Over 18 years after the democratic government took power in South Africa, environmental changes relevant to the land reform policies are taking place because of unintended consequences of land reform policy. This study aimed at investigating the effects of the South African land reform policy on land use and land cover change on a land restitution project in Makotopong, Limpopo province, South Africa. The study used remote sensing techniques through the analysis of Landsat TM images acquired in 1994 and 2007 to produce landscape maps and derive land cover change. Statistics deriving the nature of the decline in the general condition of the land restitution project gave an insight into the kind of landscape transformation that has taken place before and after land restitution program. Quantification of land cover classes have shown a decline in post-transfer activities with a decline in agricultural productivity, as illustrated by the decline in area covered by agricultural crops (showing a decline from 78.03 ha in 1994 to 20.43 ha in 2007). The study recommends that spatial data analysis through remote sensing procedures should form the information base in monitoring and evaluating the land reform projects. Results of this study demonstrated that quantification of the changes in land use and land cover types can be very useful in deriving the nature of the general environmental and social condition of the land reform project. / Thesis (M. Sc (Geography) North-West University, Mafikeng Campus, 2012

The population, employment and housing units along the Gulf Coast of Mississippi have been increasing since the 1970s through the 2000s. In this study, an overall increasing trend in land cover was found in developed land area near interstates and highways along all three coastal counties. A strong positive correlation was observed in Hancock County between developed land and population and developed land and housing units. A strong negative correlation was observed between vegetation and housing units. Weak positive correlations were found in Harrison County between developed land and population, marsh and population, and marsh and housing units. A weak positive correlation was found in Jackson County between bare soil and population. Several study limitations such as unsupervised classification and misclassification are discussed to explain why a strong correlation was not found in Harrison and Jackson Counties.

While many studies have completed accuracy assessments on the National Land Cover Dataset (NLCD), little research has utilized a detailed digitized land cover dataset, like that available for the Town of Blacksburg, for this comparison. This study aims to evaluate the information available from a detailed land cover dataset and compare it with the National Land Cover Dataset (NLCD) at a localized scale. More specifically, it utilizes the detailed land cover dataset for the Town of Blacksburg to analyze the land cover distribution for varying land uses including single-family residential, multi-family residential, and non-residential. In addition, an application scenario assigns an area-weighted curve number to watersheds based on each land cover dataset. This study exhibits the importance of obtaining detailed land cover datasets for cities and towns. Furthermore, it shows the comprehensive information and subsequent quantifications that can be surmised from a detailed land cover dataset. / Master of Science

No description available.

This research explores multiple geo-visualisations to enhance the comprehension of changing reality. It establishes a cognitive visualisation model as a framework and a multiple visualisation approach for implementation. Multiple visualisations of land cover changes including 2D and 3D, abstract and realistic simulations with static and dynamic components are created and tested through a formal user survey. It is concluded that although the real world cannot be perfectly represented, comprehension and interpretation can be improved and enhanced by providing effective multiple visualisations in accordance with users’ specific needs and tasks.

This research explores multiple geo-visualisations to enhance the comprehension of changing reality. It establishes a cognitive visualisation model as a framework and a multiple visualisation approach for implementation. Multiple visualisations of land cover changes including 2D and 3D, abstract and realistic simulations with static and dynamic components are created and tested through a formal user survey. It is concluded that although the real world cannot be perfectly represented, comprehension and interpretation can be improved and enhanced by providing effective multiple visualisations in accordance with users’ specific needs and tasks.

Cloud-to-Ground (CG) lightning activity was analyzed across the lower Mississippi River valley. The goal was to determine whether certain land use/ land cover (LULC) types supported convective thunderstorms’ generation of CG lightning more than other LULC types. Results indicate that forested regions receive more CG lightning than any other LULC type represented in the study area. However, results also indicate that CG lightning activity can be enhanced locally by very large and/or sprawling areas of urban LULC. When cities from previous research, including Atlanta, GA, and Birmingham, AL, are combined in the rankings with cities in this study, the urban size difference between Little Rock, Arkansas, and Birmingham, Alabama, appears to highlight the area of urban LULC needed to enhance convection. Future research should focus on more cities within this gap of urban LULC area in order to identify the minimum areal expanse needed to alter convective ability over cities.

No description available.

Habitat loss and human-caused mortality are the most serious threats facing grizzly bear (<i>Ursus arctosi</i> L.) populations in Alberta, with conflicts between people and bears in agricultural areas being especially important. For this reason, information is needed about grizzly bears in agricultural areas. The objectives of this research were to find the best possible classification approach for determining multiple classes of agricultural and herbaceous land cover for the purpose of grizzly bear habitat mapping, and to determine what, if any, spatial and compositional components of the landscape affected the bears in these agricultural areas. Spectral and environmental data for five different land-cover types of interest were acquired in late July, 2007, from Landsat Thematic Mapper satellite imagery and field data collection in two study areas in Alberta. Three different classification methods were analyzed, the best method being the Supervised Sequential Masking (SSM) technique, which gave an overall accuracy of 88% and a Kappa Index of Agreement (KIA) of 83%. The SSM classification was then expanded to cover 6 more Landsat scenes, and combined with bear GPS location data. Analysis of this data revealed that bears in agricultural areas were found in grasses / forage crops 77% of the time, with small grains and bare soil / fallow fields making up the rest of the visited land-cover. Locational data for 8 bears were examined in an area southwest of Calgary, Alberta. The 4494 km2 study area was divided into 107 sub-landscapes of 42 km2. Five-meter spatial resolution IRS panchromatic imagery was used to classify the area and derive compositional and configurational metrics for each sub-landscape. It was found that the amount of agricultural land did not explain grizzly bear use; however, secondary effects of agriculture on landscape configuration did. High patch density and variation in distances between neighboring similar patch types were seen as the most significant metrics in the abundance models; higher variation in patch shape, greater contiguity between patches, and lower average distances between neighboring similar patches were the most consistently significant predictors in the bear presence / absence models. Grizzly bears appeared to prefer areas that were structurally correlated to natural areas, and avoided areas that were structurally correlated to agricultural areas. Grizzly bear presence could be predicted in a particular sub-landscape with 87% accuracy using a logistic regression model. Between 30% and 35% of the grizzlies‟ landscape scale habitat selection was explained.

Part of the problem with implementing a rangeland insurance program is that the acreage of different pasture types, which is required in order to determine an indemnity payment, is difficult to measure on the ground over large areas. Remote sensing techniques provide a potential solution to this problem. This study applied single-date SPOT (Satellite Pour IObservation de la Terre) imagery, field collected data, and geographic information system (GIS) data to study the classification of land cover and vegetation at species level. Two topographic correction models, Minnaert model and C-correction, and two classifying algorithms, maximum likelihood classifier (MLC) and artificial neural network (ANN), were evaluated. The feasibility of discriminating invasive crested wheatgrass from natives was investigated, and an exponential normalized difference vegetation index (ExpNDMI) was developed to increase the separability between crested wheatgrass and natives. Spectral separability index (SSI) was used to select proper bands and vegetation indices for classification. The results show that topographic corrections can be effective to reduce intra-class rediometric variation caused by topographic effect in the study area and improve the classification. An overall accuracy of 90.5% was obtained by MLC using Minnaert model corrected reflectance, and MLC obtained higher classification accuracy (~5%) than back-propagation based ANN. Topographic correction can reduce intra-class variation and improve classification accuracy at about 4% comparing to the original reflectance. The crested wheatgrass was over-estimated in this study, and the result indicated that single-date SPOT 5 image could not classify crested wheatgrass with satisfactory accuracy. However, the proposed ExpNDMI can reduce intra-class variation and enlarge inter-class variation, further, improve the ability to discriminate invasive crested wheatgrass from natives at 4% of overall accuracy. This study revealed that single-date SPOT image may perform an effective classification on land cover, and will provide a useful tool to update the land cover information in order to implement a rangeland insurance program.