Hydrological dynamics of temporary wetlands in the southern Great Plains as a function of surrounding land use

Collins, Steven; Starr, Scott M.; Wright, Christopher K.; Henebry, Geoffrey M.; McIntyre, Nancy E.

doi:10.1016/j.jaridenv.2014.05.006

Cited by 46 publications

(41 citation statements)

References 35 publications

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“…With the improved spatial resolution of remotely sensed imagery, the object-oriented strategy has also been proposed [22][23][24]. However, the accuracy and robustness of the existing classification methods are not yet satisfactory for wetland management, bearing big omission and commission errors due to the sparse yet variable vegetation and the hydrological fluctuation in the wetlands of arid areas [25,26]. 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].…”

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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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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“…Satellite imagery processing followed the protocols developed by Collins et al [17]. The digital satellite data (i.e., optical band data) were first calibrated to top of atmosphere reflectance in ENVI 5.1 software (Exelis Visual Information Solutions, Inc.; Boulder, CO, USA).…”

Section: Methodsmentioning

confidence: 99%

“…The majority of playas of the Great Plains occur in the Texas panhandle [7], where large-scale, irrigated agriculture began in the 1930s-1940s and accelerated in intensity and distribution after extensive groundwater mining of the Ogallala Aquifer began after World War II [15,16]. Currently, this region is approximately 26%-32% tilled (and typically irrigated) cropland and 36%-42% non-irrigated pasture or grassland (including former cropland currently enrolled in the Conservation Reserve Program) [17], although some counties exceed 80% of land under cultivation [18], and an estimated 90% of playas in this region have at least some portion of their watershed under cultivation [7]. Changes in irrigation practices were initiated in the 1980s, with a transition from row-flood (furrow) irrigation to the current practices of center-pivot and drip irrigation [19].…”

Section: Background and Rationalementioning

confidence: 99%

“…The presence of such sediments can cause the basins to no longer pond water (the sediments displace water as well as absorb water), but reclassification reflects a semantic loss in playa numbers that can preclude legal wetland protection or allocation of funds for wetland conservation or restoration [7]. Playa loss rates range from 17% to 85.7% [17,28,29], with this wide range reflecting differences in methodology and region. For example, Johnson et al [28] compared historic (~1970) and more recent (2008) aerial imagery for 602 playas in Texas and New Mexico to estimate that~17% of basins in the southern Great Plains were filled entirely (no depression remaining) and~60% no longer held water despite the presence of a depression.…”

Section: Background and Rationalementioning

confidence: 99%

“…For example, Johnson et al [28] compared historic (~1970) and more recent (2008) aerial imagery for 602 playas in Texas and New Mexico to estimate that~17% of basins in the southern Great Plains were filled entirely (no depression remaining) and~60% no longer held water despite the presence of a depression. Similarly, Collins et al [17] used satellite imagery to examine 8404 playa basins in Texas over a four-year period (2008)(2009)(2010)(2011) and determined that 4078 basins (i.e., 51.5%) never held water, even after above-average precipitation. In both of these studies, land conversion for tillage agriculture was implicated as the ultimate cause of sediment accumulation that led to basin infill and impaired ability to pond water.…”

Section: Background and Rationalementioning

confidence: 99%

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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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Surface‐water dynamics and land use influence landscape connectivity across a major dryland region

Bishop‐Taylor

Tulbure

Broich

2017

Ecological Applications

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Landscape connectivity is important for the long-term persistence of species inhabiting dryland freshwater ecosystems, with spatiotemporal surface-water dynamics (e.g., flooding) maintaining connectivity by both creating temporary habitats and providing transient opportunities for dispersal. Improving our understanding of how landscape connectivity varies with respect to surface-water dynamics and land use is an important step to maintaining biodiversity in dynamic dryland environments. Using a newly available validated Landsat TM and ETM+ surface-water time series, we modelled landscape connectivity between dynamic surface-water habitats within Australia's 1 million km semiarid Murray Darling Basin across a 25-yr period (1987-2011). We identified key habitats that serve as well-connected "hubs," or "stepping-stones" that allow long-distance movements through surface-water habitat networks. We compared distributions of these habitats for short- and long-distance dispersal species during dry, average, and wet seasons, and across land-use types. The distribution of stepping-stones and hubs varied both spatially and temporally, with temporal changes driven by drought and flooding dynamics. Conservation areas and natural environments contained higher than expected proportions of both stepping-stones and hubs throughout the time series; however, highly modified agricultural landscapes increased in importance during wet seasons. Irrigated landscapes contained particularly high proportions of well-connected hubs for long-distance dispersers, but remained relatively disconnected for less vagile organisms. The habitats identified by our study may serve as ideal high-priority targets for land-use specific management aimed at maintaining or improving dispersal between surface-water habitats, potentially providing benefits to biodiversity beyond the immediate site scale. Our results also highlight the importance of accounting for the influence of spatial and temporal surface-water dynamics when studying landscape connectivity within highly variable dryland environments.

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Hydrological dynamics of temporary wetlands in the southern Great Plains as a function of surrounding land use

Cited by 46 publications

References 35 publications

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

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

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

Surface‐water dynamics and land use influence landscape connectivity across a major dryland region

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