Free Standard AU & NZ Shipping For All Book Orders Over $80!
Register      Login
International Journal of Wildland Fire International Journal of Wildland Fire Society
Journal of the International Association of Wildland Fire
RESEARCH ARTICLE (Open Access)

Burned vegetation recovery trajectory and its driving factors using satellite remote-sensing datasets in the Great Xing’An forest region of Inner Mongolia

Qiyue Zhang https://orcid.org/0000-0003-1732-0767 A B , Saeid Homayouni B , Pengwu Zhao A and Mei Zhou A *
+ Author Affiliations
- Author Affiliations

A Forestry College, Inner Mongolia Agricultural University, No. 275, East Xin Jian Street, Hohhot, 010011, China.

B Centre Eau Terre Environnement, Institut National de la Recherche Scientifique, Québec, Canada.

* Correspondence to: dxal528@aliyun.com

International Journal of Wildland Fire 32(2) 244-261 https://doi.org/10.1071/WF21167
Submitted: 22 November 2021  Accepted: 30 November 2022   Published: 19 January 2023

© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

Forest fire is one of the most important factors that alter a forest ecosystem’s biogeochemical cycle. Large-scale distributed burned areas lose their original vegetation structure and are more impacted by climate change in the vegetation recovery process, thus making it harder to restore their original vegetation structure. In this study, we used historical Landsat imagery and the LandTrendr algorithm in the Google Earth Engine platform to study and identify post-fire stages in the Great Xing’An Range of Inner Mongolia. Moreover, we categorized different post-fire vegetation recovery trajectories. The usefulness of spectral indices was also evaluated in the study region. We applied the Geodetector model to analyze the driving factors of the burned area vegetation regeneration process. The results show that burn severity and earth–atmosphere hydrological cycle are two main impacting factors in the short term after the fire (e.g. 5–6 years). Other climatical conditions affect vegetation recovery, including prolonged vegetation recovery process, hydrothermal circulation process and topographical conditions, seasonally frozen soil, freeze–thaw processes, and climate events. This study improves understanding of the dynamic successional processes in the burned area and the driving factors. Also, the outcomes can facilitate and support sustainable forest management of the Great Xing’An Range.

Keywords: burned area, driving factors, geodector, Great Xing’An Range, Inner Mongolia, LandTrendr, remote sensing data, vegetation recovery trajectory.


References

Abatzoglou JT, Dobrowski SZ, Parks SA, Hegewisch KC (2018) TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Scientific Data 5, 170191
TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015.Crossref | GoogleScholarGoogle Scholar |

Adelabu SA, Adepoju KA, Mofokeng OD (2020) Estimation of fire potential index in mountainous protected region using remote sensing. Geocarto International 35, 29–46.
Estimation of fire potential index in mountainous protected region using remote sensing.Crossref | GoogleScholarGoogle Scholar |

Aponte C, de Groot WJ, Wotton BM (2016) Forest fires and climate change: causes, consequences and management options. International Journal of Wildland Fire 25, i–ii.
Forest fires and climate change: causes, consequences and management options.Crossref | GoogleScholarGoogle Scholar |

Artés T, Oom D, de Rigo D, Durrant TH, Maianti P, Libertà G, San-Miguel-Ayanz J (2019) A global wildfire dataset for the analysis of fire regimes and fire behaviour. Scientific Data 6, 296
A global wildfire dataset for the analysis of fire regimes and fire behaviour.Crossref | GoogleScholarGoogle Scholar |

Bartels SF, Chen HYH, Wulder MA, White JC (2016) Trends in post-disturbance recovery rates of Canada’s forests following wildfire and harvest. Forest Ecology and Management 361, 194–207.
Trends in post-disturbance recovery rates of Canada’s forests following wildfire and harvest.Crossref | GoogleScholarGoogle Scholar |

Beamish A, Raynolds MK, Epstein H, Frost GV, Macander MJ, Bergstedt H, Bartsch A, Kruse S, Miles V, Tanis CM, Heim B, Fuchs M, Chabrillat S, Shevtsova I, Verdonen M, Wagner J (2020) Recent trends and remaining challenges for optical remote sensing of Arctic tundra vegetation: A review and outlook. Remote Sensing of Environment 246, 111872
Recent trends and remaining challenges for optical remote sensing of Arctic tundra vegetation: A review and outlook.Crossref | GoogleScholarGoogle Scholar |

Bhalla A, Durham RL, Al-Tabaa N, Yeager C (2016) The development and initial psychometric validation of the eHealth readiness scale. Computers in Human Behavior 65, 460–467.
The development and initial psychometric validation of the eHealth readiness scale.Crossref | GoogleScholarGoogle Scholar |

Bholowalia P, Kumar A (2014) EBK-means: A clustering technique based on elbow method and k-means in WSN. International Journal of Computer Applications 105, 17–24.

Bonney MT, He Y, Myint SW (2020) Contextualizing the 2019–2020 Kangaroo Island Bushfires: Quantifying Landscape-Level Influences on Past Severity and Recovery with Landsat and Google Earth Engine. Remote Sensing 12, 3942
Contextualizing the 2019–2020 Kangaroo Island Bushfires: Quantifying Landscape-Level Influences on Past Severity and Recovery with Landsat and Google Earth Engine.Crossref | GoogleScholarGoogle Scholar |

Booij MJ (2002) Extreme daily precipitation in Western Europe with climate change at appropriate spatial scales. International Journal of Climatology 22, 69–85.
Extreme daily precipitation in Western Europe with climate change at appropriate spatial scales.Crossref | GoogleScholarGoogle Scholar |

Bright BC, Hudak AT, Kennedy RE, Braaten JD, Henareh Khalyani A (2019) Examining post-fire vegetation recovery with Landsat time series analysis in three western North American forest types. Fire Ecology 15, 8
Examining post-fire vegetation recovery with Landsat time series analysis in three western North American forest types.Crossref | GoogleScholarGoogle Scholar |

Cardil A, Monedero S, Schag G, de-Miguel S, Tapia M, Stoof CR, Silva CA, Mohan M, Cardil A, Ramirez J (2021) Fire behavior modeling for operational decision-making. Current Opinion in Environmental Science & Health 23, 100291
Fire behavior modeling for operational decision-making.Crossref | GoogleScholarGoogle Scholar |

Center CCC (2020) ‘Blue Book on Climate Change in China (2020).’ (Science Press: Beijing)

Chen W, Moriya K, Sakai T, Koyama L, Cao C (2014) Post-fire forest regeneration under different restoration treatments in the Greater Hinggan Mountain area of China. Ecological Engineering 70, 304–311.
Post-fire forest regeneration under different restoration treatments in the Greater Hinggan Mountain area of China.Crossref | GoogleScholarGoogle Scholar |

Chen S-t, Guo B, Zhang R, Zang W-q, Wei C-x, Wu H-w, Yang X, Zhen X-y, Li X, Zhang D-f, Han B-m, Zhang H-l (2021) Quantitatively determine the dominant driving factors of the spatial—temporal changes of vegetation npp in the hengduan mountain area during 2000–2015. Journal of Mountain Science 18, 427–445.
Quantitatively determine the dominant driving factors of the spatial—temporal changes of vegetation npp in the hengduan mountain area during 2000–2015.Crossref | GoogleScholarGoogle Scholar |

Chen J, Wu T, Zou D, Liu L, Wu X, Gong W, Zhu X, Li R, Hao J, Hu G, Pang Q, Zhang J, Yang S (2022a) Magnitudes and patterns of large-scale permafrost ground deformation revealed by Sentinel-1 InSAR on the central Qinghai-Tibet Plateau. Remote Sensing of Environment 268, 112778
Magnitudes and patterns of large-scale permafrost ground deformation revealed by Sentinel-1 InSAR on the central Qinghai-Tibet Plateau.Crossref | GoogleScholarGoogle Scholar |

Chen X, Chen W, Xu M (2022b) Remote-Sensing Monitoring of Postfire Vegetation Dynamics in the Greater Hinggan Mountain Range Based on Long Time-Series Data: Analysis of the Effects of Six Topographic and Climatic Factors. Remote Sensing 14, 2958
Remote-Sensing Monitoring of Postfire Vegetation Dynamics in the Greater Hinggan Mountain Range Based on Long Time-Series Data: Analysis of the Effects of Six Topographic and Climatic Factors.Crossref | GoogleScholarGoogle Scholar |

Chuvieco E (2009) Global Impacts of Fire. In ‘Earth Observation of Wildland Fires in Mediterranean Ecosystems’. (Ed. E Chuvieco) pp. 1–10. (Springer, Berlin, Heidelberg)
| Crossref |

Crist EP (1985) A TM tasseled cap equivalent transformation for reflectance factor data. Remote Sensing of Environment 17, 301–306.
A TM tasseled cap equivalent transformation for reflectance factor data.Crossref | GoogleScholarGoogle Scholar |

Danahy EE, Agaian SS, Panetta KA (2007) Algorithms for the resizing of binary and grayscale images using a logical transform. In ‘Image Processing: Algorithms and Systems V. Vol. 6497’. (Eds JT Astola, KO Egiazarian, ER Dougherty) (SPIE)
| Crossref |

Demek J, Embleton C (1978) ‘Guide to medium-scale geomorphological mapping.’ (Schweizerbart Science Publishers: Stuttgart, Germany)

Dymond CC, Mladenoff DJ, Radeloff VC (2002) Phenological differences in Tasseled Cap indices improve deciduous forest classification. Remote Sensing of Environment 80, 460–472.
Phenological differences in Tasseled Cap indices improve deciduous forest classification.Crossref | GoogleScholarGoogle Scholar |

Fang L, Yang J, Zu J, Li G, Zhang J (2015) Quantifying influences and relative importance of fire weather, topography, and vegetation on fire size and fire severity in a Chinese boreal forest landscape. Forest Ecology and Management 356, 2–12.
Quantifying influences and relative importance of fire weather, topography, and vegetation on fire size and fire severity in a Chinese boreal forest landscape.Crossref | GoogleScholarGoogle Scholar |

Frappart F, Wigneron J-P, Li X, Liu X, Al-Yaari A, Fan L, Wang M, Moisy C, Le Masson E, Aoulad Lafkih Z, Vallé C, Ygorra B, Baghdadi N (2020) Global Monitoring of the Vegetation Dynamics from the Vegetation Optical Depth (VOD): A Review. Remote Sensing 12, 2915
Global Monitoring of the Vegetation Dynamics from the Vegetation Optical Depth (VOD): A Review.Crossref | GoogleScholarGoogle Scholar |

Fu Y, He HS, Zhao J, Larsen DR, Zhang H, Sunde MG, Duan S (2018) Climate and Spring Phenology Effects on Autumn Phenology in the Greater Khingan Mountains, Northeastern China. Remote Sensing 10, 449
Climate and Spring Phenology Effects on Autumn Phenology in the Greater Khingan Mountains, Northeastern China.Crossref | GoogleScholarGoogle Scholar |

Girardin MP (2007) Interannual to decadal changes in area burned in Canada from 1781 to 1982 and the relationship to Northern Hemisphere land temperatures. Global Ecology and Biogeography 16, 557–566.
Interannual to decadal changes in area burned in Canada from 1781 to 1982 and the relationship to Northern Hemisphere land temperatures.Crossref | GoogleScholarGoogle Scholar |

Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202, 18–27.
Google Earth Engine: Planetary-scale geospatial analysis for everyone.Crossref | GoogleScholarGoogle Scholar |

Gouveia C, DaCamara CC, Trigo RM (2010) Post-fire vegetation recovery in Portugal based on spot/vegetation data. Natural Hazards and Earth System Sciences 10, 673–684.
Post-fire vegetation recovery in Portugal based on spot/vegetation data.Crossref | GoogleScholarGoogle Scholar |

Guo Y, Zhang X, Wang Q, Chen H, Du X, Ma Y (2020) Temporal changes in vegetation around a shale gas development area in a subtropical karst region in southwestern China. Science of the Total Environment 701, 134769
Temporal changes in vegetation around a shale gas development area in a subtropical karst region in southwestern China.Crossref | GoogleScholarGoogle Scholar |

Guo M, Li J, Yu F, Yin S, Huang S, Wen L (2021) Estimation of post-fire vegetation recovery in boreal forests using solar-induced chlorophyll fluorescence (SIF) data. International Journal of Wildland Fire 30, 365–377.
Estimation of post-fire vegetation recovery in boreal forests using solar-induced chlorophyll fluorescence (SIF) data.Crossref | GoogleScholarGoogle Scholar |

Guo J, Wang J, Xu C, Song Y (2022) Modeling of spatial stratified heterogeneity. GIScience & Remote Sensing 59, 1660–1677.
Modeling of spatial stratified heterogeneity.Crossref | GoogleScholarGoogle Scholar |

Hao B, Xu X, Wu F, Tan L (2022) Long-Term Effects of Fire Severity and Climatic Factors on Post-Forest-Fire Vegetation Recovery. Forests 13, 883
Long-Term Effects of Fire Severity and Climatic Factors on Post-Forest-Fire Vegetation Recovery.Crossref | GoogleScholarGoogle Scholar |

Hawe RG, Fuquay DM (1969) Remote sensing of lightning in forest fire research. Remote Sensing of Environment VI, 1193

Hu L, Fan W, Ren H, Liu S, Cui Y, Zhao P (2018) Spatiotemporal Dynamics in Vegetation GPP over the Great Khingan Mountains Using GLASS Products from 1982 to 2015. Remote Sensing 10, 488
Spatiotemporal Dynamics in Vegetation GPP over the Great Khingan Mountains Using GLASS Products from 1982 to 2015.Crossref | GoogleScholarGoogle Scholar |

Hu T, Hu H, Li F, Zhao B, Wu S, Zhu G, Sun L (2019) Long-term effects of post-fire restoration types on nitrogen mineralisation in a Dahurian larch (Larix gmelinii) forest in boreal China. Science of the Total Environment 679, 237–247.
Long-term effects of post-fire restoration types on nitrogen mineralisation in a Dahurian larch (Larix gmelinii) forest in boreal China.Crossref | GoogleScholarGoogle Scholar |

Hu Y, Xu E, Dong N, Tian G, Kim G, Song P, Ge S, Liu S (2022) Driving Mechanism of Habitat Quality at Different Grid-Scales in a Metropolitan City. Forests 13, 248
Driving Mechanism of Habitat Quality at Different Grid-Scales in a Metropolitan City.Crossref | GoogleScholarGoogle Scholar |

Huang C, Goward SN, Masek JG, Thomas N, Zhu Z, Vogelmann JE (2010) An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. Remote Sensing of Environment 114, 183–198.
An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks.Crossref | GoogleScholarGoogle Scholar |

Huo H, Sun C (2021) Spatiotemporal variation and influencing factors of vegetation dynamics based on Geodetector: A case study of the northwestern Yunnan Plateau, China. Ecological Indicators 130, 108005
Spatiotemporal variation and influencing factors of vegetation dynamics based on Geodetector: A case study of the northwestern Yunnan Plateau, China.Crossref | GoogleScholarGoogle Scholar |

Jenks GF (1967) The data model concept in statistical mapping. International Yearbook of Cartography 7, 186–190.

Jin S, Sader SA (2005) Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances. Remote Sensing of Environment 94, 364–372.
Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances.Crossref | GoogleScholarGoogle Scholar |

Jin Y, Randerson JT, Goetz SJ, Beck PSA, Loranty MM, Goulden ML (2012) The influence of burn severity on postfire vegetation recovery and albedo change during early succession in North American boreal forests. Journal of Geophysical Research: Biogeosciences 117, G01036
The influence of burn severity on postfire vegetation recovery and albedo change during early succession in North American boreal forests.Crossref | GoogleScholarGoogle Scholar |

Jin X-Y, Jin H-J, Iwahana G, Marchenko SS, Luo D-L, Li X-Y, Liang S-H (2021) Impacts of climate-induced permafrost degradation on vegetation: A review. Advances in Climate Change Research 12, 29–47.
Impacts of climate-induced permafrost degradation on vegetation: A review.Crossref | GoogleScholarGoogle Scholar |

João T, João G, Bruno M, João H (2018) Indicator-based assessment of post-fire recovery dynamics using satellite NDVI time-series. Ecological Indicators 89, 199–212.
Indicator-based assessment of post-fire recovery dynamics using satellite NDVI time-series.Crossref | GoogleScholarGoogle Scholar |

Kennedy RE, Yang Z, Cohen WB (2010) Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms. Remote Sensing of Environment 114, 2897–2910.
Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms.Crossref | GoogleScholarGoogle Scholar |

Kennedy RE, Yang Z, Cohen WB, Pfaff E, Braaten J, Nelson P (2012) Spatial and temporal patterns of forest disturbance and regrowth within the area of the Northwest Forest Plan. Remote Sensing of Environment 122, 117–133.
Spatial and temporal patterns of forest disturbance and regrowth within the area of the Northwest Forest Plan.Crossref | GoogleScholarGoogle Scholar |

Kennedy RE, Yang Z, Gorelick N, Braaten J, Cavalcante L, Cohen WB, Healey S (2018) Implementation of the LandTrendr Algorithm on Google Earth Engine. Remote Sensing 10, 691
Implementation of the LandTrendr Algorithm on Google Earth Engine.Crossref | GoogleScholarGoogle Scholar |

Key CH, Benson NC (2006) Landscape Assessment (LA): Sampling and Analysis Methods. In ‘FIREMON: Fire Effects Monitoring and Inventory System’ Gen. Tech. Rep. RMRS-GTR-164-CD. (Eds DC Lutes, RE Keane, JF Caratti, CH Key, NC Benson, S Sutherland, LJ Gangi) pp. LA-1–LA-55. (Ogden, UT: USDA Forest Service, Rocky Mountain Research Station) Available at https://www.fs.usda.gov/research/treesearch/24066

Kourtz PH (1968) Computers and Forest Fire Detection. The Forestry Chronicle 44, 22–24.
Computers and Forest Fire Detection.Crossref | GoogleScholarGoogle Scholar |

Lee H-J, Choi YE, Lee S-W (2018) Complex Relationships of the Effects of Topographic Characteristics and Susceptible Tree Cover on Burn Severity. Sustainability 10, 295
Complex Relationships of the Effects of Topographic Characteristics and Susceptible Tree Cover on Burn Severity.Crossref | GoogleScholarGoogle Scholar |

Li W, Jiang Y, Dong M, Du E, Wu F, Zhao S, Xu H (2021a) Species-specific growth-climate responses of Dahurian larch (Larix gmelinii) and Mongolian pine (Pinus sylvestris var. mongolica) in the Greater Khingan Range, northeast China. Dendrochronologia 65, 125803
Species-specific growth-climate responses of Dahurian larch (Larix gmelinii) and Mongolian pine (Pinus sylvestris var. mongolica) in the Greater Khingan Range, northeast China.Crossref | GoogleScholarGoogle Scholar |

Li Y, Liu H, Zhu X, Yue Y, Xue J, Shi L (2021b) How permafrost degradation threatens boreal forest growth on its southern margin? Science of the Total Environment 762, 143154
How permafrost degradation threatens boreal forest growth on its southern margin?Crossref | GoogleScholarGoogle Scholar |

Liu Z (2016) Effects of climate and fire on short-term vegetation recovery in the boreal larch forests of Northeastern China. Scientific Reports 6, 37572
Effects of climate and fire on short-term vegetation recovery in the boreal larch forests of Northeastern China.Crossref | GoogleScholarGoogle Scholar |

Liu L, Lu S (2020) Spatial-Temporal Landscape Dynamics in the Hulunbeir Forest-Steppe Ecotone. Multifunctional Grasslands in a Changing World 1, 276

Liu Y, Xie M, Liu J, Wang H, Chen B (2022) Vegetation Disturbance and Recovery Dynamics of Different Surface Mining Sites via the LandTrendr Algorithm: Case Study in Inner Mongolia, China. Land 11, 856
Vegetation Disturbance and Recovery Dynamics of Different Surface Mining Sites via the LandTrendr Algorithm: Case Study in Inner Mongolia, China.Crossref | GoogleScholarGoogle Scholar |

Luo W, Jasiewicz J, Stepinski T, Wang J, Xu C, Cang X (2016) Spatial association between dissection density and environmental factors over the entire conterminous United States. Geophysical Research Letters 43, 692–700.
Spatial association between dissection density and environmental factors over the entire conterminous United States.Crossref | GoogleScholarGoogle Scholar |

Marlon JR, Bartlein PJ, Carcaillet C, Gavin DG, Harrison SP, Higuera PE, Joos F, Power MJ, Prentice IC (2008) Climate and human influences on global biomass burning over the past two millennia. Nature Geoscience 1, 697–702.
Climate and human influences on global biomass burning over the past two millennia.Crossref | GoogleScholarGoogle Scholar |

Masson D, Knutti R (2011) Spatial-Scale Dependence of Climate Model Performance in the CMIP3 Ensemble. Journal of Climate 24, 2680–2692.
Spatial-Scale Dependence of Climate Model Performance in the CMIP3 Ensemble.Crossref | GoogleScholarGoogle Scholar |

McKetta CW (1969) Some effects of terrain on the application of airborne infrared line scanners to forest fire detection. Doctoral dissertation.

Meng R, Dennison PE, Huang C, Moritz MA, D’Antonio C (2015) Effects of fire severity and post-fire climate on short-term vegetation recovery of mixed-conifer and red fir forests in the Sierra Nevada Mountains of California. Remote Sensing of Environment 171, 311–325.
Effects of fire severity and post-fire climate on short-term vegetation recovery of mixed-conifer and red fir forests in the Sierra Nevada Mountains of California.Crossref | GoogleScholarGoogle Scholar |

Pickell PD, Hermosilla T, Frazier RJ, Coops NC, Wulder MA (2016) Forest recovery trends derived from Landsat time series for North American boreal forests. International Journal of Remote Sensing 37, 138–149.
Forest recovery trends derived from Landsat time series for North American boreal forests.Crossref | GoogleScholarGoogle Scholar |

Powell SL, Cohen WB, Healey SP, Kennedy RE, Moisen GG, Pierce KB, Ohmann JL (2010) Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches. Remote Sensing of Environment 114, 1053–1068.
Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches.Crossref | GoogleScholarGoogle Scholar |

Qiu J, Wang H, Shen W, Zhang Y, Su H, Li M (2021) Quantifying Forest Fire and Post-Fire Vegetation Recovery in the Daxin’anling Area of Northeastern China Using Landsat Time-Series Data and Machine Learning. Remote Sensing 13, 792
Quantifying Forest Fire and Post-Fire Vegetation Recovery in the Daxin’anling Area of Northeastern China Using Landsat Time-Series Data and Machine Learning.Crossref | GoogleScholarGoogle Scholar |

Reygadas Langarica Y, Spera SA, Salisbury D, Galati V (2020) Mapping Forest Disturbances Across the Southwestern Amazon: Evaluation and Comparison of Optical Remote Sensing Algorithms. In ‘American Geophysical Union, Fall Meeting 2020, abstract #GC103-0003’. Available at https://ui.adsabs.harvard.edu/abs/2020AGUFMGC1030003R/abstract

Rupp TS, Chapin III FS, Starfield AM (2000) Response of subarctic vegetation to transient climatic change on the Seward Peninsula in north-west Alaska. Global Change Biology 6, 541–555.
Response of subarctic vegetation to transient climatic change on the Seward Peninsula in north-west Alaska.Crossref | GoogleScholarGoogle Scholar |

San-Miguel-Ayanz J, Moreno JM, Camia A (2013) Analysis of large fires in European Mediterranean landscapes: Lessons learned and perspectives. Forest Ecology and Management 294, 11–22.
Analysis of large fires in European Mediterranean landscapes: Lessons learned and perspectives.Crossref | GoogleScholarGoogle Scholar |

San-Miguel-Ayanz J, Durrant T, Boca R, Maianti P, Liberta G, Artes Vivancos T, Jacome Felix Oom D, Branco A, De Rigo D, Ferrari D, Pfeiffer H, Grecchi R, Nuitjen D (2020) Advance EFFIS report on forest fires in Europe, Middle East and North Africa 2019. EUR 30222 EN, Publications Office of the European Union, Luxembourg, JRC120692. Available at https://publications.jrc.ec.europa.eu/repository/handle/JRC120692

Seidl R, Thom D, Kautz M, Martin-Benito D, Peltoniemi M, Vacchiano G, Wild J, Ascoli D, Petr M, Honkaniemi J, Lexer MJ, Trotsiuk V, Mairota P, Svoboda M, Fabrika M, Nagel TA, Reyer CPO (2017) Forest disturbances under climate change. Nature Climate Change 7, 395–402.
Forest disturbances under climate change.Crossref | GoogleScholarGoogle Scholar |

Shi L, Dech JP, Liu H, Zhao P, Bayin D, Zhou M (2019) Post-fire vegetation recovery at forest sites is affected by permafrost degradation in the Da Xing’an Mountains of northern China. Journal of Vegetation Science 30, 940–949.
Post-fire vegetation recovery at forest sites is affected by permafrost degradation in the Da Xing’an Mountains of northern China.Crossref | GoogleScholarGoogle Scholar |

Siscawati M (1998) Underlying causes of deforestation and forest degradation in Indonesia: A case study on forest fire. In ‘Proceedings of the IGES International Workshop on Forest Conservation Strategies for the Asia and Pacific Region’ 21–23 July 1998. pp. 44–57 (Institute for Global Environmental Strategies) Available at https://www.iges.or.jp/en/publication_documents/pub/conferenceproceedings/en/744/1ws-7-mia.pdf

Song Y, Wright G, Wu P, Thatcher D, McHugh T, Li Q, Li SJ, Wang X (2018) Segment-Based Spatial Analysis for Assessing Road Infrastructure Performance Using Monitoring Observations and Remote Sensing Data. Remote Sensing 10, 1696
Segment-Based Spatial Analysis for Assessing Road Infrastructure Performance Using Monitoring Observations and Remote Sensing Data.Crossref | GoogleScholarGoogle Scholar |

Su Y, Li T, Cheng S, Wang X (2020) Spatial distribution exploration and driving factor identification for soil salinisation based on geodetector models in coastal area. Ecological Engineering 156, 105961
Spatial distribution exploration and driving factor identification for soil salinisation based on geodetector models in coastal area.Crossref | GoogleScholarGoogle Scholar |

Tadono T, Ishida H, Oda F, Naito S, Minakawa K, Iwamoto H (2014) Precise global DEM generation by ALOS PRISM. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences II-4, 71–76.
Precise global DEM generation by ALOS PRISM.Crossref | GoogleScholarGoogle Scholar |

Takaku J, Tadono T, Tsutsui K (2014) Generation of High Resolution Global DSM from ALOS PRISM. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-4, 243–248.
Generation of High Resolution Global DSM from ALOS PRISM.Crossref | GoogleScholarGoogle Scholar |

Takaku J, Tadono T, Tsutsui K, Ichikawa M (2016) Validation of “AW3D” global DSM generated from ALOS PRISM. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences III-4, 25–31.
Validation of “AW3D” global DSM generated from ALOS PRISM.Crossref | GoogleScholarGoogle Scholar |

Tang H, Li Z, Zhu Z, Chen B, Zhang B, Xin X (2015) Variability and climate change trend in vegetation phenology of recent decades in the Greater Khingan Mountain area, Northeastern China. Remote Sensing 7, 11914–11932.
Variability and climate change trend in vegetation phenology of recent decades in the Greater Khingan Mountain area, Northeastern China.Crossref | GoogleScholarGoogle Scholar |

Thomas Ambadan J, Oja M, Gedalof ZE, Berg AA (2020) Satellite-Observed Soil Moisture as an Indicator of Wildfire Risk. Remote Sensing 12, 1543
Satellite-Observed Soil Moisture as an Indicator of Wildfire Risk.Crossref | GoogleScholarGoogle Scholar |

Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8, 127–150.
Red and photographic infrared linear combinations for monitoring vegetation.Crossref | GoogleScholarGoogle Scholar |

van Wagtendonk JW, Root RR, Key CH (2004) Comparison of AVIRIS and Landsat ETM+ detection capabilities for burn severity. Remote Sensing of Environment 92, 397–408.
Comparison of AVIRIS and Landsat ETM+ detection capabilities for burn severity.Crossref | GoogleScholarGoogle Scholar |

Verbesselt J, Hyndman R, Newnham G, Culvenor D (2010) Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment 114, 106–115.
Detecting trend and seasonal changes in satellite image time series.Crossref | GoogleScholarGoogle Scholar |

Viana-Soto A, Aguado I, Martínez S (2017) Assessment of Post-Fire Vegetation Recovery Using Fire Severity and Geographical Data in the Mediterranean Region (Spain). Environments 4, 90
Assessment of Post-Fire Vegetation Recovery Using Fire Severity and Geographical Data in the Mediterranean Region (Spain).Crossref | GoogleScholarGoogle Scholar |

Viana-Soto A, Aguado I, Salas J, García M (2020) Identifying Post-Fire Recovery Trajectories and Driving Factors Using Landsat Time Series in Fire-Prone Mediterranean Pine Forests. Remote Sensing 12, 1499
Identifying Post-Fire Recovery Trajectories and Driving Factors Using Landsat Time Series in Fire-Prone Mediterranean Pine Forests.Crossref | GoogleScholarGoogle Scholar |

Wang Z (2020) The Response of Soil Respiration to Wildfire Interference and Burned Area Management in the Permafrost Region of Da xing’an Mountains, Inner Mongolia (in Chinese). PhD thesis, Inner Mongolia Agricultural University.

Wang JF, Li XH, Christakos G, Liao YL, Zhang T, Gu X, Zheng XY (2010) Geographical detectors‐based health risk assessment and its application in the neural tube defects study of the Heshun Region, China. International Journal of Geographical Information Science 24, 107–127.
Geographical detectors‐based health risk assessment and its application in the neural tube defects study of the Heshun Region, China.Crossref | GoogleScholarGoogle Scholar |

Wang J-F, Zhang T-L, Fu B-J (2016) A measure of spatial stratified heterogeneity. Ecological Indicators 67, 250–256.
A measure of spatial stratified heterogeneity.Crossref | GoogleScholarGoogle Scholar |

Wang J, Wang C, Zang S (2017) Assessing re-composition of Xing’an larch in boreal forests after the 1987 fire, Northeast China. Remote Sensing 9, 504
Assessing re-composition of Xing’an larch in boreal forests after the 1987 fire, Northeast China.Crossref | GoogleScholarGoogle Scholar |

Wang S, Zhong R, Liu L, Zhang J (2021) Ecological Effect of Ecological Engineering Projects on Low-Temperature Forest Cover in Great Khingan Mountain, China. International Journal of Environmental Research and Public Health 18, 10625
Ecological Effect of Ecological Engineering Projects on Low-Temperature Forest Cover in Great Khingan Mountain, China.Crossref | GoogleScholarGoogle Scholar |

White J, Ryan K, Key C, Running S (1996) Remote Sensing of Forest Fire Severity and Vegetation Recovery. International Journal of Wildland Fire 6, 125–136.
Remote Sensing of Forest Fire Severity and Vegetation Recovery.Crossref | GoogleScholarGoogle Scholar |

Wittenberg L, van der Wal H, Keesstra S, Tessler N (2020) Post-fire management treatment effects on soil properties and burned area restoration in a wildland-urban interface, Haifa Fire case study. Science of The Total Environment 716, 135190
Post-fire management treatment effects on soil properties and burned area restoration in a wildland-urban interface, Haifa Fire case study.Crossref | GoogleScholarGoogle Scholar |

Xiao X, Boles S, Liu J, Zhuang D, Liu M (2002) Characterization of forest types in Northeastern China, using multi-temporal SPOT-4 VEGETATION sensor data. Remote Sensing of Environment 82, 335–348.
Characterization of forest types in Northeastern China, using multi-temporal SPOT-4 VEGETATION sensor data.Crossref | GoogleScholarGoogle Scholar |

Xu C, Wang J (2016) Geodetector. Available at http://www.geodetector.cn/

Xu Q, Dong Y, Wang Y, Yang R, Xu C (2018) Determinants and identification of the northern boundary of China’s tropical zone. Journal of Geographical Sciences 28, 31–45.
Determinants and identification of the northern boundary of China’s tropical zone.Crossref | GoogleScholarGoogle Scholar |

Yang J, Cooper DJ, Li Z, Song W, Zhang Y, Zhao B, Han S, Wang X (2020) Differences in tree and shrub growth responses to climate change in a boreal forest in China. Dendrochronologia 63, 125744
Differences in tree and shrub growth responses to climate change in a boreal forest in China.Crossref | GoogleScholarGoogle Scholar |

Yang S, Lupascu M, Meel KS (2021) Predicting Forest Fire Using Remote Sensing Data And Machine Learning. Proceedings of the AAAI Conference on Artificial Intelligence 35, 14983–14990.
Predicting Forest Fire Using Remote Sensing Data And Machine Learning.Crossref | GoogleScholarGoogle Scholar |

Yao Q, Brown PM, Liu S, Rocca ME, Trouet V, Zheng B, Chen H, Li Y, Liu D, Wang X (2017) Pacific-Atlantic Ocean influence on wildfires in northeast China (1774 to 2010). Geophysical Research Letters 44, 1025–1033.
Pacific-Atlantic Ocean influence on wildfires in northeast China (1774 to 2010).Crossref | GoogleScholarGoogle Scholar |

Ying H, Zhang H, Zhao J, Shan Y, Zhang Z, Guo X, Rihan W, Deng G (2020) Effects of spring and summer extreme climate events on the autumn phenology of different vegetation types of Inner Mongolia, China, from 1982 to 2015. Ecological Indicators 111, 105974
Effects of spring and summer extreme climate events on the autumn phenology of different vegetation types of Inner Mongolia, China, from 1982 to 2015.Crossref | GoogleScholarGoogle Scholar |

Yue Y, Liu H, Xue J, Li Y, Guo W (2020) Ecological indicators of near-surface permafrost habitat at the southern margin of the boreal forest in China. Ecological Indicators 108, 105714
Ecological indicators of near-surface permafrost habitat at the southern margin of the boreal forest in China.Crossref | GoogleScholarGoogle Scholar |

Zhang Z, Wu Q, Xun X, Wang B, Wang X (2018) Climate change and the distribution of frozen soil in 1980–2010 in northern northeast China. Quaternary International 467, 230–241.
Climate change and the distribution of frozen soil in 1980–2010 in northern northeast China.Crossref | GoogleScholarGoogle Scholar |

Zhang Y, Li D, Liu L, Liang Z, Shen J, Wei F, Li S (2021) Spatiotemporal Characteristics of the Surface Urban Heat Island and Its Driving Factors Based on Local Climate Zones and Population in Beijing, China. Atmosphere 12, 1271
Spatiotemporal Characteristics of the Surface Urban Heat Island and Its Driving Factors Based on Local Climate Zones and Population in Beijing, China.Crossref | GoogleScholarGoogle Scholar |

Zhao FR, Meng R, Huang C, Zhao M, Zhao FA, Gong P, Yu L, Zhu Z (2016) Long-Term Post-Disturbance Forest Recovery in the Greater Yellowstone Ecosystem Analyzed Using Landsat Time Series Stack. Remote Sensing 8, 898
Long-Term Post-Disturbance Forest Recovery in the Greater Yellowstone Ecosystem Analyzed Using Landsat Time Series Stack.Crossref | GoogleScholarGoogle Scholar |

Zheng K, Tan L, Sun Y, Wu Y, Duan Z, Xu Y, Gao C (2021) Impacts of climate change and anthropogenic activities on vegetation change: Evidence from typical areas in China. Ecological Indicators 126, 107648
Impacts of climate change and anthropogenic activities on vegetation change: Evidence from typical areas in China.Crossref | GoogleScholarGoogle Scholar |

Zhu Z (2017) Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications. ISPRS Journal of Photogrammetry and Remote Sensing 130, 370–384.
Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications.Crossref | GoogleScholarGoogle Scholar |

Zhu Z, Woodcock CE, Olofsson P (2012) Continuous monitoring of forest disturbance using all available Landsat imagery. Remote Sensing of Environment 122, 75–91.
Continuous monitoring of forest disturbance using all available Landsat imagery.Crossref | GoogleScholarGoogle Scholar |

Zhu L, Meng J, Zhu L (2020) Applying Geodetector to disentangle the contributions of natural and anthropogenic factors to NDVI variations in the middle reaches of the Heihe River Basin. Ecological Indicators 117, 106545
Applying Geodetector to disentangle the contributions of natural and anthropogenic factors to NDVI variations in the middle reaches of the Heihe River Basin.Crossref | GoogleScholarGoogle Scholar |