{
  "abstract": "During the summer of 2022, Chongqing experienced record-breaking stress from extremely high temperatures, persistent decreases in precipitation, and severe wildfires. Remote sensing has been widely applied to monitor natural disasters and vegetation recovery because of its ability to collect continuous spatiotemporal dynamics observations. In this study, the normalised difference vegetation index (NDVI) and normalised burn ratio (NBR) were comprehensively used to assess the effects of high temperatures and wildfires on vegetation. The statistical results indicate that the average values of the NDVI and NBR decreased by 28% and 22%, respectively, between June and September. Moreover, both the NDVI and NBR sharply decreased across temporal and spatial dimensions after wildfires. The NBR outperforms the NDVI in wildfire warnings and burned area extractions. In early-stage recovery, the NDVI and NBR in the burned areas began to gradually recover but had not yet reached normal levels. The NDVI is superior to the NBR in terms of assessing the resistance and resilience of vegetation to extreme climate events. Furthermore, vegetation recovery response mechanisms are correlated with fire intensities, burned areas and vegetation types. Severely burned areas exhibited weaker resistance. Forests demonstrated strong resistance but weaker resilience; grasslands and shrubs, conversely, showed weaker resistance but stronger resilience.",
  "abstract_shown": true,
  "affiliations": [
    "State Key Laboratory of Loess Science, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, China",
    "National Observation and Research Station of the Earth Critical Zone on the Loess Plateau in Shaanxi, Xi’an, China",
    "Xi’an University of Technology, Xi’an, Shaanxi, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yixin Zhao"
    },
    {
      "affs": [
        1
      ],
      "name": "Yao Jiang"
    },
    {
      "affs": [
        2
      ],
      "name": "Mei Han"
    },
    {
      "affs": [
        0
      ],
      "name": "Yi Song"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/280bttbb2td08.bib",
  "cited_by": 0,
  "doi": "10.1080/19475705.2026.2751771",
  "fulltext_url": "https://www.tandfonline.com/doi/10.1080/19475705.2026.2751771",
  "id": "280bttbb2td08",
  "issns": [
    "1947-5713",
    "1947-5705"
  ],
  "landing_url": "https://doi.org/10.1080/19475705.2026.2751771",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "GE1-350"
    ],
    [
      "lcc",
      "HD61"
    ],
    [
      "lcc",
      "TD1-1066"
    ],
    [
      "openalex",
      "2303|Remote Sensing in Agriculture"
    ]
  ],
  "openalex": "W7223271824",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": "Taylor & Francis Group",
  "ris": "https://research.draconitic.com/paper/280bttbb2td08.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:44:15Z",
      "native_id": "62a9724cd59540409edee2c7425fdf1d",
      "source": "doaj",
      "url": "https://doi.org/10.1080/19475705.2026.2751771"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2303|Remote Sensing in Agriculture",
      "subfield": "ecology"
    }
  ],
  "title": "Using remote sensing to monitor vegetation recovery after extreme climate events in Chongqing, China",
  "title_text": "Using remote sensing to monitor vegetation recovery after extreme climate events in Chongqing, China",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:44:15Z",
  "url": "https://research.draconitic.com/paper/280bttbb2td08",
  "venue": "Geomatics, Natural Hazards & Risk",
  "versions": []
}