Recent advances in unmanned-aerial-vehicle- (UAV-) based remote sensing utilizing lightweight multispectral and thermal infrared sensors allow for rapid wide-area landmine contamination detection and mapping surveys. We present results of a study focused on developing and testing an automated technique of remote landmine detection and identification of scatterable antipersonnel landmines in wide-area surveys. Our methodology is calibrated for the detection of scatterable plastic landmines which utilize a liquid explosive encapsulated in a polyethylene or plastic body in their design. We base our findings on analysis of multispectral and thermal datasets collected by an automated UAV-survey system featuring scattered PFM-1-type landmines as test objects and present results of an effort to automate landmine detection, relying on supervised learning algorithms using a Faster Regional-Convolutional Neural Network (Faster R-CNN). The RGB visible light Faster R-CNN demo yielded a 99.3% testing accuracy for a partially withheld testing set and 71.5% testing accuracy for a completely withheld testing set. Across multiple test environments, using centimeter scale accurate georeferenced datasets paired with Faster R-CNN, allowed for accurate automated detection of test PFM-1 landmines. This method can be calibrated to other types of scatterable antipersonnel mines in future trials to aid humanitarian demining initiatives. With millions of remnant PFM-1 and similar scatterable plastic mines across post-conflict regions and considerable stockpiles of these landmines posing long-term humanitarian and economic threats to impacted communities, our methodology could considerably aid in efforts to demine impacted regions.

Use of landmines as a weapon of unconventional warfare rapidly increased in armed conflicts of the last century and some estimates suggest that at least 100 million remain in place across post-conflict nations. Among munitions and explosives of concern (MECs), aerially deployed plastic anti-personnel mines are particularly challenging in terms of their detection and subsequent disposal. Detection and identification of MECs largely relies on the geophysical principles of magnetometry and electromagnetic-induction (EMI), which makes non-magnetic plastic MECs particularly difficult to detect and extremely dangerous to clear. In a recent study we demonstrated the potential of time-lapse thermal-imaging technology to detect unique thermal signatures associated with plastic MECs. Here, we present the results of a series of field trials demonstrating the viability of low-cost unmanned aerial vehicles (UAVs) equipped with infrared cameras to detect and identify the most notorious plastic landmines—the Soviet-era PFM-1 aerially deployed antipersonnel mine. We present results of an experiment simulating analysis of a full-scale ballistic PFM-1 minefield and demonstrate our ability to accurately detect and identify all elements associated with this type of deployment. We report significantly reduced time and equipment costs associated with the use of a UAV-mounted infrared system and anticipate its utility to both the scientific and non-governmental organization (NGO) community.

Submarine groundwater discharge (SGD) is an important component of many coastal environments and hydrologic processes, providing sources of nutrients to marine ecosystems, and potentially, an important source of fresh water for human populations. Here, we use a combination of unpiloted aerial systems (UAS) thermal infrared (TIR) imaging and salinity measurements to characterize SGD on the remote East Polynesian island of Rapa Nui (Easter Island, Chile). Previous research has shown that coastal freshwater seeps are abundant on Rapa Nui and strongly associated with the locations of ancient settlement sites. We currently lack, however, information on the differential magnitude or quality of these sources of fresh water. Our UAS-based TIR results from four locations on Rapa Nui suggest that locations of variably-sized SGD plumes are associated with many ancient settlement sites on the island and that these water sources are resilient to drought events. These findings support previous work indicating that ancient Rapa Nui communities responded to the inherent and climate-induced hydrological challenges of the island by focusing on these abundant and resilient freshwater sources. Our results highlight the efficacy of using UAS-based TIR for detecting relatively small SGD locations and provide key insights on the potential uses of these water sources for past and current Rapa Nui communities.

Orphaned oil and gas wells represent an important environmental and economic development concern in areas where historical energy exploration and production activity pre-dated regulations on well abandonment documentation practices. From an economic development perspective, these wells pose a subsurface risk to infrastructure development, while the environmental impact of orphaned wells is largely associated with uncontrolled emissions of both fluids and gasses, most notably atmospheric methane. Often neglected in regulatory oversight, methane emissions from orphaned wells contribute to global greenhouse gas concentrations and without proper mitigation, these emissions contribute to and further exacerbate global climate change processes. An inherent challenge of remediation efforts targeting orphaned wells is the consistent observation that only a fraction of located and identified wells produce the majority of methane emissions, yet no methodology exists to effectively separate out heavy emitters without visiting and assessing each and every well. We demonstrate that emitting wells have certain defined characteristics largely pertaining to the presence and integrity of metal casing to the surface, which can be distinguished as an organized high intensity magnetic anomaly in low-altitude UAV-based aeromagnetic surveys. In this paper, we present a database of identified and ground-truthed wellsites correlated to their magnetic signatures and provide a roadmap to initial prioritization of wellsites for subsequent remediation activities that can be implemented in complex environments where other survey options are not feasible. In a regulatory environment where resources dedicated to wellsite remediation are limited, we propose implementing this methodology as a key element of a targeted approach to wellsite prioritization for subsequent remediation activity.

Ground-penetrating radar has emerged as a prominent non-destructive evaluation tool for the study of inaccessible subsurface elements of cultural heritage structures. Often of central interest is the desire to image the remains of a pre-existing historic structure that is located directly beneath a more recently built one. The interpretation of GPR images in such cases is usually difficult due to ambiguities caused by the presence of pervasive clutter, environmental noise, and overlapping target signatures. Sites with abundant ground truth allow for more confident interpretations and serve as a useful testbed to assist similar studies at other places, where little or no ground truth is available. This study reports GPR interpretations of structures belonging to the 19th century Citadel beneath the main prison cellhouse at Alcatraz. At this site, lidar scans, direct observations, and historical documents are available to facilitate identification of radar target signatures. A general interpretation of the acquired radargrams is made in this paper, while the companion paper presents more advanced analysis of target signatures based on curvelet image processing. This study points to the development of a radar facies classification scheme that is specific to cultural heritage investigations.