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    陈冀岱, 牛树奎. 多时相高分辨率遥感影像的森林可燃物分类和变化分析[J]. 北京林业大学学报, 2018, 40(12): 38-48. DOI: 10.13332/j.1000-1522.20180269
    引用本文: 陈冀岱, 牛树奎. 多时相高分辨率遥感影像的森林可燃物分类和变化分析[J]. 北京林业大学学报, 2018, 40(12): 38-48. DOI: 10.13332/j.1000-1522.20180269
    Chen Jidai, Niu Shukui. Classification and change analysis of forest fuels by multi-temporal high resolution remote sensing images[J]. Journal of Beijing Forestry University, 2018, 40(12): 38-48. DOI: 10.13332/j.1000-1522.20180269
    Citation: Chen Jidai, Niu Shukui. Classification and change analysis of forest fuels by multi-temporal high resolution remote sensing images[J]. Journal of Beijing Forestry University, 2018, 40(12): 38-48. DOI: 10.13332/j.1000-1522.20180269

    多时相高分辨率遥感影像的森林可燃物分类和变化分析

    Classification and change analysis of forest fuels by multi-temporal high resolution remote sensing images

    • 摘要:
      目的本文针对国内外利用多时相高分辨率遥感影像进行森林可燃物分类研究匮乏的情况,探索高分辨率影像的分类方法,并研究多时相森林可燃物分类结果的差异,以及与海拔、坡度变化的关系。
      方法以鹫峰林场为研究区,针对鹫峰林场内植被状况及以往研究成果,主要依据植物群落、林型和燃烧特性划分可燃物类别,研究对比不同森林可燃物类型的光谱特征曲线,建立遥感图像与森林可燃物的联系。选用GF-1号5、8、10月的遥感影像为原始数据,利用EnMAP-box中的支持向量机(SVM)算法、随机森林(RF)以及基于CART的决策树分类方法进行森林可燃物分类,将可燃物类别最终划分为:针叶林、阔叶林、针阔混交林、灌木林和非林地5种类别,并分别对其特征进行描述,之后将最优分类方法应用到多时相的遥感影像中,并使用变化检测算法来确定非防火期(5—10月)森林可燃物类型之间土地面积的变化情况。同时,我们将数字高程模型(DEM)分为4类(1类(< 250 m)、2类(250~500 m)、3类((500~750 m)和4类(>750 m)),坡度分3类:缓坡(< 15°)、斜坡(15°~35°)、陡坡(>35°),并使用Jenks方法分别对海拔和坡度每个类别土地面积变化计算百分比,研究随着海拔和坡度变化,森林可燃物面积的变化规律。
      结果划分的5种森林可燃物类别的光谱特征具有很好区分性,SVM分类最为准确,取得惩罚参数(C)为1 000和核参数(g)为10使得SVM分类模型达到最优,其总体分类精度为91.88%,Kappa系数为0.89,精度相对RF和CART分别提高了2.72%和9.36%。非防火期内(5—11月)森林可燃物类型变化有一定的规律,针叶林、混交林属于中等稳定类别,没有显著变化,分别保持93.74%和94.87%不变。相比之下,阔叶林和灌木林发生了较大变化,分别发生14.64%和13.36%。随着海拔的增加和坡度的变化,森林可燃物类型土地面积也发生了变化,海拔500~750 m和坡度16°~35°的土地上面积变化最大,达到了20%以上。
      结论多时相高分辨率遥感影像的森林可燃物分类中,基于SVM分类方法能够将可燃物更好地分类,且随着时间、海拔和坡度的变化,森林可燃物面积的变化有一定的规律,5—10月阔叶林和灌木林在海拔500~750 m和坡度16°~35°变化最大。

       

      Abstract:
      ObjectiveThe classification of forest fuels based on high resolution remote sensing images is very important for the modern management of forest fire, but the study for classification by multi-temporal high resolution remote sensing images is scanty at home and abroad. This study explored the classification method of high-resolution images, the differences in classification results of multi-temporal forest fuels, and researched the relationship with altitude and slope change.
      MethodAccording to the vegetation status and previous research results in the Jiufeng Forest Farm of Beijing, the fuels were classified by plant community, forest types and combustion characteristics. Then we studied and compared the spectral characteristic curves of different forest fuel types, and established the connection between remote sensing images and forest fuels. The remote sensing images of May, August and October of GF-1 were used as the original data. The classification of forest fuels was carried out by the support vector machine (SVM) algorithm, random forest (RF) and the decision tree method based on CART of EnMAP-box in the Jiufeng Forest Farm, and the classification results were as follows: coniferous forest, broadleaved forest, coniferous and broadleaved mixed forest, shrub forest and non-forest land. After describing their characteristics separately, the optimal classification method was applied into multi-temporal remote sensing images, and the change detection algorithm was used to determine the changes among the types of forest fuels during non-fireproof period (May to November). At the same time, we divided the digital elevation model (DEM) into four categories (1 (< 250 m), 2 (250-500 m), 3 (500-750 m) and 4 (>750 m)). Similarly, we divided slope into three types: gentle slope (< 15°), slope (15°-35°), steep slope (>35°), and used the Jenks method to calculate the percentage of land area change for each category of elevation and slope, respectively. Then we studied the changes in the classification results of forest fuels with changes of altitude and slope.
      ResultThe results showed that the spectral characteristics of the five forest fuel categories were well differentiated. The SVM classification was the most accurate. The penalty parameter (C) was 1 000 and the kernel parameter (g) was 10, which made the SVM classification model optimal. The overall classification accuracy was 91.88%, the kappa coefficient was 0.89. And the accuracy was improved relative to RF and CART. The classification accuracy was 2.72% and 9.36% higher than RF and CART, respectively. The types of forest fuels during non-fireproof period (May to November) had certain change regularity, and there were no significant changes in coniferous and mixed forest which belong to moderate stable types, keeping 93.74% and 94.87%, respectively. In contrast, broadleaved and shrub forest changed greatly by 14.64% and 13.36%, respectively; with the increase of altitude and the change of slope, the land area of forest fuels had also changed. The area with altitude above 500-750 m and slope of 16°-35° had the largest change, reaching more than 20%.
      ConclusionIn the classification of forest fuels with multi-temporal high-resolution remote sensing images, the SVM classification method can classify fuels better, and with the change of time, altitude and slope, the change of forest fuel area has certain regularity. From May to October, broadleaved forests and shrubs vary most at altitudes of 500-750 m and slopes of 16°-35°.

       

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