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M.S.

Whitetop Mountain is Virginia's second highest peak and hosts two globally rare, insular ecosystems: a southern Appalachian grass bald and a red spruce-dominated forest. These areas provide important ecosystem services and habitat for rare and endangered species. They are highly prized for their cultural value and recreational areas that support nearby rural economies. This thesis investigated spatial patterns in both ecosystems on Whitetop. We documented a 24.73% decrease of in the extent of the southern Appalachian grass bald across 68 years through analysis of historical aerial photography. In the red spruce-dominated forest, we used a consumer grade unmanned aerial vehicle (UAV) to survey the health of all trees within a 46 ha sample plot. We assessed (dead, dying, healthy) over 9,000 individual trees based on visual patterns in the imagery and produced spatial products that will inform land managers about where resources are most needed. About 7.4% of the red spruce trees in our study area were classified as dead or dying. A model relating spruce mortality to biophysical landscape factors identified no single predictive factor related to mortality. The addition of optical information from the UAV imagery into the model proved utility for remotely-sensed data in identification of dead spruce within the forest canopy at Whitetop and possibly in other similarly structured forests. This research contributed to the limited body of knowledge surrounding the decline of both southern Appalachian grass balds and red spruce forests and provided technical insights for future mortality monitoring. / Master of Science / This thesis investigates land cover changes in two rare ecosystems on Whitetop Mountain, Virginia. The mountain has important biological significance and is a cultural landmark. The high-elevation summit hosts plant and animal species characteristic of northern climates, including a red spruce-dominated forest and a southern Appalachian grass bald. This work documented a 24.73% decrease in the size of the rare southern Appalachian grass bald ecosystem at Whitetop Mountain over 68 years and discussed potential drivers and proposed management for conservation. We also successfully used a camera-equipped unmanned aerial vehicle (drone) to produce high quality imagery for spruce mortality detection within the red spruce forest. Of over 9,000 standing spruce trees, 7.4% were categorized as either dead or dying. We built a predictive model to investigate the relationship between mortality and biophysical environmental factors, but did not identify a single causal factor. A second model that included the color band information from the drone camera revealed that different types of aerial imagery could play a valuable role in detection of tree mortality in forests of similar structure. Overall this research contributes to the body of knowledge surrounding the decline of both southern Appalachian grass bald and red spruce ecosystems and provides insights for management.

<p>This&nbsp / thesis&nbsp / examines&nbsp / the&nbsp / changes&nbsp / that&nbsp / have&nbsp / occurred&nbsp / within&nbsp / West-Coast Renosterveld within&nbsp / the&nbsp / last 350 years, and assesses&nbsp / the viability of&nbsp / the&nbsp / remaining fragments.</p>

Historically the U.S. Forest Service has used uncorrected aerial photographs to delineate proposed and past management activities on the land base it manages. Transferring a boundary from an image not planimetrically correct to a planimetrically corrected image introduces errors. Positional accuracy of boundaries affects the number of acres the Forest is accountable for managing, and the annual sale quantity (ASQ) or annual board feet targets. The purpose of this study was to develop a methodology that eliminated the need to transfer the boundary from an uncorrected to a corrected image. Raster and vector warping methods were evaluated with reference to positional accuracy and efficiency. Due to the rugged topography of the Siuslaw National Forest, selection of ground control points (GCPs) was an important function in the accurate transformation of images. A Vector warping method, Rubber Sheeting the ARC/INFO projective transformation for all digital GCPs, to all of the Global Position System (GPS) ground control points, provided the most accurate rectification of vector boundaries that had been digitized or scanned from an uncontrolled low elevation photograph. / Graduation date: 1992

Thesis (M.S.)--Mississippi State University. Department of Plant and Soil Sciences. / Title from title screen. Includes bibliographical references.

Leaf area index (LAI) provides forestry information that is important for regional scale ecological models and in studies of global change. This research examines the effects of mountainous terrain on the radiometric properties of multispectral CASI imagery in estimating ground-based optical measurements of LAI, obtained using the TRAC and LAI- 2000 systems. Field and image data were acquired summer 1998 in Kananaskis, Alberta, Canada. To account for the influence of terrain a new modified approach using the Li and Strahler Geometric Optical Mutual Shadowing (GOMS) model in 'multiple forward mode' (MFM) was developed. This new methodology was evaluated against four traditional radiometric corrections used in comination with spectral mixture analysis (SMA) and NDVI. The MFM approach provided the best overall predictions of LAI measured with ground-based optical instruments, followed by terrain normalized SMA, SMA without terrain normalization and NDVI. / xiv, 151 leaves : ill. (some col.), map ; 29 cm.

<p>This&nbsp / thesis&nbsp / examines&nbsp / the&nbsp / changes&nbsp / that&nbsp / have&nbsp / occurred&nbsp / within&nbsp / West-Coast Renosterveld within&nbsp / the&nbsp / last 350 years, and assesses&nbsp / the viability of&nbsp / the&nbsp / remaining fragments.</p>

This study determines and predicts multi-temporal Land-Use-Land-Cover Change (LULC) in a peripheral urban landscape over a 22 year period in relation to the study area‘s greenery. A change detection analysis using post classification Maximum Likelihood algorithm on three multispectral SPOT-4 images was used to determine land-cover transformation. To predict future land coverage, a Land-Cover Change Modeler (LCM) and a Markov Chain were used. Results show that between the year 2000-2006, 2006-2011 and 2000-2011 the study area experienced varied changes in the different LULCs. Built-up areas increased by 10.08%, 3.15% and 13.23% in 2000-2006, 2006-2011, and 2000-2011 respectively. Areas covered by thicket decreased by 0.59% in 2000-2006 but increased by 0.56%, 0.07% in 2006-2011 and 2000-2011 respectively. Forest land-cover increased by 2.59% in 2000-2006, 2.82% in 2006-2011, and 5.41% in 2000-2011. Grassland declined by 8.46% and 2.64% in 2000-2006 and 2000-2011 respectively while degraded grassland declined by 3.62%, 12.45% and 16.07% in 2000-2006, 2006-2011, and 2000-2011 respectively. Projection results indicate a consistent pattern of growth or decline to those experienced between 2000-2011. This study provides insight into LULC patterns within the eThekwini metro area and offers invaluable understanding of the transformation of the urban green spaces. Key words: Land-Use-Land-Cover Change, Change detection, Land-Cover Change Modeler, Markov Chain Process, Land-Cover Change Prediction. / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2013.

Mémoire (M.Ress.Renouv.)--Université du Québec à Chicoutimi, 1996. / Document électronique également accessible en format PDF. CaQCU