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Metodologia de classificação de imagens multiespectrais aplicada ao mapeamento do uso da terra e cobertura vegetal na Amazônia: exemplo de caso na região de São Félix do Xingu, sul do Pará.; Methodology for multispectral image classification applied to the mapping of land use and land cover in Amazonia: a case example in the region of Sao Felix do Xingu, south of Para.

Kawakubo, Fernando Shinji
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 05/08/2010 PT
Relevância na Pesquisa
66.43%
Este trabalho apresenta uma metodologia de classificação de imagens multiespectrais aplicada a análise e mapeamento da evolução do uso da terra/cobertura vegetal em São Félix do Xingu, Sul do Pará. Imagens frações representando as proporções de sombra, vegetação e solo foram estimadas a partir das bandas 1 a 5 e 7 do Landsat-5 TM e relacionadas com as estruturas das classes de uso da terra/cobertura vegetal. As imagens frações geradas do modelo linear de mistura espectral foram importantes para reduzir a massa de dados e ao mesmo tempo realçar alvos de interesse na imagem. A banda do infravermelho próximo (TM-4) foi importante para realçar áreas de queimadas. A classificação adotada foi divida em etapas combinando técnica de segmentação por crescimento de regiões e uso de máscaras. Por meio da máscara foi possível restringir o processo de segmentação em regiões pré-estabelecidas com o intuito de adquirir um melhor particionamento da imagem. Adotando este procedimento, ao invés de realizar uma única segmentação para mapear todas as classes em uma única vez, foram realizadas várias segmentações ao longo das etapas. As regiões segmentadas foram agrupadas com um classificador não-supervionado batizado de ISOSEG. Os resultados mostram que a metodologia é bastante eficiente. A matriz de erro gerada para a classificação de 2008 apontou que as confusões mais freqüentes ocorreram entre as classes que apresentaram em certas localidades proporções de misturas parecidas: Capoeira e Campo/Pastagem-2; Campo/Pastagem-1 e Campo/Pastagem-2; Queimada-1 e Queimada-2; Solo Exposto e Campo/Pastagem-1. Considerando nove classes...

Toward Satellite-Based Land Cover Classification Through Optimum-Path Forest

Pisani, Rodrigo Jose; Mizobe Nakamura, Rodrigo Yuji; Riedel, Paulina Setti; Lopes Zimback, Celia Regina; Falcao, Alexandre Xavier; Papa, Joao Paulo
Fonte: Ieee-inst Electrical Electronics Engineers Inc Publicador: Ieee-inst Electrical Electronics Engineers Inc
Tipo: Artigo de Revista Científica Formato: 6075-6085
ENG
Relevância na Pesquisa
96.3%
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP); Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq); Processo FAPESP: 09/16206-1; Processo FAPESP: 10/11676-7; Land cover classification has been paramount in the last years. Since the amount of information acquired by satellite on-board imaging systems has increased, there is a need for automatic tools that can tackle such problem. Despite the fact that one can find several works in the literature, we propose a novel methodology for land cover classification by means of the optimum-path forest (OPF) framework, which has never been applied to this context up to date. Experiments were conducted in supervised and unsupervised situations against some state-of-the-art pattern recognition techniques, such as support vector machines, Bayesian classifier, k-means, and mean shift. We had shown that supervised OPF can outperform such approaches, being much faster than all. In regard to clustering techniques, all classifiers have achieved similar results.

Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques

Furtado, Luiz Felipe de Almeida; Silva, Thiago Sanna Freire; Fernandes, Pedro José Farias; Novo, Evelyn Márcia Leão de Moraes
Fonte: Instituto Nacional de Pesquisas da Amazônia Publicador: Instituto Nacional de Pesquisas da Amazônia
Tipo: Artigo de Revista Científica Formato: 195-202
ENG
Relevância na Pesquisa
66.38%
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq); Fundação de Amparo à Pesquisa no Estado de São Paulo (FAPESP); Processo FAPESP: 2010/11269-2; Processo FAPESP: 2011/23594-8; Given the limitations of different types of remote sensing images, automated land-cover classifications of the Amazon várzea may yield poor accuracy indexes. One way to improve accuracy is through the combination of images from different sensors, by either image fusion or multi-sensor classifications. Therefore, the objective of this study was to determine which classification method is more efficient in improving land cover classification accuracies for the Amazon várzea and similar wetland environments - (a) synthetically fused optical and SAR images or (b) multi-sensor classification of paired SAR and optical images. Land cover classifications based on images from a single sensor (Landsat TM or Radarsat-2) are compared with multi-sensor and image fusion classifications. Object-based image analyses (OBIA) and the J.48 data-mining algorithm were used for automated classification, and classification accuracies were assessed using the kappa index of agreement and the recently proposed allocation and quantity disagreement measures. Overall...

The contribution of multitemporal information from multispectral satellite images for automatic land cover classification at the national scale

Carrão, Hugo Miguel Saiote
Fonte: Universidade Nova de Lisboa Publicador: Universidade Nova de Lisboa
Tipo: Tese de Doutorado
Publicado em 27/01/2010 ENG
Relevância na Pesquisa
76.45%
Thesis submitted to the Instituto Superior de Estatística e Gestão de Informação da Universidade Nova de Lisboa in partial fulfillment of the requirements for the Degree of Doctor of Philosophy in Information Management – Geographic Information Systems; Imaging and sensing technologies are constantly evolving so that, now, the latest generations of satellites commonly provide with Earth’s surface snapshots at very short sampling periods (i.e. daily images). It is unquestionable that this tendency towards continuous time observation will broaden up the scope of remotely sensed activities. Inevitable also, such increasing amount of information will prompt methodological approaches that combine digital image processing techniques with time series analysis for the characterization of land cover distribution and monitoring of its dynamics on a frequent basis. Nonetheless, quantitative analyses that convey the proficiency of three-dimensional satellite images data sets (i.e. spatial, spectral and temporal) for the automatic mapping of land cover and land cover time evolution have not been thoroughly explored. In this dissertation, we investigate the usefulness of multispectral time series sets of medium spatial resolution satellite images for the regular land cover characterization at the national scale. This study is carried out on the territory of Continental Portugal and exploits satellite images acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) and MEdium Resolution Imaging Spectrometer (MERIS). In detail...

Urban land use and land cover change analysis and modeling a case study area Malatya, Turkey

Baysal, Gülendam
Fonte: Universidade Nova de Lisboa Publicador: Universidade Nova de Lisboa
Tipo: Dissertação de Mestrado
Publicado em 30/01/2013 ENG
Relevância na Pesquisa
66.37%
Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.; This research was conducted to analyze the land use and land cover changes and to model the changes for the case study area Malatya, Turkey. The first step of the study was acquisition of multi temporal data in order to detect the changes over the time. For this purpose satellite images (Landsat 1990-2000-2010) have been used. In order to acquire data from satellite images object oriented image classification method have been used. To observe the success of the classification accuracy assessment has been done by comparing the control points with the classification results and measured with kappa. According to results of accuracy assessment the overall kappa value found around 75%. The second step was to perform the suitability analysis for the urban category to use in modeling process and it has been done using the Multi Criteria Evaluation method. The third step was to observe the changes between the defined years in the study area. In order to observe the changes land use/cover maps belongs to different years compared with cross tabulation and overlay methods, according to the results it has been observed that the main changes in the study area were the transformation of agricultural lands and orchards to urban areas. Every ten years around 1000ha area of agricultural land and orchards were transformed to urban. After detecting the changes in the study area simulation for the future has been performed. For the simulation two different methods have been used which are; the combination of Cellular Automata and Markov Chain methods and the combination of Multilayer Perceptron and Markov Chain methods with the support of the suitability analysis. In order to validate the models; both of them has been used to simulate the year 2010 land categories using the 1990 and 2000 data. Simulation results compared with the existing 2010 map for the accuracy assessment (validation). For accuracy assessment the quantity and allocation based disagreements and location and quantity based kappa agreements has been calculated. According to the results it has been observed that the combination of Multilayer Perceptron and Markov Chain methods had a higher accuracy in overall...

Detecting and evaluating land cover change in the eastern half of East Timor (1972-2011)

Costa, Helder António Bento da
Fonte: Universidade Nova de Lisboa Publicador: Universidade Nova de Lisboa
Tipo: Dissertação de Mestrado
Publicado em 28/02/2013 ENG
Relevância na Pesquisa
66.39%
Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.; Land use / land cover (LULC) change detection based on remote sensing (RS) data is an important source of information for various decision support systems. In East Timor where forest plays a key role in sustaining communities’ livelihoods the information derived from LULC change detection is invaluable to the conservation, sustainable development and management of forest resources. To assess the patterns of land cover change, as a result of complex socio-economic factors, satellite imagery and image processing techniques can be useful. This study is concerned with identifying change in land use and land cover types in East Timor between 1972 and 2011, using satellite images from Landsat MSS, TM and ETM+ sensors. Seven major cover types were identified in this study including forest, mixed rangeland, grassland, farmland, built-up areas, bare soil and water. A combination of NDVI differencing, supervised and unsupervised classification was used to derive final classification maps. Due to the lack of ground truth data, further processing were performed to improve the final classification maps by applying rationality change test. Post-classification comparison change detection technique was used to assess categorical changes between 1972 and 2011. The results highlight a significant level of deforestation due to uncontrolled illegal logging and increase in farmland...

Analysis of urban land use and land cover changes: a case of study in Bahir Dar, Ethiopia

Sahalu, Atalel Getu
Fonte: Universidade Nova de Lisboa Publicador: Universidade Nova de Lisboa
Tipo: Dissertação de Mestrado
Publicado em 28/02/2014 ENG
Relevância na Pesquisa
66.42%
Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies; The high rate of urbanization coupled with population growth has caused changes in land use and land cover in Bahir Dar, Ethiopia. Therefore, understanding and quantifying the spatio- temporal dynamics of urban land use and land cover changes and its driving factors is essential to put forward the right policies and monitoring mechanisms on urban growth for decision making. Thus, the objective of this study was to analyze land use and land cover changes in Bahir Dar area, Ethiopia by applying geospatial and land use change modeling tools. In order to achieve this, satellite data of Landsat TM for 1986 and ETM for 2001 and 2010 have been obtained and preprocessed using ArcGIS. The Maximum Liklihood Algorithm of Supervised Classification has been used to generate land use and land cover maps. For the accuracy of classified land use and land cover maps, a confusion matrix was used to derive overall accuracy and results were above the minimum and acceptable threshold level. The generated land cover maps have been run with Land Change Modeler for quantifying land use and land cover changes, to examine land use transitions between land cover classes...

Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques

FURTADO,Luiz Felipe de Almeida; SILVA,Thiago Sanna Freire; FERNANDES,Pedro José Farias; NOVO,Evelyn Márcia Leão de Moraes
Fonte: Instituto Nacional de Pesquisas da Amazônia Publicador: Instituto Nacional de Pesquisas da Amazônia
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/06/2015 EN
Relevância na Pesquisa
66.38%
Given the limitations of different types of remote sensing images, automated land-cover classifications of the Amazon várzea may yield poor accuracy indexes. One way to improve accuracy is through the combination of images from different sensors, by either image fusion or multi-sensor classifications. Therefore, the objective of this study was to determine which classification method is more efficient in improving land cover classification accuracies for the Amazon várzea and similar wetland environments - (a) synthetically fused optical and SAR images or (b) multi-sensor classification of paired SAR and optical images. Land cover classifications based on images from a single sensor (Landsat TM or Radarsat-2) are compared with multi-sensor and image fusion classifications. Object-based image analyses (OBIA) and the J.48 data-mining algorithm were used for automated classification, and classification accuracies were assessed using the kappa index of agreement and the recently proposed allocation and quantity disagreement measures. Overall, optical-based classifications had better accuracy than SAR-based classifications. Once both datasets were combined using the multi-sensor approach, there was a 2% decrease in allocation disagreement...

Land Cover Classification in a Complex Urban-Rural Landscape with Quickbird Imagery

Moran, Emilio Federico.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em /10/2010 EN
Relevância na Pesquisa
66.42%
High spatial resolution images have been increasingly used for urban land use/cover classification, but the high spectral variation within the same land cover, the spectral confusion among different land covers, and the shadow problem often lead to poor classification performance based on the traditional per-pixel spectral-based classification methods. This paper explores approaches to improve urban land cover classification with Quickbird imagery. Traditional per-pixel spectral-based supervised classification, incorporation of textural images and multispectral images, spectral-spatial classifier, and segmentation-based classification are examined in a relatively new developing urban landscape, Lucas do Rio Verde in Mato Grosso State, Brazil. This research shows that use of spatial information during the image classification procedure, either through the integrated use of textural and spectral images or through the use of segmentation-based classification method, can significantly improve land cover classification performance.

A Simple Semi-Automatic Approach for Land Cover Classification from Multispectral Remote Sensing Imagery

Jiang, Dong; Huang, Yaohuan; Zhuang, Dafang; Zhu, Yunqiang; Xu, Xinliang; Ren, Hongyan
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
Publicado em 26/09/2012 EN
Relevância na Pesquisa
66.48%
Land cover data represent a fundamental data source for various types of scientific research. The classification of land cover based on satellite data is a challenging task, and an efficient classification method is needed. In this study, an automatic scheme is proposed for the classification of land use using multispectral remote sensing images based on change detection and a semi-supervised classifier. The satellite image can be automatically classified using only the prior land cover map and existing images; therefore human involvement is reduced to a minimum, ensuring the operability of the method. The method was tested in the Qingpu District of Shanghai, China. Using Environment Satellite 1(HJ-1) images of 2009 with 30 m spatial resolution, the areas were classified into five main types of land cover based on previous land cover data and spectral features. The results agreed on validation of land cover maps well with a Kappa value of 0.79 and statistical area biases in proportion less than 6%. This study proposed a simple semi-automatic approach for land cover classification by using prior maps with satisfied accuracy, which integrated the accuracy of visual interpretation and performance of automatic classification methods. The method can be used for land cover mapping in areas lacking ground reference information or identifying rapid variation of land cover regions (such as rapid urbanization) with convenience.

Land-cover classification in the Brazilian Amazon with the integration of Landsat ETM + and Radarsat data.

LU, D.; BATISTELLA, M.; MORAN, E.
Fonte: International Journal of Remote Sensing, v. 28, n. 24, p. 5447-5459, 2007. Publicador: International Journal of Remote Sensing, v. 28, n. 24, p. 5447-5459, 2007.
Tipo: Artigo em periódico indexado (ALICE)
EN
Relevância na Pesquisa
86.36%
Land-cover classification with remotely sensed data in moist tropical regions in a challenge due to the complex biophysical conditions. This paper explores techniques to improve land-cover classification accuracy through a comparative analysis of different combinations of spectral signatures and textures from Landsat Enhanced Thematic Mapper Plus (ETM +) and Radarsat data. A wavelet-merging technique was used to integrate Landsat ETM + multispectral and panchromatic data or Radarsat data. Grey-level co-occurrence matrix (GLCM) textures based on Landsat ETM + panchromatic of Radarsat data and different sizes of moving windows were examined. A maximum-likelihood classifier was used to implement image classification for different combinations. This research indicates the important role of textures in improving land-cover classification accuracies in Amazonian environments. ...; 2007

A comparison of multisensor integration methods for land cover classification in the Brazilian Amazon.

LU, D.; LI, G.; MORAN, E.; DUTRA, L.; BATISTELLA, M.
Fonte: GIScience & Remote Sensing, v. 48, n. 3, p. 345-370, 2011. Publicador: GIScience & Remote Sensing, v. 48, n. 3, p. 345-370, 2011.
Tipo: Artigo em periódico indexado (ALICE)
PT_BR
Relevância na Pesquisa
66.38%
Many data fusion methods are available, but it is poorly understood which fusion method is suitable for integrating Landsat Thematic Mapper (TM) and radar data for land cover classification. This research explores the integration of Landsat TM and radar images (i.e., ALOS PALSAR L-band and RADARSAT-2 C-band) for land cover classification in a moist tropical region of the Brazilian Amazon. Different data fusion methods?principal component analysis (PCA), wavelet-merging technique (Wavelet), high-pass filter resolution-merging (HPF), and normalized multiplication (NMM)?were explored. Land cover classification was conducted with maximum likelihood classification based on different scenarios. This research indicates that individual radar data yield much poorer land cover classifications than TM data, and PALSAR L-band data perform relatively better than RADARSAT-2 C-band data. Compared to the TM data, the Wavelet multisensor fusion improved overall classification by 3.3%?5.7%, HPF performed similarly, but PCA and NMM reduced overall classification accuracy by 5.1%?6.1% and 7.6% ?12.7%, respectively. Different polarization options, such as HH and HV, work similarly when used in data fusion. This research underscores the importance of selecting a suitable data fusion method that can preserve spectral fidelity while improving spatial resolution.; 2011

A comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region.

LI, G.; LU, D.; DUTRA, L.; BATISTELLA, M.
Fonte: ISPRS Journal of Photogrammetry and Remote Sensing, v. 70, p. 26-38, 2012. Publicador: ISPRS Journal of Photogrammetry and Remote Sensing, v. 70, p. 26-38, 2012.
Tipo: Artigo em periódico indexado (ALICE) Formato: p. 26-38.
EN
Relevância na Pesquisa
96.51%
This paper explores the use of ALOS (Advanced Land Observing Satellite) PALSARL-band (Phased Array type L-band Synthetic Aperture Radar) and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. Transformed divergence was used to identify potential textural images which were calculated with the gray-level co-occurrence matrix method. The standard deviation of selected textural images and correlation coefficients between them were then used to determine the best combination of texture images for land-cover classification. Classification results based on different scenarios with maximum likelihood classifier were compared. Based on the identified best scenarios, different classification algorithms ? maximum likelihood classifier, classification tree analysis, Fuzzy ARTMAP (a neural-network method), k-nearest neighbor, object-based classification, and support vector machine were compared for examining which algorithm was suitable for land-cover classification in the tropical moist region. This research indicates that the combination of radiometric images and their textures provided considerably better classification accuracies than individual datasets. The L-band data provided much better landcover classification than C-band data but neither L-band nor C-band was suitable for fine land-cover classification system...

A land cover map of Latin America and the Caribbean in the framework of the SERENA project

Blanco P.D.; Colditz R.R.; Lopez Saldana G.; Hardtke L.A.; Llamas R.M.; Mari N.A.; Fischer A.; Caride C.; Acenolaza P.G.; del Valle H.F.; Lillo-Saavedra M.; Coronato F.; Opazo S.A.; Morelli F.; Anaya J.A.; Sione W.F.; Zamboni P.; Arroyo V.B.
Fonte: Universidade de Medellín Publicador: Universidade de Medellín
Tipo: Article; info:eu-repo/semantics/article
ENG
Relevância na Pesquisa
76.47%
Land cover maps at different resolutions and mapping extents contribute to modeling and support decision making processes. Because land cover affects and is affected by climate change, it is listed among the 13 terrestrial essential climate variables. This paper describes the generation of a land cover map for Latin America and the Caribbean (LAC) for the year 2008. It was developed in the framework of the project Latin American Network for Monitoring and Studying of Natural Resources (SERENA), which has been developed within the GOFC-GOLD Latin American network of remote sensing and forest fires (RedLaTIF). The SERENA land cover map for LAC integrates: 1) the local expertise of SERENA network members to generate the training and validation data, 2) a methodology for land cover mapping based on decision trees using MODIS time series, and 3) class membership estimates to account for pixel heterogeneity issues. The discrete SERENA land cover product, derived from class memberships, yields an overall accuracy of 84% and includes an additional layer representing the estimated per-pixel confidence. The study demonstrates in detail the use of class memberships to better estimate the area of scarce classes with a scattered spatial distribution. The land cover map is already available as a printed wall map and will be released in digital format in the near future. The SERENA land cover map was produced with a legend and classification strategy similar to that used by the North American Land Change Monitoring System (NALCMS) to generate a land cover map of the North American continent...

MERIS AO # 516 - Land Cover Mapping at BOREAS Study Area

Hu, B.; Miller, J. R.; Zarco-Tejada, Pablo J.; Freemantle, J.; Zwick, H.
Fonte: Conselho Superior de Investigações Científicas Publicador: Conselho Superior de Investigações Científicas
Tipo: Comunicación de congreso Formato: 766688 bytes; application/pdf
ENG
Relevância na Pesquisa
76.33%
The authors are grateful for the financial support from research grants provided through GEOmatics for Informed Decisions (GEOIDE), part of the Canadian Networks of Centres of Excellence (NCE).úÒ’ûŒÉ; The objective of this study is to validate the MERIS vegetation land cover classification product. The full resolution MERIS radiance data sets obtained over the BOREAS (Boreal Ecosystem-Atmosphere Study) South Study Area in May and August 2003 were used. The MERIS radiance data were first converted to at-canopy reflectance data, which were compared to CASI data obtained during BOREAS (1994), followed by unsupervised classification performed based on seasonal variation of pigments as inferred from visible and near-infrared spectral bands. Three modified normalized Difference Vegetation Indices (mNDVI), sensitive to relative proportions among pigments and pigment content, and a red-edge spectral parameter, the wavelength at the reflectance minimum (ë0) were used in the unsupervised classification. Accuracy assessments of the derived vegetation classification maps were performed using a forest inventory map provided by the Saskatchewan Environment and Resource Management Forestry Branch-Inventory Unit (SERM-FBIU). The forest vegetation classification using seasonal changes in optical indices (mNDVIs and ë0)...

Land Cover Change and Ecosystem Services on the North Carolina Piedmont 1985 to 2005

Donohue, Michael John
Fonte: Universidade Duke Publicador: Universidade Duke
Tipo: Masters' project Formato: 4256311 bytes; application/pdf
EN_US
Relevância na Pesquisa
76.42%
Analyses of ecosystem processes are advanced through remote sensing and geostatistical modeling methods capable of capturing landscape pattern over broad spatial and temporal scales. Many ecological studies rely on land cover data classified from satellite imagery. In this, changes in land cover are often presumed to correlate with changes in ecosystem processes or services provided by ecosystems (e.g., watershed protection). Documenting changes in land cover requires that images be classified over time, often using historical images to document landscape change. But this is difficult to do for historical images because we cannot ground-truth old images, lacking actual land cover data from the past. I developed a land cover classification scheme using a classification and regression tree (CART) model generated from 2001 National Land Cover Dataset (NLCD) and Summer, Fall, and Winter triplets of Landsat 5 Thematic Mapper (TM) imagery. The model is robust to inter-annual variability in surface reflectance, and thus can be extended in time to classify land cover from images from any time, past or future. The model was used to predict land cover from 1985 to 2005, for a study region in the Piedmont of North Carolina. Temporal and spatial analyses focused on ecosystem services of carbon sequestration and biodiversity support as affected by forest fragmentation. This study offers a landscape-level identification of the relationships between spatial and temporal development patterns and the provision of ecosystem services. The project also represents the creation of a multi-annual land cover classification dataset of which few exist...

A Multi-temporal fusion-based approach for land cover mapping in support of nuclear incident response

Sah, Shagan
Fonte: Rochester Instituto de Tecnologia Publicador: Rochester Instituto de Tecnologia
Tipo: Tese de Doutorado
EN_US
Relevância na Pesquisa
66.48%
An increasingly important application of remote sensing is to provide decision support during emergency response and disaster management efforts. Land cover maps constitute one such useful application product during disaster events; if generated rapidly after any disaster, such map products can contribute to the efficacy of the response effort. In light of recent nuclear incidents, e.g., after the earthquake/tsunami in Japan (2011), our research focuses on constructing rapid and accurate land cover maps of the impacted area in case of an accidental nuclear release. The methodology involves integration of results from two different approaches, namely coarse spatial resolution multi-temporal and fine spatial resolution imagery, to increase classification accuracy. Although advanced methods have been developed for classification using high spatial or temporal resolution imagery, only a limited amount of work has been done on fusion of these two remote sensing approaches. The presented methodology thus involves integration of classification results from two different remote sensing modalities in order to improve classification accuracy. The data used included RapidEye and MODIS scenes over the Nine Mile Point Nuclear Power Station in Oswego (New York...

Land cover classification of landsat thematic mapper images using pseudo invariant feature normalization applied to change detection

Hawes, Tim
Fonte: Rochester Instituto de Tecnologia Publicador: Rochester Instituto de Tecnologia
Tipo: Tese de Doutorado
EN_US
Relevância na Pesquisa
86.41%
A radiometric normalization technique for compensating illumination and atmospheric differences between multi-temporal images should allow classification of the images with a single classification algorithm. This allows a simpler approach to land cover change detection. Land cover classification of Landsat Thematic Mapper Imagery with and without Pseudo Invariant Feature Normalization was performed to demonstrate the effect on classification and change detection accuracy. A post-classification change detection method using two separate classification algorithms, one for each date, was performed as a baseline comparison. Land cover classification using one classification algorithm was attempted with and without gain and offset correction to serve as another comparison. Accuracy verification was performed on the classification results by comparing random samples against ground truth.

A land use and land cover accuracy assessment based on Landsat 7 imagery within The Canandaigua watershed: Natural Heritage Program and The James Anderson Classification System

Money, Travis
Fonte: Rochester Instituto de Tecnologia Publicador: Rochester Instituto de Tecnologia
Tipo: Tese de Doutorado
EN_US
Relevância na Pesquisa
76.55%
Research that incorporates GIS and remotely sensed imagery has become increasingly popular and important for large-scale environmental applications, such as generating land use and land cover maps. One of the critical aspects of land cover analyses is assigning a land use and land cover classification scheme. This research evaluated two classification schemes, the 2002 Natural Heritage Classification and the 1976 James Anderson System in a land cover analysis of the Canandaigua Lake Watershed using Landsat imagery. It was hypothesized that the Landsat imagery could be used to identify unique ecological communities such as those delineated by the Natural Heritage Classification. A composite image, created from an August 15, 2003 Landsat image using bands 1, 3 and 5, was used for the fine cluster analysis, which produced 38 unique clusters. Using the Canandaigua Lake Watershed Council's land use and land cover map as a truth image (26 single NHC classes and 14 mixed NHC classes), the clustered Landsat image was used in an unsupervised classification analysis that resulted in generalized land use and land cover maps using the Natural Heritage and James Anderson Classification schemes (5 and 6 dominant land covers respectively). Because many clusters were associated with several land cover classes...

Land-cover classification with an expert classification algorithm using digital aerial photographs

Perea,Alberto J.; Meroño,José E.; Aguilera,María J.; Cruz,José L. de la
Fonte: South African Journal of Science Publicador: South African Journal of Science
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/06/2010 EN
Relevância na Pesquisa
96.32%
The purpose of this study was to evaluate the usefulness of the spectral information of digital aerial sensors in determining land-cover classification using new digital techniques. The land covers that have been evaluated are the following, (1) bare soil, (2) cereals, including maize (Zea mays L.), oats (Avena sativa L.), rye (Secale cereale L.), wheat (Triticum aestivum L.) and barley (Hordeun vulgare L.), (3) high protein crops, such as peas (Pisum sativum L.) and beans (Vicia faba L.), (4) alfalfa (Medicago sativa L.), (5) woodlands and scrublands, including holly oak (Quercus ilex L.) and common retama (Retama sphaerocarpa L.), (6) urban soil, (7) olive groves (Olea europaea L.) and (8) burnt crop stubble. The best result was obtained using an expert classification algorithm, achieving a reliability rate of 95%. This result showed that the images of digital airborne sensors hold considerable promise for the future in the field of digital classifications because these images contain valuable information that takes advantage of the geometric viewpoint. Moreover, new classification techniques reduce problems encountered using high-resolution images; while reliabilities are achieved that are better than those achieved with traditional methods.