基于多时相遥感数据的吉林西部土地覆被分类提取

为深化计算机自动提取土地覆被类型在遥感分类研究中的应用,以吉林西部的镇赉县为试验区,利用Landsat8多时相遥感数据的季节变化信息、地表植被、水体与土壤等特征参量,构建多维分类特征数据集对试验区进行土地覆被分类研究,提取了11种地表覆被类型。结果表明:1)多维变量组合方案的总体分类精度为95.50%,Kappa系数为0.9504。该方案自动提取地类达到了一个比较理想的分类结果,方案有效可行;2)方案中,3个主要特征分类变量的引入能很好改善易混淆地类的可分性,尤其,地表植被季节变化信息和土地信息的引入能明显提高土地覆被的分类精度;3)实际情况表明,引入的分类特征量不是越多越好,只有将多种分类特征有效结合才能够提高土地覆被分类精度。该文为农牧交错带上的土地覆被遥感监测提供了一个可行的方案,该方案有效可行。

Extracting land cover types in western Jilin based on multi-temporal remote sensing data

Abstract: Recently, it's still difficult to entirely replace the artificial visual interpretation for the computer automatic classification, which is used to extract land cover types' information from the remote sensing imagery, because the automatic method needs more efforts to improve the precision of the classification results. Furthermore, this problem has become the key joint of the automatic classification extraction. How to extract land cover types' information in western area of Jilin, is one of the important problems, and the confused land cover types needs to be distinguished. The aim of this study is to deepen the application of remote sensing classification method that is used to extract land cover information automatically and quickly from the satellite imagery. The western area of Jilin is selected as the main research area. A new total solution to extract land cover information, based on the spatial variation theory, has been designed for the convenient automatic classification with the remote sensing technology. The remote sensing classification scheme is carried out by coding the R language algorithm and operating the remote sensing software ERDAS platform. The land cover types in Zhenlai County in the western area of Jilin, have been extracted and monitored through the combined utilization of 4 indices, including semivariance value of normalized difference vegetation index (NDVI) dataset, local variance of image texture, modified soil-adjusted vegetation index and normalized difference water index, which have significant meaning for the land cover types in the transition zone between cropping area and nomadic area. These variances have definite physical meaning (including vegetation, water, and soil drought conditions), so that the phenological information was used to build a multi-dimensional feature space classification data set. The results indicated that: 1) A total of 11 land cover types are extracted, using the multi-temporal remote sensing information to build a multidimensional classification characteristics data set based on the Landsat 8 data. The overall classification accuracy of the algorithm is 95.50%; the Kappa coefficient of classification is 0.9504. The automatic extracting approach implemented obtains a comparatively ideal classification result; 2) The introduction of 3 characteristic variables of the classification in the scheme significantly improves the separability of the confused land cover types. Considering the vegetation classification, the vegetation growth information has practical life-activity significance, and is a real-time dynamic method for the vegetation change monitoring; 3) Improving the land cover classification accuracy is not to introduce more characteristic parameters of the classification, but to effectively combine multiple appropriate classification variables. The new method can broaden the application vision and the scope of the ecological remote sensing investigation of surface vegetation. Moreover, the introduction of new variables not only makes the macro monitoring more convenient, but also improves the accuracy of classification of remote sensing interpretation. It's noted that the extracted classification has obvious regional feature, and the regional feature is consistent with the farming cultivation characteristics on the Northeast Plain. In a word, the results can provide a credible approach and valuable example for extracting and monitoring land cover type in farming-pastoral transitional zone. It is feasible to use the spatial variation theory to extract and monitor land cover type by combining the several evaluation indices.