Mapping recent burned patches in Siberian larch forest using landsat and MODIS data

Zhihua Liu, Jian Yang, Francis Dwomoh

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Mapping burned area at relatively high spatio-temporal resolution is important to assess the causes and consequences of landscape fire in forest ecosystems. A simple algorithm, Burned Area Extraction and Dating (BAED) algorithm, was developed to automatically extract burned patches and determine approximate date of fire occurrence for each burned perimeter. Burned perimeters were extracted by a two stage approach, including determining “core burned” pixels and shape of the burned patches, from successive Landsat images. A time series model that has components of seasonality and trend was fitted from MODIS (MODerate-resolution Imaging Spectroradiometer) NDVI (Normalized Difference Vegetation Index) product. The approximate date of fire occurrence for each burned patch was assigned when the difference between the observed NDVI and the predicted NDVI from the time series model exceeded a threshold for three consecutive times. The BAED algorithm was tested in Siberian larch forest, an ecosystem with unique fire regime and considerable contribution to the global carbon balance. The results suggested that correct rate detected by BAED algorithm increases sharply when fire size < 200 ha, and then levels at 90% thereafter. The BAED provides ecologists an easy way to map burned area for assessing ecological effects of landscape fire in Landsat data poor regions.

Original languageEnglish
Pages (from-to)861-887
Number of pages27
JournalEuropean Journal of Remote Sensing
StatePublished - Dec 13 2016


This research was funded by National Natural Science Foundation of China (31100345 and 31470517) and CAS Pioneer Hundred Talents Program. The BAED algorithm was coded in R programming language, and available at

FundersFunder number
National Natural Science Foundation of China31100345, 31470517
Chinese Academy of Sciences


    • Boreal forest
    • Landscape ecology
    • Larch
    • Mapping
    • Remote sensing
    • Wildfire


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