Description and validation of a new set of object-based temporal geostatistical features for land-use/land-cover change detection

Gil-Yepes, José Luis; Ruiz, Luis Á.; Recio, J. A.; Balaguer-Beser, Ángel; Hermosilla, Txomin

doi:10.1016/j.isprsjprs.2016.08.010

Cited by 67 publications

(29 citation statements)

References 48 publications

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“…Previous studies have shown that pixel-based change detection can suffer from not including additional information regarding the spatial context [32]. The inclusion of spatial information can reduce the small or spurious changes [33] and also allow for the incorporation of supplemental spatial information, such as geostatistical features [34][35][36][37]. Although pixel-based approaches also generate spatial information, the neighborhood used to retrieve the information is often defined by a square window that is easy to implement.…”

Section: Introductionmentioning

confidence: 99%

Using Spatial Features to Reduce the Impact of Seasonality for Detecting Tropical Forest Changes from Landsat Time Series

et al. 2018

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In forested areas that experience strong seasonality and are undergoing rapid land cover conversion (e.g., Brazilian savannas), the accuracy of remote sensing change detection is affected by seasonal changes that are erroneously classified as having changed. To improve the quality and consistency of regionally important forest change maps, we aim to separate process related change (for example, spectral variability due to phenology) from changes related to deforestations or fires. Seasonal models are typically used to account for seasonality, but fitting a model is difficult when there are insufficient data points in the time series. In this research, we utilize remotely sensed data and related spectral trends and the spatial context at the object level to evaluate the performance of geostatistical features to reduce the impact of seasonality from the NDVI (Normalized Difference Vegetation Index) of Landsat time series. The study area is the São Romão municipality, totaling 2440 km 2 , and is part of the Brazilian savannas biome. We first create image objects via multiresolution segmentation, basing the objects on the characteristics found in the first image (2003) of the 13-year time series. We intersected the objects with the NDVI images in order to extract semivariogram indices, the RVF (Ratio Variance-First lag) and AFM (Area First lag-First Maximum), and spectral information (average and standard deviation of NDVI values) to generate the time series from these features and to derive Spatio-Temporal Metrics (change and trend) to train a Random Forest (RF) algorithm. The NDVI spatial variability, captured by the AFM semivariogram index time series produced the best result, reaching 96.53% of the overall accuracy (OA) to separate no-change from forest change, while the greatest inter-class confusion occurred using the average of the NDVI values time series (OA = 63.72%). The spatial context approach we presented is a novel approach for the detection of forest change events that are subject to seasonality (and possible miss-classification of change) and mitigating the effects of forest phenology without the need for specific de-seasoning models.

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Section: Introductionmentioning

confidence: 99%

Using Spatial Features to Reduce the Impact of Seasonality for Detecting Tropical Forest Changes from Landsat Time Series

et al. 2018

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“…These have been used as measures of texture (Curran 1988;Woodcock;Strahler;Jupp et al, 1988), for improved image classification (Balaguer et al, 2010;Balaguer-Beser et al, 2011;Wu et al, 2015Yue et al, 2013Powers et al, 2015), and more recently, in change detection studies (Costantini et al, 2012;Sertel et al, 2007;Gil-Yepes et al, 2016). Acerbi Junior et al (2015) demonstrated the potential of semivariogram parameters (derived from bitemporal NDVI images) to detect changes in Brazilian savanna vegetation, showing that these parameters increased on deforested areas and remained constant in regions where the land cover had not changed.…”

Section: Introductionmentioning

confidence: 99%

“…Balaguer et al (2010) achieved high-accuracy measurements by combining semivariogram features and spectral information in land cover mapping. Gil-Yepes et al (2016) proposed and evaluated a set of new temporal geostatistical features for object-based change detection (OBCD) analysis within agricultural plots at two different dates, showing that the new set of cross-semivariogram and codispersion features provided high global accuracy measures when compared to the use of only spectral information.…”

Section: Introductionmentioning

confidence: 99%

Object-based change detection using semivariogram indices derived from NDVI images: The environmental disaster in Mariana, Brazil

et al. 2017

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Object-based change detection is a powerful analysis tool for remote sensing data, but few studies consider the potential of temporal semivariogram indices for mapping land-cover changes using object-based approaches. In this study, we explored and evaluated the performance of semivariogram indices calculated from remote sensing imagery, using the Normalized Differential Vegetation Index (NDVI) to detect changes in spatial features related to land cover caused by a disastrous 2015 dam failure in Brazil's Mariana district. We calculated the NDVI from Landsat 8 images acquired before and after the disaster, then created objects by multiresolution segmentation analysis based on post-disaster images. Experimental semivariograms were computed within the image objects and semivariogram indices were calculated and selected by principal component analysis. We used the selected indices as input data to a support vector machine algorithm for classifying change and no-change classes. The selected semivariogram indices showed their effectiveness as input data for object-based change detection analysis, producing highly accurate maps of areas affected by post-dam-failure flooding in the region. This approach can be used in many other contexts for rapid and accurate assessment of such land-cover changes.Index terms: Remote sensing; geostatistics; feature extraction. RESUMORecentemente, variáveis geoestatísticas derivadas de imagens de sensoriamento remoto ganharam espaço dentre os procedimentos de detecção de mudanças, porém, o potencial temporal destas variáveis para o mapeamento das mudanças baseado na análise por objetos ainda é pouco estudado. Neste estudo, o desempenho de um conjunto de índices calculados de semivariogramas derivados de imagens NDVI bitemporais para detectar mudanças na cobertura do solo foi analisado e avaliado. O município de Mariana foi selecionado para teste e validação da metodologia devido ao grande impacto ocasionado pelo desastre. O processo iniciou-se com a aquisição de imagens Landsat 8 antes e após o desastre e o cálculo do NDVI. Os objetos foram criados através da segmentação em multiresolução baseada na imagem pós-desastre. Os semivariogramas experimentais foram gerados dentro de cada objeto e os índices foram extraídos e selecionados através da análise de componentes principais. Os índices selecionados foram utilizados como dados de entrada para o algoritmo support vector machines para a classificação de áreas de mudança e não mudança. Os índices selecionados se mostraram efetivos para a detecção de mudanças, indicando a possibilidade de utilização para a detecção de mudanças baseada em objetos, resultando em um mapa precisos das áreas inundadas afetadas pelo desastre. Esta abordagem pode ser usada em muitos outros contextos para uma avaliação rápida e precisa de tais mudanças na cobertura do solo.Termos para indexação: Sensoriamento remoto; geostatistica; extração de atributos.

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“…12 Indeed, geostatistics can provide parameters for describing spatial patterns, 13 modeling uncertainty in spatial analyses, 14 measuring the spatial autocorrelation 15 and radiometric coregionalization, 16 and can also support downscaling techniques 17 and classification. 18 Authors of Ref. 19 presented a method based on variogram analysis that analyzed the spatial pattern of MODIS images using variogram tools to identify the images with higher quality, adopting MODIS band 03 (459 to 479 nm, blue) as the band that defines the structural parameters of the variogram.…”

Section: Introductionmentioning

confidence: 99%

Spatial and spectral pattern identification for the automatic selection of high-quality MODIS images

Pesquer

Domingo‐Marimon

Pons

2019

J. Appl. Rem. Sens.

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Remote sensing is providing an increasing number of crucial data about Earth. Systematic revisitation time allows the analysis of long time series as well as imagery utilization in the most interesting moments. Nevertheless, the current huge amount of data makes essential the usage of automatic methods to select the best captures, as many of them are not useful because of clouds, shadows, etc. Because of that, one of the characteristics of the more recent missions is the distribution, along with the spectral data, of a large amount of quality ancillary datasets. These datasets can act synergistically in the aim of selecting the best quality images, but the criteria they provide are not always enough. Indeed, these datasets are often used on a per pixel basis and the spatial pattern of the different spectral bands is forgotten, so ignoring the key information they can provide for our goals. With this aim, our work takes one of the most successful instruments in remote sensing, MODIS, and demonstrates, through geostatistical techniques, that the role of the spatial patterns of the spectral bands can effectively improve image selection in a complex (for climate, relief, and vegetation and crop phenology) region of 63;700 km 2. The results show that band 01 (red) is the preferred one, as it achieves a 13% higher success than when only using quality bands criteria: a 94% global accuracy (66 true classifications, and only four omissions and one commission error). A second, important finding, is that the geostatistical selection improves results when using any band, except for band 02 (NIR1), which makes our proposal potentially useful for most remote sensing missions. Finally, the method can be executed in a reasonable computing time due to previously developed high-performance computing techniques. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Description and validation of a new set of object-based temporal geostatistical features for land-use/land-cover change detection

Cited by 67 publications

References 48 publications

Using Spatial Features to Reduce the Impact of Seasonality for Detecting Tropical Forest Changes from Landsat Time Series

Using Spatial Features to Reduce the Impact of Seasonality for Detecting Tropical Forest Changes from Landsat Time Series

Object-based change detection using semivariogram indices derived from NDVI images: The environmental disaster in Mariana, Brazil

Spatial and spectral pattern identification for the automatic selection of high-quality MODIS images

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