The role of remote sensing and human–environment interactions (HEI) research in social and environmental decision-making has steadily increased along with numerous technological and methodological advances in the global environmental change field. Given the growing inter- and trans-disciplinary nature of studies focused on understanding the human dimensions of global change (HDGC), the need for a synchronization of agendas is evident. We conduct a bibliometric assessment and review of the last two decades of peer-reviewed literature to ascertain what the trends and current directions of integrating remote sensing into HEI research have been and discuss emerging themes, challenges, and opportunities. Despite advances in applying remote sensing to understanding ever more complex HEI fields such as land use/land cover change and landscape degradation, agricultural dynamics, urban geography and ecology, natural hazards, water resources, epidemiology, or paleo HEIs, challenges remain in acquiring and leveraging accurately georeferenced social data and establishing transferable protocols for data integration. However, recent advances in micro-satellite, unmanned aerial systems (UASs), and sensor technology are opening new avenues of integration of remotely sensed data into HEI research at scales relevant for decision-making purposes that simultaneously catalyze developments in HDGC research. Emerging or underutilized methodologies and technologies such as thermal sensing, digital soil mapping, citizen science, UASs, cloud computing, mobile mapping, or the use of “humans as sensors” will continue to enhance the relevance of HEI research in achieving sustainable development goals and driving the science of HDGC further.

With predicted alterations in climate and land use, managing water resources is of the utmost importance, especially in areas such as the United States (U.S.) Coastal Plain where extensive connections exist between surface and groundwater systems. These changes create the need for models that effectively assess shifting hydrologic regimes and, in that context, we examine the performance of the Soil and Water Assessment Tool (SWAT) in a low-gradient, shallow-aquifer-dominated watershed of the U.S. Coastal Plain using a gridded reanalysis dataset. We evaluate accuracy, uncertainty, and parameter sensitivity by comparing observed and predicted streamflow at two gaging stations and assess model predictions for yearly average runoff (SURQ), percolation (PERC), and sediment loss (SYLD). Streamflow performance was acceptable during calibration (NSE = 0.67 and 0.60) and very good during validation (NSE = 0.84 and 0.91). Model predictions for SURQ, PERC, and SYLD coincided with expected ranges for this region. Parameters related to shallow aquifer properties or groundwater were highly sensitive, which indicates the need for continued study of spatial and temporal variability within the sub-surface components of these hydrologic systems. Our findings highlight the applicability of this reanalysis dataset for modeling hydrologic processes in poorly gaged watersheds and adds to the body of research that seeks to develop effective assessment tools for shallow-aquifer-dominated systems. Our methodology can effectively assist watershed managers in establishing baseline rates of hydrologic processes as is crucial with future predicted shifts in hydrologic regimes due to land-use alteration and climate change.

Wetlands provide critical ecosystem services across a range of environmental gradients and are at heightened risk of degradation from anthropogenic pressures and continued development, especially in coastal regions. There is a growing need for high-resolution (spatially and temporally) habitat identification and precise delineation of wetlands across a variety of stakeholder groups, including wetlands loss mitigation programs. Traditional wetland delineations are costly, time-intensive and can physically degrade the systems that are being surveyed, while aerial surveys are relatively fast and relatively unobtrusive. To assess the efficacy and feasibility of using two variable-cost LiDAR sensors mounted on a commercial hexacopter unmanned aerial system (UAS) in deriving high resolution topography, we conducted nearly concomitant flights over a site located in the Atlantic Coastal plain that contains a mix of palustrine forested wetlands, upland coniferous forest, upland grass and bare ground/dirt roads. We compared point clouds and derived topographic metrics acquired using the Quanergy M8 and the Velodyne HDL-32E LiDAR sensors with airborne LiDAR and results showed that the less expensive and lighter payload sensor outperforms the more expensive one in deriving high resolution, high accuracy ground elevation measurements under a range of canopy cover densities and for metrics of point cloud density and digital terrain computed both globally and locally using variable size tessellations. The mean point cloud density was not significantly different between wetland and non-wetland areas, but the two sensors were significantly different by wetland/non-wetland type. Ultra-high-resolution LiDAR-derived topography models can fill evolving wetlands mapping needs and increase accuracy and efficiency of detection and prediction of sensitive wetland ecosystems, especially for heavily forested coastal wetland systems.

There is a growing demand for the collection of ultra-high spatial resolution imagery using unmanned aerial systems (UASs). UASs are a cost-effective solution for data collection on small scales and can fly at much lower altitudes, thus yielding spatial resolutions not previously achievable with manned aircraft or satellites. The use of commercially available software for image processing has also become commonplace due to the relative ease at which imagery can be processed and the minimal knowledge of traditional photogrammetric processes required by users. Commercially available software such as AgiSoft Photoscan and Pix4Dmapper Pro are capable of generating the high-quality data that are in demand for environmental remote sensing applications. We quantitatively assess the implications of processing parameter decision-making on UAS product accuracy and quality for orthomosaic and digital surface models for RGB and multispectral imagery. We iterated 40 processing workflows by incrementally varying two key processing parameters in Pix4Dmapper Pro, and conclude that maximizing for the highest intermediate parameters may not always translate into effective final products. We also show that multispectral imagery can effectively be leveraged to derive three-dimensional models of higher quality despite the lower resolution of sensors when compared to RGB imagery, reducing time in the field and the need for multiple flights over the same area when collecting multispectral data is a priority. We conclude that when users plan to use the highest processing parameter values, to ensure quality end-products it is important to increase initial flight coverage in advance.

Knowledge of temperature variation within and across beach-nesting bird habitat, and how such variation may affect the nesting success and survival of these species, is currently lacking. This type of data is furthermore needed to refine predictions of population changes due to climate change, identify important breeding habitat, and guide habitat restoration efforts. Thermal imagery collected with unmanned aerial vehicles (UAVs) provides a potential approach to fill current knowledge gaps and accomplish these goals. Our research outlines a novel methodology for collecting and implementing active thermal ground control points (GCPs) and assess the accuracy of the resulting imagery using an off-the-shelf commercial fixed-wing UAV that allows for the reconstruction of thermal landscapes at high spatial, temporal, and radiometric resolutions. Additionally, we observed and documented the behavioral responses of beach-nesting birds to UAV flights and modifications made to flight plans or the physical appearance of the UAV to minimize disturbance. We found strong evidence that flying on cloudless days and using sky-blue camouflage greatly reduced disturbance to nesting birds. The incorporation of the novel active thermal GCPs into the processing workflow increased image spatial accuracy an average of 12 m horizontally (mean root mean square error of checkpoints in imagery with and without GCPs was 0.59 m and 23.75 m, respectively). The final thermal indices generated had a ground sampling distance of 25.10 cm and a thermal accuracy of less than 1 °C. This practical approach to collecting highly accurate thermal data for beach-nesting bird habitat while avoiding disturbance is a crucial step towards the continued monitoring and modeling of beach-nesting birds and their habitat.