Fine Land-Cover Mapping in China Using Landsat Datacube and an Operational SPECLib-Based Approach
Fine Land-Cover Mapping in China Using Landsat Datacube and an Operational SPECLib-Based Approach
复制标题
DOI：
10.3390/rs11091056
复制
发表时间：
2019-05
期刊：
影响因子：
--
通讯作者：
Xiao Zhang;Liangyun Liu;Xidong Chen;Shuai Xie;Yuan Gao
中科院分区：
文献类型：
--
作者：
Xiao Zhang;Liangyun Liu;Xidong Chen;Shuai Xie;Yuan Gao
高分辨率土地覆盖信息是地球科学的重要基础。本文提出了一种新的基于SPECLib的业务方法，用于多时相Landsat影像分类使用的反射光谱从时空光谱库（SPECLib）的30米的土地覆盖制图全国。首先，利用欧洲空间局（ESA）气候变化倡议全球土地覆盖（CCI_LC）产品和MODIS Version 6最低点双向反射分布函数调整反射率（NBAR）产品（MCD 43 A4），在正弦投影下建立了空间分辨率为158.85 km（相当于赤道1.43°）、时间分辨率为8天的全球SPECLib。然后，利用2015年Landsat OLI图像的所有可用观测数据开发了覆盖整个中国的Landsat数据立方体。第三，提出了基于SPECLib的多时相随机森林方法，利用Landsat数据立方体生成了包含22种土地覆盖类型的年度土地覆盖图。最后，利用两个不同的验证系统，使用大约11 000个判读点，对中国土地覆盖图进行了验证。结果表明：2级验证系统（19种土地覆盖类型）和1级验证系统（9种土地覆盖类型）的总体精度分别为71.3%和80.7%，Kappa系数分别为0.664和0.757。因此，在中国的案例研究表明，建议SPECLib方法是一个可操作的和准确的方法，区域/全球土地覆盖精细制图的空间分辨率为30米。
Fine resolution land cover information is a vital foundation of Earth science. In this paper, a novel SPECLib-based operational method is presented for the classification of multi-temporal Landsat imagery using reflectance spectra from the spatial-temporal spectral library (SPECLib) for 30 m land-cover mapping for the whole of China. Firstly, using the European Space Agency (ESA) Climate Change Initiative Global Land Cover (CCI_LC) product and the MODIS Version 6 Nadir bidirectional reflectance distribution function adjusted reflectance (NBAR) product (MCD43A4), a global SPECLib with a spatial resolution of 158.85 km (equivalent to 1.43° at the equator) and a temporal resolution of eight days was developed in the sinusoidal projection. Then, the Landsat datacube covering the whole of China was developed using all available observations of Landsat OLI imagery in 2015. Thirdly, the multi-temporal random forest method based on SPECLib was presented to produce an annual land-cover map with 22 land-cover types using the Landsat datacube. Finally, the annual China land-cover map was validated by two different validation systems using approximately 11,000 interpretation points. The mapping results achieved the overall accuracy of 71.3% and 80.7% and the kappa coefficient of 0.664 and 0.757 for the level-2 validation system (19 land-cover types) and the level-1 validation system (nine land-cover types), respectively. Therefore, the case study in China indicates that the proposed SPECLib method is an operational and accurate method for regional/global fine land-cover mapping at a spatial resolution of 30 m.