Topographic Metrics for Improved Mapping of Forested Wetlands

Lang, Megan W.; McCarty, Greg; Oesterling, Robert; Yeo, In‐Young

doi:10.1007/s13157-012-0359-8

Cited by 108 publications

(95 citation statements)

References 31 publications

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“…These discrepancies relate to differences in the amount of vegetation detectable by each sensor. In leaf-off conditions, sensing water through a vegetated canopy near nadir will obscure branches and trunks due to low contrast between dark features including tree bark, shadow and water [13][14][15]. At oblique angles and low levels of vegetation cover however, radar still produces double bounce off of trunks and standing water acting as corner reflectors.…”

Section: Discussionmentioning

confidence: 99%

“…Mapping flooded vegetation from optical sensors is mature and has known limitations [10], while research to map flooded vegetation from radar has been active in recent years [11,12]. Detecting water beneath vegetation in optical imagery is challenging during the growing season because the canopy obscures the water surface [13][14][15], however sufficient absorption generally occurs to detect water during leaf-off in early spring or late fall in wavelengths from visible to infrared. Landsat's look direction within ±7.5 • of nadir [16] enhances its ability to sense water in leaf-off conditions due to the predominantly vertical structure of vegetation, while radar is able to detect water at an oblique angle beneath leaf-on canopies under certain conditions, depending on leaf size, shape and orientation, as well as wavelength, polarization and incidence angle.…”

Section: Introductionmentioning

confidence: 99%

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Comparing Landsat and RADARSAT for Current and Historical Dynamic Flood Mapping

Olthof

Tolszczuk-Leclerc

2018

Remote Sensing

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Abstract:Mapping the historical occurrence of flood water in time and space provides information that can be used to help mitigate damage from future flood events. In Canada, flood mapping has been performed mainly from RADARSAT imagery in near real-time to enhance situational awareness during an emergency, and more recently from Landsat to examine historical surface water dynamics from the mid-1980s to present. Here, we seek to integrate the two data sources for both operational and historical flood mapping. A main challenge of a multi-sensor approach is ensuring consistency between surface water mapped from sensors that fundamentally interact with the target differently, particularly in areas of flooded vegetation. In addition, automation of workflows that previously relied on manual interpretation is increasingly needed due to large data volumes contained within satellite image archives. Despite differences between data received from both sensors, common approaches to surface water and flooded vegetation mapping including multi-channel classification and region growing can be applied with sensor-specific adaptations for Derived historical products depicting inundation frequency and trends were also generated from each sensor's time-series of surface water maps and compared.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparing Landsat and RADARSAT for Current and Historical Dynamic Flood Mapping

Olthof

Tolszczuk-Leclerc

2018

Remote Sensing

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“…The vertical accuracy of the dataset had a RMSE of 15 cm. The near-infrared lidar returns had a pulse rate of 126,000 Hz and scan frequency of 50 Hz [46]. Although coarser in derived spatial resolution, the original pulse density was higher for the December data collection (~2.8 points·m −2 ) than the previous effort (~1.4 points·m −2 ).…”

Section: Lidar Demsmentioning

confidence: 99%

“…This stream dataset has been shown to be more comprehensive and accurate relative to the U.S. Geological Survey National Hydrography Dataset (NHD) high-resolution stream dataset [46]. The semi-automated streams were buffered by 2 m to account for stream and riparian width.…”

Section: Depression/stream Filtermentioning

confidence: 99%

“…First, a topographic wetness index (TWI) was derived using the equation: ln(a/tanβ), in which "a" represented the upslope contributing area per unit contour length and "β" was the local slope [55]. Using the open-source software program System for Automated Geoscientific Analysis (SAGA) v. 2.0.8, the FD8 flow routing algorithm [56] was calculated from the DEM and applied to the SAGA Wetness Index module [57], to create the FD8 TWI layer [46]. ArcGIS was then used to create a local terrain normalized relief (LTNR) map that was added to the FD8 TWI layer to produce an enhanced topographic wetness index (ETWI), where FD8-based wetness index values within identified depressions were increased by 10% [46].…”

Section: Enhanced Topographic Wetness Indexmentioning

confidence: 99%

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Integrating Radarsat-2, Lidar, and Worldview-3 Imagery to Maximize Detection of Forested Inundation Extent in the Delmarva Peninsula, USA

et al. 2017

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Natural variability in surface-water extent and associated characteristics presents a challenge to gathering timely, accurate information, particularly in environments that are dominated by small and/or forested wetlands. This study mapped inundation extent across the Upper Choptank River Watershed on the Delmarva Peninsula, occurring within both Maryland and Delaware. We integrated six quad-polarized Radarsat-2 images, Worldview-3 imagery, and an enhanced topographic wetness index in a random forest model. Output maps were filtered using light detection and ranging (lidar)-derived depressions to maximize the accuracy of forested inundation extent. Overall accuracy within the integrated and filtered model was 94.3%, with 5.5% and 6.0% errors of omission and commission for inundation, respectively. Accuracy of inundation maps obtained using Radarsat-2 alone were likely detrimentally affected by less than ideal angles of incidence and recent precipitation, but were likely improved by targeting the period between snowmelt and leaf-out for imagery collection. Across the six Radarsat-2 dates, filtering inundation outputs by lidar-derived depressions slightly elevated errors of omission for water (+1.0%), but decreased errors of commission (−7.8%), resulting in an average increase of 5.4% in overall accuracy. Depressions were derived from lidar datasets collected under both dry and average wetness conditions. Although antecedent wetness conditions influenced the abundance and total area mapped as depression, the two versions of the depression datasets showed a similar ability to reduce error in the inundation maps. Accurate mapping of surface water is critical to predicting and monitoring the effect of human-induced change and interannual variability on water quantity and quality.

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GIS‐based prediction of stream chemistry using landscape composition, wet areas, and hydrological flow pathways

et al. 2017

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Landscape morphology exerts strong, scale‐dependent controls on stream hydrology and biogeochemistry in heterogeneous catchments. We applied three descriptors of landscape structure at different spatial scales based on new geographic information system tools to predict variability in stream concentrations for a wide range of solutes (Al, Ba, Be, Ca, Fe, K, Mg, Na, S, Si, Sr, Sc, Co, Cr, Ni, Cu, As, Se, Rb, Y, Cd, Sb, Cs, La, Pb, Th, U, DOC, and Cl) using a linear regression analysis. Results showed that less reactive elements, which can be expected to behave more conservatively in the landscape (e.g., Na, K, Ca, Mg, Cl, and Si), generally were best predicted from the broader‐scale description of landscape composition (areal coverage of peat, tills, and sorted sediments). These results highlight the importance of mineral weathering as a source of some elements, which was best captured by landscape‐scale descriptors of catchment structure. By contrast, more nonconservative elements (e.g., DOC, Al, Cd, Cs, Co, Th, Y, and U), were best predicted by defining wet areas and/or flow path lengths of different patches in the landscape. This change in the predictive models reflect the importance of peat deposits, such as organic‐rich riparian zones and mire ecosystems, which are favorable environments for biogeochemical reactions of more nonconservative elements. As such, using this understanding of landscape influences on stream chemistry can provide improved mitigation strategies and management plans that specifically target source areas, so as to minimize mobilization of undesired elements into streams.

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Topographic Metrics for Improved Mapping of Forested Wetlands

Cited by 108 publications

References 31 publications

Comparing Landsat and RADARSAT for Current and Historical Dynamic Flood Mapping

Comparing Landsat and RADARSAT for Current and Historical Dynamic Flood Mapping

Integrating Radarsat-2, Lidar, and Worldview-3 Imagery to Maximize Detection of Forested Inundation Extent in the Delmarva Peninsula, USA

GIS‐based prediction of stream chemistry using landscape composition, wet areas, and hydrological flow pathways

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