Priority list of biodiversity metrics to observe from space
Monitoring global biodiversity from space through remotely sensing geospatial patterns has high potential to add to our knowledge acquired by field observation. Although a framework of essential biodiversity variables (EBVs) is emerging for monitoring biodiversity, its poor alignment with remote sen...
Saved in:
| Published in | Nature ecology & evolution Vol. 5; no. 7; pp. 896 - 906 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
01.07.2021
Nature Publishing Group |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | Monitoring global biodiversity from space through remotely sensing geospatial patterns has high potential to add to our knowledge acquired by field observation. Although a framework of essential biodiversity variables (EBVs) is emerging for monitoring biodiversity, its poor alignment with remote sensing products hinders interpolation between field observations. This study compiles a comprehensive, prioritized list of remote sensing biodiversity products that can further improve the monitoring of geospatial biodiversity patterns, enhancing the EBV framework and its applicability. The ecosystem structure and ecosystem function EBV classes, which capture the biological effects of disturbance as well as habitat structure, are shown by an expert review process to be the most relevant, feasible, accurate and mature for direct monitoring of biodiversity from satellites. Biodiversity products that require satellite remote sensing of a finer resolution that is still under development are given lower priority (for example, for the EBV class species traits). Some EBVs are not directly measurable by remote sensing from space, specifically the EBV class genetic composition. Linking remote sensing products to EBVs will accelerate product generation, improving reporting on the state of biodiversity from local to global scales.
Remote sensing of geospatial biodiversity patterns is an important complement to field observations. This priority list suggests how remote sensing observations can be better integrated into the essential biodiversity variables. |
|---|---|
| AbstractList | Monitoring global biodiversity from space through remotely sensing geospatial patterns has high potential to add to our knowledge acquired by field observation. Although a framework of essential biodiversity variables (EBVs) is emerging for monitoring biodiversity, its poor alignment with remote sensing products hinders interpolation between field observations. This study compiles a comprehensive, prioritized list of remote sensing biodiversity products that can further improve the monitoring of geospatial biodiversity patterns, enhancing the EBV framework and its applicability. The ecosystem structure and ecosystem function EBV classes, which capture the biological effects of disturbance as well as habitat structure, are shown by an expert review process to be the most relevant, feasible, accurate and mature for direct monitoring of biodiversity from satellites. Biodiversity products that require satellite remote sensing of a finer resolution that is still under development are given lower priority (for example, for the EBV class species traits). Some EBVs are not directly measurable by remote sensing from space, specifically the EBV class genetic composition. Linking remote sensing products to EBVs will accelerate product generation, improving reporting on the state of biodiversity from local to global scales. Monitoring global biodiversity from space through remotely sensing geospatial patterns has high potential to add to our knowledge acquired by field observation. Although a framework of essential biodiversity variables (EBVs) is emerging for monitoring biodiversity, its poor alignment with remote sensing products hinders interpolation between field observations. This study compiles a comprehensive, prioritized list of remote sensing biodiversity products that can further improve the monitoring of geospatial biodiversity patterns, enhancing the EBV framework and its applicability. The ecosystem structure and ecosystem function EBV classes, which capture the biological effects of disturbance as well as habitat structure, are shown by an expert review process to be the most relevant, feasible, accurate and mature for direct monitoring of biodiversity from satellites. Biodiversity products that require satellite remote sensing of a finer resolution that is still under development are given lower priority (for example, for the EBV class species traits). Some EBVs are not directly measurable by remote sensing from space, specifically the EBV class genetic composition. Linking remote sensing products to EBVs will accelerate product generation, improving reporting on the state of biodiversity from local to global scales. Remote sensing of geospatial biodiversity patterns is an important complement to field observations. This priority list suggests how remote sensing observations can be better integrated into the essential biodiversity variables. Monitoring global biodiversity from space through remotely sensing geospatial patterns has high potential to add to our knowledge acquired by field observation. Although a framework of essential biodiversity variables (EBVs) is emerging for monitoring biodiversity, its poor alignment with remote sensing products hinders interpolation between field observations. This study compiles a comprehensive, prioritized list of remote sensing biodiversity products that can further improve the monitoring of geospatial biodiversity patterns, enhancing the EBV framework and its applicability. The ecosystem structure and ecosystem function EBV classes, which capture the biological effects of disturbance as well as habitat structure, are shown by an expert review process to be the most relevant, feasible, accurate and mature for direct monitoring of biodiversity from satellites. Biodiversity products that require satellite remote sensing of a finer resolution that is still under development are given lower priority (for example, for the EBV class species traits). Some EBVs are not directly measurable by remote sensing from space, specifically the EBV class genetic composition. Linking remote sensing products to EBVs will accelerate product generation, improving reporting on the state of biodiversity from local to global scales.Remote sensing of geospatial biodiversity patterns is an important complement to field observations. This priority list suggests how remote sensing observations can be better integrated into the essential biodiversity variables. Monitoring global biodiversity from space through remotely sensing geospatial patterns has high potential to add to our knowledge acquired by field observation. Although a framework of essential biodiversity variables (EBVs) is emerging for monitoring biodiversity, its poor alignment with remote sensing products hinders interpolation between field observations. This study compiles a comprehensive, prioritized list of remote sensing biodiversity products that can further improve the monitoring of geospatial biodiversity patterns, enhancing the EBV framework and its applicability. The ecosystem structure and ecosystem function EBV classes, which capture the biological effects of disturbance as well as habitat structure, are shown by an expert review process to be the most relevant, feasible, accurate and mature for direct monitoring of biodiversity from satellites. Biodiversity products that require satellite remote sensing of a finer resolution that is still under development are given lower priority (for example, for the EBV class species traits). Some EBVs are not directly measurable by remote sensing from space, specifically the EBV class genetic composition. Linking remote sensing products to EBVs will accelerate product generation, improving reporting on the state of biodiversity from local to global scales.Monitoring global biodiversity from space through remotely sensing geospatial patterns has high potential to add to our knowledge acquired by field observation. Although a framework of essential biodiversity variables (EBVs) is emerging for monitoring biodiversity, its poor alignment with remote sensing products hinders interpolation between field observations. This study compiles a comprehensive, prioritized list of remote sensing biodiversity products that can further improve the monitoring of geospatial biodiversity patterns, enhancing the EBV framework and its applicability. The ecosystem structure and ecosystem function EBV classes, which capture the biological effects of disturbance as well as habitat structure, are shown by an expert review process to be the most relevant, feasible, accurate and mature for direct monitoring of biodiversity from satellites. Biodiversity products that require satellite remote sensing of a finer resolution that is still under development are given lower priority (for example, for the EBV class species traits). Some EBVs are not directly measurable by remote sensing from space, specifically the EBV class genetic composition. Linking remote sensing products to EBVs will accelerate product generation, improving reporting on the state of biodiversity from local to global scales. |
| Author | Van De Kerchove, Ruben Lock, Marcelle C. Turner, Woody Geijzendorffer, Ilse Wang, Tiejun Vihervaara, Petteri Mücher, Caspar A. Schaepman, Michael E. Heiden, Uta Fernandez, Miguel O’Connor, Brian Paganini, Marc Ali, Abebe Vis, Jan Kees Wingate, Vladimir Gorelick, Noel Leitão, Pedro J. Heurich, Marco Skidmore, Andrew K. Darvishzadeh, Roshanak Wegmann, Martin Hobern, Donald Fernández, Néstor Muller-Karger, Frank E. Rocchini, Duccio Holzwarth, Stefanie Lausch, Angela Kissling, W. Daniel Feilhauer, Hannes Roeoesli, Claudia Coops, Nicholas C. Neinavaz, Elnaz |
| Author_xml | – sequence: 1 givenname: Andrew K. orcidid: 0000-0002-7446-8429 surname: Skidmore fullname: Skidmore, Andrew K. email: a.k.skidmore@utwente.nl organization: Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Department of Earth and Environmental Science, Macquarie University – sequence: 2 givenname: Nicholas C. orcidid: 0000-0002-0151-9037 surname: Coops fullname: Coops, Nicholas C. organization: Department of Forest Resources Management, University of British Columbia – sequence: 3 givenname: Elnaz orcidid: 0000-0003-4117-2354 surname: Neinavaz fullname: Neinavaz, Elnaz organization: Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente – sequence: 4 givenname: Abebe surname: Ali fullname: Ali, Abebe organization: Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Department of Geography and Environmental Studies, Wollo University – sequence: 5 givenname: Michael E. orcidid: 0000-0002-9627-9565 surname: Schaepman fullname: Schaepman, Michael E. organization: Remote Sensing Laboratories, Department of Geography, University of Zurich – sequence: 6 givenname: Marc surname: Paganini fullname: Paganini, Marc organization: European Space Research Institute (ESRIN), European Space Agency – sequence: 7 givenname: W. Daniel orcidid: 0000-0002-7274-6755 surname: Kissling fullname: Kissling, W. Daniel organization: Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam – sequence: 8 givenname: Petteri orcidid: 0000-0002-5889-8402 surname: Vihervaara fullname: Vihervaara, Petteri organization: Biodiversity Centre, Finnish Environment Institute (SYKE) – sequence: 9 givenname: Roshanak orcidid: 0000-0001-7512-0574 surname: Darvishzadeh fullname: Darvishzadeh, Roshanak organization: Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente – sequence: 10 givenname: Hannes orcidid: 0000-0001-5758-6303 surname: Feilhauer fullname: Feilhauer, Hannes organization: Institute of Geographical Sciences, Freie Universität Berlin, Remote Sensing Center for Earth System Research, University of Leipzig – sequence: 11 givenname: Miguel orcidid: 0000-0002-8301-1340 surname: Fernandez fullname: Fernandez, Miguel organization: NatureServe, George Mason University – sequence: 12 givenname: Néstor orcidid: 0000-0002-9645-8571 surname: Fernández fullname: Fernández, Néstor organization: German Centre for Integrative Biodiversity Research (iDiv), Institute of Biology, Martin Luther University Halle-Wittenberg – sequence: 13 givenname: Noel orcidid: 0000-0002-5548-2436 surname: Gorelick fullname: Gorelick, Noel organization: Google – sequence: 14 givenname: Ilse surname: Geijzendorffer fullname: Geijzendorffer, Ilse organization: Tour du Valat – sequence: 15 givenname: Uta orcidid: 0000-0002-3865-1912 surname: Heiden fullname: Heiden, Uta organization: Earth Observation Center (EOC), Remote Sensing Technology Institute, German Aerospace Center (DLR) – sequence: 16 givenname: Marco orcidid: 0000-0003-0051-2930 surname: Heurich fullname: Heurich, Marco organization: Department of Visitor Management and National Park Monitoring, Bavarian Forest National Park Administration, Albert Ludwigs University of Freiburg – sequence: 17 givenname: Donald surname: Hobern fullname: Hobern, Donald organization: GBIF Secretariat – sequence: 18 givenname: Stefanie orcidid: 0000-0001-7364-7006 surname: Holzwarth fullname: Holzwarth, Stefanie organization: Earth Observation Center (EOC), Remote Sensing Technology Institute, German Aerospace Center (DLR) – sequence: 19 givenname: Frank E. orcidid: 0000-0003-3159-5011 surname: Muller-Karger fullname: Muller-Karger, Frank E. organization: College of Marine Science, University of South Florida – sequence: 20 givenname: Ruben surname: Van De Kerchove fullname: Van De Kerchove, Ruben organization: Flemish Institute for Technological Research (VITO) – sequence: 21 givenname: Angela orcidid: 0000-0002-4490-7232 surname: Lausch fullname: Lausch, Angela organization: Computational Landscape Ecology, Helmholtz Centre for Environmental Research (UFZ), Geography Department, Humboldt University of Berlin – sequence: 22 givenname: Pedro J. orcidid: 0000-0003-3038-9531 surname: Leitão fullname: Leitão, Pedro J. organization: Technische Universität Braunschweig, Humboldt-Universität zu Berlin – sequence: 23 givenname: Marcelle C. orcidid: 0000-0001-7759-0852 surname: Lock fullname: Lock, Marcelle C. organization: Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Department of Earth and Environmental Science, Macquarie University – sequence: 24 givenname: Caspar A. surname: Mücher fullname: Mücher, Caspar A. organization: Wageningen Environmental Research, Wageningen University & Research – sequence: 25 givenname: Brian surname: O’Connor fullname: O’Connor, Brian organization: UN Environment World Conservation Monitoring Centre – sequence: 26 givenname: Duccio orcidid: 0000-0003-0087-0594 surname: Rocchini fullname: Rocchini, Duccio organization: Department of Biological, Geological and Environmental Sciences, University of Bologna, Department of Applied Geoinformatics and Spatial Planning, Faculty of Environmental Sciences, Czech University of Life Sciences – sequence: 27 givenname: Claudia surname: Roeoesli fullname: Roeoesli, Claudia organization: Remote Sensing Laboratories, Department of Geography, University of Zurich – sequence: 28 givenname: Woody surname: Turner fullname: Turner, Woody organization: Earth Science Division, NASA – sequence: 29 givenname: Jan Kees surname: Vis fullname: Vis, Jan Kees organization: Unilever Europe B.V – sequence: 30 givenname: Tiejun orcidid: 0000-0002-1138-8464 surname: Wang fullname: Wang, Tiejun organization: Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente – sequence: 31 givenname: Martin orcidid: 0000-0003-0335-9601 surname: Wegmann fullname: Wegmann, Martin organization: Institute of Geography and Geology, University of Wuerzburg – sequence: 32 givenname: Vladimir surname: Wingate fullname: Wingate, Vladimir organization: Land Systems and Sustainable Land Management, Geographisches Institut, Universität Bern |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33986541$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kE1LBCEYgCWKtrb-QIcY6NJlSkcdlU4RfcFCHQq6ieu8Ey4z46buUv8-a_tiD50UeR59fXbR5uAHQOiA4BOCqTyNjHCuSlyREhPGSfm6gXYqqkRJKXva_LMfof0YZxhjIgRXdb2NRpQqWXNGdtDZfXA-uPRWdC6mwrfF1PnGLSHEj8MeUnA2FskXfhohLKFog--LODcW9tBWa7oI-1_rGD1eXT5c3JSTu-vbi_NJaRlRqaSSm0pCy6ZKilaCBcYsGG6gEQJEJUhjagoNZU0trTJCMDASt7JWNQdp6Bgdr-6dB_-ygJh076KFrjMD-EXUFa8kkST_NaNHa-jML8KQp8sUk4xKpVSmDr-oxbSHRs-D6014099ZMlCtABt8jAHaH4Rg_ZFfr_LrnF9_5tevWZJrknXJJOeHFIzr_lfpSo35neEZwu_Y_1jvGyiZTA |
| CitedBy_id | crossref_primary_10_1080_11263504_2023_2165569 crossref_primary_10_3390_rs14236165 crossref_primary_10_3390_rs15174128 crossref_primary_10_1016_j_ecolind_2022_109004 crossref_primary_10_1038_s41598_024_64615_2 crossref_primary_10_1002_ecs2_70026 crossref_primary_10_1109_LGRS_2023_3319548 crossref_primary_10_1098_rsta_2023_0058 crossref_primary_10_1093_biosci_biae052 crossref_primary_10_3390_rs13245105 crossref_primary_10_1111_rec_14338 crossref_primary_10_1038_s41598_023_44384_0 crossref_primary_10_1007_s12524_022_01569_w crossref_primary_10_1016_j_srs_2023_100114 crossref_primary_10_3390_rs15030820 crossref_primary_10_1007_s12080_024_00587_3 crossref_primary_10_3390_rs16132425 crossref_primary_10_1016_j_scitotenv_2022_161365 crossref_primary_10_1080_17538947_2022_2130461 crossref_primary_10_3390_land11030371 crossref_primary_10_3390_rs15081969 crossref_primary_10_1111_2041_210X_14245 crossref_primary_10_1016_j_jclepro_2023_136331 crossref_primary_10_1016_j_ecoinf_2023_102082 crossref_primary_10_1016_j_geoderma_2024_117058 crossref_primary_10_1016_j_ecolind_2024_112940 crossref_primary_10_1093_biosci_biab114 crossref_primary_10_3389_fcosc_2024_1298962 crossref_primary_10_1016_j_jag_2022_103131 crossref_primary_10_1016_j_rse_2023_113614 crossref_primary_10_1002_eap_2624 crossref_primary_10_1016_j_envres_2024_119432 crossref_primary_10_1016_j_ecoinf_2024_102702 crossref_primary_10_1038_s41559_023_02206_6 crossref_primary_10_1098_rstb_2023_0208 crossref_primary_10_3390_f15060946 crossref_primary_10_1007_s44177_022_00013_5 crossref_primary_10_1038_s41598_023_50812_y crossref_primary_10_1098_rstb_2022_0192 crossref_primary_10_1029_2022JG007026 crossref_primary_10_3390_f15071198 crossref_primary_10_1073_pnas_2107662119 crossref_primary_10_5194_essd_15_1871_2023 crossref_primary_10_3389_fmars_2021_740953 crossref_primary_10_1016_j_oneear_2024_10_005 crossref_primary_10_1007_s41064_023_00235_1 crossref_primary_10_1016_j_asr_2025_02_052 crossref_primary_10_1109_MCG_2023_3321989 crossref_primary_10_1016_j_ecolind_2025_113085 crossref_primary_10_7780_kjrs_2024_40_5_2_8 crossref_primary_10_1080_15481603_2024_2427305 crossref_primary_10_1038_s41598_021_96047_7 crossref_primary_10_1016_j_rsase_2025_101499 crossref_primary_10_1016_j_rse_2022_113338 crossref_primary_10_1016_j_tree_2024_09_006 crossref_primary_10_1016_j_ecocom_2023_101029 crossref_primary_10_1016_j_jag_2022_103074 crossref_primary_10_1016_j_rse_2023_113918 crossref_primary_10_1007_s41324_023_00558_6 crossref_primary_10_1016_j_jenvman_2022_114944 crossref_primary_10_1016_j_rse_2023_113591 crossref_primary_10_1016_j_rse_2022_113170 crossref_primary_10_1016_j_indic_2022_100179 crossref_primary_10_1007_s11356_022_22469_z crossref_primary_10_3390_plants11202803 crossref_primary_10_3390_rs14071631 crossref_primary_10_1038_s41559_022_01702_5 crossref_primary_10_1080_01431161_2023_2240520 crossref_primary_10_1111_geb_13516 crossref_primary_10_7717_peerj_13534 crossref_primary_10_1016_j_ecolind_2024_112970 crossref_primary_10_1016_j_rse_2024_114518 crossref_primary_10_2139_ssrn_4784271 crossref_primary_10_1038_s41597_024_04143_w crossref_primary_10_1016_j_agrformet_2023_109337 crossref_primary_10_1016_j_isprsjprs_2022_12_028 crossref_primary_10_1088_2752_664X_ad4beb crossref_primary_10_3390_rs16071139 crossref_primary_10_1080_10106049_2024_2387087 crossref_primary_10_3390_insects15040229 crossref_primary_10_3390_su13179897 crossref_primary_10_1016_j_rse_2022_113024 crossref_primary_10_1016_j_jag_2024_103732 crossref_primary_10_1002_rse2_333 crossref_primary_10_1080_01431161_2023_2234093 crossref_primary_10_1002_bse_3208 crossref_primary_10_1007_s10661_024_12993_5 crossref_primary_10_1016_j_scitotenv_2021_148994 crossref_primary_10_3390_rs15235593 crossref_primary_10_1038_s44185_024_00071_5 crossref_primary_10_1016_j_jag_2024_104142 crossref_primary_10_3897_neobiota_94_126714 crossref_primary_10_1016_j_compag_2025_110178 crossref_primary_10_1080_01431161_2023_2205982 crossref_primary_10_1016_j_rse_2024_114082 crossref_primary_10_3389_fenvs_2022_788843 crossref_primary_10_3390_rs14051083 crossref_primary_10_1016_j_apgeog_2024_103249 crossref_primary_10_1109_JSTARS_2023_3270884 crossref_primary_10_3390_rs14092279 crossref_primary_10_1016_j_rsase_2023_101121 crossref_primary_10_1016_j_rse_2023_113530 crossref_primary_10_1016_j_ecolind_2023_110773 crossref_primary_10_1016_j_fecs_2024_100196 crossref_primary_10_1002_rse2_248 crossref_primary_10_3390_rs14153633 crossref_primary_10_1002_spe_3098 crossref_primary_10_1126_science_abn1406 crossref_primary_10_1016_j_ecoinf_2025_102993 crossref_primary_10_1088_2515_7620_ad3c16 crossref_primary_10_1002_fee_2587 crossref_primary_10_1016_j_scitotenv_2024_178132 crossref_primary_10_1038_s41598_024_65954_w crossref_primary_10_3390_data9120143 crossref_primary_10_1080_01431161_2021_2024912 crossref_primary_10_1016_j_rse_2024_114379 crossref_primary_10_3390_rs14215363 crossref_primary_10_1111_oik_09435 crossref_primary_10_1038_s41893_022_01020_5 crossref_primary_10_3390_rs15164061 crossref_primary_10_3390_land13101660 crossref_primary_10_1038_s43247_023_00758_w crossref_primary_10_1111_brv_12792 crossref_primary_10_1007_s41324_022_00457_2 crossref_primary_10_1016_j_techfore_2023_122806 crossref_primary_10_1109_TGRS_2024_3353410 crossref_primary_10_1002_eap_2603 crossref_primary_10_3389_fenvs_2024_1333762 crossref_primary_10_1126_sciadv_abl8214 crossref_primary_10_7717_peerj_16972 crossref_primary_10_1109_JSTARS_2024_3456587 crossref_primary_10_3390_rs16111833 crossref_primary_10_1016_j_isprsjprs_2024_05_014 crossref_primary_10_1016_j_rse_2024_114028 crossref_primary_10_3390_rs14153618 crossref_primary_10_1016_j_foreco_2024_122094 crossref_primary_10_1080_10106049_2022_2087756 crossref_primary_10_3390_f13030445 |
| Cites_doi | 10.1016/j.cosust.2018.02.005 10.1016/j.envsci.2015.08.005 10.1111/gcb.13168 10.1038/s41559-018-0551-1 10.1038/s41559-018-0667-3 10.1186/s40462-015-0036-7 10.2307/1468118 10.1016/j.rse.2008.06.018 10.1002/ldr.636 10.1016/j.rse.2015.09.001 10.1016/j.ecocom.2013.06.003 10.3390/rs8020161 10.1016/j.rse.2016.08.013 10.1016/j.rse.2016.10.018 10.1146/annurev.polisci.11.053106.141706 10.1175/BAMS-D-13-00047.1 10.1117/1.JRS.9.094096 10.1016/j.isprsjprs.2007.03.003 10.1111/geb.12601 10.1016/j.ecolind.2016.06.022 10.1080/01431160701736489 10.14358/PERS.70.6.703 10.1007/s10531-017-1386-9 10.1016/j.gecco.2017.01.007 10.3390/rs12061044 10.1016/j.isprsjprs.2014.03.009 10.1016/S0034-4257(01)00182-1 10.1007/s10531-017-1388-7 10.1016/S0098-8472(99)00052-0 10.1016/j.rse.2008.02.010 10.1080/01431160412331330275 10.1046/j.0022-0477.2001.00634.x 10.1023/B:LAND.0000021711.40074.ae 10.1016/j.isprsjprs.2018.02.002 10.3390/rs10020157 10.1073/pnas.0912668107 10.1890/1051-0761(1998)008[1022:HOSCSA]2.0.CO;2 10.1002/eap.1682 10.1016/j.rse.2016.05.019 10.3390/rs10030443 10.1038/nature18608 10.1126/science.1229931 10.1038/523403a 10.1111/2041-210X.12510 10.1016/j.rse.2011.02.030 10.2307/2937116 10.1073/pnas.0701135104 10.3390/rs6042682 10.1126/science.1244693 10.1111/j.2006.0906-7590.04596.x 10.1111/1365-2664.12417 10.1109/AERO.2005.1559323 10.1002/rse2.4 10.1890/08-0588.1 10.1016/j.rse.2010.12.015 10.1002/ece3.6469 10.1111/j.1755-263X.2011.00211.x 10.1016/j.isprsjprs.2020.10.017 10.1016/j.biocon.2014.11.048 10.3390/rs8030221 10.1109/LGRS.2017.2657778 10.3390/rs10071120 10.1038/nplants.2016.24 10.1111/j.1365-2435.2010.01769.x 10.1038/nature16489 10.1111/nph.16771 10.1038/nature20584 10.1016/j.rse.2004.12.011 10.1007/s10021-005-0041-6 10.1126/science.aau2650 10.1016/j.ecolind.2015.08.048 10.1016/bs.aecr.2015.03.001 10.1126/science.1256014 10.1098/rstb.2010.0081 10.1016/j.rse.2006.07.013 10.1080/01431168708954756 10.1111/j.1365-2664.2012.02205.x 10.1016/j.landurbplan.2005.05.006 10.1111/mms.12544 10.1126/science.aaj1987 10.3390/rs70708830 10.1109/MIC.2011.20 10.1016/j.ecolmodel.2014.08.018 10.1371/journal.pone.0115989 10.2307/2404920 10.1111/1365-2664.12179 10.1016/j.rse.2008.05.013 10.1029/95JD01536 10.1002/ece3.601 10.1016/j.rse.2010.10.011 10.1016/j.isprsjprs.2013.05.003 10.3390/rs8030196 10.1016/B978-0-12-396992-7.00002-2 10.1126/science.aaa2478 10.1016/j.inffus.2020.07.004 10.1016/j.rse.2004.12.016 10.1016/j.ecoinf.2019.101033 10.5194/acp-10-11707-2010 10.1111/geb.12182 10.1016/j.landusepol.2003.10.011 10.1002/rse2.15 10.1007/s11629-018-5200-2 10.1111/j.1365-2745.2008.01419.x 10.3390/rs9090878 10.1038/s41559-017-0176 10.1007/s100219900062 10.1016/j.foreco.2006.01.013 10.1016/S0034-4257(02)00074-3 10.1002/rse2.29 10.1111/brv.12359 10.1046/j.1365-2435.2002.00664.x |
| ContentType | Journal Article |
| Copyright | Springer Nature Limited 2021. corrected publications 2021 Springer Nature Limited 2021. corrected publications 2021. |
| Copyright_xml | – notice: Springer Nature Limited 2021. corrected publications 2021 – notice: Springer Nature Limited 2021. corrected publications 2021. |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 8FE 8FH AEUYN AFKRA ATCPS AZQEC BBNVY BENPR BHPHI BKSAR CCPQU DWQXO GNUQQ HCIFZ LK8 M7P PATMY PCBAR PHGZM PHGZT PKEHL PQEST PQGLB PQQKQ PQUKI PYCSY 7X8 |
| DOI | 10.1038/s41559-021-01451-x |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest SciTech Collection ProQuest Natural Science Collection ProQuest One Sustainability ProQuest Central UK/Ireland Agricultural & Environmental Science Collection ProQuest Central Essentials Biological Science Collection ProQuest Central Natural Science Collection Earth, Atmospheric & Aquatic Science Collection ProQuest One ProQuest Central Korea ProQuest Central Student SciTech Premium Collection Biological Sciences Biological Science Database Environmental Science Database Earth, Atmospheric & Aquatic Science Database ProQuest Central Premium ProQuest One Academic ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition Environmental Science Collection MEDLINE - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Central Essentials SciTech Premium Collection ProQuest One Community College ProQuest Natural Science Collection Earth, Atmospheric & Aquatic Science Collection ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Sustainability Natural Science Collection ProQuest Central Korea Agricultural & Environmental Science Collection Biological Science Collection ProQuest Central (New) ProQuest Biological Science Collection ProQuest One Academic Eastern Edition Earth, Atmospheric & Aquatic Science Database Biological Science Database ProQuest SciTech Collection Environmental Science Collection ProQuest One Academic UKI Edition Environmental Science Database ProQuest One Academic ProQuest One Academic (New) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE ProQuest Central Student MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 3 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Zoology Ecology |
| EISSN | 2397-334X |
| EndPage | 906 |
| ExternalDocumentID | 33986541 10_1038_s41559_021_01451_x |
| Genre | Research Support, U.S. Gov't, Non-P.H.S Review Research Support, Non-U.S. Gov't Journal Article |
| GrantInformation_xml | – fundername: National Aeronautics and Space Administration (NASA) grantid: NNX14AP62A; 80NSSC20K0017; NA19NOS0120199 funderid: https://doi.org/10.13039/100000104 – fundername: EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council) grantid: 834709 funderid: https://doi.org/10.13039/100010663 – fundername: Suomen Akatemia | Strategic Research Council (Strategisen Tutkimuksen Neuvosto) grantid: 312559 funderid: https://doi.org/10.13039/501100009047 – fundername: EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020) grantid: 820852; 730329 funderid: https://doi.org/10.13039/100010661 |
| GroupedDBID | 0R~ 53G AAEEF AAHBH AARCD AAYZH ABJNI ABLJU ACBWK ACGFS ADBBV AEUYN AFKRA AFSHS AFWHJ AHSBF AIBTJ ALFFA ALMA_UNASSIGNED_HOLDINGS ARMCB ATCPS AXYYD BBNVY BENPR BHPHI BKKNO BKSAR CCPQU EBS EJD FSGXE FZEXT HCIFZ M7P NNMJJ O9- ODYON PATMY PCBAR PYCSY RNT SHXYY SIXXV SNYQT SOJ TAOOD TBHMF TDRGL TSG AAYXX AFANA ATHPR CITATION PHGZM PHGZT CGR CUY CVF ECM EIF NFIDA NPM PQGLB 8FE 8FH AZQEC DWQXO GNUQQ LK8 PKEHL PQEST PQQKQ PQUKI 7X8 |
| ID | FETCH-LOGICAL-c419t-385a28ef4b987f8ece44cea5aed77e7271da63ed34d68c9a774ea80f86965e8a3 |
| IEDL.DBID | BENPR |
| ISSN | 2397-334X |
| IngestDate | Fri Jul 11 16:17:00 EDT 2025 Wed Jul 16 16:23:48 EDT 2025 Mon Jul 21 05:59:00 EDT 2025 Tue Jul 01 00:52:48 EDT 2025 Thu Apr 24 22:54:03 EDT 2025 Fri Feb 21 02:40:12 EST 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 7 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c419t-385a28ef4b987f8ece44cea5aed77e7271da63ed34d68c9a774ea80f86965e8a3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Review-3 content type line 23 |
| ORCID | 0000-0002-7274-6755 0000-0002-1138-8464 0000-0002-7446-8429 0000-0001-7759-0852 0000-0003-3038-9531 0000-0002-9627-9565 0000-0001-7364-7006 0000-0003-3159-5011 0000-0003-0087-0594 0000-0002-5548-2436 0000-0002-4490-7232 0000-0003-0335-9601 0000-0001-7512-0574 0000-0002-9645-8571 0000-0002-5889-8402 0000-0003-0051-2930 0000-0002-8301-1340 0000-0003-4117-2354 0000-0001-5758-6303 0000-0002-3865-1912 0000-0002-0151-9037 |
| OpenAccessLink | https://www.nature.com/articles/s41559-021-01451-x.pdf |
| PMID | 33986541 |
| PQID | 2548438999 |
| PQPubID | 4669716 |
| PageCount | 11 |
| ParticipantIDs | proquest_miscellaneous_2528181334 proquest_journals_2548438999 pubmed_primary_33986541 crossref_primary_10_1038_s41559_021_01451_x crossref_citationtrail_10_1038_s41559_021_01451_x springer_journals_10_1038_s41559_021_01451_x |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2021-07-01 |
| PublicationDateYYYYMMDD | 2021-07-01 |
| PublicationDate_xml | – month: 07 year: 2021 text: 2021-07-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | Nature ecology & evolution |
| PublicationTitleAbbrev | Nat Ecol Evol |
| PublicationTitleAlternate | Nat Ecol Evol |
| PublicationYear | 2021 |
| Publisher | Nature Publishing Group UK Nature Publishing Group |
| Publisher_xml | – name: Nature Publishing Group UK – name: Nature Publishing Group |
| References | FassnachtFEReview of studies on tree species classification from remotely sensed dataRemote Sens. Environ.2016186648710.1016/j.rse.2016.08.013 Copernicus Global Land Service: FAPAR Copernicushttps://land.copernicus.eu/global/about (2020). VogelDPrivate global business regulationAnnu. Rev. Polit. Sci.20081126128210.1146/annurev.polisci.11.053106.141706 PotterCRecent history of large-scale ecosystem disturbances in North America derived from the AVHRR satellite recordEcosystems2005880882410.1007/s10021-005-0041-6 SchmellerDBuilding capacity in biodiversity monitoring at the global scaleBiodivers. Conserv.2017262765279010.1007/s10531-017-1388-7 CollMEcological indicators to capture the effects of fishing on biodiversity and conservation status of marine ecosystemsEcol. Indic.20166094796210.1016/j.ecolind.2015.08.048 ZhaoMSHeinschFANemaniRRRunningSWImprovements of the MODIS terrestrial gross and net primary production global data setRemote Sens. Environ.20059516417610.1016/j.rse.2004.12.011 Verger, A. & Descals, A. Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)—300 m Version 1; Algorithm Theoretical Basis Document (ATBD), Issue 1.00 (Framework Service Contract No. 199494-JRC) (Copernicus Global Land Operations CGLOPS-1, 2020). WuSBMonitoring tree-crown scale autumn leaf phenology in a temperate forest with an integration of PlanetScope and drone remote sensing observationsISPRS J. Photogramm. Remote Sens.2021171364810.1016/j.isprsjprs.2020.10.017 GravesSJTree species abundance predictions in a tropical agricultural landscape with a supervised classification model and imbalanced dataRemote Sens.2016816110.3390/rs8020161 PekelJCottamAGorelickNBelwardASHigh-resolution mapping of global surface water and its long-term changesNature20165404184221:CAS:528:DC%2BC28XitVWmurbJ2792673310.1038/nature20584 PereiraHMEssential biodiversity variablesScience20133392772781:CAS:528:DC%2BC3sXhslagsbc%3D2332903610.1126/science.1229931 Surface Biology and Geology (SBG) NASA Sciencehttps://science.nasa.gov/earth-science/decadal-sbg (2020). BaiYFGrazing alters ecosystem functioning and C:N:P stoichiometry of grasslands along a regional precipitation gradientJ. Appl. Ecol.201249120412151:CAS:528:DC%2BC3sXislSmt7k%3D10.1111/j.1365-2664.2012.02205.x GrataniLBombelliACorrelation between leaf age and other leaf traits in three Mediterranean maquis shrub species: Quercus ilex, Phillyrea latifolia and Cistus incanusEnviron. Exp. Bot.20004314115310.1016/S0098-8472(99)00052-0 LucasRRowlandsABrownAKeyworthSBuntingPRule-based classification of multi-temporal satellite imagery for habitat and agricultural land cover mappingISPRS J. Photogramm. Remote Sens.20076216518510.1016/j.isprsjprs.2007.03.003 KaysRCrofootMCJetzWWikelskiMTerrestrial animal tracking as an eye on life and planetScience2015348aaa24782606885810.1126/science.aaa2478 HansenMCStehmanSVPotapovPVQuantification of global gross forest cover lossProc. Natl Acad. Sci. USA2010107865086551:CAS:528:DC%2BC3cXmsFWmtLs%3D20421467288935410.1073/pnas.0912668107 HealyCGotelliNJPotvinCPartitioning the effects of biodiversity and environmental heterogeneity for productivity and mortality in a tropical tree plantationJ. Ecol.20089690391310.1111/j.1365-2745.2008.01419.x CzyzEAIntraspecific genetic variation of a Fagus sylvatica population in a temperate forest derived from airborne imaging spectroscopy time seriesEcol. Evol.2020107419743032760538739131910.1002/ece3.6469 ReichPBWaltersMBEllsworthDSLeaf life-span in relation to leaf, plant, and stand characteristics among diverse ecosystemsEcol. Monogr.19926236539210.2307/2937116 JonesMOJonesLAKimballJSMcDonaldKCSatellite passive microwave remote sensing for monitoring global land surface phenologyRemote Sens. Environ.20111151102111410.1016/j.rse.2010.12.015 MyneniRBGlobal products of vegetation leaf area and fraction absorbed PAR from year one of MODIS dataRemote Sens. Environ.20028321423110.1016/S0034-4257(02)00074-3 WengQHRemote sensing of impervious surfaces in the urban areas: requirements, methods, and trendsRemote Sens. Environ.2012117344910.1016/j.rse.2011.02.030 Salcedo-SanzSMachine learning information fusion in Earth observation: a comprehensive review of methods, applications and data sourcesInf. Fusion20206325627210.1016/j.inffus.2020.07.004 AsnerGPAirborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservationScience20173553853891:CAS:528:DC%2BC2sXhsVGrsLc%3D2812681510.1126/science.aaj1987 LauschAUnderstanding forest health with remote sensing, part III: requirements for a scalable multi-source forest health monitoring network based on data science approachesRemote Sens.201810112010.3390/rs10071120 BuchananGMFree satellite data key to conservationScience20183611391401:CAS:528:DC%2BC1cXhtlahtr%2FO30002246 SchmellerDSAn operational definition of essential biodiversity variablesBiodivers. Conserv.2017262967297210.1007/s10531-017-1386-9 LakePSDisturbance, patchiness, and diversity in streamsJ. N. Am. Benthol. Soc.20001957359210.2307/1468118 HustonMAWolvertonSThe global distribution of net primary production: resolving the paradoxEcol. Monogr.20097934337710.1890/08-0588.1 RoyDPBoschettiLJusticeCOJuJThe collection 5 MODIS burned area product—global evaluation by comparison with the MODIS active fire productRemote Sens. Environ.20081123690370710.1016/j.rse.2008.05.013 PettorelliNFraming the concept of satellite remote sensing essential biodiversity variables: challenges and future directionsRemote Sens. Ecol. Conserv.2016212213110.1002/rse2.15 WalkerBKinzigALangridgeJPlant attribute diversity, resilience, and ecosystem function: the nature and significance of dominant and minor speciesEcosystems199929511310.1007/s100219900062 Morsdorf, F. et al. in Remote Sensing of Plant Biodiversity (eds Cavender-Bares, J. et al.) 83–104 (Springer International, 2020). SchmidtKSMapping coastal vegetation using an expert system and hyperspectral imageryPhotogramm. Eng. Remote Sens.20047070371510.14358/PERS.70.6.703 BushAConnecting Earth observation to high-throughput biodiversity dataNat. Ecol. Evol.20171017610.1038/s41559-017-0176 YangZSpotting East African mammals in open savannah from spacePLoS ONE20149e11598925551561428122110.1371/journal.pone.0115989 Cavender-BaresJAssociations of leaf spectra with genetic and phylogenetic variation in oaks: prospects for remote detection of biodiversityRemote Sens.2016822110.3390/rs8030221 O’Connor, B., Bojinski, S., Roosli, C. & Schaepman, M. E. Monitoring global changes in biodiversity and climate essential as ecological crisis intensifies. Ecol. Inform. 55, 101033 (2020). JoshiCIndirect remote sensing of a cryptic forest understorey invasive speciesFor. Ecol. Manag.200622524525610.1016/j.foreco.2006.01.013 VihervaaraPHow essential biodiversity variables and remote sensing can help national biodiversity monitoringGlob. Ecol. Conserv.201710435910.1016/j.gecco.2017.01.007 KhosravipourASkidmoreAKIsenburgMGenerating spike-free digital surface models using LiDAR raw point clouds: a new approach for forestry applicationsInt. J. Appl. Earth Obs. Geoinf.201652104114 NavarroLMMonitoring biodiversity change through effective global coordinationCurr. Opin. Environ. Sustain.20172915816910.1016/j.cosust.2018.02.005 NiklasKJ‘Diminishing returns’ in the scaling of functional leaf traits across and within species groupsProc. Natl Acad. Sci. USA2007104889188961:CAS:528:DC%2BD2sXmt1Wgt7g%3D17502616188559810.1073/pnas.0701135104 DíazSFunctional traits, the phylogeny of function, and ecosystem service vulnerabilityEcol. Evol.201332958297524101986379054310.1002/ece3.601 WuJGEffects of changing scale on landscape pattern analysis: scaling relationsLandsc. Ecol.20041912513810.1023/B:LAND.0000021711.40074.ae RamoeloAChoMMathieuRSkidmoreAKPotential of Sentinel-2 spectral configuration to assess rangeland qualityJ. Appl. Remote Sens.2015909409610.1117/1.JRS.9.094096 ShiYFWangTJSkidmoreAKHeurichMImportant LiDAR metrics for discriminating forest tree species in Central EuropeISPRS J. Photogramm. Remote Sens.201813716317410.1016/j.isprsjprs.2018.02.002 RichardsJAWoodgatePWSkidmoreAKAn explanation of enhanced radar backscattering from flooded forestsInt. J. Remote Sens.198781093110010.1080/01431168708954756 VerstraeteMMDinerDJBezyJLPlanning for a spaceborne Earth observation mission: from user expectations to measurement requirementsEnviron. Sci. Policy20155441942710.1016/j.envsci.2015.08.005 ThackeraySJPhenological sensitivity to climate across taxa and trophic levelsNature20165352412451:CAS:528:DC%2BC28XhtVOns7bP2736222210.1038/nature18608 Belward, A. The Global Observing System for Climate: Implementation Needs Report No. GCOS-200 (Global Climate Observing System, 2016). SkidmoreAKAn expert system classifies eucalypt forest types using Landsat thematic mapper data and a digital terrain modelPhotogramm. Eng. Remote Sens.19895514491464 BaldeckCAAsnerGPImproving remote species identification through efficient training data collectionRemote Sens.201462682269810.3390/rs6042682 GarriguesSAllardDBaretFWeissMInfluence of landscape spatial heterogeneity on the non-linear estimation of leaf area index from moderate spatial resolution remote sensing dataRemote Sens. Environ.200610528629810.1016/j.rse.2006.07.013 TranquilliSLack of conservation effort rapidly increases African great ape extinction riskConserv. Lett.20125485510.1111/j.1755-263X.2011.00211.x HerktKMBSkidmoreAKFahrJMacroecological conclusions based on IUCN expert maps: a call for cautionGlob. Ecol. Biogeogr.20172693094110.1111/geb.12601 TurnerWSensing biodiversityScience20143463013021:CAS:528:DC%2BC2cXhvFSjtbrI2532437210.1126/science.1256014 HomolovaLMaenovskyZCleversJGarcia-SantosGSchaeprnanMEReview of optical-based remote sensing for plant trait mappingEcol. Complex.20131511610.1016/j.ecocom.2013.06.003 Díaz, S. et al. Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IP W Jetz (1451_CR5) 2016; 2 P Wilkes (1451_CR80) 2016; 7 PS Lake (1451_CR17) 2000; 19 J Transon (1451_CR82) 2018; 10 1451_CR46 CA Baldeck (1451_CR76) 2014; 6 DS Schmeller (1451_CR58) 2017; 26 PL Nagler (1451_CR49) 2009; 113 A Khosravipour (1451_CR25) 2016; 52 KMB Herkt (1451_CR35) 2017; 26 S Madonsela (1451_CR86) 2017; 58 AK Skidmore (1451_CR98) 2015; 523 A Bush (1451_CR87) 2017; 1 GP Asner (1451_CR71) 1998; 8 S Lavorel (1451_CR109) 2002; 16 YF Xue (1451_CR20) 2017; 9 J Hyyppa (1451_CR81) 2008; 29 I Garonna (1451_CR54) 2016; 22 MC Hansen (1451_CR36) 2013; 342 BW Brassard (1451_CR50) 2011; 25 AK Schweiger (1451_CR122) 2018; 2 J Cavender-Bares (1451_CR123) 2016; 8 C Joshi (1451_CR68) 2006; 225 W Neumann (1451_CR94) 2015; 3 R Kays (1451_CR88) 2015; 348 KS Schmidt (1451_CR28) 2004; 70 HM Pereira (1451_CR4) 2013; 339 S Tranquilli (1451_CR116) 2012; 5 M Schlerf (1451_CR19) 2005; 95 1451_CR3 B Walker (1451_CR56) 1999; 2 1451_CR26 1451_CR27 A Ramoelo (1451_CR85) 2015; 9 1451_CR1 M Paganini (1451_CR2) 2016; 2 D Schmeller (1451_CR113) 2017; 26 PJ Curran (1451_CR23) 2001; 76 H Ye (1451_CR38) 2019; 16 MN Tuanmu (1451_CR32) 2014; 23 KJ Niklas (1451_CR55) 2007; 104 J Elith (1451_CR120) 2006; 29 JP Gibert (1451_CR107) 2015; 52 1451_CR14 MS Zhao (1451_CR21) 2005; 95 S Salcedo-Sanz (1451_CR42) 2020; 63 SB Wu (1451_CR41) 2021; 171 L Gratani (1451_CR47) 2000; 43 F Urbano (1451_CR91) 2010; 365 S Bojinski (1451_CR15) 2014; 95 1451_CR103 1451_CR95 1451_CR96 1451_CR100 S Díaz (1451_CR111) 2013; 3 R De Jong (1451_CR74) 2011; 115 MO Jones (1451_CR53) 2011; 115 F Chen (1451_CR64) 2018; 10 R Barga (1451_CR9) 2011; 15 FE Muller-Karger (1451_CR10) 2018; 28 D Vogel (1451_CR115) 2008; 11 S Saura (1451_CR73) 2014; 51 EA Czyz (1451_CR121) 2020; 10 A Lausch (1451_CR78) 2016; 70 N Pettorelli (1451_CR7) 2016; 2 A Lausch (1451_CR8) 2018; 10 AK Skidmore (1451_CR67) 1997; 63 W Turner (1451_CR112) 2014; 346 GM Buchanan (1451_CR117) 2018; 361 A Lausch (1451_CR33) 2015; 295 J Pekel (1451_CR37) 2016; 540 AS Belward (1451_CR114) 2015; 103 PB Reich (1451_CR51) 1992; 62 W Turner (1451_CR118) 2015; 182 MC Hansen (1451_CR101) 2010; 107 WD Kissling (1451_CR43) 2018; 93 P Vihervaara (1451_CR102) 2017; 10 MA Huston (1451_CR52) 2009; 79 RS Defries (1451_CR69) 1995; 100 JA Richards (1451_CR45) 1987; 8 WL Qi (1451_CR84) 2016; 187 JG Masek (1451_CR99) 2008; 112 C Potter (1451_CR59) 2005; 8 1451_CR124 Z Yang (1451_CR93) 2014; 9 MM Verstraete (1451_CR97) 2015; 54 GJ Scott (1451_CR66) 2017; 14 LM Navarro (1451_CR6) 2017; 29 GR Van der Werf (1451_CR62) 2010; 10 B O’Connor (1451_CR11) 2015; 1 IR Geijzendorffer (1451_CR12) 2016; 53 JG Wu (1451_CR16) 2004; 19 AK Skidmore (1451_CR31) 1989; 55 C Healy (1451_CR44) 2008; 96 DP Roy (1451_CR60) 2008; 112 K Kitayama (1451_CR48) 2002; 90 UB Nidumolu (1451_CR63) 2004; 15 J Peterseil (1451_CR72) 2004; 21 GP Asner (1451_CR104) 2017; 355 S Díaz (1451_CR106) 2016; 529 SJ Thackeray (1451_CR110) 2016; 535 MA Wulder (1451_CR119) 2015; 170 KM McManus (1451_CR90) 2016; 8 S Rohde (1451_CR13) 2006; 78 SJ Graves (1451_CR18) 2016; 8 RB Myneni (1451_CR22) 2002; 83 JE Meireles (1451_CR89) 2020; 228 YF Shi (1451_CR79) 2018; 137 QH Weng (1451_CR65) 2012; 117 HC Cubaynes (1451_CR92) 2019; 35 YF Bai (1451_CR57) 2012; 49 L Guanter (1451_CR83) 2015; 7 L Homolova (1451_CR24) 2013; 15 WD Kissling (1451_CR75) 2018; 2 S Garrigues (1451_CR40) 2006; 105 D Arvor (1451_CR29) 2013; 82 M Buchhorn (1451_CR34) 2020; 12 M Coll (1451_CR105) 2016; 60 R Lucas (1451_CR30) 2007; 62 FE Fassnacht (1451_CR77) 2016; 186 PJ Curran (1451_CR39) 2005; 26 M Hagen (1451_CR108) 2012; 46 AM Cunliffe (1451_CR70) 2016; 183 J Russell-Smith (1451_CR61) 1997; 34 34031569 - Nat Ecol Evol. 2021 May 24 34697457 - Nat Ecol Evol. 2021 Dec;5(12):1639 34282320 - Nat Ecol Evol. 2021 Jul 19 36100676 - Nature. 2022 Sep;609(7927):467 |
| References_xml | – reference: GeijzendorfferIRBridging the gap between biodiversity data and policy reporting needs: an essential biodiversity variables perspectiveJ. Appl. Ecol.2016531341135010.1111/1365-2664.12417 – reference: LauschALinking earth observation and taxonomic, structural and functional biodiversity: local to ecosystem perspectivesEcol. Indic.20167031733910.1016/j.ecolind.2016.06.022 – reference: SchmellerDSAn operational definition of essential biodiversity variablesBiodivers. Conserv.2017262967297210.1007/s10531-017-1386-9 – reference: MeirelesJELeaf reflectance spectra capture the evolutionary history of seed plantsNew Phytol.202022848549332579721754050710.1111/nph.16771 – reference: DefriesRSMapping the land surface for global atmosphere–biosphere models—toward continuous distributions of vegetation’s functional propertiesJ. Geophys. Res. Atmos.1995100208672088210.1029/95JD01536 – reference: Cavender-BaresJAssociations of leaf spectra with genetic and phylogenetic variation in oaks: prospects for remote detection of biodiversityRemote Sens.2016822110.3390/rs8030221 – reference: PaganiniMLeidnerAKGellerGTurnerWWegmannMThe role of space agencies in remotely sensed essential biodiversity variablesRemote Sens. Ecol. Conserv.2016213214010.1002/rse2.29 – reference: JonesMOJonesLAKimballJSMcDonaldKCSatellite passive microwave remote sensing for monitoring global land surface phenologyRemote Sens. Environ.20111151102111410.1016/j.rse.2010.12.015 – reference: ChenFFast automatic airport detection in remote sensing images using convolutional neural networksRemote Sens.20181044310.3390/rs10030443 – reference: KisslingWDTowards global data products of essential biodiversity variables on species traitsNat. Ecol. Evol.20182153115403022481410.1038/s41559-018-0667-3 – reference: SkidmoreAKTurnerBJBrinkhofWKnowlesEPerformance of a neural network: mapping forests using GIS and remotely sensed dataPhotogramm. Eng. Remote Sens.199763501514 – reference: XueYFWangTJSkidmoreAKAutomatic counting of large mammals from very high resolution panchromatic satellite imageryRemote Sens.2017987810.3390/rs9090878 – reference: HansenMCHigh-resolution global maps of 21st-century forest cover changeScience20133428508531:CAS:528:DC%2BC3sXhslCrsrbO2423372210.1126/science.1244693 – reference: PekelJCottamAGorelickNBelwardASHigh-resolution mapping of global surface water and its long-term changesNature20165404184221:CAS:528:DC%2BC28XitVWmurbJ2792673310.1038/nature20584 – reference: Weiss, J. R., Smythe, W. D. & Lu, W. W. Science Traceability. In Proc. IEEE Aerospace Conference 292–299 (IEEE, 2005). – reference: TurnerWSensing biodiversityScience20143463013021:CAS:528:DC%2BC2cXhvFSjtbrI2532437210.1126/science.1256014 – reference: BushAConnecting Earth observation to high-throughput biodiversity dataNat. Ecol. Evol.20171017610.1038/s41559-017-0176 – reference: BelwardASSkoienJOWho launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellitesISPRS J. Photogramm. Remote Sens.201510311512810.1016/j.isprsjprs.2014.03.009 – reference: SkidmoreAKAn expert system classifies eucalypt forest types using Landsat thematic mapper data and a digital terrain modelPhotogramm. Eng. Remote Sens.19895514491464 – reference: Russell-SmithJRyanPGDurieuRA LANDSAT MSS-derived fire history of Kakadu National Park, monsoonal northern Australia, 1980–94: seasonal extent, frequency and patchinessJ. Appl. Ecol.19973474876610.2307/2404920 – reference: Copernicus Global Land Service: FAPAR Copernicushttps://land.copernicus.eu/global/about (2020). – reference: Belward, A. The Global Observing System for Climate: Implementation Needs Report No. GCOS-200 (Global Climate Observing System, 2016). – reference: SchmellerDBuilding capacity in biodiversity monitoring at the global scaleBiodivers. Conserv.2017262765279010.1007/s10531-017-1388-7 – reference: MasekJGNorth American forest disturbance mapped from a decadal Landsat recordRemote Sens. Environ.20081122914292610.1016/j.rse.2008.02.010 – reference: SchweigerAKPlant spectral diversity integrates functional and phylogenetic components of biodiversity and predicts ecosystem functionNat. Ecol. Evol.201829769822976044010.1038/s41559-018-0551-1 – reference: ZhaoMSHeinschFANemaniRRRunningSWImprovements of the MODIS terrestrial gross and net primary production global data setRemote Sens. Environ.20059516417610.1016/j.rse.2004.12.011 – reference: VerstraeteMMDinerDJBezyJLPlanning for a spaceborne Earth observation mission: from user expectations to measurement requirementsEnviron. Sci. Policy20155441942710.1016/j.envsci.2015.08.005 – reference: SchlerfMAtzbergerCHillJRemote sensing of forest biophysical variables using HyMap imaging spectrometer dataRemote Sens. Environ.20059517719410.1016/j.rse.2004.12.016 – reference: ReichPBWaltersMBEllsworthDSLeaf life-span in relation to leaf, plant, and stand characteristics among diverse ecosystemsEcol. Monogr.19926236539210.2307/2937116 – reference: NeumannWOpportunities for the application of advanced remotely-sensed data in ecological studies of terrestrial animal movementMov. Ecol.2015325941571441810410.1186/s40462-015-0036-7 – reference: Muller-KargerFESatellite sensor requirements for monitoring essential biodiversity variables of coastal ecosystemsEcol. Appl.20182874976029509310594726410.1002/eap.1682 – reference: McManusKMPhylogenetic structure of foliar spectral traits in tropical forest canopiesRemote Sens.2016819610.3390/rs8030196 – reference: MyneniRBGlobal products of vegetation leaf area and fraction absorbed PAR from year one of MODIS dataRemote Sens. Environ.20028321423110.1016/S0034-4257(02)00074-3 – reference: UrbanoFWildlife tracking data management: a new visionPhil. Trans. R. Soc. B Biol. Sci.20103652177218510.1098/rstb.2010.0081 – reference: O’ConnorBEarth observation as a tool for tracking progress towards the Aichi Biodiversity TargetsRemote Sens. Ecol. Conserv.20151192810.1002/rse2.4 – reference: BuchananGMFree satellite data key to conservationScience20183611391401:CAS:528:DC%2BC1cXhtlahtr%2FO30002246 – reference: WulderMAVirtual constellations for global terrestrial monitoringRemote Sens. Environ.2015170627610.1016/j.rse.2015.09.001 – reference: FassnachtFEReview of studies on tree species classification from remotely sensed dataRemote Sens. Environ.2016186648710.1016/j.rse.2016.08.013 – reference: GaronnaIde JongRSchaepmanMEVariability and evolution of global land surface phenology over the past three decades (1982–2012)Glob. Change Biol.2016221456146810.1111/gcb.13168 – reference: ShiYFWangTJSkidmoreAKHeurichMImportant LiDAR metrics for discriminating forest tree species in Central EuropeISPRS J. Photogramm. Remote Sens.201813716317410.1016/j.isprsjprs.2018.02.002 – reference: CunliffeAMBrazierREAndersonKUltra-fine grain landscape-scale quantification of dryland vegetation structure with drone-acquired structure-from-motion photogrammetryRemote Sens. Environ.201618312914310.1016/j.rse.2016.05.019 – reference: MadonselaSMulti-phenology WorldView-2 imagery improves remote sensing of savannah tree speciesInt. J. Appl. Earth Obs. Geoinf.2017586573 – reference: HealyCGotelliNJPotvinCPartitioning the effects of biodiversity and environmental heterogeneity for productivity and mortality in a tropical tree plantationJ. Ecol.20089690391310.1111/j.1365-2745.2008.01419.x – reference: TranquilliSLack of conservation effort rapidly increases African great ape extinction riskConserv. Lett.20125485510.1111/j.1755-263X.2011.00211.x – reference: HyyppaJReview of methods of small-footprint airborne laser scanning for extracting forest inventory data in boreal forestsInt. J. Remote Sens.2008291339136610.1080/01431160701736489 – reference: YeHImproving remote sensing-based net primary production estimation in the grazed land with defoliation formulation modelJ. Mt. Sci.20191632333610.1007/s11629-018-5200-2 – reference: SauraSBodinOFortinMJStepping stones are crucial for species’ long-distance dispersal and range expansion through habitat networksJ. Appl. Ecol.20145117118210.1111/1365-2664.12179 – reference: LucasRRowlandsABrownAKeyworthSBuntingPRule-based classification of multi-temporal satellite imagery for habitat and agricultural land cover mappingISPRS J. Photogramm. Remote Sens.20076216518510.1016/j.isprsjprs.2007.03.003 – reference: BojinskiSThe concept of essential climate variables in support of climate research, applications, and policyBull. Am. Meteorol. Soc.2014951431144310.1175/BAMS-D-13-00047.1 – reference: BaldeckCAAsnerGPImproving remote species identification through efficient training data collectionRemote Sens.201462682269810.3390/rs6042682 – reference: HagenMBiodiversity, species interactions and ecological networks in a fragmented worldAdv. Ecol. Res.2012468921010.1016/B978-0-12-396992-7.00002-2 – reference: TuanmuMNJetzWA global 1-km consensus land-cover product for biodiversity and ecosystem modellingGlob. Ecol. Biogeogr.2014231031104510.1111/geb.12182 – reference: WuSBMonitoring tree-crown scale autumn leaf phenology in a temperate forest with an integration of PlanetScope and drone remote sensing observationsISPRS J. Photogramm. Remote Sens.2021171364810.1016/j.isprsjprs.2020.10.017 – reference: LavorelSGarnierEPredicting changes in community composition and ecosystem functioning from plant traits: revisiting the Holy GrailFunct. Ecol.20021654555610.1046/j.1365-2435.2002.00664.x – reference: QiWLDubayahROCombining Tandem-X InSAR and simulated GEDI LiDAR observations for forest structure mappingRemote Sens. Environ.201618725326610.1016/j.rse.2016.10.018 – reference: O’Connor, B., Bojinski, S., Roosli, C. & Schaepman, M. E. Monitoring global changes in biodiversity and climate essential as ecological crisis intensifies. Ecol. Inform. 55, 101033 (2020). – reference: BargaRGannonDReedDThe client and the cloud democratizing research computingIEEE Internet Comput.201115727510.1109/MIC.2011.20 – reference: KaysRCrofootMCJetzWWikelskiMTerrestrial animal tracking as an eye on life and planetScience2015348aaa24782606885810.1126/science.aaa2478 – reference: Van der WerfGRGlobal fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009)Atmos. Chem. Phys.20101011707117351:CAS:528:DC%2BC3MXmvFemsb0%3D10.5194/acp-10-11707-2010 – reference: WalkerBKinzigALangridgeJPlant attribute diversity, resilience, and ecosystem function: the nature and significance of dominant and minor speciesEcosystems199929511310.1007/s100219900062 – reference: RamoeloAChoMMathieuRSkidmoreAKPotential of Sentinel-2 spectral configuration to assess rangeland qualityJ. Appl. Remote Sens.2015909409610.1117/1.JRS.9.094096 – reference: Morsdorf, F. et al. in Remote Sensing of Plant Biodiversity (eds Cavender-Bares, J. et al.) 83–104 (Springer International, 2020). – reference: AsnerGPAirborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservationScience20173553853891:CAS:528:DC%2BC2sXhsVGrsLc%3D2812681510.1126/science.aaj1987 – reference: What are EBVs? GEO BONhttps://geobon.org/ebvs/what-are-ebvs/ (2020). – reference: SchmidtKSMapping coastal vegetation using an expert system and hyperspectral imageryPhotogramm. Eng. Remote Sens.20047070371510.14358/PERS.70.6.703 – reference: PeterseilJEvaluating the ecological sustainability of Austrian agricultural landscapes—the SINUS approachLand Use Policy20042130732010.1016/j.landusepol.2003.10.011 – reference: PettorelliNFraming the concept of satellite remote sensing essential biodiversity variables: challenges and future directionsRemote Sens. Ecol. Conserv.2016212213110.1002/rse2.15 – reference: GibertJPDellAIDeLongJPPawarSScaling-up trait variation from individuals to ecosystemsAdv. Ecol. Res.20155211710.1016/bs.aecr.2015.03.001 – reference: HustonMAWolvertonSThe global distribution of net primary production: resolving the paradoxEcol. Monogr.20097934337710.1890/08-0588.1 – reference: KisslingWDBuilding essential biodiversity variables (EBVs) of species distribution and abundance at a global scaleBiol. Rev.2018936006252876690810.1111/brv.12359 – reference: LauschAUnderstanding forest health with remote sensing, part III: requirements for a scalable multi-source forest health monitoring network based on data science approachesRemote Sens.201810112010.3390/rs10071120 – reference: TurnerWFree and open-access satellite data are key to biodiversity conservationBiol. Conserv.201518217317610.1016/j.biocon.2014.11.048 – reference: DíazSFunctional traits, the phylogeny of function, and ecosystem service vulnerabilityEcol. Evol.201332958297524101986379054310.1002/ece3.601 – reference: NidumoluUBDe BieCVan KeulenHSkidmoreAKEnhancement of area-specific land-use objectives for land developmentLand Degrad. Dev.20041551352510.1002/ldr.636 – reference: HomolovaLMaenovskyZCleversJGarcia-SantosGSchaeprnanMEReview of optical-based remote sensing for plant trait mappingEcol. Complex.20131511610.1016/j.ecocom.2013.06.003 – reference: AsnerGPWessmanCASchimelDSHeterogeneity of savanna canopy structure and function from imaging spectrometry and inverse modelingEcol. Appl.199881022103610.1890/1051-0761(1998)008[1022:HOSCSA]2.0.CO;2 – reference: GravesSJTree species abundance predictions in a tropical agricultural landscape with a supervised classification model and imbalanced dataRemote Sens.2016816110.3390/rs8020161 – reference: RichardsJAWoodgatePWSkidmoreAKAn explanation of enhanced radar backscattering from flooded forestsInt. J. Remote Sens.198781093110010.1080/01431168708954756 – reference: GrataniLBombelliACorrelation between leaf age and other leaf traits in three Mediterranean maquis shrub species: Quercus ilex, Phillyrea latifolia and Cistus incanusEnviron. Exp. Bot.20004314115310.1016/S0098-8472(99)00052-0 – reference: VogelDPrivate global business regulationAnnu. Rev. Polit. Sci.20081126128210.1146/annurev.polisci.11.053106.141706 – reference: GuanterLThe EnMAP spaceborne imaging spectroscopy mission for Earth observationRemote Sens.201578830885710.3390/rs70708830 – reference: Díaz, S. et al. Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES Secretariat, 2019). – reference: WilkesPUsing discrete-return airborne laser scanning to quantify number of canopy strata across diverse forest typesMethods Ecol. Evol.2016770071210.1111/2041-210X.12510 – reference: National Academies of Sciences, Engineering, and Medicine Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space (National Academies Press, 2018). – reference: RoyDPBoschettiLJusticeCOJuJThe collection 5 MODIS burned area product—global evaluation by comparison with the MODIS active fire productRemote Sens. Environ.20081123690370710.1016/j.rse.2008.05.013 – reference: YangZSpotting East African mammals in open savannah from spacePLoS ONE20149e11598925551561428122110.1371/journal.pone.0115989 – reference: De JongRde BruinSde WitASchaepmanMEDentDLAnalysis of monotonic greening and browning trends from global NDVI time-seriesRemote Sens. Environ.201111569270210.1016/j.rse.2010.10.011 – reference: NiklasKJ‘Diminishing returns’ in the scaling of functional leaf traits across and within species groupsProc. Natl Acad. Sci. USA2007104889188961:CAS:528:DC%2BD2sXmt1Wgt7g%3D17502616188559810.1073/pnas.0701135104 – reference: VihervaaraPHow essential biodiversity variables and remote sensing can help national biodiversity monitoringGlob. Ecol. Conserv.201710435910.1016/j.gecco.2017.01.007 – reference: CollMEcological indicators to capture the effects of fishing on biodiversity and conservation status of marine ecosystemsEcol. Indic.20166094796210.1016/j.ecolind.2015.08.048 – reference: HansenMCStehmanSVPotapovPVQuantification of global gross forest cover lossProc. Natl Acad. Sci. USA2010107865086551:CAS:528:DC%2BC3cXmsFWmtLs%3D20421467288935410.1073/pnas.0912668107 – reference: Walters, M. et al. Essential Biodiversity Variables UNEP/CBD/SBSTTA/17/INF/7 (Convention on Biological Diversity, 2013). – reference: HerktKMBSkidmoreAKFahrJMacroecological conclusions based on IUCN expert maps: a call for cautionGlob. Ecol. Biogeogr.20172693094110.1111/geb.12601 – reference: ElithJNovel methods improve prediction of species’ distributions from occurrence dataEcography20062912915110.1111/j.2006.0906-7590.04596.x – reference: NaglerPLGlennEPHinojosa-HuertaOSynthesis of ground and remote sensing data for monitoring ecosystem functions in the Colorado River Delta, MexicoRemote Sens. Environ.20091131473148510.1016/j.rse.2008.06.018 – reference: BaiYFGrazing alters ecosystem functioning and C:N:P stoichiometry of grasslands along a regional precipitation gradientJ. Appl. Ecol.201249120412151:CAS:528:DC%2BC3sXislSmt7k%3D10.1111/j.1365-2664.2012.02205.x – reference: ScottGJEnglandMRStarmsWAMarcumRADavisCHTraining deep convolutional neural networks for land-cover classification of high-resolution imageryIEEE Geosci. Remote Sens. Lett.20171454955310.1109/LGRS.2017.2657778 – reference: CurranPJDunganJLPetersonDLEstimating the foliar biochemical concentration of leaves with reflectance spectrometry testing the Kokaly and Clark methodologiesRemote Sens. Environ.20017634935910.1016/S0034-4257(01)00182-1 – reference: ArvorDDurieuxLAndresSLaporteMAAdvances in geographic object-based image analysis with ontologies: a review of main contributions and limitations from a remote sensing perspectiveISPRS J. Photogramm. Remote Sens.20138212513710.1016/j.isprsjprs.2013.05.003 – reference: LakePSDisturbance, patchiness, and diversity in streamsJ. N. Am. Benthol. Soc.20001957359210.2307/1468118 – reference: RohdeSHostmannMPeterAEwaldKCRoom for rivers: an integrative search strategy for floodplain restorationLandsc. Urban Plan.200678507010.1016/j.landurbplan.2005.05.006 – reference: JetzWMonitoring plant functional diversity from spaceNat. Plants20162160242724935710.1038/nplants.2016.24 – reference: Salcedo-SanzSMachine learning information fusion in Earth observation: a comprehensive review of methods, applications and data sourcesInf. Fusion20206325627210.1016/j.inffus.2020.07.004 – reference: DíazSThe global spectrum of plant form and functionNature20165291671712670081110.1038/nature16489 – reference: CurranPJSteeleCMMERIS: the re-branding of an ocean sensorInt. J. Remote Sens.2005261781179810.1080/01431160412331330275 – reference: WuJGEffects of changing scale on landscape pattern analysis: scaling relationsLandsc. Ecol.20041912513810.1023/B:LAND.0000021711.40074.ae – reference: CubaynesHCFretwellPTBamfordCGerrishLJacksonJAWhales from space: four mysticete species described using new VHR satellite imageryMar. Mammal. Sci.20193546649110.1111/mms.12544 – reference: WengQHRemote sensing of impervious surfaces in the urban areas: requirements, methods, and trendsRemote Sens. Environ.2012117344910.1016/j.rse.2011.02.030 – reference: KhosravipourASkidmoreAKIsenburgMGenerating spike-free digital surface models using LiDAR raw point clouds: a new approach for forestry applicationsInt. J. Appl. Earth Obs. Geoinf.201652104114 – reference: BuchhornMCopernicus global land cover layers—Collection 2Remote Sens.202012104410.3390/rs12061044 – reference: TransonJd’AndrimontRMaugnardADefournyPSurvey of hyperspectral earth observation applications from space in the Sentinel-2 contextRemote Sens.20181015710.3390/rs10020157 – reference: LauschAUnderstanding and quantifying landscape structure—a review on relevant process characteristics, data models and landscape metricsEcol. Model.2015295314110.1016/j.ecolmodel.2014.08.018 – reference: NavarroLMMonitoring biodiversity change through effective global coordinationCurr. Opin. Environ. Sustain.20172915816910.1016/j.cosust.2018.02.005 – reference: SkidmoreAKAgree on biodiversity metrics to track from spaceNature20155234034051:CAS:528:DC%2BC2MXht1WksrbO2620158210.1038/523403a – reference: JoshiCIndirect remote sensing of a cryptic forest understorey invasive speciesFor. Ecol. Manag.200622524525610.1016/j.foreco.2006.01.013 – reference: CzyzEAIntraspecific genetic variation of a Fagus sylvatica population in a temperate forest derived from airborne imaging spectroscopy time seriesEcol. Evol.2020107419743032760538739131910.1002/ece3.6469 – reference: GarriguesSAllardDBaretFWeissMInfluence of landscape spatial heterogeneity on the non-linear estimation of leaf area index from moderate spatial resolution remote sensing dataRemote Sens. Environ.200610528629810.1016/j.rse.2006.07.013 – reference: Surface Biology and Geology (SBG) NASA Sciencehttps://science.nasa.gov/earth-science/decadal-sbg (2020). – reference: PotterCRecent history of large-scale ecosystem disturbances in North America derived from the AVHRR satellite recordEcosystems2005880882410.1007/s10021-005-0041-6 – reference: ThackeraySJPhenological sensitivity to climate across taxa and trophic levelsNature20165352412451:CAS:528:DC%2BC28XhtVOns7bP2736222210.1038/nature18608 – reference: KitayamaKAibaSIEcosystem structure and productivity of tropical rain forests along altitudinal gradients with contrasting soil phosphorus pools on Mount Kinabalu, BorneoJ. Ecol.200290375110.1046/j.0022-0477.2001.00634.x – reference: Verger, A. & Descals, A. Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)—300 m Version 1; Algorithm Theoretical Basis Document (ATBD), Issue 1.00 (Framework Service Contract No. 199494-JRC) (Copernicus Global Land Operations CGLOPS-1, 2020). – reference: PereiraHMEssential biodiversity variablesScience20133392772781:CAS:528:DC%2BC3sXhslagsbc%3D2332903610.1126/science.1229931 – reference: BrassardBWChenHYHBergeronYPareDDifferences in fine root productivity between mixed- and single-species standsFunct. Ecol.20112523824610.1111/j.1365-2435.2010.01769.x – volume: 29 start-page: 158 year: 2017 ident: 1451_CR6 publication-title: Curr. Opin. Environ. Sustain. doi: 10.1016/j.cosust.2018.02.005 – volume: 54 start-page: 419 year: 2015 ident: 1451_CR97 publication-title: Environ. Sci. Policy doi: 10.1016/j.envsci.2015.08.005 – volume: 22 start-page: 1456 year: 2016 ident: 1451_CR54 publication-title: Glob. Change Biol. doi: 10.1111/gcb.13168 – volume: 2 start-page: 976 year: 2018 ident: 1451_CR122 publication-title: Nat. Ecol. Evol. doi: 10.1038/s41559-018-0551-1 – volume: 2 start-page: 1531 year: 2018 ident: 1451_CR75 publication-title: Nat. Ecol. Evol. doi: 10.1038/s41559-018-0667-3 – volume: 3 year: 2015 ident: 1451_CR94 publication-title: Mov. Ecol. doi: 10.1186/s40462-015-0036-7 – volume: 19 start-page: 573 year: 2000 ident: 1451_CR17 publication-title: J. N. Am. Benthol. Soc. doi: 10.2307/1468118 – volume: 113 start-page: 1473 year: 2009 ident: 1451_CR49 publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2008.06.018 – volume: 15 start-page: 513 year: 2004 ident: 1451_CR63 publication-title: Land Degrad. Dev. doi: 10.1002/ldr.636 – volume: 170 start-page: 62 year: 2015 ident: 1451_CR119 publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2015.09.001 – volume: 15 start-page: 1 year: 2013 ident: 1451_CR24 publication-title: Ecol. Complex. doi: 10.1016/j.ecocom.2013.06.003 – volume: 8 start-page: 161 year: 2016 ident: 1451_CR18 publication-title: Remote Sens. doi: 10.3390/rs8020161 – volume: 186 start-page: 64 year: 2016 ident: 1451_CR77 publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2016.08.013 – volume: 187 start-page: 253 year: 2016 ident: 1451_CR84 publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2016.10.018 – volume: 11 start-page: 261 year: 2008 ident: 1451_CR115 publication-title: Annu. Rev. Polit. Sci. doi: 10.1146/annurev.polisci.11.053106.141706 – volume: 95 start-page: 1431 year: 2014 ident: 1451_CR15 publication-title: Bull. Am. Meteorol. Soc. doi: 10.1175/BAMS-D-13-00047.1 – volume: 9 start-page: 094096 year: 2015 ident: 1451_CR85 publication-title: J. Appl. Remote Sens. doi: 10.1117/1.JRS.9.094096 – ident: 1451_CR27 – volume: 62 start-page: 165 year: 2007 ident: 1451_CR30 publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2007.03.003 – volume: 26 start-page: 930 year: 2017 ident: 1451_CR35 publication-title: Glob. Ecol. Biogeogr. doi: 10.1111/geb.12601 – volume: 70 start-page: 317 year: 2016 ident: 1451_CR78 publication-title: Ecol. Indic. doi: 10.1016/j.ecolind.2016.06.022 – volume: 29 start-page: 1339 year: 2008 ident: 1451_CR81 publication-title: Int. J. Remote Sens. doi: 10.1080/01431160701736489 – volume: 70 start-page: 703 year: 2004 ident: 1451_CR28 publication-title: Photogramm. Eng. Remote Sens. doi: 10.14358/PERS.70.6.703 – volume: 26 start-page: 2967 year: 2017 ident: 1451_CR58 publication-title: Biodivers. Conserv. doi: 10.1007/s10531-017-1386-9 – ident: 1451_CR103 – volume: 10 start-page: 43 year: 2017 ident: 1451_CR102 publication-title: Glob. Ecol. Conserv. doi: 10.1016/j.gecco.2017.01.007 – volume: 12 start-page: 1044 year: 2020 ident: 1451_CR34 publication-title: Remote Sens. doi: 10.3390/rs12061044 – volume: 103 start-page: 115 year: 2015 ident: 1451_CR114 publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2014.03.009 – volume: 76 start-page: 349 year: 2001 ident: 1451_CR23 publication-title: Remote Sens. Environ. doi: 10.1016/S0034-4257(01)00182-1 – volume: 26 start-page: 2765 year: 2017 ident: 1451_CR113 publication-title: Biodivers. Conserv. doi: 10.1007/s10531-017-1388-7 – volume: 43 start-page: 141 year: 2000 ident: 1451_CR47 publication-title: Environ. Exp. Bot. doi: 10.1016/S0098-8472(99)00052-0 – volume: 112 start-page: 2914 year: 2008 ident: 1451_CR99 publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2008.02.010 – volume: 26 start-page: 1781 year: 2005 ident: 1451_CR39 publication-title: Int. J. Remote Sens. doi: 10.1080/01431160412331330275 – volume: 90 start-page: 37 year: 2002 ident: 1451_CR48 publication-title: J. Ecol. doi: 10.1046/j.0022-0477.2001.00634.x – volume: 19 start-page: 125 year: 2004 ident: 1451_CR16 publication-title: Landsc. Ecol. doi: 10.1023/B:LAND.0000021711.40074.ae – volume: 58 start-page: 65 year: 2017 ident: 1451_CR86 publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 137 start-page: 163 year: 2018 ident: 1451_CR79 publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2018.02.002 – volume: 10 start-page: 157 year: 2018 ident: 1451_CR82 publication-title: Remote Sens. doi: 10.3390/rs10020157 – volume: 107 start-page: 8650 year: 2010 ident: 1451_CR101 publication-title: Proc. Natl Acad. Sci. USA doi: 10.1073/pnas.0912668107 – volume: 8 start-page: 1022 year: 1998 ident: 1451_CR71 publication-title: Ecol. Appl. doi: 10.1890/1051-0761(1998)008[1022:HOSCSA]2.0.CO;2 – volume: 28 start-page: 749 year: 2018 ident: 1451_CR10 publication-title: Ecol. Appl. doi: 10.1002/eap.1682 – volume: 183 start-page: 129 year: 2016 ident: 1451_CR70 publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2016.05.019 – volume: 10 start-page: 443 year: 2018 ident: 1451_CR64 publication-title: Remote Sens. doi: 10.3390/rs10030443 – volume: 535 start-page: 241 year: 2016 ident: 1451_CR110 publication-title: Nature doi: 10.1038/nature18608 – volume: 339 start-page: 277 year: 2013 ident: 1451_CR4 publication-title: Science doi: 10.1126/science.1229931 – volume: 523 start-page: 403 year: 2015 ident: 1451_CR98 publication-title: Nature doi: 10.1038/523403a – volume: 7 start-page: 700 year: 2016 ident: 1451_CR80 publication-title: Methods Ecol. Evol. doi: 10.1111/2041-210X.12510 – volume: 117 start-page: 34 year: 2012 ident: 1451_CR65 publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2011.02.030 – volume: 62 start-page: 365 year: 1992 ident: 1451_CR51 publication-title: Ecol. Monogr. doi: 10.2307/2937116 – volume: 104 start-page: 8891 year: 2007 ident: 1451_CR55 publication-title: Proc. Natl Acad. Sci. USA doi: 10.1073/pnas.0701135104 – volume: 6 start-page: 2682 year: 2014 ident: 1451_CR76 publication-title: Remote Sens. doi: 10.3390/rs6042682 – volume: 342 start-page: 850 year: 2013 ident: 1451_CR36 publication-title: Science doi: 10.1126/science.1244693 – volume: 29 start-page: 129 year: 2006 ident: 1451_CR120 publication-title: Ecography doi: 10.1111/j.2006.0906-7590.04596.x – volume: 53 start-page: 1341 year: 2016 ident: 1451_CR12 publication-title: J. Appl. Ecol. doi: 10.1111/1365-2664.12417 – ident: 1451_CR95 doi: 10.1109/AERO.2005.1559323 – volume: 1 start-page: 19 year: 2015 ident: 1451_CR11 publication-title: Remote Sens. Ecol. Conserv. doi: 10.1002/rse2.4 – ident: 1451_CR3 – volume: 79 start-page: 343 year: 2009 ident: 1451_CR52 publication-title: Ecol. Monogr. doi: 10.1890/08-0588.1 – volume: 115 start-page: 1102 year: 2011 ident: 1451_CR53 publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2010.12.015 – volume: 10 start-page: 7419 year: 2020 ident: 1451_CR121 publication-title: Ecol. Evol. doi: 10.1002/ece3.6469 – volume: 5 start-page: 48 year: 2012 ident: 1451_CR116 publication-title: Conserv. Lett. doi: 10.1111/j.1755-263X.2011.00211.x – volume: 171 start-page: 36 year: 2021 ident: 1451_CR41 publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2020.10.017 – volume: 182 start-page: 173 year: 2015 ident: 1451_CR118 publication-title: Biol. Conserv. doi: 10.1016/j.biocon.2014.11.048 – volume: 8 start-page: 221 year: 2016 ident: 1451_CR123 publication-title: Remote Sens. doi: 10.3390/rs8030221 – volume: 14 start-page: 549 year: 2017 ident: 1451_CR66 publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2017.2657778 – volume: 10 start-page: 1120 year: 2018 ident: 1451_CR8 publication-title: Remote Sens. doi: 10.3390/rs10071120 – volume: 2 start-page: 16024 year: 2016 ident: 1451_CR5 publication-title: Nat. Plants doi: 10.1038/nplants.2016.24 – volume: 25 start-page: 238 year: 2011 ident: 1451_CR50 publication-title: Funct. Ecol. doi: 10.1111/j.1365-2435.2010.01769.x – volume: 529 start-page: 167 year: 2016 ident: 1451_CR106 publication-title: Nature doi: 10.1038/nature16489 – volume: 228 start-page: 485 year: 2020 ident: 1451_CR89 publication-title: New Phytol. doi: 10.1111/nph.16771 – volume: 540 start-page: 418 year: 2016 ident: 1451_CR37 publication-title: Nature doi: 10.1038/nature20584 – ident: 1451_CR1 – ident: 1451_CR46 – volume: 95 start-page: 164 year: 2005 ident: 1451_CR21 publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2004.12.011 – volume: 8 start-page: 808 year: 2005 ident: 1451_CR59 publication-title: Ecosystems doi: 10.1007/s10021-005-0041-6 – volume: 361 start-page: 139 year: 2018 ident: 1451_CR117 publication-title: Science doi: 10.1126/science.aau2650 – volume: 60 start-page: 947 year: 2016 ident: 1451_CR105 publication-title: Ecol. Indic. doi: 10.1016/j.ecolind.2015.08.048 – volume: 52 start-page: 1 year: 2015 ident: 1451_CR107 publication-title: Adv. Ecol. Res. doi: 10.1016/bs.aecr.2015.03.001 – volume: 346 start-page: 301 year: 2014 ident: 1451_CR112 publication-title: Science doi: 10.1126/science.1256014 – volume: 365 start-page: 2177 year: 2010 ident: 1451_CR91 publication-title: Phil. Trans. R. Soc. B Biol. Sci. doi: 10.1098/rstb.2010.0081 – volume: 105 start-page: 286 year: 2006 ident: 1451_CR40 publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2006.07.013 – volume: 8 start-page: 1093 year: 1987 ident: 1451_CR45 publication-title: Int. J. Remote Sens. doi: 10.1080/01431168708954756 – volume: 49 start-page: 1204 year: 2012 ident: 1451_CR57 publication-title: J. Appl. Ecol. doi: 10.1111/j.1365-2664.2012.02205.x – volume: 78 start-page: 50 year: 2006 ident: 1451_CR13 publication-title: Landsc. Urban Plan. doi: 10.1016/j.landurbplan.2005.05.006 – volume: 35 start-page: 466 year: 2019 ident: 1451_CR92 publication-title: Mar. Mammal. Sci. doi: 10.1111/mms.12544 – ident: 1451_CR96 – volume: 355 start-page: 385 year: 2017 ident: 1451_CR104 publication-title: Science doi: 10.1126/science.aaj1987 – volume: 7 start-page: 8830 year: 2015 ident: 1451_CR83 publication-title: Remote Sens. doi: 10.3390/rs70708830 – ident: 1451_CR124 – volume: 15 start-page: 72 year: 2011 ident: 1451_CR9 publication-title: IEEE Internet Comput. doi: 10.1109/MIC.2011.20 – volume: 295 start-page: 31 year: 2015 ident: 1451_CR33 publication-title: Ecol. Model. doi: 10.1016/j.ecolmodel.2014.08.018 – volume: 9 start-page: e115989 year: 2014 ident: 1451_CR93 publication-title: PLoS ONE doi: 10.1371/journal.pone.0115989 – volume: 34 start-page: 748 year: 1997 ident: 1451_CR61 publication-title: J. Appl. Ecol. doi: 10.2307/2404920 – volume: 51 start-page: 171 year: 2014 ident: 1451_CR73 publication-title: J. Appl. Ecol. doi: 10.1111/1365-2664.12179 – volume: 112 start-page: 3690 year: 2008 ident: 1451_CR60 publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2008.05.013 – ident: 1451_CR26 – volume: 100 start-page: 20867 year: 1995 ident: 1451_CR69 publication-title: J. Geophys. Res. Atmos. doi: 10.1029/95JD01536 – volume: 3 start-page: 2958 year: 2013 ident: 1451_CR111 publication-title: Ecol. Evol. doi: 10.1002/ece3.601 – volume: 115 start-page: 692 year: 2011 ident: 1451_CR74 publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2010.10.011 – volume: 82 start-page: 125 year: 2013 ident: 1451_CR29 publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2013.05.003 – volume: 8 start-page: 196 year: 2016 ident: 1451_CR90 publication-title: Remote Sens. doi: 10.3390/rs8030196 – volume: 46 start-page: 89 year: 2012 ident: 1451_CR108 publication-title: Adv. Ecol. Res. doi: 10.1016/B978-0-12-396992-7.00002-2 – volume: 348 start-page: aaa2478 year: 2015 ident: 1451_CR88 publication-title: Science doi: 10.1126/science.aaa2478 – volume: 55 start-page: 1449 year: 1989 ident: 1451_CR31 publication-title: Photogramm. Eng. Remote Sens. – volume: 63 start-page: 256 year: 2020 ident: 1451_CR42 publication-title: Inf. Fusion doi: 10.1016/j.inffus.2020.07.004 – volume: 95 start-page: 177 year: 2005 ident: 1451_CR19 publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2004.12.016 – ident: 1451_CR100 doi: 10.1016/j.ecoinf.2019.101033 – volume: 52 start-page: 104 year: 2016 ident: 1451_CR25 publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 10 start-page: 11707 year: 2010 ident: 1451_CR62 publication-title: Atmos. Chem. Phys. doi: 10.5194/acp-10-11707-2010 – volume: 23 start-page: 1031 year: 2014 ident: 1451_CR32 publication-title: Glob. Ecol. Biogeogr. doi: 10.1111/geb.12182 – volume: 21 start-page: 307 year: 2004 ident: 1451_CR72 publication-title: Land Use Policy doi: 10.1016/j.landusepol.2003.10.011 – volume: 2 start-page: 122 year: 2016 ident: 1451_CR7 publication-title: Remote Sens. Ecol. Conserv. doi: 10.1002/rse2.15 – volume: 16 start-page: 323 year: 2019 ident: 1451_CR38 publication-title: J. Mt. Sci. doi: 10.1007/s11629-018-5200-2 – volume: 96 start-page: 903 year: 2008 ident: 1451_CR44 publication-title: J. Ecol. doi: 10.1111/j.1365-2745.2008.01419.x – volume: 9 start-page: 878 year: 2017 ident: 1451_CR20 publication-title: Remote Sens. doi: 10.3390/rs9090878 – volume: 1 start-page: 0176 year: 2017 ident: 1451_CR87 publication-title: Nat. Ecol. Evol. doi: 10.1038/s41559-017-0176 – volume: 2 start-page: 95 year: 1999 ident: 1451_CR56 publication-title: Ecosystems doi: 10.1007/s100219900062 – volume: 225 start-page: 245 year: 2006 ident: 1451_CR68 publication-title: For. Ecol. Manag. doi: 10.1016/j.foreco.2006.01.013 – volume: 83 start-page: 214 year: 2002 ident: 1451_CR22 publication-title: Remote Sens. Environ. doi: 10.1016/S0034-4257(02)00074-3 – volume: 2 start-page: 132 year: 2016 ident: 1451_CR2 publication-title: Remote Sens. Ecol. Conserv. doi: 10.1002/rse2.29 – ident: 1451_CR14 – volume: 63 start-page: 501 year: 1997 ident: 1451_CR67 publication-title: Photogramm. Eng. Remote Sens. – volume: 93 start-page: 600 year: 2018 ident: 1451_CR43 publication-title: Biol. Rev. doi: 10.1111/brv.12359 – volume: 16 start-page: 545 year: 2002 ident: 1451_CR109 publication-title: Funct. Ecol. doi: 10.1046/j.1365-2435.2002.00664.x – reference: 34282320 - Nat Ecol Evol. 2021 Jul 19;: – reference: 34031569 - Nat Ecol Evol. 2021 May 24;: – reference: 34697457 - Nat Ecol Evol. 2021 Dec;5(12):1639 – reference: 36100676 - Nature. 2022 Sep;609(7927):467 |
| SSID | ssj0001775966 |
| Score | 2.5827353 |
| SecondaryResourceType | review_article |
| Snippet | Monitoring global biodiversity from space through remotely sensing geospatial patterns has high potential to add to our knowledge acquired by field... |
| SourceID | proquest pubmed crossref springer |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 896 |
| SubjectTerms | 631/158/670 704/158 Benchmarking Biodiversity Biological and Physical Anthropology Biological effects Biomedical and Life Sciences Ecological function Ecology Ecosystem Ecosystem structure Evolutionary Biology Interpolation Knowledge acquisition Life Sciences Monitoring Paleontology Perspective Remote observing Remote sensing Satellites Structure-function relationships Zoology |
| Title | Priority list of biodiversity metrics to observe from space |
| URI | https://link.springer.com/article/10.1038/s41559-021-01451-x https://www.ncbi.nlm.nih.gov/pubmed/33986541 https://www.proquest.com/docview/2548438999 https://www.proquest.com/docview/2528181334 |
| Volume | 5 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1LS8QwEB50vXgRxVd9EcGbBrdN2iZ4EBVFBEVEwVtJ0gkIul3dFfz5ZrrpLiJ6bpqGmXTyZV4fwIEXWVooI3ku26baTnJbOuTSpdpbnea2ptrh27vi-knePOfP0eE2immVnU1sDXXdOPKRH4eLjCKmbq1Ph--cWKMouhopNOZhIZhgpXqwcH55d_8w87KUZR4AfayW6Qt1PKITVHPKTGhZavnXzxPpF8z8FSJtT56rZViKkJGdTXS8AnM4WIWT-4-Xhojn2GtQFGs8sy9N3SVZsDciynIjNm5YY8nxiowKSViwHw7X4Onq8vHimkciBO5kqsdcqNxkCr20WpVeoUMpHZrcYF2WGBBIWptCYC1kXSinTYB0aFTfq0IXOSoj1qE3aAa4Ccyj65dO2ADrrKT2fVnqay2tKzBD48sE0k4YlYtdwoms4rVqo9VCVRMBVkGAVSvA6iuBw-k7w0mPjH9H73QyruL_Mqpm2k1gf_o47HQKX5gBNp80hjpXhTu1TGBjopvp54TQihjNEzjqlDWb_O-1bP2_lm1YzNqNQtm5O9Abf3zibsAgY7sXN9o3o2nZeA |
| linkProvider | ProQuest |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LT9wwEB7R5dBeqlZ9hUfrSu2ptdjETmKrQlUfoKXAClUgcXNtZywh0Q2wi0r_FL8RTx67qlC5cY7jROPPnhnP4wN4F0SWFspKnsumqbaX3JUeufSpDk6nuauodnh_XIyO5I_j_HgJrvtaGEqr7M_E5qCuak935BvRkVHE1K3157NzTqxRFF3tKTRaWOzi3z_RZZtu7nyP6_s-y7a3Dr-NeMcqwL1M9YwLldtMYZAuuttBoUcpPdrcYlWWGNV5WtlCYCVkVSivbbSP0KphUIUuclRWxHkfwLIUxTAbwPLXrfHBz8WtTlnm0YHoqnOGQm1MSWNrTpkQDSsuv_pXA94ya2-FZBtNt_0EHncmKvvSYuopLOHkGXw6uDipieiOnUZgsDowd1JXfVIH-03EXH7KZjWrHV30IqPCFRbPK4_P4eheRPQCBpN6gq-ABfTD0gsXzUgnqV1gloZKS-cLzNCGMoG0F4bxXVdyIsc4NU10XCjTCtBEAZpGgOYqgQ_zd87anhx3jl7rZWy6_Tk1CzQl8Hb-OO4sCpfYCdaXNIY6ZUUfXibwsl2b-eeE0IoY1BP42C_WYvL__8vK3f_yBh6ODvf3zN7OeHcVHmUNaCgzeA0Gs4tLXI_2z8y97kDH4Nd94_wGUpsXsA |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Priority+list+of+biodiversity+metrics+to+observe+from+space&rft.jtitle=Nature+ecology+%26+evolution&rft.au=Skidmore%2C+Andrew+K&rft.au=Coops%2C+Nicholas+C&rft.au=Neinavaz+Elnaz&rft.au=Abebe%2C+Ali&rft.date=2021-07-01&rft.pub=Nature+Publishing+Group&rft.eissn=2397-334X&rft.volume=5&rft.issue=7&rft.spage=896&rft.epage=906&rft_id=info:doi/10.1038%2Fs41559-021-01451-x |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2397-334X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2397-334X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2397-334X&client=summon |