Prototype-Scale Physical Model of Wave Attenuation Through a Mangrove Forest of Moderate Cross-Shore Thickness: LiDAR-Based Characterization and Reynolds Scaling for Engineering With Nature

This study investigates the potential of a Rhizophora mangrove forest of moderate cross-shore thickness to attenuate wave heights using an idealized prototype-scale physical model constructed in a 104 m long wave flume. An 18 m long cross-shore transect of an idealized red mangrove forest based on the trunk-prop root system was constructed in the flume. Two cases with forest densities of 0.75 and 0.375 stems/m2 and a third baseline case with no mangroves were considered. LiDAR was used to quantify the projected area per unit height and to estimate the effective diameter of the system. The methodology was accurate to within 2% of the known stem diameters and 10% of the known prop root diameters. Random and regular wave conditions seaward, throughout, and inland of the forest were measured to determine wave height decay rates and drag coefficients for relative water depths ranging 0.36 to 1.44. Wave height decay rates ranged 0.008–0.021 m–1 for the high-density cases and 0.004–0.010 m–1 for the low-density cases and were found to be a function of water depth. Doubling the forest density increased the decay rate by a factor two, consistent with previous studies for other types of emergent vegetation. Drag coefficients ranged 0.4–3.8, and were found to be dependent on the Reynolds number. Uncertainty in the estimates of the drag coefficient due to the measured projected area and measured wave attenuation was quantified and found to have average combined standard deviations of 0.58 and 0.56 for random and regular waves, respectively. Two previous reduced-scale studies of wave attenuation by mangroves compared well with the present study when their Reynolds numbers were re-scaled by λ3/2 where λ is the prototype-to-model geometric scale ratio. Using the combined data sets, an equation is proposed to estimate the drag coefficient for a Rhizophora mangrove forest: CD = 0.6 + 3e04/ReDBH with an uncertainty of 0.69 over the range 5e03 < ReDBH < 1.9e05, where ReDBH is based on the tree diameter at breast height. These results may improve engineering guidance for the use of mangroves and other emergent vegetation in coastal wave attenuation.

Integrating SAR and Optical Remote Sensing for Conservation-Targeted Wetlands Mapping

The Prairie Pothole Region (PPR) contains numerous depressional wetlands known as potholes that provide habitats for waterfowl and other wetland-dependent species. Mapping these wetlands is essential for identifying viable waterfowl habitat and conservation planning scenarios, yet it is a challenging task due to the small size of the potholes, and the presence of emergent vegetation. This study develops an open-source process within the Google Earth Engine platform for mapping the spatial distribution of wetlands through the integration of Sentinel-1 C-band SAR (synthetic aperture radar) data with high-resolution (10-m) Sentinel-2 bands. We used two machine-learning algorithms (random forest (RF) and support vector machine (SVM)) to identify wetlands across the study area through supervised classification of the multisensor composite. We trained the algorithms with ground truth data provided through field studies and aerial photography. The accuracy was assessed by comparing the predicted and actual wetland and non-wetland classes using statistical coefficients (overall accuracy, Kappa, sensitivity, and specificity). For this purpose, we used four different out-of-sample test subsets, including the same year, next year, small vegetated, and small non-vegetated test sets to evaluate the methods on different spatial and temporal scales. The results were also compared to Landsat-derived JRC surface water products, and the Sentinel-2-derived normalized difference water index (NDWI). The wetlands derived from the RF model (overall accuracy 0.76 to 0.95) yielded favorable results, and outperformed the SVM, NDWI, and JRC products in all four testing subsets. To provide a further characterization of the potholes, the water bodies were stratified based on the presence of emergent vegetation using Sentinel-2-derived NDVI, and, after excluding permanent water bodies, using the JRC surface water product. The algorithm presented in the study is scalable and can be adopted for identifying wetlands in other regions of the world.

Comparison of Generic and Proprietary Aquatic Herbicides for Control of Invasive Vegetation : Part 2. Emergent Plants

Aquatic herbicides are one of the most effective and widespread ways to manage nuisance vegetation in the US After the active ingredient is selected, often there are numerous proprietary and generic branded products to select from. To date, limited efforts have been made to compare the efficacy of brand name and generic herbicides head to head; therefore, at tot al of 20 mesocosm trials were conducted to evaluate various 2,4 -D, glyphosate, imazapyr, and triclopyr products against alligatorweed (Alternanthera philoxeroides (Mart.) Griseb.), southern cattail (hereafter referred to as cattail, Typha domingensis Pers.), and creeping water primrose (hereafter referred as primrose, Ludwigia peploides (Kunth) P.H. Raven). All active ingredients were applied to foliage at broadcast rates commonly used in applications to public waters. Proprietary and generic 2,4 -D, glyphosate, imazapyr, and triclopyr were efficacious and provided 39 to 99% control of alligatorweed, cattail and primrose in 19 of the 20 trials. There were no significant differences i n product performance except glyphosate vs. alligatorweed (trial 1, Rodeo vs. Roundup Custom) and glyphosate vs. cattail (trial 1, Rodeo vs. Glyphosate 5.4). These results demonstrate under small -scale conditions, the majority of the generic and proprietary herbicides provided similar control of emergent vegetation, regardless of active ingredient