Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, National Engineering Research Centre of Geospatial Information Technology, Spatial Information Research Centre of Fujian Province, Fuzhou University, Fuzhou 350116, Fujian, China.
Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, National Engineering Research Centre of Geospatial Information Technology, Spatial Information Research Centre of Fujian Province, Fuzhou University, Fuzhou 350116, Fujian, China.
Sci Total Environ. 2017 Nov 15;598:581-592. doi: 10.1016/j.scitotenv.2017.03.221. Epub 2017 Apr 25.
Spatiotemporal explicit information on paddy rice distribution is essential for ensuring food security and sustainable environmental management. Paddy rice mapping algorithm through the Combined Consideration of Vegetation phenology and Surface water variations (CCVS) has been efficiently applied based on the 8day composites time series datasets. However, the great challenge for phenology-based algorithms introduced by unpromising data availability in middle/high spatial resolution imagery, such as frequent cloud cover and coarse temporal resolution, remained unsolved. This study addressed this challenge through developing an automatic and Adaptive paddy Rice Mapping Method (ARMM) based on the cloud frequency and spectral separability. The proposed ARMM method was tested on the Landsat 8 Operational Land Imager (OLI) image (path/row 118/028) in the Songnen Plain in Northeast China in 2015. First, the whole study region was automatically and adaptively subdivided into undisturbed and disturbed regions through a per-pixel strategy based on Landsat image data availability during key phenological stage. Second, image objects were extracted from approximately cloud-free images in disturbed and undisturbed regions, respectively. Third, phenological metrics and other feature images from individual or multiple images were developed. Finally, a flexible automatic paddy rice mapping strategy was implemented. For undisturbed region, an object-oriented CCVS method was utilized to take the full advantages of phenology-based method. For disturbed region, Random Forest (RF) classifier was exploited using training data from CCVS-derived results in undisturbed region and feature images adaptively selected with full considerations of spectral separability and the spatiotemporal coverage. The ARMM method was verified by 473 reference sites, with an overall accuracy of 95.77% and kappa index of 0.9107. This study provided an efficient strategy to accommodate the challenges of phenology-based approaches through transferring knowledge in parts of a satellite scene with finer time series to targets (other parts) with deficit data availability.
稻田分布的时空显式信息对于确保粮食安全和可持续环境管理至关重要。基于 8 天合成时间序列数据集，通过综合考虑植被物候和地表水体变化（CCVS）的水稻制图算法已经得到了有效应用。然而，基于物候的算法面临的一个重大挑战是，在中/高空间分辨率图像中，数据可用性不理想，如频繁的云层覆盖和较粗的时间分辨率，这一问题仍然没有得到解决。本研究通过开发一种基于云频率和光谱可分离性的自动和自适应水稻制图方法（ARMM）来解决这一挑战。该方法在 2015 年中国东北松嫩平原的 Landsat 8 陆地成像仪（OLI）图像（路径/行 118/028）上进行了测试。首先，通过基于 Landsat 图像在关键物候期的数据可用性的逐像素策略，将整个研究区域自动且自适应地划分为未受干扰和受干扰区域。其次，分别从受干扰和未受干扰区域中提取近似无云的图像中的图像对象。第三，从单个或多个图像中开发物候指标和其他特征图像。最后，实现了灵活的自动水稻制图策略。对于未受干扰的区域，利用基于对象的 CCVS 方法充分利用基于物候的方法的优势。对于受干扰的区域，利用随机森林（RF）分类器，使用未受干扰区域中 CCVS 衍生结果的训练数据，并自适应地选择特征图像，充分考虑光谱可分离性和时空覆盖范围。该方法通过将更精细时间序列卫星场景的部分知识转移到目标（其他部分），从而解决了基于物候方法的挑战，在 473 个参考点上进行了验证，总体精度为 95.77%，kappa 指数为 0.9107。
