Identificação regional da Floresta Estacional Decidual na bacia do Rio Paranã a partir da análise multitemporal de imagens MODIS

Júnior, Osmar Abı́lio de Carvalho; Hermuche, Potira Meirelles; Guimarães, Renato Fontes

doi:10.1590/s0102-261x2006000300002

Cited by 18 publications

(17 citation statements)

References 27 publications

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“…In this research, we used 552 images for the period 2001-2012 over the same area. A representation of the NBR image collection can be obtained by building the cube of MODIS temporal series [64,65]. The cube is formed by images of the temporal series with its three dimensions: x, y and z (NBR temporal curve) acquired in the same geographical area at different times.…”

Section: Modis/terra Time-series Datamentioning

confidence: 99%

Standardized Time-Series and Interannual Phenological Deviation: New Techniques for Burned-Area Detection Using Long-Term MODIS-NBR Dataset

et al. 2015

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Typically, digital image processing for burned-areas detection combines the use of a spectral index and the seasonal differencing method. However, the seasonal differencing has many errors when applied to a long-term time series. This article aims to develop and test two methods as an alternative to the traditional seasonal difference. The study area is the Chapada dos Veadeiros National Park (Central Brazil) that comprises different vegetation of the Cerrado biome. We used the MODIS/Terra Surface Reflectance 8-Day composite data, considering a 12-year period. The normalized burn ratio was calculated from the band 2 (250-meter resolution) and the band 7 (500-meter resolution reasampled to 250-meter). In this context, the normalization methods aim to eliminate all possible sources of spectral variation and highlight the burned-area features. The proposed normalization methods were the standardized time-series and the interannual phenological deviation. The standardized time-series calculate for each pixel the z-scores of its temporal curve, obtaining a mean of 0 and a standard deviation of 1. The second method establishes a reference curve for each pixel from the average interannual phenology that is subtracted for every year of its respective time series. Optimal threshold value between burned and unburned area for each method was determined from accuracy assessment curves, which compare different threshold values and its accuracy indices with a reference classification using Landsat TM. The different methods have similar accuracy for the burning event, where the standardized method has slightly better results. However, the seasonal difference OPEN ACCESS Remote Sens. 2015, 7 6951 method has a very false positive error, especially in the period between the rainy and dry seasons. The interannual phenological deviation method minimizes false positive errors, but some remain. In contrast, the standardized time series shows excellent results not containing this type of error. This precision is due to the design method that does not perform a subtraction with a baseline (prior year or average phenological curve). Thus, this method allows a high stability and can be implemented for the automatic detection of burned areas using long-term time series.

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Section: Modis/terra Time-series Datamentioning

confidence: 99%

Standardized Time-Series and Interannual Phenological Deviation: New Techniques for Burned-Area Detection Using Long-Term MODIS-NBR Dataset

et al. 2015

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“…All MODIS-NDVI images obtained over 2011-2013 were merged in a three-dimensional image cube; "X" and "Y" are related to geographical coordinates (longitude and latitude), and "Z" is the behavior of the target over time [43,44].…”

Section: Image Cube Of Ndvi-modis Time Seriesmentioning

confidence: 99%

Comparative Analysis of MODIS Time-Series Classification Using Support Vector Machines and Methods Based upon Distance and Similarity Measures in the Brazilian Cerrado-Caatinga Boundary

et al. 2015

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Abstract:We have mapped the primary native and exotic vegetation that occurs in the Cerrado-Caatinga transition zone in Central Brazil using MODIS-NDVI time series (product MOD09Q1) data over a two-year period (2011)(2012)(2013). Our methodology consists of the following steps: (a) the development of a three-dimensional cube composed of the NDVI-MODIS time series; (b) the removal of noise; (c) the selection of reference temporal curves and classification using similarity and distance measures; and (d) classification using support vector machines (SVMs). We evaluated different temporal classifications using similarity and distance measures of land use and land cover considering several combinations of attributes. Among the classification using distance and similarity measures, the best result employed the Euclidean distance with the NDVI-MODIS data by considering more than one reference temporal curve per class and adopting six mapping classes. In the majority of tests, the SVM classifications yielded better results than other methods. The best OPEN ACCESSRemote Sens. 2015, 7 12161 result among all the tested methods was obtained using the SVM classifier with a fourth-degree polynomial kernel; an overall accuracy of 80.75% and a Kappa coefficient of 0.76 were obtained. Our results demonstrate the potential of vegetation studies in semiarid ecosystems using time-series data.

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“…This procedure is very different from the conventional methods of noise removal in radar images, which operates on a single image. Therefore, this new approach using multi-components has no similarity with other methods applied to radar data, but is compatible with the procedures used in hyperspectral images [98][99][100][101], aerial gamma-ray surveys [33,34,102,103] and time-series data [35][36][37]. The key to success is in the reconstruction of a valid signal and the attenuation of noise from the PDC components.…”

Section: Discussionmentioning

confidence: 99%

“…In the hyperspectral data, linear transformation techniques are often used to eliminate noise, such as Maximum Noise Fraction (MNF) [31] and Noise-Adjusted Principal Components (NAPCs) [32]. However, these methods are also adequate to eliminate noise interferences of a larger amount of data, such as an aerial gamma-ray survey [33,34] and a time series of remote sensing data [35][36][37]. The MNF transform adopts similar arguments to the PCA to derivate its components.…”

Section: Figurementioning

confidence: 99%

Probability Density Components Analysis: A New Approach to Treatment and Classification of SAR Images

Júnior

Maciel²,

Carvalho³

et al. 2014

Remote Sensing

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Speckle noise (salt and pepper) is inherent to synthetic aperture radar (SAR), which causes a usual noise-like granular aspect and complicates the image classification. In SAR image analysis, the spatial information might be a particular benefit for denoising and mapping classes characterized by a statistical distribution of the pixel intensities from a complex and heterogeneous spectral response. This paper proposes the Probability Density Components Analysis (PDCA), a new alternative that combines filtering and frequency histogram to improve the classification procedure for the single-channel synthetic aperture radar (SAR) images. This method was tested on L-band SAR data from the Advanced Land Observation System (ALOS) Phased-Array Synthetic-Aperture Radar (PALSAR) sensor. The study area is localized in the Brazilian Amazon rainforest, northern Rondônia State (municipality of Candeias do Jamari), containing forest and land use patterns. The proposed algorithm uses a moving window over the image, estimating the probability density curve in different image components. Therefore, a single input image generates an output with multi-components. Initially the multi-components should be treated by OPEN ACCESSRemote Sens. 2014, 6 2990 noise-reduction methods, such as maximum noise fraction (MNF) or noise-adjusted principal components (NAPCs). Both methods enable reducing noise as well as the ordering of multi-component data in terms of the image quality. In this paper, the NAPC applied to multi-components provided large reductions in the noise levels, and the color composites considering the first NAPC enhance the classification of different surface features. In the spectral classification, the Spectral Correlation Mapper and Minimum Distance were used. The results obtained presented as similar to the visual interpretation of optical images from TM-Landsat and Google Maps.

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Identificação regional da Floresta Estacional Decidual na bacia do Rio Paranã a partir da análise multitemporal de imagens MODIS

Cited by 18 publications

References 27 publications

Standardized Time-Series and Interannual Phenological Deviation: New Techniques for Burned-Area Detection Using Long-Term MODIS-NBR Dataset

Standardized Time-Series and Interannual Phenological Deviation: New Techniques for Burned-Area Detection Using Long-Term MODIS-NBR Dataset

Comparative Analysis of MODIS Time-Series Classification Using Support Vector Machines and Methods Based upon Distance and Similarity Measures in the Brazilian Cerrado-Caatinga Boundary

Probability Density Components Analysis: A New Approach to Treatment and Classification of SAR Images

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