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.

Buruli ulcer (BU) is an environmental bacterium caused by Mycobacterium ulcerans. Modes of transmission and hosts of the disease remain unknown. The purposes of this study are to explore the environmental factors that are possibly explain the spatial distribution of BU, to predict BU cases by using the environmental factors, and to investigate the impact of land use and land cover change on the BU distribution. The study area covers the southwest portion of Ghana, 74 districts in 6 regions. The results show that the highest endemic areas occur in the center and expand to the southern portion of the study area. Statistically, the incidence rates of BU are positively correlated to the percentage of forest cover and inversely correlated to the percentages of grassland, soil, and urban areas in the study area. That is, forest is the most important environmental risk factor in this study. Model from zero-inflated Poisson regression is used in this paper to explain the impact of each land use and land cover type on the spatial distribution of BU. The results confirm that the changes of land use and land cover affect the spatial distribution of BU in the study area.

Land cover classification using remotely sensed data is a critical first step in large-scale environmental monitoring, resource management and regional planning. The classification task is made difficult by severe atmospheric scattering and absorption, seasonal variation, spatial dependence, complex surface dynamics and geometries, and large intra-class variability. Most of the recent research effort in land cover classification has gone into the development of increasingly robust and accurate (and also increasingly complex) classifiers by constructing–often in an ad hoc manner–multispectral, multitemporal, multisource classifiers using modern machine learning techniques such as artificial neural networks, fuzzy-sets, and expert systems. However, the focus has always been (almost exclusively) on increasing the classification accuracy of newly developed classifiers. We would of course like to perform land cover classification (i) as accurately as possible, but also (ii) as quickly as possible. Unfortunately there exists a tradeoff between these two requirements, since the faster we must make a decision, the lower we expect our classification accuracy to be, and conversely, a higher classification accuracy typically requires that we observe more samples (i.e., we must wait longer for a decision). Sequential analysis provides an attractive (indeed an optimal) solution to handling this tradeoff between the classification accuracy and the detection delay–and it is the aim of this study to apply sequential analysis to the land cover classification task. Furthermore, this study deals exclusively with the binary classification of coarse resolution MODIS time series data in the Gauteng region in South Africa, and more specifically, the task of discriminating between residential areas and vegetation is considered. / Dissertation (MEng)--University of Pretoria, 2011. / Electrical, Electronic and Computer Engineering / unrestricted

Land cover classification is necessary for understanding the state of the surface of the Earth at varying regions of interest. Knowledge of the Earth’s surface is critical in land-use planning, especially for the project study area Jupiter Inlet Lighthouse Outstanding Natural Area, where various vegetation, wild-life, and cultural components rely on adequate land-cover knowledge. The purpose of this research is to demonstrate the capability of UAV true color imagery for land cover classification. In addition to the objective of land cover classification, comparison of varying spatial resolutions of the imagery will be analyzed in the accuracy assessment of the output thematic maps. These resolutions will also be compared at varying training sample sizes to see which configuration performed best. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection

Hydrologic models were used to examine the effects of land cover change on the flow regime of a watershed located in North-Central Texas. Additionally, the effect of spatial resolution was examined by conducting the simulations using sub-watersheds of different sizes to account for the watershed. Using the Army Corps of Engineers, Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS), two different modeling methods were evaluated at the different sub-watershed resolutions for four rainfall events. Calibration results indicate using the smaller spatial resolutions improves the model results. Different scenarios for land cover change were evaluated for all resolutions using both models. As land cover change increased, the amount of flow from the watershed increased.

Diploma thesis deals with the land cover classification in Sidama region of Ethiopia and 2 kebeles, Chancho and Dangora Morocho. High resolution Sentinel-2 and very high resolution PlanetScope satellite images are used. The development of the classification algorithm is done in the Google Earth Engine cloud based environment. Ten combinations of the 4 most important parameters of the Random Forest classification method are tested. The defined legend contains 8 land cover classes, namely built-up, crops, grassland/pasture, forest, scrubland, bareland, wetland and water body. The training dataset is collected in the field during the fall 2020. The classification results of the two data types at two scales are compared. The highest overall accuracy for land cover classification of Sidama region came out to be 84.1% and kappa index of 0.797, with Random Forest method parameters of 100 trees, 4 spectral bands entering each tree, value of 1 for leaf population and 40% of training data used for each tree. For the land cover classification of Chancho and Dangora Morocho kebele with the same method settings, the overall accuracy came out to be 66.00 and 73.73% and kappa index of 0.545 and 0.601. For the classification of Chancho kebele, a different combination of parameters (80, 3, 1, 0.4) worked out better...

In Sweden, reporting is voluntary for most common felled game, and the number of voluntary reports can vary between hunting teams, HMP, and counties. In 2020, an improved harvest estimation model was developed, which reduced the sensitivity to low reporting. However, there were still some limits to the model, where large, credible intervals were estimated. Therefore, additional variables were considered as the model does not take into account landcover among HMPs, [2] the impact of climate, [4] wildlife accidents, and [4] geographical distribution, creating the covariate model. This study aimed to compare the new model with the covariate model to see if covariates would reduce the large, credible intervals. Two hypothesis tests were performed: evaluation of predictive performance using leave one out cross-validation and evaluation of the 95 % credible interval. Evaluation of predictive performance was performed by examining the difference in expected log-pointwise predictive density (ELPD) and standard error (SE) for each species and model. The results show that the covariates model ranked highest for all ten species, and out of the ten species, six had an (ELPD) difference of two to four, which implies that there is support that the covariate model will be a better predictor for other datasets than this one. At least one covariate had an apparent effect on harvest estimates for nine out of ten species. Finally, the covariate model reduced the large uncertainties, which was an improvement of the null model, indicating that harvest estimates can be improved by taking covariates into account.

The Space Shuttle Program at the John F. Kennedy Space Center (KSC) in Brevard County made a significant impact on the aerospace industry, but what is unknown is how it impacted the county surrounding it, specifically through land use/land cover (LU/LC) change and population dynamics. This research collected land cover and population data throughout the program to determine the impact, while also creating a record of the state of LU/LC and population in Brevard County in general during the same period. Urbanization and tourism were also evaluated as possible catalysts for change when analyzing the LU/LC maps created in ArcMap and the population graphs from Microsoft Excel. Calculated area for different LU/LC classes were the main focus of this research, which led to the finding that urbanization has been a major factor of change in Brevard County through expanding residential areas rather than tourism and change from the Space Shuttle Program was centered in cities closest to the KSC. / Includes bibliography. / Thesis (MS)--Florida Atlantic University, 2021. / FAU Electronic Theses and Dissertations Collection

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