Cited by 126

The DOI matches its Crossref record.

Standardizing Ecosystem Morphological Traits from 3D Information Sources

Valbuena, R., O’Connor, B., Zellweger, F., Simonson, W., Vihervaara, P., Maltamo, M., Silva, C.A., Almeida, D.R.A., Danks, F., Morsdorf, F., Chirici, G., Lucas, R., Coomes, D.A., Coops, N.C.·2020·Trends in Ecology & Evolution

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.tree.2020.03.006

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 68

  1. Wulder, 2014, Satellites: make Earth observations open access, Nature, 513, 30, 10.1038/513030a · DOI
  2. Dubayah, 2020, The global ecosystem dynamics investigation: high-resolution laser ranging of the Earth’s forests and topography, Sci. Remote Sens., 1, 100002, 10.1016/j.srs.2020.100002 · DOI · Paper page
  3. Bae, 2019, Radar vision in the mapping of forest biodiversity from space, Nat. Comm., 10, 4757, 10.1038/s41467-019-12737-x · DOI
  4. Tang, 2019, Characterizing global forest canopy cover distribution using spaceborne lidar, Remote Sens. Environ., 231, 111262, 10.1016/j.rse.2019.111262 · DOI
  5. Vihervaara, 2017, How essential biodiversity variables and remote sensing can help national biodiversity monitoring, Global Ecol. Conserv., 10, 43, 10.1016/j.gecco.2017.01.007 · DOI
  6. Kissling, 2018, Towards global data products of essential biodiversity variables on species traits, Nat. Ecol. Evol., 2, 1531, 10.1038/s41559-018-0667-3 · DOI
  7. Navarro, 2017, Monitoring biodiversity change through effective global coordination, Curr. Opin. Environ. Sust., 29, 158, 10.1016/j.cosust.2018.02.005 · DOI
  8. Jetz, 2019, Essential biodiversity variables for mapping and monitoring species populations, Nat. Ecol. Evol., 3, 539, 10.1038/s41559-019-0826-1 · DOI
  9. O'Connor, 2015, Earth observation as a tool for tracking progress towards the Aichi biodiversity targets, Remote Sens. Ecol. Conserv., 1, 19, 10.1002/rse2.4 · DOI
  10. Skidmore, 2015, Environmental science: agree on biodiversity metrics to track from space, Nature, 523, 403, 10.1038/523403a · DOI
  11. Dantas de Paula, 2020, Combining European earth observation products with dynamic global vegetation models for estimating essential biodiversity variables, Int. J. Digit. Earth, 13, 262, 10.1080/17538947.2019.1597187 · DOI
  12. Simonson, 2014, Applications of airborne lidar for the assessment of animal species diversity, Methods Ecol. Evol., 5, 719, 10.1111/2041-210X.12219 · DOI
  13. Schneider, 2017, Mapping functional diversity from remotely sensed morphological and physiological forest traits, Nat. Comm., 8, 1441, 10.1038/s41467-017-01530-3 · DOI
  14. Almeida, 2019, Persistent effects of fragmentation on tropical rainforest canopy structure after 20 yr of isolation, Ecol. Appl., 29, 10.1002/eap.1952 · DOI
  15. Asner, 2017, Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation, Science, 355, 385, 10.1126/science.aaj1987 · DOI
  16. Calders, 2019, 3D imaging insights into forests and coral reefs, Trends Ecol. Evol., 35, 6, 10.1016/j.tree.2019.10.004 · DOI
  17. Bakx, 2019, Use and categorization of light detection and ranging vegetation metrics in avian diversity and species distribution research, Divers. Distrib., 25, 1045, 10.1111/ddi.12915 · DOI
  18. Beland, 2019, On promoting the use of lidar systems in forest ecosystem research, For. Ecol. Manag., 450, 117484, 10.1016/j.foreco.2019.117484 · DOI
  19. Hancock, 2019, The GEDI Simulator: a large-footprint waveform Lidar simulator for calibration and validation of spaceborne missions, Earth Space Sci., 6, 294, 10.1029/2018EA000506 · DOI
  20. Nelson, 2017, Lidar-based estimates of aboveground biomass in the continental US and Mexico using ground, airborne, and satellite observations, Remote Sens. Environ., 188, 127, 10.1016/j.rse.2016.10.038 · DOI
  21. Meyer, 2015, Global priorities for an effective information basis of biodiversity distributions, Nat. Comm., 6, 8221, 10.1038/ncomms9221 · DOI
  22. Kellner, 2019, New opportunities for forest remote sensing through ultra-high-density drone lidar, Surv. Geophys., 40, 959, 10.1007/s10712-019-09529-9 · DOI
  23. Almeida, 2019, Monitoring the structure of forest restoration plantations with a drone-lidar system, Int. J. Appl. Earth Obs., 6379, 192
  24. Geijzendorffer, 2017, How can global conventions for biodiversity and ecosystem services guide local conservation actions?, Curr. Opin. Environ. Sust., 29, 145, 10.1016/j.cosust.2017.12.011 · DOI
  25. Duvall, 2019, Collapsing complexity: quantifying multiscale properties of reef topography, J. Geophys. Res. Oceans, 124, 5021, 10.1029/2018JC014859 · DOI
  26. Fahey, 2019, Defining a spectrum of integrative trait-based vegetation canopy structural types, Ecol. Lett., 22, 2049, 10.1111/ele.13388 · DOI
  27. Gallagher, 2020, Open science principles for accelerating trait-based science across the Tree of Life, Nat. Ecol. Evol., 4, 294, 10.1038/s41559-020-1109-6 · DOI
  28. Guo, 2017, Regional mapping of vegetation structure for biodiversity monitoring using airborne lidar data, Ecol. Infor., 38, 50, 10.1016/j.ecoinf.2017.01.005 · DOI
  29. Gough, 2019, High rates of primary production in structurally complex forests, Ecology, 100, 10.1002/ecy.2864 · DOI
  30. Milanesi, 2017, Three-dimensional habitat structure and landscape genetics: a step forward in estimating functional connectivity, Ecology, 98, 393, 10.1002/ecy.1645 · DOI
  31. Gwenzi, 2014, Modeling canopy height in a savanna ecosystem using spaceborne lidar waveforms, Remote Sens. Environ., 154, 338, 10.1016/j.rse.2013.11.024 · DOI
  32. Lopatin, 2016, Comparing generalized linear models and random forest to model vascular plant species richness using LiDAR data in a natural forest in central Chile, Remote Sens. Environ., 173, 200, 10.1016/j.rse.2015.11.029 · DOI
  33. Marselis, 2018, Distinguishing vegetation types with airborne waveform Lidar data in a tropical forest-savanna mosaic: a case study in Lopé National Park, Gabon, Remote Sens. Environ., 216, 626, 10.1016/j.rse.2018.07.023 · DOI
  34. Valbuena, 2017, Key structural features of boreal forests may be detected directly using L-moments from airborne lidar data, Remote Sens. Environ., 194, 437, 10.1016/j.rse.2016.10.024 · DOI
  35. Zellweger, 2016, Environmental predictors of species richness in forest landscapes: abiotic factors versus vegetation structure, J. Biogeogr., 43, 1080, 10.1111/jbi.12696 · DOI
  36. Sankey, 2015, Remote sensing of Sonoran Desert vegetation structure and phenology with ground-based LiDAR, Remote Sens.-Basel, 7, 342, 10.3390/rs70100342 · DOI
  37. Coops, 2016, A forest structure habitat index based on airborne laser scanning data, Ecol. Ind., 67, 346, 10.1016/j.ecolind.2016.02.057 · DOI
  38. Asner, 2014, Mapping tropical forest carbon: calibrating plot estimates to a simple LiDAR metric, Remote Sens. Environ., 140, 614, 10.1016/j.rse.2013.09.023 · DOI
  39. Adnan, 2019, A simple approach to forest structure classification using airborne laser scanning that can be adopted across bioregions, For. Ecol. Manag., 43, 111, 10.1016/j.foreco.2018.10.057 · DOI
  40. Qi, 2016, Combining tandem-X InSAR and simulated GEDI lidar observations for forest structure mapping, Remote Sens. Environ., 187, 253, 10.1016/j.rse.2016.10.018 · DOI
  41. Disney, 2019, Innovations in ground and airborne technologies as reference and for training and validation: terrestrial laser scanning (TLS), Surv. Geophys., 40, 937, 10.1007/s10712-019-09527-x · DOI
  42. Bush, 2017, Connecting Earth observation to high-throughput biodiversity data, Nat. Ecol. Evol., 1, 0176, 10.1038/s41559-017-0176 · DOI
  43. Vihervaara, 2015, How to integrate remotely sensed data and biodiversity for ecosystem assessments at landscape scale, Landscape Ecol., 30, 501, 10.1007/s10980-014-0137-5 · DOI
  44. Silva, 2018, Comparison of small- and large-footprint lidar characterization of tropical forest aboveground structure and biomass: a case study from Central Gabon, IEEE J. Sel. Top. Appl., 11, 3512
  45. Zhao, 2018, Utility of multitemporal lidar for forest and carbon monitoring: tree growth, biomass dynamics, and carbon flux, Remote Sens. Environ., 204, 883, 10.1016/j.rse.2017.09.007 · DOI
  46. Cao, 2016, Estimation of forest biomass dynamics in subtropical forests using multi-temporal airborne LiDAR data, Remote Sens. Environ., 178, 158, 10.1016/j.rse.2016.03.012 · DOI
  47. Greaves, 2015, Estimating aboveground biomass and leaf area of low-stature Arctic shrubs with terrestrial LiDAR, Remote Sens. Environ., 164, 26, 10.1016/j.rse.2015.02.023 · DOI
  48. Thers, 2017, Lidar-derived variables as a proxy for fungal species richness and composition in temperate Northern Europe, Remote Sens. Environ., 200, 102, 10.1016/j.rse.2017.08.011 · DOI
  49. Moeslund, 2019, Light detection and ranging explains diversity of plants, fungi, lichens, and bryophytes across multiple habitats and large geographic extent, Ecol. Appl., 29, 10.1002/eap.1907 · DOI
  50. Davies, 2017, Canopy structure drives orangutan habitat selection in disturbed Bornean forests, Proc. Natl. Acad. Sci. U. S. A., 114, 8307, 10.1073/pnas.1706780114 · DOI
  51. Ferrari, 2018, Habitat structural complexity metrics improve predictions of fish abundance and distribution, Ecography, 41, 1077, 10.1111/ecog.02580 · DOI
  52. Abelleira Martínez, 2016, Scaling up functional traits for ecosystem services with remote sensing: concepts and methods, Ecol. Evol., 6, 4359, 10.1002/ece3.2201 · DOI
  53. Mononen, 2018, Usability of citizen science observations together with airborne laser scanning data in determining the habitat preferences of forest birds, For. Ecol. Manag., 430, 498, 10.1016/j.foreco.2018.08.040 · DOI
  54. Matasci, 2018, Three decades of forest structural dynamics over Canada’s forested ecosystems using Landsat time-series and lidar plots, Remote Sens. Environ., 216, 697, 10.1016/j.rse.2018.07.024 · DOI
  55. Almeida, 2019, A new era in forest restoration monitoring, Restor. Ecol., 28, 8, 10.1111/rec.13067 · DOI
  56. Lucas, 2020, Structural characterisation of mangrove forests achieved through combining multiple sources of remote sensing data, Remote Sens. Environ., 237, 111543, 10.1016/j.rse.2019.111543 · DOI
  57. Smith, 2019, Seasonal and drought-related changes in leaf area profiles depend on height and light environment in an Amazon forest, New Phytol., 222, 1284, 10.1111/nph.15726 · DOI
  58. Fisher, 2018, A novel technique using LiDAR to identify native-dominated and tame-dominated grasslands in Canada, Remote Sens. Environ., 218, 201, 10.1016/j.rse.2018.10.003 · DOI
  59. Maguire, 2019, Terrestrial lidar scanning reveals fine-scale linkages between microstructure and photosynthetic functioning of small-stature spruce trees at the forest-tundra ecotone, Agric. For. Meteorol., 269–270, 157, 10.1016/j.agrformet.2019.02.019 · DOI
  60. Turak, 2017, Essential biodiversity variables for measuring change in global freshwater biodiversity, Biol. Conserv., 213, 272, 10.1016/j.biocon.2016.09.005 · DOI · Paper page
  61. Shao, 2019, Towards high throughput assessment of canopy dynamics: the estimation of leaf area structure in Amazonian forests with multitemporal multi-sensor airborne lidar, Remote Sens. Environ., 221, 1, 10.1016/j.rse.2018.10.035 · DOI
  62. Hilmers, 2018, Biodiversity along temperate forest succession, J. Appl. Ecol., 55, 2756, 10.1111/1365-2664.13238 · DOI
  63. Scaranello, 2019, Estimation of coarse dead wood stocks in intact and degraded forests in the Brazilian Amazon using airborne lidar, Biogeosciences, 16, 3457, 10.5194/bg-16-3457-2019 · DOI
  64. McCarley, 2017, Multi-temporal LiDAR and Landsat quantification of fire-induced changes to forest structure, Remote Sens. Environ, 191, 419, 10.1016/j.rse.2016.12.022 · DOI
  65. Hu, 2019, A simple and integrated approach for fire severity assessment using bi-temporal airborne LiDAR data, Int. J. Appl. Earth Obs., 6378, 25
  66. Duncanson, 2018, Monitoring individual tree-based change with airborne lidar, Ecol. Evol., 8, 5079, 10.1002/ece3.4075 · DOI
  67. Reddy, 2015, Quantifying soil carbon loss and uncertainty from a peatland wildfire using multi-temporal LiDAR, Remote Sens. Environ., 170, 306, 10.1016/j.rse.2015.09.017 · DOI
  68. Song, 2016, Estimation of broad-leaved canopy growth in the urban forested area using multi-temporal airborne LiDAR datasets, Urban For. Urban Gree., 16, 142, 10.1016/j.ufug.2016.02.007 · DOI

Cited by

Sorted by citations

See all 126

Metadata

Authors
Valbuena, R., O’Connor, B., Zellweger, F., Simonson, W., Vihervaara, P., Maltamo, M., Silva, C. A., Almeida, D. R. A., Danks, F., Morsdorf, F., Chirici, G., Lucas, R., Coomes, D. A., & Coops, N. C.
Venue
Trends in Ecology & Evolution
Publisher
Elsevier BV
Published
2020-08
Volume
35
Issue
8
Pages
656-667
ISSN
0169-5347
Cited by
126
References
0
Language
en
Funders
  • Horizon 2020 (LORENZLIDAR-658180)
  • Swiss National Science Foundation (172198)
  • São Paulo Research Foundation (#2018/21338-3, #2019/14697-0)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review