Wetland classification using optical and radar data and neural network classification

Augusteijn, Marijke F.; Warrender, Christina E.

doi:10.1080/014311698215342

Cited by 74 publications

(26 citation statements)

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“…The Tasseled Cap transformation also has been used by others to improve wetland mapping [81,94]. We found that using radar backscatter was more useful than using the polarimetric decompositions; in particular, our findings further confirm those of others documenting the importance of co-and cross-polarization radar backscatter (HH and HV, respectively) in classifying land cover [95][96][97].…”

Section: Cowardin Wetland Classification (Level 2)supporting

confidence: 81%

Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota

2013

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Abstract:Wetland mapping at the landscape scale using remotely sensed data requires both affordable data and an efficient accurate classification method. Random forest classification offers several advantages over traditional land cover classification techniques, including a bootstrapping technique to generate robust estimations of outliers in the training data, as well as the capability of measuring classification confidence. Though the random forest classifier can generate complex decision trees with a multitude of input data and still not run a high risk of over fitting, there is a great need to reduce computational and operational costs by including only key input data sets without sacrificing a significant level of accuracy. Our main questions for this study site in Northern Minnesota were: (1) how does classification accuracy and confidence of mapping wetlands compare using different remote sensing platforms and sets of input data; (2) what are the key input variables for accurate differentiation of upland, water, and wetlands, including wetland type; and (3) which datasets and seasonal imagery yield the best accuracy for wetland classification. Our results show the key input variables include terrain (elevation and curvature) and soils descriptors (hydric), along with an assortment of remotely sensed data collected in the spring (satellite visible, near infrared, and thermal bands; satellite normalized vegetation index and Tasseled Cap greenness and wetness; and horizontal-horizontal (HH) and horizontal-vertical (HV) polarization using L-band satellite radar). We undertook this exploratory analysis to inform decisions by natural resource OPEN ACCESS Remote Sens. 2013, 5 3213 managers charged with monitoring wetland ecosystems and to aid in designing a system for consistent operational mapping of wetlands across landscapes similar to those found in Northern Minnesota.

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Section: Cowardin Wetland Classification (Level 2)supporting

confidence: 81%

Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota

2013

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“…There are many studies on wetland mapping over China also, and remote sensing has been used in monitoring changes in the Poyang Lake, Honghe wetlands and Zhalong River wetlands [13][14][15]. 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].…”

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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“…Whereas several researchers have evaluated the use of optical or SAR data alone for mapping wetlands, until more recently few had evaluated SAR and optical fusion for wetland mapping [6,[8][9][10][11][12][13][14] and most ignored the coarser-resolution thermal bands.…”

Section: Introductionmentioning

confidence: 99%

Development of a Bi-National Great Lakes Coastal Wetland and Land Use Map Using Three-Season PALSAR and Landsat Imagery

et al. 2015

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Methods using extensive field data and three-season Landsat TM and PALSAR imagery were developed to map wetland type and identify potential wetland stressors (i.e., adjacent land use) for the United States and Canadian Laurentian coastal Great Lakes. The mapped area included the coastline to 10 km inland to capture the region hydrologically connected to the Great Lakes. Maps were developed in cooperation with the overarching Great Lakes Consortium plan to provide a comprehensive regional baseline map suitable for coastal wetland assessment and management by agencies at the local, tribal, state, and federal levels. The goal was to provide not only land use and land cover (LULC) baseline data at moderate spatial resolution (20-30 m), but a repeatable methodology to monitor change into the future. The prime focus was on mapping wetland ecosystem types, such as emergent wetland and forested wetland, as well as to delineate wetland monocultures (Typha, OPEN ACCESSRemote Sens. 2015, 7 8656Phragmites, Schoenoplectus) and differentiate peatlands (fens and bogs) from other wetland types. The overall accuracy for the coastal Great Lakes map of all five lake basins was 94%, with a range of 86% to 96% by individual lake basin (Huron, Ontario, Michigan, Erie and Superior).

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Wetland classification using optical and radar data and neural network classification

Cited by 74 publications

References 0 publications

Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota

Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota

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

Development of a Bi-National Great Lakes Coastal Wetland and Land Use Map Using Three-Season PALSAR and Landsat Imagery

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