Stakeholder Education About the Designation of Coastal Zones for the Protection of the Florida Manatee (Trichechus manatus latirostris): The Manatee Awareness and Protection Resource (MAPR) Web Site

In Florida, boaters often have close encounters with Florida manatees (Trichechus manatus latirostris), a protected marine mammal. Manatee protection zones are established to regulate boating traffic in coastal areas where manatees may be at risk of injury. Public debate over the demarcation of the zones is acrimonious. The Boating and Waterways Management Program of Florida Sea Grant created the Manatee Awareness and Protection Resource (MAPR) Web site to promote stakeholder education and awareness surrounding the designation of manatee protection zones. The Web site includes an interactive map that allows visitors to visualize some of the factors involved in delineating manatee protection zones

Remotely Estimating Beneficial Arthropod Populations: Implications of a Low-Cost Small Unmanned Aerial System

Studies show that agricultural land requires investment in the habitat management of non-cropped areas to support healthy beneficial arthropods and the ecosystem services they provide. In a previous small plot study, we manually counted blooms over the season, and found that plots providing greater numbers of flowers supported significantly higher pollinator populations over that of spontaneous weed plots. Here, we examined the potential of deploying an inexpensive small unmanned aerial vehicle (UAV) as a tool to remotely estimate floral resources and corresponding pollinator populations. Data were collected from previously established native wildflower plots in 19 locations on the University of Georgia experimental farms in South Georgia, USA. A UAV equipped with a lightweight digital camera was deployed to capture images of the flowers during the months of June and September 2017. Supervised image classification using a geographic information system (GIS) was carried out on the acquired images, and classified images were used to evaluate the floral area. The floral area obtained from the images positively correlated with the floral counts gathered from the quadrat samples. Furthermore, the floral area derived from imagery significantly predicted pollinator populations, with a positive correlation indicating that plots with greater area of blooming flowers contained higher numbers of pollinators

Perennial Grass and Native Wildflowers: A Synergistic Approach to Habitat Management

Marginal agricultural land provides opportunities to diversify landscapes by producing biomass for biofuel, and through floral provisioning that enhances arthropod-mediated ecosystem service delivery. We examined the effects of local spatial context (adjacent to woodland or agriculture) and irrigation (irrigation or no irrigation) on wildflower bloom and visitation by arthropods in a biofeedstocks-wildflower habitat buffer design. Twenty habitat buffer plots were established containing a subplot of Napier grass (Pennisetum perpureum Schumach) for biofeedstock, three commercial wildflower mix subplots, and a control subplot containing spontaneous weeds. Arthropods and flowers were visually observed in quadrats throughout the season. At the end of the season we measured soil nutrients and harvested Napier biomass. We found irrespective of buffer location or irrigation, pollinators were observed more frequently early in the season and on experimental plots with wildflowers than on weeds in the control plots. Natural enemies showed a tendency for being more common on plots adjacent to a wooded border, and were also more commonly observed early in the season. Herbivore visits were infrequent and not significantly influenced by experimental treatments. Napier grass yields were high and typical of first-year yields reported regionally, and were not affected by location context or irrigation. Our results suggest habitat management designs integrating bioenergy crop and floral resources provide marketable biomass and habitat for beneficial arthropods

C-band synthetic aperture radar (SAR) imagery for the classification of diverse cropping systems

International audienceCloudy conditions reduce the utility of optical imagery for crop monitoring. New constellations of satellites – including the RADARSAT Constellation Mission (RCM) and Sentinel-1A/B, both available under free and open data policies – can be used to create stacks of dense seasonal C-band Synthetic Aperture Radar (SAR) data. Yet to date, the contribution of SAR imagery to operational crop mapping is often limited to that of a gap-filler, compensating for optical data obscured by clouds. The Joint Experiment for Crop Assessment and Monitoring (JECAM) SAR Inter-Comparison Experiment is a multi-year, multi-partner project focused on evaluating methods for SAR-based crop classification. Stacks of dense time-series SAR imagery, from RADARSAT-2 and Sentinel-1 satellites, were acquired for 10 sites located in six countries. Decision Tree (DT) and Random Forest (RF) classification methodologies were applied to these SAR data-stacks, as well as to data-stacks of optical only, and optimized SAR/optical data combinations. For the dense time-series SAR stacks, overall classification accuracies above 85% and 80% were obtained for 6 of 10 and 8 of 10 sites, respectively. For maize, the SAR-only data delivered user’s and producer’s accuracies greater than 90% for half the sites. For soya bean, accuracies greater than 80% were reported for 5 of 9 sites and classification accuracies were greater than 80% for wheat on half the sites. Classification results were influenced by the mix and number of agriculture classes present at each site, the available SAR imagery, as well as the training and validation data sets for individual crop types. These results have important operational implications for regions of the world dominated by cloudy conditions and the lack of adequate amounts of optical imagery to support satellite-based crop monitoring

Responses to environmental variability by herbivorous insects and their natural enemies within a bioenergy crop, Miscanthus x giganteus.

Precision agriculture (PA) is the application of management decisions based on identifying, quantifying, and responding to space-time variability. However, knowledge of crop pest responses to within-field environmental variability, and the spatial distribution of their natural enemies, is limited. Quantitative methods providing insights on how pest-predator relationships vary within fields are potentially important tools. In this study, phloem feeders and their natural enemies, were observed over two years across 81 locations within a field of the perennial feedstock grass in Georgia, USA. Geographically weighted regression (GWR) was used to spatially correlate their abundance with environmental factors. Variables included distance to forest edge, Normalized Difference of Vegetation Index (NDVI), slope, aspect, elevation, soil particle size distribution, and weather values. GWR methods were compared with generalized linear regression methods that do not account for spatial information. Non-spatial models indicated positive relationships between phloem-feeder abundance and wind speed, but negative relationships between elevation, proportions of silt and sand, and NDVI. With data partitioned into three seasonal groups, terrain and soil variables remained significant, and natural enemies and spiders became relevant. Results from GWR indicated that magnitudes and directions of responses varied within the field, and that relationships differed among seasons. Strong negative relationships between response and explanatory factors occurred: with NDVI during mid-season; with percent silt, during mid-, and late seasons; and with spider abundance during early and late seasons. In GWR models, slope, elevation, and aspect were mostly positive indicating further that associations with elevation depended on whether models incorporated spatial information or not. By using spatially explicit models, the analysis provided a complex, nuanced understanding of within-field relationships between phloem feeders and environmental covariates. This approach provides an opportunity to learn about the variability within agricultural fields and, with further analysis, has potential to inform and improve PA and habitat management decisions

Evaluation of SMAP Core Validation Site Representativeness Errors using Dense Networks of in situ Sensors and Random Forests

© 2008-2012 IEEE. In order to validate its soil moisture products, the NASA Soil Moisture Active Passive (SMAP) mission utilizes sites with permanent networks of in situ soil moisture sensors maintained by independent calibration and validation partners in a variety of ecosystems around the world. Measurements from each core validation site (CVS) are combined in a weighted average to produce an estimate of soil moisture at a 33-km scale that represents the SMAP's radiometer-based retrievals. Since upscaled estimates produced in this manner are dependent on the weighting scheme applied, an independent method of quantifying their biases is needed. Here, we present one such method that uses soil moisture measurements taken from a dense, but temporary, network of soil moisture sensors deployed at each CVS to train a random forests regression expressing soil moisture in terms of a set of spatial variables. The regression then serves as an independent source of upscaled estimates against which permanent network upscaled estimates can be compared in order to calculate bias statistics. This method, which offers a systematic and unified approach to estimate bias across a variety of validation sites, was applied to estimate biases at four CVSs. The results showed that the magnitude of the uncertainty in the permanent network upscaling bias can sometimes exceed 80% of the upper limit on SMAP's entire allowable unbiased root-mean-square error (ubRMSE). Such large CVS bias uncertainties could make it more difficult to assess biases in soil moisture estimates from SMAP