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.

Entender como o balanço hídrico de uma bacia hidrográfica é afetado pela conversão de florestas em cultivos agrícolas e pastagens permite avaliar os possíveis impactos da mudança de uso e cobertura do solo na sustentabilidade regional. Com o modelo hidrológico de grande escala VIC (Variable Infiltration Capacity) calibrado para as condições locais simulou-se os possíveis efeitos em três cenários de substituição da cobertura vegetal na bacia do rio Ji-Paraná, Rondônia. Um cenário imagina a substituição total da floresta pela cultura da soja, e nessas condições a vazão do rio aumentou em 28% durante o período chuvoso, e em até 70% na época seca, quando comparados às vazões atuais. Quando a substituição foi por pastagens esse aumento foi um pouco menor, 11% no período chuvoso e 16% no período seco. Esses dois cenários mutuamente exclusivos foram selecionados por representar condições extremas de uso do solo. Um terceiro cenário simulou a possível condição original da bacia coberta totalmente pela floresta. Esse último cenário permite inferir sobre as possíveis conseqüências das mudanças já ocorridas visto que as medidas de vazão são recentes (menos de 30 anos). Nessa condição a vazão sofreu redução de 9% durante o período chuvoso e de 20% no período seco. O modelo indicou ainda redução de 30% na evapotranspiração dessas culturas, durante o período seco, quando comparado ao cenário de floresta. / The understanding of how the water balance of a hydrographic basin is affected by the conversion of forests into crops and pasture allow to evaluate the possible impacts of the land use and land cover changes in the regional sustainability. Using a macro-scale hydrologic VIC (Variable Infiltration Capacity) model calibrated for local conditions we simulated the possible effects on the hydrology under three scenarios of land cover substitution of the Ji-Paraná basin, state of Rondônia (western region of Brazil). One scenario simulated the total replacement of forest by soybeans crops, under this condition the outflow of the river increased in 28% during the rainy period, and up to 70% at the dry period, when comparative to the current outflows. When forest was replaced by pastures, this increase was a little lower, 11% in rainy season and 16% in the dry period. These two exclusive scenarios have been selected by representing extreme conditions of land use. One third scenario simulated the possible original condition of the basin covered by forest. This last scenario allows to infer on the possible consequences of the actual changes since recent measures of outflow (less than 30 years). In this condition the outflow suffered a reduction of 9% during the rainy period and 20% in the dry period. The model indicated a decrease of 30% in evapotranspiration for these crops in dry period compared to the forest scenario.

Entender como o balanço hídrico de uma bacia hidrográfica é afetado pela conversão de florestas em cultivos agrícolas e pastagens permite avaliar os possíveis impactos da mudança de uso e cobertura do solo na sustentabilidade regional. Com o modelo hidrológico de grande escala VIC (Variable Infiltration Capacity) calibrado para as condições locais simulou-se os possíveis efeitos em três cenários de substituição da cobertura vegetal na bacia do rio Ji-Paraná, Rondônia. Um cenário imagina a substituição total da floresta pela cultura da soja, e nessas condições a vazão do rio aumentou em 28% durante o período chuvoso, e em até 70% na época seca, quando comparados às vazões atuais. Quando a substituição foi por pastagens esse aumento foi um pouco menor, 11% no período chuvoso e 16% no período seco. Esses dois cenários mutuamente exclusivos foram selecionados por representar condições extremas de uso do solo. Um terceiro cenário simulou a possível condição original da bacia coberta totalmente pela floresta. Esse último cenário permite inferir sobre as possíveis conseqüências das mudanças já ocorridas visto que as medidas de vazão são recentes (menos de 30 anos). Nessa condição a vazão sofreu redução de 9% durante o período chuvoso e de 20% no período seco. O modelo indicou ainda redução de 30% na evapotranspiração dessas culturas, durante o período seco, quando comparado ao cenário de floresta. / The understanding of how the water balance of a hydrographic basin is affected by the conversion of forests into crops and pasture allow to evaluate the possible impacts of the land use and land cover changes in the regional sustainability. Using a macro-scale hydrologic VIC (Variable Infiltration Capacity) model calibrated for local conditions we simulated the possible effects on the hydrology under three scenarios of land cover substitution of the Ji-Paraná basin, state of Rondônia (western region of Brazil). One scenario simulated the total replacement of forest by soybeans crops, under this condition the outflow of the river increased in 28% during the rainy period, and up to 70% at the dry period, when comparative to the current outflows. When forest was replaced by pastures, this increase was a little lower, 11% in rainy season and 16% in the dry period. These two exclusive scenarios have been selected by representing extreme conditions of land use. One third scenario simulated the possible original condition of the basin covered by forest. This last scenario allows to infer on the possible consequences of the actual changes since recent measures of outflow (less than 30 years). In this condition the outflow suffered a reduction of 9% during the rainy period and 20% in the dry period. The model indicated a decrease of 30% in evapotranspiration for these crops in dry period compared to the forest scenario.

Changes in land use and land cover are associated with many environmental issues observed on the earth’s surface. In the last decades, these changes were unprece-dented, mainly in tropical forest areas. The Brazilian Amazon, the world’s largest tropical forest, lost around 200.000 km² of primary forest in the last ten years (INPE, 2005). Considering this, and the consequences caused by this deforestation, it is important to know and define correctly the responsible agents, aiming at better pub-lic policies that can help preserve the forest. Searching for indicators that could help to identify the deforestation agents, some studies, such as Mertens and Lambin (1997), suggest that every deforestation process shapes the forest land in a specific way, producing a spatial pattern that can be interpreted as indicative of the agents with specific economic activities. Based on this hypothesis, the objective of this study was to contribute to a better understanding of land change processes in the Amazon forest, investigating the linkages between spatial patterns of deforestation, as visualized in satellite images, and different agents and their specific economic activities. To reach this objective, our methodological approach was based on socio-economic data acquired at a household level combined with data from satellite im-ages. First, different spatial patterns of deforestation were identified on the satellite images, based on the typologies proposed by Husson et al. (1995). Then, some of the identified spatial patterns were isolated and analyzed for specific aspects, such as, the deforestation rate calculated through satellite images. socio-economic character-istics based on household survey data and evolution of land use and land cover based on thematic maps derived from satellite images. In addition, cluster analysis was applied using the socio-economic data (household survey) and land use and land cover data (satellite images) in a search for homogeneous groups related to the spa-tial pattern. In the end, an Analysis of Variance (ANOVA) was applied to confirm the differences between spatial patterns. The results suggested that the different spatial patterns of deforestation found in the study area can be related to specific economic activities. Nevertheless, the re-sults have indicated that the spatial configuration is not a consequence of its main economic activity. They suggest that the spatial configuration is linked to the settle-ment project, and the main economic activity in the spatial patterns is a consequence of a set of factors such as: size of property, location and disposition of the property, presence or absence of infrastructure (road, market, transportation, economic and technical).

Lic.-avh. Uppsala : Sveriges lantbruksuniversitet, 2007.

Thesis (Ph. D.)--Oregon State University, 2008. / Printout. Includes bibliographical references (leaves 125-136). Also available on the World Wide Web.

abstract: This dissertation research studies long-term spatio-temporal patterns of surface urban heat island (SUHI) intensity, urban evapotranspiration (ET), and urban outdoor water use (OWU) using Phoenix metropolitan area (PMA), Arizona as the case study. This dissertation is composed of three chapters. The first chapter evaluates the SUHI intensity for PMA using Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) product and a time-series trend analysis to discover areas that experienced significant changes of SUHI intensity between 2000 and 2017. The heating and cooling effects of different urban land use land cover (LULC) types was also examined using classified Landsat satellite images. The second chapter is focused on urban ET and the impacts of urban LULC change on ET. An empirical model of urban ET for PMA was built using flux tower data and MODIS land products using multivariate regression analysis. A time-series trend analysis was then performed to discover areas in PMA that experienced significant changes of ET between 2001 and 2015. The impact of urban LULC change on ET was examined using classified LULC maps. The third chapter models urban OWU in PMA using a surface energy balance model named METRIC (Mapping Evapotranspiration at high spatial Resolution with Internalized Calibration) and time-series Landsat Thematic Mapper 5 imagery for 2010. The relationship between urban LULC types and OWU was examined with the use of very high-resolution land cover classification data generated from the National Agriculture Imagery Program (NAIP) imagery and regression analysis. Socio-demographic variables were selected from census data at the census track level and analyzed against OWU to study their relationship using correlation analysis. This dissertation makes significant contributions and expands the knowledge of long-term urban climate dynamics for PMA and the influence of urban expansion and LULC change on regional climate. Research findings and results can be used to provide constructive suggestions to urban planners, decision-makers, and city managers to formulate new policies and regulations when planning new constructions for the purpose of sustainable development for a desert city. / Dissertation/Thesis / Doctoral Dissertation Geography 2018

Charles University in Prague Faculty of Science Department of Social Geography and Regional Development Robin Rašín The Landscape of the Czech-Austrian Borderland: Development and Heritage Summary Roztoky u Křivoklátu 2010 1 Landscape is a key geographical concept and geographical research on land change and land use has a tradition that can be dated back for almost a hundred years. The development of the subject orientation of land change science can be divided into two phases. (i) During the first phase (50's/60's of the 20th century) the description of landscape and its morphology (structure) were at the centre of research interest along with the research of the potential for agro-production. This particular type of study was motivated by the need to find a solution to the problem of the increasing inability to supply a growing (European) population with agricultural products. (ii) In the second phase (circa from 70's of the 20th century), the attention of scientists towards the accelerated rate of land change on a global scale that can be associated with: the increase of human population, changes in agricultural techniques, mining, environmental exploitation et cetera. There is an obvious need to solve the negative impacts of the human activities on the landscape at different scales (from local to...