As sea levels rise and temperatures increase, vegetation communities in tropical and sub-tropical coastal areas will be stressed; some will migrate northward and inland. The transition from coastal marshes and scrub–shrubs to woody mangroves is a fundamental change to coastal community structure and species composition. However, this transition will likely be episodic, complicating monitoring efforts, as mangrove advances are countered by dieback from increasingly impactful storms. Coastal habitat monitoring has traditionally been conducted through satellite and ground-based surveys. Here we investigate the use of UAV-LiDAR (unoccupied aerial vehicle–light detection and ranging) and multispectral photogrammetry to study a Florida coastal wetland. These data have higher resolution than satellite-derived data and are cheaper and faster to collect compared to crewed aircraft or ground surveys. We detected significant canopy change in the period between our survey (2020–2022) and a previous survey (2015), including loss at the scale of individual buttonwood trees (Conocarpus erectus), a woody mangrove associate. The UAV-derived data were collected to investigate the utility of simplified processing and data inputs for habitat classification and were validated with standard metrics and additional ground truth. UAV surveys combined with machine learning can streamline coastal habitat monitoring, facilitating repeat surveys to assess the effects of climate change and other change agents.

Barrier island beaches provide important protection, but human development, loss of natural cover, hurricanes, and tropical storms have contributed to widespread beach erosion. Some coastal regions resort to jetties, shore protection structures, and beach nourishment, whereby offshore or other nearby sediment sources are mined and added to the beach. These projects are costly, and their effectiveness must be closely monitored. This paper investigates the ability of photogrammetry from an Unoccupied Aerial Vehicle (UAV) to quantify geomorphic changes of the subaerial section to a newly nourished beach. On October 10th, 2018, Hurricane Michael moved up the Gulf of Mexico past Tampa Bay, coinciding with an ongoing nourishment project at Indian Rocks Beach. We conducted UAV and ground surveys before and after the hurricane passage and compared point clouds and across-shore profiles at three locations. We compare observed erosional regimes to probabilistically forecasted erosional regimes and found they were only minimally present, perhaps reflecting the recent sand additions. An average volume loss of ~31 m ³ /m was measured across the three studied sections.

Monitoring coastal wetlands, particularly mangroves, is increasingly important as the impacts of climate change increase. As sea levels rise and temperature increase, vegetation communities traditionally associated with tropical and sub-tropical coastlines will migrate northward and also inland, along waterways. The transition from coastal marshes and subshrubs to woody mangroves is a fundamental change to coastal community structure and species composition, requiring monitoring. However, this transition is likely to be episodic, complicating monitoring efforts, as mangrove advances are countered by dieback resulting from increasingly impactful storms. Coastal habitat monitoring has traditionally been done through satellite and ground-based surveys. This project investigates the use of UAV lidar and multispectral photogrammetry which can be obtained routinely at higher resolution than satellite derived data, and cheaper and faster than ground-surveys. Using UAV-based methods we monitor and classify coastal habitats, including mangroves, using simple machine learning methods. Between 2020 and 2022 we investigated the use of remote sensing to monitor a multiple use Florida coastal ecosystem. Using UAV lidar we mapped vegetation communities and detected sections of significant canopy loss. Ground truthing verified the occurrence of recent canopy loss at the scale of individual snag remnants of woody mangrove associates, i.e., buttonwood trees (Conocarpus erectus). Using UAV lidar and multispectral photogrammetry data as inputs into a random forest model, we created several models of habitat classification. Training inputs included 2000-pixel and 5000-pixel data subsets. Initial results were resampled to match the size of tree crown in the field area creating four classification schemes. All classifications were validated using standard metrics. Mangrove habitat identification using the resampled 2000-pixel model has 85% producer’s accuracy and 80% user’s accuracy. UAV surveys combined with machine-learning streamline coastal habitat monitoring and facilitate repeat surveys to assess the effects of climate change.

Mapping of magnetic anomalies in volcanic areas is a valuable tool to better understand lava flow geometries and dynamics. The high magnetization of basaltic lava allows us to easily identify buried lava flows, providing constraints on the total volume of old erupted material and the flow geometry, while magnetic mapping of volcanic intrusions in country rock enables us to model feeder dike geometry. The low magnetic signals within recent lava flows can identify areas that are still above the Curie temperature, constraining the dynamic of recent flow, and negative anomalies above old empty lava tubes can allow us to identify these hidden conduits.Traditionally, magnetic anomaly mapping for small regions is performed by walking through the survey area and for larger regions using crewed aircraft. Walking is often daunting, labor-intensive, and potentially dangerous. On the other hand, crewed aircraft are normally expensive and require a significant logistical organization before the survey. Uncrewed aerial vehicles (UAVs) have the potential to bridge the gap and collect a significant amount of high-resolution data in a relatively short time, possibly in areas not easily accessible. UAVs also provide the opportunity to collect data at multiple altitudes, providing a full gradient of the measured field.Here we present the results from both old and recent lava flows. Little Cones in Nevada (USA) consists of two visible cones erupted ~0.8Ma in the vicinity of the proposed nuclear repository of Yucca Mountain, Nevada. The lava flows from the two cones are partially buried by alluvium and not visible above ground. Our UAV data collection and data inversion allowed us to map the full extent of the lava flow and estimate the total volume of effused material. Our surveys of Hell’s Half Acre (~3000 BCE) lava flow, and Kings’ Bowl (~300 BCE) flow in Idaho (USA) are examples of the use of magnetic anomalies to identify lava tubes, feeding dikes, and flow morphology. Our 2022 survey of the 2018 Pacaya (Guatemala) lava flow is an example of a hard-to-access flow in which we can identify the warmer core that has not yet cooled below the Curie temperature. A survey of the 2018 Lower East Rift Zone eruption of Kīlauea in Hawaiʻi (USA) conducted in 2022 is another example of a negative anomaly possibly associated with areas that are still hot, or lava tubes. In all the surveys we collected a few hundred line kms of data in a few days using two 200 Hz triaxial fluxgate magnetometers mounted on a medium-lift drone. UAV magnetic surveys in volcanic regions are a powerful tool for understanding old and recent volcanic processes.

Improvements in miniaturization and affordability of lidar technology, mainly due to innovation in self-driving cars, means that UAV lidar is now an accessible option for geoscience research. We present applications in which UAV lidar contributes to data collection in ways that would otherwise not be possible in the time frame, budget, and/or with the resolution required.Volcanoes: Lava flow surface texture can provide information on lava flow dynamics and emplacement. The transition between pahoehoe and a’a flow textures can indicate changes in flow rates and flow thickness, and the morphology of ripples in ropey pahoehoe flows can indicate flow direction. Hell’s Half Acre, Idaho, USA, is a basaltic lava flow that was erupted ~5000 y.a. Analysis of UAV lidar data at this lava field shows lava flow surface texture in sufficient resolution to define cm-scale pahoehoe ripples. In addition, larger scale lava features such as channels and inflation/deflation ridges can be mapped which allows us to understand the dynamics of the lava flow emplacement.Vegetation: UAV Lidar can be useful for analysis of vegetation canopy, both in stripping canopy (lidar last return) and in using it for tree height (lidar first return). By combining UAV lidar with other airborne data, e.g. multispectral imaging, we can identify and map tree species at Ft de Soto Park, in Florida, USA.Permafrost: Permafrost thermokarst features can develop rapidly and climate change will cause an increase in these rapid thaw events. With UAV lidar we can strip the vegetation to reveal the underlying ground surface which can then be used to assess and model permafrost processes. UAV surveys are quick and relatively inexpensive (as compared to crewed aviation) and data can be collected in response to a thaw event. We present data from Alaska, USA, at known sites of rapid thermokarst thaw.UAV lidar, both as a stand-alone dataset, and when integrated with other data streams e.g. multispectral and visible imagery, can provide high-resolution data (both spatial and temporal) on a platform that is relatively low-cost and logistically straightforward to deploy.