Quantifying Vegetation Vulnerability to Climate Variability in China Article Swipe
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14143491
Climate variability has profound effects on vegetation. Spatial distributions of vegetation vulnerability that comprehensively consider vegetation sensitivity and resilience are not well understood in China. Furthermore, the combination of cumulative climate effects and a one-month-lagged autoregressive model represents an advance in the technical approach for calculating vegetation sensitivity. In this study, the spatiotemporal characteristics of vegetation sensitivity to climate variability and vegetation resilience were investigated at seasonal scales. Further analysis explored the spatial distributions of vegetation vulnerability for different regions. The results showed that the spatial distribution pattern of vegetation vulnerability exhibited spatial heterogeneity in China. In spring, vegetation vulnerability values of approximately 0.9 were mainly distributed in northern Xinjiang and northern Inner Mongolia, while low values were scattered in Yunnan Province and the central region of East China. The highest proportion of severe vegetation vulnerability to climate variability was observed in the subhumid zone (28.94%), followed by the arid zone (26.27%). In summer and autumn, the proportions of severe vegetation vulnerability in the arid and humid zones were higher than those in the other climate zones. Regarding different vegetation types, the highest proportions of severe vegetation vulnerability were found in sparse vegetation in different seasons, while the highest proportions of slight vegetation vulnerability were found in croplands in different seasons. In addition, vegetation with high vulnerability is prone to change in Northeast and Southwest China. Although ecological restoration projects have been implemented to increase vegetation cover in northern China, low vegetation resilience and high vulnerability were observed in this region. Most grasslands, which were mainly concentrated on the Qinghai–Tibet Plateau, had high vulnerability. Vegetation areas with low resilience were likely to be degraded in this region. The areas with highly vulnerable vegetation on the Qinghai–Tibet Plateau could function as warning signals of vegetation degradation. Knowledge of spatial patterns of vegetation resilience and vegetation vulnerability will help provide scientific guidance for regional environmental protection.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs14143491
- OA Status
- gold
- Cited By
- 17
- References
- 79
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4286433294
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4286433294Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/rs14143491Digital Object Identifier
- Title
-
Quantifying Vegetation Vulnerability to Climate Variability in ChinaWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-07-21Full publication date if available
- Authors
-
Liangliang Jiang, Bing Liu, Ye YuanList of authors in order
- Landing page
-
https://doi.org/10.3390/rs14143491Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3390/rs14143491Direct OA link when available
- Concepts
-
Vegetation (pathology), Environmental science, Physical geography, Vulnerability (computing), Arid, Spatial distribution, Geography, Ecology, Remote sensing, Pathology, Medicine, Computer science, Biology, Computer securityTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
17Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 11, 2024: 3, 2023: 1, 2022: 2Per-year citation counts (last 5 years)
- References (count)
-
79Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4286433294 |
|---|---|
| doi | https://doi.org/10.3390/rs14143491 |
| ids.doi | https://doi.org/10.3390/rs14143491 |
| ids.openalex | https://openalex.org/W4286433294 |
| fwci | 2.32309587 |
| type | article |
| title | Quantifying Vegetation Vulnerability to Climate Variability in China |
| biblio.issue | 14 |
| biblio.volume | 14 |
| biblio.last_page | 3491 |
| biblio.first_page | 3491 |
| topics[0].id | https://openalex.org/T13377 |
| topics[0].field.id | https://openalex.org/fields/23 |
| topics[0].field.display_name | Environmental Science |
| topics[0].score | 0.9962000250816345 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2306 |
| topics[0].subfield.display_name | Global and Planetary Change |
| topics[0].display_name | Ecosystem dynamics and resilience |
| topics[1].id | https://openalex.org/T10226 |
| topics[1].field.id | https://openalex.org/fields/23 |
| topics[1].field.display_name | Environmental Science |
| topics[1].score | 0.9955999851226807 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2306 |
| topics[1].subfield.display_name | Global and Planetary Change |
| topics[1].display_name | Land Use and Ecosystem Services |
| topics[2].id | https://openalex.org/T10005 |
| topics[2].field.id | https://openalex.org/fields/23 |
| topics[2].field.display_name | Environmental Science |
| topics[2].score | 0.9940999746322632 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2309 |
| topics[2].subfield.display_name | Nature and Landscape Conservation |
| topics[2].display_name | Ecology and Vegetation Dynamics Studies |
| is_xpac | False |
| apc_list.value | 2500 |
| apc_list.currency | CHF |
| apc_list.value_usd | 2707 |
| apc_paid.value | 2500 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 2707 |
| concepts[0].id | https://openalex.org/C2776133958 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7900000214576721 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q7918366 |
| concepts[0].display_name | Vegetation (pathology) |
| concepts[1].id | https://openalex.org/C39432304 |
| concepts[1].level | 0 |
| concepts[1].score | 0.5735942125320435 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q188847 |
| concepts[1].display_name | Environmental science |
| concepts[2].id | https://openalex.org/C100970517 |
| concepts[2].level | 1 |
| concepts[2].score | 0.5551102161407471 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q52107 |
| concepts[2].display_name | Physical geography |
| concepts[3].id | https://openalex.org/C95713431 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5517504215240479 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q631425 |
| concepts[3].display_name | Vulnerability (computing) |
| concepts[4].id | https://openalex.org/C150772632 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5416188836097717 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q1330709 |
| concepts[4].display_name | Arid |
| concepts[5].id | https://openalex.org/C2777016058 |
| concepts[5].level | 2 |
| concepts[5].score | 0.43453603982925415 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q7574061 |
| concepts[5].display_name | Spatial distribution |
| concepts[6].id | https://openalex.org/C205649164 |
| concepts[6].level | 0 |
| concepts[6].score | 0.41865119338035583 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[6].display_name | Geography |
| concepts[7].id | https://openalex.org/C18903297 |
| concepts[7].level | 1 |
| concepts[7].score | 0.2362707555294037 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q7150 |
| concepts[7].display_name | Ecology |
| concepts[8].id | https://openalex.org/C62649853 |
| concepts[8].level | 1 |
| concepts[8].score | 0.13102403283119202 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q199687 |
| concepts[8].display_name | Remote sensing |
| concepts[9].id | https://openalex.org/C142724271 |
| concepts[9].level | 1 |
| concepts[9].score | 0.0 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q7208 |
| concepts[9].display_name | Pathology |
| concepts[10].id | https://openalex.org/C71924100 |
| concepts[10].level | 0 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[10].display_name | Medicine |
| concepts[11].id | https://openalex.org/C41008148 |
| concepts[11].level | 0 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[11].display_name | Computer science |
| concepts[12].id | https://openalex.org/C86803240 |
| concepts[12].level | 0 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[12].display_name | Biology |
| concepts[13].id | https://openalex.org/C38652104 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q3510521 |
| concepts[13].display_name | Computer security |
| keywords[0].id | https://openalex.org/keywords/vegetation |
| keywords[0].score | 0.7900000214576721 |
| keywords[0].display_name | Vegetation (pathology) |
| keywords[1].id | https://openalex.org/keywords/environmental-science |
| keywords[1].score | 0.5735942125320435 |
| keywords[1].display_name | Environmental science |
| keywords[2].id | https://openalex.org/keywords/physical-geography |
| keywords[2].score | 0.5551102161407471 |
| keywords[2].display_name | Physical geography |
| keywords[3].id | https://openalex.org/keywords/vulnerability |
| keywords[3].score | 0.5517504215240479 |
| keywords[3].display_name | Vulnerability (computing) |
| keywords[4].id | https://openalex.org/keywords/arid |
| keywords[4].score | 0.5416188836097717 |
| keywords[4].display_name | Arid |
| keywords[5].id | https://openalex.org/keywords/spatial-distribution |
| keywords[5].score | 0.43453603982925415 |
| keywords[5].display_name | Spatial distribution |
| keywords[6].id | https://openalex.org/keywords/geography |
| keywords[6].score | 0.41865119338035583 |
| keywords[6].display_name | Geography |
| keywords[7].id | https://openalex.org/keywords/ecology |
| keywords[7].score | 0.2362707555294037 |
| keywords[7].display_name | Ecology |
| keywords[8].id | https://openalex.org/keywords/remote-sensing |
| keywords[8].score | 0.13102403283119202 |
| keywords[8].display_name | Remote sensing |
| language | en |
| locations[0].id | doi:10.3390/rs14143491 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S43295729 |
| locations[0].source.issn | 2072-4292 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2072-4292 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Remote Sensing |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Remote Sensing |
| locations[0].landing_page_url | https://doi.org/10.3390/rs14143491 |
| locations[1].id | pmh:oai:doaj.org/article:1f072fc605a04d1fb9817b5fff434f86 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306401280 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].source.host_organization_lineage | |
| locations[1].license | cc-by-sa |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | https://openalex.org/licenses/cc-by-sa |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Remote Sensing, Vol 14, Iss 14, p 3491 (2022) |
| locations[1].landing_page_url | https://doaj.org/article/1f072fc605a04d1fb9817b5fff434f86 |
| locations[2].id | pmh:oai:mdpi.com:/2072-4292/14/14/3491/ |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306400947 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | True |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | MDPI (MDPI AG) |
| locations[2].source.host_organization | https://openalex.org/I4210097602 |
| locations[2].source.host_organization_name | Multidisciplinary Digital Publishing Institute (Switzerland) |
| locations[2].source.host_organization_lineage | https://openalex.org/I4210097602 |
| locations[2].license | cc-by |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | https://openalex.org/licenses/cc-by |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Remote Sensing; Volume 14; Issue 14; Pages: 3491 |
| locations[2].landing_page_url | https://dx.doi.org/10.3390/rs14143491 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5053405826 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-6264-474X |
| authorships[0].author.display_name | Liangliang Jiang |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].raw_affiliation_string | Chongqing Key Laboratory of GIS Application, Chongqing 401331, China |
| authorships[0].affiliations[1].institution_ids | https://openalex.org/I126924076 |
| authorships[0].affiliations[1].raw_affiliation_string | School of Geography and Tourism, Chongqing Normal University, Chongqing 401331, China |
| authorships[0].institutions[0].id | https://openalex.org/I126924076 |
| authorships[0].institutions[0].ror | https://ror.org/01dcw5w74 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I126924076 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Chongqing Normal University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Liangliang Jiang |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Chongqing Key Laboratory of GIS Application, Chongqing 401331, China, School of Geography and Tourism, Chongqing Normal University, Chongqing 401331, China |
| authorships[1].author.id | https://openalex.org/A5100339941 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-2608-6434 |
| authorships[1].author.display_name | Bing Liu |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I126924076 |
| authorships[1].affiliations[0].raw_affiliation_string | College of Chemistry, Chongqing Normal University, Chongqing 401331, China |
| authorships[1].institutions[0].id | https://openalex.org/I126924076 |
| authorships[1].institutions[0].ror | https://ror.org/01dcw5w74 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I126924076 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Chongqing Normal University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Bing Liu |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | College of Chemistry, Chongqing Normal University, Chongqing 401331, China |
| authorships[2].author.id | https://openalex.org/A5100337346 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-9762-368X |
| authorships[2].author.display_name | Ye Yuan |
| authorships[2].countries | BE, CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I32597200 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Geography, Ghent University, 9000 Ghent, Belgium |
| authorships[2].affiliations[1].institution_ids | https://openalex.org/I19820366, https://openalex.org/I4210103115 |
| authorships[2].affiliations[1].raw_affiliation_string | State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China |
| authorships[2].institutions[0].id | https://openalex.org/I32597200 |
| authorships[2].institutions[0].ror | https://ror.org/00cv9y106 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I32597200 |
| authorships[2].institutions[0].country_code | BE |
| authorships[2].institutions[0].display_name | Ghent University |
| authorships[2].institutions[1].id | https://openalex.org/I19820366 |
| authorships[2].institutions[1].ror | https://ror.org/034t30j35 |
| authorships[2].institutions[1].type | government |
| authorships[2].institutions[1].lineage | https://openalex.org/I19820366 |
| authorships[2].institutions[1].country_code | CN |
| authorships[2].institutions[1].display_name | Chinese Academy of Sciences |
| authorships[2].institutions[2].id | https://openalex.org/I4210103115 |
| authorships[2].institutions[2].ror | https://ror.org/01a8ev928 |
| authorships[2].institutions[2].type | facility |
| authorships[2].institutions[2].lineage | https://openalex.org/I19820366, https://openalex.org/I4210103115 |
| authorships[2].institutions[2].country_code | CN |
| authorships[2].institutions[2].display_name | Xinjiang Institute of Ecology and Geography |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Ye Yuan |
| authorships[2].is_corresponding | True |
| authorships[2].raw_affiliation_strings | Department of Geography, Ghent University, 9000 Ghent, Belgium, State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.3390/rs14143491 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Quantifying Vegetation Vulnerability to Climate Variability in China |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T13377 |
| primary_topic.field.id | https://openalex.org/fields/23 |
| primary_topic.field.display_name | Environmental Science |
| primary_topic.score | 0.9962000250816345 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2306 |
| primary_topic.subfield.display_name | Global and Planetary Change |
| primary_topic.display_name | Ecosystem dynamics and resilience |
| related_works | https://openalex.org/W2249491925, https://openalex.org/W2385855844, https://openalex.org/W2358296458, https://openalex.org/W3003911445, https://openalex.org/W2367225526, https://openalex.org/W15285571, https://openalex.org/W2141204001, https://openalex.org/W2124264691, https://openalex.org/W574916529, https://openalex.org/W4309269549 |
| cited_by_count | 17 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 11 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 3 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 1 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 2 |
| locations_count | 3 |
| best_oa_location.id | doi:10.3390/rs14143491 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S43295729 |
| best_oa_location.source.issn | 2072-4292 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2072-4292 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Remote Sensing |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Remote Sensing |
| best_oa_location.landing_page_url | https://doi.org/10.3390/rs14143491 |
| primary_location.id | doi:10.3390/rs14143491 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S43295729 |
| primary_location.source.issn | 2072-4292 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2072-4292 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Remote Sensing |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Remote Sensing |
| primary_location.landing_page_url | https://doi.org/10.3390/rs14143491 |
| publication_date | 2022-07-21 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W2744649443, https://openalex.org/W2108886061, https://openalex.org/W2128467912, https://openalex.org/W75231382, https://openalex.org/W2990292985, https://openalex.org/W2169245074, https://openalex.org/W3119824811, https://openalex.org/W3190241818, https://openalex.org/W3119605586, https://openalex.org/W2898691000, https://openalex.org/W3035368969, https://openalex.org/W3128694539, https://openalex.org/W3180835887, https://openalex.org/W3179019636, https://openalex.org/W2610523921, https://openalex.org/W6721852620, https://openalex.org/W6779744877, https://openalex.org/W1494272296, https://openalex.org/W1687968548, https://openalex.org/W3205624694, https://openalex.org/W4226390814, https://openalex.org/W2514339160, https://openalex.org/W6752494383, https://openalex.org/W2954347045, https://openalex.org/W2414581830, https://openalex.org/W2920943407, https://openalex.org/W2804504707, https://openalex.org/W2613288273, https://openalex.org/W3038954342, https://openalex.org/W2028948228, https://openalex.org/W2885080026, https://openalex.org/W3157980307, https://openalex.org/W2912077313, https://openalex.org/W2518198314, https://openalex.org/W2997645500, https://openalex.org/W3080553423, https://openalex.org/W2119470700, https://openalex.org/W2919607174, https://openalex.org/W6863605553, https://openalex.org/W2282122840, https://openalex.org/W2128175703, https://openalex.org/W2086050408, https://openalex.org/W1627669854, https://openalex.org/W2922289806, https://openalex.org/W2039361154, https://openalex.org/W2140886475, https://openalex.org/W2076669576, https://openalex.org/W2468044511, https://openalex.org/W1901442568, https://openalex.org/W2160362513, https://openalex.org/W2283727311, https://openalex.org/W4286681062, https://openalex.org/W2804441190, https://openalex.org/W1966512215, https://openalex.org/W2007818389, https://openalex.org/W2318680928, https://openalex.org/W2144697829, https://openalex.org/W2136161015, https://openalex.org/W2060906109, https://openalex.org/W2519102139, https://openalex.org/W2051447204, https://openalex.org/W2801966091, https://openalex.org/W2994021699, https://openalex.org/W4285793930, https://openalex.org/W3092137207, https://openalex.org/W2551704037, https://openalex.org/W2906803362, https://openalex.org/W2779442419, https://openalex.org/W2885747415, https://openalex.org/W2904356162, https://openalex.org/W2809772488, https://openalex.org/W2297302604, https://openalex.org/W2754859101, https://openalex.org/W3128858573, https://openalex.org/W2973303088, https://openalex.org/W3036207951, https://openalex.org/W2806045290, https://openalex.org/W2481197961, https://openalex.org/W4398938743 |
| referenced_works_count | 79 |
| abstract_inverted_index.a | 33 |
| abstract_inverted_index.In | 48, 96, 152, 211 |
| abstract_inverted_index.an | 38 |
| abstract_inverted_index.as | 289 |
| abstract_inverted_index.at | 65 |
| abstract_inverted_index.be | 272 |
| abstract_inverted_index.by | 147 |
| abstract_inverted_index.in | 23, 40, 94, 107, 119, 141, 162, 172, 190, 193, 206, 208, 221, 237, 248, 274 |
| abstract_inverted_index.is | 217 |
| abstract_inverted_index.of | 9, 28, 54, 74, 88, 101, 126, 132, 158, 184, 200, 292, 296, 299 |
| abstract_inverted_index.on | 5, 257, 283 |
| abstract_inverted_index.to | 57, 136, 219, 233, 271 |
| abstract_inverted_index.0.9 | 103 |
| abstract_inverted_index.The | 80, 129, 277 |
| abstract_inverted_index.and | 17, 32, 60, 110, 122, 154, 165, 223, 243, 302 |
| abstract_inverted_index.are | 19 |
| abstract_inverted_index.for | 44, 77, 310 |
| abstract_inverted_index.had | 261 |
| abstract_inverted_index.has | 2 |
| abstract_inverted_index.low | 115, 240, 267 |
| abstract_inverted_index.not | 20 |
| abstract_inverted_index.the | 26, 41, 51, 71, 84, 123, 142, 148, 156, 163, 173, 181, 197, 258, 284 |
| abstract_inverted_index.was | 139 |
| abstract_inverted_index.East | 127 |
| abstract_inverted_index.Most | 251 |
| abstract_inverted_index.arid | 149, 164 |
| abstract_inverted_index.been | 231 |
| abstract_inverted_index.have | 230 |
| abstract_inverted_index.help | 306 |
| abstract_inverted_index.high | 215, 244, 262 |
| abstract_inverted_index.than | 170 |
| abstract_inverted_index.that | 12, 83 |
| abstract_inverted_index.this | 49, 249, 275 |
| abstract_inverted_index.well | 21 |
| abstract_inverted_index.were | 63, 104, 117, 168, 188, 204, 246, 254, 269 |
| abstract_inverted_index.will | 305 |
| abstract_inverted_index.with | 214, 266, 279 |
| abstract_inverted_index.zone | 144, 150 |
| abstract_inverted_index.Inner | 112 |
| abstract_inverted_index.areas | 265, 278 |
| abstract_inverted_index.could | 287 |
| abstract_inverted_index.cover | 236 |
| abstract_inverted_index.found | 189, 205 |
| abstract_inverted_index.humid | 166 |
| abstract_inverted_index.model | 36 |
| abstract_inverted_index.other | 174 |
| abstract_inverted_index.prone | 218 |
| abstract_inverted_index.those | 171 |
| abstract_inverted_index.which | 253 |
| abstract_inverted_index.while | 114, 196 |
| abstract_inverted_index.zones | 167 |
| abstract_inverted_index.China, | 239 |
| abstract_inverted_index.China. | 24, 95, 128, 225 |
| abstract_inverted_index.Yunnan | 120 |
| abstract_inverted_index.change | 220 |
| abstract_inverted_index.higher | 169 |
| abstract_inverted_index.highly | 280 |
| abstract_inverted_index.likely | 270 |
| abstract_inverted_index.mainly | 105, 255 |
| abstract_inverted_index.region | 125 |
| abstract_inverted_index.severe | 133, 159, 185 |
| abstract_inverted_index.showed | 82 |
| abstract_inverted_index.slight | 201 |
| abstract_inverted_index.sparse | 191 |
| abstract_inverted_index.study, | 50 |
| abstract_inverted_index.summer | 153 |
| abstract_inverted_index.types, | 180 |
| abstract_inverted_index.values | 100, 116 |
| abstract_inverted_index.zones. | 176 |
| abstract_inverted_index.Climate | 0 |
| abstract_inverted_index.Further | 68 |
| abstract_inverted_index.Plateau | 286 |
| abstract_inverted_index.Spatial | 7 |
| abstract_inverted_index.advance | 39 |
| abstract_inverted_index.autumn, | 155 |
| abstract_inverted_index.central | 124 |
| abstract_inverted_index.climate | 30, 58, 137, 175 |
| abstract_inverted_index.effects | 4, 31 |
| abstract_inverted_index.highest | 130, 182, 198 |
| abstract_inverted_index.pattern | 87 |
| abstract_inverted_index.provide | 307 |
| abstract_inverted_index.region. | 250, 276 |
| abstract_inverted_index.results | 81 |
| abstract_inverted_index.scales. | 67 |
| abstract_inverted_index.signals | 291 |
| abstract_inverted_index.spatial | 72, 85, 92, 297 |
| abstract_inverted_index.spring, | 97 |
| abstract_inverted_index.warning | 290 |
| abstract_inverted_index.Although | 226 |
| abstract_inverted_index.Plateau, | 260 |
| abstract_inverted_index.Province | 121 |
| abstract_inverted_index.Xinjiang | 109 |
| abstract_inverted_index.analysis | 69 |
| abstract_inverted_index.approach | 43 |
| abstract_inverted_index.consider | 14 |
| abstract_inverted_index.degraded | 273 |
| abstract_inverted_index.explored | 70 |
| abstract_inverted_index.followed | 146 |
| abstract_inverted_index.function | 288 |
| abstract_inverted_index.guidance | 309 |
| abstract_inverted_index.increase | 234 |
| abstract_inverted_index.northern | 108, 111, 238 |
| abstract_inverted_index.observed | 140, 247 |
| abstract_inverted_index.patterns | 298 |
| abstract_inverted_index.profound | 3 |
| abstract_inverted_index.projects | 229 |
| abstract_inverted_index.regional | 311 |
| abstract_inverted_index.regions. | 79 |
| abstract_inverted_index.seasonal | 66 |
| abstract_inverted_index.seasons, | 195 |
| abstract_inverted_index.seasons. | 210 |
| abstract_inverted_index.subhumid | 143 |
| abstract_inverted_index.(26.27%). | 151 |
| abstract_inverted_index.(28.94%), | 145 |
| abstract_inverted_index.Knowledge | 295 |
| abstract_inverted_index.Mongolia, | 113 |
| abstract_inverted_index.Northeast | 222 |
| abstract_inverted_index.Regarding | 177 |
| abstract_inverted_index.Southwest | 224 |
| abstract_inverted_index.addition, | 212 |
| abstract_inverted_index.croplands | 207 |
| abstract_inverted_index.different | 78, 178, 194, 209 |
| abstract_inverted_index.exhibited | 91 |
| abstract_inverted_index.scattered | 118 |
| abstract_inverted_index.technical | 42 |
| abstract_inverted_index.Vegetation | 264 |
| abstract_inverted_index.cumulative | 29 |
| abstract_inverted_index.ecological | 227 |
| abstract_inverted_index.proportion | 131 |
| abstract_inverted_index.represents | 37 |
| abstract_inverted_index.resilience | 18, 62, 242, 268, 301 |
| abstract_inverted_index.scientific | 308 |
| abstract_inverted_index.understood | 22 |
| abstract_inverted_index.vegetation | 10, 15, 46, 55, 61, 75, 89, 98, 134, 160, 179, 186, 192, 202, 213, 235, 241, 282, 293, 300, 303 |
| abstract_inverted_index.vulnerable | 281 |
| abstract_inverted_index.calculating | 45 |
| abstract_inverted_index.combination | 27 |
| abstract_inverted_index.distributed | 106 |
| abstract_inverted_index.grasslands, | 252 |
| abstract_inverted_index.implemented | 232 |
| abstract_inverted_index.proportions | 157, 183, 199 |
| abstract_inverted_index.protection. | 313 |
| abstract_inverted_index.restoration | 228 |
| abstract_inverted_index.sensitivity | 16, 56 |
| abstract_inverted_index.variability | 1, 59, 138 |
| abstract_inverted_index.vegetation. | 6 |
| abstract_inverted_index.Furthermore, | 25 |
| abstract_inverted_index.concentrated | 256 |
| abstract_inverted_index.degradation. | 294 |
| abstract_inverted_index.distribution | 86 |
| abstract_inverted_index.investigated | 64 |
| abstract_inverted_index.sensitivity. | 47 |
| abstract_inverted_index.approximately | 102 |
| abstract_inverted_index.distributions | 8, 73 |
| abstract_inverted_index.environmental | 312 |
| abstract_inverted_index.heterogeneity | 93 |
| abstract_inverted_index.vulnerability | 11, 76, 90, 99, 135, 161, 187, 203, 216, 245, 304 |
| abstract_inverted_index.autoregressive | 35 |
| abstract_inverted_index.spatiotemporal | 52 |
| abstract_inverted_index.vulnerability. | 263 |
| abstract_inverted_index.Qinghai–Tibet | 259, 285 |
| abstract_inverted_index.characteristics | 53 |
| abstract_inverted_index.comprehensively | 13 |
| abstract_inverted_index.one-month-lagged | 34 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5100337346 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I19820366, https://openalex.org/I32597200, https://openalex.org/I4210103115 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.5699999928474426 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.85862004 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |