skip to main content

Search for: All records

Creators/Authors contains: "Jones, Benjamin M"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Free, publicly-accessible full text available September 1, 2021
  2. Free, publicly-accessible full text available September 1, 2021
  3. Deep learning (DL) convolutional neural networks (CNNs) have been rapidly adapted in very high spatial resolution (VHSR) satellite image analysis. DLCNN-based computer visions (CV) applications primarily aim for everyday object detection from standard red, green, blue (RGB) imagery, while earth science remote sensing applications focus on geo object detection and classification from multispectral (MS) imagery. MS imagery includes RGB and narrow spectral channels from near- and/or middle-infrared regions of reflectance spectra. The central objective of this exploratory study is to understand to what degree MS band statistics govern DLCNN model predictions. We scaffold our analysis on a case study thatmore »uses Arctic tundra permafrost landform features called ice-wedge polygons (IWPs) as candidate geo objects. We choose Mask RCNN as the DLCNN architecture to detect IWPs from eight-band Worldview-02 VHSR satellite imagery. A systematic experiment was designed to understand the impact on choosing the optimal three-band combination in model prediction. We tasked five cohorts of three-band combinations coupled with statistical measures to gauge the spectral variability of input MS bands. The candidate scenes produced high model detection accuracies for the F1 score, ranging between 0.89 to 0.95, for two different band combinations (coastal blue, blue, green (1,2,3) and green, yellow, red (3,4,5)). The mapping workflow discerned the IWPs by exhibiting low random and systematic error in the order of 0.17–0.19 and 0.20–0.21, respectively, for band combinations (1,2,3). Results suggest that the prediction accuracy of the Mask-RCNN model is significantly influenced by the input MS bands. Overall, our findings accentuate the importance of considering the image statistics of input MS bands and careful selection of optimal bands for DLCNN predictions when DLCNN architectures are restricted to three spectral channels.« less
    Free, publicly-accessible full text available September 1, 2021
  4. Free, publicly-accessible full text available July 1, 2021
  5. Free, publicly-accessible full text available May 12, 2021
  6. Free, publicly-accessible full text available March 4, 2021
  7. The intensification of coastal storms, combined with declining sea ice cover, sea level rise, and changes to permafrost conditions, will likely increase the incidence and impact of storm surge flooding in Arctic coastal environments. In coastal communities accurate information on the exposure of infrastructure can make an important contribution to adaptation planning. In this study, we use high resolution elevation data from airborne LiDAR to generate storm flooding scenarios for three coastal communities (Utqiag_ vik, Wainwright, and Kaktovik) in northern Alaska. To estimate the potential for damage to infrastructure caused by flooding for each community, we generated data on replacementmore »costs and used it to estimate the financial impact of 24 storm flooding scenarios of varying intensities. This analysis shows that all three communities are exposed to storm surges, but highlights the fact that infrastructure in Utqiag_ vik (the administrative center of the North Slope Borough) is significantly more exposed than buildings in Wainwright and Kaktovik. Our findings show that flooding scenarios can complement information gained from past events and help to inform local-decision making.« less
    Free, publicly-accessible full text available March 18, 2021
  8. State-of-the-art deep learning technology has been successfully applied to relatively small selected areas of very high spatial resolution (0.15 and 0.25 m) optical aerial imagery acquired by a fixed-wing aircraft to automatically characterize ice-wedge polygons (IWPs) in the Arctic tundra. However, any mapping of IWPs at regional to continental scales requires images acquired on different sensor platforms (particularly satellite) and a refined understanding of the performance stability of the method across sensor platforms through reliable evaluation assessments. In this study, we examined the transferability of a deep learning Mask Region-Based Convolutional Neural Network (R-CNN) model for mapping IWPs in satellitemore »remote sensing imagery (~0.5 m) covering 272 km2 and unmanned aerial vehicle (UAV) (0.02 m) imagery covering 0.32 km2. Multi-spectral images were obtained from the WorldView-2 satellite sensor and pan-sharpened to ~0.5 m, and a 20 mp CMOS sensor camera onboard a UAV, respectively. The training dataset included 25,489 and 6022 manually delineated IWPs from satellite and fixed-wing aircraft aerial imagery near the Arctic Coastal Plain, northern Alaska. Quantitative assessments showed that individual IWPs were correctly detected at up to 72% and 70%, and delineated at up to 73% and 68% F1 score accuracy levels for satellite and UAV images, respectively. Expert-based qualitative assessments showed that IWPs were correctly detected at good (40–60%) and excellent (80–100%) accuracy levels for satellite and UAV images, respectively, and delineated at excellent (80–100%) level for both images. We found that (1) regardless of spatial resolution and spectral bands, the deep learning Mask R-CNN model effectively mapped IWPs in both remote sensing satellite and UAV images; (2) the model achieved a better accuracy in detection with finer image resolution, such as UAV imagery, yet a better accuracy in delineation with coarser image resolution, such as satellite imagery; (3) increasing the number of training data with different resolutions between the training and actual application imagery does not necessarily result in better performance of the Mask R-CNN in IWPs mapping; (4) and overall, the model underestimates the total number of IWPs particularly in terms of disjoint/incomplete IWPs.« less
    Free, publicly-accessible full text available April 1, 2021
  9. In the Alaskan Arctic, permafrost coastal systems are eroding at rates more than double those of the past. Rampant environmental change is putting new pressures on Arctic coastal dynamics, with the loss of landscapes, cultural heritage, infrastructure, and communities. Dr Benjamin Jones (Water and Environmental Research Center, Institute of Northern Engineering, University of Alaska Fairbanks) and Professor Hugues Lantuit (Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Germany) are working with scientists and stakeholders from across Arctic nations to develop the Permafrost Coastal Systems Network (PerCS-Net), a forum for reinvigorated research, knowledge integration, and management of environmental changemore »at the fringes of the Arctic.« less
    Free, publicly-accessible full text available November 7, 2020
  10. Free, publicly-accessible full text available February 1, 2021