Estimating the area burned by agricultural fires from Landsat 8 Data using the Vegetation Difference Index and Burn Scar Index

被引:13
|
作者
Wang, Shudong [1 ]
Baig, Muhammad Hasan Ali [2 ]
Liu, Suhong [3 ]
Wan, Huawei [4 ]
Wu, Taixia [4 ]
Yang, Yingying [5 ]
机构
[1] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China
[2] Pir Mehr Ali Shah Arid Agr Univ, IGEO, Rawalpindi 46300, Pakistan
[3] Beijing Normal Univ, Sch Geog, Beijing 100875, Peoples R China
[4] Minist Environm Protect, Satellite Environm Ctr, Beijing 100094, Peoples R China
[5] Hohai Univ, Sch Earth Sci & Engn, Nanjing 211100, Jiangsu, Peoples R China
基金
美国国家科学基金会;
关键词
remote sensing of environment; SPECTRAL INDEXES; BOREAL FOREST; SEVERITY; REGION; EMISSION; AEROSOLS; CARBON; FIELD; NDVI;
D O I
10.1071/WF17069
中图分类号
S7 [林业];
学科分类号
0829 ; 0907 ;
摘要
Obtaining an accurate estimate of the area of burned crops through remote sensing provides extremely useful data for the assessment of fire-induced trace gas emissions and grain loss in agricultural areas. A new method, incorporating the Vegetation Difference Index (VDI) and Burn Scar Index (BSI) models, is proposed for the extraction of burned crops area. The VDI model can greatly reduce the confounding effect of background information pertaining to green vegetation (forests and grasslands), water bodies and buildings; subsequent use of the BSI model could improve the accuracy of burned area estimations because of the reduction in the influence of background information. The combination of VDI and BSI enables the VDI to reduce the effect of non-farmland information, which in turn improves the accuracy and speed of the BSI model. The model parameters were established, and an effects analysis was performed, using a normalized dispersion value simulation based on a comparison of different types of background information. The efficacy of the VDI and BSI models was tested for a winter wheat planting area in the Haihe River Basin in central China. In comparison with other models, it was found that this method could effectively extract burned area information.
引用
收藏
页码:217 / 227
页数:11
相关论文
共 50 条
  • [1] An improved combined vegetation difference index and burn scar index approach for mapping cropland burned areas using combined data from Landsat 8 multispectral and thermal infrared bands
    Liu, Shufu
    Wang, Shudong
    Chi, Tianhe
    Wen, Congcong
    Wu, Taixia
    Wang, Dacheng
    INTERNATIONAL JOURNAL OF WILDLAND FIRE, 2020, 29 (06) : 499 - 512
  • [2] Downscaling of MODIS leaf area index using landsat vegetation index
    Ovakoglou, Georgios
    Alexandridis, Thomas K.
    Clevers, Jan G. P. W.
    Gitas, Ioannis Z.
    GEOCARTO INTERNATIONAL, 2022, 37 (09) : 2466 - 2489
  • [3] Estimating the leaf area index in Indian tropical forests using Landsat-8 OLI data
    Middinti, Suresh
    Thumaty, Kiran Chand
    Gopalakrishnan, Rajashekar
    Jha, Chandra Shekhar
    Thatiparthi, Byragi Reddy
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2017, 38 (23) : 6769 - 6789
  • [4] Estimating crop biomass using leaf area index derived from Landsat 8 and Sentinel-2 data
    Dong, Taifeng
    Liu, Jiangui
    Qian, Budong
    He, Liming
    Liu, Jane
    Wang, Rong
    Jing, Qi
    Champagne, Catherine
    McNairn, Heather
    Powers, Jarrett
    Shi, Yichao
    Chen, Jing M.
    Shang, Jiali
    ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2020, 168 : 236 - 250
  • [5] ESTIMATING LEAF-AREA INDEX OF WHEAT WITH LANDSAT DATA
    POLLOCK, RB
    KANEMASU, ET
    REMOTE SENSING OF ENVIRONMENT, 1979, 8 (04) : 307 - 312
  • [6] A novel fire index-based burned area change detection approach using Landsat-8 OLI data
    Liu, Sicong
    Zheng, Yongjie
    Dalponte, Michele
    Tong, Xiaohua
    EUROPEAN JOURNAL OF REMOTE SENSING, 2020, 53 (01) : 104 - 112
  • [7] ESTIMATION OF THE LEAF AREA INDEX USING A MODIFIED TRIANGULAR DIFFERENCE VEGETATION INDEX
    Huang, Linsheng
    Jiang, Jing
    Song, Furan
    Zhao, Jinling
    Huang, Wenjiang
    2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019), 2019, : 7200 - 7203
  • [8] Assessment of the Analytic Burned Area Index for Forest Fire Severity Detection Using Sentinel and Landsat Data
    Guo, Rentao
    Yan, Jilin
    Zheng, He
    Wu, Bo
    FIRE-SWITZERLAND, 2024, 7 (01):
  • [9] Seasonal variations of leaf area index of agricultural fields retrieved from Landsat data
    Gonzalez-Sanpedro, M. C.
    Le Toan, T.
    Moreno, J.
    Kergoat, L.
    Rubio, E.
    REMOTE SENSING OF ENVIRONMENT, 2008, 112 (03) : 810 - 824
  • [10] Estimating the Biomass of Waterhyacinth (Eichhornia crassipes) Using the Normalized Difference Vegetation Index Derived from Simulated Landsat 5 TM
    Robles, Wilfredo
    Madsen, John D.
    Wersal, Ryan M.
    INVASIVE PLANT SCIENCE AND MANAGEMENT, 2015, 8 (02) : 203 - 211