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

Mémoire (M.Ress.Renouv.)--Université du Québec à Chicoutimi, 1997. / Document électronique également accessible en format PDF. CaQCU

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

Thesis (MSc (Geography and Environmental Studies))--University of Stellenbosch, 2010. / ENGLISH ABSTRACT: Supervised classifiers are the most popular approach for image classification due to their high accuracies, ease of use and strong theoretical grounding. Their primary disadvantage is the high level of user input required during the creation of the data needed to train the classifier. One alternative to supervised classification is an expert-system rule-based approach where expert knowledge is used to create a set of rules which can be applied to multiple images. This research compared supervised and expert-system rule-based approaches for forest mapping. For this purpose two SPOT 5 images were acquired and atmospherically corrected. Field visits, aerial photography, high resolution imagery and expert forestry knowledge were used for the compilation of the training data and the development of a rule-set. Both approaches were evaluated in an object-orientated environment. It was found that the accuracy of the resulting maps was equivalent, with both techniques returning an overall classification accuracy of 90%. This suggests that cost-effectiveness is the decisive factor for determining which method is superior. Although the development of the rule-set was time-consuming and challenging, it did not require any training data. In contrast, the supervised approach required a large number of training areas for each image classified, which was time-consuming and costly. Significantly more training areas will be required when the technique is applied to large areas, especially when multiple images are used. It was concluded that the rule-set is more cost-effective when applied at regional scale, but it is not viable for mapping small areas. / AFRIKAANSE OPSOMMING: Gerigte klassifiseerders is die gewildste benadering tot beeldklassifikasie as gevolg van hulle hoë graad van akkuraatheid, maklike aanwending en kragtige teoretiese fundering. Die primere nadeel van gerigte klassifikasie is die hoë vlak van gebruikersinsette wat benodig word tydens die skepping van opleidingsdata. 'n Alternatief vir gerigte klassifikasie is 'n deskundige stelsel waarin ‘n reëlgebaseerde benadering gevolg word om deskundige kennis aan te wend vir die opstel van 'n stel reëls wat op meervoudige beelde toegepas kan word. Hierdie navorsing het gerigte en deskundige stelsel benaderings toegepas vir bosboukartering om die twee benaderings met mekaar te vergelyk. Vir dié doel is twee SPOT 5 beelde verkry en atmosferies gekorrigeer. Veldbesoeke, lugfotografie, hoë-resolusie beelde en deskundige bosboukennis is aangewend om opleidingsdata saam te stel en die stel reëls te ontwikkel. Beide benaderings is in 'n objekgeoriënteerde omgewing beoordeel. Die akkuraatheidsvlakke van die resulterende kaarte was ewe hoog vir beide tegnieke met 'n algehele klassifikasie-akkuraatheid van 90%. Dit wil dus voorkom asof koste-effektiwiteit eerder as akkuraatheid die deurslaggewende faktor is om te bepaal watter metode die beste is. Alhoewel die ontwikkeling van die stel reëls tydrowend en uitdagend was, het dit geen opleidingsdata vereis nie. In teenstelling hiermee is 'n groot aantal opleidingsgebiede geskep vir elke beeld wat met gerigte klassifikasie verwerk is – 'n tydrowende en duur opsie. Dit is duidelik dat meer opleidingsgebiede benodig sal word wanneer die tegniek op groot gebiede toegepas word, veral omdat meervoudige beelde gebruik sal word. Gevolglik sal die stel reëls meer kosteeffektief wees wanneer dit op streekskaal toegepas word. ‘n Deskundige stelsel benadering is egter nie lewensvatbaar vir die kartering van klein gebiede nie.

Orientador: Marcos Cesar Ferreira / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Geociencias / Made available in DSpace on 2018-08-13T02:47:13Z (GMT). No. of bitstreams: 1 Gomes_PriscilaBrochado_D.pdf: 6950162 bytes, checksum: fc83241a2e03189e1a609b95c290e928 (MD5) Previous issue date: 2009 / Resumo: O surgimento de sensores de alta resolução espacial permitiu que o nível de detalhamento das imagens de satélite chegasse ao ponto em que estudos de ecologia florestal pudessem ser realizados, através deste instrumento, com base em informações de espécies e indivíduos arbóreos. Tal tema de pesquisa é um campo promissor, já que a possibilidade de se estudar árvores de uma floresta à distância, pode contribuir para maior agilidade e menor custo do manejo florestal, minimizando custos de boa parte dos levantamentos de campo. Os objetivos deste trabalho foram investigar alguns procedimentos metodológicos utilizando imagens do satélite Quickbird, combinadas a dados de campo e a ferramentas computacionais para detecção, identificação e mapeamento de árvores de uma floresta semidecidua e de um cerradão no estado de São Paulo. O método de classificação orientada a objetos foi adequado para a identificação das espécies Sclerolobium paniculatum e Mabea fistulifera nas imagens Quickbird. A baixa resolução espectral destas imagens pode ser compensada pelo uso de informações espaciais dos indivíduos de interesse na classificação orientada a objetos. O método utilizado foi apropriado para a separação entre árvores decíduas e perenefólias da floresta semidecidua, porém, para esta formação florestal, a identificação de espécies não foi possível. Índices de vegetação obtidos através da álgebra de bandas de imagens Quickbird e Aster se mostraram boas fontes de informação para a elaboração de mapas de distribuição potencial de espécies arbóreas. Estes resultados indicam que, dependendo da formação florestal estudada e da espécie de interesse, a resolução espacial do satélite Quickbird pode ser satisfatória para a identificação de espécies arbóreas e que essas imagens têm grande aplicação em estudos biogeográficos. / Abstract: New high resolution satellite images, like Ikonos and Quickbird, provide great potential to monitor and study trees of tropical forest through species identification and mapping. This topic of research is a promising field since remote sensing has potential to provide, at lower cost, forest information with great coverage than is attainable using field sampling. The objective of this thesis was to investigate some methodological proceedings using Quickbird images, combined with field data and informatics tools, to detection, identification and mapping of trees from a cerradão (Savanna Forest) and a semi-deciduous seasonal forest in the state of São Paulo. Sclerolobium paniculatum and Mabea fistulifera had good results in their identification by object oriented classification in Quickbird images. The low spectral resolution of Quickbird images is somewhat compensated by the spatial information available for the species of interest defined in our object-oriented classification. The species identification for semi-deciduous forest was not satisfactory, however in this kind of vegetation it was possible the separation among deciduous and evergreen trees. Vegetation indexes from Quickbird and Aster band math were good information source for tree species potential distribution maps. The results show that, depending on the forest type and species of interest, the spatial resolution of Quickbird images is sufficient for the identification of individual tree crowns. These images have big application in biogeography studies. / Doutorado / Análise Ambiental e Dinâmica Territorial / Doutor em Ciências

Forest ecosystems are being altered by climate change, invasive species, and additional stressors. Our ability to detect these changes and quantify their impacts relies on detailed data across spatial and temporal scales. This dissertation expands the ecological utility of long-term satellite imagery by developing high quality forest mapping products and examining spatiotemporal changes in tree species abundance and phenology across the northeastern United States (US; the ‘Northeast’). Species/genus-level forest composition maps were developed by integrating field data and Landsat images to model abundance at a sub-pixel scale. These abundance maps were then used to 1) produce a more detailed, accurate forest classification compared to similar products and 2) construct a 30-year time-series of abundance for eight common species/genera. Analyzing the time-series data revealed significant abundance trends in notable species, including increases in American beech (Fagus grandifolia) at the expense of sugar maple (Acer saccharum). Climate was the dominant predictor of abundance trends, indicating climate change may be altering competitive relationships. Spatiotemporal trends in deciduous forest phenology – start and end of the growing season (SOS/EOS) – were examined based on MODIS imagery from 2001-2015. SOS exhibited a slight advancing trend across the Northeast, but with a distinct spatial pattern: eastern ecoregions showed advance and western ecoregions delay. EOS trended substantially later almost everywhere. SOS trends were linked to winter-spring temperature and precipitation trends; areas with higher elevation and fall precipitation anomalies had negative associations with EOS trends. Together, this work demonstrates the value of remote sensing in furthering our understanding of long-term forest responses to changing environmental conditions. By highlighting potential changes in forest composition and function, the research presented here can be used to develop forest conservation and management strategies in the Northeast.

Effective management of forest resources requires reliable and timely information on their status. In this regard, remote sensing techniques have played an important role, as they allow collection of data on extensive, remote and inaccessible areas. Historically, aerial photographs were the primary remote sensing data source in forest inventory and mapping, and they are still extensively for visual photo-interpretation. In this thesis, we show that their use can be improved thanks to automatic processing and an application using digitised orthophotos is provided. Satellite-based remote sensing has been regarded as an alternative, low-cost and rapid, data source to aerial photography and ground survey. Indeed, it has proved to be effective at the continental and global scales, but applications for local forest management purposes are still rare. The main reason for this is that the spatial resolution of satellite remote sensing data that was available until recently (mainly from Landsat TM/ETM and SPOT HRV) was too coarse for stand level information. Satellite images with enhanced spatial resolution (such as IKONOS) should overcome this limitation. This thesis investigates their actual capabilities for forest stand mapping and characterisation. We show that they are well suited for forest stand type classification and for retrieval of several dendrometric variables in coniferous stands with an accuracy similar to that of field sampling. For the sake of solutions to provide more precise and detailed information on forest stand, we assessed also the contribution of hyperspectral and multiple-view angle data acquired by CHRIS/PROBA. Although the winter season scene did not fully permit utilisation of the hyperspectral dimension of this dataset, the study provides insights into directional effects. This work makes, hopefully, a step towards automated processing and effective integration of satellite-based remote sensing data into the forest management information system and, by upscaling, into the national forest inventories.

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

The causes of recent episodes of forest decline in Canada have not yet been fully determined. Current explanations include the effects of acid depositon as well as natural causes such as climate stress. This thesis takes a geographic approach and undertakes risk-response comparisons in order to examined the problem of maple forest dieback in the Appalachian region of Quebec. Geographic Information Systems are used to map a series of forest decline risk factors relating to soil characteristics and topography. The individual risk factors, as well as models comprising weighted combinations of risk factors, are compared with actual defoliation patterns. Forest defoliation is determined using areal survey data and satellite imagery. Although statistically significant associations were obtained between defoliation patterns and several risk factors, the amount of association was not strong enough to conclude that these factors are dominant causes of forest decline.

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