The Corredor Bioceanico is a major transportation project connecting the agricultural heartlands of South America to the Atlantic and Pacific coasts. The final link is in southeastern Bolivia - an underdeveloped area that is home to two indigenous groups and globally-significant woodlands and wetlands. Infrastructure developments - comprising a major highway upgrade, revitalized railway services and increased flows along gas pipelines to Brazil - pose major threats to livelihoods and the region's ecological integrity. There are two broad objectives: (i) to map and quantify the spatial patterns of land change using a time-series of coarse and medium resolution satellite imagery; and (ii) to understand the socio-economic and political drivers of change by linking household surveys and interviews with farmers; environmental, climatic, and political data; and classified satellite imagery. Overall, large-scale deforestation has occurred along the Corredor Bioceanico for mechanized commercial production of oil-seed crops such as soybeans and sunflower. The significance of these findings is that agriculture-driven deforestation is pushing into sensitive areas threatening world-renowned ecosystems such as the Chaco, Chiquitano and Pantanal as well as noteworthy national parks. Though quantity remains relatively small compared to other parts of South America, rates of forest loss match or exceed those of more publicized regions such as Rondonia or Mato Grosso, Brazil. Moreover, rates of forest loss are accelerating linearly with time due to policies implemented by incumbent president Evo Morales. Results also show that in the first years of cultivation, pasture is the dominant land-use, but it quickly gives way to intensively cropped farmland. The main findings in terms of percentage area cleared according to forest type is that farmers appear to be favoring transitional forest types on deep and poorly drained soils of alluvial plains. Semi-structured interviews with farmers and representatives of key institutions illustrate that price determined by the global market is not proportionally the most dominant motive driving LULCC in the lowlands of Santa Cruz, Bolivia - an area seen as a quintessential neoliberal frontier.

Master of Arts / Geography / Marcellus M. Caldas / As concerns increase over climate change, energy independence, and higher fuel prices, Brazilian sugarcane ethanol is seen as a part of a clean energy future. Brazilian sugarcane ethanol was developed with a long history of government support, and with the introduction of the flex fueled car in 2003, demand rose dramatically. These factors have helped sugarcane expand beyond its traditional regions of Brazil into the Cerrado. More recently however, private capital from both domestic and foreign companies have started investing in Brazilian agriculture and these investments have helped fuel the sugarcane expansion into the Cerrado in the last 15 years. Over 22 sugarcane mills have been constructed in the Brazilian state of Goiás, located in the heart of the Cerrado. The increased investments driving the expansion of sugarcane into the Cerrado brings numerous questions regarding its environmental and social impacts. Thus, the goal of this thesis is to understand how the structural organization of the sugarcane ethanol mills’ affects development at a municipality level in the state of Goiás, Brazil. More specifically, this thesis has two objectives; to evaluate the effects of the sugarcane mills’ influence on land use and land cover change in these municipalities; and to compare how domestic owned mills, foreign owned mills, and jointly owned mills affect socioeconomic development on the municipalities. Three municipalities were analyzed, Edéia, Caçu, and Quirinópolis. Results showed that land use and land cover change varied by municipality. The majority of Edéia’s sugarcane expansion came from lands already in agricultural use. On the other hand, Caçu’s and Quirinópolis’s sugarcane expansion came from pasture lands. However, throughout all the municipalities, sugarcane expansion over native vegetation was small. All three municipalities increased their socioeconomic development levels over the past 20 years as reported on the Human Development Index. In addition, urban survey responses revealed that the residents of Edéia perceived the sugarcane mill had made their lives better than respondents in either Quirinópolis or Caçu. However, this analysis covers only a brief period of time, and future analysis of these, and other municipalities that host sugarcane mills throughout the Cerrado will be needed.

This dissertation addresses gender dimensions of Land Use and Land Cover Change (GLULCC) in the last few decades in a collective land titled to Afro-descendant communities in the Pacific region of Colombia, South America, and examines socio-economic and political signifiers affecting land use decisions, rights, and responsibilities. It shows how contrasting but complementary subfields of investigation, Political Ecology and Land Use Science, have contributed ontological, epistemological and practical scholarly works to help better understand the Gender Dimensions of Land Use and Land Cover Change (GLULCC). Historical and current information on environmental, socioeconomic and settlement processes provided a comprehensive portrait of the study area. The remote sensing process (a mainstream method for identifying land use and land cover change) helped exploring the spatial setting of land cover/use, and to reflect on the opportunities and constrains of the steps undertaken during this procedure under the lenses of researching their gendered dimensions. Statistical analyses on both census data (secondary data) and survey sample data (fieldwork data) allowed to establish a set of three groups of gendered land uses, namely, women-akin, men-akin, and gender-blind uses. Exploratory statistics, pairwise correlations, and binary and multinomial logit regression models helped to reassert the latter gendered categories’ assertions. A concluding narrative perspective of GLULCC seeks to further contribute to work streaming/ mainstreaming what I consider may be a scholarly-fertile research line. It hopes to bond, with another perspective, previous theoretical, spatial and quantitative outcomes, under the lenses of the practical experience of fieldwork, which also by way of participatory observation and semi-unstructured interviews brought to the researcher (me) valuable insights and information besides the previous outcomes. Empirical evidence allowed identifying gender-based time allocation, resource-use power relations, and reproductive strategies. Finally, the found rearrangement of settlement spaces and production systems provides practical indications that women´s role on LULCC is well beyond the establishment of small gardens and orchards, or the collection of fuel wood to provide for their families. In contrast, inside this collective title, women’s decisions/strategies have also restructured settlement patterns, and thus, land use dynamics of larger areas at heterogeneous spatial and temporal scales. / text

Research has demonstrated that equivalent temperature (TE), which incorporates both the surface air temperature (T) and moist heat content associated with atmospheric moisture, is a better indicator of overall heat content. This thesis follows up on a study that used TE to determine the impacts of land use/land cover and air masses on the atmospheric heat content over Kentucky during the growing season (April-September). The study, which used data from the Kentucky Mesonet, reveals that moist weather types dominate the growing season and, as expected, differences between T and TE are smaller under dry atmospheric conditions but larger under moist conditions. For example, the lowest TE-T difference was 10.04 °C on a dry weather day on the 18th of April, 2010 (T = 8.91 °C and TE = 18.95 °C). On the other hand, the highest estimated difference for a day of moist tropical weather was 46.54 °C on the 11th of August, 2010 (T = 26.54 °C and TE = 73.08 °C). Since land cover type influences both moisture availability and temperature in the lower atmosphere, the research shows that TE is larger in areas with higher physical evaporation and transpiration rates. Results support the hypothesis that the influence of different weather types over a region is a likely cause of interannual variation in TE.

Rapid urban growth and unprecedented rural to urban transition, along with a huge population growth are new phenomena for both high and low income countries, which started in the mid-20th century. However, urban growth rates and patterns are different in developed countries and developing ones. In less developed countries, urbanization and rural to urban transition usually takes place in an unmanaged way and they are associated with a series of socioeconomical and environmental issues and problems. Identification of the city growth trends in past decades can help urban planners and managers to minimize these negative impacts. In this research, urban growth in the city of Isfahan, Iran, is the subject of study. Isfahan the third largest city in Iran has experienced a huge urban growth and population boom during the last three decades. This transition led to the destruction of natural and agricultural lands and environmental pollutions. Historical and recent remotely sensed data, along with different remote sensing techniques and methods have been used by researchers for urban land use and land cover change detection. In this study three Landsat TM and ETM+ images of the study site, acquired in 1985, 2000 and 2009 are used. Before starting processing, radiometric normalization is done to minimize the atmospheric effects. Then, processing methods including principal component analysis (PCA), vegetation indices and supervised classification are implemented on the images. Accuracy assessment of the PCA method showed that the first PC was responsible for more than 81% of the total variance, and therefore used for analysis of PCA differencing. ΔPC1t1-t2 shows the amount of changes in land use and land cover during the period of study. In this study ten vegetation indices were selected to be applied to the 1985 image. Accuracy assessments showed that Transformed Differencing Vegetation Index (TDVI) is the most sensitive and accurate index for mapping vegetation in arid and semi-arid urban areas. Hence, TDVI was applied to the 2000 and 2009 images. ΔTDVIt1-t2 showed the changes in land use and land cover especially the land use transformation from vegetation cover into the urban class. Supervised classification is the last method applied to the images. Training sites were assigned for the selected classes and accuracy was monitored during the process of training site selection. The results of classification show the expansion of urban class and diminishment in natural and agricultural lands.

Detecting the changes of land use and land cover of the earth’s surface is extremely important to achieve continual and precise information about study area for any kinds of planning of the development. Geographic information system and remote sensing technologies have shown their great capabilities to solve the study issues like land use and land cover changes. The aim of this thesis is to produce maps of land use and land cover of Shijiazhuang on year 1993, 2000 and 2009 to monitor the possible changes that may occur particularly in agricultural land and urban or built-up land, and detect the process of urbanization in this city. Three multi-temporal satellite image data, Thematic Mapper image data from year 1993, Enhanced Thematic Mapper image data from 2000 and China Brazil Earth Resource Satellite image data from 2009 were used in this thesis. In this study, supervised classification was the major classification approach to provide classified maps, and five land use and land cover categories were identified and mapped. Post-classification approach was used to improve the qualities of the classified map. The noises in the classified maps will be removed after post-classification process. Normalized difference vegetation index was used to detect the changes of vegetated land and non-vegetated land. Change detection function in Erdas Imagine was used to detect the urban growth and the intensity of changes surrounding the urban areas. Cellular automata Markov was used to simulate the trends of land use and cover change during the period of 1993 to 2000 and 2000 to 2009, and a future land use map was simulated based on the land use maps of year 2000 and 2009. From this performance, the cross-tabulation matrices between different periods were produced to analyze the trends of land use and cover changes, and these statistic data directly expressed the change of land use and land cover. The results show that the agricultural land and urban or built-up land were changed a lot, approximately half of agricultural land was converted into urban or built-up land. This indicates that the loss of agricultural land is associated with the growth of urban or built-up land. Thus, the urbanization took place in Shijiazhuang, and the results of this urban expansion lead to the loss of agricultural land and environmental problems. During the process of detecting the land use and cover change, obtaining of high-precision classified maps was the main problem.

Rapid urban growth and unprecedented rural to urban transition, along with a huge population growth are new phenomena for both high and low income countries, which started in the mid-20th century. However, urban growth rates and patterns are different in developed countries and developing ones. In less developed countries, urbanization and rural to urban transition usually takes place in an unmanaged way and they are associated with a series of socioeconomical and environmental issues and problems. Identification of the city growth trends in past decades can help urban planners and managers to minimize these negative impacts. In this research, urban growth in the city of Isfahan, Iran, is the subject of study. Isfahan the third largest city in Iran has experienced a huge urban growth and population boom during the last three decades. This transition led to the destruction of natural and agricultural lands and environmental pollutions. Historical and recent remotely sensed data, along with different remote sensing techniques and methods have been used by researchers for urban land use and land cover change detection. In this study three Landsat TM and ETM+ images of the study site, acquired in 1985, 2000 and 2009 are used. Before starting processing, radiometric normalization is done to minimize the atmospheric effects. Then, processing methods including principal component analysis (PCA), vegetation indices and supervised classification are implemented on the images. Accuracy assessment of the PCA method showed that the first PC was responsible for more than 81% of the total variance, and therefore used for analysis of PCA differencing. ΔPC1t1-t2 shows the amount of changes in land use and land cover during the period of study. In this study ten vegetation indices were selected to be applied to the 1985 image. Accuracy assessments showed that Transformed Differencing Vegetation Index (TDVI) is the most sensitive and accurate index for mapping vegetation in arid and semi-arid urban areas. Hence, TDVI was applied to the 2000 and 2009 images. ΔTDVIt1-t2 showed the changes in land use and land cover especially the land use transformation from vegetation cover into the urban class. Supervised classification is the last method applied to the images. Training sites were assigned for the selected classes and accuracy was monitored during the process of training site selection. The results of classification show the expansion of urban class and diminishment in natural and agricultural lands.

abstract: Spatial uncertainty refers to unknown error and vagueness in geographic data. It is relevant to land change and urban growth modelers, soil and biome scientists, geological surveyors and others, who must assess thematic maps for similarity, or categorical agreement. In this paper I build upon prior map comparison research, testing the effectiveness of similarity measures on misregistered data. Though several methods compare uncertain thematic maps, few methods have been tested on misregistration. My objective is to test five map comparison methods for sensitivity to misregistration, including sub-pixel errors in both position and rotation. Methods included four fuzzy categorical models: fuzzy kappa's model, fuzzy inference, cell aggregation, and the epsilon band. The fifth method used conventional crisp classification. I applied these methods to a case study map and simulated data in two sets: a test set with misregistration error, and a control set with equivalent uniform random error. For all five methods, I used raw accuracy or the kappa statistic to measure similarity. Rough-set epsilon bands report the most similarity increase in test maps relative to control data. Conversely, the fuzzy inference model reports a decrease in test map similarity. / Dissertation/Thesis / M.A. Geography 2010

Rapid urban growth and unprecedented rural to urban transition, along with a huge population growth are new phenomena for both high and low income countries, which started in the mid-20th century. However, urban growth rates and patterns are different in developed countries and developing ones. In less developed countries, urbanization and rural to urban transition usually takes place in an unmanaged way and they are associated with a series of socioeconomical and environmental issues and problems. Identification of the city growth trends in past decades can help urban planners and managers to minimize these negative impacts. In this research, urban growth in the city of Isfahan, Iran, is the subject of study. Isfahan the third largest city in Iran has experienced a huge urban growth and population boom during the last three decades. This transition led to the destruction of natural and agricultural lands and environmental pollutions. Historical and recent remotely sensed data, along with different remote sensing techniques and methods have been used by researchers for urban land use and land cover change detection. In this study three Landsat TM and ETM+ images of the study site, acquired in 1985, 2000 and 2009 are used. Before starting processing, radiometric normalization is done to minimize the atmospheric effects. Then, processing methods including principal component analysis (PCA), vegetation indices and supervised classification are implemented on the images. Accuracy assessment of the PCA method showed that the first PC was responsible for more than 81% of the total variance, and therefore used for analysis of PCA differencing. ΔPC1t1-t2 shows the amount of changes in land use and land cover during the period of study. In this study ten vegetation indices were selected to be applied to the 1985 image. Accuracy assessments showed that Transformed Differencing Vegetation Index (TDVI) is the most sensitive and accurate index for mapping vegetation in arid and semi-arid urban areas. Hence, TDVI was applied to the 2000 and 2009 images. ΔTDVIt1-t2 showed the changes in land use and land cover especially the land use transformation from vegetation cover into the urban class. Supervised classification is the last method applied to the images. Training sites were assigned for the selected classes and accuracy was monitored during the process of training site selection. The results of classification show the expansion of urban class and diminishment in natural and agricultural lands.

Land alterations including deforestation, unsustainable land management practices and an increase in cultivated areas have occurred in the Upper Ruvu watershed in recent decades threatening water and natural resources. This study, which used a combination of remote sensing techniques, field experiments, watershed monitoring, and modeling was designed to investigate impacts of environmental changes on hydrology and soil erosion. The objectives were to: map the extent of land use and land cover change and its influence on soil erosion; correlate the contribution of climate variability and human activities to the changes in hydrology at headwater and watershed scales; estimate surface runoff, sediments and Curve Number at plot scale, and model streamflow responses to changes in land use and land cover using the SWAT watershed model. Results indicate that areas covered by forest decreased from 17% in 1991 to 4% of the total watershed area in 2015. However, areas covered by cropland increased from 14% to 30% of the total watershed area from 1991 to 2015, respectively. Further, results indicate that site characteristics affect runoff and sediment yield as higher soil loss was estimated from cropland with a mean of 28.4 tha-1 in 2015 from 19.8 tha-1 in 1991. Results from monitoring show high sediment loads were from the most disturbed watersheds, compared to Mbezi. Analysis of trends for the long term records at the watershed showed that rainfall had significant decreasing trends. At annual scale, climate variability contributed 46% and human activities contributed 54% of the changes in streamflow. Results from the rainfall simulation experiments show upland rice had higher runoff (48 mmh-1) and soil loss (94 gm-2) compared to grassland and forest. Results from the model outputs showed that average streamflow decreased by 13% between 1991 and 2015. Average peak flows increased by 5% and 12% for 2000 and 2015, respectively compared to the baseline. Land alterations had impacts on surface runoff which increased by 75% and baseflow decreased by 66% in 2015 from the baseline. These results highlight the main areas of changes and provide quantitative information to decision makers for sustainable land and water resources planning and management. / Ph. D.