Forest health estimation in Sholayar Reserve Forest, Kerala using AVIRIS-NG hyperspectral data View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03-25

AUTHORS

Shahbaz Ahmad, Arvind Chandra Pandey, Amit Kumar, Nikhil V. Lele, Bimal K. Bhattacharya

ABSTRACT

The present study deals with analyzing forest health, its parameters, and suitability of hyperspectral data for vegetation health-related studies. Sholayar reserve forest in Kerala has a huge reserve of equatorial moist evergreen forest and demands preservation in every respect. Due to increased human interferences coupled with possible climate change, its health is undergoing a stage of deterioration. Stress levels in the canopy were assessed using a number of stress-related pigments. Detailed study of vegetation response to canopy leaf pigments have been carried out in the study. Airborne Visible Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) data provides immense possibilities to study a number of stress-related pigments like anthocyanin, carotenoid, lignin, chlorophyll-a, b etc. Dominant species in these forests are Holigarna arnottiana, Grevillea robusta, Grewia tiliifolia, Syzygium cumini, Alstonia Scholaris, Cinnamomum verum, Artocarpus heterophyllus, Bischofia javanica, Mangifera indica, Bombax ceiba, Anogeissus latifolia, Terminalia paniculata etc. Apart from luscious natural vegetation, plantation of teak (Tectona Grandis), rubber (Hevea brasiliensis), tea (Camellia sinensis), Coffee (Coffee Arabica), Palm-Oil tree (Elaeis guineensis) etc. also exists. Field data pertaining to one of the selected pigments was correlated with remotely sensed pigment estimates. Correlation of field measured chlorophyll concentration and EVI showed R2 = 0.421. Similarly, the anthocyanin index showed a correlation of R2 = 0.319. In the Sholayar Reserve Forest (493.0 km2) an area of 141.0 km2 was found to be in a healthy state. Whereas about 218.0 km2 of area exhibit moderately healthy condition and 77.0 km2 area was in the least healthy state. More... »

PAGES

1-14

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s41324-019-00260-6

DOI

http://dx.doi.org/10.1007/s41324-019-00260-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112986210


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/2008/0705", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Forestry Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/07", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Agricultural and Veterinary Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Central University of Jharkhand", 
          "id": "https://www.grid.ac/institutes/grid.448765.c", 
          "name": [
            "Department of Geoinformatics, School of Natural Resource Management, Central University of Jharkhand, 835205, Ranchi, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ahmad", 
        "givenName": "Shahbaz", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Central University of Jharkhand", 
          "id": "https://www.grid.ac/institutes/grid.448765.c", 
          "name": [
            "Department of Geoinformatics, School of Natural Resource Management, Central University of Jharkhand, 835205, Ranchi, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Pandey", 
        "givenName": "Arvind Chandra", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Central University of Jharkhand", 
          "id": "https://www.grid.ac/institutes/grid.448765.c", 
          "name": [
            "Department of Geoinformatics, School of Natural Resource Management, Central University of Jharkhand, 835205, Ranchi, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kumar", 
        "givenName": "Amit", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Indian Space Research Organisation", 
          "id": "https://www.grid.ac/institutes/grid.418654.a", 
          "name": [
            "Space Application Centre, ISRO, 380015, Ahmedabad, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lele", 
        "givenName": "Nikhil V.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Indian Space Research Organisation", 
          "id": "https://www.grid.ac/institutes/grid.418654.a", 
          "name": [
            "Space Application Centre, ISRO, 380015, Ahmedabad, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bhattacharya", 
        "givenName": "Bimal K.", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/0034-4257(95)00193-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000734517"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0304-4238(99)00045-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006747143"
        ], 
        "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/s0034-4257(02)00010-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022719585"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1146/annurev.py.33.090195.002421", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027151418"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ijleo.2016.05.115", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027720823"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0034-4257(92)90059-s", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028679046"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0034-4257(92)90059-s", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028679046"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1161-0301(01)00125-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029277415"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0034-4257(02)00011-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032751093"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.compag.2011.09.012", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032760047"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01431169308953986", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034176421"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5772/8283", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037539426"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1641/0006-3568(2004)054[0523:uistse]2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037737013"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0176-1617(11)81633-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039444668"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-94-011-3294-7_39", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042752147", 
          "https://doi.org/10.1007/978-94-011-3294-7_39"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-94-011-3294-7_39", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042752147", 
          "https://doi.org/10.1007/978-94-011-3294-7_39"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01904168609363475", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044375202"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0065-2113(08)60326-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044590099"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0034-4257(02)00182-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047135422"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0034-4257(02)00182-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047135422"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01431169308954010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051077999"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0034-4257(01)00191-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053639055"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tgrs.2003.813214", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061608854"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2134/agronj2001.931125x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1068994385"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/igarss.2008.4779612", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093787285"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/igarss.2004.1369826", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095323697"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1201/9781420032857", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095905311"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-03-25", 
    "datePublishedReg": "2019-03-25", 
    "description": "The present study deals with analyzing forest health, its parameters, and suitability of hyperspectral data for vegetation health-related studies. Sholayar reserve forest in Kerala has a huge reserve of equatorial moist evergreen forest and demands preservation in every respect. Due to increased human interferences coupled with possible climate change, its health is undergoing a stage of deterioration. Stress levels in the canopy were assessed using a number of stress-related pigments. Detailed study of vegetation response to canopy leaf pigments have been carried out in the study. Airborne Visible Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) data provides immense possibilities to study a number of stress-related pigments like anthocyanin, carotenoid, lignin, chlorophyll-a, b etc. Dominant species in these forests are Holigarna arnottiana, Grevillea robusta, Grewia tiliifolia, Syzygium cumini, Alstonia Scholaris, Cinnamomum verum, Artocarpus heterophyllus, Bischofia javanica, Mangifera indica, Bombax ceiba, Anogeissus latifolia, Terminalia paniculata etc. Apart from luscious natural vegetation, plantation of teak (Tectona Grandis), rubber (Hevea brasiliensis), tea (Camellia sinensis), Coffee (Coffee Arabica), Palm-Oil tree (Elaeis guineensis) etc. also exists. Field data pertaining to one of the selected pigments was correlated with remotely sensed pigment estimates. Correlation of field measured chlorophyll concentration and EVI showed R2 = 0.421. Similarly, the anthocyanin index showed a correlation of R2 = 0.319. In the Sholayar Reserve Forest (493.0 km2) an area of 141.0 km2 was found to be in a healthy state. Whereas about 218.0 km2 of area exhibit moderately healthy condition and 77.0 km2 area was in the least healthy state.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s41324-019-00260-6", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1153043", 
        "issn": [
          "2366-3286", 
          "2366-3294"
        ], 
        "name": "Spatial Information Research", 
        "type": "Periodical"
      }
    ], 
    "name": "Forest health estimation in Sholayar Reserve Forest, Kerala using AVIRIS-NG hyperspectral data", 
    "pagination": "1-14", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "6d714466065bdbf6c8b2a7b545c8f45d475345b85518c8a066fbdb5a7be22881"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s41324-019-00260-6"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1112986210"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s41324-019-00260-6", 
      "https://app.dimensions.ai/details/publication/pub.1112986210"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T13:04", 
    "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/v90/0000000366_0000000366/records_112042_00000001.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs41324-019-00260-6"
  }
]
 

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/s41324-019-00260-6'

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/s41324-019-00260-6'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s41324-019-00260-6'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s41324-019-00260-6'


 

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

157 TRIPLES      21 PREDICATES      49 URIs      16 LITERALS      5 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s41324-019-00260-6 schema:about anzsrc-for:07
2 anzsrc-for:0705
3 schema:author Nc70187c816844a4583cee52de23f6323
4 schema:citation sg:pub.10.1007/978-94-011-3294-7_39
5 https://doi.org/10.1016/0034-4257(92)90059-s
6 https://doi.org/10.1016/0034-4257(95)00193-x
7 https://doi.org/10.1016/j.compag.2011.09.012
8 https://doi.org/10.1016/j.ijleo.2016.05.115
9 https://doi.org/10.1016/s0034-4257(01)00191-2
10 https://doi.org/10.1016/s0034-4257(02)00010-x
11 https://doi.org/10.1016/s0034-4257(02)00011-1
12 https://doi.org/10.1016/s0034-4257(02)00182-7
13 https://doi.org/10.1016/s0034-4257(96)00112-5
14 https://doi.org/10.1016/s0065-2113(08)60326-0
15 https://doi.org/10.1016/s0176-1617(11)81633-0
16 https://doi.org/10.1016/s0304-4238(99)00045-x
17 https://doi.org/10.1016/s1161-0301(01)00125-3
18 https://doi.org/10.1080/01431169308953986
19 https://doi.org/10.1080/01431169308954010
20 https://doi.org/10.1080/01904168609363475
21 https://doi.org/10.1109/igarss.2004.1369826
22 https://doi.org/10.1109/igarss.2008.4779612
23 https://doi.org/10.1109/tgrs.2003.813214
24 https://doi.org/10.1146/annurev.py.33.090195.002421
25 https://doi.org/10.1201/9781420032857
26 https://doi.org/10.1641/0006-3568(2004)054[0523:uistse]2.0.co;2
27 https://doi.org/10.2134/agronj2001.931125x
28 https://doi.org/10.5772/8283
29 schema:datePublished 2019-03-25
30 schema:datePublishedReg 2019-03-25
31 schema:description The present study deals with analyzing forest health, its parameters, and suitability of hyperspectral data for vegetation health-related studies. Sholayar reserve forest in Kerala has a huge reserve of equatorial moist evergreen forest and demands preservation in every respect. Due to increased human interferences coupled with possible climate change, its health is undergoing a stage of deterioration. Stress levels in the canopy were assessed using a number of stress-related pigments. Detailed study of vegetation response to canopy leaf pigments have been carried out in the study. Airborne Visible Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) data provides immense possibilities to study a number of stress-related pigments like anthocyanin, carotenoid, lignin, chlorophyll-a, b etc. Dominant species in these forests are Holigarna arnottiana, Grevillea robusta, Grewia tiliifolia, Syzygium cumini, Alstonia Scholaris, Cinnamomum verum, Artocarpus heterophyllus, Bischofia javanica, Mangifera indica, Bombax ceiba, Anogeissus latifolia, Terminalia paniculata etc. Apart from luscious natural vegetation, plantation of teak (Tectona Grandis), rubber (Hevea brasiliensis), tea (Camellia sinensis), Coffee (Coffee Arabica), Palm-Oil tree (Elaeis guineensis) etc. also exists. Field data pertaining to one of the selected pigments was correlated with remotely sensed pigment estimates. Correlation of field measured chlorophyll concentration and EVI showed R2 = 0.421. Similarly, the anthocyanin index showed a correlation of R2 = 0.319. In the Sholayar Reserve Forest (493.0 km2) an area of 141.0 km2 was found to be in a healthy state. Whereas about 218.0 km2 of area exhibit moderately healthy condition and 77.0 km2 area was in the least healthy state.
32 schema:genre research_article
33 schema:inLanguage en
34 schema:isAccessibleForFree false
35 schema:isPartOf sg:journal.1153043
36 schema:name Forest health estimation in Sholayar Reserve Forest, Kerala using AVIRIS-NG hyperspectral data
37 schema:pagination 1-14
38 schema:productId N3a42d9338e0841ea8e222bc1cc1bb3cf
39 N4b4daa1ab7b74183a66e754805b68865
40 Nf5c6d90f30484040aec1aabb6c9ca97a
41 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112986210
42 https://doi.org/10.1007/s41324-019-00260-6
43 schema:sdDatePublished 2019-04-11T13:04
44 schema:sdLicense https://scigraph.springernature.com/explorer/license/
45 schema:sdPublisher N73ab9415bbf94c76af6abd4bee45c0e7
46 schema:url https://link.springer.com/10.1007%2Fs41324-019-00260-6
47 sgo:license sg:explorer/license/
48 sgo:sdDataset articles
49 rdf:type schema:ScholarlyArticle
50 N007c018ac3c24b219011bc4ad9bde5d1 schema:affiliation https://www.grid.ac/institutes/grid.448765.c
51 schema:familyName Kumar
52 schema:givenName Amit
53 rdf:type schema:Person
54 N3a42d9338e0841ea8e222bc1cc1bb3cf schema:name readcube_id
55 schema:value 6d714466065bdbf6c8b2a7b545c8f45d475345b85518c8a066fbdb5a7be22881
56 rdf:type schema:PropertyValue
57 N3bc05d5ed8164c749e9e0decc8533614 rdf:first N007c018ac3c24b219011bc4ad9bde5d1
58 rdf:rest Nd15a07e830b54580bf550d4118cad59c
59 N4b4daa1ab7b74183a66e754805b68865 schema:name doi
60 schema:value 10.1007/s41324-019-00260-6
61 rdf:type schema:PropertyValue
62 N5a3ab2811d6441ffb0f7d5d404ed0ebf schema:affiliation https://www.grid.ac/institutes/grid.448765.c
63 schema:familyName Pandey
64 schema:givenName Arvind Chandra
65 rdf:type schema:Person
66 N5e74a05ea40f4f32ba2bda189fb52a56 schema:affiliation https://www.grid.ac/institutes/grid.448765.c
67 schema:familyName Ahmad
68 schema:givenName Shahbaz
69 rdf:type schema:Person
70 N73ab9415bbf94c76af6abd4bee45c0e7 schema:name Springer Nature - SN SciGraph project
71 rdf:type schema:Organization
72 N93e36997f1294bc9b7161fffc00a0718 rdf:first N93f1cb8385f140a38ff6d5940b756047
73 rdf:rest rdf:nil
74 N93f1cb8385f140a38ff6d5940b756047 schema:affiliation https://www.grid.ac/institutes/grid.418654.a
75 schema:familyName Bhattacharya
76 schema:givenName Bimal K.
77 rdf:type schema:Person
78 Na77e01d027364112b7f1bba0b2ebab1a rdf:first N5a3ab2811d6441ffb0f7d5d404ed0ebf
79 rdf:rest N3bc05d5ed8164c749e9e0decc8533614
80 Nc70187c816844a4583cee52de23f6323 rdf:first N5e74a05ea40f4f32ba2bda189fb52a56
81 rdf:rest Na77e01d027364112b7f1bba0b2ebab1a
82 Ncc6fd69357684f11967a94e1a3fc1735 schema:affiliation https://www.grid.ac/institutes/grid.418654.a
83 schema:familyName Lele
84 schema:givenName Nikhil V.
85 rdf:type schema:Person
86 Nd15a07e830b54580bf550d4118cad59c rdf:first Ncc6fd69357684f11967a94e1a3fc1735
87 rdf:rest N93e36997f1294bc9b7161fffc00a0718
88 Nf5c6d90f30484040aec1aabb6c9ca97a schema:name dimensions_id
89 schema:value pub.1112986210
90 rdf:type schema:PropertyValue
91 anzsrc-for:07 schema:inDefinedTermSet anzsrc-for:
92 schema:name Agricultural and Veterinary Sciences
93 rdf:type schema:DefinedTerm
94 anzsrc-for:0705 schema:inDefinedTermSet anzsrc-for:
95 schema:name Forestry Sciences
96 rdf:type schema:DefinedTerm
97 sg:journal.1153043 schema:issn 2366-3286
98 2366-3294
99 schema:name Spatial Information Research
100 rdf:type schema:Periodical
101 sg:pub.10.1007/978-94-011-3294-7_39 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042752147
102 https://doi.org/10.1007/978-94-011-3294-7_39
103 rdf:type schema:CreativeWork
104 https://doi.org/10.1016/0034-4257(92)90059-s schema:sameAs https://app.dimensions.ai/details/publication/pub.1028679046
105 rdf:type schema:CreativeWork
106 https://doi.org/10.1016/0034-4257(95)00193-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1000734517
107 rdf:type schema:CreativeWork
108 https://doi.org/10.1016/j.compag.2011.09.012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032760047
109 rdf:type schema:CreativeWork
110 https://doi.org/10.1016/j.ijleo.2016.05.115 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027720823
111 rdf:type schema:CreativeWork
112 https://doi.org/10.1016/s0034-4257(01)00191-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053639055
113 rdf:type schema:CreativeWork
114 https://doi.org/10.1016/s0034-4257(02)00010-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1022719585
115 rdf:type schema:CreativeWork
116 https://doi.org/10.1016/s0034-4257(02)00011-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032751093
117 rdf:type schema:CreativeWork
118 https://doi.org/10.1016/s0034-4257(02)00182-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047135422
119 rdf:type schema:CreativeWork
120 https://doi.org/10.1016/s0034-4257(96)00112-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017802574
121 rdf:type schema:CreativeWork
122 https://doi.org/10.1016/s0065-2113(08)60326-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044590099
123 rdf:type schema:CreativeWork
124 https://doi.org/10.1016/s0176-1617(11)81633-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039444668
125 rdf:type schema:CreativeWork
126 https://doi.org/10.1016/s0304-4238(99)00045-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1006747143
127 rdf:type schema:CreativeWork
128 https://doi.org/10.1016/s1161-0301(01)00125-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029277415
129 rdf:type schema:CreativeWork
130 https://doi.org/10.1080/01431169308953986 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034176421
131 rdf:type schema:CreativeWork
132 https://doi.org/10.1080/01431169308954010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051077999
133 rdf:type schema:CreativeWork
134 https://doi.org/10.1080/01904168609363475 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044375202
135 rdf:type schema:CreativeWork
136 https://doi.org/10.1109/igarss.2004.1369826 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095323697
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1109/igarss.2008.4779612 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093787285
139 rdf:type schema:CreativeWork
140 https://doi.org/10.1109/tgrs.2003.813214 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061608854
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1146/annurev.py.33.090195.002421 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027151418
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1201/9781420032857 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095905311
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1641/0006-3568(2004)054[0523:uistse]2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037737013
147 rdf:type schema:CreativeWork
148 https://doi.org/10.2134/agronj2001.931125x schema:sameAs https://app.dimensions.ai/details/publication/pub.1068994385
149 rdf:type schema:CreativeWork
150 https://doi.org/10.5772/8283 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037539426
151 rdf:type schema:CreativeWork
152 https://www.grid.ac/institutes/grid.418654.a schema:alternateName Indian Space Research Organisation
153 schema:name Space Application Centre, ISRO, 380015, Ahmedabad, India
154 rdf:type schema:Organization
155 https://www.grid.ac/institutes/grid.448765.c schema:alternateName Central University of Jharkhand
156 schema:name Department of Geoinformatics, School of Natural Resource Management, Central University of Jharkhand, 835205, Ranchi, India
157 rdf:type schema:Organization
 




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


...