CAJ | 학술논문

森林火灾发生后，为及时、准确地掌握森林受灾情况，利用高分一号卫星（GF －1）16m 宽幅影像各波段反射率信息，结合计算的归一化植被指数（NDVI）、过火区识别指数（BAI）、阴影植被指数（SVI）、归一化差异水体指数（NDWI）和全球环境监测指数（GEMI）等5种光谱指数，构建森林火烧迹地识别决策树模型（CART）；在选取的研究区对该模型方法进行验证，并与最大似然监督分类法和非监督分类（ISODATA）方法所得到的结果精度进行了对比分析，结果表明：采用基于 CART 模型的决策树方法对火烧迹地识别结果精度较最大似然法总体分类精度提高了4.38％，Kappa 系数提高了0.1024，制图精度提高了14.96％，用户精度提高了8.50％；而采用ISODATA 方法识别的火烧迹地的总体精度和 Kappa 系数都较低，制图精度和用户精度都没有达到1％。
삼림화재발생후，위급시、준학지장악삼림수재정황，이용고분일호위성（GF －1）16m 관폭영상각파단반사솔신식，결합계산적귀일화식피지수（NDVI）、과화구식별지수（BAI）、음영식피지수（SVI）、귀일화차이수체지수（NDWI）화전구배경감측지수（GEMI）등5충광보지수，구건삼림화소적지식별결책수모형（CART）；재선취적연구구대해모형방법진행험증，병여최대사연감독분류법화비감독분류（ISODATA）방법소득도적결과정도진행료대비분석，결과표명：채용기우 CART 모형적결책수방법대화소적지식별결과정도교최대사연법총체분류정도제고료4.38％，Kappa 계수제고료0.1024，제도정도제고료14.96％，용호정도제고료8.50％；이채용ISODATA 방법식별적화소적지적총체정도화 Kappa 계수도교저，제도정도화용호정도도몰유체도1％。
This paper describes the technique to be needed for rapidly and accurately identifying the burn-ed area by forest fires,following the catastrophic fires by the vegetation index CART decision tree methods using the wide coverage image of GF-1(GF-1 WFV).They were compared between the maximum likeli-hood classification of supervised and unsupervised classification(ISODATA),within burned area indexes, to improve the accuracy of the burned area,shaded vegetation index,global environment monitoring in-dex,improved shadows and bare commission or omission burned phenomenon.The results showed that the decision tree classification method based on CART algorithms for burned area identification has signifi-cantly improved the overall accuracy by 4.38% compared with the maximum likelihood method;Kappa coefficient increased by 0.1024.GF-1 satellite imagery for unsupervised classification(ISODATA)identi-fies the burned area poorly,the overall accuracy and Kappa coefficient are low,the map making accuracy and user accuracy have not reached 1%.