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1. Transfer Learning for Wildfire Identification in UAV Imagery

   https://doi.org/10.1109/CISS48834.2020.1570617429  

   Wu, Haiyu; Li, Huayu; Shamsoshoara, Alireza; Razi, Abolfazl; Afghah, Fatemeh (March 2020, 54th Annual Conference on Information Sciences and Systems (CISS))

   null (Ed.)

   Due to Wildfire's huge destructive impacts on agriculture and food production, wildlife habitat, climate, human life and ecosystem, timely discovery of fires enable swift response to fires before they go out of control, in order to minimize the resulting damage and impacts. One of the emerging technologies for fire monitoring is deploying Unmanned Aerial Vehicles, due to their high flexibility and maneuverability, less human risk, and on-demand high quality imaging capabilities. In order to realize a real-time system for fire detection and expansion analysis, fast and high-accuracy image-processing algorithms are required. Several studies have shown that deep learning methods can provide the most accurate response, however the training time can be prohibitively long, especially when using online learning for constant refinement of the developed model. Another challenge is the lack of large datasets for training a deep learning algorithm. In this respect, we propose to use a pretrained mobileNetV2 architecture to implement transfer learning, which requires a smaller dataset and reduces the computational complexity while not compromising the accuracy. In addition, we conduct an effective data augmentation pipeline to simulate some extreme scenarios, which could promise the robustness of our approach. The testing results illustrate that our method maintains a high identification accuracy in different situations - original dataset (99.7%), adding Gaussian blurred (95.3%), and additive Gaussian noise (99.3%). 

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2. Wildland Fire Detection and Monitoring Using a Drone-Collected RGB/IR Image Dataset

   Chen, Xiwen and (January 2022, IEEE Access)

   Current forest monitoring technologies including satellite remote sensing, manned/piloted aircraft, and observation towers leave uncertainties about a wildfire’s extent, behavior, and conditions in the fire’s near environment, particularly during its early growth. Rapid mapping and real-time fire monitoring can inform in-time intervention or management solutions to maximize beneficial fire outcomes. Drone systems’ unique features of 3D mobility, low flight altitude, and fast and easy deployment make them a valuable tool for early detection and assessment of wildland fires, especially in remote forests that are not easily accessible by ground vehicles. In addition, the lack of abundant, well-annotated aerial datasets – in part due to unmanned aerial vehicles’ (UAVs’) flight restrictions during prescribed burns and wildfires – has limited research advances in reliable data-driven fire detection and modeling techniques. While existing wildland fire datasets often include either color or thermal fire images, here we present (1) a multi-modal UAV-collected dataset of dual-feed side-by-side videos including both RGB and thermal images of a prescribed fire in an open canopy pine forest in Northern Arizona and (2) a deep learning-based methodology for detecting fire and smoke pixels at accuracy much higher than the usual single-channel video feeds. The collected images are labeled to “fire” or “no-fire” frames by two human experts using side-by-side RGB and thermal images to determine the label. To provide context to the main dataset’s aerial imagery, the included supplementary dataset provides a georeferenced pre-burn point cloud, an RGB orthomosaic, weather information, a burn plan, and other burn information. By using and expanding on this guide dataset, research can develop new data-driven fire detection, fire segmentation, and fire modeling techniques. 

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3. Airborne Lidar and Electro-Optical Imagery along Surface Ruptures of the 2019 Ridgecrest Earthquake Sequence, Southern California

   https://doi.org/10.1785/0220190338  

   Hudnut, Kenneth W.; Brooks, Benjamin A.; Scharer, Katherine; Hernandez, Janis L.; Dawson, Timothy E.; Oskin, Michael E.; Ramon Arrowsmith, J.; Goulet, Christine A.; Blake, Kelly; Boggs, Matthew L.; et al (April 2020, Seismological Research Letters)

   Abstract Surface rupture from the 2019 Ridgecrest earthquake sequence, initially associated with the Mw 6.4 foreshock, occurred on 4 July on a ∼17  km long, northeast–southwest-oriented, left-lateral zone of faulting. Following the Mw 7.1 mainshock on 5 July (local time), extensive northwest–southeast-oriented, right-lateral faulting was then also mapped along a ∼50  km long zone of faults, including subparallel splays in several areas. The largest slip was observed in the epicentral area and crossing the dry lakebed of China Lake to the southeast. Surface fault rupture mapping by a large team, reported elsewhere, was used to guide the airborne data acquisition reported here. Rapid rupture mapping allowed for accurate and efficient flight line planning for the high-resolution light detection and ranging (lidar) and aerial photography. Flight line planning trade-offs were considered to allocate the medium (25 pulses per square meter [ppsm]) and high-resolution (80 ppsm) lidar data collection polygons. The National Center for Airborne Laser Mapping acquired the airborne imagery with a Titan multispectral lidar system and Digital Modular Aerial Camera (DiMAC) aerial digital camera, and U.S. Geological Survey acquired Global Positioning System ground control data. This effort required extensive coordination with the Navy as much of the airborne data acquisition occurred within their restricted airspace at the China Lake ranges. 

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4. Streaming Scene Maps for Co-Robotic Exploration in Bandwidth Limited Environments

   https://doi.org/10.1109/ICRA.2019.8794132  

   Girdhar, Yogesh; Cai, Levi; Jamieson, Stewart; McGuire, Nathan; Flaspohler, Genevieve; Suman, Stefano; Claus, Brian (May 2019, 2019 International Conference on Robotics and Automation (ICRA))

   This paper proposes a bandwidth tunable technique for real-time probabilistic scene modeling and mapping to enable co-robotic exploration in communication constrained environments such as the deep sea. The parameters of the system enable the user to characterize the scene complexity represented by the map, which in turn determines the bandwidth requirements. The approach is demonstrated using an underwater robot that learns an unsupervised scene model of the environment and then uses this scene model to communicate the spatial distribution of various high-level semantic scene constructs to a human operator. Preliminary experiments in an artificially constructed tank environment, as well as simulated missions over a 10m x 10m coral reef using real data, show the tunability of the maps to different bandwidth constraints and science interests. To our knowledge this is the first paper to quantify how the free parameters of the unsupervised scene model impact both the scientific utility of and bandwidth required to communicate the resulting scene model. 

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5. Interannual variation in climate contributes to contingency in post‐fire restoration outcomes in seeded sagebrush steppe

   https://doi.org/10.1111/csp2.12737  

   Simler‐Williamson, Allison_B; Applestein, Cara; Germino, Matthew_J (June 2022, Conservation Science and Practice)

   Abstract Interannual variation, especially weather, is an often‐cited reason for restoration “failures”; yet its importance is difficult to experimentally isolate across broad spatiotemporal extents, due to correlations between weather and site characteristics. We examined post‐fire treatments within sagebrush‐steppe ecosystems to ask: (1) Is weather following seeding efforts a primary reason why restoration outcomes depart from predictions? and (2) Does the management‐relevance of weather differ across space and with time since treatment? Our analysis quantified range‐wide patterns of sagebrush (Artemisiaspp.) recovery, by integrating long‐term records of restoration and annual vegetation cover estimates from satellite imagery following thousands of post‐fire seeding treatments from 1984 to 2005. Across the Great Basin, sagebrush growth increased in wetter, cooler springs; however, the importance of spring weather varied with sites' long‐term climates, suggesting differing ecophysiological limitations across sagebrush's range. Incorporation of spring weather, including from the “planting year,” improved predictions of sagebrush recovery, but these advances were small compared to contributions of time‐invariant site characteristics. Given extreme weather conditions threatening this ecosystem, explicit consideration of weather could improve the allocation of management resources, such as by identifying areas requiring repeated treatments; but improved forecasts of shifting mean conditions with climate change may more significantly aid the prediction of sagebrush recovery. 

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