An area and a point-based technique were used together at each of the same 75 sampling sites (stands), on a Sourish-Mixed Bushveld farm, to collect data for the classification and mapping of the vegetation. Both sets of data were synthesized using the same computer program package and the efficacy of the resulting classifications as well as the efficiency of the two field sampling techniques was compared. Following this, a continuous 7 752 point (1 m apart) transect was carried out, traversing the farm, in order to determine the optimum scales at which to sample Sourish-Mixed Bushveld so as to increase classification efficacy and improve community boundary recognition. The results indicated that (1) the arbitrarily chosen sampling scale of 1:8 000 was too large for "farm-scale" studies; (2) the area-based method proved to be satisfactory in that the classification and vegetation map produced with this method were verified spatially and environmentally; (3) the point-based method was deficient as a classificatory and mapping tool at large scales, since too few species were recorded with this method to make any sense of the classification and mapping of the vegetation was not possible; (4) less time per species was spent using the area-based method but because more species per stand were recorded with this method, the point-based method was quicker per stand; (5) the area-based method was easier to use in dense vegetation and irregular terrain; and (6) the optimum sampling scales for Sourish-Mixed Bushveld, as indicated by the synthesis of the continuous transect data, are about 1:12 000, 1:50 000 and 1:250 000. / Thesis (M.Sc.)-University of Natal, Pietermaritzburg, 1995.

Vegetation monitoring plays a significant role in improving the quality of life above the earth's surface. However, vegetation resources management is challenging due to climate change, global warming, and urban development. The research aims to identify and extract vegetation communities for Jupiter Inlet Lighthouse Outstanding Natural Area (JILONA) using developed Unmanned Aerial Systems (UAS) deployed with five bands of RedEdge Micasence Multispectral Sensor. UAS has a lot of potential for various applications as it provides high-resolution imagery at lower altitudes. In this study, spectral reflectance values for each vegetation species were collected using a spectroradiometer instrument. Those values were correlated with five band UAS Image values to understand the sensor's performance, also added with reflectance’s similarities and divergence for vegetation species. Pixel and Object-based classification methods were performed using 0.15 ft Multispectral Imagery to identify the vegetation classes. Supervised Machine Learning Support Vector Machine (SVM) and Random Forest (RF) algorithms with topographical information were used to produce thematic vegetation maps. The Pixel-based procedure using the SVM algorithm generated an overall accuracy and kappa coefficient of above 90 percent. Both classification approaches have provided aesthetic vegetation thematic maps. According to statistical cross-validation findings and visual interpretation of vegetation communities, the pixel classification method outperformed object-based classification. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2021. / FAU Electronic Theses and Dissertations Collection

Pixel based and object based vegetation community classification methods were performed using 30 meter spatial resolution Landsat satellite imagery of the Arthur R. Marshall Loxahatchee National Wildlife Refuge (Refuge), a remnant of the northern Everglades. Supervised classification procedures using maximum likelihood and parallelepiped algorithms were used to produce thematic maps with the following vegetation communities : wet prairie, sawgrass, cattail, tree island, brush, aquatic/open water. Spectral data, as well as NDVI, texture and principal component data were used to produce vegetation community classification maps. The accuracy levels of the thematic maps produced were calculated and compared to one another. The pixel based approach using the parallelepiped classification algorithm on the spectral and NDVI dataset had the highest accuracy level. A generalized form of this classification using only three vegetation communities (all wet prairie, tree island/brush and aquatic/open water) was compared to a previously published classification which used 1987 SPOT imagery in order to extract information on possible vegetation community transitions that are occurring within the Refuge. Results of the study indicate that 30 meter spatial resolution may be useful for understanding broad vegetation community trends but not species level trends. Pixel based procedures provide a more accurate classification than object based procedures for this landscape when using 30 meter imagery. Lastly, since 1987 there may be a trend of tree island/brush communities replacing wet prairie communities in the northern part of the Refuge and a transition to wet prairie communities in place of tree island/brush communities in the southern portion of the Refuge. / by Dorianne M. Barone. / Thesis (M.S.)--Florida Atlantic University, 2008. / Includes bibliography. / Electronic reproduction. Boca Raton, FL : 2008 Mode of access: World Wide Web.

Alvars are globally rare rock barren ecosystems on limestone pavement. This thesis focused on the quantitative classification of vegetation of Manitoba alvars, the relationships between vegetation patterns and environmental factors and the effects of grazing on vegetation. Vegetation plots were completed across twenty sites. Cluster analysis, indicator species analysis and PCA were used to describe eight vegetation types. A RDA revealed moisture regime, soil depth, bare rock cover and disturbance (grazing and browsing) are the most important factors affecting floristic composition. Grazing effects were studied at two sites using paired plots on either side of a fenceline dividing grazed and ungrazed areas. PCA and RDA showed significant difference between vegetation compositions based on grazing. A partitioning of species richness and diversity by introduced and native species revealed that both sites experienced significant replacement by introduced species. Current grazing levels on Manitoba alvars are severely impacting the vegetation of this ecosystem. / October 2016

In complex environments, increasing demand for exploring natural resources by both decision makers and the public is driving the search for sustainable planning initiatives. Among these is the use of virtual environments to support effective communication and informed decision-making. Central to the use of virtual environments is their development at low cost and with high realism. / This paper explores intelligent approaches to objects placement, orientation and scaling in virtual environments such that the process is both accurate and cost-effective. The work involves: (1) determining of the key rules to be applied for the classification of vegetation objects and the ways to build an object library according to ecological classes; (2) exploring rules for the placement of vegetation objects based on vegetation behaviours and the growth potential value collected for the research area; (3) developing GIS algorithms for implementation of these rules; and (4) integrating of the GIS algorithms into the existing SIEVE Direct software in such a way that the rules find expression in the virtual environment. / This project is an extension of an integrated research project SIEVE (Spatial Information Exploration and Visualization Environment) that looks at converting 2D GIS data into 3D models which are used for visualization. The aims of my contribution to this research are to develop rules for the classification and intelligent placement of objects, to build a normative object database for rural objects and to output these as 2D billboards or 3D models using the developed intelligent placement algorithms. / Based on Visual Basic Language and ArcObjects tools (ESRI ArcGIS and Game Engine), the outcomes of the intelligent placement process for vegetation objects are shown in the SIEVE environment with 2D images and 3D models. These GIS algorithms were tested in the integrated research project. According to the case study in Victoria, rule-based intelligent placement is based on the idea that certain decision-making processes can be codified into rules which, if followed automatically, would yield results similar to those which would occur in the natural environment. Final product produces Virtual Reality (VR) scenes similar to the natural landscapes. Considering the 2D images and 3D models represented in the SIEVE scenario and the rules (for natural and plantation vegetation) developed in conjunction with scientists in the Victorian Department of Primary Industries (DPI) and other agencies, outcomes will contribute to the development of policies for better land and resource management and link to wide ranging vegetation assessment projects.

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This pamphlet provides a summary of a fuller report issued under the same title.

This full report presents the first approximation of vegetation classification of trembling aspen ecosystems in interior British Columbia. The classification is based on a total of 186 plots sampled during the summers of 1995, 1997 and 1998. We used multivariate and tabular methods to synthesize and classify ecosystems according to the Braun-Blanquet approach and the methods of biogeoclimatic ecosystem classification. The aspen ecosystems were classified into 15 basic vegetation units (associations or subassociations) that were grouped into four alliances. Communities of the Populus tremuloides – Mertensia paniculata, and Populus tremuloides – Elymus innovatus alliances were aligned with the boreal Picea glauca & mariana order and were distributed predominantly in the Boreal White and Black Spruce zone; communities of the Populus tremuloides – Thalictrum occidentale alliance were also aligned with the same order, but were distributed predominantly in the Sub-Boreal Spruce zone; communities of the Populus tremuloides – Symphoricarpos albus alliance were aligned with the wetter cool temperate Tsuga heterophylla order and the drier cool temperate Pseudotsuga menziesii order and were distributed in the Sub-boreal Spruce, Interior Western Hemlock, Montane Spruce, and Interior Douglas-fir zones. We describe the vegatation and environmental features of these units and present vegetation and environmental tables for individual plots and units.

This pamphlet provides a summary of a fuller report issued under the title: Classification of mid-seral black spruce ecosystems of northern British Columbia

The difficulty of gathering information on field and coarse resolution of Landsat images forced to use ancillary data in vegetation mapping. The aim of this study is to increase the accuracy of species level vegetation classification incorporating environmental variables in the Amanos region. In the first part of the study, coarse vegetation classification is attained by using maximum likelihood method with the help of forest management maps. Canonical Correspondence analysis is used to explore the relationships among the environmental variables and vegetation classes. Discriminant Analysis is used in the second part of the study in two different stages. Firstly Fisher&rsquo / s linear equations for each of the previously defined nine groups calculated and the pixels are included in one of these groups by looking at the probability of that pixel being in that group. In the second stage Distance raster value of maximum likelihood classification is used. Distance raster pixels having a value less than one is accepted as misclassified and replaced with a value of first stage result of that pixel. As a result of this study 19.6 % increase in the overall accuracy is obtained by using the relationships between environmental variables and vegetation distribution.