CoChat
Sign Up
Cited by 270

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Neural Network Guided Interpolation for Mapping Canopy Height of China’s Forests by Integrating GEDI and ICESat-2 Data

Liu, Xiaoqiang, Su, Yanjun, Hu, Tianyu, Yang, Qiuli, Liu, Bingbing, Deng, Yufei, Tang, Hao, Tang, Zhiyao, Fang, Jingyun, Guo, Qinghua·2022·Remote Sensing of Environment
DOI 10.1016/j.rse.2021.112844
Open this paper in CoChat — free account

Overview

Integrity signals

Registry

Crossref · 10.1016/j.rse.2021.112844

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

  • The main objective of the GEDI mission is to provide high-accuracy and high global coverage data on canopy structure, which can be used to estimate biomass and canopy height.

    “The main objective of the GEDI mission is to provide high-accuracy and high global coverage data on canopy structure, which can be used to estimate biomass [39][40][41] and canopy height [30,38,42].”

    From ANADEM: A Digital Terrain Model for South America (2024) · cites this paper as [30]

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 65

  1. Asner, 2009, Tropical forest carbon assessment: integrating satellite and airborne mapping approaches, Environ. Res. Lett., 4, 10.1088/1748-9326/4/3/034009 · DOI
  2. Bentley, 1975, Multidimensional binary search trees used for associative searching, Commun. ACM, 18, 509, 10.1145/361002.361007 · DOI
  3. Bergen, 2009, Remote sensing of vegetation 3-D structure for biodiversity and habitat: review and implications for lidar and radar spaceborne missions, J. Geophys. Res. Biogeosci., 114, 10.1029/2008JG000883 · DOI
  4. Chen, 2015, Global land cover mapping at 30 m resolution: a POK-based operational approach, ISPRS J. Photogramm. Remote Sens., 103, 7, 10.1016/j.isprsjprs.2014.09.002 · DOI
  5. Davies, 2019, Combining behavioural and LiDAR data to reveal relationships between canopy structure and orangutan nest site selection in disturbed forests, Biol. Conserv., 232, 97, 10.1016/j.biocon.2019.01.032 · DOI
  6. Donoghue, 2006, Using LiDAR to compare forest height estimates from IKONOS and Landsat ETM+ data in Sitka spruce plantation forests, Int. J. Remote Sens., 27, 2161, 10.1080/01431160500396493 · DOI
  7. Drusch, 2012, Sentinel-2: ESA’s optical high-resolution mission for GMES operational services, Remote Sens. Environ., 120, 25, 10.1016/j.rse.2011.11.026 · DOI
  8. Dubayah, 2020, The global ecosystem dynamics investigation: high-resolution laser ranging of the Earth’s forests and topography, Sci. Remote Sens., 1
  9. Farr, 2000, Shuttle radar topography Mission produces a wealth of data, EOS Trans. Am. Geophys. Union, 81, 583, 10.1029/EO081i048p00583 · DOI
  10. Fatoyinbo, 2021, The NASA AfriSAR campaign: airborne SAR and lidar measurements of tropical forest structure and biomass in support of current and future space missions, Remote Sens. Environ., 264, 10.1016/j.rse.2021.112533 · DOI
  11. Fick, 2017, WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas, Int. J. Climatol., 37, 4302, 10.1002/joc.5086 · DOI · Paper page
  12. Gao, 2016, A spatial conditioned Latin hypercube sampling method for mapping using ancillary data, Trans. GIS, 20, 735, 10.1111/tgis.12176 · DOI
  13. García, 2018, Modelling forest canopy height by integrating airborne LiDAR samples with satellite radar and multispectral imagery, Int. J. Appl. Earth Obs. Geoinf., 66, 159
  14. Gorelick, 2017, Google Earth Engine: Planetary-scale geospatial analysis for everyone, Remote Sens. Environ., 202, 18, 10.1016/j.rse.2017.06.031 · DOI
  15. Guo, 2010, Effects of topographic variability and lidar sampling density on several DEM interpolation methods, Photogramm. Eng. Remote. Sens., 76, 701, 10.14358/PERS.76.6.701 · DOI
  16. Guo, 2017, An integrated UAV-borne lidar system for 3D habitat mapping in three forest ecosystems across China, Int. J. Remote Sens., 38, 2954, 10.1080/01431161.2017.1285083 · DOI
  17. Guo, 2021, Lidar boosts 3D ecological observations and Modelings: a review and perspective, IEEE Geosci. Remote Sens. Magaz., 9, 232, 10.1109/MGRS.2020.3032713 · DOI
  18. Hengl, 2004, A generic framework for spatial prediction of soil variables based on regression-kriging, Geoderma, 120, 75, 10.1016/j.geoderma.2003.08.018 · DOI
  19. Hengl, 2007, About regression-kriging: from equations to case studies, Comput. Geosci., 33, 1301, 10.1016/j.cageo.2007.05.001 · DOI
  20. Hou, 2001, Vegetation atlas of China, Chinese Acad. Sci., the editorial board of vegetation map of China, 113
  21. Hu, 2021, Development and performance evaluation of a very low-cost UAV-Lidar system for forestry applications, Remote Sens., 13, 77, 10.3390/rs13010077 · DOI
  22. Huang, 2017, Mapping vegetation heights in China using slope correction ICESat data, SRTM, MODIS-derived and climate data, ISPRS J. Photogramm. Remote Sens., 129, 189, 10.1016/j.isprsjprs.2017.04.020 · DOI
  23. Hudak, 2002, Integration of lidar and Landsat ETM+ data for estimating and mapping forest canopy height, Remote Sens. Environ., 82, 397, 10.1016/S0034-4257(02)00056-1 · DOI
  24. Lefsky, 2010, A global forest canopy height map from the moderate resolution imaging spectroradiometer and the geoscience laser altimeter system, Geophys. Res. Lett., 37, 10.1029/2010GL043622 · DOI
  25. Lefsky, 2005, Estimates of forest canopy height and aboveground biomass using ICESat, Geophys. Res. Lett., 32, 10.1029/2005GL023971 · DOI
  26. Li, 2015, Lidar with multi-temporal MODIS provide a means to upscale predictions of forest biomass, ISPRS J. Photogramm. Remote Sens., 102, 198, 10.1016/j.isprsjprs.2015.02.007 · DOI
  27. Li, 2017, Estimating ground-level PM2. 5 by fusing satellite and station observations: a geo-intelligent deep learning approach, Geophys. Res. Lett., 44, 10.1002/2017GL075710 · DOI
  28. Li, 2020, High-resolution mapping of forest canopy height using machine learning by coupling ICESat-2 LiDAR with Sentinel-1, Sentinel-2 and Landsat-8 data, Int. J. Appl. Earth Obs. Geoinf., 92
  29. Liang, 2018, International benchmarking of terrestrial laser scanning approaches for forest inventories, ISPRS J. Photogramm. Remote Sens., 144, 137, 10.1016/j.isprsjprs.2018.06.021 · DOI
  30. Lim, 2003, LiDAR remote sensing of forest structure, Prog. Phys. Geogr., 27, 88, 10.1191/0309133303pp360ra · DOI
  31. Liu, 2018, Estimating forest structural attributes using UAV-LiDAR data in Ginkgo plantations, ISPRS J. Photogramm. Remote Sens., 146, 465, 10.1016/j.isprsjprs.2018.11.001 · DOI
  32. Liu, 2021, Performance evaluation of GEDI and ICESat-2 laser altimeter data for terrain and canopy height retrievals, Remote Sens. Environ., 264, 10.1016/j.rse.2021.112571 · DOI
  33. Lu, 2004, Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin, For. Ecol. Manag., 198, 149, 10.1016/j.foreco.2004.03.048 · DOI
  34. MacArthur, 1961, On bird species diversity, Ecology, 42, 594, 10.2307/1932254 · DOI · Paper page
  35. Markus, 2017, The Ice, Cloud, and land Elevation satellite-2 (ICESat-2): science requirements, concept, and implementation, Remote Sens. Environ., 190, 260, 10.1016/j.rse.2016.12.029 · DOI · Paper page
  36. Masek, 2015, The role of remote sensing in process-scaling studies of managed forest ecosystems, For. Ecol. Manag., 355, 109, 10.1016/j.foreco.2015.05.032 · DOI
  37. McCombs, 2003, Influence of fusing lidar and multispectral imagery on remotely sensed estimates of stand density and mean tree height in a managed loblolly pine plantation, For. Sci., 49, 457
  38. Milanesi, 2017, Three-dimensional habitat structure and landscape genetics: a step forward in estimating functional connectivity, Ecology, 98, 393, 10.1002/ecy.1645 · DOI
  39. Neuenschwander, 2019, The ATL08 land and vegetation product for the ICESat-2 Mission, Remote Sens. Environ., 221, 247, 10.1016/j.rse.2018.11.005 · DOI
  40. Neuenschwander, 2020, Validation of ICESat-2 terrain and canopy heights in boreal forests, Remote Sens. Environ., 251, 10.1016/j.rse.2020.112110 · DOI
  41. Pedregosa, 2011, Scikit-learn: machine learning in Python, J. Machine Learn. Res., 12, 2825
  42. Popescu, 2018, Photon counting LiDAR: an adaptive ground and canopy height retrieval algorithm for ICESat-2 data, Remote Sens. Environ., 208, 154, 10.1016/j.rse.2018.02.019 · DOI
  43. Potapov, 2021, Mapping global forest canopy height through integration of GEDI and Landsat data, Remote Sens. Environ., 253, 10.1016/j.rse.2020.112165 · DOI · Paper page
  44. Rabus, 2003, The shuttle radar topography mission—a new class of digital elevation models acquired by spaceborne radar, ISPRS J. Photogramm. Remote Sens., 57, 241, 10.1016/S0924-2716(02)00124-7 · DOI
  45. Schutz, 2005, Overview of the ICESat mission, Geophys. Res. Lett., 32, 10.1029/2005GL024009 · DOI
  46. Simard, 2011, Mapping forest canopy height globally with spaceborne lidar, J. Geophys. Res. Biogeosci., 116, 10.1029/2011JG001708 · DOI
  47. Srivastava, 2014, Dropout: a simple way to prevent neural networks from overfitting, J. Machine Learn. Res., 15, 1929
  48. Su, 2014, A practical method for SRTM DEM correction over vegetated mountain areas, ISPRS J. Photogramm. Remote Sens., 87, 216, 10.1016/j.isprsjprs.2013.11.009 · DOI
  49. Su, 2016, Spatial distribution of forest aboveground biomass in China: estimation through combination of spaceborne lidar, optical imagery, and forest inventory data, Remote Sens. Environ., 173, 187, 10.1016/j.rse.2015.12.002 · DOI
  50. Su, 2017, Fine-resolution forest tree height estimation across the Sierra Nevada through the integration of spaceborne LiDAR, airborne LiDAR, and optical imagery, Int. J. Digital Earth, 10, 307, 10.1080/17538947.2016.1227380 · DOI
  51. Su, 2020, The development and evaluation of a backpack LiDAR system for accurate and efficient forest inventory, IEEE Geosci. Remote Sens. Lett.
  52. Tao, 2016, Global patterns and determinants of forest canopy height, Ecology, 97, 3265, 10.1002/ecy.1580 · DOI
  53. Tobler, 1970, A computer movie simulating urban growth in the Detroit region, Econ. Geogr., 46, 234, 10.2307/143141 · DOI
  54. Turner, 2003, Remote sensing for biodiversity science and conservation, Trends Ecol. Evol., 18, 306, 10.1016/S0169-5347(03)00070-3 · DOI
  55. Van Leeuwen, 1999, MODIS vegetation index compositing approach: a prototype with AVHRR data, Remote Sens. Environ., 69, 264, 10.1016/S0034-4257(99)00022-X · DOI
  56. Vierling, 2008, Lidar: shedding new light on habitat characterization and modeling, Front. Ecol. Environ., 6, 90, 10.1890/070001 · DOI · Paper page
  57. Wang, 2019, Remote sensing of terrestrial plant biodiversity, Remote Sens. Environ., 231, 10.1016/j.rse.2019.111218 · DOI
  58. Wang, 2012, A review of spatial sampling, Spatial Stat., 2, 1, 10.1016/j.spasta.2012.08.001 · DOI
  59. Wang, 2016, A combined GLAS and MODIS estimation of the global distribution of mean forest canopy height, Remote Sens. Environ., 174, 24, 10.1016/j.rse.2015.12.005 · DOI
  60. Wang, 2019, Is field-measured tree height as reliable as believed–a comparison study of tree height estimates from field measurement, airborne laser scanning and terrestrial laser scanning in a boreal forest, ISPRS J. Photogramm. Remote Sens., 147, 132, 10.1016/j.isprsjprs.2018.11.008 · DOI
  61. Wilkes, 2015, Mapping forest canopy height across large areas by upscaling ALS estimates with freely available satellite data, Remote Sens., 7, 12563, 10.3390/rs70912563 · DOI
  62. Yang, 2015, Forest canopy height mapping over China using GLAS and MODIS data, Sci. China Earth Sci., 58, 96, 10.1007/s11430-014-4905-5 · DOI
  63. Zhao, 2016, Improved progressive TIN densification filtering algorithm for airborne LiDAR data in forested areas, ISPRS J. Photogramm. Remote Sens., 117, 79, 10.1016/j.isprsjprs.2016.03.016 · DOI
  64. Zhao, 2018, A global corrected SRTM DEM product for vegetated areas, Remote Sens. Lett., 9, 393, 10.1080/2150704X.2018.1425560 · DOI
  65. Zhu, 2018, Spatial prediction based on third law of geography, Ann. GIS, 24, 225, 10.1080/19475683.2018.1534890 · DOI

Cited by

Sorted by citations

Metadata

Authors
Liu, X., Su, Y., Hu, T., Yang, Q., Liu, B., Deng, Y., Tang, H., Tang, Z., Fang, J., & Guo, Q.
Venue
Remote Sensing of Environment
Publisher
Elsevier BV
Published
2022-02
Volume
269
Pages
112844
ISSN
0034-4257
DOI
10.1016/j.rse.2021.112844
Cited by
270
References
3
Language
en
Licence
Funders
  • Chinese Academy of Sciences
  • National Natural Science Foundation of China (31971575, 41871332, 41901358)

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