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Özesmi, Stacy L.; Bauer, Marvin E.

doi:10.1023/a:1020908432489

Cited by 912 publications

(261 citation statements)

References 48 publications

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“…The UN Millennium Ecosystem Assessment identifies environmental degradation to be most pronounced in wetlands compared with other aquatic or terrestrial ecosystems. Accurate and updated wetland inventories are essential to protect wetlands from degradation and to prevent damage (Ozesmi and Bauer 2002). Wetland environments, therefore, require frequent monitoring and mapping to underpin sustainable management.…”

Section: Current Status Of Wetlandsmentioning

confidence: 99%

“…Moreover, information related to temporal changes that potentially affect the hydrological regime and water balance of the wetlands can be acquired using satellite images (Ozesmi and Bauer 2002). Importantly, satellite imagery can go back years and in some instances several decades.…”

Section: Remote Sensing For Wetland Monitoringmentioning

confidence: 99%

“…Tahmoor colliery has been extracting coal from the Bulli coal seam at a depth of approximately 300 m below ground by longwall methods since 1981. The mining operations were approximately 660 m east of Lake Couridjah during the period 1996-2002(TCPL 2012.…”

Section: Study Areamentioning

confidence: 99%

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Evaluation of rainfall and wetland water area variability at Thirlmere Lakes using Landsat time-series data

Banerjee

Raval

Timms

2016

Int. J. Environ. Sci. Technol.

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Thirlmere Lakes is a group of five freshwater wetlands in the southwest fringe of Sydney, Australia, that is subject to cyclic wetting and drying. The lakes are surrounded by activities that have led to increasing pressure on the local surface and groundwater supply including farming and mining. The mine has been operating for more than 30 years, and in recent times, there has been speculation that the surface subsidence and underground pumping may have some impact on surface water and groundwater hydrology. A study was undertaken using satellite imagery to examine the relation between water area changes and rainfall variability. The study utilised Landsat time-series data during the period 1982-2014 to calculate changes in the lake water area (LA), through the normalised difference water index (NDWI) threshold. High classification accuracy was achieved using NDWI against high-resolution data that are available for the years 2008 (88.4 %), 2010 (92.8 %), and 2013 (96.9 %). The LA measurement was correlated against 11 historic observations that occurred in 2009, 2010, and 2011 during drier wetland conditions. Correlation analysis of the LA with the residual rainfall mass spread across the past 30 years has found that rainfall variability is a major dominant factor associated with the wetland changes. The underground mining operations, if verified by independent investigations, probably play a minor or negligible contributor to variations in total wetland area during the study period.This study has demonstrated that remote sensing is a technique that can be used to augment limited historic data.

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Section: Current Status Of Wetlandsmentioning

confidence: 99%

Section: Remote Sensing For Wetland Monitoringmentioning

confidence: 99%

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Evaluation of rainfall and wetland water area variability at Thirlmere Lakes using Landsat time-series data

Banerjee

Raval

Timms

2016

Int. J. Environ. Sci. Technol.

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“…Playas were identified in ENVI 5.1 and 5.2 with a band math wetland classification rule with a modification made for the Landsat 8 images rather than Landsat 5 as used in Collins et al [17]; multispectral bands 6 and 4 were used for the Landsat 8 images, and bands 5 and 3 were used for the Landsat 5 image to correspond to the appropriate short-wave infrared and visible red spectra used to distinguish water from non-water. This technique is one of the most common means of identifying water in remotely sensed images [34,35,56,57]. The four classified images were then converted to shapefiles projected to UTM zone 14N (geographic coordinate system NAD 1983, datum WGS 1984) in ArcMap 10.2.2 (Esri; Redlands, CA, USA).…”

Section: Methodsmentioning

confidence: 99%

Using Remotely Sensed Imagery to Document How Land Use Drives Turbidity of Playa Waters in Texas

et al. 2016

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Sedimentation (primarily from human land use) is a major threat to runoff-fed wetlands of the Great Plains of North America (playas), but it is unknown how many playas are turbid, how prevalence of turbidity has changed over time, and how turbidity is related to surrounding land use. We used remotely sensed imagery to assess sedimentation in the waters of over 7700 playa basins in Texas on four dates during a 29-year span: 25 July 1986 (a regionally wet time), 3 May 2014 (during drought), 4 June 2014 (after the drought was broken), and 25 July 2015 (one year post-drought). Even on the wettest date examined, 64% of playa basins did not hold water. Turbidity varied over time, was already present in over half of the basins examined in 1986, and prevalence of turbidity was not simply proportional to overall wet playa abundance. There was an increase in total and irrigated cropland in our focal region and a statistically significant association between sedimentation and land use within 100 m of a playa: clear playas were associated with more urban development and pasture/grassland, and turbid playas were surrounded mostly by cropland.

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“…For classification algorithms, Ozesmi and Bauer [48] provided a thorough review about various classification methods for coastal and wetland studies. Among these methods, the machine-learning algorithm of SVM classifier demonstrates high robustness and reliability through numerous successful applications even with limited sample size, which is critical for sample-based studies considering the challenging field accessibility in wetland environments [49][50][51][52].…”

Section: Stage 2: Multi-input Classification Comparisonmentioning

confidence: 99%

Photogrammetric UAV Mapping of Terrain under Dense Coastal Vegetation: An Object-Oriented Classification Ensemble Algorithm for Classification and Terrain Correction

et al. 2017

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Photogrammetric UAV sees a surge in use for high-resolution mapping, but its use to map terrain under dense vegetation cover remains challenging due to a lack of exposed ground surfaces. This paper presents a novel object-oriented classification ensemble algorithm to leverage height, texture and contextual information of UAV data to improve landscape classification and terrain estimation. Its implementation incorporates multiple heuristics, such as multi-input machine learning-based classification, object-oriented ensemble, and integration of UAV and GPS surveys for terrain correction. Experiments based on a densely vegetated wetland restoration site showed classification improvement from 83.98% to 96.12% in overall accuracy and from 0.7806 to 0.947 in kappa value. Use of standard and existing UAV terrain mapping algorithms and software produced reliable digital terrain model only over exposed bare grounds (mean error = −0.019 m and RMSE = 0.035 m) but severely overestimated the terrain by~80% of mean vegetation height in vegetated areas. The terrain correction method successfully reduced the mean error from 0.302 m to −0.002 m (RMSE from 0.342 m to 0.177 m) in low vegetation and from 1.305 m to 0.057 m (RMSE from 1.399 m to 0.550 m) in tall vegetation. Overall, this research validated a feasible solution to integrate UAV and RTK GPS for terrain mapping in densely vegetated environments.

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Cited by 912 publications

References 48 publications

Evaluation of rainfall and wetland water area variability at Thirlmere Lakes using Landsat time-series data

Evaluation of rainfall and wetland water area variability at Thirlmere Lakes using Landsat time-series data

Using Remotely Sensed Imagery to Document How Land Use Drives Turbidity of Playa Waters in Texas

Photogrammetric UAV Mapping of Terrain under Dense Coastal Vegetation: An Object-Oriented Classification Ensemble Algorithm for Classification and Terrain Correction

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