The Moderate Resolution Imaging Spectroradiometer (MODIS): land remote sensing for global change research

Justice, Chris; Vermote, Éric; Townshend, J. R. G.; DeFries, Ruth; Roy, David P.; Hall, Dorothy K.; Salomonson, V. V.; Privette, Jeffrey L.; Riggs, George A.; Strahler, Alan H.; Lucht, Wolfgang; Myneni, Ranga B.; Knyazikhin, Yuri; Running, Steven W.; Nemani, R. R.; Wan, Zhengming; Huete, Alfredo; Leeuwen, Willem J. D. van; Wolfe, Robert E.; Giglio, L.; Müller, Jan-Peter; Lewis, P.; Barnsley, Michael J.

doi:10.1109/36.701075

Cited by 1,330 publications

(699 citation statements)

References 78 publications

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“…where crown relative height =1 (Wanner et al, 1995, Justice et al, 1998 r b crown relative shape =2 (Wanner et al, 1995, Justice et al, 1998 k i, k g and k v are the empirical components (kernel weights) derived from mathematical inversion of the linear model using multi-angular radiation observations. For a surface 14 that behaves in a more Lambertian manner (e.g.…”

Section: Brdf Notationmentioning

confidence: 99%

Separating physiologically and directionally induced changes in PRI using BRDF models

Hilker¹,

Coops²,

Hall³

et al. 2008

Remote Sensing of Environment

170

150

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Section: Brdf Notationmentioning

confidence: 99%

Separating physiologically and directionally induced changes in PRI using BRDF models

Hilker¹,

Coops²,

Hall³

et al. 2008

Remote Sensing of Environment

170

150

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“…This increases the possibility of obtaining cloud free data [11,12] and makes it possible to track crops along the season. The derived temporal profiles of vegetation indices such as Normalized Difference Vegetation Index (NDVI) can be used for automatic classification and land cover mapping [5,13,14].…”

Section: Introductionmentioning

confidence: 99%

Self-Guided Segmentation and Classification of Multi-Temporal Landsat 8 Images for Crop Type Mapping in Southeastern Brazil

et al. 2015

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Only well-chosen segmentation parameters ensure optimum results of object-based image analysis (OBIA). Manually defining suitable parameter sets can be a time-consuming approach, not necessarily leading to optimum results; the subjectivity of the manual approach is also obvious. For this reason, in supervised segmentation as proposed by Stefanski et al. (2013) one integrates the segmentation and classification tasks. The segmentation is optimized directly with respect to the subsequent classification. In this contribution, we build on this work and developed a fully autonomous workflow for supervised object-based classification, combining image segmentation and random forest (RF) classification. Starting from a fixed set of randomly selected and manually interpreted training samples, suitable segmentation parameters are automatically identified. A sub-tropical study site located in São Paulo State (Brazil) was used to evaluate the OPEN ACCESSRemote Sens. 2015, 7 14483proposed approach. Two multi-temporal Landsat 8 image mosaics were used as input (from August 2013 and January 2014) together with training samples from field visits and VHR (RapidEye) photo-interpretation. Using four test sites of 15 × 15 km 2 with manually interpreted crops as independent validation samples, we demonstrate that the approach leads to robust classification results. On these samples (pixel wise, n ≈ 1 million) an overall accuracy (OA) of 80% could be reached while classifying five classes: sugarcane, soybean, cassava, peanut and others. We found that the overall accuracy obtained from the four test sites was only marginally lower compared to the out-of-bag OA obtained from the training samples. Amongst the five classes, sugarcane and soybean were classified best, while cassava and peanut were often misclassified due to similarity in the spatio-temporal feature space and high within-class variabilities. Interestingly, misclassified pixels were in most cases correctly identified through the RF classification margin, which is produced as a by-product to the classification map.

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“…No entanto, esse índice mostra-se adequado para ambientes de cerrado (cerrado típico), cujo valor de IAF fica em torno de 1 (MIURA et al, 2003). O EVI foi desenvolvido para evidenciar as regiões com alta biomassa, reduzindo a influencia do solo e da atmosfera, sendo expresso pela seguinte formulação (JUSTICE et al, 1998):…”

Section: Aquisição Dos Dados Modisunclassified

“…Para o MODIS, esses valores são C 1 = 6; C 2 = 7,5; L = 1; e G = 2,5 (JUSTICE et al, 1998;HUETE et al, 1994;1997).…”

Section: Aquisição Dos Dados Modismentioning

confidence: 99%

Tratamento de ruídos e caracterização de fisionomias do Cerrado utilizando séries temporais do sensor MODIS

et al. 2011

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RESUMO -O Cerrado é formado por um mosaico de fisionomias campestres, savânicas e florestais que possuem um típico ciclo fenológico. Nesse contexto, os dados do MODIS fornecem medidas diárias que permitem monitorar a sazonal fenologia da vegetação. O objetivo deste trabalho foi caracterizar formações savânicas, formações florestais e áreas de cerrado convertido pela ação antrópica, utilizando séries temporais de NDVI e EVI do sensor MODIS, após a suavização de ruídos. A metodologia adotada pode ser subdividida nos seguintes passos: (a) confecção do cubo temporal com NDVI e EVI, onde o perfil em z corresponde à assinatura temporal, (b) tratamento do ruído e (c) detecção das assinaturas temporais. A Transformação pela Fração Mínima de Ruído (MNF) foi aplicada para suavizar ruídos contidos nas séries temporais. Os resultados indicaram que os valores de NDVI foram maiores que os valores de EVI e existiu relação com as estações do ano. As formações florestais foram as que apresentaram maiores valores de NDVI e EVI, possuindo ainda as menores variações entre as estações. As áreas convertidas apresentaram os menores valores dos dois índices, tendo grande queda em seus valores no início da estação seca, provavelmente por se tratar do período de colheita. O estudo concluiu que as séries temporais NDVI e EVI são úteis na diferenciação dos tipos de vegetação.Palavras-chave: Cerrado, MODIS e Série temporal. DENOISING AND CARACTERIZATION OF CERRADO PHYSIOGNOMIES USING MODIS TIMES SERIES

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The Moderate Resolution Imaging Spectroradiometer (MODIS): land remote sensing for global change research

Cited by 1,330 publications

References 78 publications

Separating physiologically and directionally induced changes in PRI using BRDF models

Separating physiologically and directionally induced changes in PRI using BRDF models

Self-Guided Segmentation and Classification of Multi-Temporal Landsat 8 Images for Crop Type Mapping in Southeastern Brazil

Tratamento de ruídos e caracterização de fisionomias do Cerrado utilizando séries temporais do sensor MODIS

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