatially and temporally characterize land use/land cover response to drought and quantify water consumption is crucial. This dissertation evaluates changes in `undisturbed' desert vegetation in response to water availability to characterize climate-driven variability. A new model coupling phenology and spectral unmixing was applied to Landsat time series (1987-2010) in order to derive fractional cover (FC) maps of annuals, perennials, and evergreen vegetation. Results show that annuals FC is controlled by short term water availability and antecedent soil moisture. Perennials FC follow wet-dry multi-year regime shifts, while evergreen is completely decoupled from short term changes in water availability. Trend analysis suggests that different processes operate at the local scale. Regionally, evergreen cover increased while perennials and annuals cover decreased. Subsequently, urban land cover was compared with its surrounding desert. A distinct signal of rain use efficiency and aridity index was documented from remote sensing and a soil-water-balance model. It was estimated that a total of 295 mm of water input is needed to sustain current greenness. Finally, an energy balance model was developed to spatio-temporally estimate evapotranspiration (ET) as a proxy for water consumption, and evaluate land use/land cover types in response to drought. Agricultural fields show an average ET of 9.3 mm/day with no significant difference between drought and wet conditions, implying similar level of water usage regardless of climatic conditions. Xeric neighborhoods show significant variability between dry and wet conditions, while mesic neighborhoods retain high ET of 400-500 mm during drought due to irrigation. Considering the potentially limited water availability, land use/land cover changes due to population increases, and the threat of a warming and drying climate, maintaining large water-consuming, irrigated landscapes challenges sustainable practices of water conservation and the need to provide amenities of this desert area for enhancing quality of life. / Dissertation/Thesis / Ph.D. Geography 2014

The 2015 flood event in the Shire River basin was characterised by Malawi Government’s Department of Disaster Management (DoDMA) as the worst on record. It led to the damage in property worth millions of dollars with recovery still ongoing 3 years later. Over 150 fatalities were confirmed at the time with hundreds of others missing. The extent of the damage of the disaster was perhaps underlined by the swift adoption of the disaster management policy which was still in draft format then and the adoption of the climate change management policy a year later. In the aftermath of the disaster, as with most extreme weather events elsewhere around the world, questions were asked as to whether climate change might have had a hand in the occurrence of such an event and whether, going into a warmer climate, events of that nature of extremity will be the new normal. By using the risk-based event attribution methodology based on dedicated attribution experiments with a global climate model, and focusing on one of the sub-catchments of the Shire River basin, this study explored whether climate change from anthropogenic sources might have influenced the likelihood of such an event occurring. However, given the nature of hydrological events and the land use history of the basin, land use and cover change is another potential flood risk factor which, if overlooked, might affect conclusions with regards to the contribution of external factors to the risk of flooding. To account for both climate change and land use and land change, four sets of rainfallrunoff simulations were run using the Hydrologiska Byrans Vattenbalans-avdelning (HBV) hydrological model which has the ability to simulate the impact of land use and climate change on rainfall-runoff relationships. Each set was a combination of a climate scenario-either “factual” or “counter-factual”- and land use and cover change scenario-either factual (historical) or counterfactual (current). The climate scenarios were based on simulated rainfall and temperature from the HadAM3p model run in two modes-the “factual” and “counter-factual”- simulating the climate with atmospheric conditions closely resembling the atmosphere at the time of occurrence of the event and the climate as it would have been without human emissions of greenhouse gases. The proportion of the risk was calculated to determine how the risk of experiencing a flood of the January-April 2015 magnitude (for 1-day, 10- day, and 30-day maximum flows) changes with climate change only, land use and cover change only, as well as both climate change and land use and cover change. The results demonstrated that the probability of exceeding the 1-day maximum flow of the 2015 magnitude was lower in the factual (current) climate than in the counter-factual. However, changes in land use modify the flood risk such that, when land use change was accounted for, the extent of the reduction in the risk was lower. On the other hand, exceedance probabilities for 10-day and 30-day maximum flows were higher in the factual (current) climate. This was further heightened by changes in land use and cover. The study also established that observational uncertainties typical of the region may influence event attribution results to some extent. The results, which are based on a single attribution method and a single global climate model, do not span the method-model uncertainty range. As a consequence, the results are limited and do not constitute a fully defensible attribution statement.

京都大学 / 0048 / 新制・課程博士 / 博士(農学) / 甲第20816号 / 農博第2256号 / 新制||農||1055(附属図書館) / 学位論文||H30||N5098(農学部図書室) / 京都大学大学院農学研究科森林科学専攻 / (主査)教授 神﨑 護, 教授 北島 薫, 教授 德地 直子 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DGAM

This study compares suspended sediment and land use/land cover in the watershed of Weeks Bay, Alabama. Using Landsat thematic mapper imagery, potential high and low erosion sites were determined based on the increase in urban development form 2002 to 2005. In situ sediment sampling was used to test the hypothesis that the high erosion potential sites have larger amounts of suspended sediments. Additionally, sampling was performed along the Fish and Magnolia rivers to establish a background total suspended sediment level. The background study established an average total suspended sediment concentration of 18.71 mg/L for the Fish River and 17.47 mg/L for the Magnolia River, which are higher than previous studies. The results of the comparison between suspended sediments and land use/land cover proved to be more complex than expected due to variation in precipitation, to the 30 m satellite resolution, and to the criteria for classifying urban land use.

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Malaria is one of the leading causes of mortality and morbidity globally. Land use/land cover (LULC) change have been found to affect the transmission and distribution of malaria in other regions, but no study has attempted to examine such relationships in Nepal. Therefore, this study was conducted in Nepal to assess LULC change between 2000 and 2010, to study the spatial and temporal trend of malaria incidence rate (MIR) between 1999 and 2015, and to understand the relationship between LULC and malaria. The land cover types used for this study are forest, water bodies, agriculture, grassland, shrubland, barren areas, built-up areas and paddy areas. Change detection techniques were used to study LULC change. The temporal trend of MIR in 58 districts, and the relationship between MIR and LULC were evaluated using Poisson and negative binomial regression. Forest, water bodies, snow cover, and built-up area increased in Nepal by 28.5%, 2.96%, 55.12% and 21.19% respectively while the rest of the LULC variables decreased. MIR decreased significantly in 21 districts; however, four districts namely Pyuthan, Kaski, Rupandehi and Siraha had a significantly increasing trend of MIR. During 2001, 2002, and 2003, MIR was positively related to water bodies and paddy areas. Similarly, MIR of 2010 was negatively related to grassland. However, there was no relationship between LULC and MIR in 2000, 2011, 2012 and 2013. It may be because MIR is decreasing significantly in the country and thus the influence of LULC change is also decreasing. / MS

Recently, there is a tremendous amount of high resolution imagery that wasn’t available years ago, mainly because of the advancement of the technology in capturing such images. Most of the very high resolution (VHR) imagery comes in three bands only the red, green and blue (RGB), whereas, the importance of using such imagery in remote sensing studies has been only considered lately, despite that, there are no enough studies examining the usefulness of these imagery in urban applications. This research proposes a method to investigate high resolution imagery to analyse an urban area using UAV imagery for land use and land cover classification. Remote sensing imagery comes in various characteristics and format from different sources, most commonly from satellite and airborne platforms. Recently, unmanned aerial vehicles (UAVs) have become a very good potential source to collect geographic data with new unique properties, most important asset is the VHR of spatiotemporal data structure. UAV systems are as a promising technology that will advance not only remote sensing but GIScience as well. UAVs imagery has been gaining popularity in the last decade for various remote sensing and GIS applications in general, and particularly in image analysis and classification. One of the concerns of UAV imagery is finding an optimal approach to classify UAV imagery which is usually hard to define, because many variables are involved in the process such as the properties of the image source and purpose of the classification. The main objective of this research is evaluating land use / land cover (LULC) classification for urban areas, whereas the data of the study area consists of VHR imagery of RGB bands collected by a basic, off-shelf and simple UAV. LULC classification was conducted by pixel and object-based approaches, where supervised algorithms were used for both approaches to classify the image. In pixel-based image analysis, three different algorithms were used to create a final classified map, where one algorithm was used in the object-based image analysis. The study also tested the effectiveness of object-based approach instead of pixel-based in order to minimize the difficulty in classifying mixed pixels in VHR imagery, while identifying all possible classes in the scene and maintain the high accuracy. Both approaches were applied to a UAV image with three spectral bands (red, green and blue), in addition to a DEM layer that was added later to the image as ancillary data. Previous studies of comparing pixel-based and object-based classification approaches claims that object-based had produced better results of classes for VHR imagery. Meanwhile several trade-offs are being made when selecting a classification approach that varies from different perspectives and factors such as time cost, trial and error, and subjectivity.       Classification based on pixels was approached in this study through supervised learning algorithms, where the classification process included all necessary steps such as selecting representative training samples and creating a spectral signature file. The process in object-based classification included segmenting the UAV’s imagery and creating class rules by using feature extraction. In addition, the incorporation of hue, saturation and intensity (IHS) colour domain and Principle Component Analysis (PCA) layers were tested to evaluate the ability of such method to produce better results of classes for simple UAVs imagery. These UAVs are usually equipped with only RGB colour sensors, where combining more derived colour bands such as IHS has been proven useful in prior studies for object-based image analysis (OBIA) of UAV’s imagery, however, incorporating the IHS domain and PCA layers in this research did not provide much better classes. For the pixel-based classification approach, it was found that Maximum Likelihood algorithm performs better for VHR of UAV imagery than the other two algorithms, the Minimum Distance and Mahalanobis Distance. The difference in the overall accuracy for all algorithms in the pixel-based approach was obvious, where the values for Maximum Likelihood, Minimum Distance and Mahalanobis Distance were respectively as 86%, 80% and 76%. The Average Precision (AP) measure was calculated to compare between the pixel and object-based approaches, the result was higher in the object-based approach when applied for the buildings class, the AP measure for object-based classification was 0.9621 and 0.9152 for pixel-based classification. The results revealed that pixel-based classification is still effective and can be applicable for UAV imagery, however, the object-based classification that was done by the Nearest Neighbour algorithm has produced more appealing classes with higher accuracy. Also, it was concluded that OBIA has more power for extracting geographic information and easier integration within the GIS, whereas the result of this research is estimated to be applicable for classifying UAV’s imagery used for LULC applications.

Master of Science / Department of Geography / Douglas G. Goodin / Volyn Oblast in Western Ukraine has experienced substantial land use/land cover change over the last 25 years as a result of a change in political systems. Remote sensing provides a framework to quantify this change without extensive field work or historical land cover records. In this study, land change is quantified utilizing a post-classification change detection technique comparing Landsat imagery from 1986-2011(Post-Soviet era began 1991). A variety of remote sensing classification methods are explored to take advantage of spectral and spatial variation within this complex study area, and a hybrid scheme is ultimately utilized. Land cover from the CORINE classification scheme is then converted to the EUNIS habitat classification scheme to analyze how land cover change has affected habitat fragmentation. I found large scale agricultural abandonment, increases in forested areas, shifts towards smaller scale farming practices, shifts towards mixed forest structures, and increases in fragmentation of both forest and agricultural habitat types. These changes could have several positive and negative on biodiversity, ecosystems, and human well-being.