Bradley C. Autrey

Segmentation and object-oriented classification of wetlands in a karst Florida landscape using multi-season Landsat-7 ETM+ imagery

Segmentation and object-oriented processing of single-season and multi-season Landsat-7 Enhanced Thematic Mapper Plus (ETM+) data was utilized for the classification of wetlands in a 1560 km2 study area of north central Florida. This segmentation and object-oriented classification outperformed the traditional maximum likelihood algorithm (MLC) in accurately mapping wetlands, with overall accuracies of 90.2% (single-season imagery) and 90.8% (multi-season imagery), compared to overall accuracies for the MLC classifiers of 78.4 and 79.0%, respectively. Kappa coefficients were over 1.5-times greater for the segmentation/object-oriented classifications than for the MLC classifications, and producer and user accuracies were also higher. The producer accuracies of the segmentation/object-oriented classifications were 90.8% (single-season) and 91.6% (multi-season), compared to 70.6 and 74.4%, respectively, for the MLC classifications. User accuracies were 73.9 and 73.5% for the single-season and multi-season segmentation/object-oriented classifications, respectively, compared to 54.1% (single-season) and 55.0% (multi-season) for the MLC classifications. The use of multi-seasonal data resulted in only a slight increase in overall accuracy over the single-season imagery. This small increase was primarily due to better discrimination of riparian wetlands in the multi-season data. Segmentation and object-oriented processing provides a low-cost, high-accuracy method for classification of wetlands on a local, regional, or national basis.

Biota connect aquatic habitats throughout freshwater ecosystem mosaics

Freshwater ecosystems are linked at various spatial and temporal scales by movements of biota adapted to life in water. We review the literature on movements of aquatic organisms that connect different types of freshwater habitats, focusing on linkages from streams and wetlands to downstream waters. Here, streams, wetlands, rivers, lakes, ponds, and other freshwater habitats are viewed as dynamic freshwater ecosystem mosaics (FEMs) that collectively provide the resources needed to sustain aquatic life. Based on existing evidence, it is clear that biotic linkages throughout FEMs have important consequences for biological integrity and biodiversity. All aquatic organisms move within and among FEM components, but differ in the mode, frequency, distance, and timing of their movements. These movements allow biota to recolonize habitats, avoid inbreeding, escape stressors, locate mates, and acquire resources. Cumulatively, these individual movements connect populations within and among FEMs and contribute to local and regional diversity, resilience to disturbance, and persistence of aquatic species in the face of environmental change. Thus, the biological connections established by movement of biota among streams, wetlands, and downstream waters are critical to the ecological integrity of these systems. Future research will help advance our understanding of the movements that link FEMs and their cumulative effects on downstream waters.

Featured collection introduction: Connectivity of streams and wetlands to downstream waters

Connectivity is a fundamental but highly dynamic property of watersheds. Variability in the types and degrees of aquatic ecosystem connectivity presents challenges for researchers and managers seeking to accurately quantify its effects on critical hydrologic, biogeochemical, and biological processes. However, protecting natural gradients of connectivity is key to protecting the range of ecosystem services that aquatic ecosystems provide. In this featured collection, we review the available evidence on connections and functions by which streams and wetlands affect the integrity of downstream waters such as large rivers, lakes, reservoirs, and estuaries. The reviews in this collection focus on the types of waters whose protections under the U.S. Clean Water Act have been called into question by U.S. Supreme Court cases. We synthesize 40+ years of research on longitudinal, lateral, and vertical fluxes of energy, material, and biota between aquatic ecosystems included within the Act’s frame of reference. Many questions about the roles of streams and wetlands in sustaining downstream water integrity can be answered from currently available literature, and emerging research is rapidly closing data gaps with exciting new insights into aquatic connectivity and function at local, watershed, and regional scales. Synthesis of foundational and emerging research is needed to support science-based efforts to provide safe, reliable sources of fresh water for present and future generations. (KEY TERMS: ecological integrity; river networks; streams; wetlands; floodplains; riparian areas; watersheds; U.S. Clean Water Act.) Alexander, Laurie C., Ken M. Fritz, Kate A. Schofield, Bradley C. Autrey, Julie E. DeMeester, Heather E. Golden, David C. Goodrich, William G. Kepner, Hadas R. Kiperwas, Charles R. Lane, Stephen D. LeDuc, Scott G. Leibowitz, Michael G. McManus, Amina I. Pollard, Caroline E. Ridley, Melanie K. Vanderhoof, and Parker J. Wigington, Jr., 2018. Featured Collection Introduction: Connectivity of Streams and Wetlands to Downstream Waters. Journal of the American Water Resources Association (JAWRA) 54(2): 287–297. https://doi.org/10.1111/ 1752-1688.12630 Paper No. JAWRA-17-0107-P of the Journal of the American Water Resources Association (JAWRA). Received July 24, 2017; accepted January 22, 2018.

Comparing Pixel- and Object-Based Approaches in Effectively Classifying Wetland-Dominated Landscapes

Wetland ecosystems straddle both terrestrial and aquatic habitats, performing many ecological functions directly and indirectly benefitting humans. However, global wetland losses are substantial. Satellite remote sensing and classification informs wise wetland management and monitoring. Both pixel- and object-based classification approaches using parametric and non-parametric algorithms may be effectively used in describing wetland structure and habitat, but which approach should one select? We conducted both pixel- and object-based image analyses (OBIA) using parametric (Iterative Self-Organizing Data Analysis Technique, ISODATA, and maximum likelihood, ML) and non-parametric (random forest, RF) approaches in the Barguzin Valley, a large wetland (~500 km2) in the Lake Baikal, Russia, drainage basin. Four Quickbird multispectral bands plus various spatial and spectral metrics (e.g., texture, Non-Differentiated Vegetation Index, slope, aspect, etc.) were analyzed using field-based regions of interest sampled to characterize an initial 18 ISODATA-based classes. Parsimoniously using a three-layer stack (Quickbird band 3, water ratio index (WRI), and mean texture) in the analyses resulted in the highest accuracy, 87.9% with pixel-based RF, followed by OBIA RF (segmentation scale 5, 84.6% overall accuracy), followed by pixel-based ML (83.9% overall accuracy). Increasing the predictors from three to five by adding Quickbird bands 2 and 4 decreased the pixel-based overall accuracy while increasing the OBIA RF accuracy to 90.4%. However, McNemar’s chi-square test confirmed no statistically significant difference in overall accuracy among the classifiers (pixel-based ML, RF, or object-based RF) for either the three- or five-layer analyses. Although potentially useful in some circumstances, the OBIA approach requires substantial resources and user input (such as segmentation scale selection—which was found to substantially affect overall accuracy). Hence, we conclude that pixel-based RF approaches are likely satisfactory for classifying wetland-dominated landscapes.

Decision-Tree, Rule-Based, and Random Forest Classification of High-Resolution Multispectral Imagery for Wetland Mapping and Inventory

Efforts are increasingly being made to classify the world’s wetland resources, an important ecosystem and habitat that is diminishing in abundance. There are multiple remote sensing classification methods, including a suite of nonparametric classifiers such as decision-tree (DT), rule-based (RB), and random forest (RF). High-resolution satellite imagery can provide more specificity to the classified end product, and ancillary data layers such as the Normalized Difference Vegetation Index, and hydrogeomorphic layers such as distance-to-a-stream can be coupled to improve overall accuracy (OA) in wetland studies. In this paper, we contrast three nonparametric machine-learning algorithms (DT, RB, and RF) using a large field-based dataset (n = 228) from the Selenga River Delta of Lake Baikal, Russia. We also explore the use of ancillary data layers selected to improve OA, with a goal of providing end users with a recommended classifier to use and the most parsimonious suite of input parameters for classifying wetland-dominated landscapes. Though all classifiers appeared suitable, the RF classification outperformed both the DT and RB methods, achieving OA >81%. Including a texture metric (homogeneity) substantially improved the classification OA. However, including vegetation/soil/water metrics (based on WorldView-2 band combinations), hydrogeomorphic data layers, and elevation data layers to increase the descriptive content of the input parameters surprisingly did not markedly improve the OA. We conclude that, in most cases, RF should be the classifier of choice. The potential exception to this recommendation is under the circumstance where the end user requires narrative rules to best manage his or her resource. Though not useful in this study, continuously increasing satellite imagery resolution and band availability suggests the inclusion of ancillary contextual data layers such as soil metrics or elevation data, the granularity of which may define its utility in subsequent wetland classifications.

Sediment accretion and accumulation of P, N and organic C in depressional wetlands of three ecoregions of the United States

Wetland depressions without surface channel connections to aquatic systems are substantial sinks for nitrogen (N), phosphorus (P) and organic carbon (org. C). We assessed accretion, N, P and org.-C accumulation rates in 43 depressional wetlands across three ecoregions of the USA (Erie Drift Plain, EDP; Middle Atlantic Coastal Plain, MACP; Southern Coastal Plain, SCP) using caesium-137 (137Cs). The mean sediment accretion rate in minimally affected (reference) sites was 0.6 ± 0.4 mm year-1 and did not differ among ecoregions. Accumulation rates for N and org. C averaged 3.1 ± 3.1 g N m-2 year-1and 43.4 ± 39.0 g org. C m-2 year-1 respectively, and did not differ across minimally affected sites. Phosphorus accumulation rates were significantly greater in EDP (0.10 ± 0.10 g P m-2 year-1) than MACP (0.01 ± 0.01 g P m-2 year-1) or SCP (0.04 ± 0.04 g P m-2 year-1) sites. Land-use modality and wetland-type effects were analysed in SCP, with few differences being found. Depressional wetlands sequester substantive amounts of nutrients and C; their cumulative contributions may significantly affect landscape nutrient and C dynamics because of the abundance of wetland depressions on the landscape, warranting further investigation and potential watershed-scale conservation approaches.

Remotely mapping river water quality using multivariate regression with prediction validation

Remote spectral sensing offers an attractive means of mapping river water quality over wide spatial regions. While previous research has focused on development of spectral indices and models to predict river water quality based on remote images, little attention has been paid to subsequent validation of these predictions. To address this oversight, we describe a retrospective analysis of remote, multispectral Compact Airborne Spectrographic Imager (CASI) images of the Ohio River and its Licking River and Little Miami River tributaries. In conjunction with the CASI acquisitions, ground truth measurements of chlorophyll-a concentration and turbidity were made for a small set of locations in the Ohio River. Partial least squares regression models relating the remote river images to ground truth measurements of chlorophyll-a concentration and turbidity for the Ohio River were developed. Employing these multivariate models, chlorophyll-a concentrations and turbidity levels were predicted in river pixels lacking ground truth measurements, generating detailed estimated water quality maps. An important but often neglected step in the regression process is to validate prediction results using a spectral residual statistic. For both the chlorophyll-a and turbidity regression models, a spectral residual value was calculated for each river pixel and compared to the associated statistical confidence limit for the model. These spectral residual statistic results revealed that while the chlorophyll-a and turbidity models could validly be applied to a vast majority of Ohio River and Licking River pixels, application of these models to Little Miami River pixels was inappropriate due to an unmodeled source of spectral variation.