Phragmites australis is a perennial grass presently invading many intertidal and freshwater wetlands throughout much of the Atlantic Coast of North America. The spread of Phragmites into coastal wetlands is in part determined by available freshwater and nutrients, especially nitrogen, within the watershed where Phragmites populations occur. The Eastern Shore of Virginia is an intensive agricultural area, and watershed landcover may play a major role in Phragmites invasion. Forty-five Phragmites patches were sampled in eight VA Eastern Shore mainland watersheds and on a barrier island. Regardless of watershed landcover characteristics, there was little variation in Phragmites australis patch characteristics along the oceanside of the entire Eastern Shore of Virginia. Phragmites is a generalist with broad environmental tolerances. Thus, successful management and eradication plans may have broad scale application for this invasive grass.

Land cover change detection in urban areas provides valuable data on loss of forest and agricultural land to residential and commercial development. Using Landsat 5 Thematic Mapper (1991) and Landsat 7 ETM+ (2000) imagery of Gwinnett County, GA, change images were obtained using image differencing of Normalized Difference Vegetation Index (NDVI), principal components analysis (PCA), and Tasseled Cap-transformed images. Ground truthing and accuracy assessment determined that land cover change detection using the NDVI and Tasseled Cap image transformation methods performed best in the study area, while PCA performed the worst of the three methods assessed. Analyses on vegetative and vegetation changes from 1991- 2000 revealed that these methods perform well for detecting changes in vegetation and/or vegetative characteristics but do not always correspond with changes in land use. Gwinnett County lost an estimated 13,500 hectares of vegetation cover during the study period to urban sprawl, with the majority of the loss coming from forested areas.

Land and vegetation changes are part of the continuous and dynamic cycle of earth system variation. This research examines vegetation changes in the 21-county eco-region along coastal Georgia. The Advanced Very High Resolution Radiometer (AVHRR) with Normalized Difference Vegetation Index (NDVI) data is used in tandem with a Principal Component Analysis (PCA) and climatic variables to determine where, and to what extent vegetation and land cover change is occurring. This research is designed around a 16 year time-series from 1990-2005. Findings were that mean NDVI values were either steady or slightly improved, and that PC1 (Healthiness) and PC2 (Time-Change) explained nearly 99 percent of the total mean variance. Healthiness declines are primarily the result of expanding urban districts and decreased soil moisture while increases are the results of restoration, and increased soil moisture. This research aims to use this analysis for the assessment of land changes as the conduit for future environmental research.

Pattern recognition in urban areas is one of the most challenging issues in classifying satellite remote sensing data. Parametric pixel-by-pixel classification algorithms tend to perform poorly in this context. This is because urban areas comprise a complex spatial assemblage of disparate land cover types - including built structures, numerous vegetation types, bare soil and water bodies. Thus, there is a need for more powerful spectral pattern recognition techniques, utilizing pixel-by-pixel spectral information as the basis for automated urban land cover detection. This paper adopts the multi-layer perceptron classifier suggested and implemented in [5]. The objective of this study is to analyse the performance and stability of this classifier - trained and tested for supervised classification (8 a priori given land use classes) of a Landsat-5 TM image (270 x 360 pixels) from the city of Vienna and its northern surroundings - along with varying the training data set in the single-training-site case. The performance is measured in terms of total classification, map user's and map producer's accuracies. In addition, the stability with initial parameter conditions, classification error matrices, and error curves are analysed in some detail. (authors' abstract) / Series: Discussion Papers of the Institute for Economic Geography and GIScience

Groundwater is a vital non-renewable resource that is being over exploited at an alarming rate. In Saudi Arabia, the majority of groundwater is used for agricultural activities. As such, the mapping of irrigated lands is a crucial step for managing available water resources. Even though traditional in-field mapping is effective, it is expensive, physically demanding, and spatially restricted. The use of remote sensing combined with advanced computational approaches provide a potential solution to this scale problem. However, when attempted at large scales, traditional computing tends to have significant processing and storage limitations. To address the scalability challenge, this project explores open-source cloud-based resources to map and quantify center-pivot irrigation fields on a national scale. This is achieved by first applying a land cover classification using Random Forest which is a machine learning approach, and then implementing a circle detection algorithm. While the analysis represents a preliminary exploration of these emerging cloud-based techniques, there is clear potential for broad application to many problems in the Earth and environmental sciences.

Obtaining an accurate estimate of a land-cover classifier's performance over a wide geographic area is a challenging problem due to the need to generate the ground truth that covers the entire area that may be thousands of square kilometers in size. The current best approach constructs a testing dataset by drawing samples randomly from the entire area --- with a human supplying the true label for each such sample --- with the hope that the selections thus made statistically capture all of the data diversity in the area. A major shortcoming of this approach is that it is difficult for a human to ensure that the information provided by the next data element chosen by the random sampler is non-redundant with respect to the data already collected. In order to reduce the annotation burden, it makes sense to remove any redundancies from the entire dataset before presenting its samples to a human for annotation. This dissertation presents a framework that uses a combination of clustering and compression to create a concise-set representation of the land-cover data for a large geographic area. Whereas clustering is achieved by applying Locality Sensitive Hashing (LSH) to the data elements, compression is achieved through choosing a single data element to represent a given cluster. This framework reduces the annotation burden on the human and makes it more likely that the human would persevere during the annotation stage. We validate our framework experimentally by comparing it with the traditional random sampling approach using WorldView2 satellite imagery.

An important portion of the large northern permafrost soil organic carbon (SOC) pool might be released into the atmosphere as greenhouse gases following permafrost thawing and subsequent SOC decomposition under future warming conditions, resulting in a warming amplification known as the permafrost carbon feedback. Improved knowledge about the amount, composition and distribution of the permafrost SOC pool is essential when assessing the potential magnitude and timing of the permafrost carbon feedback. This study investigates and compares the SOC storage, composition and distribution in two contrasting permafrost environments: a lowland tundra area in NE Siberia (Tiksi study site), and an alpine area in the Russian Altai Mountains (Aktru Valley study site). Soil pedons were sampled down to 1 m depth and analyzed for key soil properties, i.e., DBD, water content, coarse fraction content, %OC, %IC, C/N ratios and δ¹⁵N values. These soil properties are upscaled by vertical subdivisions based on land cover classes. The role of geomorphology in the accumulation and distribution of SOC in the alpine study site is tested by using a landform and a combined land cover-land form upscaling approach. The estimated mean SOC storage in the upper meter of soils in the alpine site is 3.5 ± 0.8 kg C m¯² compared to 21.4 ± 3.2 kg C m¯² in the lowland tundra site (95% confidence intervals). The inclusion of geomorphology in the upscaling in some cases allows identification of SOC hotspots and areas with very low SOC storage within former land cover classes, therefore improving the landscape SOC storage distribution in the area. The much lower SOC stocks in the alpine site of Aktru Valley can be largely explained by the presence of extensive unvegetated areas in high altitudes (60%), the occurrence active layers deeper than the active soil formation, the enhanced SOM decomposition due to coarse grained, well-drained non-frozen soils, and the negligible occurrence of peatlands and buried organics. Instead, the lowland tundra site in NE Siberia presents important amounts of relatively undecomposed SOM in the permafrost layer. Thus, under future climate warming, alpine permafrost environments such as Aktru Valley may become a net C sink due to an upward shift of vegetation zones and an increase in plant productivity and soil development. Contrarily, lowland tundra areas such as Tiksi may become important C sources since the small increase in C uptake by photosynthetic plants will be outweighed by the thawing and subsequent decomposition of the much larger permafrost SOC pool.

No description available.

One of the major consequences of urbanization is the transformation of land surfaces from rural/natural environments to built-up land that supports diverse forms of human activity. These transformations impact the local geology, climate, hydrology, flora and fauna and human-life supporting ecosystem services in the region. Mapping and analysis of land use/land cover change in urban regions and tracking their environmental impact is therefore of vital importance for evaluating policy options for future growth and promoting sustainable urban development. The overall objective of this research is to investigate the extent of urban growth and/or sprawl and its potential environmental impact in the regions surrounding a few selected major cities in North America, Europe and Asia using landscape metrics and other environmental indicators to assess the landscape changes. The urban regions examined are the Greater Toronto Area (GTA) in Canada, Stockholm region and County in Sweden and Shanghai in China. The analyses are based on classificatons of optical satellite imagery (Landsat TM/ETM+ or SPOT 1/5) between 1985 and 2010. Maximum likelihood classification (MLC) under urban/rural masks, objectbased image analysis (OBIA) with rule-based classification and support vector machines (SVM) classification methods were used with grey level cooccurrence matrix (GLCM) texture features as input to help obtain higher accuracies. Based on the classification results, landscape metrics, selected environmental indicators and indices, and ecosystem service valuation were calculated and used to estimate environmental impact of urban growth. The results show that urban areas in the GTA grew by nearly 40% between 1985 and 2005. Results from the landscape metrics and urban compactness indicators show that low-density built-up areas increased significantly in the GTA between 1985 and 2005, mainly at the expense of agricultural areas. The majority of environmentally significant areas were increasingly surrounded by urban areas between 1985 and 2005, furthering their isolation from other natural areas. Urban areas in the Stockholm region increased by 10% between 1986 and 2006. The landscape metrics indicated that natural areas became more isolated or shrank whereas new small urban patches came into being. The most noticeable changes in terms of environmental impact and urban expansion were in the east and north of the study area. Large forested areas in the northeast dropped the most in terms of environmental impact ranking, while the most improved analysis units were close to the central Stockholm area. The study comparing Shanghai and Stockholm County revealed that urban areas increased ten times as much in Shanghai as they did in Stockholm, at 120% and 12% respectively. The landscape metrics results show that fragmentation in both study regions occurred mainly due to the growth of high density built-up areas in previously more natural environments, while the expansion of low density built-up areas was for the most part in conjunction with pre-existing patches. The growth in urban areas resulted in ecosystem service value losses of approximately 445 million USD in Shanghai, mostly due to the decrease in natural coastal wetlands, while in Stockholm the value of ecosystem services changed very little. This study demonstrates the utility of urban and environmental indicators derived from remote sensing data via GIS techniques in assessing both the spatio-temporal dynamics of urban growth and its environmental impact in different metropolitan regions. High accuracy classifications of optical medium resolution remote sensing data are achieved thanks in part to the incorporation of texture features for both object- and pixel-based classification methods, and to the use of urban/rural masks with the latter. The landscape metrics calculated based on the classifications are useful in quantifying urban growth trends and potential environmental impact as well as facilitating their comparison. The environmental indicator results highlight the challenges in terms of sustainable urban growth unique to each landscape, both spatially and temporally. The next phase of this PhD research will involve finding valid methods of comparing and contrasting urban growth patterns and estimated environmental impact in different regions of the world and further exploration of how to link urbanizing landscapes to changes in ecosystem services via environmental indicators. / <p>QC 20141212</p>

No description available.