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Fire is a cultural phenomenon. It is among man's oldest tools, the first product of the natural world he learned to domesticate. Since the 1970's, fire has been utilized extensively in forest management practices. This study was designed to compare prescribed burning in the fall or the spring with hand cutting to reduce the overall height of vegetation. Ten scenic overlooks on the Blue Ridge Parkway were selected for treatment. The experiment is a randomized incomplete block design. Four permanent transects were delineated in each unit for vegetation sampling. Four one-by-five meter plots were sampled on each transect for the species and number of root crowns in three height classes: less than one meter, one to three meters and greater than three meters. Vegetation sampling was completed before and after treatment. Rate of spread was determined by non-directional grid sampling. Flame length was measured at five points within the sampling grid and fire intensity was calculated. Prescribed burning and hand cutting stimulate sprouting of existing vegetation. Repetitive burning is necessary to effectively control hardwood sprouting on the Parkway. Fire stimulated the herbaceous community and resulted in a significant increase in the species richness. Changes in soil characteristics were slight and did not degrade the site. Personnel costs were similar but burning required fewer hours of work. Decreases in the number of personal accidents and an expected decrease in the number of personnel required to successfully complete the burns favor the use of fire to control vegetation for forest vista management. / Master of Science

Surface coal mining leads to inevitable changes and notable impact on the physical environment of the earth and engenders immense damage to the landscape and the ecological environment. The dramatic high-speed rock digging and disturbance unavoidably causes ecosystem degradation and destruction. Detecting how surface coal mining affects the environment on the process of land use/cover change is one of the primary concerns to preserve nature and minimize the environmental impacts. Therefore, monitoring and understanding the environmental impact processes in mining areas is critical for sustainable management of the Earth's environment. In this thesis, remote sensing and Geographic Information System (GIS) are applied to assess the spatial environmental impact caused by surface coal mining in southwestern Indiana. The goal of this research is to develop a methodology to classify the coal mining field using satellite imagery and to quantify and assess land use /cover changes using remote sensing and GIS. The specific methods include classification of Landsat Thermal Mapper (TM) data and comparison of the spatial patterns of the classification results in the study region. The results are presented with a 3-D model to better understand and visualize the coal mining effects on the landscape. Results obtained in this study indicate the change area of land use/cover and the potential area for planting crops in southwestern Indiana. Based on the observation of the data results, vegetation in the study area was found to have changed significantly over the study period. In particular, the developed areas have been increasing quickly and the areas of agriculture and forests have been decreasing appreciably. / Department of Geography

<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>

Understanding variations in remote sensing data with illumination and sensor angle changes is important in agricultural crop monitoring. This research investigated field bidirectional reflectance factor (BRF) in crop differentiation and PROSAIL leaf area index (LAI) estimation. BRF and LAI data were collected for planophile and erectophile crops at three growth stages. In the solar principal plane, BRF differed optimally at 860 nm 60 days after planting (DAP) for canola and pea, at 860 nm 45 and 60 DAP for wheat and barley, and at 860 nm and 670 nm 45 and 60 DAP for planophiles versus erectophiles. The field BRF data helped better understand PROSAIL LAI estimation. NDVI was preferred for estimating LAI, however the MTVI2 vegetation index showed high sensitivity to view angles, particularly for erectophiles. The hotspot was important for crop differentiation and LAI. Availability of more along-track, off-nadir looking spaceborne sensors was recommended for agricultural crop monitoring. / xiii, 161 leaves : ill., 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>

Mammoth Cave National Park (MCNP) contains a diversity of forest types due to a complex mosaic of landform, rock types and land-use history. The point-centered quarter method was used to collect data for a forested vegetation classification. Stratified random sampling was done in each of the various site types found within the boundaries of MCNP. A classification based on the information available in the matrix of species importance values from each site type was constructed using two-way indicator species analysis (TWINSPAN). Geographic Information System (GIS) analysis was used to devise an automated vegetation mapping model that can be used to predict vegetation from environmental variables. A tool to assess the accuracy of model predictions was devised. The predicted vegetation map was stored within the GIS, and allows access to a variety of data associated with inventored, classifed, and predicted plant community types. / Department of Biology

Thesis (M.S.)--St. Cloud State University, 1995. / Includes bibliographical references (leaves 48-53).

Thesis (MSc)--Stellenbosch University, 2005. / ENGLISH ABSTRACT: Remote sensing and image processing tools provide speedy and up-to-date information on land resources. Although remote sensing is the most effective means of land cover and land use mapping, it is not without limitations. The accuracy of image analysis depends on a number of factors, of which the image classifier used is probably the most significant. It is noted that there is no perfect classifier, but some robust classifiers achieve higher accuracy results than others. For certain land cover/uses, discrimination based only on spectral properties is extremely difficult and often produces poor results. The use of ancillary data can improve the classification process. Some classifiers incorporate ancillary data before or after the classification process, which limits the full utilization of the information contained in the ancillary data. Expert classification, on the other hand, makes better use of ancillary data by incorporating data directly into the classification process. In this study an expert classification model was developed based on spatial operations designed to identify a specific land cover/use, by integrating both spectral and available ancillary data. Ancillary data were derived either from the spectral channels or from other spatial data sources such as DEM (Digital Elevation Model) and topographical maps. The model was developed in ERDAS Imagine image-processing software, using the expert engineer as a final integrator of the different constituent spatial operations. An attempt was made to identify the Level I land cover classes in the South African National Land Cover classification scheme hierarchy. Rules were determined on the basis of expert knowledge or statistical calculations of mean and variance on training samples. Although rules could be determined by using statistical applications, such as the classification analysis regression tree (CART), the absence of adequate and accurate training data for all land cover classes and the fact that all land cover classes do not require the same predictor variables makes this option less desirable. The result of the accuracy assessment showed that the overall classification accuracy was 84.3% and kappa statistics 0.829. Although this level of accuracy might be suitable for most applications, the model is flexible enough to be improved further. / AFRIKAANSE OPSOMMING: Afstandswaameming-en beeldverwerkingstegnieke kan akkurate informasie oorbodemhulpbronne weergee. Alhoewel afstandswaameming die mees effektiewe manier van grondbedekking en grondgebruikkartering is, is dit nie sonder beperkinge nie. Die akkuraatheid van beeldverwerking is afhanklik van verskeie faktore, waarvan die beeld klassifiseerder wat gebruik word, waarskynlik die belangrikste faktor is. Dit is welbekend dat daar geen perfekte klassifiseerder is nie, alhoewel sekere kragtige klassifiseerders hoër akkuraatheid as ander behaal. Vir sekere grondbedekking en -gebruike is uitkenning gebaseer op spektrale eienskappe uiters moeilik en dikwels word swak resultate behaal. Die gebruik van aanvullende data, kan die klassifikasieproses verbeter. Sommige klassifiseerders inkorporeer aanvullende data voor of na die klassifikasieproses, wat die volle aanwending van die informasie in die aanvullende data beperk. Deskundige klassifikasie, aan die ander kant, maak beter gebruik van aanvullende data deurdat dit data direk in die klassifikasieproses inkorporeer. Tydens hierdie studie is 'n deskundige klassifikasiemodel ontwikkel gebaseer op ruimtelike verwerkings, wat ontwerp is om spesifieke grondbedekking en -gebruike te identifiseer. Laasgenoemde is behaal deur beide spektrale en beskikbare aanvullende data te integreer. Aanvullende data is afgelei van, óf spektrale eienskappe, óf ander ruimtelike bronne soos 'n DEM (Digitale Elevasie Model) en topografiese kaarte. Die model is ontwikkel in ERDAS Imagine beeldverwerking sagteware, waar die 'expert engineer' as finale integreerder van die verskillende samestellende ruimtelike verwerkings gebruik is. 'n Poging is aangewend om die Klas I grondbedekkingklasse, in die Suid-Afrikaanse Nasionale Grondbedekking klassifikasiesisteem te identifiseer. Reëls is vasgestel aan die hand van deskundige begrippe of eenvoudige statistiese berekeninge van die gemiddelde en variansie van opleidingsdata. Alhoewel reëls met behulp van statistiese toepassings, soos die 'classification analysis regression tree (CART)' vasgestel kon word, maak die afwesigheid van genoegsame en akkurate opleidingsdata vir al die grondbedekkingsklasse hierdie opsie minder aantreklik. Bykomend tot laasgenoemde, vereis alle grondbedekkingsklasse nie dieselfde voorspellingsveranderlikes nie. Die resultaat van hierdie akkuraatheidsskatting toon dat die algehele klassifikasie-akkuraatheid 84.3% was en die kappa statistieke 0.829. Alhoewel hierdie vlak van akkuraatheid vir die meeste toepassings geskik is, is die model aanpasbaar genoeg om verder te verbeter.

Made available in DSpace on 2014-06-11T19:31:33Z (GMT). No. of bitstreams: 0 Previous issue date: 2008-12-02Bitstream added on 2014-06-13T20:02:19Z : No. of bitstreams: 1 celinski_tm_dr_botfca.pdf: 1773028 bytes, checksum: 4e269402cffb336eabab0615c60d49d5 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Este trabalho teve por objetivo a discriminação de classes de cobertura do solo em imagens de sensoriamento remoto do satélite CBERS-2 por meio do Classificador Árvore de Decisão. O estudo incluiu a avaliação de combinações de atributos da imagem para melhor discriminação entre classes e a verificação da acurácia da metodologia proposta comparativamente ao Classificador Máxima Verossimilhança (MAXVER). A área de estudo está localizada na região dos Campos Gerais, no Estado do Paraná, que apresenta diversidade quanto aos tipos de vegetação: culturas de inverno e de verão, áreas de reflorestamento, mata natural e pastagens. Foi utilizado um conjunto de dezesseis (16) atributos a partir das imagens, composto por: bandas do sensor CCD (1, 2, 3, 4), índices de vegetação (CTVI, DVI, GEMI, NDVI, SR, SAVI, TVI), componentes de mistura (solo, sombra, vegetação) e os dois primeiros componentes principais. A acurácia da classificação foi avaliada por meio da matriz de erros de classificação e do coeficiente kappa. A coleta de amostras de verdade terrestre foi realizada utilizando-se um aparelho GPS de navegação para o processo de georreferenciamento, para serem usadas na fase de treinamento dos classificadores e também na verificação da acurácia. O processamento das imagens e a geração dos mapas temáticos foram realizados por meio do Sistema de Informações Geográficas SPRING, sendo as rotinas desenvolvidas na linguagem de programação LEGAL. Para a geração do Classificador Árvore de Decisão foi utilizada a ferramenta See5. Na definição das classes, buscou-se um alto nível discriminatório a fim de permitir a separação dos diferentes tipos de culturas presentes na região nas épocas de inverno e de verão. A classificação por árvore de decisão apresentou uma acurácia total de 94,5% e coeficiente kappa igual a 0,9389, para a cena 157/128; para... / This work aimed to discriminate classes of land cover in remote sensing images of the satellite CBERS-2, using the Decision Tree Classifier. The study includes the evaluation of combinations of attributes of the image to a better discrimination between classes and the verification of the accuracy of the proposed methodology, comparatively to the Maximum Likelihood Classifier (MLC). The geographical area used is situated in the region of the “Campos Gerais”, in the Paraná State, which presents diversities concerning the different kinds of vegetations: summer and winter crops, reforestation areas, natural forests and pastures. It was used a set of sixteen (16) attributes from images, composed by bands of the sensor CCD (1, 2, 3, 4), vegetation indices (CTVI, DVI, GEMI, NDVI, SR, SAVI, TVI), mixture components (soil, shadow, vegetation) and the two first principal components. The accuracy of the classifications was evaluated using the classification error matrix and the kappa coefficient. The collect of the samples of ground truth was performed using a navigation device GPS to the georeference process to be used in the training stage of the classifiers and in the verification of the accuracy, as well. The processing of the images and the generation of the thematic maps were made using the Geographic Information System SPRING, and the routines were developed in the programming language LEGAL. The generation of the Decision Tree Classifier was made using the tool See5. A high discriminatory level was aimed during the definition of the classes in order to allow the separation of the different kinds of winter and summer crops. The classification accuracy by decision tree was 94.5% and kappa coefficient was 0.9389 to the scene 157/128; to the scene 158/127, it presented the values 88% and 0.8667, respectively. Results showed that the performance of the Decision Tree Classifier was better... (Complete abstract click electronic access below)