文章基本信息

标题：Forest type mapping using object-based classification method in Kapilvastu district, Nepal
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作者：A. K. Chaudhary ; A. K. Acharya ; S. Khanal 等
期刊名称：Banko Janakari : A Journal of Forestry Information for Nepal
印刷版ISSN：1016-0582
出版年度：2016
卷号：26
期号：1
页码：38-44
DOI：10.3126/banko.v26i1.15500
语种：English
出版社：Department of Forest Research and Survey
摘要：In the recent years, object-based image analysis (OBIA) approach has emerged with an attempt to overcome limitations inherited in conventional pixel-based approaches. OBIA was performed using Landsat 8 image to map the forest types in Kapilvastu district of Nepal. Systematic sampling design was adopted to establish sample points in the field, and 70% samples were used for classification and 30% samples for accuracy assessment. Landsat image was pre-processed, and the slope and aspect derived from the ASTER DEM were used as additional predictors for classification. Segmentation was done using eCognition v8.0 with the scale parameter of 20, ratios of 0.1 and 0.9 for shape and color, respectively. Classification and Regression Tree (CART) and nearest neighbor classifier (k-NN) methods were used for object-based classification. The major forest types observed in the district were KS ( Acacia catechu / Dalbergia sissoo ), Sal ( Shorea robusta ) and Tropical Mixed Hardwood. The k-NN classification technique showed higher overall accuracy than the CART method. The classification approach used in this study can also be applied to classify forest types in other districts. Improvement in classification accuracy can be potentially obtained through inclusion of sufficient samples from all classes. Banko Janakari A Journal of Forestry Information for Nepal Vol. 26, No. 1, Page: 38-44, 2016
其他摘要：In the recent years, object-based image analysis (OBIA) approach has emerged with an attempt to overcome limitations inherited in conventional pixel-based approaches. OBIA was performed using Landsat 8 image to map the forest types in Kapilvastu district of Nepal. Systematic sampling design was adopted to establish sample points in the field, and 70% samples were used for classification and 30% samples for accuracy assessment. Landsat image was pre-processed, and the slope and aspect derived from the ASTER DEM were used as additional predictors for classification. Segmentation was done using eCognition v8.0 with the scale parameter of 20, ratios of 0.1 and 0.9 for shape and color, respectively. Classification and Regression Tree (CART) and nearest neighbor classifier (k-NN) methods were used for object-based classification. The major forest types observed in the district were KS ( Acacia catechu / Dalbergia sissoo ), Sal ( Shorea robusta ) and Tropical Mixed Hardwood. The k-NN classification technique showed higher overall accuracy than the CART method. The classification approach used in this study can also be applied to classify forest types in other districts. Improvement in classification accuracy can be potentially obtained through inclusion of sufficient samples from all classes. Banko Janakari A Journal of Forestry Information for Nepal Vol. 26, No. 1, Page: 38-44, 2016
关键词：Landsat;Machine learning algorithm;Object-based classification