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1
Lymburner, Leo. "Mapping riparian vegetation functions using remote sensing and terrain analysis." Connect to thesis, 2005. http://repository.unimelb.edu.au/10187/2821.
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Land use practices over the last 200 years have dramatically altered the distribution and amount of riparian vegetation throughout many catchments in Australia. This has lead to a number of negative impacts including a decrease in water quality, an increase in sediment transport and a decrease in the quality of terrestrial and aquatic habitats. The task of restoring the functions of riparian zones is an enormous one and requires spatial and temporal prioritisation. An analysis of the existing and historical functions of riparian zones and their spatial distribution is a major aid to this process and will enable efficient use of remediation resources. The approach developed in this thesis combines remote sensing, field measurement and terrain analysis to describe the distribution of five riparian zone functions: sediment trapping, bank stabilization, denitrification, stream shading and large woody debris production throughout a large semi-arid catchment in central Queensland.
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Heumann, Benjamin W. "Mapping vegetation phenology in the Sahel and Soudan, Africa, 1982 to 2005." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=101139.
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The Sahel and Soudan regions of Africa are hot-spots for vegetation change due to climatic and anthropogenic causes. Recent studies using remote sensing have demonstrated that vegetation recovery has occurred across the region questioning the occurrence of widespread land degradation in the region. These studies have focused on proxy measurements of net primary productivity, but have not addressed seasonal characteristics of vegetation such as phenology. This thesis examines how vegetation phenology has changed from 1982--2005 in the Sahel and Soudan regions and how phenology relates to observed bio-productivity and regional precipitation patterns. This is the first research to assess multi-decadal phenology change for a tropical ecosystem. Results show that while bio-productivity has significantly increased in the Sahel, significant phenology change has primarily been detected in the Soudan region. Furthermore, the relationship between phenology and bio-productivity and precipitation differs between the Sahel and Soudan. This research demonstrates the utility of measuring phenological change of a tropical ecosystem for vegetation monitoring applications.
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Mayr, Thomas. "The evaluation of PMI data for vegetation mapping in the Somerset Levels." Thesis, Cranfield University, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.281899.
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Palm, Fredrik. "Urban Vegetation Mapping Using Remote Sensing Techniques : A Comparison of Methods." Thesis, Stockholms universitet, Institutionen för naturgeografi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-117108.
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The aim of this study is to compare remote sensing methods in the context of a vegetation mapping of an urban environment. The methods used was (1) a traditional per-pixel based method; maximum likelihood supervised classification (ENVI), (2) a standard object based method; example based feature extraction (ENVI) and (3) a newly developed method; Window Independent Contextual Segmentation (WICS) (Choros Cognition). A four-band SPOT5 image with a pixel size of 10x10m was used for the classifications. A validation data-set was created using a ortho corrected aerial image with a pixel size of 1x1m. Error matrices was created by cross-tabulating the classified images with the validation data-set. From the error matrices, overall accuracy and kappa coefficient was calculated. The object-based method performed best with a overall accuracy of 80% and a kappa value of 0.6, followed by the WICS method with an overall accuracy of 77% and a kappa value of 0.53, placing the supervised classification last with an overall accuracy of 71% and a kappa value of 0.38. The results of this study suggests object-based method and WICS to perform better than the supervised classification in an urban environment.
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Miglhorance, Edmar. "Mapping Wild Leek with UAV and Satellite Remote Sensing." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/38865.
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Wild leek (Allium tricoccum) is a spring ephemeral of northeastern North America. In the Canadian province of Quebec, it is listed as threatened due to human harvesting, and in Gatineau Park its presence is used as an indicator of human impact. Wild leek grows in patches on the forest floor, and before the tree canopy develops its green leaves are clearly visible through the bare branches of deciduous forests, allowing it to be observed with optical remote sensing. This study developed and tested a new method for monitoring wild leek across large geographic areas by integrating field observations, UAV video, and satellite imagery. Three-cm resolution orthomosaics were generated for five <0.1 km2 sites from the UAV video using Structure-from-Motion, segmented, and classified into wild leek (WL) or other (OT) surface types using a simple greenness threshold. The resulting maps, validated using the field observations, had a high overall accuracy (F1-scores between 0.64 to 0.94). These maps were then used to calibrate a linear model predicting the per-pixel percentage cover of wild leek (%WL) from NDVI in the satellite imagery. The linear model calibrated for a Sentinel-2 image from 2018, covering all of Gatineau Park (~361 km2), allowed %WL to be predicted with an RMSE of 10.32. A similar model calibrated for a WorldView-2 image from 2018 was noisy (RMSE = 37.64), though much improved by resampling this image to match the spatial resolution of Sentinel-2, due to MAUP scale effect (RMSE = 13.06). Testing the potential for satellite-based monitoring of wild leek, the %WL prediction errors were similar when a new linear model was developed using the Sentinel-2 image from 2017 (RMSE = 12.84) and when the model calibrated with the 2018 Sentinel-2 image was applied to the 2017 satellite data (RMSE = 16.97). The linear models developed for the Sentinel-2 and WorldView-2 images from 2018 were used to map wild leek cover for Gatineau Park. Both images allowed production of similar wild leek maps that, based on field experience and visual inspection of the imagery, provide good descriptions of the actual distribution of wild leek at Gatineau Park.
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Hassan, Bukar. "Applications of remote sensing to arid grasslands : experimental and Nigerian case studies." Thesis, Bangor University, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.329703.
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Kamalesh, Vidhya Lakshmi. "Vegetation parameter retrieval from hyperspectral, multiple view angle PROBA/CHRIS data." Thesis, Swansea University, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.678514.
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Hurst, Rebecca Jeanne. "Use of satellite imagery to measure cover of prairie vegetation for the detection of change." Thesis, Montana State University, 2006. http://etd.lib.montana.edu/etd/2006/hurst/HurstR0506.pdf.
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Koon, Michael. "A spatial and temporal analysis of conifers using remote sensing and GIS." Huntington, WV : [Marshall University Libraries], 2004. http://www.marshall.edu/etd/descript.asp?ref=401.
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Thesis (M.S.)--Marshall University, 2004.Title from document title page. Document formatted into pages; contains viii, 40 p. including illustrations. Includes abstract. Includes bibliographical references (p. 39-40).
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Sulieman, Hussein Mohamed. "Mapping and Modelling of Vegetation Changes in the Southern Gadarif Region, Sudan, Using Remote Sensing." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2008. http://nbn-resolving.de/urn:nbn:de:bsz:14-ds-1199964393472-79860.
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The study was conducted at the vicinity of the rural town of Doka in an area of about 55 x 40 km2. The aim of the study was to map and model the influences of the introduction of mechanized rain-fed agriculture and its rapid expansion on the natural vegetation in the southern Gadarif Region. To achieve these objectives the study utilized a series of techniques. Beside the intensive use of remote sensing imagery, interviews with key informants and farmers as well as detailed field surveys were carried out. Multi-temporal analyses of remote sensing imagery showed that during the seventies the average natural vegetation clearing rate increased most rapidly and then began to slow down. Farmers are aware that land degradation, in various forms, is taking place on their cultivated agricultural land. This is based on their perception and the interpretation of indicators such as weed infestation, reduced soil fertility and soil compaction. Continuous cropping, mono-cropping, rainfall shortage and the use of inferior seeds were the main reasons of land degradation indicated by the farmers. Abandonment of agricultural land to restore soil fertility is a common practice among farmers in the Gadarif Region. The study proved that the subsequent natural regeneration of plant species and the vegetation development on abandoned agricultural land are subject to the previous cultivation period and the duration of the fallow. The current regeneration capacity of the abandoned land may not be sufficient to reach full restoration of the previous vegetation climax except for some pockets which received more regenerative resources. Field surveys in conjunction with remotely sensed and topographic data have the potential to explain the restoration and rehabilitation patterns of degraded/abandoned agricultural land to a good extent. The findings of the study seem to be representative not only for the whole Gadarif Region or other areas in Sudan, but also for other regions in the Sahel Zone with similar problems and environmental and social conditions. One of the most practical conservation approaches is to let farmers play an active role in managing their abandoned land. Such management aims to allow for a certain level of use and benefits while maintaining the natural vegetation development on theses area in order to achieve maximal restoration. Although the study investigated the vegetation development in abandoned mechanized rainfed agricultural land, a full understanding of the path-way needs surveys that include more types of abandoned land and investigation of the effects of other local environmental factors (e.g. fire, grazing, distance from forests etc.) for more than one season.
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Aleong-Mackay, Kathryn. "Landsat imagery and small-scale vegetation maps : data supplementation and verification : a case study of the Maralal area, northern Kenya." Thesis, McGill University, 1987. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=66182.
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Cherrington, Emil. "Towards ecologically consistent remote sensing mapping of tree communities in French Guiana:." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-222860.
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Tropical forests, which provide important ecosystem functions and services, are increasingly threatened by anthropogenic pressures. This has resulted in an urgent need to understand tree species diversity of those forests. Where knowledge of that diversity is largely from the botanical surveys and local ecological studies, data must inevitably be up-scaled from point observations to the landscape and regional level if a holistic perspective is required. This thesis explores aspects of the spatio-temporal heterogeneity of canopy reflectance patterns over the forests of French Guiana, in order to assess whether this information could help defining an ecologically consistent forest typology.
To gain insight into both the spatial and temporal heterogeneity of French Guiana’s forests, instrumental artefacts affecting the satellite data first had to be addressed. Data used in this study represent the spectral response of forest canopies, and the way in which such data are captured makes them susceptible to the ‘bi-directional reflectance distribution function’ (BRDF). BRDF indicates that objects do not reflect light in equal proportions in all directions (isotropically). Thus, forest canopies will reflect light anisotropically depending on factors including canopy roughness, leaf optical properties and inclination, and the position of the sun relative to the sensor. The second chapter of this thesis examines how BRDF affects the canopy reflectance of forests in French Guiana, and how not correcting for BRDF affects spectral classifications of those forests. When monthly reflectance data corrected for the artefact are examined, these suggest seasonally-occurring changes in forest structure or spectral properties of French Guiana’s forests.
The third chapter of this thesis thus examines temporal effects of BRDF, and used cross-regional comparisons and plot-level radiative transfer modelling to seek to understand the drivers of the monthly variation of the forests’ canopy reflectance. For the latter, the Discrete Anisotropic Radiative Transfer (DART) model was used along with aerial laser scanning (ALS) observations over different forest structures, indicating that the observed variation in reflectance (and derivatives known as vegetation indices) could not be explained by monthly variations in solar direction. At the regional scale, it was also demonstrated that forests in the Guiana Shield possess temporal variation distinct from forests in central Africa or northern Borneo, forests also lying just above the Equator. Had the observed temporal variation in vegetation indices been the result of BRDF, it would have been expected that the forests in the three zones would have similar patterns of variation, which they did not. Central African forests appear to have their greening synchronized with rainfall, whereas forests in the Guianas appear synchronized with the availability of solar radiation.
Further analysis of the vegetation index time-series of observations also indicated that different types of forests in French Guiana possess distinct patterns of temporal variation, suggesting that tropical forest types can be discriminated on the basis of their respective “temporal signatures.” That was exploited in the fourth chapter of the thesis, which maps forests in French Guiana based on their combined spatio-temporal canopy reflectance patterns and by so doing presents a novel way of addressing forest typology, based on ecologically meaningful information.
The thesis presented demonstrates that it is possible to adequately address remote sensing data artefacts to examine patterns of spatial and temporal variation in tropical forests. It has shown that phenological patterns of tropical rainforests can be deduced from remote sensing data, and that forest types can be mapped based on spatio-temporal canopy reflectance patterns. It is thus an important contribution to understand the ecology of tropical forests in French Guiana and to improve the toolbox of scientists dealing with the identification of spatio-temporal patterns observable in forests at the landscape level.
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Mehner, Henny. "The potential of high spatial resolution remote sensing for mapping upland vegetation using advanced classification methods." Thesis, University of Newcastle Upon Tyne, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.417524.
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Jackson, Anthony Edward. "Mapping the effects of dry sclerophyll vegetation within the battlespace using the Leica ADS40 and GIS." Queensland University of Technology, 2006. http://eprints.qut.edu.au/16189/.
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1st Topographical Survey Squadron, Royal Australian Engineers, provides deployable geospatial support to the Australian Defence Force. Part of this role is the production of products for use by commanders. These products provide commanders at all levels with mission specific and up to date knowledge of the terrain that he will encounter on the battlespace. Currently 1st Topographical Survey Squadron provides products that contain manmade features, hydrography, slope, surface configuration and vegetation as displayed on current mapping products. They do not provide an accurate portrayal of the effect that vegetation characteristics have on the battlespace.
Introducing these types of features will give the commander a greater knowledge of the terrain and environment that he will encounter, and will greatly improve the planning and success of the campaign.
This research explored to what extent the accuracy of the terrain analysis products currently produced by 1st Topographical Survey Squadron would increase by adding a more detailed portrayal of vegetation extracted from remote sensing data.
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Bispo, Rafael Carlos 1982. "Utilização de dados do sensor Modis no monitoramento e mapeamento da cultura de café." [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/256799.
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Orientadores: Rubens Augusto Camargo Lamparelli, Jansle Vieira RochaDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Agrícola
Made available in DSpace on 2018-08-22T16:59:27Z (GMT). No. of bitstreams: 1 Bispo_RafaelCarlos_M.pdf: 6682271 bytes, checksum: 3672bc3cd1d328c4edf329eabbd31ffe (MD5) Previous issue date: 2013
Resumo: A produção de café esteve intimamente ligada ao desenvolvimento econômico do Brasil e ainda hoje o café é um importante produto da agricultura nacional. O Estado de Minas Gerais responde atualmente por 52% de toda a área de café do Brasil. Dessa forma, dada a importância da cafeicultura para a economia brasileira, é necessário desenvolver e melhorar as metodologias para seu monitoramento. Dados de sensoriamento remoto podem fornecer informações para o monitoramento e o mapeamento de café de maneira mais rápida e menos onerosa do que os métodos convencionais. Nesse contexto, os objetivos desta pesquisa foram identificar a bienalidade da cultura de café por meio de dados do sensor MODIS, juntamente com dados de estações meteorológicas, entre os anos de 2004 a 2012, e avaliar a eficácia das imagens-fração derivadas do sensor MODIS no mapeamento automático das áreas de café do município de Monte Santo de Minas/MG. Foi utilizada uma série temporal com 163 imagens da banda NIR do MODIS, produto MOD13Q1, para se extrair os valores de refletância dos pixels com pelo menos 80% de café. Dados diários de temperatura e precipitação foram agrupados de acordo com a resolução temporal das imagens (16 dias) para o cálculo do balanço hídrico. Para o mapeamento das áreas de café, foram utilizadas imagens do MODIS, bandas MIR, NIR e RED, dos períodos seco e chuvoso. Através do Modelo Linear de Mistura Espectral foram derivadas imagens-fração de solo, café e água/sombra. Estas imagens-fração serviram como dados de entrada para a classificação automática supervisionada com o método SVM - Support Vector Machine. Os resultados mostraram que para o monitoramento do café os dados de refletância dos períodos de colheita apresentaram maior correlação com a alternância da quantidade da produção. A partir da matriz de erro montada entre as classificações e as máscaras de referência, observou-se que os melhores resultados de Exatidão Global e Índice Kappa foram obtidos na classificação do período seco, sendo 67% e 0,41, respectivamente. Análises estatísticas de correlação e coeficiente de variação aplicadas sobre as imagens-fração de café permitiram melhor compreensão da complexidade do mapeamento do café
Abstract: Coffee production was closely linked to the economic development of Brazil and even today coffee is an important product of national agriculture. The State of Minas Gerais currently accounts for 52% of the whole area of coffee in Brazil. Thus, given the importance of the coffee crops to Brazilian economy, it is necessary to develop and improve methodologies for its monitoring. Then, remote sensing data can provide information for monitoring and mapping of coffee crops faster and cheaper than conventional methods. In this context, the objectives of this study were to identify the biennial yield of the coffee crop using data from MODIS and meteorological stations, over the period between 2004 and 2012, and assess the effectiveness of the fraction-images derived from MODIS in the automatic mapping of the areas of coffee in Monte Santo de Minas/MG. Were used a time series of 163 images of NIR band from MODIS, MOD13Q1 product, to extract the values of reflectance of pixels with at least 80% of coffee. Daily data of air temperature and precipitation were compiled to 16-day intervals to match the temporal resolution of MODIS imagery and to calculate the water balance. For coffee mapping, we used MODIS imagery, MIR, NIR and RED bands, of dry and rainy seasons. Through the Spectral Linear Mixing Model were derived fraction images of soil, coffee and water/shadow. These fraction images served as input data for supervised classification with SVM - Support Vector Machine approach. The results showed that for coffee monitoring the reflectance data of harvest period presented higher correlation with the alternation of coffee production. From the error matrix between the classifications and reference masks, it was observed that the best results of Overall Accuracy and Kappa Index were obtained in the classification of the dry season, with 67% and 0.41, respectively. Statistical analyses of correlation and coefficient of variation applied over images fraction of coffee allowed a better understanding about the complexity of mapping coffee
Mestrado
Planejamento e Desenvolvimento Rural Sustentável
Mestre em Engenharia Agrícola
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Amundsen, Kelly J. "Mapping Riparian Vegetation in the Lower Colorado River Using Low Resolution Satellite Imagery." Cleveland State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=csu1292855785.
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Williams, Danielle M. "Time series analysis of vegetation dynamics and burn scar mapping at Smoky Hill Air National Guard Range, Kansas using moderate resolution satellite imagery." Thesis, Kansas State University, 2016. http://hdl.handle.net/2097/34462.
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Master of ArtsDepartment of Geography
J. M. Shawn Hutchinson
Military installations are important assets for the proper training of armed forces. To ensure the continued viability of training lands, management practices need to be implemented to sustain the necessary environmental conditions for safe and effective training. For this study two analyses were done, a contemporary burn history and a time series analysis. The study area is Smoky Hill Air National Guard Range (ANGR), an Impact Area (within the range) and a non-military Comparison Site. Landsat 5 TM / 7 ETM+ imagery was used to create an 11 year composite burn history image. NDVI values were derived from MODIS imagery for the time series analysis using the statistical package BFAST. Results from both studies were combined to make conclusions about training impacts at Smoky Hill ANGR and determine if BFAST is a viable environmental management tool. Based on this study the training within Smoky Hill ANGR does not seem to be having a negative effect on the overall vegetation condition. It was also discovered that BFAST was able to accurately detect known vegetation disturbances. BFAST is a viable environmental management tool if the limitations are understood.
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Kaloki, McNichol Kitavi. "MAPPING VEGETATION STATUS AT LAKE NAKURU NATIONAL PARK AND SURROUNDS, KENYA." Miami University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=miami1498015331943846.
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McCall, David S. "Expanding the Application of Spectral Reflectance Measurement in Turfgrass Systems." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/77971.
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Light reflectance from plants can be used as a non-invasive predictor of health and yield for many cropping systems, and has been investigated to a lesser extent with managed turfgrass systems. The frequent agronomic inputs associated with maintaining golf course grasses allow for exceptional stand quality under harsh growing conditions, but often expend resources inefficiently, leading to either stand loss or unnecessary inputs in localized areas. Turfgrass researchers have adopted some basic principles of light reflectance formerly developed for cropping systems, but field radiometric-derived narrow-band algorithms for turfgrass-specific protocols are lacking. Research was conducted to expand the feasibility of using radiometry to detect various turfgrass stressors and improve speed and geographic specificity of turfgrass management. Methods were developed to detect applied turfgrass stress from herbicide five days before visible symptoms developed under normal field growing conditions. Soil volumetric water content was successfully estimated using a water band index of creeping bentgrass canopy reflectance. The spectral reflectance of turfgrass treated with conventional synthetic pigments was characterized and found to erroneously influence plant health interpretation of common vegetation indices because of near infrared interference by such pigments. Finally, reflectance data were used to estimate root zone temperatures and root depth of creeping bentgrass systems using a gradient of wind velocities created with turf fans. Collectively, these studies provide a fundamental understanding of several turfgrass-specific reflectance algorithms and support unique opportunities to detect stresses and more efficiently allocate resources to golf course turf.Ph. D.
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Rolfson, David, and University of Lethbridge Faculty of Arts and Science. "Collection of endmembers and their separability for spectral unmixing in rangeland applications." Thesis, Lethbridge, Alta. : University of Lethbridge, Dept. of Geography, 2010, 2010. http://hdl.handle.net/10133/2527.
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Rangelands are an important resource to Alberta. Due to their size, mapping
rangeland features is difficult. However, the use of aerial and satellite data for mapping
has increased the area that can be studied at one time. The recent success in applying
hyperspectral data to vegetation mapping has shown promise in rangeland classification.
However, classification mapping of hyperspectral data requires existing data for input
into classification algorithms. The research reported in this thesis focused on acquiring a
seasonal inventory of in-situ reflectance spectra of rangeland plant species (endmembers)
and comparing them to evaluate their separability as an indicator of their suitability for
hyperspectral image classification analysis. The goals of this research also included
determining the separability of species endmembers at different times of the growing
season.
In 2008, reflectance spectra were collected for three shrub species (Artemisia
cana, Symphoricarpos occidentalis, and Rosa acicularis), five rangeland grass species
native to southern Alberta (Koeleria gracilis, Stipa comata, Bouteloua gracilis,
Agropyron smithii, Festuca idahoensis) and one invasive grass species (Agropyron
cristatum). A spectral library, built using the SPECCHIO spectral database software,
was populated using these spectroradiometric measurements with a focus on vegetation
spectra.
Average endmembers of plant spectra acquired during the peak of sample
greenness were compared using three separability measures – normalized Euclidean
distance (NED), correlation separability measure (CSM) and Modified Spectral Angle
Mapper (MSAM) – to establish the degree to which the species were separable. Results
were normalized to values between 0 and 1 and values above the established thresholds
indicate that the species were not separable . The endmembers for Agropyron cristatum,
Agropyron smithii, and Rosa acicularis were not separable using CSM (threshold =
0.992) or MSAM (threshold = 0.970). NED (threshold = 0.950) was best able to separate
species endmembers.
Using reflectance data collected throughout the summer and fall, species
endmembers obtained within two-week periods were analyzed using NED to plot their
separability. As expected, separability of sample species changed as they progressed
through their individual phenological patterns. Spectra collected during different solar
zenith angles were compared to see if they affected the separability measures. Sample
species endmembers were generally separable using NED during the periods in which
they were measured and compared. However, Koeleria gracilis and Festuca idahoensis
endmembers were inseparable from June to mid-August when measurements were taken
at solar zenith angles between 25° – 30° and 45° – 60°. However, between 30° and 45°,
Bouteloua gracilis and Festuca idahoensis endmembers, normally separable during other
solar zenith angles, became spectrally similar during the same sampling period.
Findings suggest that the choice of separability measures is an important factor
when analyzing hyperspectral data. The differences observed in the separability results
over time also suggest that the consideration of phenological patterns in planning data
acquisition for rangeland classification mapping has a high level of importance.xii, 93 leaves : ill. (some col.) ; 29 cm
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Wang, Wei J. "Monitoring the impact of surface coal mining on vegetation in southwestern Indiana using remote sensing and GIS." Virtual Press, 2008. http://liblink.bsu.edu/uhtbin/catkey/1399198.
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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
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Yadav, Shweta. "SATELLITE-BASED APPROACH FOR MONITORING AND MAPPING THE SUBMERGED AQUATIC VEGETATION IN THE EUTROPHIC SHALLOW BASIN OF LAKE BIWA, JAPAN." Kyoto University, 2017. http://hdl.handle.net/2433/227617.
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Smit, Walter J. (Walter Johan). "A comparison of selected satellite remote sensing techniques for mapping fire scars in limestone fynbos." Thesis, Stellenbosch : Stellenbosch University, 2001. http://hdl.handle.net/10019.1/52064.
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Thesis (MA.)--Stellenbosch University, 2001.ENGLISH ABSTRACT: There are many reasons to conserve fynbos. Not only does fynbos form part of the Cape floral kingdom, one of the richest floral kingdoms in the world, but the contribution that it makes to the regional economy through utilisation, education, recreation and tourist opportunities is immeasurable. Fire plays an integral role in fynbos ecosystems. According to Van Wilgen, Richardson & Seydack (1994: 322) " ... managing fynbos equates to managing fire". Therefore managers need accurate fire information about a fynbos area to manage it properly. This is where satellite remote sensing can provide the manager with useful information about the fire regime. In other words, satellite remote sensing can help a manager establish where and when an area has burnt. Using readily available satellite data, this study attempts to establish (through comparison) what techniques would be most suitable and affordable to compile a fire information database. Landsat Thematic Mapper data from 1990 - 1996 of the southwestern Cape was used and compared with existing fire records of the area. The results show that techniques such as supervised and unsupervised classification are reliable in identifying burnt areas, but a major drawback of these techniques is that they require a large amount of user input and knowledge. They are thus not regarded as simple or easily repeatable. - The' more simple techniques like image differencing and image ratioing were also found to be reliable in identifying burnt areas. These techniques require less user input and in some instances less data (image bands) to produce similar (or better) results than supervised and unsupervised classification techniques. The results show that differencing temporally different Images, obtained from applying principle components analysis, produces reliable results with very little confusion and little user input. Using such a technique could enable users to procure only two bands of Landsat data and still produce reliable fire information for managing a fynbos ecosystem.
AFRIKAANSE OPSOMMING: Daar is verskeie redes waarom fynbos bewaar moet word. Nie net vorm dit deel van een van die rykste blommeryke in die wereld nie, maar die bydrae wat dit tot die streeksekonomie maak, deur die benutting van veldblomme en die geleenthede wat dit bied vir toerisme en ontspanning, is enorm. Vuur speel 'n belangrike rol in die bestuur van fynbos ekosisteme. Soos beklemtoon deur Van Wilgen, Richardson & Seydack (1994: 322) se stelling: " ... managing fynbos equates to managing fire". Om hierdie rede is dit belangrik dat 'n bestuurder akkurate inligting oor die verspreiding van veldbrande moet he. Satellietafstandwaarneming kan hier 'n belangrike rol speel deur sulke inligting te verskaf Deur gebruik te maak van maklik bekombare satellietdata, poog hierdie studie om te bepaal (d.m.v. vergelyking) watter tegnieke die mees geskikte is in terme van bekostigbaarheid en gebruikersvriendelikheid. Landsat Thematic Mapper data van 1990 tot 1996 van die suidwes-Kaap is gebruik en vergelyk met bestaande branddata van die studiegebied. Daar is gevind dat tegnieke soos gerigte en nie-gerigte klassifikasie in staat is om gebrande dele betroubaar uit te ken. Hierdie tegnieke verg egter baie insette en kennis van die gebruiker en is ook nie maklik om jaar na jaar te herhaal nie. Daarom word hierdie tegnieke nie aanbeveel nie. Daar is gevind dat die eenvoudiger tegnieke soos veranderingsanalise ook gebrande dele betroubaar kon uitken. Hierdie tegnieke het die voordeel dat die gebruiker nie baie' kennis van die gebied hoef te he nie en ook nie so baie insette hoef te lewer nie. Hierdie tegnieke word bo gerigte en nie-gerigte klassifikasie aanbeveel. - Die resultate dui daarop dat betroubare resultate verkry kan word deur tempo reel verskillende beeIde, verkry deur hoofkomponentanalise, van mekaar af te trek. Hierdie tegniek vereis relatief min gebruikersinsette en daar kan selfs met slegs twee Landsat bande gewerk word. So 'n tegniek kan beslis 'n bekostigbare en effektiewe manier wees om nodige inligting vir die bestuur van 'n fynbos ekosisteem te bekom.
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Hickson, Benjamin. "Using Classification and Regression Tree and Valley Bottom Modeling Techniques to Identify Riparian Vegetation in Pinal County, Arizona." The University of Arizona, 2015. http://hdl.handle.net/10150/626257.
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The ecological value and functionality of riparian systems along ephemeral, intermittent, and perennial streams in the Southwest is well established. In Pinal County, Arizona the existing datasets available to environmental managers and governing bodies drastically underestimate the extent and presence of riparian zones. This study addresses the issue through the use of remote sensing land cover classification techniques. Landsat 8 data, topographic data, and high-resolution color infrared (CIR) imagery, and several derived vegetation indices are used to construct a classification and regression tree (CART) model. Using training data, the CART model is used for the identification and delineation of basic land cover classes across the County. Woody annual and perennial species are identified and associated to riparian zones using a valley bottom model (VBM) developed by the United States Department of Agriculture.
The CART model (kappa value of 0.76) found that 929 square-miles of annual vegetation and 651 square-miles of perennial vegetation are present across Pinal County. Annual and perennial vegetation classifications are assessed for density using a 0.33 acre moving window. The density values for both classes are then used in conjunction to differentiate upland, xeroriparian, mesoriparian, and hydropriarian vegetation zones. Vegetation zones are clipped to regions where the VBM identifies valley bottom probability to be 62 percent or greater. The results generated provide a sufficiently comprehensive dataset that gives County managers and environmental professionals improved insight into the presence and distribution of important riparian habitats.
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Sulieman, Hussein Mohamed. "Mapping and Modelling of Vegetation Changes in the Southern Gadarif Region, Sudan, Using Remote Sensing: Land-Use Impacts on Biophysical Processes." Doctoral thesis, Dresden TUDpress, 2007. http://deposit.d-nb.de/cgi-bin/dokserv?id=3058481&prov=M&dok_var=1&dok_ext=htm.
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Vargas, Juan Jose Quiros. "Multispectral aerial images to phenotype yield potential and tree inventory mapping: case studies in dry pea (Pisum sativum) and apple (Malus domestica) nursery." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/11/11152/tde-28022018-180550/.
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Field data collection involves time and money consuming processes, additionally carrying possible measurement errors. With the technological advance in the last years, low cost remote sensing tools have emerged to facilitate procedures for in-field measurements, being one of the most known techniques the use of multispectral cameras coupled to RPA. These tools are complemented by the implementation of procedures in GIS and image-processing software, from which are developed methodologies leading to extract target values from a certain original set of data. In this work, multispectral images were used in two case studies: (1) for yield estimation in pea plots for breeding research, and (2) for plant counting in an apple nursery planted directly on the soil; both fields are located in Washington State, USA. In the first case, a reliable and replicable methodology for yield estimation was created as a high throughput phenotyping technique; while in the second case an algorithm capable of identifying the number of apple plants with more than 95% accuracy was developed. In both studies, remote sensing is used as an efficient and practical way to improve field operations under the specified conditions of each case.A coleta de dados de campo envolve processos de grande consumo em tempo e dinheiro, ademais de levar o risco de possíveis erros de medição. Com o avanço tecnológico nos últimos anos, surgiram ferramentas de sensoriamento remoto de baixo custo para facilitar procedimentos de medição em campo, sendo uma das técnicas mais conhecidas o uso de câmeras multiespectrales acopladas a um ARP. Essas ferramentas são complementadas pela implementação de procedimentos em programas SIG e de processamento de imagens, a partir dos quais são desenvolvidas metodologias que visam extrair valores alvo desde um determinado conjunto original de dados. Neste trabalho, foram utilizadas imagens multiespectrais no desenvolvimento de dois estudos de caso: (1) para estimativa de produtividade em parcelas para pesquisa de ervilha, e (2) para contagem de plantas em um viveiro de maçã plantado diretamente no solo; ambos os campos localizados no estado de Washington, EUA. No primeiro caso, foi criada uma metodologia confiável e replicável para estimativa de produtividade como técnica de fenotipagem de alto rendimento; enquanto no segundo caso, foi desenvolvido um algoritmo capaz de identificar o número de plantas de maçã com mais de 95% de exatidão. Em ambos os estudos, o sensoriamento remoto é usado como uma ferramenta eficiente e prática na melhora de operações de campo.
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Mashimbye, Zama Eric. "Remote sensing-based identification and mapping of salinised irrigated land between Upington and Keimoes along the lower Orange River, South Africa." Thesis, Link to the online version, 2005. http://hdl.handle.net/10019/1647.
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Mazumdar, Deepayan Dutta. "Multiangular crop differentiation and LAI estimation using PROSAIL model inversion." Thesis, Lethbridge, Alta. : University of Lethbridge, Dept. of Geography, c2011, 2011. http://hdl.handle.net/10133/3103.
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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
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Sulieman, Hussein Mohamed [Verfasser]. "Mapping and modelling of vegetation changes in the Southern Gadarif Region, Sudan, using remote sensing : land-use impacts on biophysical processes / Hussein Mohamed Sulieman." Dresden : TUDpress, 2008. http://d-nb.info/997493739/34.
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Kidane, Dawit K. "Rule-based land cover classification model : expert system integration of image and non-image spatial data." Thesis, Stellenbosch : Stellenbosch University, 2005. http://hdl.handle.net/10019.1/50445.
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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.
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Thapa, Vivek. "Analysis of the One-Horned Rhinoceros (Rhinoceros Unicornis) Habitat in the Royal Chitwan National Park, Nepal." Thesis, University of North Texas, 2005. https://digital.library.unt.edu/ark:/67531/metadc4926/.
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This study analyzes the remaining suitable habitat of the one-horned rhinoceros, Rhinoceros unicornis, in Royal Chitwan National Park of Nepal. An April 2003 Landsat image was classified into eight land cover types: wetland, sand, water, mixed forest, sal forest, agriculture, settlement, and grassland. This image was converted into habitat suitability maps using cover, food, and water. The rhinoceros prefers grassland habitat with oxbow lakes and closed canopy during the monsoon season. Nominal values of five parameters were used to create a map of habitat suitability index. The map was categorized into four habitat classes: highly unsuitable, unsuitable, moderately suitable habitat, and suitable. Landscape metrics, patch metrics and class metrics associated with habitat were determined through the use of FRAGSTATS.
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Newton, Ian Paul. "Recent transformations in West-Coast Renosterveld: patterns, processes and ecological significance." Thesis, University of the Western Cape, 2008. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_8396_1263521893.
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This
thesis
examines
the
changes
that
have
occurred
within
West-Coast Renosterveld within
the
last 350 years, and assesses
the viability of
the
remaining fragments.
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Young, Andrea Ferraz. "Aplicação de indices relativos de vegetação e temperatura para estudo das mudanças do uso e ocupação do solo : estudo de caso de Curitiba (PR), 1986 a 2002." [s.n.], 2004. http://repositorio.unicamp.br/jspui/handle/REPOSIP/257217.
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Orientadores: Jansle Vieira Rocha, Maria Victoria Ramos BallesterTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Agricola
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Resumo: O objetivo principal do trabalho foi o de criar índices relativos de vegetação e temperatura da superfície, sensíveis a mudanças, que sintetizassem as alterações ocorridas nos padrões da cobertura vegetal e urbanização, em função das transformações evidenciadas no tecido urbano do município de Curitiba (PR). Baseando-se na análise dos resultados obtidos através do cálculo desses índices, procedeu-se a análise conjunta com os dados de população. Portanto, esse processo envolveu o estudo dos padrões de uso do solo, das interações entre as diferentes classes inseridas na paisagem e de como esses padrões e interações mudam ao longo do tempo. Assim sendo, três regiões de Curitiba foram selecionadas por meio da definição de critérios específicos e comparadas em termos de mudanças de padrões e tendências.Imagens Landsat TM e ETM+ foram utilizadas para identificar diferentes padrões de cobertura da terra fornecendo uma classificação do uso do solo. Para isolar as áreas com vegetação das superfícies urbanas construídas, o Índice de Vegetação por Diferença Normalizada (NDVI) foi utilizado como um indicador da presença de vegetação, a partir do qual o índice de vegetação relativo (NDVI-R) foi criado. Ao mesmo tempo, valores das bandas termais do satélite Landsat (bandas 6) foram extraídos como indicadores das diferenças termais entre usos do solo, servido de base para a proposição de um índice de temperatura relativo (DN-R), que foi comparado com o índice de vegetação relativo (NDVI-R). Um banco de dados identificando as principais características da população de cada região foi construído, servindo de suporte para a análise entre as variáveis, fornecendo cenários da realidade e subseqüentes conflitos causados pelas mudanças na paisagem. Através da aplicação dessas técnicas foi possível verificar a importância do tamanho e distribuição das áreas de vegetação na caracterização das áreas urbanizadas e semi-urbanizadas. Esta abordagem comparativa demonstrou como a paisagem pode ser derivada do imageamento por satélite fornecendo uma representação das mudanças ocorridas na estrutura espacial urbana. Além disso, demonstrou como o rápido crescimento populacional e o desenvolvimento urbano tendem a competir com condições ambientais mais sensíveis, tais como parques e áreas de proteção ambiental. Todo o processo envolveu mudanças na composição, estrutura e função da paisagem, que ocorreu sobre um pano de fundo de manchas naturais remanescentes alteradas pelas transformações da morfologia urbana. A maioria dessas alterações evidencia mudanças no micro clima. Certamente, a análise das distribuições espaciais forneceu novos esclarecimentos sobre a estrutura da paisagem, que poderão ser explorados no planejamento do uso do solo. Esta é uma abordagem que fornece uma nova direção e oportunidade de pesquisa no que se refere a questões ambientais relativas ao processo de tomada de decisão, endereçada a objetivos ambientais com vistas ao desenvolvimento sócio-econômico, especialmente porque ressalta-se o fato de que não apenas os atributos físicos dos elementos da paisagem, mas também suas configurações espaciais são importantes determinantes na dinâmica do uso do solo
Abstract: The aim of this study was to propose relative vegetation and temperature indices which could reflect the changes occurred in the vegetation cover and urbanization patterns caused by transformations along the city of Curitiba (PR). Based on the results of these analysis it was achieved the analysis of the mainly population characteristics. Thus, it involved the study of land use patterns, the interactions between them within the landscape, and how these patterns and interactions change over the time. Therefore, three areas of Curitiba were selected by specific criteria and were studied and compared in terms of changing patterns and tendencies. Landsat TM and ETM+ images were used to identify different patterns of land cover providing a land use classification. In order to separate vegetated from built-up surfaces, the Normalized Difference Vegetation Index (NDVI) was used as an indicator of vegetation presence, from of what the relative vegetation index (NDVI-R) was created. At the same time, values from the thermal band (band 6) of Landsat satellite were extracted as an indicator of thermal differences between land uses, based on that a relative temperature index (DN-R) was proposed and compared with the relative vegetation index (NDVI-R). A database was built identifying the main characteristics of population of each area, serving as support for an analysis between variables, providing scenarios for subsequent conflicts caused by landscape changes. By applying these techniques it was possible to verify the importance of the size and distribution of the vegetated areas in characterizing urbanized and semi urbanized areas. This comparative approach has demonstrated how landscape can be derived from satellite imagery providing a representation of changes in the urban spatial structure. Besides, it has showed how the rapid population growth and urban development trends along the city compete with sensitive environmental conditions in areas such as municipal parks and conservation areas. Every process involved changes in landscape composition, structure and function, which occurred on a backdrop of natural remaining patches altered by transformations of urban morphology. Most of these changes shows up micro climate changes. Certainly, the analysis of spatial distributions provided new insights about the landscape structure, which could be exploited in the land use planning. This is an approach that provides a new direction and research opportunity in terms of environmental issues on the agenda of policy makers, addressed towards environmental goals for social-economic development proposals specially because it highlighted the fact that not only the physical attributes of the landscape elements but also their spatial configuration were important determinants of land use dynamics
Doutorado
Planejamento e Desenvolvimento Rural Sustentável
Doutor em Engenharia Agrícola
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Schwieder, Marcel. "Landsat derived land surface phenology metrics for the characterization of natural vegetation in the Brazilian savanna." Doctoral thesis, Humboldt-Universität zu Berlin, 2018. http://dx.doi.org/10.18452/19368.
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Die Brasilianische Savanne, auch bekannt als der Cerrado, bedeckt ca. 24% der Landoberfläche Brasiliens. Der Cerrado ist von einer einzigartigen Biodiversität und einem starken Gradienten in der Vegetationsstruktur gekennzeichnet. Großflächige Landnutzungsveränderungen haben dazu geführt, dass annähernd die Hälfte der Cerrado in bewirtschaftetes Land umgewandelt wurde. Die Kartierung ökologischer Prozesse ist nützlich, um naturschutzpolitische Entscheidungen auf räumlich explizite Informationen zu stützen, sowie um das Verständnis der Ökosystemdynamik zu verbessern. Neue Erdbeobachtungssensoren, frei verfügbare Daten, sowie Fortschritte in der Datenverarbeitung ermöglichen erstmalig die großflächige Erfassung saisonaler Vegetationsdynamiken mit hohem räumlichen Detail. In dieser Arbeit wird der Mehrwert von Landsat-basierten Landoberflächenphänologischen (LSP) Metriken, für die Charakterisierung der Cerrado-Vegetation, hinsichtlich ihrer strukturellen und phänologischen Diversität, sowie zur Schätzung des oberirdischen Kohlenstoffgehaltes (AGC), analysiert. Die Ergebnisse zeigen, dass LSP-Metriken die saisonale Vegetatiosdynamik erfassen und für die Kartierung von Vegetationsphysiognomien nützlich sind, wobei hier die Grenzen der Einteilung von Vegetationsgradienten in diskrete Klassen erreicht wurden. Basierend auf Ähnlichkeiten in LSP wurden LSP Archetypen definiert, welche die Erfassung und Darstellung der phänologischen Diversität im gesamten Cerrado ermöglichten und somit zur Optimierung aktueller Kartierungskonzepte beitragen können. LSP-Metriken ermöglichten die räumlich explizite Quantifizierung von AGC in drei Untersuchungsgebieten und sollten bei zukünftigen Kohlenstoffschätzungen berücksichtigt werden. Die Erkenntnisse dieser Dissertation zeigen die Vorteile und Nutzungsmöglichkeiten von LSP Metriken im Bereich der Ökosystemüberwachung und haben demnach direkte Implikationen für die Entwicklung und Bewertung nachhaltiger Landnutzungsstrategien.The Brazilian savanna, known as the Cerrado, covers around 24% of Brazil. It is characterized by a unique biodiversity and a strong gradient in vegetation structure. Land-use changes have led to almost half of the Cerrado being converted into cultivated land. The mapping of ecological processes is, therefore, an important prerequisite for supporting nature conservation policies based on spatially explicit information and for deepening our understanding of ecosystem dynamics. New sensors, freely available data, and advances in data processing allow the analysis of large data sets and thus for the first time to capture seasonal vegetation dynamics over large extents with a high spatial detail. This thesis aimed to analyze the benefits of Landsat based land surface phenological (LSP) metrics, for the characterization of Cerrado vegetation, regarding its structural and phenological diversity, and to assess their relation to above ground carbon. The results revealed that LSP metrics enable to capture the seasonal dynamics of photosynthetically active vegetation and are beneficial for the mapping of vegetation physiognomies. However, the results also revealed limitations of hard classification approaches for mapping vegetation gradients in complex ecosystems. Based on similarities in LSP metrics, which were for the first time derived for the whole extent of the Cerrado, LSP archetypes were proposed, which revealed the spatial patterns of LSP diversity at a 30 m spatial resolution and offer potential to enhance current mapping concepts. Further, LSP metrics facilitated the spatially explicit quantification of AGC in three study areas in the central Cerrado and should thus be considered as a valuable variable for future carbon estimations. Overall, the insights highlight that Landsat based LSP metrics are beneficial for ecosystem monitoring approaches, which are crucial to design sustainable land management strategies that maintain key ecosystem functions and services.
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Tamstorf, Mikkel P. "Satellitbaseret vegetationskortlægning i Vestgrønland." [København] : Miljø- og Energiministeriet, Danmarks Miljøundersøgelser, 2001. http://www.dmu.dk/1_viden/2_Publikationer/3_Ovrige/rapporter/PHD_mpt.pdf.
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Deblauwe, Vincent. "Modulation des structures de végétation auto-organisées en milieu aride." Doctoral thesis, Universite Libre de Bruxelles, 2010. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210121.
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À l’échelle macroscopique, à savoir celle du paysage, la couverture végétale des milieux arides apparaît comme fortement hétérogène. Cette organisation spatiale des peuplements est habituellement attribuée aux processus de facilitation et de compétition qui opèrent à l’échelle microscopique des individus végétaux. Le qualificatif d’auto-organisées fut donc attribué à ces végétations qui se structurent en l’absence d’hétérogénéité préexistante du milieu physique. L’auto-organisation de la végétation fut particulièrement bien étudiée dans le cas des structures périodiques connues dès les années '50 sous le nom de brousses tigrées. Depuis les années '90, un pas en avant dans la compréhension de ce phénomène fut accompli grâce au développement de modèles mécanistes de la dynamique de la phytomasse et des ressources, émanant du cadre théorique de l'auto-organisation des structures dissipatives. Ces modèles se rejoignent sur un ensemble de prédictions robustes et vérifiables concernant la formation, le maintien et la modulation par l'environnement des structures macroscopiques. Durant le même laps de temps, notre niveau d’analyse a connu une expansion sans précédent, à la fois dans le temps et dans l’espace, grâce au développement de l’imagerie satellitaire et des outils d’analyse spatiale. Nous nous trouvons dès lors à un moment charnière pour la validation macroscopique des théories d’auto-organisation des végétations en milieu aride.
Le présent travail s'articule en quatre études, chacune traitant d'une prédiction différente. Nous avons mis en évidence les principales variables responsables de la formation des structures et de leur modulation en termes d’échelle et de géométrie. Enfin avons démontré la mobilité des structures sous l’effet d’une pente de terrain.
Doctorat en Sciences
info:eu-repo/semantics/nonPublished
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Strawbridge, Fiona. "Passive microwave remote sensing of vegetation." Thesis, University of Bristol, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.242948.
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Cazals, Cécile. "Apport des données Sentinel-1 pour la cartographie des milieux humides." Thesis, Paris Est, 2017. http://www.theses.fr/2017PESC1226/document.
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Les milieux humides sont menacés par le changement climatique et l'anthropisation des milieux naturels. La télédétection est un moyen efficace de suivre des variables environnementales sur de grands espaces. Cependant, lorsqu’il s’agit de l’étude de la dynamique hydrologique, une résolution temporelle importante est indispensable. Or celle-ci est difficilement accessible par l'imagerie optique satellitaire du fait de l’important couvert nuageux qui masque le sol. Les capteurs radars, travaillant dans le domaine des micro-ondes, sont tout particulièrement adaptés à la caractérisation des dynamiques hydrologiques grâce à la sensibilité de leurs mesures en présence d’eau et ce quelque soit la végétation en place. Il en découle que toutes les acquisitions Radar à Synthèse d’Ouverture (RSO) sont exploitables, acquises de jour comme de nuit, quel que soit le couvert nuageux.Récemment la télédétection radar satellitaire a vécu une véritable révolution grâce au lancement du satellite Sentinel-1A en 2014 suivi de son jumeaux Sentinel-1B deux ans plus tard par l'Agence Spatiale Européenne dans le cadre du programme Copernicus. Ces capteurs acquièrent des données en bande C (λ = 5,6 cm) sur toute l'Europe chaque 12 jours par satellite et leur diffusion est libre et gratuite pour tous. Le présent travail de thèse vise à évaluer le potentiel de ces données à haute résolution temporelle pour le suivi des surfaces d'eau et des milieux humides.Dans une première partie consacrée à la cartographie des surfaces d'eau, nous avons identifié une confusion la réponse radar en bande C des surfaces d'eau et celle de certains sols nus. Nous avons alors montré que la période hivernale est celle qui présente le moins d’ambiguïté et que la polarisation VH est la plus adaptée à la cartographie des surfaces en eau. Quatre méthodes de détection des zones en eau ont été comparées, il résulte que l’utilisation de méthodes non-supervisées sans données a priori n'est pas envisageable et que les méthodes prenant en compte le voisinage spatial donnent de meilleurs résultats. Un filtrage temporel a été mis au point et a permis d'améliorer la détection et de s'affranchir des confusions entre sols nus et surfaces d'eau permanentes. Les surfaces d'eau de plus de 0,5 ha ont plus de 80 % de chances d'être détectées.Un second volet de cette thèse est consacré au suivi d'une zone humide prairiale par télédétection radar. L'utilisation d'images pleinement polarimétriques a montré que la configuration de polarimétrie partielle VV/VH disponible sur le capteur de Sentinel-1 permet de caractériser l'inondation prairiale avec ou sans végétation. Une méthode prenant en compte l'information de voisinage temporel a permis de traiter une série de 14 images Sentinel-1 pour obtenir 14 cartes d'inondation pour l’année 2015. Une estimation de la précision à l'échelle intra-parcellaire a été mise au point, il apparaît que si la précision est relativement bonne (80 %), le rappel est assez bas (40 %). Cette méthode permet d'établir des bilans d'évolution intra- et inter-annuels.Cette thèse a montré le potentiel de l'utilisation d'images radar à haute résolution temporelle tant pour la cartographie des surfaces en eau que pour le suivi d'une zone humide prairialeWetlands are threatened by climate change and the anthropization of natural environments. Satellite remote sensing is useful for environmental monitoring at large areas. However, when it comes to the study of hydrological dynamics, a significant temporal resolution is essential. The latter is difficult to reach with optical satellite imagery because of the cloud cover that masks the ground. Radar sensors are well suited to the characterization of hydrological dynamics thanks to the sensitivity of their measurements in the presence of water, whatever the vegetation in place. As a result, all Synthetic Aperture Radar (SAR) acquisitions are available, both day and night, regardless of cloud cover.Satellite radar remote sensing has gone through a revolution with the launch of the Sentinel-1A satellite, followed by its twins Sentinel-1B by the European Space Agency as part of the Copernicus program in 2014. These sensors acquire C-band data (λ = 5.6 cm) on a regular basis on Europe and their distribution is free for all users. Their temporal frequency initially of 12 days has decreased to 6 days from the end of 2016. This work aims at evaluating the potential of these data with high temporal resolution for the monitoring of water bodies and wetlands.The first part of this thesis focuses on water bodies mapping. We found confusion in the C-band radar response between water surfaces and that of some bare soils. We showed that the winter period is the least ambiguous and that the VH polarization is the most suitable for the mapping of water surfaces. Four methods of water detection have been compared. It appears that the use of unsupervised methods without a priori data is not conceivable and that the methods taking into account the spatial neighborhood give better results. Temporal filtering has been developed and has improved detection and avoided confusion between bare soil and permanent water surfaces. Water surfaces of more than 0.5 ha are more than 80% likely to be detected.A second part of this thesis is devoted to the monitoring of wet grasslands by radar remote sensing. The use of fully polarimetric data has shown that the VV/VH partial polarimetry configuration available on the Sentinel-1 sensor is able to characterize the prairial floods with or without vegetation. A method taking into account the temporal neighborhood allowed to process a series of 14 Sentinel-1 images to obtain 14 flood maps. The accuracy of floods maps at the intra-parcel scale has been estimated, it appears that if the precision is relatively good (80%), but the recall is rather low (40%). This method allow to establish intra- and inter-annual monitoring.This thesis has shown the potential of high temporal resolution radar images for the mapping of the water surfaces and for the monitoring of a wetland meadow
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Akkok, Inci. "Geological Mapping Using Remote Sensing Technologies." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/3/12610626/index.pdf.
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In an area of interest- Sivas Basin, Turkey- where most of the units are sedimentary and show similar spectral characteristics, spectral settings of ASTER sensor may not be enough by itself. Therefore, considering other aspects, such as morphological variables, is reasonable in addition to spectral classifiers. The main objective of this study is to test usefulness of integration of spectral analysis and morphological information for geological mapping. Remotely sensed imagery obtained from ASTER sensor is used to classify different lithological units while DEM is used to characterize landforms related to these lithological units.
Maximum Likelihood Classification (MLC) is used to integrate data streaming from different sources. The methodology involves integrating the surface properties of the classified geological units in addition to the spectral reflectances. Seven different classification trials were conducted: : 1. MLC using only nine ASTER bands, 2. MLC using ASTER bands and DEM, 3. MLC using ASTER bands and slope, 4. MLC using ASTER bands and plan curvature, 5. MLC using ASTER bands and profile curvature, 6. MLC using ASTER bands and drainage density and finally 7. MLC using ASTER bands and all ancillary data.
The results revealed that integrating topographical parameters aid in improvement of classification where spectral information is not sufficient to discriminate between classes of interest. An increase of more than 5% is observed in overall accuracy for the all ancillary data integration case. Moreover more than 10% improvement for most of the classes was identified. However from the results it is evident that the areal extent of the classified units causes constraints on application of the methodology.
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Yetkin, Erdem. "Alteration mapping by remote mapping by remote sensing Application to Hasandağ- Melendiz volcanic complex /." Ankara : METU, 2003. http://etd.lib.metu.edu.tr/upload/1090927/index.pdf.
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Sheffield, Kathryn Jane, and kathryn sheffield@dpi vic gov au. "Multi-spectral remote sensing of native vegetation condition." RMIT University. Mathematical and Geospatial Sciences, 2009. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20091110.112816.
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Native vegetation condition provides an indication of the state of vegetation health or function relative to a stated objective or benchmark. Measures of vegetation condition provide an indication of the vegetation's capacity to provide habitat for a range of species and ecosystem functions through the assessment of selected vegetation attributes. Subsets of vegetation attributes are often combined into vegetation condition indices or metrics, which are used to provide information for natural resource management. Despite their value as surrogates of biota and ecosystem function, measures of vegetation condition are rarely used to inform biodiversity assessments at scales beyond individual stands. The extension of vegetation condition information across landscapes, and approaches for achieving this, using remote sensing technologies, is a key focus of the work presented in this thesis. The aim of this research is to assess the utility of multi-spectral remotely sensed data for the recovery of stand-level attributes of native vegetation condition at landscape scales. The use of remotely sensed data for the assessment of vegetation condition attributes in fragmented landscapes is a focus of this study. The influence of a number of practical issues, such as spatial scale and ground data sampling methodology, are also explored. This study sets limitations on the use of this technology for vegetation condition assessment and also demonstrates the practical impact of data quality issues that are frequently encountered in these types of applied integrated approaches. The work presented in this thesis demonstrates that while some measures of vegetation condition, such as vegetation cover and stem density, are readily recoverable from multi-spectral remotely sensed data, others, such as hollow-bearing trees and log length, are not easily derived from this type of data. The types of information derived from remotely sensed data, such as texture measures and vegetation indices, that are useful for vegetation condition assessments of this nature are also highlighted. The utility of multi-spectral remotely sensed data for the assessment of stand-level vegetation condition attributes is highly dependent on a number of factors including the type of attribute being measured, the characteristics of the vegetation, the sensor characteristics (i.e. the spatial, spectral, temporal, and radiometric resolution), and other spatial data quality considerations, such as site homogeneity and spatial scale. A series of case studies are presented in this thesis that explores the effects of these factors. These case studies demonstrate the importance of different aspects of spatial data and how data manipulation can greatly affect the derived relationships between vegetation attributes and remotely sensed data. The work documented in this thesis provides an assessment of what can be achieved from two sources of multi-spectral imagery in terms of recovery of individual vegetation attributes from remotely sensed data. Potential surrogate measures of vegetation condition that can be derived across broad scales are identified. This information could provide a basis for the development of landscape scale multi-spectral remotely sensed based vegetation condition assessment approaches, supplementing information provided by established site-based vegetation condition assessment approaches.
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Al, Sghair Fathi Goma. "Remote sensing and GIS for wetland vegetation study." Thesis, University of Glasgow, 2013. http://theses.gla.ac.uk/4581/.
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Remote Sensing (RS) and Geographic Information System (GIS) approaches, combined with ground truthing, are providing new tools for advanced ecosystem management, by providing the ability to monitor change over time at local, regional, and global scales. In this study, remote sensing (Landsat TM and aerial photographs) and GIS, combined with ground truthing work, were used to assess wetland vegetation change over time at two contrasting wetland sites in the UK: freshwater wetland at Wicken Fen between 1984 and 2009, and saltmarsh between 1988 and 2009 in Caerlaverock Reserve. Ground truthing studies were carried out in Wicken Fen (UK National Grid Reference TL 5570) during 14th - 18th June 2010: forty 1 m2 quadrats were taken in total, placed randomly along six transects in different vegetation types. The survey in the second Study Area Caerlaverock Reserve (UK National Grid Reference NY0464) was conducted on 5th - 9th July 2011, with a total of forty-eight 1 m2 quadrats placed randomly along seven transects in different vegetation types within the study area. Two-way indicator species (TWINSPAN) was used for classification the ground truth samples, taking separation on eigenvalues with high value (>0.500), to define end-groups of samples. The samples were classified into four sample-groups based on data from 40 quadrats in Wicken Fen, while the data were from 48 quadrats divided into five sample-groups in Caerlaverock Reserve. The primary analysis was conducted by interpreting vegetation cover from aerial photographs, using GIS combined with ground truth data. Unsupervised and supervised classifications with the same technique for aerial photography interpretation were used to interpret the vegetation cover in the Landsat TM images. In Wicken Fen, Landsat TM images were used from 18th August 1984 and 23rd August 2009; for Caerlaverock Reserve Landsat TM imagery used was taken from 14th May 1988 and 11th July 2009. Aerial photograph imagery for Wicken Fen was from 1985 and 2009; and for Caerlaverock Reserve, from 1988 and 2009. Both the results from analysis of aerial photographs and Landsat TM imagery showed a substantial temporal change in vegetation during the period of study at Wicken Fen, most likely primarily produced by the management programme, rather than being due to natural change. In Cearlaverock Reserve, results from aerial photography interpretation indicated a slight change in the cover of shrubs during the period 1988 to 2009, but little other change over the study period. The results show that the classification accuracy using aerial photography was higher than that of Landsat TM data. The difference of classification accuracy between aerial photography and Landsat TM, especially in Caerlaverock Reserve, was due to the low resolution of Landsat TM images, and the fact that some vegetation classes occupied an area less than that of the pixel size of the TM image. Based on the mapping exercise, the aerial photographs produced better vegetation classes (when compared with ground truthing data) than Landsat TM images, because aerial photos have a higher spatial resolution than the Landsat TM images. Perhaps the most important conclusion of this study is that it provides evidence that the RS/GIS approach can provide useful baseline data about wetland vegetation change over time, and across quite expansive areas, which can therefore provide valuable information to aid the management and conservation of wetland habitats.
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Dandois, Jonathan P. "Remote sensing of vegetation structure using computer vision." Thesis, University of Maryland, Baltimore County, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3637314.
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High-spatial resolution measurements of vegetation structure are needed for improving understanding of ecosystem carbon, water and nutrient dynamics, the response of ecosystems to a changing climate, and for biodiversity mapping and conservation, among many research areas. Our ability to make such measurements has been greatly enhanced by continuing developments in remote sensing technology—allowing researchers the ability to measure numerous forest traits at varying spatial and temporal scales and over large spatial extents with minimal to no field work, which is costly for large spatial areas or logistically difficult in some locations. Despite these advances, there remain several research challenges related to the methods by which three-dimensional (3D) and spectral datasets are joined (remote sensing fusion) and the availability and portability of systems for frequent data collections at small scale sampling locations. Recent advances in the areas of computer vision structure from motion (SFM) and consumer unmanned aerial systems (UAS) offer the potential to address these challenges by enabling repeatable measurements of vegetation structural and spectral traits at the scale of individual trees. However, the potential advances offered by computer vision remote sensing also present unique challenges and questions that need to be addressed before this approach can be used to improve understanding of forest ecosystems. For computer vision remote sensing to be a valuable tool for studying forests, bounding information about the characteristics of the data produced by the system will help researchers understand and interpret results in the context of the forest being studied and of other remote sensing techniques. This research advances understanding of how forest canopy and tree 3D structure and color are accurately measured by a relatively low-cost and portable computer vision personal remote sensing system: 'Ecosynth'. Recommendations are made for optimal conditions under which forest structure measurements should be obtained with UAS-SFM remote sensing. Ultimately remote sensing of vegetation by computer vision offers the potential to provide an 'ecologist's eye view', capturing not only canopy 3D and spectral properties, but also seeing the trees in the forest and the leaves on the trees.
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Beckett, Heath. "Remote sensing of water stress in fynbos vegetation." Bachelor's thesis, University of Cape Town, 2010. http://hdl.handle.net/11427/25902.
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I aim to determine whether or not remote sensing, through multispectral, satellite and digital photography, is a feasible and accurate method for determining drought stress in Fynbos vegetation. I hypothesize that (1) water stress in fynbos is detectable with the use of a remote sensing index, namely NDVI and (2) that the remotely sensed trends will correlate with ground truth measures of water stress.
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Flaherty, Silvia Susana. "Red squirrel habitat mapping using remote sensing." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/7607.
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The native Eurasian red squirrel is considered endangered in the UK and is under strict legal protection. Long-term management of its habitat is a key goal of the UK conservation strategy. Current selection criteria of reserves and subsequent management mainly consider species composition and food availability. However, there exists a critical gap in understanding and quantifying the relationship between squirrel abundance, their habitat use and forest structural characteristics. This has partly resulted from the limited availability of structural data along with cost-efficient data collection methods. This study investigated the relationship between squirrel feeding activity and structural characteristics of Scots pine forests. Field data were collected from two study areas: Abernethy and Aberfoyle Forests. Canopy closure, diameter at breast height, height and number of trees were measured in 56 plots. Abundance of squirrel feeding signs was used as an index of habitat use. A GLM was used to model the response of cones stripped by squirrels in relation to the field collected structural variables. Results show that forest structural characteristics are significant predictors of feeding sign presence, with canopy closure, number of trees and tree height explaining 43% of the variation in stripped cones. The GLM was also implemented using LiDAR data to assess at wider scales the number of cones stripped by squirrels. The use of remote sensing -in particular Light Detection and Ranging (LiDAR) - enables cost efficient assessments of forest structure at large scales and can be used to retrieve the three variables explored in this study; canopy cover, tree height and number of trees, that relate to red squirrel feeding behaviour. Correlation between field-predicted and LiDAR-predicted number of stripped cones was performed to assess LiDAR-based model performance. LiDAR data acquired at Aberfoyle and Abernethy Forests had different characteristics (in particular pulse density), which influences the accuracy of LiDAR derived metrics. Therefore correlations between field predicted and LiDAR predicted number of cones (LSC) were assessed for each study area separately. Strong correlations (rs=0.59 for Abernethy and 0.54 for Aberfoyle) suggest that LiDAR-based model performed relatively well over the study areas. The LiDAR-based model was not expected to provide absolute numbers of cones stripped by squirrels but a relative measure of habitat use. This can be interpreted as different levels of habitat suitability for red squirrels. LiDAR-based GLM maps were classified into three levels of suitability: unsuitable (LSC = 0), Low (LSC < 10) and Medium to High Suitability (LSC >=10). These thresholds were defined based on expert knowledge. Such a classification of habitat suitability allows for further differentiation of habitat quality for red squirrels and therefore for a refined estimation of the carrying capacity that was used to inform population viability analysis (PVA) at Abernethy Forest. PVA assists the evaluation of the probability of a species population to become extinct over a specified period of time, given a set of data on environmental conditions and species characteristics. In this study, two scenarios were modelled in a PVA package (VORTEX). For the first scenario (Basic) carrying capacity was calculated for the whole forest, while for the second scenario (LiDAR) only Medium-to-High suitable patches were considered. Results suggest a higher probability of extinction for the LiDAR scenario (74%) than for the Basic scenario (55%). Overall the findings of this study highlight 1) the importance of considering forest structure when managing habitat for squirrel conservation and 2) the usefulness of LiDAR remote sensing as a tool to assist red squirrel, and potentially other species, habitat management.
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McGonigle, Chris. "Mapping benthic habitat using acoustic remote sensing." Thesis, University of Ulster, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.551582.
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Backscatter imagery from multibeam echosounders (MBES) is increasingly used for benthic habitat mapping. This research explores MBES backscatter classification using QTC-Multiview on data from Stanton Banks (UK) and Cashes Ledge (USA). Image-processing algorithms are used to extract values from samples of backscatter data, which are reduced by principal components analysis and are objectively clustered. This process is initially evaluated using 2005 data from Stanton Banks and compared with ground-truth data to determine their biological validity. Low-levels of agreement are observed between acoustic class and ground- truth data «35%); video is determined to be the most spatially appropriate method for comparison. Subsequently, the area was resurveyed in 2006 using the same MBES with different operational parameters, acquiring low- and high-density data coverage. Percentage agreement between classifications was 78%, determined to be due to operational parameters as opposed to environmental change. Agreement with ground truth data improved from 71 % to 77% with increased data density. In 2008, a 2 km2 area was resurveyed at two different orientations and vessel speeds within the same 24 hr period. Classification revealed 53% similarity at 4 rns-1 and 49% at 2 rns-1 from opposing orientations. The same orientations surveyed at different speeds were between 68% (k=0.583) and 53% (k=0.384) similar. These results suggest that both orientation and speed are significant considerations in image-based classification. Finally, the significance of water-column biomass in backscatter classification was examined at Cashes Ledge using MBES data from kelp beds. Two approaches were examined for detecting the presence of macrophytes; image-based and manual picking. Comparison with video data revealed comparable success, with both methods most successful at predicting Laminaria sp. (77.3%-82.6% correct) in shallow water «30m). This research demonstrates the significance of MBES backscatter and image-based classification as potential tools for the emergent discipline of benthic habitat mapping.
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Tooke, Thoreau Rory. "Remote sensing applications for vegetation management in urban environments." Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/11502.
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Vegetation has been identified as an essential component of healthy urban environments, and the
benefits of urban vegetation range widely from influences on the physical conditions of the city
to the social well-being of the people who reside in these areas. As a result, ongoing research is
important to understand the dynamic spatial components of urban vegetation to help urban
planners and scholars manage this valuable resource.
Advanced remote sensing technologies, such as high spatial resolution sensors and laser
scanning devices, are useful tools for examining urban environments since they can capture
detailed information regarding the material and structural composition of the urban surface. By
providing a complete coverage of urban environments remote sensing technologies enable new
possibilities to quantify the contributions of urban vegetation for a wealth of active processes in
urban areas. The studies in this thesis examine several remote sensing devices to demonstrate
the influence of urban vegetation on both physical and social aspects of urban environments.
Three studies comprise the body of this work. They present new geographic techniques using
remote sensing for: 1) the detailed classification of urban vegetation conditions; 2) quantifying
the contribution of trees to solar radiation available for building rooftops; and 3) examining
socioeconomic disparities related to urban green-space.
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Stephen, Haroon. "Microwave Remote Sensing of Saharan Ergs and Amazon Vegetation." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1461.pdf.
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Tyoda, Zipho. "Landslide susceptibility mapping : remote sensing and GIS approach." Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019.1/79856.
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Thesis (MSc)--Stellenbosch University, 2013.Landslide susceptibility maps are important for development planning and disaster management. The current synthesis of landslide susceptibility maps largely applies GIS and remote sensing techniques. One of the most critical stages on landslide susceptibility mapping is the selection of landslide causative factors and weighting of the selected causative factors, in accordance to their influence to slope instability. GIS is ideal when deriving static factors i.e. slope and aspect and most importantly in the synthesis of landslide susceptibility maps. The integration of landslide causative thematic maps requires the selection of the weighting method; in order to weight the causative thematic maps in accordance to their influence to slope instability. Landslide susceptibility mapping is based on the assumption that future landslides will occur under similar circumstances as historic landslides. The weight of evidence method is ideal for landslide susceptibility mapping, as it calculates the weights of the causative thematic maps using known landslides points. This method was applied in an area within the Western Cape province of South Africa, the area is known to be highly susceptible to landslide occurrences. A prediction rate of 80.37% was achieved. The map combination approach was also applied and achieved a prediction rate of 50.98%. Satellite remote sensing techniques can be used to derive the thematic information needed to synthesize landslide susceptibility maps and to monitor the variable parameters influencing landslide susceptibility. Satellite remote sensing techniques can contribute to landslide investigation at three distinct phases namely: (1) detection and classification of landslides (2) monitoring landslide movement and identification of conditions leading up to an event (3) analysis and prediction of slope failures. Various sources of remote sensing data can contribute to these phases. Although the detection and classification of landslides through the remote sensing techniques is important to define landslide controlling parameters, the ideal is to use remote sensing data for monitoring of areas susceptible to landslide occurrence in an effort to provide an early warning. In this regard, optical remote sensing data was used successfully to monitor the variable conditions (vegetation health and productivity) that make an area susceptible to landslide occurrence.
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Jones, Gwawr Angharad. "Coastal habitat mapping and monitoring utilising remote sensing." Thesis, Aberystwyth University, 2017. http://hdl.handle.net/2160/cfb598d7-9bb7-44a7-8725-bcf13d81657b.
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Coastal habitats are highly sensitive to change and highly diverse. Degrading environmental conditions have led to a global decline in biodiversity through loss, modification and fragmentation of habitats, triggering an increased effort to conserve these ecosystems. Remote sensing is important tool for filling in critical information gaps for monitoring habitats, yet significant barriers exist for operational use within the ecological and conservation communities. Reporting on both extent and condition of habitats are critical to fulfil policy requirements, specifically the ECs Habitat’s Directive. This study focuses on the use of Very High Resolution (VHR) optical imagery for retrieving parameters to identifyassociations that can separate habitat boundaries for extent mapping down to species level for indicators of condition, with a focus on operational use. The Earth Observation Data for Habitat Monitoring (EODHaM) system was implemented using Worldview-2 data from two periods (July and September), in situ data and local ecological knowledge for two sites in Wales, Kenfig Burrows SAC and Castlemartin SSSI. The system utilises the Food and Agricultural Organisation’s (FAO) Land Cover Classification System (LCCS) but translations between land cover and habitat schemes are not straight forward and need special consideration that are likely to be site specific. Limitations within therule-based method of the EODHaM system were identified and therefore augmented with machine learning based classification algorithms creating a hybrid method of classification generating accurate (>80% overall accuracy) baseline maps with a more automated and repeatable method. Quantitative methods of validation traditionally used within the remote sensing community do not consider spatial aspects of maps. Therefore, qualitative assessments carried out in the field were used in addition to error matrices, overall accuracy and the kappa coefficient. This required input from ecologists and site specialists, enhancing communication and understanding between the different communities. Generating baseline maps required significant amount of training data and updating baselines through change detection methods is recommended for monitoring. An automated, novel map-to-image change detection was therefore implemented. Natural and anthropogenic changes were successfully detected from Worldview-2 and Sentinel-2 data at Kenfig Burrows. An innovative component of this research was the development of methods, which were demonstrated to be transferable between both sites and increased understanding between remote sensing scientist and ecologist. Through this approach, a more operational method for monitoring site specific habitats through satellite data is proposed, with direct benefits for conservation, environment and policy.