@article{reckling_levine_nelson_mitasova_2023, title={Predicting residential septic system malfunctions for targeted drone inspections}, volume={30}, ISSN={2352-9385}, url={http://dx.doi.org/10.1016/j.rsase.2023.100936}, DOI={10.1016/j.rsase.2023.100936}, abstractNote={Septic system malfunctions can cause untreated sewage to pond in yards or contaminate drinking water wells leading to environmental and health problems. While most malfunction detections rely on reports by individuals, machine learning and remote sensing can be used to identify potentially failing systems. We propose a methodology that combines a machine learning technique implemented in Maxent with unmanned aerial system (UAS) mapping to create a priority queue for inspection and detecting malfunctions apparent in the collected imagery. We demonstrate the approach in Wake County, North Carolina, a County with 73,347 septic systems located within drinking water supply watersheds. The predictive modeling identified 102 systems with a 99.9% probability of failure. Four properties from the queue were mapped by UAS and the acquired imagery was visually analyzed in the visible spectrum for signs of malfunction. Our results suggest that the proposed approach can assist in the early identification of failing systems minimizing the environmental impacts and saving resource time and funds.}, journal={Remote Sensing Applications: Society and Environment}, publisher={Elsevier BV}, author={Reckling, William and Levine, Jay and Nelson, Stacy A.C. and Mitasova, Helena}, year={2023}, month={Apr}, pages={100936} }
 @article{white_reckling_petrasova_meentemeyer_mitasova_2022, title={Rapid-DEM: Rapid Topographic Updates through Satellite Change Detection and UAS Data Fusion}, volume={14}, ISSN={["2072-4292"]}, url={https://www.mdpi.com/2072-4292/14/7/1718}, DOI={10.3390/rs14071718}, abstractNote={As rapid urbanization occurs in cities worldwide, the importance of maintaining updated digital elevation models (DEM) will continue to increase. However, due to the cost of generating high-resolution DEM over large spatial extents, the temporal resolution of DEMs is coarse in many regions. Low-cost unmanned aerial vehicles (UAS) and DEM data fusion provide a partial solution to improving the temporal resolution of DEM but do not identify which areas of a DEM require updates. We present Rapid-DEM, a framework that identifies and prioritizes locations with a high likelihood of an urban topographic change to target UAS data acquisition and fusion to provide up-to-date DEM. The framework uses PlanetScope 3 m satellite imagery, Google Earth Engine, and OpenStreetMap for land cover classification. GRASS GIS generates a contextualized priority queue from the land cover data and outputs polygons for UAS flight planning. Low-cost UAS fly the identified areas, and WebODM generates a DEM from the UAS survey data. The UAS data is fused with an existing DEM and uploaded to a public data repository. To demonstrate Rapid-DEM a case study in the Walnut Creek Watershed in Wake County, North Carolina is presented. Two land cover classification models were generated using random forests with an overall accuracy of 89% (kappa 0.86) and 91% (kappa 0.88). The priority queue identified 109 priority locations representing 1.5% area of the watershed. Large forest clearings were the highest priority locations, followed by newly constructed buildings. The highest priority site was a 0.5 km2 forest clearing that was mapped with UAS, generating a 15 cm DEM. The UAS DEM was resampled to 3 m resolution and fused with USGS NED 1/9 arc-second DEM data. Surface water flow was simulated over the original and updated DEM to illustrate the impact of the topographic change on flow patterns and highlight the importance of timely DEM updates.}, number={7}, journal={REMOTE SENSING}, publisher={MDPI AG}, author={White, Corey T. and Reckling, William and Petrasova, Anna and Meentemeyer, Ross K. and Mitasova, Helena}, year={2022}, month={Apr} }
 @article{reckling_mitasova_wegmann_kauffman_reid_2021, title={Efficient Drone-Based Rare Plant Monitoring Using a Species Distribution Model and AI-Based Object Detection}, volume={5}, ISSN={["2504-446X"]}, url={https://www.mdpi.com/2504-446X/5/4/110}, DOI={10.3390/drones5040110}, abstractNote={Monitoring rare plant species is used to confirm presence, assess health, and verify population trends. Unmanned aerial systems (UAS) are ideal tools for monitoring rare plants because they can efficiently collect data without impacting the plant or endangering personnel. However, UAS flight planning can be subjective, resulting in ineffective use of flight time and overcollection of imagery. This study used a Maxent machine-learning predictive model to create targeted flight areas to monitor Geum radiatum, an endangered plant endemic to the Blue Ridge Mountains in North Carolina. The Maxent model was developed with ten environmental layers as predictors and known plant locations as training data. UAS flight areas were derived from the resulting probability raster as isolines delineated from a probability threshold based on flight parameters. Visual analysis of UAS imagery verified the locations of 33 known plants and discovered four previously undocumented occurrences. Semi-automated detection of plant species was explored using a neural network object detector. Although the approach was successful in detecting plants in on-ground images, no plants were identified in the UAS aerial imagery, indicating that further improvements are needed in both data acquisition and computer vision techniques. Despite this limitation, the presented research provides a data-driven approach to plan targeted UAS flight areas from predictive modeling, improving UAS data collection for rare plant monitoring.}, number={4}, journal={DRONES}, publisher={MDPI AG}, author={Reckling, William and Mitasova, Helena and Wegmann, Karl and Kauffman, Gary and Reid, Rebekah}, year={2021}, month={Dec} }