Land Cover Classification over East Asian Region Using Recent MODIS NDVI Data (2006-2008)
Land Cover Classification over East Asian Region Using Recent MODIS NDVI Data (2006-2008)
复制标题
使用最新 MODIS NDVI 数据对东亚地区土地覆盖进行分类（2006-2008 年）
作者：
Jeon‐Ho Kang;M. Suh;Chong
利用支持向量机（SVM）对东亚地区土地覆盖图（国立公州大学土地覆盖图：KLC）进行分类，并使用地面实况数据进行评估。基本输入数据为近三年（2006-2008年）的MODIS（中分辨率成像光谱仪）NDVI（归一化植被指数）数据。空间分辨率为1 km，时间频率为16天。为了最大限度地减少在MODIS NDVI数据中的云污染的像素的数量，最大值的复合被应用到16天的数据。并对月NDVI数据进行了基于时空连续性假设的云污染像元校正。为了减少数据量，提高分类质量，从校正后的月NDVI数据中提取了NDVI最大值、幅值、平均值等9个物候数据。3种类型的土地覆盖图（国际地圈生物圈计划：IGBP，马里兰州大学：UMD，和中分辨率成像分光光度计）被用来建立一个“准”地面实况数据集，这是由像素的三个土地覆盖图分类为相同的土地覆盖类型。分类结果表明，与IGBP和Umd相比，阔叶树和草地的比例较大，而农田和针叶树的比例较小。利用实测数据进行验证，结果表明，MODIS、KLC、IGBP、Umd土地覆盖数据的像元与观测值的符合率分别为80%、77%、63%、57%。MODIS、IGBP、Umd和KLC在土地覆盖类型上的显著差异主要表现在中国南方和东北地区，这两个地区夏季和冬季的像元主要受到云和雪的污染。结果表明，原始数据的质量是影响土地覆盖分类的重要因素之一。
A Land cover map over East Asian region (Kongju national university Land Cover map: KLC) is classified by using support vector machine (SVM) and evaluated with ground truth data. The basic input data are the recent three years (2006-2008) of MODIS (MODerate Imaging Spectriradiometer) NDVI (normalized difference vegetation index) data. The spatial resolution and temporal frequency of MODIS NDVI are 1km and 16 days, respectively. To minimize the number of cloud contaminated pixels in the MODIS NDVI data, the maximum value composite is applied to the 16 days data. And correction of cloud contaminated pixels based on the spatiotemporal continuity assumption are applied to the monthly NDVI data. To reduce the dataset and improve the classification quality, 9 phenological data, such as, NDVI maximum, amplitude, average, and others, derived from the corrected monthly NDVI data. The 3 types of land cover maps (International Geosphere Biosphere Programme: IGBP, University of Maryland: UMd, and MODIS) were used to build up a "quasi" ground truth data set, which were composed of pixels where the three land cover maps classified as the same land cover type. The classification results show that the fractions of broadleaf trees and grasslands are greater, but those of the croplands and needleleaf trees are smaller compared to those of the IGBP or UMd. The validation results using in-situ observation database show that the percentages of pixels in agreement with the observations are 80%, 77%, 63%, 57% in MODIS, KLC, IGBP, UMd land cover data, respectively. The significant differences in land cover types among the MODIS, IGBP, UMd and KLC are mainly occurred at the southern China and Manchuria, where most of pixels are contaminated by cloud and snow during summer and winter, respectively. It shows that the quality of raw data is one of the most important factors in land cover classification.
影响因子：
3.2
作者：
Chen, F;Dudhia, J
通讯作者：
Dudhia, J