{"data":{"id":"10.6084/m9.figshare.11409444","type":"dois","attributes":{"doi":"10.6084/m9.figshare.11409444","prefix":"10.6084","suffix":"m9.figshare.11409444","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Garbarino, Matteo","nameType":"Personal","givenName":"Matteo","familyName":"Garbarino","affiliation":[],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-9010-1731","nameIdentifierScheme":"ORCID"}]},{"name":"Morresi, Donato","nameType":"Personal","givenName":"Donato","familyName":"Morresi","affiliation":[],"nameIdentifiers":[]},{"name":"Urbinati, Carlo","nameType":"Personal","givenName":"Carlo","familyName":"Urbinati","affiliation":[],"nameIdentifiers":[]},{"name":"Vitali, Alessandro","nameType":"Personal","givenName":"Alessandro","familyName":"Vitali","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Land cover raster data of the Italian mountain ranges (Alps \u0026amp; Apennines) for the 1954 - 2012 period"}],"publisher":"figshare","container":{},"publicationYear":2019,"subjects":[{"subject":"Ecology"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Biological sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"50206 Environmental Monitoring","subjectScheme":"FOR"},{"subject":"FOS: Earth and related environmental sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Earth and related environmental sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"50104 Landscape Ecology","subjectScheme":"FOR"},{"subject":"70504 Forestry Management and Environment","subjectScheme":"FOR"},{"subject":"FOS: Agriculture, forestry and fisheries","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Agriculture, forestry and fisheries","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2019-12-19","dateType":"Created"},{"date":"2019-12-20","dateType":"Updated"},{"date":"2019","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["30807362 Bytes"],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"\u003cb\u003eDescription\u003c/b\u003e The dataset is composed by 32 rasters representing land cover maps of 16 landscapes (1954 - 2012 period) divided by mountain region membership (Alps or Apennines) as indicated in the following list:ALPS Bagni – BAN - 1574.3 haMello – MEL- 1433.7 ha Sapè – SAP- 959.4 haPesio – PES- 1599.2 haVentina – VEN - 630.4 haMusella – MUS - 921.4 haVeglia – VEG - 1407.2 haDevero – DEV - 1570.0 haAPENNINESCimone - CIM - 1593.4 haSibillini - SIB - 1601.3 haGran Sasso - GRS - 1602.4 haTerminillo - TER - 1600.7 haMorrone - MOR - 1603.7 haGenzana - GEN - 1603.4 haMonte Mare - MMA - 1604.0 ha Matese - MAT - 1605.4 ha\u003cb\u003e\u003cbr\u003e\u003c/b\u003e\u003cb\u003eMetadata\u003c/b\u003e - Cell Size (X, Y): 1, 1 meters - Coordinate System: WGS84 UTM Zone 32N (EPSG: 32632) - NoData Value: 255 - Data type: 8 Bit unsigned integer - Format: GeoTIFF - Compression: LZW - Land cover classification: object-oriented segmentation and video photointerpretation (Garbarino et al. 2013). - Land class values: 1 = forest, 2 = grassland, 3 = cropland, 4 = human, 5 = unvegetated\u003cbr\u003e","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiPz4KPHJlc291cmNlIHhtbG5zPSJodHRwOi8vZGF0YWNpdGUub3JnL3NjaGVtYS9rZXJuZWwtNCIgeG1sbnM6eHNpPSJodHRwOi8vd3d3LnczLm9yZy8yMDAxL1hNTFNjaGVtYS1pbnN0YW5jZSIgeHNpOnNjaGVtYUxvY2F0aW9uPSJodHRwOi8vZGF0YWNpdGUub3JnL3NjaGVtYS9rZXJuZWwtNCBodHRwOi8vc2NoZW1hLmRhdGFjaXRlLm9yZy9tZXRhL2tlcm5lbC00LjEvbWV0YWRhdGEueHNkIj4KICA8aWRlbnRpZmllciBpZGVudGlmaWVyVHlwZT0iRE9JIj4xMC42MDg0L005LkZJR1NIQVJFLjExNDA5NDQ0PC9pZGVudGlmaWVyPgogIDxjcmVhdG9ycz4KICAgIDxjcmVhdG9yPgogICAgICA8Y3JlYXRvck5hbWU+TWF0dGVvIEdhcmJhcmlubzwvY3JlYXRvck5hbWU+CiAgICAgIDxuYW1lSWRlbnRpZmllciBuYW1lSWRlbnRpZmllclNjaGVtZT0iT1JDSUQiIHNjaGVtZVVSST0iaHR0cDovL29yY2lkLm9yZyI+MDAwMC0wMDAyLTkwMTAtMTczMTwvbmFtZUlkZW50aWZpZXI+CiAgICA8L2NyZWF0b3I+CiAgICA8Y3JlYXRvcj4KICAgICAgPGNyZWF0b3JOYW1lPkRvbmF0byBNb3JyZXNpPC9jcmVhdG9yTmFtZT4KICAgIDwvY3JlYXRvcj4KICAgIDxjcmVhdG9yPgogICAgICA8Y3JlYXRvck5hbWU+Q2FybG8gVXJiaW5hdGk8L2NyZWF0b3JOYW1lPgogICAgPC9jcmVhdG9yPgogICAgPGNyZWF0b3I+CiAgICAgIDxjcmVhdG9yTmFtZT5BbGVzc2FuZHJvIFZpdGFsaTwvY3JlYXRvck5hbWU+CiAgICA8L2NyZWF0b3I+CiAgPC9jcmVhdG9ycz4KICA8dGl0bGVzPgogICAgPHRpdGxlPkxhbmQgY292ZXIgcmFzdGVyIGRhdGEgb2YgdGhlIEl0YWxpYW4gbW91bnRhaW4gcmFuZ2VzIChBbHBzICZhbXA7IEFwZW5uaW5lcykgZm9yIHRoZSAxOTU0IC0gMjAxMiBwZXJpb2Q8L3RpdGxlPgogIDwvdGl0bGVzPgogIDxkZXNjcmlwdGlvbnM+CiAgICA8ZGVzY3JpcHRpb24gZGVzY3JpcHRpb25UeXBlPSJBYnN0cmFjdCI+Jmx0O3AmZ3Q7Jmx0Oy9wJmd0OyZsdDtwJmd0OyZsdDsvcCZndDsmbHQ7cCZndDsmbHQ7L3AmZ3Q7Jmx0O3AmZ3Q7Jmx0O2ImZ3Q7RGVzY3JpcHRpb24mbHQ7L2ImZ3Q7Jmx0Oy9wJmd0OyZsdDtwJmd0OyZsdDsvcCZndDsmbHQ7cCZndDsKCiZsdDsvcCZndDsmbHQ7cCZndDtUaGUgZGF0YXNldAppcyBjb21wb3NlZCBieSAzMiByYXN0ZXJzIHJlcHJlc2VudGluZyBsYW5kIGNvdmVyIG1hcHMgb2YgMTYgbGFuZHNjYXBlcyAoMTk1NCAtIDIwMTIgcGVyaW9kKSBkaXZpZGVkIGJ5IG1vdW50YWluIHJlZ2lvbgptZW1iZXJzaGlwIChBbHBzIG9yIEFwZW5uaW5lcykgYXMgaW5kaWNhdGVkIGluCnRoZSBmb2xsb3dpbmcgbGlzdDombHQ7L3AmZ3Q7Jmx0O3AmZ3Q7QUxQUyAmbHQ7L3AmZ3Q7Jmx0O3AmZ3Q7Jmx0Oy9wJmd0OyZsdDtwJmd0O0JhZ25pIOKAkyBCQU4KLSAxNTc0LjMgaGEmbHQ7L3AmZ3Q7Jmx0O3AmZ3Q7Jmx0Oy9wJmd0OyZsdDtwJmd0O01lbGxvIOKAkwpNRUwtIDE0MzMuNyBoYSAmbHQ7L3AmZ3Q7Jmx0O3AmZ3Q7U2Fww6gg4oCTIFNBUC0KOTU5LjQgaGEmbHQ7L3AmZ3Q7Jmx0O3AmZ3Q7UGVzaW8g4oCTClBFUy0gMTU5OS4yIGhhJmx0Oy9wJmd0OyZsdDtwJmd0O1ZlbnRpbmEg4oCTClZFTiAtIDYzMC40IGhhJmx0Oy9wJmd0OyZsdDtwJmd0O011c2VsbGEg4oCTCk1VUyAtIDkyMS40IGhhJmx0Oy9wJmd0OyZsdDtwJmd0O1ZlZ2xpYSDigJMKVkVHIC0gMTQwNy4yIGhhJmx0Oy9wJmd0OyZsdDtwJmd0O0RldmVybyDigJMKREVWIC0gMTU3MC4wIGhhJmx0Oy9wJmd0OyZsdDtwJmd0O0FQRU5OSU5FUyZsdDsvcCZndDsmbHQ7cCZndDtDaW1vbmUgLQpDSU0gLSAxNTkzLjQgaGEmbHQ7L3AmZ3Q7Jmx0O3AmZ3Q7U2liaWxsaW5pIC0KU0lCIC0gMTYwMS4zIGhhJmx0Oy9wJmd0OyZsdDtwJmd0O0dyYW4gU2Fzc28KLSBHUlMgLSAxNjAyLjQgaGEmbHQ7L3AmZ3Q7Jmx0O3AmZ3Q7VGVybWluaWxsbwotIFRFUiAtIDE2MDAuNyBoYSZsdDsvcCZndDsmbHQ7cCZndDtNb3Jyb25lIC0KTU9SIC0gMTYwMy43IGhhJmx0Oy9wJmd0OyZsdDtwJmd0O0dlbnphbmEgLQpHRU4gLSAxNjAzLjQgaGEmbHQ7L3AmZ3Q7Jmx0O3AmZ3Q7TW9udGUgTWFyZQotIE1NQSAtIDE2MDQuMCBoYSZsdDsvcCZndDsmbHQ7cCZndDsKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKJmx0Oy9wJmd0OyZsdDtwJmd0O01hdGVzZSAtCk1BVCAtIDE2MDUuNCBoYSZsdDsvcCZndDsmbHQ7cCZndDsmbHQ7L3AmZ3Q7Jmx0O3AmZ3Q7Jmx0Oy9wJmd0OyZsdDtwJmd0OyZsdDtiJmd0OyZsdDticiZndDsmbHQ7L2ImZ3Q7Jmx0Oy9wJmd0OyZsdDtwJmd0OyZsdDtiJmd0O01ldGFkYXRhJmx0Oy9iJmd0OyZsdDsvcCZndDsKCiZsdDtwJmd0Oy0gICAgICAgICAKQ2VsbApTaXplIChYLCBZKTogMSwgMSBtZXRlcnMmbHQ7L3AmZ3Q7CgombHQ7cCZndDstICAgICAgICAgCkNvb3JkaW5hdGUKU3lzdGVtOiBXR1M4NCBVVE0gWm9uZSAzMk4gKEVQU0c6IDMyNjMyKSZsdDsvcCZndDsKCiZsdDtwJmd0Oy0gICAgICAgICAKTm9EYXRhClZhbHVlOiAyNTUmbHQ7L3AmZ3Q7CgombHQ7cCZndDstICAgICAgICAgCkRhdGEKdHlwZTogOCBCaXQgdW5zaWduZWQgaW50ZWdlciZsdDsvcCZndDsKCiZsdDtwJmd0Oy0gICAgICAgICAKRm9ybWF0OgpHZW9USUZGJmx0Oy9wJmd0OwoKJmx0O3AmZ3Q7LSAgICAgICAgIApDb21wcmVzc2lvbjoKTFpXJmx0Oy9wJmd0OwoKJmx0O3AmZ3Q7LSAgICAgICAgIApMYW5kCmNvdmVyIGNsYXNzaWZpY2F0aW9uOiBvYmplY3Qtb3JpZW50ZWQgc2VnbWVudGF0aW9uIGFuZCB2aWRlbwpwaG90b2ludGVycHJldGF0aW9uIChHYXJiYXJpbm8gZXQgYWwuIDIwMTMpLiZsdDsvcCZndDsKCiZsdDtwJmd0Oy0gICAgICAgICAKTGFuZApjbGFzcyB2YWx1ZXM6IDEgPSBmb3Jlc3QsIDIgPSBncmFzc2xhbmQsIDMgPSBjcm9wbGFuZCwgNCA9IGh1bWFuLCA1ID0KdW52ZWdldGF0ZWQmbHQ7L3AmZ3Q7Jmx0O2JyJmd0OyZsdDtwJmd0OyZsdDsvcCZndDsmbHQ7cCZndDsmbHQ7L3AmZ3Q7PC9kZXNjcmlwdGlvbj4KICA8L2Rlc2NyaXB0aW9ucz4KICA8c3ViamVjdHM+CiAgICA8c3ViamVjdD5FY29sb2d5PC9zdWJqZWN0PgogICAgPHN1YmplY3Qgc2NoZW1lVVJJPSJodHRwOi8vd3d3LmFicy5nb3YuYXUvYXVzc3RhdHMvYWJzQC5uc2YvMC82QkI0MjdBQjk2OTZDMjI1Q0EyNTc0MTgwMDA0NDYzRSIgc3ViamVjdFNjaGVtZT0iRk9SIj41MDIwNiBFbnZpcm9ubWVudGFsIE1vbml0b3Jpbmc8L3N1YmplY3Q+CiAgICA8c3ViamVjdCBzY2hlbWVVUkk9Imh0dHA6Ly93d3cuYWJzLmdvdi5hdS9hdXNzdGF0cy9hYnNALm5zZi8wLzZCQjQyN0FCOTY5NkMyMjVDQTI1NzQxODAwMDQ0NjNFIiBzdWJqZWN0U2NoZW1lPSJGT1IiPjUwMTA0IExhbmRzY2FwZSBFY29sb2d5PC9zdWJqZWN0PgogICAgPHN1YmplY3Qgc2NoZW1lVVJJPSJodHRwOi8vd3d3LmFicy5nb3YuYXUvYXVzc3RhdHMvYWJzQC5uc2YvMC82QkI0MjdBQjk2OTZDMjI1Q0EyNTc0MTgwMDA0NDYzRSIgc3ViamVjdFNjaGVtZT0iRk9SIj43MDUwNCBGb3Jlc3RyeSBNYW5hZ2VtZW50IGFuZCBFbnZpcm9ubWVudDwvc3ViamVjdD4KICA8L3N1YmplY3RzPgogIDxwdWJsaXNoZXI+Zmlnc2hhcmU8L3B1Ymxpc2hlcj4KICA8cHVibGljYXRpb25ZZWFyPjIwMTk8L3B1YmxpY2F0aW9uWWVhcj4KIC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