Integrating GEDI and Landsat: Spaceborne Lidar and Four Decades of Optical Imagery for the Analysis of Forest Disturbances and Biomass Changes in Italy
Forests play a prominent role in the battle against climate change, as they absorb a relevant part of human carbon emissions. However, precisely because of climate change, forest disturbances are expected to increase and alter forests’ capacity to absorb carbon. In this context, forest monitoring us...
Saved in:
| Published in | Sensors (Basel, Switzerland) Vol. 22; no. 5; p. 2015 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Switzerland
MDPI AG
04.03.2022
MDPI |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1424-8220 1424-8220 |
| DOI | 10.3390/s22052015 |
Cover
| Abstract | Forests play a prominent role in the battle against climate change, as they absorb a relevant part of human carbon emissions. However, precisely because of climate change, forest disturbances are expected to increase and alter forests’ capacity to absorb carbon. In this context, forest monitoring using all available sources of information is crucial. We combined optical (Landsat) and photonic (GEDI) data to monitor four decades (1985–2019) of disturbances in Italian forests (11 Mha). Landsat data were confirmed as a relevant source of information for forest disturbance mapping, as forest harvestings in Tuscany were predicted with omission errors estimated between 29% (in 2012) and 65% (in 2001). GEDI was assessed using Airborne Laser Scanning (ALS) data available for about 6 Mha of Italian forests. A good correlation (r2 = 0.75) between Above Ground Biomass Density GEDI estimates (AGBD) and canopy height ALS estimates was reported. GEDI data provided complementary information to Landsat. The Landsat mission is capable of mapping disturbances, but not retrieving the three-dimensional structure of forests, while our results indicate that GEDI is capable of capturing forest biomass changes due to disturbances. GEDI acquires useful information not only for biomass trend quantification in disturbance regimes but also for forest disturbance discrimination and characterization, which is crucial to further understanding the effect of climate change on forest ecosystems. |
|---|---|
| AbstractList | Forests play a prominent role in the battle against climate change, as they absorb a relevant part of human carbon emissions. However, precisely because of climate change, forest disturbances are expected to increase and alter forests’ capacity to absorb carbon. In this context, forest monitoring using all available sources of information is crucial. We combined optical (Landsat) and photonic (GEDI) data to monitor four decades (1985–2019) of disturbances in Italian forests (11 Mha). Landsat data were confirmed as a relevant source of information for forest disturbance mapping, as forest harvestings in Tuscany were predicted with omission errors estimated between 29% (in 2012) and 65% (in 2001). GEDI was assessed using Airborne Laser Scanning (ALS) data available for about 6 Mha of Italian forests. A good correlation (r2 = 0.75) between Above Ground Biomass Density GEDI estimates (AGBD) and canopy height ALS estimates was reported. GEDI data provided complementary information to Landsat. The Landsat mission is capable of mapping disturbances, but not retrieving the three-dimensional structure of forests, while our results indicate that GEDI is capable of capturing forest biomass changes due to disturbances. GEDI acquires useful information not only for biomass trend quantification in disturbance regimes but also for forest disturbance discrimination and characterization, which is crucial to further understanding the effect of climate change on forest ecosystems. Forests play a prominent role in the battle against climate change, as they absorb a relevant part of human carbon emissions. However, precisely because of climate change, forest disturbances are expected to increase and alter forests' capacity to absorb carbon. In this context, forest monitoring using all available sources of information is crucial. We combined optical (Landsat) and photonic (GEDI) data to monitor four decades (1985-2019) of disturbances in Italian forests (11 Mha). Landsat data were confirmed as a relevant source of information for forest disturbance mapping, as forest harvestings in Tuscany were predicted with omission errors estimated between 29% (in 2012) and 65% (in 2001). GEDI was assessed using Airborne Laser Scanning (ALS) data available for about 6 Mha of Italian forests. A good correlation (r2 = 0.75) between Above Ground Biomass Density GEDI estimates (AGBD) and canopy height ALS estimates was reported. GEDI data provided complementary information to Landsat. The Landsat mission is capable of mapping disturbances, but not retrieving the three-dimensional structure of forests, while our results indicate that GEDI is capable of capturing forest biomass changes due to disturbances. GEDI acquires useful information not only for biomass trend quantification in disturbance regimes but also for forest disturbance discrimination and characterization, which is crucial to further understanding the effect of climate change on forest ecosystems.Forests play a prominent role in the battle against climate change, as they absorb a relevant part of human carbon emissions. However, precisely because of climate change, forest disturbances are expected to increase and alter forests' capacity to absorb carbon. In this context, forest monitoring using all available sources of information is crucial. We combined optical (Landsat) and photonic (GEDI) data to monitor four decades (1985-2019) of disturbances in Italian forests (11 Mha). Landsat data were confirmed as a relevant source of information for forest disturbance mapping, as forest harvestings in Tuscany were predicted with omission errors estimated between 29% (in 2012) and 65% (in 2001). GEDI was assessed using Airborne Laser Scanning (ALS) data available for about 6 Mha of Italian forests. A good correlation (r2 = 0.75) between Above Ground Biomass Density GEDI estimates (AGBD) and canopy height ALS estimates was reported. GEDI data provided complementary information to Landsat. The Landsat mission is capable of mapping disturbances, but not retrieving the three-dimensional structure of forests, while our results indicate that GEDI is capable of capturing forest biomass changes due to disturbances. GEDI acquires useful information not only for biomass trend quantification in disturbance regimes but also for forest disturbance discrimination and characterization, which is crucial to further understanding the effect of climate change on forest ecosystems. Forests play a prominent role in the battle against climate change, as they absorb a relevant part of human carbon emissions. However, precisely because of climate change, forest disturbances are expected to increase and alter forests' capacity to absorb carbon. In this context, forest monitoring using all available sources of information is crucial. We combined optical (Landsat) and photonic (GEDI) data to monitor four decades (1985-2019) of disturbances in Italian forests (11 Mha). Landsat data were confirmed as a relevant source of information for forest disturbance mapping, as forest harvestings in Tuscany were predicted with omission errors estimated between 29% (in 2012) and 65% (in 2001). GEDI was assessed using Airborne Laser Scanning (ALS) data available for about 6 Mha of Italian forests. A good correlation (r = 0.75) between Above Ground Biomass Density GEDI estimates (AGBD) and canopy height ALS estimates was reported. GEDI data provided complementary information to Landsat. The Landsat mission is capable of mapping disturbances, but not retrieving the three-dimensional structure of forests, while our results indicate that GEDI is capable of capturing forest biomass changes due to disturbances. GEDI acquires useful information not only for biomass trend quantification in disturbance regimes but also for forest disturbance discrimination and characterization, which is crucial to further understanding the effect of climate change on forest ecosystems. Forests play a prominent role in the battle against climate change, as they absorb a relevant part of human carbon emissions. However, precisely because of climate change, forest disturbances are expected to increase and alter forests’ capacity to absorb carbon. In this context, forest monitoring using all available sources of information is crucial. We combined optical (Landsat) and photonic (GEDI) data to monitor four decades (1985–2019) of disturbances in Italian forests (11 Mha). Landsat data were confirmed as a relevant source of information for forest disturbance mapping, as forest harvestings in Tuscany were predicted with omission errors estimated between 29% (in 2012) and 65% (in 2001). GEDI was assessed using Airborne Laser Scanning (ALS) data available for about 6 Mha of Italian forests. A good correlation (r 2 = 0.75) between Above Ground Biomass Density GEDI estimates (AGBD) and canopy height ALS estimates was reported. GEDI data provided complementary information to Landsat. The Landsat mission is capable of mapping disturbances, but not retrieving the three-dimensional structure of forests, while our results indicate that GEDI is capable of capturing forest biomass changes due to disturbances. GEDI acquires useful information not only for biomass trend quantification in disturbance regimes but also for forest disturbance discrimination and characterization, which is crucial to further understanding the effect of climate change on forest ecosystems. |
| Author | Vangi, Elia Chirici, Gherardo Francini, Saverio D’Amico, Giovanni Borghi, Costanza |
| AuthorAffiliation | 2 Department of Bioscience and Territory, University of Molise, 86100 Campobasso, Italy 1 Department of Agriculture, Food, Environment and Forestry, University of Florence, 50145 Florence, Italy; saverio.francini@unifi.it (S.F.); elia.vangi@unifi.it (E.V.); costanza.borghi@unifi.it (C.B.); gherardo.chirici@unifi.it (G.C.) |
| AuthorAffiliation_xml | – name: 2 Department of Bioscience and Territory, University of Molise, 86100 Campobasso, Italy – name: 1 Department of Agriculture, Food, Environment and Forestry, University of Florence, 50145 Florence, Italy; saverio.francini@unifi.it (S.F.); elia.vangi@unifi.it (E.V.); costanza.borghi@unifi.it (C.B.); gherardo.chirici@unifi.it (G.C.) |
| Author_xml | – sequence: 1 givenname: Saverio orcidid: 0000-0001-6991-0289 surname: Francini fullname: Francini, Saverio – sequence: 2 givenname: Giovanni orcidid: 0000-0002-2341-3268 surname: D’Amico fullname: D’Amico, Giovanni – sequence: 3 givenname: Elia orcidid: 0000-0002-9772-2258 surname: Vangi fullname: Vangi, Elia – sequence: 4 givenname: Costanza orcidid: 0000-0002-5334-7548 surname: Borghi fullname: Borghi, Costanza – sequence: 5 givenname: Gherardo orcidid: 0000-0002-0669-5726 surname: Chirici fullname: Chirici, Gherardo |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35271161$$D View this record in MEDLINE/PubMed |
| BookMark | eNplks1uEzEQx1eoiH7AgRdAlrjAIdQfu2ubQ6WSNGWlSD0AZ8trezeOdu1ge5HyJLwuTtJWbbnY1sx_fjOemfPixHlniuI9gl8I4fAyYgwrDFH1qjhDJS5nLBtOnrxPi_MYNxBiQgh7U5ySClOEanRW_G1cMn2Qyboe3N4sGiCdBqt8RJm-gh9bqUzrgzNgZbUMB-_STwEsjJLaROA7cLdNVskBNKPsTdiBzgeQ1gZcOznsoj1olj6YmMDCxjSFVjqVQ_esb9aPMkYwX0vXZ5t1oEk57G3xupNDNO_u74vi1_Lm5_z7bHV328yvVzNV1jzNENIUmU4TnhvBtWZKE40Rhtp0rVaGatW1bcUqKClta2001NggzHELa0oYuSiaI1d7uRHbYEcZdsJLKw4GH3ohQ_7eYASqGVSMw5ZKWnKDOe0IxyWvSqa1QmVmXR1Z26kdTc7uUpDDM-hzj7Nr0fs_gnFU1iXPgE_3gOB_T7lfYrRRmWGQzvgpClwTRlFOuc_18YV0k6eSG35QUcoRZFVWfXha0WMpD_PPgsujQAUfYzCdUDblZfD7Au0gEBT7DROPG5YjPr-IeID-r_0HbWDP_w |
| CitedBy_id | crossref_primary_10_1016_j_fecs_2022_100050 crossref_primary_10_1016_j_jag_2023_103403 crossref_primary_10_3390_rs15112919 crossref_primary_10_34133_remotesensing_0086 crossref_primary_10_3390_f15122133 crossref_primary_10_1109_JSTARS_2023_3313251 crossref_primary_10_1016_j_ecoinf_2023_102404 crossref_primary_10_3390_rs17020320 crossref_primary_10_1038_s43017_023_00508_8 crossref_primary_10_1080_22797254_2024_2334717 crossref_primary_10_3390_f14061116 crossref_primary_10_3390_rs15061638 crossref_primary_10_1080_15481603_2024_2396807 crossref_primary_10_3389_fpls_2024_1428268 crossref_primary_10_1016_j_srs_2025_100195 crossref_primary_10_1016_j_envsoft_2022_105580 crossref_primary_10_1109_JSTARS_2023_3296468 crossref_primary_10_1016_j_ecoinf_2023_102082 crossref_primary_10_1126_science_abp8622 crossref_primary_10_1088_1748_9326_acde8f crossref_primary_10_1016_j_jag_2022_103175 crossref_primary_10_26848_rbgf_v17_2_p1127_1146 crossref_primary_10_1088_1748_9326_ac9c1d crossref_primary_10_1016_j_jag_2024_103935 crossref_primary_10_1016_j_isprsjprs_2023_06_002 crossref_primary_10_3390_rs14133172 crossref_primary_10_3390_f14061098 crossref_primary_10_1007_s41748_024_00437_6 crossref_primary_10_3390_rs17050796 crossref_primary_10_3390_land12081479 crossref_primary_10_1016_j_srs_2024_100181 crossref_primary_10_3390_rs15225274 crossref_primary_10_1016_j_ecolind_2024_112157 crossref_primary_10_3390_s24123947 crossref_primary_10_1007_s10661_023_12066_z crossref_primary_10_3390_rs16071281 crossref_primary_10_1016_j_rse_2024_114534 crossref_primary_10_34133_remotesensing_0132 crossref_primary_10_1016_j_jenvman_2023_118736 crossref_primary_10_1016_j_rse_2022_113367 crossref_primary_10_1016_j_catena_2024_108488 crossref_primary_10_3390_rs14184466 crossref_primary_10_1007_s12145_022_00915_3 crossref_primary_10_1080_01431161_2024_2373344 crossref_primary_10_3390_rs15123107 crossref_primary_10_4081_gh_2022_1106 crossref_primary_10_3390_f14061270 crossref_primary_10_1016_j_envsoft_2024_106268 crossref_primary_10_1016_j_rse_2024_114382 crossref_primary_10_3390_rs14205158 crossref_primary_10_1029_2022EF003314 crossref_primary_10_1080_22797254_2024_2399209 crossref_primary_10_1016_j_scitotenv_2023_164281 crossref_primary_10_3390_rs15040923 |
| Cites_doi | 10.1080/01431160110075532 10.1109/JSTARS.2012.2228167 10.1016/j.rse.2004.08.008 10.1126/science.1201609 10.1111/gcb.15679 10.1016/j.rse.2007.07.004 10.3390/f12060680 10.1080/01431168608954695 10.1016/j.jclepro.2018.01.050 10.1016/0034-4257(87)90015-0 10.1007/s13595-020-00936-2 10.3390/rs11111275 10.1007/s13595-017-0674-6 10.1016/j.rse.2020.111779 10.1080/02757259609532305 10.1016/S0034-4257(01)00259-0 10.1016/j.rse.2015.09.004 10.1080/01431161.2021.1899334 10.1016/j.rse.2017.12.020 10.1016/j.isprsjprs.2016.01.006 10.1016/j.rse.2017.11.015 10.1080/07038992.2014.945827 10.1016/j.rse.2011.08.024 10.1126/science.1244693 10.3390/rs13081530 10.1111/j.1365-2486.2011.02452.x 10.1046/j.1523-1739.1996.10030695.x 10.3390/rs13030442 10.1080/17538947.2016.1187673 10.1007/s13595-016-0590-1 10.1016/j.rse.2010.07.008 10.1016/j.rse.2010.12.007 10.1080/22797254.2020.1806734 10.1126/science.1155121 10.1007/978-94-017-8663-8 10.1016/j.rse.2022.112904 10.1016/j.srs.2020.100002 10.1126/science.aac6759 10.3390/rs13051038 10.1016/S0304-4238(99)00019-9 10.3390/rs12223720 10.14214/sf.10247 10.1109/Multi-Temp.2013.6866024 10.3832/ifor3648-014 10.1038/nclimate3303 10.1016/j.rse.2020.112234 10.1016/j.rse.2017.06.031 10.1016/j.rse.2021.112764 10.1016/j.rse.2007.05.017 10.1016/j.rse.2011.09.025 10.1023/A:1007452223027 10.1080/22797254.2022.2042397 10.1016/j.isprsjprs.2016.03.008 10.5721/EuJRS20144725 10.1016/j.rse.2017.10.034 10.1080/07038992.2016.1207484 10.1016/j.rse.2009.08.011 10.3389/fclim.2020.576740 10.1590/1809-4392201500835 10.1002/2018GL077253 10.1016/j.foreco.2016.07.007 10.1016/j.rse.2021.112845 10.1038/s41893-020-00609-y 10.3390/rs10111832 10.1126/science.1155458 10.1016/j.rse.2020.112165 |
| ContentType | Journal Article |
| Copyright | 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2022 by the authors. 2022 |
| Copyright_xml | – notice: 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2022 by the authors. 2022 |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7X7 7XB 88E 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU DWQXO FYUFA GHDGH K9. M0S M1P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS 7X8 5PM DOA |
| DOI | 10.3390/s22052015 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) ProQuest Hospital Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One Community College ProQuest Central Korea Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) ProQuest Health & Medical Collection PML(ProQuest Medical Library) ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Central China ProQuest Central ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Health & Medical Research Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | Publicly Available Content Database MEDLINE - Academic MEDLINE CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 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: 3 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 4 dbid: BENPR name: ProQuest Central (New) url: http://www.proquest.com/pqcentral?accountid=15518 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1424-8220 |
| ExternalDocumentID | oai_doaj_org_article_1680c890b7a749e297f39249548ddc14 PMC8914649 35271161 10_3390_s22052015 |
| Genre | Journal Article |
| GeographicLocations | Italy |
| GeographicLocations_xml | – name: Italy |
| GroupedDBID | --- 123 2WC 53G 5VS 7X7 88E 8FE 8FG 8FI 8FJ AADQD AAHBH AAYXX ABDBF ABUWG ACUHS ADBBV ADMLS AENEX AFKRA AFZYC ALIPV ALMA_UNASSIGNED_HOLDINGS BENPR BPHCQ BVXVI CCPQU CITATION CS3 D1I DU5 E3Z EBD ESX F5P FYUFA GROUPED_DOAJ GX1 HH5 HMCUK HYE IAO ITC KQ8 L6V M1P M48 MODMG M~E OK1 OVT P2P P62 PHGZM PHGZT PIMPY PQQKQ PROAC PSQYO RNS RPM TUS UKHRP XSB ~8M 3V. ABJCF ARAPS CGR CUY CVF ECM EIF HCIFZ KB. M7S NPM PDBOC 7XB 8FK AZQEC DWQXO K9. PJZUB PKEHL PPXIY PQEST PQUKI PRINS 7X8 PUEGO 5PM |
| ID | FETCH-LOGICAL-c469t-11d71efd393399dd8cd3d2120defbdce7dcfbb5850a77b6ded0d2e1292b067383 |
| IEDL.DBID | M48 |
| ISSN | 1424-8220 |
| IngestDate | Wed Aug 27 01:17:38 EDT 2025 Thu Aug 21 18:12:06 EDT 2025 Thu Sep 04 16:13:31 EDT 2025 Fri Jul 25 19:50:38 EDT 2025 Wed Feb 19 02:27:30 EST 2025 Tue Jul 01 02:41:47 EDT 2025 Thu Apr 24 23:08:19 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 5 |
| Keywords | disturbance GEDI biomass harvest regeneration Landsat lidar |
| Language | English |
| License | https://creativecommons.org/licenses/by/4.0 Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c469t-11d71efd393399dd8cd3d2120defbdce7dcfbb5850a77b6ded0d2e1292b067383 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0001-6991-0289 0000-0002-5334-7548 0000-0002-9772-2258 0000-0002-0669-5726 0000-0002-2341-3268 |
| OpenAccessLink | https://www.proquest.com/docview/2637791085?pq-origsite=%requestingapplication%&accountid=15518 |
| PMID | 35271161 |
| PQID | 2637791085 |
| PQPubID | 2032333 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_1680c890b7a749e297f39249548ddc14 pubmedcentral_primary_oai_pubmedcentral_nih_gov_8914649 proquest_miscellaneous_2638712494 proquest_journals_2637791085 pubmed_primary_35271161 crossref_citationtrail_10_3390_s22052015 crossref_primary_10_3390_s22052015 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 20220304 |
| PublicationDateYYYYMMDD | 2022-03-04 |
| PublicationDate_xml | – month: 3 year: 2022 text: 20220304 day: 4 |
| PublicationDecade | 2020 |
| PublicationPlace | Switzerland |
| PublicationPlace_xml | – name: Switzerland – name: Basel |
| PublicationTitle | Sensors (Basel, Switzerland) |
| PublicationTitleAlternate | Sensors (Basel) |
| PublicationYear | 2022 |
| Publisher | MDPI AG MDPI |
| Publisher_xml | – name: MDPI AG – name: MDPI |
| References | Trumbore (ref_6) 2015; 349 Galford (ref_50) 2008; 112 White (ref_29) 2016; 42 Senf (ref_38) 2021; 27 McRoberts (ref_72) 2016; 378 Gorelick (ref_45) 2017; 202 Barriopedro (ref_77) 2018; 45 Woodcock (ref_53) 2001; 78 Dubayah (ref_35) 2020; 1 Chirici (ref_44) 2011; 115 ref_16 White (ref_82) 2016; 116 Liang (ref_27) 2016; 115 ref_61 ref_60 Jensen (ref_51) 1995; 61 Giannetti (ref_70) 2018; 75 Bullock (ref_13) 2020; 2 Leite (ref_32) 2021; 268 ref_24 Saarela (ref_25) 2016; 73 Kennedy (ref_12) 2010; 114 Wulder (ref_48) 2008; 112 ref_67 Francini (ref_15) 2020; 53 ref_66 ref_21 Dalponte (ref_10) 2020; 93 Bottalico (ref_43) 2014; 47 ref_64 Bartolozzi (ref_76) 1999; 81 Francini (ref_19) 2021; 42 ref_62 Hansen (ref_58) 2012; 122 Marcelli (ref_79) 2020; 54 ref_28 Canadell (ref_85) 2008; 320 ref_26 Vangi (ref_40) 2021; 14 Seto (ref_52) 2002; 23 Chirici (ref_68) 2020; 77 Yang (ref_18) 2018; 178 Kubat (ref_71) 1998; 30 Seidl (ref_8) 2017; 7 White (ref_86) 2022; 271 Bonan (ref_4) 2008; 320 Duncanson (ref_33) 2020; 242 Horler (ref_63) 1986; 7 Zheng (ref_54) 2004; 93 ref_78 Woodcock (ref_47) 1987; 21 Roy (ref_57) 2010; 114 ref_31 ref_75 Griffiths (ref_59) 2013; 6 Anderson (ref_65) 2007; 112 ref_73 Senf (ref_2) 2020; 4 Hansen (ref_22) 2013; 342 Coppin (ref_49) 1996; 13 Seidl (ref_7) 2011; 17 Potapov (ref_30) 2020; 253 Reiche (ref_14) 2018; 204 ref_37 Fragal (ref_17) 2016; 46 Silva (ref_34) 2020; 253 Baumann (ref_74) 2021; 269 ref_81 ref_80 White (ref_55) 2014; 40 Hermosilla (ref_56) 2015; 170 Pflugmacher (ref_46) 2012; 122 Hunter (ref_84) 1996; 10 Francini (ref_20) 2021; 106 ref_42 ref_41 ref_1 Laurin (ref_11) 2020; 94 ref_3 Cohen (ref_69) 2018; 205 Hermosilla (ref_83) 2016; 9 Matasci (ref_23) 2018; 209 Chirici (ref_39) 2019; 84 Duncanson (ref_36) 2022; 270 ref_9 Pan (ref_5) 2011; 333 |
| References_xml | – volume: 23 start-page: 1985 year: 2002 ident: ref_52 article-title: Monitoring land-use change in the Pearl River Delta using Landsat TM publication-title: Int. J. Remote Sens. doi: 10.1080/01431160110075532 – volume: 6 start-page: 2088 year: 2013 ident: ref_59 article-title: A Pixel-Based Landsat Compositing Algorithm for Large Area Land Cover Mapping publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2012.2228167 – volume: 93 start-page: 402 year: 2004 ident: ref_54 article-title: Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2004.08.008 – ident: ref_78 – volume: 333 start-page: 988 year: 2011 ident: ref_5 article-title: A Large and Persistent Carbon Sink in the World’s Forests publication-title: Science doi: 10.1126/science.1201609 – volume: 27 start-page: 3605 year: 2021 ident: ref_38 article-title: Storm and fire disturbances in Europe: Distribution and trends publication-title: Glob. Chang. Biol. doi: 10.1111/gcb.15679 – volume: 112 start-page: 955 year: 2008 ident: ref_48 article-title: Landsat continuity: Issues and opportunities for land cover monitoring publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2007.07.004 – ident: ref_21 doi: 10.3390/f12060680 – volume: 7 start-page: 405 year: 1986 ident: ref_63 article-title: Forestry information content of Thematic Mapper data publication-title: Int. J. Remote Sens. doi: 10.1080/01431168608954695 – volume: 178 start-page: 353 year: 2018 ident: ref_18 article-title: Detecting the dynamics of vegetation disturbance and recovery in surface mining area via Landsat imagery and LandTrendr algorithm publication-title: J. Clean. Prod. doi: 10.1016/j.jclepro.2018.01.050 – volume: 21 start-page: 311 year: 1987 ident: ref_47 article-title: The factor of scale in remote sensing publication-title: Remote Sens. Environ. doi: 10.1016/0034-4257(87)90015-0 – volume: 77 start-page: 40 year: 2020 ident: ref_68 article-title: Monitoring clearcutting and subsequent rapid recovery in Mediterranean coppice forests with Landsat time series publication-title: Ann. For. Sci. doi: 10.1007/s13595-020-00936-2 – ident: ref_42 – ident: ref_61 – ident: ref_1 – volume: 61 start-page: 199 year: 1995 ident: ref_51 article-title: Inland Wetland Change Detection in the Everglades Water Conservation Area 2A Using a Time Series of Normalized Remotely Sensed Data publication-title: Photogramm. Eng. Remote Sens. – ident: ref_24 doi: 10.3390/rs11111275 – volume: 75 start-page: 6 year: 2018 ident: ref_70 article-title: European Forest Types: Toward an automated classification publication-title: Ann. For. Sci. doi: 10.1007/s13595-017-0674-6 – volume: 242 start-page: 111779 year: 2020 ident: ref_33 article-title: Biomass estimation from simulated GEDI, ICESat-2 and NISAR across environmental gradients in Sonoma County, California publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2020.111779 – volume: 13 start-page: 207 year: 1996 ident: ref_49 article-title: Digital change detection in forest ecosystems with remote sensing imagery publication-title: Remote Sens. Rev. doi: 10.1080/02757259609532305 – volume: 78 start-page: 194 year: 2001 ident: ref_53 article-title: Monitoring Large Areas for Forest Change Using Landsat: Generalization across Space, Time and Landsat Sensors publication-title: Remote Sens. Environ. doi: 10.1016/S0034-4257(01)00259-0 – volume: 170 start-page: 121 year: 2015 ident: ref_56 article-title: Regional detection, characterization, and attribution of annual forest change from 1984 to 2012 using Landsat-derived time-series metrics publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2015.09.004 – volume: 42 start-page: 4693 year: 2021 ident: ref_19 article-title: The Three Indices Three Dimensions (3I3D) algorithm: A new method for forest disturbance mapping and area estimation based on optical remotely sensed imagery publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2021.1899334 – volume: 209 start-page: 90 year: 2018 ident: ref_23 article-title: Large-area mapping of Canadian boreal forest cover, height, biomass and other structural attributes using Landsat composites and lidar plots publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2017.12.020 – volume: 115 start-page: 63 year: 2016 ident: ref_27 article-title: Terrestrial laser scanning in forest inventories publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2016.01.006 – volume: 205 start-page: 131 year: 2018 ident: ref_69 article-title: A LandTrendr multispectral ensemble for forest disturbance detection publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2017.11.015 – volume: 40 start-page: 192 year: 2014 ident: ref_55 article-title: Pixel-Based Image Compositing for Large-Area Dense Time Series Applications and Science publication-title: Can. J. Remote Sens. doi: 10.1080/07038992.2014.945827 – volume: 122 start-page: 66 year: 2012 ident: ref_58 article-title: A review of large area monitoring of land cover change using Landsat data publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2011.08.024 – volume: 342 start-page: 850 year: 2013 ident: ref_22 article-title: High-resolution global maps of 21st-century forest cover change publication-title: Science doi: 10.1126/science.1244693 – ident: ref_9 doi: 10.3390/rs13081530 – ident: ref_66 – volume: 17 start-page: 2842 year: 2011 ident: ref_7 article-title: Unraveling the drivers of intensifying forest disturbance regimes in Europe publication-title: Glob. Chang. Biol. doi: 10.1111/j.1365-2486.2011.02452.x – ident: ref_62 – volume: 10 start-page: 695 year: 1996 ident: ref_84 article-title: Benchmarks for managing ecosystems: Are human activities natural? publication-title: Conserv. Biol. doi: 10.1046/j.1523-1739.1996.10030695.x – volume: 94 start-page: 407 year: 2020 ident: ref_11 article-title: Satellite open data to monitor forest damage caused by extreme climate-induced events: A case study of the Vaia storm in Northern Italy publication-title: For. Int. J. For. Res. – ident: ref_31 doi: 10.3390/rs13030442 – volume: 9 start-page: 1035 year: 2016 ident: ref_83 article-title: Mass data processing of time series Landsat imagery: Pixels to data products for forest monitoring publication-title: Int. J. Digit. Earth doi: 10.1080/17538947.2016.1187673 – volume: 73 start-page: 895 year: 2016 ident: ref_25 article-title: Hierarchical model-based inference for forest inventory utilizing three sources of information publication-title: Ann. For. Sci. doi: 10.1007/s13595-016-0590-1 – volume: 114 start-page: 2897 year: 2010 ident: ref_12 article-title: Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr—Temporal segmentation algorithms publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2010.07.008 – volume: 115 start-page: 1025 year: 2011 ident: ref_44 article-title: Large-scale monitoring of coppice forest clearcuts by multitemporal very high resolution satellite imagery. A case study from central Italy publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2010.12.007 – volume: 53 start-page: 233 year: 2020 ident: ref_15 article-title: Near-real time forest change detection using PlanetScope imagery publication-title: Eur. J. Remote Sens. doi: 10.1080/22797254.2020.1806734 – volume: 269 start-page: 112849 year: 2021 ident: ref_74 article-title: Ecoregion-wide, multi-sensor biomass mapping highlights a major underestimation of dry forests carbon stocks publication-title: Remote Sens. Environ. – ident: ref_3 – volume: 320 start-page: 1444 year: 2008 ident: ref_4 article-title: Forests and Climate Change: Forcings, Feedbacks, and the Climate Benefits of Forests publication-title: Science doi: 10.1126/science.1155121 – ident: ref_28 doi: 10.1007/978-94-017-8663-8 – volume: 271 start-page: 112904 year: 2022 ident: ref_86 article-title: Mapping, validating, and interpreting spatio-temporal trends in post-disturbance forest recovery publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2022.112904 – volume: 1 start-page: 100002 year: 2020 ident: ref_35 article-title: The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth’s forests and topography publication-title: Sci. Remote Sens. doi: 10.1016/j.srs.2020.100002 – volume: 349 start-page: 814 year: 2015 ident: ref_6 article-title: Forest health and global change publication-title: Science doi: 10.1126/science.aac6759 – ident: ref_41 doi: 10.3390/rs13051038 – volume: 81 start-page: 309 year: 1999 ident: ref_76 article-title: Assessment of frost tolerance in olive (Olea europaea L.) publication-title: Sci. Hortic. doi: 10.1016/S0304-4238(99)00019-9 – ident: ref_80 doi: 10.3390/rs12223720 – ident: ref_67 – volume: 54 start-page: 10247 year: 2020 ident: ref_79 article-title: Large-scale two-phase estimation of wood production by poplar plantations exploiting Sentinel-2 data as auxiliary information publication-title: Silva Fenn. doi: 10.14214/sf.10247 – ident: ref_16 doi: 10.1109/Multi-Temp.2013.6866024 – ident: ref_37 – volume: 106 start-page: 102663 year: 2021 ident: ref_20 article-title: An open science and open data approach for the statistically robust estimation of forest disturbance areas publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 14 start-page: 144 year: 2021 ident: ref_40 article-title: Are we ready for a National Forest Information System? State of the art of forest maps and airborne laser scanning data availability in Italy publication-title: iForest doi: 10.3832/ifor3648-014 – volume: 7 start-page: 395 year: 2017 ident: ref_8 article-title: Forest disturbances under climate change publication-title: Nat. Clim. Chang. doi: 10.1038/nclimate3303 – volume: 84 start-page: 101959 year: 2019 ident: ref_39 article-title: Wall-to-wall spatial prediction of growing stock volume based on Italian National Forest Inventory plots and remotely sensed data publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 253 start-page: 112234 year: 2020 ident: ref_34 article-title: Fusing simulated GEDI, ICESat-2 and NISAR data for regional aboveground biomass mapping publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2020.112234 – volume: 202 start-page: 18 year: 2017 ident: ref_45 article-title: Google Earth Engine: Planetary-scale geospatial analysis for everyone publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2017.06.031 – volume: 268 start-page: 112764 year: 2021 ident: ref_32 article-title: Large scale multi-layer fuel load characterization in tropical savanna using GEDI spaceborne lidar data publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2021.112764 – volume: 112 start-page: 576 year: 2008 ident: ref_50 article-title: Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2007.05.017 – volume: 122 start-page: 146 year: 2012 ident: ref_46 article-title: Using Landsat-derived disturbance history (1972–2010) to predict current forest structure publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2011.09.025 – volume: 112 start-page: D11 year: 2007 ident: ref_65 article-title: A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 2. Surface moisture climatology publication-title: J. Geophys. Res. Earth Surf. – volume: 30 start-page: 195 year: 1998 ident: ref_71 article-title: Machine Learning for the Detection of Oil Spills in Satellite Radar Images publication-title: Mach. Learn. doi: 10.1023/A:1007452223027 – ident: ref_73 doi: 10.1080/22797254.2022.2042397 – volume: 116 start-page: 55 year: 2016 ident: ref_82 article-title: Optical remotely sensed time series data for land cover classification: A review publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2016.03.008 – ident: ref_75 – volume: 93 start-page: 102206 year: 2020 ident: ref_10 article-title: Mapping forest windthrows using high spatial resolution multispectral satellite images publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 47 start-page: 437 year: 2014 ident: ref_43 article-title: Classifying silvicultural systems (coppices vs. high forests) in Mediterranean oak forests by Airborne Laser Scanning data publication-title: Eur. J. Remote Sens. doi: 10.5721/EuJRS20144725 – ident: ref_81 – volume: 204 start-page: 147 year: 2018 ident: ref_14 article-title: Improving near-real time deforestation monitoring in tropical dry forests by combining dense Sentinel-1 time series with Landsat and ALOS-2 PALSAR-2 publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2017.10.034 – volume: 42 start-page: 619 year: 2016 ident: ref_29 article-title: Remote Sensing Technologies for Enhancing Forest Inventories: A Review publication-title: Can. J. Remote Sens. doi: 10.1080/07038992.2016.1207484 – volume: 114 start-page: 35 year: 2010 ident: ref_57 article-title: Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2009.08.011 – volume: 2 start-page: 576740 year: 2020 ident: ref_13 article-title: A Suite of Tools for Continuous Land Change Monitoring in Google Earth Engine publication-title: Front. Clim. doi: 10.3389/fclim.2020.576740 – volume: 46 start-page: 13 year: 2016 ident: ref_17 article-title: Reconstructing historical forest cover change in the Lower Amazon floodplains using the LandTrendr algorithm publication-title: Acta Amaz. doi: 10.1590/1809-4392201500835 – volume: 45 start-page: 1955 year: 2018 ident: ref_77 article-title: June 2017: The Earliest European Summer Mega-heatwave of Reanalysis Period publication-title: Geophys. Res. Lett. doi: 10.1002/2018GL077253 – volume: 378 start-page: 44 year: 2016 ident: ref_72 article-title: Hybrid estimators for mean aboveground carbon per unit area publication-title: For. Ecol. Manag. doi: 10.1016/j.foreco.2016.07.007 – ident: ref_64 – volume: 270 start-page: 112845 year: 2022 ident: ref_36 article-title: Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2021.112845 – volume: 4 start-page: 63 year: 2020 ident: ref_2 article-title: Mapping the forest disturbance regimes of Europe publication-title: Nat. Sustain. doi: 10.1038/s41893-020-00609-y – ident: ref_26 doi: 10.3390/rs10111832 – ident: ref_60 – volume: 320 start-page: 1456 year: 2008 ident: ref_85 article-title: Managing Forests for Climate Change Mitigation publication-title: Science doi: 10.1126/science.1155458 – volume: 253 start-page: 112165 year: 2020 ident: ref_30 article-title: Mapping global forest canopy height through integration of GEDI and Landsat data publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2020.112165 |
| SSID | ssj0023338 |
| Score | 2.573457 |
| Snippet | Forests play a prominent role in the battle against climate change, as they absorb a relevant part of human carbon emissions. However, precisely because of... |
| SourceID | doaj pubmedcentral proquest pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 2015 |
| SubjectTerms | Biomass Carbon Climate Change Datasets disturbance Ecosystem Ecosystems Forest management Forests GEDI harvest Humans Landsat lidar Mapping regeneration Remote sensing Sensors Vegetation |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Nb9NAEF2hnuCAoHwZ2mpAHLhY9Xo3Xi83ShoaVOAAlXqz9ssiEjhV7Bz6S_p3O7N2rKSqxIVjvKPVZmfW816y84ax95iVCys4MtUyN6k0XqW2LEzqCi8nMleYZKga-dv34uxCfr2cXG61-qI7Yb08cL9xx7woM1fqzCqjpA65VrUgzoBI23sXW1jnmc42ZGqgWgKZV68jJJDUH7dUToqpbrKTfaJI_33I8u4Fya2MM3vCHg9QET71S3zKHoRmnz3aEhB8xm7mg9oDfoIvp9M5mMbDOZXvmu4j_ERCTO-TJsD5wptVHJ3hvDANdC--hWUNP67iz9kw_0tyFteAKBYQFcJGroRsqIFn28EUY2K9shQobZzrZEHXi1roaxRaWDQwRzR__ZxdzE5_fT5Lh1YLqUN-3KWce8VD7YXG_dKeWhoJj1kt86G2uA3Ku9papBaZUcoWPvjM5wGxQm6p000pXrC9ZtmEVww4d04F9I6wTiJf0ug9WVv0IjeitC5hHzYuqNygQ07tMP5UyEfIW9XorYS9G02vevGN-4xOyI-jAellxwcYRdUQRdW_oihhB5soqIZD3FZ5QWqMVJ6RsLfjMB4_-k_FNGG5jjZIOXEmnOJlHzTjShDbKo6IOmFqJ5x2lro70ix-R4nvUmMGk_r1__hub9jDnGo26OKcPGB73WodDhFJdfYoHppbQ3gcEg priority: 102 providerName: Directory of Open Access Journals – databaseName: ProQuest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Lb9NAEB6V9AIHxLsuBS2IAxerXnvjtZEQIiShQSUgoFJv1r4MkagdYufQX8LfZcYvGlRxjHe0We3M7sxnz3wD8AK9cqwjjkg1CZUvlJW-TmLlm9iKsQglOhmqRv64jE_OxIfz8fkeLPtaGEqr7O_E5qK2paF35MdhTNR4lCv_Zv3Lp65R9HW1b6GhutYK9nVDMXYD9vFKHgcj2J_Mlp-_DBAsQkTW8gtFCPaPKyozRRc43vFKDXn_dRHnv4mTVzzR_A7c7kJI9rbV-V3Yc8U9uHWFWPA-_F50LBD4i72fTRdMFZadUlmvql-xrwiU6Z4pHDtdWbVpRuc4L5s6ypevWJmzT-vmNTdbXBDNxSXD6JZhtMh6GhOSocaeVc2maCvbjSYDqpq5JitKO6pYW7tQsVXBFhjlXz6As_ns27sTv2vB4BvEzbXPuZXc5TZKcb9SS62OIotbG1iXa9wGaU2uNUKOQEmpY-tsYEOHMUSoqQNOEj2EUVEW7gAY58ZIJxIbaSMQR6UmSUWuk8BwFSXaePCyV0FmOn5yapPxM0OcQtrKBm158HwQXbekHNcJTUiPgwDxaDcPys33rDuWGY_x_5M00FJJkbowlXlEiBRxnLWGCw-OeivIusNdZX9N0YNnwzAeS_rWogpXbhsZhKI4E07xqDWaYSUY80qOkbYHcsecdpa6O1KsfjTU30mKnk2kh_9f1mO4GVKVBqXKiSMY1Zute4KxU62fdgfiD2faG2M priority: 102 providerName: ProQuest |
| Title | Integrating GEDI and Landsat: Spaceborne Lidar and Four Decades of Optical Imagery for the Analysis of Forest Disturbances and Biomass Changes in Italy |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/35271161 https://www.proquest.com/docview/2637791085 https://www.proquest.com/docview/2638712494 https://pubmed.ncbi.nlm.nih.gov/PMC8914649 https://doaj.org/article/1680c890b7a749e297f39249548ddc14 |
| Volume | 22 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Lj9MwELb2cYED4r1ZlsogDlwCceLGCRJClLa7RcuCgEq9RX4FKi3pbpJK9Jfwd5lxHtqiHrhUSj11Is-4M1888w0hL8ArxypigFSTUPpcGuGrJJa-jg0f8lCAk8Fq5E8X8dmcf1wMF3uk67HZLmC1E9phP6l5efnq9_XmHWz4t4g4AbK_rrBYFBzZcJ8cumMizODj_WFCGEWuoTXWdPngD4OGYGj7p1tuybH37wo5_82cvOGKpnfJnTaGpO8bpd8je7a4T27fYBZ8QP7MWhoIuKKnk_GMysLQc6zrlfUb-g2QMv7RFJaeL40s3egU5qVjiwnzFV3l9POVe89NZ7-Q52JDIbylEC7SjscEZbCzZ1XTMRjLulRoQZWba7TEvKOKNsULFV0WdAYrvHlI5tPJ9w9nftuDwdcAnGufMSOYzU2UwnqlBnsdRQbcXWBsrmAZhNG5UoA5AimEio01gQktBBGhwhY4SfSIHBSrwh4RypjWwvLEREpzAFKpTlKeqyTQTEaJ0h552akg0y1BOfbJuMwAqKC2sl5bHnnei141rBy7hEaox14AibTdF6vyR9buy4zFcP8kDZSQgqc2TEUeISQFIGeMZtwjJ50VZJ1xZmGMNI1Yt-GRZ_0w7Es8bJGFXa2dDGBRmAmmeNwYTf8kEPQKBqG2R8SWOW096vZIsfzpuL-TFFwbT4__475PyK0QazUwYY6fkIO6XNunEEHVakD2xULAZzI9HZDD0eTiy9eBexsxcDvnL7C_HmE |
| linkProvider | Scholars Portal |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9NAEB6V9AAcEG8CBRYEEherfmy8NlKFCEmIaRoQtFJvZl-GSOCE2BHKL-Hf8NuYcWzToIpbj_GuJivNeGa-9cw3AM8wKocq8BCpRr50uDTCUVEoHR0a3uO-wCBD3chH03B8wt-d9k534HfTC0NllY1PrBy1mWu6I9_3Q6LGo1r5V4sfDk2Noq-rzQgNWY9WMAcVxVjd2HFo1z8RwhUHyQD1_dz3R8PjN2OnnjLgaISGpeN5Rng2MwFC-zg2NM0nMOjQXWMzZbQVRmdKYVbtSiFUaKxxjW8xTPqKhrxEAcq9BLucLlA6sNsfTj98bCFfgAhww2eEwt39gtpaMeT2tqJgNSzgvAz330LNM5FvdB2u1Skre72xsRuwY_ObcPUMkeEt-JXUrBP4i70dDhImc8Mm1EYsy5fsEwJz8mu5ZZOZkctqdYRy2cBSfX7B5hl7v6iu1VnynWg11gyzaYbZKWtoU2gPDRItSjZA21wtFRlsUcnqz6jMqWCbXomCzXKWIKpY34aTC1HGHejk89zeA-Z5WgvLIxMozRG3xTqKeaYiV3syiJTuwotGBamu-dBpLMe3FHERaStttdWFp-3WxYYE5LxNfdJju4F4u6sH8-WXtHYDqRfi_0exq4QUPLZ-LLKAEDDiRmO0x7uw11hBWjuTIv1r-l140i6jG6BvOzK381W1B6EvSkIRdzdG054Ec2zhYWbfBbFlTltH3V7JZ18rqvEoxkjK4_v_P9ZjuDw-Ppqkk2R6-ACu-NQhQmV6fA865XJlH2LeVqpH9cvB4PNFv49_AAgWWfo |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3bbtNAEF2VVkLwgLhjKLAgkHix4svGayNViOCEmoZQQSv1zezNbSSwQ-wI5Uv4J76KGd9oUMVbH5PdTFaa2Zk59swZQl5AVA6k7wJSDT1hM6G5LcNA2CrQbMg8DkEGu5E_zoL9Y_bhZHiyRX53vTBYVtn5xNpR60LhM_KBFyA1HtbKD7K2LOIwnrxZ_LBxghS-ae3GaYh2zILeq-nG2iaPA7P-CXCu3Eti0P1Lz5uMj97t2-3EAVsBTKxs19XcNZn2AeZHkcbJPr4G5-5ok0mtDNcqkxIybEdwLgNttKM9AyHTkzjwJfRB7hWyw-HHAAR3RuPZ4ece_vmABhtuIxDuDEpscYXwO9yIiPXggIuy3X-LNs9FwclNcqNNX-nbxt5ukS2T3ybXz5Ea3iG_kpaBAj7R9-M4oSLXdIotxaJ6Tb8ASEcflxs6nWuxrFcnIJfGBmv1S1pk9NOifsROk-9IsbGmkFlTyFRpR6GCe3CoaFnRGOx0tZRovGUtazTHkqeSNn0TJZ3nNAGEsb5Lji9FGffIdl7k5gGhrqsUNyzUvlQMMFykwohlMnSUK_xQKou86lSQqpYbHUd0fEsBI6G20l5bFnneb100hCAXbRqhHvsNyOFdf1EsT9PWJaRuAP8fRo7kgrPIeBHPfETDgCG1Vi6zyG5nBWnrWMr07zWwyLN-GVwCvucRuSlW9R6AwSAJRNxvjKY_CeTb3IUs3yJ8w5w2jrq5ks_PatrxMIKoyqKH_z_WU3IV7mU6TWYHj8g1D5tFsGKP7ZLtarkyjyGFq-ST9m5Q8vWyr-MfGnVePg |
| 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=Integrating+GEDI+and+Landsat%3A+Spaceborne+Lidar+and+Four+Decades+of+Optical+Imagery+for+the+Analysis+of+Forest+Disturbances+and+Biomass+Changes+in+Italy&rft.jtitle=Sensors+%28Basel%2C+Switzerland%29&rft.au=Francini%2C+Saverio&rft.au=D%27Amico%2C+Giovanni&rft.au=Vangi%2C+Elia&rft.au=Borghi%2C+Costanza&rft.date=2022-03-04&rft.issn=1424-8220&rft.eissn=1424-8220&rft.volume=22&rft.issue=5&rft_id=info:doi/10.3390%2Fs22052015&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1424-8220&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1424-8220&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1424-8220&client=summon |