Mapping dynamic cover types in a large seasonally flooded wetland using extended principal component analysis and object-based classification

Dronova, Iryna; Gong, Peng; Wang, Lin; Zhong, Liheng

doi:10.1016/j.rse.2014.10.027

Cited by 108 publications

(64 citation statements)

References 57 publications

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“…Classification algorithms such as conventional decision tree, maximum likelihood, support vector machines (SVM), and artificial neural networks (ANN) have been employed in wetland classification [16][17][18][19][20][21]. With the improved spatial resolution of remotely sensed imagery, the object-oriented strategy has also been proposed [22][23][24]. However, the accuracy and robustness of the existing classification methods are not yet satisfactory for wetland management, bearing big omission and commission errors due to the sparse yet variable vegetation and the hydrological fluctuation in the wetlands of arid areas [25,26].…”

Section: Introductionmentioning

confidence: 99%

Random Forest Classification of Wetland Landcovers from Multi-Sensor Data in the Arid Region of Xinjiang, China

et al. 2016

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Abstract:The wetland classification from remotely sensed data is usually difficult due to the extensive seasonal vegetation dynamics and hydrological fluctuation. This study presents a random forest classification approach for the retrieval of the wetland landcover in the arid regions by fusing the Pléiade-1B data with multi-date Landsat-8 data. The segmentation of the Pléiade-1B multispectral image data was performed based on an object-oriented approach, and the geometric and spectral features were extracted for the segmented image objects. The normalized difference vegetation index (NDVI) series data were also calculated from the multi-date Landsat-8 data, reflecting vegetation phenological changes in its growth cycle. The feature set extracted from the two sensors data was optimized and employed to create the random forest model for the classification of the wetland landcovers in the Ertix River in northern Xinjiang, China. Comparison with other classification methods such as support vector machine and artificial neural network classifiers indicates that the random forest classifier can achieve accurate classification with an overall accuracy of 93% and the Kappa coefficient of 0.92. The classification accuracy of the farming lands and water bodies that have distinct boundaries with the surrounding land covers was improved 5%-10% by making use of the property of geometric shapes. To remove the difficulty in the classification that was caused by the similar spectral features of the vegetation covers, the phenological difference and the textural information of co-occurrence gray matrix were incorporated into the classification, and the main wetland vegetation covers in the study area were derived from the two sensors data. The inclusion of phenological information in the classification enables the classification errors being reduced down, and the overall accuracy was improved approximately 10%. The results show that the proposed random forest classification by fusing multi-sensor data can retrieve better wetland landcover information than the other classifiers, which is significant for the monitoring and management of the wetland ecological resources in arid areas.

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Section: Introductionmentioning

confidence: 99%

Random Forest Classification of Wetland Landcovers from Multi-Sensor Data in the Arid Region of Xinjiang, China

et al. 2016

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“…The result of the OBIA approach was successfully proved to be more accurate than that of the pixel-based approaches for land cover classification in recent studies, such as discrimination of different species of mangroves with Worldview-2 imagery [23], flood area delineation in the trans-boundary areas using the ENVISAT/ASAR and Landsat TM data [4], and crop mapping using the multi-temporal Landsat imagery [22]. Other applications of the object-based method for flood water and wetland mapping were introduced in [24][25][26]. The object-based approach is normally used for the high spatial resolution images with a pixel size smaller than those of the objects of interest [27,28].…”

Section: Introductionmentioning

confidence: 99%

Object-Based Flood Mapping and Affected Rice Field Estimation with Landsat 8 OLI and MODIS Data

Dao

Liou

2015

Remote Sensing

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Cambodia is one of the most flood-prone countries in Southeast Asia. It is geographically situated in the downstream region of the Mekong River with a lowland floodplain in the middle, surrounded by plateaus and high mountains. It usually experiences devastating floods induced by an overwhelming concentration of rainfall water over the Tonle Sap Lake's and Mekong River's banks during monsoon seasons. Flood damage assessment in the rice ecosystem plays an important role in this region as local residents rely heavily on agricultural production. This study introduced an object-based approach to flood mapping and affected rice field estimation in central Cambodia. In this approach, image segmentation processing was conducted with optimal scale parameter estimation based on the variation of objects' local variances. The inundated area was identified by using Landsat 8 images with an overall accuracy of higher than 95% compared to those derived from finer spatial resolution images. Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index products were utilized to identify the paddy rice field based on seasonal inter-variation between vegetation and water index during the transplanting stage. The rice classification result was well correlated with the statistical data at a commune level (R 2 = 0.675). The flood mapping and affected rice estimation results are useful to provide local governments with valuable information for flooding mitigation and post-flooding compensation and restoration.

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“…Although "invisible" to remote sensors, these tubers provide critical food resources to bird foragers from the tuber-feeding guild [40,43,45,59]. Some areas of Poyang Lake also retain photosynthetically active submerged and floating macrophytes from other taxa during the cool growing season [44,51]; however, the specific role of this vegetation in wintering waterbird habitat relationships remains unclear to date.…”

Section: Study Areamentioning

confidence: 99%

“…Mudflats with such properties may represent lake beds with more recent post-flood exposure [10,51] that contain aquatic invertebrates, vegetation seeds, propagules and tubers of the summer-season aquatic macrophytes [40,43] and, thus, support birds of different size and foraging preferences. The size of mudflat was previously reported to positively contribute to bird abundance, and negatively-to species richness based on the study of 10 Poyang Lake sub-lakes [7]; however, in our models the proportion of mudflat area was not as important.…”

Section: Remotely Sensed Indicators Of Poyang Lake Bird Diversity Andmentioning

confidence: 99%

“…However, the understanding of factors behind spatial patterns of bird diversity and abundance is still limited [7,45,51,52], while primary habitat requirements have been studied mainly for specific species and foraging guilds [43,45,53]. Previous multi-year analyses of waterbird diversity and abundance have investigated their associations with basin-wide hydrological and climatic factors [8] and remotely sensed extents and heterogeneity of major habitat cover types [7].…”

Section: Introductionmentioning

confidence: 99%

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Landscape-Level Associations of Wintering Waterbird Diversity and Abundance from Remotely Sensed Wetland Characteristics of Poyang Lake

et al. 2016

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Poyang Lake, the largest freshwater wetland in China, provides critical habitat for wintering waterbirds from the East Asian Flyway; however, landscape drivers of non-uniform bird diversity and abundance are not yet well understood. Using a winter 2006 waterbird survey, we examined the relationships among metrics of bird community diversity and abundance and landscape characteristics of 51 wetland sub-lakes derived by an object-based classification of Landsat satellite data. Relative importance of predictors and their sets was assessed using information-theoretic model selection and the Akaike Information Criterion. Ordinary least squares regression models were diagnosed and corrected for spatial autocorrelation using spatial autoregressive lag and error models. The strongest and most consistent landscape predictors included Normalized Difference Vegetation Index for mudflat (negative effect) and emergent grassland (positive effect), total sub-lake area (positive effect), and proportion of submerged vegetation (negative effect). Significant spatial autocorrelation in linear regression was associated with local clustering of response and predictor variables, and should be further explored for selection of wetland sampling units and management of protected areas. Overall, results corroborate the utility of remote sensing to elucidate potential indicators of waterbird diversity that complement logistically challenging ground observations and offer new hypotheses on factors underlying community distributions.

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Mapping dynamic cover types in a large seasonally flooded wetland using extended principal component analysis and object-based classification

Cited by 108 publications

References 57 publications

Random Forest Classification of Wetland Landcovers from Multi-Sensor Data in the Arid Region of Xinjiang, China

Random Forest Classification of Wetland Landcovers from Multi-Sensor Data in the Arid Region of Xinjiang, China

Object-Based Flood Mapping and Affected Rice Field Estimation with Landsat 8 OLI and MODIS Data

Landscape-Level Associations of Wintering Waterbird Diversity and Abundance from Remotely Sensed Wetland Characteristics of Poyang Lake

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