In Wangqing natural forest area of Jilin Province, usingLandsat-8 OLI_TIRS data,combined with the actual data of forest resources, took texture and spectral characteristic parameters as input element of support vector machine, chose kernel function through k-fold cross-validation, identified forest types and determined the optimal classification, and compared with spectral characteristic classification accuracy. As the results showed: it is feasiblethat forest classification accuracy with texture and spectral characteristic based on SVM. Penalty coefficientC=100.0, kernel radiusσ=1.000,radial basis kernel function of SVM classification accuracy was the highest, overall classification accuracy was 89.58%, Kappa coefficient was 0.87, classification accuracy of broad-leaved forest was better than coniferous and coniferous forest ,classification accuracy of the coniferous forest was the lowest. The accuracy of classification which only use the spectral characteristics was 81.26%. Forest classification accuracy with texture and spectral characteristic was better.%以吉林省汪清林业局天然林区为研究区，利用Landsat-8 OLI_TIRS多光谱遥感影像，结合森林资源野外调查数据，提取森林类型纹理、光谱特征参数，作为支持向量机的输入量，利用K-折交叉验证法确定最优核函数，识别森林类型，确定最优分类结果，评价分类精度，并与仅利用波段光谱特征的SVM分类结果进行精度对比。结果表明：利用纹理和光谱特征进行分类，构造SVM进行森林识别是可行的。惩罚系数C=100.0、核函数半径σ=1.000时的径向基核函数构造的支持向量机分类精度最好，总体分类精度可达89.58%，Kappa系数为0.87，单一分类精度中，阔叶林＞针叶林＞针阔混交林。只利用光谱特征的分类结果精度为81.26%，结合光谱和纹理特征的规律，能够提高分类精度。
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