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

Corcoran, Jennifer; Knight, Joseph F.; Gallant, Alisa L.

doi:10.3390/rs5073212

Cited by 188 publications

(162 citation statements)

References 79 publications

(86 reference statements)

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“…Optical and SAR imagery provide complementary information and are often used in combination, while addition of topographic variables has also been shown to improve wetland and other land cover classification [15,21,95,96]. This was confirmed in this study as variable importance analysis showed that shortwave-infrared reflectance (Landsat bands 5 and 7), PALSAR HV backscatter and several topographic variables (terrain classification index, relative slope position, elevation, and slope height, in order of importance) contributed most to overall classification accuracy.…”

Section: Random Forest Classifier Performance and Variable Importancementioning

confidence: 99%

“…The inclusion of SAR data had a relatively limited impact overall on the importance ranking of optical variables, although PALSAR HV outranked most variables when used in combination with optical variables. Corcoran, Knight and Gallant [95] discriminated upland, water, and wetland areas using RF with a similar assortment of predictors (e.g., Landsat 5 TM NIR and SWIR, elevation and curvature, hydric soils data, as well as PALSAR (L-band) cross-polarization (HV) data). Other studies have confirmed the importance of NDVI [14], NDMWI [97], Tasseled-cap components [98,99], and Landsat thermal band 6 as wetland predictors [95,98].…”

Section: Random Forest Classifier Performance and Variable Importancementioning

confidence: 99%

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Mapping the Dabus Wetlands, Ethiopia, Using Random Forest Classification of Landsat, PALSAR and Topographic Data

et al. 2017

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Abstract:The Dabus Wetland complex in the highlands of Ethiopia is within the headwaters of the Nile Basin and is home to significant ecological communities and rare or endangered species. Its many interrelated wetland types undergo seasonal and longer-term changes due to weather and climate variations as well as anthropogenic land use such as grazing and burning. Mapping and monitoring of these wetlands has not been previously undertaken due primarily to their relative isolation and lack of resources. This study investigated the potential of remote sensing based classification for mapping the primary vegetation groups in the Dabus Wetlands using a combination of dry and wet season data, including optical (Landsat spectral bands and derived vegetation and wetness indices), radar (ALOS PALSAR L-band backscatter), and elevation (SRTM derived DEM and other terrain metrics) as inputs to the non-parametric Random Forest (RF) classifier. Eight wetland types and three terrestrial/upland classes were mapped using field samples of observed plant community composition and structure groupings as reference information. Various tests to compare results using different RF input parameters and data types were conducted. A combination of multispectral optical, radar and topographic variables provided the best overall classification accuracy, 94.4% and 92.9% for the dry and wet season, respectively. Spectral and topographic data (radar data excluded) performed nearly as well, while accuracies using only radar and topographic data were 82-89%. Relatively homogeneous classes such as Papyrus Swamps, Forested Wetland, and Wet Meadow yielded the highest accuracies while spatially complex classes such as Emergent Marsh were more difficult to accurately classify. The methods and results presented in this paper can serve as a basis for development of long-term mapping and monitoring of these and other non-forested wetlands in Ethiopia and other similar environmental settings.

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Section: Random Forest Classifier Performance and Variable Importancementioning

confidence: 99%

Section: Random Forest Classifier Performance and Variable Importancementioning

confidence: 99%

Mapping the Dabus Wetlands, Ethiopia, Using Random Forest Classification of Landsat, PALSAR and Topographic Data

et al. 2017

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“…Values for the former provide an indication of the extent to which the variable generates homogeneous or pure nodes, while the latter is based on a relative change in accuracy as a result of the variable being randomly permuted or excluded from the model. In both cases, higher values indicate higher importance [14,38,44]. Importance values can also vary greatly among models built on the same predictor variables, although it has been shown that values become more stable when a high number of trees are built into the forest [38].…”

Section: The Random Forest Classifiermentioning

confidence: 99%

Assessing the Potential to Operationalize Shoreline Sensitivity Mapping: Classifying Multiple Wide Fine Quadrature Polarized RADARSAT-2 and Landsat 5 Scenes with a Single Random Forest Model

et al. 2015

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Abstract:The Random Forest algorithm was used to classify 86 Wide Fine Quadrature Polarized RADARSAT-2 scenes, five Landsat 5 scenes, and a Digital Elevation Model covering an area approximately 81,000 km 2 in size, and representing the entirety of Dease Strait, Coronation Gulf and Bathurst Inlet, Nunavut. The focus of this research was to assess the potential to operationalize shoreline sensitivity mapping to inform oil spill response and contingency planning. The impact of varying the training sample size and reducing model data load were evaluated. Results showed that acceptable accuracies could be achieved with relatively few training samples, but that higher accuracies and greater probabilities of correct class assignment were observed with larger sample sizes. Additionally, the number of inputs to the model could be greatly reduced without impacting overall performance. Optimized models reached independent accuracies of 91% for seven land cover types, and classification probabilities between 0.77 and 0.98 (values for latter represent per-class averages generated from independent validation sites). Mixed OPEN ACCESSRemote Sens. 2015, 7 13529 results were observed when assessing the potential for remote predictive mapping by simulating transferability of the model to scenes without training data.

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“…The random forest (RF) algorithm, an integrative classifier, has shown to be able to achieve high classification accuracy even when applied to analyze data with stronger noise [27,28]. Currently, the random forest classifier has been widely employed in the landcover classification of mesophyte environments, but is rarely used in the wetland classification for arid and semiarid areas [29][30][31][32][33].…”

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

Cited by 188 publications

References 79 publications

Mapping the Dabus Wetlands, Ethiopia, Using Random Forest Classification of Landsat, PALSAR and Topographic Data

Mapping the Dabus Wetlands, Ethiopia, Using Random Forest Classification of Landsat, PALSAR and Topographic Data

Assessing the Potential to Operationalize Shoreline Sensitivity Mapping: Classifying Multiple Wide Fine Quadrature Polarized RADARSAT-2 and Landsat 5 Scenes with a Single Random Forest Model

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

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