No description available.

No description available.

Land-cover identification and mapping are an integral part of natural resource planning and management. Satellite imagery provides a way to obtain land cover information, particularly for large tracts of land such as those administered by federal and state agencies. This study assesses the usefulness of the Brightness/Greenness Transformation of Landsat Thematic Mapper data for differentiating conifer forest types in northern Utah. Satellite data for the Logan Ranger District of the Wasatch-Cache National Forest were classified into 27 vegetation classes. Of these, nine were determined to be conifer classes and were used in subsequent analyses. Ten sites of each conifer class type were field checked and vegetation and physical site characteristics recorded. The Brightness/Greenness Transformation was able to distinguish conifer areas from other vegetation types. High-density conifer classes were classified at 94 percent accuracy. Low-density conifer classes were classified correctly 65 percent of the time. The Brightness/Greenness Transformation alone met with limited success in distinguish ing between conifer species. Each class showed great variability with respect to major overstory species. Analysis of variance indicated that none of the site factors measured consistently corresponded with the spectrally designated classes. While several factors differed significantly among classes, no factor was significantly different for all c l ass-pair combinations. Correlation analysis revealed that brightness, greenness, and wetness values related more to environmental values than to conifer species. Brightness was highly correlated with percent of exposed soil on the site. Greenness was highly correlated to the presence of deciduous and herbaceous vegetation. Wetness was highly correlated to total tree and conifer cover values. Adding slope and aspect data to the Brightness/Greenness Transformation classes with the highest percentages of canopy cover did allow separation of lodgepole pine and Douglas fir. High percentagecanopy cover sites on slopes less than 35 percent were classified as lodgepole pine with 89 percent accuracy. On slopes greater than or equal to 35 percent, Douglas fir was found with 79 percent accuracy.

Desert wetlands, in particular those slow moving bodies of water known as cienegas, are important sites for biodiversity in arid landscapes and serve as indicators of hydrological functioning on the landscape-level. One of the most extensive systems of cienegas, historical or extant, in southeastern Arizona lies along Cienega Creek, located southeast of Tucson, Arizona. Satellite imagery analysis is heavily utilized to determine landscape-level trends, but cienegas present a challenge to traditional analysis methods. The Normalized Difference Vegetation Index (NDVI), the classic measure of vegetation greenness, reacts counter-intuitively to open water and is affected by open ground, both common occurrences in cienega habitats. Additional remote sensing indices have been developed that balance sensitivity to these environmental elements. This research explores these remote sensing indices at Cienega Creek applying one topographic index to current elevation data and five spectral indices to Thematic Mapper imagery from 1984 to 2011. Temporal trends were identified for all spectral indices and all indices were compared for suitability in cienega habitats. Temporal trends were analyzed for spatial clustering and spatial trends identified. The Normalized Difference Infrared Index utilizing Landsat Thematic Mapper band 5 outperformed other indices at differentiating between cienega, riparian, and upland habitats and is more suitable than NDVI for analyzing cienega habitats in such circumstances.

<p> An analysis was performed using LANDSAT Thematic Mapper digital imagery to determine the feasibility of fine scale vegetation classification in southern Ontario. </p> <p> MICROPIPS, an image processing program, was used to analyse the Thematic Mapper data, based on spectral response patterns of different land cover types. Final classified images were compared with vegetation classifications as determined by the Royal Botanical Gardens, in Hamilton, Ontario. </p> <p> It was concluded, that it was possible to classify land cover types using MICROPIPS, but only at a general level. Thus, it was not feasible to classify vegetation on a fine scale. </p> / Thesis / Candidate in Philosophy

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Spectral reflectance of the Earth surface, obtained from the satellite images, should be independent from the external influences and should reflect the surface properties, specifically the proportion of the radiance reflected from the object. It was proved in this paper that the time series of the 63 images from the Landsat 5 satellite were visibly influenced by the external factors even in the case of the images already atmospherically corrected. These external factors were age of the image and WRS-2 position from which the image was obtained. Age of the image was documented with the steady decrease of the spectral reflectance values of the invariant features, especially in the visible part of the electromagnetic spectrum, caused by the sensor degradation. The influence of the WRS-2 position was documented especially in the infrared bands. The western parts of the images are lighter (have higher values of the surface reflectance) than the eastern parts. That may cause the difference between values when monitoring one spot in two overlapping WRS-2 positions. The method originally used for the relative radiometric normalization IR-MAD was here applied to normalize the surface reflectance data, and resulted in the fact that these influences did not show up any more. In order to extend the time...

Biomass burning in southern Africa has brought significant challenges to the research society as a fundamental driver of climate and land cover changes. Burned area mapping approaches have been developed that generate large-scale low and moderate resolution products made with different satellite data. This consequently afford the remote sensing community a unique opportunity to support their potential applications in e.g., examining the impact of fire on natural resources, estimating the quantities of burned biomass and gas emissions. Generally, the satellite-derived burned area products produced with dissimilar algorithms provide mapped burned areas at different levels of accuracy, as the environmental and remote sensing factors vary both spatially and temporally. This study focused on the inter-comparison and accuracy evaluation of the 500-meter Moderate Resolution Imaging Spetroradiomter (MODIS) burned area product (MCD45A1) and the Backup MODIS burned area product (hereafter BMBAP) across the main-fire prone South African biomes using reference data independently-derived from multi-temporal 30-meter Landsat 5 Thematic Mapper (TM) imagery distributed over six validation sites. The accuracy of the products was quantified using confusion matrices, linear regression and subpixel burned area measures. The results revealed that the highest burned area mapping accuracies were reported in the fynbos and grassland biomes by the MCD45A1 product, following the BMBAP product across the pine forest and savanna biomes, respectively. Further, the MCD45A1 product presented higher subpixel detection probabilities for the burned area fractions <= 50% than the BMBAP product, which appeared more reliable in detecting burned area fractions > 50% of a MODIS pixel. Finally the results demonstrated that the probability of identifying a burned area within a MODIS pixel is directly related to the proportion of the MODIS pixel burned and thus, highlights the relevance of fractional burned area during classification accuracy assessment of lower resolution remotely-sensed products using data with higher spatial resolution. / Dissertation (MSc)--University of Pretoria, 2011. / Geography, Geoinformatics and Meteorology / unrestricted

Information on deforestation and forest management is limited due to lack of understanding as to how socioeconomic factors affect deforestation. Without adequate data, it is difficult to manage deforestation; particularly in Chipuriro where deforestation is occurring at a rapid rate. This research was carried out to address the problem. Surveys and landsat images were used to collect data. To quantify deforestation, images were processed using Geographical Information System. Pearson’s Correlation Coefficient was used to deduce the relationship between socioeconomic factors and deforestation. Socioeconomic factors were obtained through household surveys and focus group discussions. Results indicated high rates of deforestation in Ward 18 (4.7% per annum) while in Ward 16 woodland cover increased throughout the study period (7.7% per annum). Population growth with its demand on fuel wood, settlement and agricultural land caused a significant decrease in woodland cover in Ward 18. Plantations increased the area under woodlands in Ward 16. / Environmental Sciences / M. Sc. (Environmental Management)

Information on deforestation and forest management is limited due to lack of understanding as to how socioeconomic factors affect deforestation. Without adequate data, it is difficult to manage deforestation; particularly in Chipuriro where deforestation is occurring at a rapid rate. This research was carried out to address the problem. Surveys and landsat images were used to collect data. To quantify deforestation, images were processed using Geographical Information System. Pearson’s Correlation Coefficient was used to deduce the relationship between socioeconomic factors and deforestation. Socioeconomic factors were obtained through household surveys and focus group discussions. Results indicated high rates of deforestation in Ward 18 (4.7% per annum) while in Ward 16 woodland cover increased throughout the study period (7.7% per annum). Population growth with its demand on fuel wood, settlement and agricultural land caused a significant decrease in woodland cover in Ward 18. Plantations increased the area under woodlands in Ward 16. / Environmental Sciences / M. Sc. (Environmental Management)