Mapping wetlands and riparian areas using Landsat ETM+ imagery and decision-tree-based models

Baker, Corey; Lawrence, Rick L.; Montagne, Cliff; Patten, Duncan T.

doi:10.1672/0277-5212(2006)26[465:mwarau]2.0.co;2

Cited by 219 publications

(125 citation statements)

References 22 publications

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“…Landsat Thematic Mapper imagery can be used to map open water at 30-m resolution (Alsdorf et al, 2007;Baker et al, 2006) appropriate for resolving smaller, shallow wetlands that provide important migratory shorebird habitat (Strum et al, 2013). There is great potential for tracking long-term surface water dynamics given Landsat's <16-day revisit period from 1983 to the present (e.g., Feng et al, 2016;Mueller et al, 2016;Tulbure et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Unsupervised classification approaches have been the most common technique in the past for mapping wetlands (Ozesmi and Bauer, 2002). Of the many supervised classification methods available, maximum likelihood has been used most frequently (Mather, 1985;Ozesmi and Bauer, 2002), however classification trees have become increasingly popular, and typically produce superior results (Baker et al, 2006;Breiman, 2001;Friedl and Brodley, 1997;Tulbure and Broich, 2013). Thresholds can also be used for land cover mapping, with an advantage of lower overall investment for the user; once developed, a classification can be generated virtually instantaneously (Chuvieco, 2016;Friedl and Brodley, 1997).…”

Section: Introductionmentioning

confidence: 99%

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Three decades of Landsat-derived spring surface water dynamics in an agricultural wetland mosaic; Implications for migratory shorebirds

Schaffer‐Smith

Swenson

Barbaree

et al. 2017

Remote Sensing of Environment

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Satellite measurements of surface water offer promise for understanding wetland habitat availability at broad spatial and temporal scales; reliable habitat is crucial for the persistence of migratory shorebirds that depend on wetland networks. We analyzed water extent dynamics within wetland habitats at a globally important shorebird stopover site for a 1983-2015 Landsat time series, and evaluated the effect of climate on water extent. A range of methods can detect open water from imagery, including supervised classification approaches and thresholds for spectral bands and indices. Thresholds provide a time advantage; however, there is no universally superior index, nor single best threshold for all instances. We used random forest to model the presence or absence of water from >6200 reference pixels, and derived an optimal water probability threshold for our study area using receiver operating characteristic curves. An optimized mid-infrared (1.5-1.7 μm) threshold identified open water in the Sacramento Valley of California at 30-m resolution with an average of 90% producer's accuracy, comparable to approaches that require more intensive user input. SLC-off Landsat 7 imagery was integrated by applying a customized interpolation that mapped water in missing data gaps with 99% user's accuracy. On average we detected open water on ~26000 ha (~3% of the study area) in early April at the peak of shorebird migration, while water extent increased five-fold after the migration rush. Over the last three decades, late March water extent declined by ~1300 ha per year, primarily due to changes in the extent and timing of agricultural flood-irrigation. Water within shorebird habitats was significantly associated with an index of water availability at the peak of migration. Our approach can be used to optimize thresholds for time series analysis and near-real-time mapping in other regions, and requires only marginally more time than generating a confusion matrix.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Three decades of Landsat-derived spring surface water dynamics in an agricultural wetland mosaic; Implications for migratory shorebirds

Schaffer‐Smith

Swenson

Barbaree

et al. 2017

Remote Sensing of Environment

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“…Accurate wetland mapping is an important 35 tool for understanding wetland functions and monitoring their response to natural and 36 anthropogenic actions (Baker et al, 2006). 37…”

Section: Introduction 31 32mentioning

confidence: 99%

Wetland monitoring using classification trees and SPOT-5 seasonal time series

Davranche

Lefebvre

Poulin

2010

Remote Sensing of Environment

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1Multi-season reflectance data from radiometrically and geometrically corrected 2 multispectral SPOT-5 images of 10-m resolution were combined with thorough field campaigns 3 and land cover digitizing using a binary classification tree algorithm to estimate the area of 4 marshes covered with common reeds (Phragmites australis) and submerged macrophytes 5 (Potamogeton pectinatus, P. pusillus, Myriophyllum spicatum, Ruppia maritima, Chara sp.) over 6 an area of 145 000 ha. Accuracy of these models was estimated by cross-validation and by 7 calculating the percentage of correctly classified pixels on the resulting maps. Robustness of this 8 approach was assessed by applying these models to an independent set of images using 9 independent field data for validation. Biophysical parameters of both habitat types were used to 10 interpret the misclassifications. The resulting trees provided a cross-validation accuracy of 98.

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“…In addition, it is well known that even small inundation regime changes may result in a substantial impact on the ecosystem characteristics and function [5]. Therefore, accurately mapping the wetland inundation is absolutely required to capture the wetland inundation dynamics to understand the influences from climate change and human activities and to monitor their responses to the terrestrial ecosystem [6,7].…”

Section: Introductionmentioning

confidence: 99%

Subpixel Inundation Mapping Using Landsat-8 OLI and UAV Data for a Wetland Region on the Zoige Plateau, China

Xia

Zhao

et al. 2017

Remote Sensing

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Abstract:Wetland inundation is crucial to the survival and prosperity of fauna and flora communities in wetland ecosystems. Even small changes in surface inundation may result in a substantial impact on the wetland ecosystem characteristics and function. This study presented a novel method for wetland inundation mapping at a subpixel scale in a typical wetland region on the Zoige Plateau, northeast Tibetan Plateau, China, by combining use of an unmanned aerial vehicle (UAV) and Landsat-8 Operational Land Imager (OLI) data. A reference subpixel inundation percentage (SIP) map at a Landsat-8 OLI 30 m pixel scale was first generated using high resolution UAV data (0.16 m). The reference SIP map and Landsat-8 OLI imagery were then used to develop SIP estimation models using three different retrieval methods (Linear spectral unmixing (LSU), Artificial neural networks (ANN), and Regression tree (RT)). Based on observations from 2014, the estimation results indicated that the estimation model developed with RT method could provide the best fitting results for the mapping wetland SIP (R 2 = 0.933, RMSE = 8.73%) compared to the other two methods. The proposed model with RT method was validated with observations from 2013, and the estimated SIP was highly correlated with the reference SIP, with an R 2 of 0.986 and an RMSE of 9.84%. This study highlighted the value of high resolution UAV data and globally and freely available Landsat data in combination with the developed approach for monitoring finely gradual inundation change patterns in wetland ecosystems.

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Mapping wetlands and riparian areas using Landsat ETM+ imagery and decision-tree-based models

Cited by 219 publications

References 22 publications

Three decades of Landsat-derived spring surface water dynamics in an agricultural wetland mosaic; Implications for migratory shorebirds

Three decades of Landsat-derived spring surface water dynamics in an agricultural wetland mosaic; Implications for migratory shorebirds

Wetland monitoring using classification trees and SPOT-5 seasonal time series

Subpixel Inundation Mapping Using Landsat-8 OLI and UAV Data for a Wetland Region on the Zoige Plateau, China

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