This master thesis deals with a classification technique for an automatic detection of different land cover types from combination of high resolution imagery and LiDAR data sets. The main aim is to introduce additional post-processing method to commonly accessible quality data sets which can replace traditional mapping techniques for certain type of applications. Classification is the process of dividing the image into land cover categories which helps with continuous and up-to-date monitoring management. Nowadays, with all the technologies and software available, it is possible to replace traditional monitoring methods with more automated processes to generate accurate and cost-effective results. This project uses object-oriented image analysis (OBIA) to classify available data sets into five main land cover classes. The automate classification rule set providing overall accuracy of 88% of correctly classified land cover types was developed and evaluated in this research. Further, the transferability of developed approach was tested upon the same type of data sets within different study area with similar success – overall accuracy was 87%. Also the limitations found during the investigation procedure are discussed and brief further approach in this field is outlined.

>Magister Scientiae - MSc / Wetlands are unique ecosystems that are acknowledged among the world’s most productive and valuable ecosystems. They are recognized as being essential to sustainable development and human welfare due to their unique environmental and socio-economic value. These highly productive ecosystems provide functions such as recycling of nutrients, watershed protection and flood control as well as grazing resources. Wetlands provide the basis for human livelihoods in Africa through ecosystem services. However, these ecosystems are affected by internal and external factors within and outside their catchments, hence the importance of monitoring those changes around these wetlands.

Healthy forests and soils are crucial for the very existence of mankind as they provide food, clean water and air, shade and protection against floods and storms. With their photosynthetic carbon storage ability, they mitigate climate change and fertilise and stabilise soils. Unfortunately, deforestation and the loss of fertile soils are the bleak reality and among the world’s most pressing challenges. Over the past decades Kenya has faced severe deforestation, but efforts are being undertaken to reverse deforestation, revegetate degraded land and combat erosion. Satellite remote sensing technology becomes increasingly useful for vegetation monitoring as the data quality improves and the costs decrease. This thesis explores the potential of free open access Sentinel-2 data for vegetation monitoring through Random Forest land cover classification and post-classification change detection on Rusinga Island, Kenya. Different single-date and multi-temporal predictor datasets differentiating respectively between five and four classes were examined to develop the most suitable model. The classification achieved acceptable results when assessed on an independent test dataset (overall accuracy of 90.06% with five classes and 96.89% with four classes), which should however be confirmed on the ground and could potentially be improved with better reference data. In this study, change detection could only be analysed over a time frame of two years, which is too short to produce meaningful results. Nevertheless, the method was proven conceptually and could be applied in the future to monitor land cover changes on Rusinga Island.

Climate change and land degradation, which is defined as the decline in the productive capacity of the land, have profound implications for resource-based livelihoods and food security. In this dissertation, I use remote sensing to improve understanding of how climate variability affects the productivity of global pasturelands and to quantify the spatial and temporal patterns of land degradation in the Southern Cone region (SCR) of South America. In the first chapter, I characterize the sensitivity of global pastureland productivity to climate variability by analyzing the relationship between MODIS enhanced vegetation index and gridded precipitation data. Results show that pasturelands are least capable of withstanding precipitation deficits in Australia, while pasturelands in Latin America recover more slowly after drought compared to other regions. In the second chapter, I use Landsat observations to measure the magnitude, geography, and rate of change in the amount of bare ground, herbaceous and woody vegetation in the SCR since 1999. Paraguay experienced the highest proportional increase in herbaceous cover as a result of agricultural expansion and intensification, while Uruguay experienced the highest proportional increase in woody cover as a result of afforestation. Argentina, the largest and most heterogeneous country in the SCR, experienced widespread land cover changes from deforestation, reforestation, afforestation, and desertification, each of which varied in extent and magnitude by ecoregion. In the third chapter, I assess patterns of land degradation in the SCR using the United Nations Sustainable Development framework. My results show that 67.5% of the SCR experienced changes in land cover properties in the 21st century, with widespread improvement (i.e., increased productive capacity), along with substantial hotspots of degradation caused by expansion of agriculture and systematic decreases in precipitation. Monitoring degradation is necessary to assess ecosystem services, ensure food security, and develop land use policies designed to increase the resilience of land systems to the joint stresses imposed by climate change and a growing global population. The methods, datasets, and results from this dissertation provide an improved basis for creating such policies in some of the world’s most vulnerable and food insecure regions.

Hydrological drought at high latitudes represents a rising environmental hazard induced byglobal climate change. Yet, we still know little about how drought events influence thebiogeochemistry of boreal streams. Here, I used 15 years of data from eight streams withinthe Krycklan Catchment to test how interannual variability in summer low flows influencesstream water chemistry. My analysis focused several key biogeochemical indicators in thesestreams, including concentrations of dissolved organic carbon (DOC), dissolved organicnitrogen (DON), nitrate (NO3) and ammonium (NH4), as well as the total C/N and NH4/NO3ratios. Overall, results revealed widespread declines in summer average DOC concentrationsand C/N ratios with greater drought severity. These responses likely reflect shifts in thebiogeochemical properties of soils that feed streams during high- versus low-flow summers.By comparison, nitrogen-based parameters were less clearly influenced by drought, exceptfor in mire-dominated headwaters, where NH4 and DON both increased during the lowestflow periods. Overall, the strong effects of flow variability drove a high degree of interannualsynchrony for DOC and C/N across all sites in the drainage system. This synchrony was morevariable overall for nitrogen-based parameters, with several sites having unique year-to-yearchanges in concentrations and ratios. However, strong temporal coherence for NH4 acrossforested streams suggest other broad-scale factors (e.g., related to forest processes) mayregulate interannual patterns for this nutrient. Collectively, results provide insight into howincreases in drought frequency and severity may alter boreal streams and rivers in the future.

>Magister Scientiae - MSc / Water is a fundamental resource and key in the provision of energy, food and health. However, water resources are currently under severe pressure as a consequence of climate change and variability, population growth and economic development. Two driving factors that affect the availability of water resources are land use land cover (LULC) change and climate variability. Increasing population influences both LULC change and climate variability by inducing changes in key hydrological parameters such as interception rates, evapotranspiration (ET), run-off, surface infiltration, soil moisture, water quality and groundwater availability thereby affecting the watershed hydrology. The effects of LULC change and climate variability on hydrologic parameters have been extensively studied.

Land cover information is a key input to many earth system models, and thus accurate and consistent land cover maps are critically important to global change science. However, existing global land cover products show unrealistically high levels of year-to-year change. This thesis explores methods to improve accuracies for global land cover classifications, with a focus on reducing spurious year-to-year variation in results derived from MODIS data. In the first part of this thesis I use clustering to identify spectrally distinct sub-groupings within defined land cover classes, and assess the spectral separability of the resulting sub-classes. Many of the sub-classes are difficult to separate due to a high degree of overlap in spectral space. In the second part of this thesis, I examine two methods to reduce year-to-year variation in classification labels. First, I evaluate a technique to construct training data for a per-pixel supervised classification algorithm by combining multiple years of spectral measurements. The resulting classifier achieves higher accuracy and lower levels of year-to-year change than a reference classifier trained using a single year of data. Second, I use a spatio-temporal Markov Random Field (MRF) model to post-process the predictions of a per-pixel classifier. The MRF framework reduces spurious label change to a level comparable to that achieved by a post-hoc heuristic stabilization technique. The timing of label change in the MRF processed maps better matched disturbance events in a reference data, whereas the heuristic stabilization results in label changes that lag several years behind disturbance events.

京都大学 / 0048 / 新制・課程博士 / 博士(地域研究) / 甲第20494号 / 地博第213号 / 未着(附属図書館) / 京都大学大学院アジア・アフリカ地域研究研究科東南アジア地域研究専攻 / (主査)教授 竹田 晋也, 教授 岩田 明久, 准教授 安藤 和雄, 准教授 鈴木 玲冶 / 学位規則第4条第1項該当 / Doctor of Area Studies / Kyoto University / DGAM

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

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