Mapping Isolated Wetlands in a Karst Landscape: GIS and Remote Sensing Methods

Reif, Molly; Frohn, Robert C.; Lane, Charles R.; Autrey, Bradley C.

doi:10.2747/1548-1603.46.2.187

Cited by 36 publications

(36 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These included peer-reviewed journal articles, conference papers and chapters of book and PhD thesis publications. This review specifically focused on vegetated wetlands (i.e., colonized by aquatic macrophytes) of diverse types [36] such as fresh-and salt-water estuarine, riverine, lacustrine, palustrine and selected marine (mangrove) ecosystems (Table 1, [5][6][7][8]12,13,[15][16][17][18][22][23][24][25][26][27][28][29][30]32,34,) and excluded studies of solely deepwater and open-water aquatic ecosystems such as seagrass beds. The OBIA applications and main research objectives fell into several broad groups (with some papers applicable to more than one of these):…”

Section: Research Objectives and Focusmentioning

confidence: 99%

“…Wetland detection & delineation: studies detecting wetland presence and/or delineating their boundaries in landscapes with significant proportion of non-wetland natural and anthropogenic land cover [14][15][16]18,26,29,[38][39][40]55,63,64,73,74,[84][85][86][87]. (2).…”

Section: Research Objectives and Focusmentioning

confidence: 99%

“…First, heterogeneity in local pixel values, variable soil moisture and spectral similarities among cover types often constrain discrimination of classes and lead to infamous "salt-and-pepper" speckle in the mapping outputs [12][13][14]. Second, spatial resolution of commonly used image datasets (such as 30-m Landsat) is frequently insufficient to detect fine-scale wetland features and class boundaries, and spectral mixing of different classes at subpixel scales may reduce mapping accuracy [15][16][17]. Finally, pixel-based approaches may be limited by the lack of ecologically meaningful information on spatial context and class neighborhood relationships in the pixel grid structure [18], unless ancillary geospatial or field information is available to inform wetland delineation and classification.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Object-Based Image Analysis in Wetland Research: A Review

2015

View full text Add to dashboard Cite

Abstract:The applications of object-based image analysis (OBIA) in remote sensing studies of wetlands have been growing over recent decades, addressing tasks from detection and delineation of wetland bodies to comprehensive analyses of within-wetland cover types and their change. Compared to pixel-based approaches, OBIA offers several important benefits to wetland analyses related to smoothing of the local noise, incorporating meaningful non-spectral features for class separation and accounting for landscape hierarchy of wetland ecosystem organization and structure. However, there has been little discussion on whether unique challenges of wetland environments can be uniformly addressed by OBIA across different types of data, spatial scales and research objectives, and to what extent technical and conceptual aspects of this framework may themselves present challenges in a complex wetland setting. This review presents a synthesis of 73 studies that applied OBIA to different types of remote sensing data, spatial scale and research objectives. It summarizes the progress and scope of OBIA uses in wetlands, key benefits of this approach, factors related to accuracy and uncertainty in its applications and the main research needs and directions to expand the OBIA capacity in the future wetland studies. Growing demands for higher-accuracy wetland characterization at both regional and local scales together with advances in very high resolution remote sensing and novel tasks in wetland restoration monitoring will likely continue active exploration of the OBIA potential in these diverse and complex environments.

show abstract

Section: Research Objectives and Focusmentioning

confidence: 99%

Section: Research Objectives and Focusmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Object-Based Image Analysis in Wetland Research: A Review

2015

View full text Add to dashboard Cite

show abstract

“…Through our stepwise process, we identified and removed 32 vernal pools that were within 10-m buffer zones of NHD features, which could have accounted for up to 15% of our errors of omission. We note that Lang et al [62] found that the horizontal accuracy of NHD maps was > 18 m, which is substantially greater than the 10-m buffer distance of NHD flowlines and areas used by Reif et al [40] and Lane et al [37] and applied in this study. However, Lane et al [37] doubled the buffer width to 20 m in a 8600 km 2 subsample of their study extent and reported an areal change in GIWs of approximately 3%, suggesting that the horizontal accuracy error in the NHD may play a small (though not insignificant) role in the outcome of the current study.…”

Section: Discussionmentioning

confidence: 90%

“…To identify potential vernal pools from surface depressions assumed to be at least intermittently connected to other water bodies, we used a series of buffered overlays. Using the 1:24,000-scale NHD data as the source data for hydrology, we created 10-m distance buffer zones from the NHD flowlines, waterbody, and area data layers, replicating Lane et al [37] and Reif et al [40]. The three buffered data layers were then merged as a single data layer to intersect with the depression layer.…”

Section: National Hydrography Dataset (Nhd)mentioning

confidence: 99%

An Effective Method for Detecting Potential Woodland Vernal Pools Using High-Resolution LiDAR Data and Aerial Imagery

Lane

Liu

2014

Remote Sensing

View full text Add to dashboard Cite

Effective conservation of woodland vernal pools-important components of regional amphibian diversity and ecosystem services-depends on locating and mapping these pools accurately. Current methods for identifying potential vernal pools are primarily based on visual interpretation and digitization of aerial photographs, with variable accuracy and low repeatability. In this paper, we present an effective and efficient method for detecting and mapping potential vernal pools using stochastic depression analysis with additional geospatial analysis. Our method was designed to take advantage of high-resolution light detection and ranging (LiDAR) data, which are becoming increasingly available, though not yet frequently employed in vernal pool studies. We successfully detected more than 2000 potential vernal pools in a ~150 km 2 study area in eastern Massachusetts. The accuracy assessment in our study indicated that the commission rates ranged from 2.5% to 6.0%, while the proxy omission rate was 8.2%, rates that are much lower than reported errors of previous vernal pool studies conducted in the northeastern United States. One significant advantage of our semi-automated approach for vernal pool identification is that it may reduce inconsistencies and alleviate repeatability concerns associated with manual photointerpretation methods. Another strength of our strategy is that, in addition to detecting the point-based vernal pool OPEN ACCESS Remote Sens. 2014, 6 11445 locations for the inventory, the boundaries of vernal pools can be extracted as polygon features to characterize their geometric properties, which are not available in the current statewide vernal pool databases in Massachusetts.

show abstract

Relative effects of geographically isolated wetlands on streamflow: a watershed‐scale analysis

et al. 2015

Self Cite

View full text Add to dashboard Cite

Geographically isolated wetlands (GIWs) are characterized as ‘isolated’ because they are embedded by uplands, though they potentially exhibit a gradient of hydrologic, biological, or chemical connections to other surface waters. In fact, recent field studies have begun to elucidate that GIWs exhibit varying degrees of hydrologic connectivity. In this study, we examine the influence of GIWs on streamflow, a potential indicator of GIW hydrologic connectivity with surface waters. We assess annual and seasonal spatially based statistical relationships between GIW characteristics (e.g. volume and extent) and streamflow across a dense network of subbasins using a hybrid modeling approach. Our method involves the Spatial Stream Network (SSN) model, which considers spatial autocorrelation of model covariates explicitly, and the Soil and Water Assessment Tool (SWAT), which predicts streamflow across a network of 579 subbasins in the lower Neuse River Basin, North Carolina, USA. Our study results suggest that GIWs, to some extent, influence streamflow. The further GIWs are from a stream, the greater their capacity to increase streamflow due to the physiographic setting, hypothesized transit times, and sequencing of watershed hydrologic connectivity in the study area. However, as the combined extent of GIWs and non‐GIWs increases in subbasins, seasonal and annual streamflow decreases. Results also suggest that other landscape indicators of watershed‐scale hydrology can, in aggregate with GIWs and non‐GIWs, explain variations in seasonal and annual simulated streamflow. Our study findings begin to elucidate the aggregate influence of GIWs on streamflow, providing insights for future decision‐making on GIW protection and management. Copyright © 2015 John Wiley & Sons, Ltd.

show abstract

Mapping Isolated Wetlands in a Karst Landscape: GIS and Remote Sensing Methods

Cited by 36 publications

References 27 publications

Object-Based Image Analysis in Wetland Research: A Review

Object-Based Image Analysis in Wetland Research: A Review

An Effective Method for Detecting Potential Woodland Vernal Pools Using High-Resolution LiDAR Data and Aerial Imagery

Relative effects of geographically isolated wetlands on streamflow: a watershed‐scale analysis

Contact Info

Product

Resources

About