A LiDAR-based decision-tree classification of open water surfaces in an Arctic delta

Crasto, N.; Hopkinson, Chris; Forbes, Donald L.; Lesack, Lance F. W.; Marsh, Philip; Spooner, Ian; Sanden, J.J. van der

doi:10.1016/j.rse.2015.04.011

Cited by 42 publications

(35 citation statements)

References 28 publications

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“…With the rapid development of remote sensing technology in recent years, water mapping and change detection based on satellite remote sensing images has become a main approach [13][14][15][16][17][18]. Synthetic Aperture Radar (SAR) data has been used to identify surface water area [19]; however, relatively limited availability of SAR data has blocked large scale and long term applications in water body mapping.…”

Section: Introductionmentioning

confidence: 99%

Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors

Zhou

Dong

Xiao

et al. 2017

Water

182

105

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Open surface water bodies play an important role in agricultural and industrial production, and are susceptible to climate change and human activities. Remote sensing data has been increasingly used to map open surface water bodies at local, regional, and global scales. In addition to image statistics-based supervised and unsupervised classifiers, spectral index-and threshold-based approaches have also been widely used. Many water indices have been proposed to identify surface water bodies; however, the differences in performances of these water indices as well as different sensors on water body mapping are not well documented. In this study, we reviewed and compared existing open surface water body mapping approaches based on six widely-used water indices, including the tasseled cap wetness index (TCW), normalized difference water index (NDWI), modified normalized difference water index (mNDWI), sum of near infrared and two shortwave infrared bands (Sum457), automated water extraction index (AWEI), land surface water index (LSWI), as well as three medium resolution sensors (Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-2 MSI). A case region in the Poyang Lake Basin, China, was selected to examine the accuracies of the open surface water body maps from the 27 combinations of different algorithms and sensors. The results showed that generally all the algorithms had reasonably high accuracies with Kappa Coefficients ranging from 0.77 to 0.92. The NDWI-based algorithms performed slightly better than the algorithms based on other water indices in the study area, which could be related to the pure water body dominance in the region, while the sensitivities of water indices could differ for various water body conditions. The resultant maps from Landsat 8 and Sentinel-2 data had higher overall accuracies than those from Landsat 7. Specifically, all three sensors had similar producer accuracies while Landsat 7 based results had a lower user accuracy. This study demonstrates the improved performance in Landsat 8 and Sentinel-2 for open surface water body mapping efforts.

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Section: Introductionmentioning

confidence: 99%

Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors

Zhou

Dong

Xiao

et al. 2017

Water

182

105

View full text Add to dashboard Cite

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“…Rasterization was performed for each input parameter individually. A tiered decision tree classifier (adapted after [29]), was used to classify water in the study area using both the positional and intensity data available in the LiDAR dataset.…”

Section: Lidar Processingmentioning

confidence: 99%

“…They are also characterized as having a low standard deviation of intensity at moderate scan angles because of the consistent reflection of the laser signal away from the receiver and subsequent low intensity returns and dropouts, and a high standard deviation of intensity at low scan angles because of the contrasting high intensity returns beneath the nadir of the plane and low intensity returns from the neighboring flight strips due to the effects of flight strip overlap [2,29]. D.…”

Section: Lidar Processingmentioning

confidence: 99%

Fusion of SAR, Optical Imagery and Airborne LiDAR for Surface Water Detection

et al. 2017

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Abstract:The detection and monitoring of surface water and its extent are critical for understanding floodwater hazards. Flooding and undermining caused by surface water flow can result in damage to critical infrastructure and changes in ecosystems. Along major transportation corridors, such as railways, even small bodies of water can pose significant hazards resulting in eroded or washed out tracks. In this study, heterogeneous data from synthetic aperture radar (SAR) satellite missions, optical satellite-based imagery and airborne light detection and ranging (LiDAR) were fused for surface water detection. Each dataset was independently classified for surface water and then fused classification models of the three datasets were created. A multi-level decision tree was developed to create an optimal water mask by minimizing the differences between models originating from single datasets. Results show a water classification uncertainty of 4-9% using the final fused models compared to 17-23% uncertainty using single polarization SAR. Of note is the use of a high resolution LiDAR digital elevation model (DEM) to remove shadow and layover effects in the SAR observations, which reduces overestimation of surface water with growing vegetation. Overall, the results highlight the advantages of fusing multiple heterogeneous remote sensing techniques to detect surface water in a predominantly natural landscape.

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“…The results presented below are from experiments performed near Destin, Florida, USA, more specifically over the East Pass, which is the entrance to Choctawhatchee Bay from the Gulf of Other spectral characteristic that will prove very useful in the future, specifically for automatic bathymetric processing, will be the ability to automatically delineate the water-land interface based solely on multispectral intensity. Several studies have already been conducted into this water-land interface delineation with single-wavelength lidar sensors [38,39], but the improvement based on multispectral intensity still needs to be investigated.…”

Section: Bathymetric Capabilitiesmentioning

confidence: 99%

Capability Assessment and Performance Metrics for the Titan Multispectral Mapping Lidar

et al. 2016

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Abstract:In this paper we present a description of a new multispectral airborne mapping light detection and ranging (lidar) along with performance results obtained from two years of data collection and test campaigns. The Titan multiwave lidar is manufactured by Teledyne Optech Inc. (Toronto, ON, Canada) and emits laser pulses in the 1550, 1064 and 532 nm wavelengths simultaneously through a single oscillating mirror scanner at pulse repetition frequencies (PRF) that range from 50 to 300 kHz per wavelength (max combined PRF of 900 kHz). The Titan system can perform simultaneous mapping in terrestrial and very shallow water environments and its multispectral capability enables new applications, such as the production of false color active imagery derived from the lidar return intensities and the automated classification of target and land covers. Field tests and mapping projects performed over the past two years demonstrate capabilities to classify five land covers in urban environments with an accuracy of 90%, map bathymetry under more than 15 m of water, and map thick vegetation canopies at sub-meter vertical resolutions. In addition to its multispectral and performance characteristics, the Titan system is designed with several redundancies and diversity schemes that have proven to be beneficial for both operations and the improvement of data quality.

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A LiDAR-based decision-tree classification of open water surfaces in an Arctic delta

Cited by 42 publications

References 28 publications

Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors

Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors

Fusion of SAR, Optical Imagery and Airborne LiDAR for Surface Water Detection

Capability Assessment and Performance Metrics for the Titan Multispectral Mapping Lidar

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