Mapping red edge-based vegetation health indicators using Landsat TM data for Australian native vegetation cover View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-12

AUTHORS

Ali Shamsoddini, Simitkumar Raval

ABSTRACT

The usefulness of red edge bands, and vegetation indices based on red edge bands, for vegetation health monitoring has already been demonstrated. There are some satellites such as WorldView-2 and Sentinel-2 acquiring images in red edge band data; while, the former data can be expensive and often lack consistent global coverage, the latter does not have a long term archive and consequently cannot be used for a long term time series analysis. This study tests the ability to predict red edge band and red edge-based vegetation indices through freely available Landsat Thematic Mapper data for an Australian Eucalyptus-dominated vegetation cover within and around a mine site. Two modelling strategies including multiple-linear regression as a linear approach and random forests as a non-linear approach were used. The results showed that it is possible to generate red edge derivatives using the Landsat Thematic Mapper data with less than 10% error using both linear and non-linear methods; however, the linear method resulted in higher estimation accuracies than non-linear methods. More... »

PAGES

545-552

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12145-018-0347-5

DOI

http://dx.doi.org/10.1007/s12145-018-0347-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1103663038


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0104", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Statistics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Tarbiat Modares University", 
          "id": "https://www.grid.ac/institutes/grid.412266.5", 
          "name": [
            "Department of Remote Sensing and GIS, Tarbiat Modares University, Tehran, Iran"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Shamsoddini", 
        "givenName": "Ali", 
        "id": "sg:person.07771612641.21", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07771612641.21"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "UNSW Australia", 
          "id": "https://www.grid.ac/institutes/grid.1005.4", 
          "name": [
            "Australian Centre for Sustainable Mining Practices, School of Mining Engineering, University of New South Wales, 2152, Sydney, NSW, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Raval", 
        "givenName": "Simitkumar", 
        "id": "sg:person.015711053135.39", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015711053135.39"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/j.jag.2011.12.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002116825"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.rse.2011.08.024", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002689855"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5194/isprsannals-ii-7-75-2014", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002964197"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01431160701395328", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003187813"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1442-9993.2010.02206.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003689765"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1442-9993.2010.02206.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003689765"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0176-1617(96)80287-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009355752"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01431161.2013.772308", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009365472"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01431160701281056", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010363419"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.rse.2002.06.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010405824"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s12145-014-0169-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016140112", 
          "https://doi.org/10.1007/s12145-014-0169-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0034-4257(96)00112-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017802574"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1011-1344(01)00145-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019698147"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01431161.2015.1024896", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022205170"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1010933404324", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024739340", 
          "https://doi.org/10.1023/a:1010933404324"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ecoenv.2005.12.013", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027499901"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.14358/pers.72.1.71", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029487124"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0034-4257(02)00096-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031931019"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/treephys/7.1-2-3-4.33", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032488212"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-60327-194-3_11", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032843129", 
          "https://doi.org/10.1007/978-1-60327-194-3_11"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-60327-194-3_11", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032843129", 
          "https://doi.org/10.1007/978-1-60327-194-3_11"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-540-25966-4_33", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033920644", 
          "https://doi.org/10.1007/978-3-540-25966-4_33"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-540-25966-4_33", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033920644", 
          "https://doi.org/10.1007/978-3-540-25966-4_33"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0034-4257(00)00169-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034288852"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jag.2010.03.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035439612"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01431161.2014.954061", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036340141"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5194/isprsannals-ii-5-w2-259-2013", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037744057"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/14498596.2012.759092", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038378494"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02183056", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039942420", 
          "https://doi.org/10.1007/bf02183056"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02183056", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039942420", 
          "https://doi.org/10.1007/bf02183056"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0034-4257(97)00001-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040040102"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1046/j.1469-8137.1999.00424.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043365402"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jag.2012.03.012", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046409442"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1179/1743286313y.0000000039", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049027284"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.rse.2003.12.013", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049531645"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/030913339401800204", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063817088"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/030913339401800204", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063817088"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1562/0031-8655(2001)074<0038:opaneo>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1068006754"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2134/jeq2004.0956", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069008557"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/1936256", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069660313"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.7763/ijmlc.2012.v2.178", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1074037965"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3390/rs9101060", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092273061"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/igarss.2004.1370611", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094527825"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-12", 
    "datePublishedReg": "2018-12-01", 
    "description": "The usefulness of red edge bands, and vegetation indices based on red edge bands, for vegetation health monitoring has already been demonstrated. There are some satellites such as WorldView-2 and Sentinel-2 acquiring images in red edge band data; while, the former data can be expensive and often lack consistent global coverage, the latter does not have a long term archive and consequently cannot be used for a long term time series analysis. This study tests the ability to predict red edge band and red edge-based vegetation indices through freely available Landsat Thematic Mapper data for an Australian Eucalyptus-dominated vegetation cover within and around a mine site. Two modelling strategies including multiple-linear regression as a linear approach and random forests as a non-linear approach were used. The results showed that it is possible to generate red edge derivatives using the Landsat Thematic Mapper data with less than 10% error using both linear and non-linear methods; however, the linear method resulted in higher estimation accuracies than non-linear methods.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s12145-018-0347-5", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1049211", 
        "issn": [
          "1865-0473", 
          "1865-0481"
        ], 
        "name": "Earth Science Informatics", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "4", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "11"
      }
    ], 
    "name": "Mapping red edge-based vegetation health indicators using Landsat TM data for Australian native vegetation cover", 
    "pagination": "545-552", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "8f5ded7058287570083b93fddfe466b239af6aea37087176b767038a4408da66"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s12145-018-0347-5"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1103663038"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s12145-018-0347-5", 
      "https://app.dimensions.ai/details/publication/pub.1103663038"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T22:49", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8690_00000609.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs12145-018-0347-5"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s12145-018-0347-5'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s12145-018-0347-5'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s12145-018-0347-5'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s12145-018-0347-5'


 

This table displays all metadata directly associated to this object as RDF triples.

190 TRIPLES      21 PREDICATES      65 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s12145-018-0347-5 schema:about anzsrc-for:01
2 anzsrc-for:0104
3 schema:author Nff0b97934fdb424da78abbdd1497acc7
4 schema:citation sg:pub.10.1007/978-1-60327-194-3_11
5 sg:pub.10.1007/978-3-540-25966-4_33
6 sg:pub.10.1007/bf02183056
7 sg:pub.10.1007/s12145-014-0169-z
8 sg:pub.10.1023/a:1010933404324
9 https://doi.org/10.1016/j.ecoenv.2005.12.013
10 https://doi.org/10.1016/j.jag.2010.03.005
11 https://doi.org/10.1016/j.jag.2011.12.005
12 https://doi.org/10.1016/j.jag.2012.03.012
13 https://doi.org/10.1016/j.rse.2002.06.002
14 https://doi.org/10.1016/j.rse.2003.12.013
15 https://doi.org/10.1016/j.rse.2011.08.024
16 https://doi.org/10.1016/s0034-4257(00)00169-3
17 https://doi.org/10.1016/s0034-4257(02)00096-2
18 https://doi.org/10.1016/s0034-4257(96)00112-5
19 https://doi.org/10.1016/s0034-4257(97)00001-1
20 https://doi.org/10.1016/s0176-1617(96)80287-2
21 https://doi.org/10.1016/s1011-1344(01)00145-2
22 https://doi.org/10.1046/j.1469-8137.1999.00424.x
23 https://doi.org/10.1080/01431160701281056
24 https://doi.org/10.1080/01431160701395328
25 https://doi.org/10.1080/01431161.2013.772308
26 https://doi.org/10.1080/01431161.2014.954061
27 https://doi.org/10.1080/01431161.2015.1024896
28 https://doi.org/10.1080/14498596.2012.759092
29 https://doi.org/10.1093/treephys/7.1-2-3-4.33
30 https://doi.org/10.1109/igarss.2004.1370611
31 https://doi.org/10.1111/j.1442-9993.2010.02206.x
32 https://doi.org/10.1177/030913339401800204
33 https://doi.org/10.1179/1743286313y.0000000039
34 https://doi.org/10.14358/pers.72.1.71
35 https://doi.org/10.1562/0031-8655(2001)074<0038:opaneo>2.0.co;2
36 https://doi.org/10.2134/jeq2004.0956
37 https://doi.org/10.2307/1936256
38 https://doi.org/10.3390/rs9101060
39 https://doi.org/10.5194/isprsannals-ii-5-w2-259-2013
40 https://doi.org/10.5194/isprsannals-ii-7-75-2014
41 https://doi.org/10.7763/ijmlc.2012.v2.178
42 schema:datePublished 2018-12
43 schema:datePublishedReg 2018-12-01
44 schema:description The usefulness of red edge bands, and vegetation indices based on red edge bands, for vegetation health monitoring has already been demonstrated. There are some satellites such as WorldView-2 and Sentinel-2 acquiring images in red edge band data; while, the former data can be expensive and often lack consistent global coverage, the latter does not have a long term archive and consequently cannot be used for a long term time series analysis. This study tests the ability to predict red edge band and red edge-based vegetation indices through freely available Landsat Thematic Mapper data for an Australian Eucalyptus-dominated vegetation cover within and around a mine site. Two modelling strategies including multiple-linear regression as a linear approach and random forests as a non-linear approach were used. The results showed that it is possible to generate red edge derivatives using the Landsat Thematic Mapper data with less than 10% error using both linear and non-linear methods; however, the linear method resulted in higher estimation accuracies than non-linear methods.
45 schema:genre research_article
46 schema:inLanguage en
47 schema:isAccessibleForFree false
48 schema:isPartOf N20e37db8977e43e18f3ba67b625b122b
49 N524ef98df5fb486e992812722b7aafc9
50 sg:journal.1049211
51 schema:name Mapping red edge-based vegetation health indicators using Landsat TM data for Australian native vegetation cover
52 schema:pagination 545-552
53 schema:productId N012babb579fd46b5a8712045b38157d1
54 Na35df54b4b91419cb589070532f27ea9
55 Nd794337f93fb4918b5aecea865e8c05f
56 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103663038
57 https://doi.org/10.1007/s12145-018-0347-5
58 schema:sdDatePublished 2019-04-10T22:49
59 schema:sdLicense https://scigraph.springernature.com/explorer/license/
60 schema:sdPublisher Ncd87af8f247c48f7970da0f6ed2da647
61 schema:url https://link.springer.com/10.1007%2Fs12145-018-0347-5
62 sgo:license sg:explorer/license/
63 sgo:sdDataset articles
64 rdf:type schema:ScholarlyArticle
65 N012babb579fd46b5a8712045b38157d1 schema:name dimensions_id
66 schema:value pub.1103663038
67 rdf:type schema:PropertyValue
68 N20e37db8977e43e18f3ba67b625b122b schema:issueNumber 4
69 rdf:type schema:PublicationIssue
70 N524ef98df5fb486e992812722b7aafc9 schema:volumeNumber 11
71 rdf:type schema:PublicationVolume
72 Na35df54b4b91419cb589070532f27ea9 schema:name doi
73 schema:value 10.1007/s12145-018-0347-5
74 rdf:type schema:PropertyValue
75 Nae5edfcc92014f62ba843eb541828f1f rdf:first sg:person.015711053135.39
76 rdf:rest rdf:nil
77 Ncd87af8f247c48f7970da0f6ed2da647 schema:name Springer Nature - SN SciGraph project
78 rdf:type schema:Organization
79 Nd794337f93fb4918b5aecea865e8c05f schema:name readcube_id
80 schema:value 8f5ded7058287570083b93fddfe466b239af6aea37087176b767038a4408da66
81 rdf:type schema:PropertyValue
82 Nff0b97934fdb424da78abbdd1497acc7 rdf:first sg:person.07771612641.21
83 rdf:rest Nae5edfcc92014f62ba843eb541828f1f
84 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
85 schema:name Mathematical Sciences
86 rdf:type schema:DefinedTerm
87 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
88 schema:name Statistics
89 rdf:type schema:DefinedTerm
90 sg:journal.1049211 schema:issn 1865-0473
91 1865-0481
92 schema:name Earth Science Informatics
93 rdf:type schema:Periodical
94 sg:person.015711053135.39 schema:affiliation https://www.grid.ac/institutes/grid.1005.4
95 schema:familyName Raval
96 schema:givenName Simitkumar
97 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015711053135.39
98 rdf:type schema:Person
99 sg:person.07771612641.21 schema:affiliation https://www.grid.ac/institutes/grid.412266.5
100 schema:familyName Shamsoddini
101 schema:givenName Ali
102 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07771612641.21
103 rdf:type schema:Person
104 sg:pub.10.1007/978-1-60327-194-3_11 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032843129
105 https://doi.org/10.1007/978-1-60327-194-3_11
106 rdf:type schema:CreativeWork
107 sg:pub.10.1007/978-3-540-25966-4_33 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033920644
108 https://doi.org/10.1007/978-3-540-25966-4_33
109 rdf:type schema:CreativeWork
110 sg:pub.10.1007/bf02183056 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039942420
111 https://doi.org/10.1007/bf02183056
112 rdf:type schema:CreativeWork
113 sg:pub.10.1007/s12145-014-0169-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1016140112
114 https://doi.org/10.1007/s12145-014-0169-z
115 rdf:type schema:CreativeWork
116 sg:pub.10.1023/a:1010933404324 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024739340
117 https://doi.org/10.1023/a:1010933404324
118 rdf:type schema:CreativeWork
119 https://doi.org/10.1016/j.ecoenv.2005.12.013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027499901
120 rdf:type schema:CreativeWork
121 https://doi.org/10.1016/j.jag.2010.03.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035439612
122 rdf:type schema:CreativeWork
123 https://doi.org/10.1016/j.jag.2011.12.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002116825
124 rdf:type schema:CreativeWork
125 https://doi.org/10.1016/j.jag.2012.03.012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046409442
126 rdf:type schema:CreativeWork
127 https://doi.org/10.1016/j.rse.2002.06.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010405824
128 rdf:type schema:CreativeWork
129 https://doi.org/10.1016/j.rse.2003.12.013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049531645
130 rdf:type schema:CreativeWork
131 https://doi.org/10.1016/j.rse.2011.08.024 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002689855
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1016/s0034-4257(00)00169-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034288852
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1016/s0034-4257(02)00096-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031931019
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1016/s0034-4257(96)00112-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017802574
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1016/s0034-4257(97)00001-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040040102
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1016/s0176-1617(96)80287-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009355752
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1016/s1011-1344(01)00145-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019698147
144 rdf:type schema:CreativeWork
145 https://doi.org/10.1046/j.1469-8137.1999.00424.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1043365402
146 rdf:type schema:CreativeWork
147 https://doi.org/10.1080/01431160701281056 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010363419
148 rdf:type schema:CreativeWork
149 https://doi.org/10.1080/01431160701395328 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003187813
150 rdf:type schema:CreativeWork
151 https://doi.org/10.1080/01431161.2013.772308 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009365472
152 rdf:type schema:CreativeWork
153 https://doi.org/10.1080/01431161.2014.954061 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036340141
154 rdf:type schema:CreativeWork
155 https://doi.org/10.1080/01431161.2015.1024896 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022205170
156 rdf:type schema:CreativeWork
157 https://doi.org/10.1080/14498596.2012.759092 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038378494
158 rdf:type schema:CreativeWork
159 https://doi.org/10.1093/treephys/7.1-2-3-4.33 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032488212
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1109/igarss.2004.1370611 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094527825
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1111/j.1442-9993.2010.02206.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1003689765
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1177/030913339401800204 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063817088
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1179/1743286313y.0000000039 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049027284
168 rdf:type schema:CreativeWork
169 https://doi.org/10.14358/pers.72.1.71 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029487124
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1562/0031-8655(2001)074<0038:opaneo>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1068006754
172 rdf:type schema:CreativeWork
173 https://doi.org/10.2134/jeq2004.0956 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069008557
174 rdf:type schema:CreativeWork
175 https://doi.org/10.2307/1936256 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069660313
176 rdf:type schema:CreativeWork
177 https://doi.org/10.3390/rs9101060 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092273061
178 rdf:type schema:CreativeWork
179 https://doi.org/10.5194/isprsannals-ii-5-w2-259-2013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037744057
180 rdf:type schema:CreativeWork
181 https://doi.org/10.5194/isprsannals-ii-7-75-2014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002964197
182 rdf:type schema:CreativeWork
183 https://doi.org/10.7763/ijmlc.2012.v2.178 schema:sameAs https://app.dimensions.ai/details/publication/pub.1074037965
184 rdf:type schema:CreativeWork
185 https://www.grid.ac/institutes/grid.1005.4 schema:alternateName UNSW Australia
186 schema:name Australian Centre for Sustainable Mining Practices, School of Mining Engineering, University of New South Wales, 2152, Sydney, NSW, Australia
187 rdf:type schema:Organization
188 https://www.grid.ac/institutes/grid.412266.5 schema:alternateName Tarbiat Modares University
189 schema:name Department of Remote Sensing and GIS, Tarbiat Modares University, Tehran, Iran
190 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...