To deliver reliable electricity to consumers on a dependable basis, electric utility companies must control undesirable woody vegetation growing on powerline rights-of-way (ROW). Six study sites were utilized for field experiments conducted in the summers of 2008 and 2009 in Neshoba County, Mississippi. This research focused on brush control on electric utility powerline distribution rights-of-way (ROW) using treatments with a recently formulated herbicide (aminocyclopyrachlor) compared to existing conventional treatments in a standard vegetation treatment program. Aminocyclopyrachlor treatments, regardless of rate or method of application, were ineffective as a stand-alone herbicide on most brush species in the study. Another experiment was conducted in the spring of 2008 on one site in Lowndes and Oktibbeha counties, Mississippi to evaluate efficacy of DAS 2706 compared with other selected bareground herbicides. Results of the experiment indicate that DAS 2706 is not a likely candidate for successful stand-alone bareground herbicide treatment.

A research report submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science (Geographical Information Systems and Remote Sensing) at the School of Geography, Archaeology & Environmental Studies) Johannesburg. 2017 / Vegetation mapping using remote sensing is a key concern in environmental application using remote sensing. The new high resolution generation has made possible, the mapping of spatial distribution of vegetation communities. The aim of this research is to test the use of new generation multispectral data for vegetation classification in Ezemvelo Game Reserve, Bronkhorspruit. Sentinel-2 and RapidEye images were used covering the study area with nine vegetation classes: eight from grassland (Mixed grassland, Wetland grass, Aristida congesta, Cynadon dactylon, Eragrostis gummiflua, Eragrostis Chloromelas, Hyparrhenia hirta, Serephium plumosum) and one from woodland (Woody vegetation). The images were pre-processed, geo-referenced and classified in order to map detailed vegetation classes of the study area. Random Forest and Support Vector Machines supervised classification methods were applied to both images to identify nine vegetation classes. The softwares used for this study were ENVI, EnMAP, ArcGIS and R statistical packages (R Development Core, 2012) .These were used for Support Vector Machines and Random Forest parameters optimization. Error matrix was created using the same reference points for Sentinel-2 and RapidEye classification. After classification, results were compared to find the best approach to create a current map for vegetation communities. Sentinel-2 achieved higher accuracies using RF with overall accuracy of 86% and Kappa value of 0.84. Sentinel-2 also achieved overall accuracy of 85% with a Kappa value of 0.83 using SVM. RapidEye achieved lower accuracies using RF with an overall accuracy of 82% and Kappa value of 0.79. RapidEye using SVM produced overall accuracy of 81% and a Kappa value of 0.79. The study concludes that Sentinel-2 multispectral data and RF have the potential to map vegetation communities. The higher accuracies achieved in the study can assist management and decision makers on assessing the current vegetation status and for future references on Ezemvelo Game Reserve. Keywords Random forest, Support Vector Machines, Sentinel-2, RapidEye, remote sensing, multispectral, hyperspectral and vegetation communities / LG2018

Thesis (M.S.)--Humboldt State University, 2007. / Includes bibliographical references (leaves 31-35). Also available via Humboldt Digital Scholar.

Thesis (Ph.D.)--University of Western Australia, 2007.

Thesis (Ph. D.(Botany))--Rhodes University, 2001.

Der Strahlungstransport (ST) in hoher Vegetation kann unter bestimmten Voraussetzungen analytisch beschrieben werden. Es sollen sogenannte Zweistrom-Modelle zur Simulation des spektralen aktinischen Flusses (AF) sowie der Photolyseraten (PR) verschiedener Spezies in Vegetation eingesetzt werden. Unter Vorgabe von Vegetationseigenschaften (Blattstreuung, Blattnormalenverteilung, Blattflächendichte etc.) können verschiedene Zweistrom-Verfahren eingeführt werden, die den Strahlungstransport vertikal aufgelöst modellieren. Dabei führt für den untersuchten Laub-Mischwald (hauptsächlich Buchen) bereits die einfache Annahme rein horizontal ausgerichteter Blätter (rein vertikale Blattnormalen) zum Erfolg: Der gemessene abwärtsgerichtete spektrale AF kann zufriedenstellend reproduziert werden, sofern bedeutsame Vegetationslücken im Kronenbereich unberücksichtigt bleiben, durch die diffuses und direktes Licht ohne Wechselwirkung mit der Vegetation zum Waldboden vordringen kann, um so den unteren Vegetationsbereich zu erhellen. / The radiation transfer in vegetation can be desribed analytically if certain assumptions are introduced. So-called two-stream methods for the simulation of the actinic flux (AF) and photolysis rates (PR) of several species in vegetation are considered. By the input of vegetation properties (leaf scattering, leaf normal distribution, leaf area density etc.) variants of two-stream methods can be introduced, which model the vertically resolved radiation field. In the case of a deciduous forest the assumption of only horizontal oriented leaves (only vertical leaf normals) already leads to good results: The measured downward spectral AF can be reproduced satisfactorily as long as significant gaps in the vegetation canopy can be left out of consideration which directly lead to an illumination of the lower layers of the vegetation.

The monitoring of continental and global scale net primary production remains a major focus of satellite-based remote sensing. Potential benefits which follow are diverse and include contributions to, and improved scientific understanding of, ecological systems, rangeland management, famine warning, agricultural commodity trading, and the study of global climate change.A NOAA-AVHRR data set containing monthly observations of green vegetation cover over a ten year period was acquired and analysed, to extract information on seasonal conditions. The data were supplied as a vegetation index, commonly known as the Normalised Difference Vegetation Index (NDVI), with a spatial resolution of approximately five km. The data set was acquired from three different satellites, and calibration problems were known to exist. A new technique was developed to estimate, and subsequently remove, the calibration bias present in the data.Monthly rainfall measurements were used as surrogate ground truth to validate the NDVI data. For regions of native vegetation, linear models relating NDVI to previous rainfall were derived, using transfer function techniques in common use in systems engineering. The models demonstrate that, in mid-latitude regions, the NDVI is a linear function of rainfall recorded over the preceding seven or eight months.Annual summaries of the image data were developed to highlight the amount and timing of plant growth. Three fundamental questions were posed as an aid to the development of the summary technique: where, when and how much? These summaries highlight the extraordinary spatial and temporal variations in plant growth, and hence rainfall, over much of Western Australia each year.Standard analysis techniques used in time series analysis, such as classical decomposition, were successfully applied to the analysis of NDVI time series. These techniques highlighted ++ / structural differences in the image data, due to land use, climatic factors and vegetation type.Overall, the results of the research undertaken in this study, using NOAA-AVHRR data in Western Australia, demonstrate that vegetation indices acquired from satellite platforms can be used to monitor continental scale seasonal conditions in an effective manner. As a consequence of these results, further research using this type of data is proposed in rangeland management and climate change modelling.

Thesis (M.S.)--Oregon State University, 2007. / Printout. Includes bibliographical references (leaves 102-105). Also available on the World Wide Web.

Thesis (M.S.)--Oregon State University, 1983. / Typescript (photocopy). Includes bibliographical references (leaves 76-80). Also available via the World Wide Web.

To overcome the limit of topography and manpower, vegetation prediction is an important method in vegetation mapping. There can be used in model prediction that concern about environment factor or in data interpolation that only consider about spatial distribution. In this research, indicator kriging was used to predict the spatial distribution of the vegetation of Laonong river basin. The distributions of associations were combined from the species in these associations which had been selected by Cluster analyst and TWINSPAN. Indicator kriging used presence/absence data to calculate the distribution pattern of these species, and the each species predicted raster had its own distinctly distribution. The distribution pattern of associations were related to species distribution directly. The stability of prediction pattern were evaluated by jackknife method. All standard errors of the prediction were under 0.01, with no significant difference in 4 different sampling measures.