Land-Cover Changes to Surface-Water Buffers in the Midwestern USA: 25 Years of Landsat Data Analyses (1993-2017).

To understand the timing, extent, and magnitude of land use/land cover (LULC) change in buffer areas surrounding Midwestern US waters, we analyzed the full imagery archive (1982-2017) of three Landsat footprints covering ~100,000 km2. The study area included urbanizing Chicago, Illinois and St. Louis, Missouri regions and agriculturally dominated landscapes (i.e., Peoria, Illinois). The Continuous Change Detection and Classification algorithm identified 1993-2017 LULC change across three Landsat footprints and in 90 m buffers for ~110,000 surface waters; waters were also size-binned into five groups for buffer LULC change analyses. Importantly, buffer-area LULC change magnitude was frequently much greater than footprint-level change. Surface-water extent in buffers increased by 14-35x the footprint rate and forest decreased by 2-9x. Development in buffering areas increased by 2-4x the footprint-rate in Chicago and Peoria area footprints but was similar to the change rate in the St. Louis area footprint. The LULC buffer-area change varied in waterbody size, with the greatest change typically occurring in the smallest waters (e.g., <0.1 ha). These novel analyses suggest that surface-water buffer LULC change is occurring more rapidly than footprint-level change, likely modifying the hydrology, water quality, and biotic integrity of existing water resources, as well as potentially affecting down-gradient, watershed-scale storages and flows of water, solutes, and particulate matter.

The Influence of Region of Interest Heterogeneity on Classification Accuracy in Wetland Systems.

Classifying and mapping natural systems such as wetlands using remote sensing frequently relies on data derived from regions of interest (ROIs), often acquired during field campaigns. ROIs tend to be heterogeneous in complex systems with a variety of land cover classes. However, traditional supervised image classification is predicated on pure single-class observations to train a classifier. This ultimately encourages end-users to create single-class ROIs, nudging ROIs away from field-based points or gerrymandering the ROI, which may produce ROIs unrepresentative of the landscape and potentially insert error into the classification. In this study, we explored WorldView-2 images and 228 field-based data points to define ROIs of varying heterogeneity levels in terms of class membership to classify and map 22 discrete classes in a large and complex wetland system. The goal was to include rather than avoid ROI heterogeneity and assess its impact on classification accuracy. Parametric and nonparametric classifiers were tested with ROI heterogeneity that varied from 7% to 100%. Heterogeneity was governed by ROI area, which we increased from the field-sampling frame of ~100 m2 nearly 19-fold to ~2124 m2. In general, overall accuracy (OA) tended downwards with increasing heterogeneity but stayed relatively high until extreme heterogeneity levels were reached. Moreover, the differences in OA were not statistically significant across several small-to-large heterogeneity levels. Per-class user's and producer's accuracies behaved similarly. Our findings suggest that ROI heterogeneity did not harm classification accuracy unless heterogeneity became extreme, and thus there are substantial practical advantages to accommodating heterogeneous ROIs in image classification. Rather than attempting to avoid ROI heterogeneity by gerrymandering, classification in wetland environments, as well as analyses of other complex environments, should embrace ROI heterogeneity.

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.

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.

Kinetic sorption of contaminants of emerging concern by a palygorskite-montmorillonite filter medium.

Kinetic sorption of bisphenol A (BPA), carbamazepine (CMZ) and ciprofloxacin (CIP) by three palygorskite-montmorillonite (Pal-Mt) granule sizes was studied. For BPA, CMZ and CIP, apparent sorption equilibrium was reached within about 3, 5 and 16 h, respectively. The highest and the lowest sorption capacities were by the small and the large granule sizes, respectively. Experimental results were compared to various sorption kinetics models to gain insights regarding the sorption processes and achieve a predictive capacity. The pseudo-second order (PSO) and the Elovich models performed the best while the pseudo-first order (PFO) model was only adequate for CMZ. The intraparticle-diffusion (IPD) model showed a two-step linear plot of BPA, CMZ and CIP sorption versus square root of time that was indicative of surface-sorption followed by IPD as a rate-limiting process before equilibrium was reached. Using the pseudo-first order (PFO) and the pseudo-second order (PSO) rate constants combined with previously-established Langmuir equilibrium sorption models, the kinetic sorption (ka) and desorption (kd) Langmuir kinetic rate constants were theoretically calculated for BPA and CIP. Kinetic sorption was then simulated using these theoretically calculated ka and kd values, and the simulations were compared to the observed behavior. The simulations fit the observed sorbed concentrations better during the early part of the experiments; the observed sorption during later times occurred more slowly than expected, supporting the hypothesis that IPD becomes a rate-limiting process during the course of the experiment.

Sorption-desorption of carbamazepine by palygorskite-montmorillonite (PM) filter medium.

Palygorskite-montmorillonite (PM) was studied as a potential sewage treatment effluent filter material for carbamazepine. Batch sorption experiments were conducted as a function of granule size (0.3-0.6, 1.7-2.0 and 2.8mm) and different sewage effluent conditions (pH, ionic strength and temperature). Results showed PM had a mix of fibrous and plate-like morphologies. Sorption and desorption isotherms were fitted to the Freundlich model. Sorption is granule size-dependent and the medium granule size would be an appropriate size for optimizing both flow and carbamazepine retention. Highest and lowest sorption capacities corresponded to the smallest and the largest granule sizes, respectively. The lowest and the highest equilibrium aqueous (Ce) and sorbed (qe) carbamazepine concentrations were 0.4 mg L(-1) and 4.5 mg L(-1), and 0.6 mg kg(-1) and 411.8 mg kg(-1), respectively. Observed higher relative sorption at elevated concentrations with a Freundlich exponent greater than one, indicated cooperative sorption. The sorption-desorption hysteresis (isotherm non-singularity) indicated irreversible sorption. Higher sorption observed at higher rather than at lower ionic strength conditions is likely due to a salting-out effect. Negative free energy and the inverse sorption capacity-temperature relationship indicated the carbamazepine sorption process was favorable or spontaneous. Solution pH had little effect on sorption.