Smartphone LIDAR can measure tree cavity dimensions for wildlife studies

Stitt, Jessica M.; Svancara, Leona K.; Vierling, L. A.; Vierling, Kerri T.

doi:10.1002/wsb.949

Cited by 8 publications

(6 citation statements)

References 29 publications

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“…There are some low-cost Lidar systems, but their accuracy does not match needs for solving this type of tasks. Considering that [ 40 , 41 , 42 ] some cars already use Lidar for autonomous driving [ 43 , 44 , 45 ], there is an intention that it will become cheaper and more affordable. In addition to the price and affordability of Lidar, another potential problem is the size of the dataset.…”

Section: Discussionmentioning

confidence: 99%

Automatic Roadside Feature Detection Based on Lidar Road Cross Section Images

Brkić

Miler

Ševrović

et al. 2022

Sensors

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The United Nations (UN) stated that all new roads and 75% of travel time on roads must be 3+ star standard by 2030. The number of stars is determined by the International Road Assessment Program (iRAP) star rating module. It is based on 64 attributes for each road. In this paper, a framework for highly accurate and fully automatic determination of two attributes is proposed: roadside severity-object and roadside severity-distance. The framework integrates mobile Lidar point clouds with deep learning-based object detection on road cross-section images. The You Only Look Once (YOLO) network was used for object detection. Lidar data were collected by vehicle-mounted mobile Lidar for all Croatian highways. Point clouds were collected in .las format and cropped to 10 m-long segments align vehicle path. To determine both attributes, it was necessary to detect the road with high accuracy, then roadside severity-distance was determined with respect to the edge of the detected road. Each segment is finally classified into one of 13 roadside severity object classes and one of four roadside severity-distance classes. The overall accuracy of the roadside severity-object classification is 85.1%, while for the distance attribute it is 85.6%. The best average precision is achieved for safety barrier concrete class (0.98), while the worst AP is achieved for rockface class (0.72).

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Section: Discussionmentioning

confidence: 99%

Automatic Roadside Feature Detection Based on Lidar Road Cross Section Images

Brkić

Miler

Ševrović

et al. 2022

Sensors

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“…Lidar data have been incorporated within wildlife or biodiversity applications across a wide variety of species, spatial scales, and with a diverse set of lidar technologies (Davies and Asner, 2014;Müller and Vierling, 2014;Olsoy et al, 2015;Stitt et al, 2019;Acebes et al, 2021;Smith et al, 2022;Shokirov et al, 2023). The potential benefit of GEDI data for wildlife applications is therefore exciting, and as with other remotely sensed products used for wildlife and biodiversity applications, it is important to understand how different accuracies and biases in spatial vegetation data could possibly affect wildlife model outcomes.…”

Section: Introductionmentioning

confidence: 99%

Women in remote sensing: 2022

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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Section: Introductionmentioning

confidence: 99%

Evaluating GEDI data fusions for continuous characterizations of forest wildlife habitat

Vogeler

Fekety

Elliott

et al. 2023

Front. Remote Sens.

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Continuous characterizations of forest structure are critical for modeling wildlife habitat as well as for assessing trade-offs with additional ecosystem services. To overcome the spatial and temporal limitations of airborne lidar data for studying wide-ranging animals and for monitoring wildlife habitat through time, novel sampling data sources, including the space-borne Global Ecosystem Dynamics Investigation (GEDI) lidar instrument, may be incorporated within data fusion frameworks to scale up satellite-based estimates of forest structure across continuous spatial extents. The objectives of this study were to: 1) investigate the value and limitations of satellite data sources for generating GEDI-fusion models and 30 m resolution predictive maps of eight forest structure measures across six western U.S. states (Colorado, Wyoming, Idaho, Oregon, Washington, and Montana); 2) evaluate the suitability of GEDI as a reference data source and assess any spatiotemporal biases of GEDI-fusion maps using samples of airborne lidar data; and 3) examine differences in GEDI-fusion products for inclusion within wildlife habitat models for three keystone woodpecker species with varying forest structure needs. We focused on two fusion models, one that combined Landsat, Sentinel-1 Synthetic Aperture Radar, disturbance, topographic, and bioclimatic predictor information (combined model), and one that was restricted to Landsat, topographic, and bioclimatic predictors (Landsat/topo/bio model). Model performance varied across the eight GEDI structure measures although all representing moderate to high predictive performance (model testing R2 values ranging from 0.36 to 0.76). Results were similar between fusion models, as well as for map validations for years of model creation (2019–2020) and hindcasted years (2016–2018). Within our wildlife case studies, modeling encounter rates of the three woodpecker species using GEDI-fusion inputs yielded AUC values ranging from 0.76–0.87 with observed relationships that followed our ecological understanding of the species. While our results show promise for the use of remote sensing data fusions for scaling up GEDI structure metrics of value for habitat modeling and other applications across broad continuous extents, further assessments are needed to test their performance within habitat modeling for additional species of conservation interest as well as biodiversity assessments.

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Smartphone LIDAR can measure tree cavity dimensions for wildlife studies

Cited by 8 publications

References 29 publications

Automatic Roadside Feature Detection Based on Lidar Road Cross Section Images

Automatic Roadside Feature Detection Based on Lidar Road Cross Section Images

Women in remote sensing: 2022

Evaluating GEDI data fusions for continuous characterizations of forest wildlife habitat

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