1
|
Horton KG, Buler JJ, Anderson SJ, Burt CS, Collins AC, Dokter AM, Guo F, Sheldon D, Tomaszewska MA, Henebry GM. Artificial light at night is a top predictor of bird migration stopover density. Nat Commun 2023; 14:7446. [PMID: 38049435 PMCID: PMC10696060 DOI: 10.1038/s41467-023-43046-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 10/30/2023] [Indexed: 12/06/2023] Open
Abstract
As billions of nocturnal avian migrants traverse North America, twice a year they must contend with landscape changes driven by natural and anthropogenic forces, including the rapid growth of the artificial glow of the night sky. While airspaces facilitate migrant passage, terrestrial landscapes serve as essential areas to restore energy reserves and often act as refugia-making it critical to holistically identify stopover locations and understand drivers of use. Here, we leverage over 10 million remote sensing observations to develop seasonal contiguous United States layers of bird migrant stopover density. In over 70% of our models, we identify skyglow as a highly influential and consistently positive predictor of bird migration stopover density across the United States. This finding points to the potential of an expanding threat to avian migrants: peri-urban illuminated areas may act as ecological traps at macroscales that increase the mortality of birds during migration.
Collapse
Affiliation(s)
- Kyle G Horton
- Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, Colorado, USA.
| | - Jeffrey J Buler
- Department of Entomology and Wildlife Ecology, University of Delaware, Newark, Delaware, USA
| | - Sharolyn J Anderson
- Natural Sounds and Night Skies Division, National Park Service, 1201 Oakridge Dr., Suite 100, Fort Collins, CO, 80525, USA
| | - Carolyn S Burt
- Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, Colorado, USA
| | - Amy C Collins
- Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, Colorado, USA
- Conservation Science Partners, Truckee, CA, USA
| | - Adriaan M Dokter
- Cornell Lab of Ornithology, Cornell University, Ithaca, New York, USA
| | - Fengyi Guo
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, USA
| | - Daniel Sheldon
- Manning College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, Massachusetts, USA
| | - Monika Anna Tomaszewska
- Center for Global Change and Earth Observations, Michigan State University, East Lansing, Michigan, USA
| | - Geoffrey M Henebry
- Center for Global Change and Earth Observations, Michigan State University, East Lansing, Michigan, USA
- Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, Michigan, USA
| |
Collapse
|
2
|
Xu C, Zhao D, Zheng Z, Zhao P, Chen J, Li X, Zhao X, Zhao Y, Liu W, Wu B, Zeng Y. Correction of UAV LiDAR-derived grassland canopy height based on scan angle. FRONTIERS IN PLANT SCIENCE 2023; 14:1108109. [PMID: 37021312 PMCID: PMC10067768 DOI: 10.3389/fpls.2023.1108109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 03/03/2023] [Indexed: 06/19/2023]
Abstract
Grassland canopy height is a crucial trait for indicating functional diversity or monitoring species diversity. Compared with traditional field sampling, light detection and ranging (LiDAR) provides new technology for mapping the regional grassland canopy height in a time-saving and cost-effective way. However, the grassland canopy height based on unmanned aerial vehicle (UAV) LiDAR is usually underestimated with height information loss due to the complex structure of grassland and the relatively small size of individual plants. We developed canopy height correction methods based on scan angle to improve the accuracy of height estimation by compensating the loss of grassland height. Our method established the relationships between scan angle and two height loss indicators (height loss and height loss ratio) using the ground-measured canopy height of sample plots with 1×1m and LiDAR-derived heigh. We found that the height loss ratio considering the plant own height had a better performance (R2 = 0.71). We further compared the relationships between scan angle and height loss ratio according to holistic (25-65cm) and segmented (25-40cm, 40-50cm and 50-65cm) height ranges, and applied to correct the estimated grassland canopy height, respectively. Our results showed that the accuracy of grassland height estimation based on UAV LiDAR was significantly improved with R2 from 0.23 to 0.68 for holistic correction and from 0.23 to 0.82 for segmented correction. We highlight the importance of considering the effects of scan angle in LiDAR data preprocessing for estimating grassland canopy height with high accuracy, which also help for monitoring height-related grassland structural and functional parameters by remote sensing.
Collapse
Affiliation(s)
- Cong Xu
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Dan Zhao
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhaoju Zheng
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
| | - Ping Zhao
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Junhua Chen
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xiuwen Li
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xueming Zhao
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yujin Zhao
- University of Chinese Academy of Sciences, Beijing, China
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, China
| | - Wenjun Liu
- School of Ecology and Environmental Science, Yunnan University, Kunming, Yunnan, China
| | - Bingfang Wu
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yuan Zeng
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| |
Collapse
|
3
|
Stevens TK, Hale AM, Williams DA. Environmental and anthropogenic variables influence the distribution of a habitat specialist (
Sylvilagus aquaticus
) in a large urban forest. CONSERVATION SCIENCE AND PRACTICE 2023. [DOI: 10.1111/csp2.12882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Affiliation(s)
- Thomas K. Stevens
- Department of Biology Texas Christian University Fort Worth Texas USA
| | - Amanda M. Hale
- Department of Biology Texas Christian University Fort Worth Texas USA
| | - Dean A. Williams
- Department of Biology Texas Christian University Fort Worth Texas USA
| |
Collapse
|
4
|
Kissling WD, Shi Y, Koma Z, Meijer C, Ku O, Nattino F, Seijmonsbergen AC, Grootes MW. Country-wide data of ecosystem structure from the third Dutch airborne laser scanning survey. Data Brief 2022; 46:108798. [PMID: 36569534 PMCID: PMC9772796 DOI: 10.1016/j.dib.2022.108798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 11/17/2022] [Accepted: 11/28/2022] [Indexed: 12/12/2022] Open
Abstract
The third Dutch national airborne laser scanning flight campaign (AHN3, Actueel Hoogtebestand Nederland) conducted between 2014 and 2019 during the leaf-off season (October-April) across the whole Netherlands provides a free and open-access, country-wide dataset with ∼700 billion points and a point density of ∼10(-20) points/m2. The AHN3 point cloud was obtained with Light Detection And Ranging (LiDAR) technology and contains for each point the x, y, z coordinates and additional characteristics (e.g. return number, intensity value, scan angle rank and GPS time). Moreover, the point cloud has been pre-processed by 'Rijkswaterstraat' (the executive agency of the Dutch Ministry of Infrastructure and Water Management), comes with a Digital Terrain Model (DTM) and a Digital Surface Model (DSM), and is delivered with a pre-classification of each point into one of six classes (0: Never Classified, 1: Unclassified, 2: Ground, 6: Building, 9: Water, 26: Reserved [bridges etc.]). However, no detailed information on vegetation structure is available from the AHN3 point cloud. We processed the AHN3 point cloud (∼16 TB uncompressed data volume) into 10 m resolution raster layers of ecosystem structure at a national extent, using a novel high-throughput workflow called 'Laserfarm' and a cluster of virtual machines with fast central processing units, high memory nodes and associated big data storage for managing the large amount of files. The raster layers (available as GeoTIFF files) capture 25 LiDAR metrics of vegetation structure, including ecosystem height (e.g. 95th percentiles of normalized z), ecosystem cover (e.g. pulse penetration ratio, canopy cover, and density of vegetation points within defined height layers), and ecosystem structural complexity (e.g. skewness and variability of vertical vegetation point distribution). The raster layers make use of the Dutch projected coordinate system (EPSG:28992 Amersfoort / RD New), are each ∼1 GB in size, and can be readily used by ecologists in a geographic information system (GIS) or analytical open-source software such as R and Python. Even though the class '1: Unclassified' mainly includes vegetation points, other objects such as cars, fences, and boats can also be present in this class, introducing potential biases in the derived data products. We therefore validated the raster layers of ecosystem structure using >180,000 hand-labelled LiDAR points in 100 randomly selected sample plots (10 m × 10 m each) across the Netherlands. Besides vegetation, objects such as boats, fences, and cars were identified in the sampled plots. However, the misclassification rate of vegetation points (i.e. non-vegetation points that were assumed to be vegetation) was low (∼0.05) and the accuracy of the 25 LiDAR metrics derived from the AHN3 point cloud was high (∼90%). To minimize existing inaccuracies in this country-wide data product (e.g. ships on water bodies, chimneys on roofs, or cars on roads that might be incorrectly used as vegetation points), we provide an additional mask that captures water bodies, buildings and roads generated from the Dutch cadaster dataset. This newly generated country-wide ecosystem structure data product provides new opportunities for ecology and biodiversity science, e.g. for mapping the 3D vegetation structure of a variety of ecosystems or for modelling biodiversity, species distributions, abundance and ecological niches of animals and their habitats.
Collapse
Affiliation(s)
- W. Daniel Kissling
- University of Amsterdam, Institute for Biodiversity and Ecosystem Dynamics (IBED), P.O. Box 94240, 1090 GE Amsterdam, The Netherlands,LifeWatch ERIC, Virtual Laboratory and Innovations Centre (VLIC), University of Amsterdam Faculty of Science, Science Park 904, 1098 XH Amsterdam,Corresponding author. @IBED_UvA
| | - Yifang Shi
- University of Amsterdam, Institute for Biodiversity and Ecosystem Dynamics (IBED), P.O. Box 94240, 1090 GE Amsterdam, The Netherlands,LifeWatch ERIC, Virtual Laboratory and Innovations Centre (VLIC), University of Amsterdam Faculty of Science, Science Park 904, 1098 XH Amsterdam
| | - Zsófia Koma
- University of Amsterdam, Institute for Biodiversity and Ecosystem Dynamics (IBED), P.O. Box 94240, 1090 GE Amsterdam, The Netherlands,Aarhus University, Department of Biology, Center for Sustainable Landscapes Under Global Change, Ny Munkegade 116, 8000 Aarhus C, Denmark
| | - Christiaan Meijer
- Netherlands eScience Center, Science Park 402 (Matrix III), 1098 XH Amsterdam, The Netherlands
| | - Ou Ku
- Netherlands eScience Center, Science Park 402 (Matrix III), 1098 XH Amsterdam, The Netherlands
| | - Francesco Nattino
- Netherlands eScience Center, Science Park 402 (Matrix III), 1098 XH Amsterdam, The Netherlands
| | - Arie C. Seijmonsbergen
- University of Amsterdam, Institute for Biodiversity and Ecosystem Dynamics (IBED), P.O. Box 94240, 1090 GE Amsterdam, The Netherlands
| | - Meiert W. Grootes
- Netherlands eScience Center, Science Park 402 (Matrix III), 1098 XH Amsterdam, The Netherlands
| |
Collapse
|
5
|
Kissling WD, Shi Y, Koma Z, Meijer C, Ku O, Nattino F, Seijmonsbergen AC, Grootes MW. Laserfarm – A high-throughput workflow for generating geospatial data products of ecosystem structure from airborne laser scanning point clouds. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
|
6
|
Kerry RG, Montalbo FJP, Das R, Patra S, Mahapatra GP, Maurya GK, Nayak V, Jena AB, Ukhurebor KE, Jena RC, Gouda S, Majhi S, Rout JR. An overview of remote monitoring methods in biodiversity conservation. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:80179-80221. [PMID: 36197618 PMCID: PMC9534007 DOI: 10.1007/s11356-022-23242-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 09/20/2022] [Indexed: 06/16/2023]
Abstract
Conservation of biodiversity is critical for the coexistence of humans and the sustenance of other living organisms within the ecosystem. Identification and prioritization of specific regions to be conserved are impossible without proper information about the sites. Advanced monitoring agencies like the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) had accredited that the sum total of species that are now threatened with extinction is higher than ever before in the past and are progressing toward extinct at an alarming rate. Besides this, the conceptualized global responses to these crises are still inadequate and entail drastic changes. Therefore, more sophisticated monitoring and conservation techniques are required which can simultaneously cover a larger surface area within a stipulated time frame and gather a large pool of data. Hence, this study is an overview of remote monitoring methods in biodiversity conservation via a survey of evidence-based reviews and related studies, wherein the description of the application of some technology for biodiversity conservation and monitoring is highlighted. Finally, the paper also describes various transformative smart technologies like artificial intelligence (AI) and/or machine learning algorithms for enhanced working efficiency of currently available techniques that will aid remote monitoring methods in biodiversity conservation.
Collapse
Affiliation(s)
- Rout George Kerry
- Department of Biotechnology, Utkal University, Vani Vihar, Bhubaneswar, Odisha 751004 India
| | | | - Rajeswari Das
- Department of Soil Science and Agricultural Chemistry, School of Agriculture, GIET University, Gunupur, Rayagada, Odisha 765022 India
| | - Sushmita Patra
- Indian Council of Agricultural Research-Directorate of Foot and Mouth Disease-International Centre for Foot and Mouth Disease, Arugul, Bhubaneswar, Odisha 752050 India
| | | | - Ganesh Kumar Maurya
- Zoology Section, Mahila MahaVidyalya, Banaras Hindu University, Varanasi, 221005 India
| | - Vinayak Nayak
- Indian Council of Agricultural Research-Directorate of Foot and Mouth Disease-International Centre for Foot and Mouth Disease, Arugul, Bhubaneswar, Odisha 752050 India
| | - Atala Bihari Jena
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115 USA
| | | | - Ram Chandra Jena
- Department of Pharmaceutical Sciences, Utkal University, Vani Vihar, Bhubaneswar, Odisha 751004 India
| | - Sushanto Gouda
- Department of Zoology, Mizoram University, Aizawl, 796009 India
| | - Sanatan Majhi
- Department of Biotechnology, Utkal University, Vani Vihar, Bhubaneswar, Odisha 751004 India
| | - Jyoti Ranjan Rout
- School of Biological Sciences, AIPH University, Bhubaneswar, Odisha 752101 India
| |
Collapse
|
7
|
Moudrý V, Cord AF, Gábor L, Laurin GV, Barták V, Gdulová K, Malavasi M, Rocchini D, Stereńczak K, Prošek J, Klápště P, Wild J. Vegetation structure derived from airborne laser scanning to assess species distribution and habitat suitability: The way forward. DIVERS DISTRIB 2022. [DOI: 10.1111/ddi.13644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Affiliation(s)
- Vítězslav Moudrý
- Department of Spatial Sciences, Faculty of Environmental Sciences Czech University of Life Sciences Prague Praha‐Suchdol Czech Republic
- Institute for Environmental Studies, Faculty of Science Charles University Prague 2 Czech Republic
- Institute of Botany of the Czech Academy of Sciences Průhonice Czech Republic
| | - Anna F. Cord
- Chair of Computational Landscape Ecology, Institute of Geography Technische Universität Dresden Dresden Germany
| | - Lukáš Gábor
- Department of Spatial Sciences, Faculty of Environmental Sciences Czech University of Life Sciences Prague Praha‐Suchdol Czech Republic
- Department of Ecology and Evolutionary Biology Yale University New Haven Connecticut USA
- Center for Biodiversity and Global Change Yale University New Haven Connecticut USA
| | - Gaia Vaglio Laurin
- Department for Innovation in Biological, Agro‐Food and Forest Systems University of Tuscia Viterbo Italy
| | - Vojtěch Barták
- Department of Spatial Sciences, Faculty of Environmental Sciences Czech University of Life Sciences Prague Praha‐Suchdol Czech Republic
| | - Kateřina Gdulová
- Department of Spatial Sciences, Faculty of Environmental Sciences Czech University of Life Sciences Prague Praha‐Suchdol Czech Republic
| | - Marco Malavasi
- Department of Spatial Sciences, Faculty of Environmental Sciences Czech University of Life Sciences Prague Praha‐Suchdol Czech Republic
- Department of Chemistry, Physics, Mathematics and Natural Sciences University of Sassari Sassari Italy
| | - Duccio Rocchini
- Department of Spatial Sciences, Faculty of Environmental Sciences Czech University of Life Sciences Prague Praha‐Suchdol Czech Republic
- BIOME Lab, Department of Biological, Geological and Environmental Sciences Alma Mater Studiorum University of Bologna Bologna Italy
| | | | - Jiří Prošek
- Department of Spatial Sciences, Faculty of Environmental Sciences Czech University of Life Sciences Prague Praha‐Suchdol Czech Republic
- Institute of Botany of the Czech Academy of Sciences Průhonice Czech Republic
| | - Petr Klápště
- Department of Spatial Sciences, Faculty of Environmental Sciences Czech University of Life Sciences Prague Praha‐Suchdol Czech Republic
| | - Jan Wild
- Department of Spatial Sciences, Faculty of Environmental Sciences Czech University of Life Sciences Prague Praha‐Suchdol Czech Republic
- Institute of Botany of the Czech Academy of Sciences Průhonice Czech Republic
| |
Collapse
|
8
|
Gábor L, Jetz W, Lu M, Rocchini D, Cord A, Malavasi M, Zarzo‐Arias A, Barták V, Moudrý V. Positional errors in species distribution modelling are not overcome by the coarser grains of analysis. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Lukáš Gábor
- Department of Spatial Sciences, Faculty of Environmental Sciences Czech University of Life Sciences Prague Praha – Suchdol Czech Republic
- Department of Ecology and Evolutionary Biology Yale University New Haven Connecticut USA
- Center for Biodiversity and Global Change Yale University New Haven Connecticut USA
| | - Walter Jetz
- Department of Ecology and Evolutionary Biology Yale University New Haven Connecticut USA
- Center for Biodiversity and Global Change Yale University New Haven Connecticut USA
| | - Muyang Lu
- Department of Ecology and Evolutionary Biology Yale University New Haven Connecticut USA
- Center for Biodiversity and Global Change Yale University New Haven Connecticut USA
| | - Duccio Rocchini
- Department of Spatial Sciences, Faculty of Environmental Sciences Czech University of Life Sciences Prague Praha – Suchdol Czech Republic
- BIOME Lab, Department of Biological, Geological and Environmental Sciences Alma Mater Studiorum University of Bologna Bologna Italy
| | - Anna Cord
- Institute of Geography Technische Universität Dresden Dresden Germany
| | - Marco Malavasi
- Department of Spatial Sciences, Faculty of Environmental Sciences Czech University of Life Sciences Prague Praha – Suchdol Czech Republic
| | - Alejandra Zarzo‐Arias
- Department of Spatial Sciences, Faculty of Environmental Sciences Czech University of Life Sciences Prague Praha – Suchdol Czech Republic
- Universidad de Oviedo Oviedo Asturias Spain
- Department of Biogeography and Global Change Museo Nacional de Ciencias Naturales (MNCN‐CSIC) Madrid Spain
| | - Vojtěch Barták
- Department of Spatial Sciences, Faculty of Environmental Sciences Czech University of Life Sciences Prague Praha – Suchdol Czech Republic
| | - Vítězslav Moudrý
- Department of Spatial Sciences, Faculty of Environmental Sciences Czech University of Life Sciences Prague Praha – Suchdol Czech Republic
| |
Collapse
|
9
|
UAV-Borne Imagery Can Supplement Airborne Lidar in the Precise Description of Dynamically Changing Shrubland Woody Vegetation. REMOTE SENSING 2022. [DOI: 10.3390/rs14092287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Airborne laser scanning (ALS) is increasingly used for detailed vegetation structure mapping; however, there are many local-scale applications where it is economically ineffective or unfeasible from the temporal perspective. Unmanned aerial vehicles (UAVs) or airborne imagery (AImg) appear to be promising alternatives, but only a few studies have examined this assumption outside economically exploited areas (forests, orchards, etc.). The main aim of this study was to compare the usability of normalized digital surface models (nDSMs) photogrammetrically derived from UAV-borne and airborne imagery to those derived from low- (1–2 pts/m2) and high-density (ca. 20 pts/m2) ALS-scanning for the precise local-scale modelling of woody vegetation structures (the number and height of trees/shrubs) across six dynamically changing shrubland sites. The success of the detection of woody plant tops was initially almost 100% for UAV-based models; however, deeper analysis revealed that this was due to the fact that omission and commission errors were approximately equal and the real accuracy was approx. 70% for UAV-based models compared to 95.8% for the high-density ALS model. The percentage mean absolute errors (%MAE) of shrub/tree heights derived from UAV data ranged between 12.2 and 23.7%, and AImg height accuracy was relatively lower (%MAE: 21.4–47.4). Combining UAV-borne or AImg-based digital surface models (DSM) with ALS-based digital terrain models (DTMs) significantly improved the nDSM height accuracy (%MAE: 9.4–13.5 and 12.2–25.0, respectively) but failed to significantly improve the detection of the number of individual shrubs/trees. The height accuracy and detection success using low- or high-density ALS did not differ. Therefore, we conclude that UAV-borne imagery has the potential to replace custom ALS in specific local-scale applications, especially at dynamically changing sites where repeated ALS is costly, and the combination of such data with (albeit outdated and sparse) ALS-based digital terrain models can further improve the success of the use of such data.
Collapse
|
10
|
Koma Z, Seijmonsbergen AC, Grootes MW, Nattino F, Groot J, Sierdsema H, Foppen RPB, Kissling D. Better together? Assessing different remote sensing products for predicting habitat suitability of wetland birds. DIVERS DISTRIB 2022. [DOI: 10.1111/ddi.13468] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Affiliation(s)
- Zsófia Koma
- Institute for Biodiversity and Ecosystem Dynamics (IBED) University of Amsterdam Amsterdam The Netherlands
- Department of Biology Center for Sustainable Landscapes Under Global Change Aarhus University Aarhus Denmark
| | - Arie C. Seijmonsbergen
- Institute for Biodiversity and Ecosystem Dynamics (IBED) University of Amsterdam Amsterdam The Netherlands
| | | | | | - Jim Groot
- Institute for Biodiversity and Ecosystem Dynamics (IBED) University of Amsterdam Amsterdam The Netherlands
| | - Henk Sierdsema
- Sovon Dutch Centre for Field Ornithology Nijmegen The Netherlands
| | - Ruud P. B. Foppen
- Sovon Dutch Centre for Field Ornithology Nijmegen The Netherlands
- Department of Animal Ecology and Ecophysiology Institute for Water and Wetland Research Radboud University Nijmegen The Netherlands
| | - Daniel Kissling
- Institute for Biodiversity and Ecosystem Dynamics (IBED) University of Amsterdam Amsterdam The Netherlands
- LifeWatch Virtual Laboratory Innovation Center (VLIC)LifeWatch ERIC Amsterdam The Netherlands
| |
Collapse
|
11
|
Berezowski V, Moffat I, Shendryk Y, MacGregor D, Ellis J, Mallett X. A multidisciplinary approach to locating clandestine gravesites in cold cases: Combining geographic profiling, LiDAR, and near surface geophysics. Forensic Sci Int Synerg 2022; 5:100281. [PMID: 35966608 PMCID: PMC9372742 DOI: 10.1016/j.fsisyn.2022.100281] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 07/08/2022] [Accepted: 08/02/2022] [Indexed: 11/03/2022]
Abstract
By nature, clandestine burials are difficult to locate, an issue that can complicate the legal process, and interrupt the natural grief process of the family. The purpose of this paper is to present a three-step process to search for clandestine graves using (1) geographic profiling, (2) light detection and ranging (LiDAR), and (3) near surface geophysics. Each process incrementally decreases the geographic area being searched, while increasing the level of detail provided to investigators. Using two well-known Australian cases and one experimental study, this paper will demonstrate how (1) can highlight potential search areas, (2) can further narrow down the location of potential burial sites within these search areas, and (3) can assist with locating the clandestine grave. Although each technique on its own can successfully locate graves, combining the techniques can provide the most efficient approach to locate those who are missing and buried.
Collapse
|
12
|
Detectability of the Critically Endangered Araucaria angustifolia Tree Using Worldview-2 Images, Google Earth Engine and UAV-LiDAR. LAND 2021. [DOI: 10.3390/land10121316] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The Brazilian Atlantic Forest is a global biodiversity hotspot and has been extensively mapped using satellite remote sensing. However, past mapping focused on overall forest cover without consideration of keystone plant resources such as Araucaria angustifolia. A. angustifolia is a critically endangered coniferous tree that is essential for supporting overall biodiversity in the Atlantic Forest. A. angustifolia’s distribution has declined dramatically because of overexploitation and land-use changes. Accurate detection and rapid assessments of the distribution and abundance of this species are urgently needed. We compared two approaches for mapping Araucaria angustifolia across two scales (stand vs. individual tree) at three study sites in Brazil. The first approach used Worldview-2 images and Random Forest in Google Earth Engine to detect A. angustifolia at the stand level, with an accuracy of >90% across all three study sites. The second approach relied on object identification using UAV-LiDAR and successfully mapped individual trees (producer’s/user’s accuracy = 94%/64%) at one study site. Both approaches can be employed in tandem to map remaining stands and to determine the exact location of A. angustifolia trees. Each approach has its own strengths and weaknesses, and we discuss their adoptability by managers to inform conservation of A. angustifolia.
Collapse
|
13
|
Disentangling LiDAR Contribution in Modelling Species–Habitat Structure Relationships in Terrestrial Ecosystems Worldwide. A Systematic Review and Future Directions. REMOTE SENSING 2021. [DOI: 10.3390/rs13173447] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Global biodiversity is threatened by unprecedented and increasing anthropogenic pressures, including habitat loss and fragmentation. LiDAR can become a decisive technology by providing accurate information about the linkages between biodiversity and ecosystem structure. Here, we review the current use of LiDAR metrics in ecological studies regarding birds, mammals, reptiles, amphibians, invertebrates, bryophytes, lichens, and fungi (BLF). We quantify the types of research (ecosystem and LiDAR sources) and describe the LiDAR platforms and data that are currently available. We also categorize and harmonize LiDAR metrics into five LiDAR morphological traits (canopy cover, height and vertical distribution, understory and shrubland, and topographic traits) and quantify their current use and effectiveness across taxonomic groups and ecosystems. The literature review returned 173 papers that met our criteria. Europe and North America held most of the studies, and birds were the most studied group, whereas temperate forest was by far the most represented ecosystem. Globally, canopy height was the most used LiDAR trait, especially in forest ecosystems, whereas canopy cover and terrain topography traits performed better in those ecosystems where they were mapped. Understory structure and shrubland traits together with terrain topography showed high effectiveness for less studied groups such as BLF and invertebrates and in open landscapes. Our results show how LiDAR technology has greatly contributed to habitat mapping, including organisms poorly studied until recently, such as BLF. Finally, we discuss the forthcoming opportunities for biodiversity mapping with different LiDAR platforms in combination with spectral information. We advocate (i) for the integration of spaceborne LiDAR data with the already available airborne (airplane, drones) and terrestrial technology, and (ii) the coupling of it with multispectral/hyperspectral information, which will allow for the exploration and analyses of new species and ecosystems.
Collapse
|
14
|
Kong F, Wang D, Yin H, Dronova I, Fei F, Chen J, Pu Y, Li M. Coupling urban 3-D information and circuit theory to advance the development of urban ecological networks. CONSERVATION BIOLOGY : THE JOURNAL OF THE SOCIETY FOR CONSERVATION BIOLOGY 2021; 35:1140-1150. [PMID: 33477199 DOI: 10.1111/cobi.13682] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 09/14/2020] [Accepted: 10/01/2020] [Indexed: 06/12/2023]
Abstract
Ongoing, rapid urban growth accompanied by habitat fragmentation and loss challenges biodiversity conservation and leads to decreases in ecosystem services. Application of the concept of ecological networks in the preservation and restoration of connections among isolated patches of natural areas is a powerful conservation strategy. However, previous approaches often failed to objectively consider the impacts of complex 3-D city environments on ecological niches. We used airborne lidar-derived information on the 3-D structure of the built environment and vegetation and detailed land use and cover data to characterize habitat quality, niche diversity, and human disturbance and to predict habitat connectivity among 38 identified habitat core areas (HCAs) in Nanjing, China. We used circuit theory and Linkage Mapper to create a landscape resistance layer, simulate habitat connectivity, and identify and prioritize important corridors. We mapped 64 links by using current flow centrality to evaluate each HCA's contribution and the links that facilitate intact connectivity. Values were highest for HCA links located in the west, south, and northeast of the study area, where natural forests with complex 3-D structures predominate. Two smaller HCA areas had high centrality scores relative to their extents, which means they could act as important stepping stones in connectivity planning. The mapped pinch-point regions had narrow and fragile links among the HCAs, suggesting they require special protection. The barriers with the highest impact scores were mainly located at the HCA connections to Purple Mountain and, based on these high scores, are more likely to indicate important locations that can be restored to improve potential connections. Our novel framework allowed us to sufficiently convey spatially explicit information to identify targets for habitat restoration and potential pathways for species movement and dispersal. Such information is critical for assessing existing or potential habitats and corridors and developing strategic plans to balance habitat conservation and other land uses based on scientifically informed connectivity planning and implementation.
Collapse
Affiliation(s)
- Fanhua Kong
- School of Geography and Ocean Science, Nanjing University, Xianlin Avenue 163, Nanjing, 210023, China
| | - Ding Wang
- School of Geography and Ocean Science, Nanjing University, Xianlin Avenue 163, Nanjing, 210023, China
| | - Haiwei Yin
- School of Architecture and Urban Planning, Nanjing University, No. 22, Hankou Road, Nanjing, 210093, China
| | - Iryna Dronova
- Department of Landscape Architecture and Environmental Planning, University of California at Berkeley, Berkeley, CA, 94720, U.S.A
| | - Fan Fei
- School of Architecture and Urban Planning, Nanjing University, No. 22, Hankou Road, Nanjing, 210093, China
| | - Jiayu Chen
- School of Geography and Ocean Science, Nanjing University, Xianlin Avenue 163, Nanjing, 210023, China
| | - Yingxia Pu
- School of Geography and Ocean Science, Nanjing University, Xianlin Avenue 163, Nanjing, 210023, China
| | - Manchun Li
- Jiangsu Provincial Key Laboratory of Geographic Information Science & Technology,School of Geography and Ocean Science, Nanjing University, Xianlin Avenue 163, Nanjing, 210023, China
| |
Collapse
|
15
|
Fricker GA, Crampton LH, Gallerani EM, Hite JM, Inman R, Gillespie TW. Application of lidar for critical endangered bird species conservation on the island of Kauai, Hawaii. Ecosphere 2021. [DOI: 10.1002/ecs2.3554] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Affiliation(s)
- Geoffrey A. Fricker
- Social Sciences Department California Polytechnic University, San Luis Obispo Building 47‐13 San Luis Obispo California93407USA
- Department of Geography University of California Los Angeles 1255 Bunche HallBox 951524 Los Angeles California90095USA
- School of Geographical Sciences and Urban Planning Arizona State University PO Box 875302 Tempe Arizona85287USA
| | - Lisa H. Crampton
- Kaua‘i Forest Bird Recovery Project Pacific Cooperative Studies Unit PO Box 27 Hanapepe Hawaii96716USA
| | - Erica M. Gallerani
- Kaua‘i Forest Bird Recovery Project Pacific Cooperative Studies Unit PO Box 27 Hanapepe Hawaii96716USA
| | - Justin M. Hite
- Kaua‘i Forest Bird Recovery Project Pacific Cooperative Studies Unit PO Box 27 Hanapepe Hawaii96716USA
| | - Richard Inman
- School of Geographical Sciences and Urban Planning Arizona State University PO Box 875302 Tempe Arizona85287USA
| | - Thomas W. Gillespie
- Department of Geography University of California Los Angeles 1255 Bunche HallBox 951524 Los Angeles California90095USA
| |
Collapse
|
16
|
Vries JPR, Koma Z, WallisDeVries MF, Kissling WD. Identifying fine‐scale habitat preferences of threatened butterflies using airborne laser scanning. DIVERS DISTRIB 2021. [DOI: 10.1111/ddi.13272] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Affiliation(s)
- Jan Peter Reinier Vries
- Institute of Biodiversity and Ecosystem Dynamics (IBED) University of Amsterdam Amsterdam The Netherlands
| | - Zsófia Koma
- Institute of Biodiversity and Ecosystem Dynamics (IBED) University of Amsterdam Amsterdam The Netherlands
| | - Michiel F. WallisDeVries
- De Vlinderstichting/Dutch Butterfly Conservation Wageningen The Netherlands
- Plant Ecology and Nature Conservation Group Wageningen University Wageningen The Netherlands
| | - W. Daniel Kissling
- Institute of Biodiversity and Ecosystem Dynamics (IBED) University of Amsterdam Amsterdam The Netherlands
| |
Collapse
|
17
|
Assessing the Performance of ICESat-2/ATLAS Multi-Channel Photon Data for Estimating Ground Topography in Forested Terrain. REMOTE SENSING 2020. [DOI: 10.3390/rs12132084] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
As a continuation of Ice, Cloud, and Land Elevation Satellite-1 (ICESat-1), the ICESat-2/Advanced Topographic Laser Altimeter System (ATLAS) employs a micro-pulse multi-beam photon counting approach to produce photon data for measuring global terrain. Few studies have assessed the accuracy of different ATLAS channels in retrieving ground topography in forested terrain. This study aims to assess the accuracy of measuring ground topography in forested terrain using different ATLAS channels and the correlation between laser intensity parameters, laser pointing angle parameters, and elevation error. The accuracy of ground topography measured by the ATLAS footprints is evaluated by comparing the derived Digital Terrain Model (DTM) from the ATL03 (Global Geolocated Photon Data) and ATL08 (Land and Vegetation Height) products with that from the airborne Light Detection And Ranging (LiDAR). Results show that the ATLAS product performed well in the study area at all laser intensities and laser pointing angles, and correlations were found between the ATLAS DTM and airborne LiDAR DTM (coefficient of determination––R2 = 1.00, root mean squared error––RMSE = 0.75 m). Considering different laser intensities, there is a significant correlation between the tx_pulse_energy parameter and elevation error. With different laser pointing angles, there is no significant correlation between the tx_pulse_skew_est, tx_pulse_width_lower, tx_pulse_width_upper parameters and the elevation error.
Collapse
|
18
|
Valbuena R, O'Connor B, Zellweger F, Simonson W, Vihervaara P, Maltamo M, Silva CA, Almeida DRA, Danks F, Morsdorf F, Chirici G, Lucas R, Coomes DA, Coops NC. Standardizing Ecosystem Morphological Traits from 3D Information Sources. Trends Ecol Evol 2020; 35:656-667. [PMID: 32423635 DOI: 10.1016/j.tree.2020.03.006] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Revised: 02/28/2020] [Accepted: 03/09/2020] [Indexed: 12/31/2022]
Abstract
3D-imaging technologies provide measurements of terrestrial and aquatic ecosystems' structure, key for biodiversity studies. However, the practical use of these observations globally faces practical challenges. First, available 3D data are geographically biased, with significant gaps in the tropics. Second, no data source provides, by itself, global coverage at a suitable temporal recurrence. Thus, global monitoring initiatives, such as assessment of essential biodiversity variables (EBVs), will necessarily have to involve the combination of disparate data sets. We propose a standardized framework of ecosystem morphological traits - height, cover, and structural complexity - that could enable monitoring of globally consistent EBVs at regional scales, by flexibly integrating different information sources - satellites, aircrafts, drones, or ground data - allowing global biodiversity targets relating to ecosystem structure to be monitored and regularly reported.
Collapse
Affiliation(s)
- R Valbuena
- United Nations Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), 219 Huntington Road, CB3 0DL Cambridge, UK; Department of Plant Sciences in the Conservation Research Institute, University of Cambridge, Downing Street, CB2 3EA Cambridge, UK; School of Natural Sciences, Bangor University, Thoday Building, Bangor LL57 2UW, UK.
| | - B O'Connor
- United Nations Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), 219 Huntington Road, CB3 0DL Cambridge, UK
| | - F Zellweger
- Department of Plant Sciences in the Conservation Research Institute, University of Cambridge, Downing Street, CB2 3EA Cambridge, UK; Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903 Birmensdorf, Switzerland
| | - W Simonson
- United Nations Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), 219 Huntington Road, CB3 0DL Cambridge, UK
| | - P Vihervaara
- Biodiversity Centre, Finnish Environment Institute (SYKE), Latokartanonkaari 11, 00790 Helsinki, Finland
| | - M Maltamo
- Faculty of Forest Sciences, University of Eastern Finland, PO Box 111, Joensuu, Finland
| | - C A Silva
- Department of Geographical Sciences, University of Maryland, College Park, MD, USA; School of Forest Resources and Conservation, University of Florida, Gainesville, FL, USA
| | - D R A Almeida
- Department of Forest Sciences, 'Luiz de Queiroz' College of Agriculture (USP/ESALQ), University of São Paulo, Piracicaba, SP, Brazil
| | - F Danks
- United Nations Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), 219 Huntington Road, CB3 0DL Cambridge, UK
| | - F Morsdorf
- Remote Sensing Laboratories, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
| | - G Chirici
- Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università degli Studi di Firenze, via San Bonaventura 13, 50145 Florence, Italy
| | - R Lucas
- Earth Observation and Ecosystem Dynamics Research Group, Aberystwyth University, Aberystwyth SY23 3DB, UK
| | - D A Coomes
- Department of Plant Sciences in the Conservation Research Institute, University of Cambridge, Downing Street, CB2 3EA Cambridge, UK
| | - N C Coops
- Department of Forest Resource Management, University of British Columbia, 2424 Main Mall, Vancouver V6T 1Z4, Canada
| |
Collapse
|