利用MODIS影像提取火烧迹地方法的研究

doi:10.3724/SP.J.1047.2016.01529

摘要/Abstract

摘要：

火烧迹地监测不仅可以反映火灾对生态系统的影响情况及损失信息,还能为全球碳循环研究提供重要的数据支持。本文利用MODIS地表反射率产品（MOD09A1）的近红外和短波红外波段构建的归一化燃烧率指数（NBR）,计算前后2期影像的NBR差值,并在光谱指数差分法的基础上,结合MODIS植被数据产品（MOD44B）提供的植被覆盖度信息,设置规则提取火烧迹地。本文选择西伯利亚地区东南部的林地、草地、农田等不同生态系统的交界地带作为实验区,利用本文算法提取该区域的火烧迹地。实验结果表明：（1）本文算法的火烧迹地提取效果较好,优于MODIS火烧迹地产品（MCD45A1）,kappa系数由0.70提高到0.75;（2）利用林木覆盖度、草本覆盖度数据,可以减少误判,提高火烧迹地提取的精度,kappa系数分别由0.69、0.73都提高到0.75。

关键词: 火烧迹地, MODIS, NBR, 植被覆盖度, 差分法

Abstract:

Fires belong to one of the main disturbance factors and play an important role in various ecosystems. Burned area detection not only indicates the impact of fires on ecosystems, but also provides a scientific support for the global carbon cycle studies. Traditional burned scar area detection approach mainly depends on ground survey and measurements, which still has several defects, such as the heavy workload, high cost, low efficiency, and poor timeliness etc. By applying remote sensing technology to map the burned area can produce burn scar information with greater spatial and temporal scale and effectively avoid the above-mentioned problems. Currently, many methods aiming to map the burned area on remote sensing images have been developed, and various global burned area products which provide the consistent assessments of fire activity at the global scale are also available; however, the efficiency of their performances differs within various ecosystems. In this study, we developed an algorithm to map the burned scar area in an ecosystem transition zone by using the Moderate Resolution Imaging Spectroradiometer (MODIS) data. This algorithm was developed based on the Normalized Burned Ratio differencing (dNBR) and the vegetation coverage data. The NBR index was originally developed specifically for mapping burned areas, and recently it has been used in the assessment of burning severity. Firstly, we used the near red and shortwave infrared bands of MODIS Surface Reflectance products (MOD09A1) to calculate the NBR values. Then, the differenced NBR (dNBR) calculated from the NBR values for a composite period with the previous 8-day range was calculated. The frequency distribution of dNBR maximum value in the burned scar area and the unburned region was analyzed. Since the change of NBR values in regions with different vegetation coverage was different, the tree cover and herbaceous cover data provided by the MODIS Vegetation Continuous Fields product (MOD44B) were also used for setting up rules to extract the burned scar area. A case study was carried out in an ecosystem transition zone within the southeast Siberia, where forest, grassland, farmland and other different ecosystems coexist. Comparison of the burned area detected by this algorithm with the adoption of high resolution burned scar information from Landsat ETM+ imagery shows a high accuracy. And the result obtained using this algorithm was better than the one using the MODIS Combined Burned Area product (MCD45A1), with the kappa coefficient increased from 0.70 to 0.75. To make a better comparison, we set up rules with the same threshold values of dNBR to extract the burned scar area, but without the usage of tree cover or herbaceous cover data. We found that the use of tree cover data as well as the herbaceous cover data can reduce mistakes during the process and improve the accuracy of burned area extraction, with the kappa coefficient increased from 0.69 and 0.73 respectively to 0.75.

Key words: burned area, MODIS, NBR, vegetation coverage, difference method

肖潇, 冯险峰, 孙庆龄. 利用MODIS影像提取火烧迹地方法的研究[J]. 地球信息科学学报, 2016, 18(11): 1529-1536.DOI:10.3724/SP.J.1047.2016.01529

XIAO Xiao,FENG Xianfeng,SUN Qingling. Burned Area Detection in the Ecosystem Transition Zone Using MODIS Data[J]. Journal of Geo-information Science, 2016, 18(11): 1529-1536.DOI:10.3724/SP.J.1047.2016.01529

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参考文献 24

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位数	参数名称	可取值	含义
2	云阴影（Cloud shadow）	0	无（No）
3-5	陆地/水（Land/water flag）	001	陆地（Land）
6-7	气溶胶（Aerosol quality）	01	少（Low）
6-7	气溶胶（Aerosol quality）	10	平均（Average）
8-9	卷云（Cirrus detected）	00	无（No）
		01	少（Low）
		10	平均（Average）
10	内部云算法（Internal cloud algorithm flag）	0	无云（No cloud）
12	MOD35 雪/冰（snow/ice flag）	0	无（No）
15	内部雪掩膜（Internal snow mask）	0	无雪（No snow）

土地覆被类型	像元个数	所占比例/（%）
水域	0	0
落叶针叶林	0	0
落叶阔叶林	0	0
混交林	91	0.56
草林混杂地	42	0.26
草地	7447	45.93
永久湿地	0	0
农用地	7158	44.15
城市和建设区	32	0.20
农用地/自然植被拼接地	1444	8.91
稀疏植被	0	0