PDF(Pubmed)
Abstract：
Arid zone grassland is a crucial component of terrestrial ecosystems and plays a significant role in ecosystem protection and soil erosion prevention. However, accurately mapping grassland spatial information in arid zones presents a great challenge. The accuracy of remote sensing grassland mapping in arid zones is affected by spectral variability caused by the highly diverse landscapes. In this study, we explored the potential of a rectangular tile classification model, constructed using the random forest algorithm and integrated images from Sentinel-1A (synthetic aperture radar imagery) and Sentinel-2 (optical imagery), to enhance the accuracy of grassland mapping in the semiarid to arid regions of Ordos, China. Monthly Sentinel-1A median value images were synthesised, and four MODIS vegetation index mean value curves (NDVI, MSAVI, NDWI and NDBI) were used to determine the optimal synthesis time window for Sentinel-2 images. Seven experimental groups, including 14 experimental schemes based on the rectangular tile classification model and the traditional global classification model, were designed. By applying the rectangular tile classification model and Sentinel-integrated images, we successfully identified and extracted grasslands. The results showed the integration of vegetation index features and texture features improved the accuracy of grassland mapping. The overall accuracy of the Sentinel-integrated images from EXP7-2 was 88.23%, which was higher than the accuracy of the single sensor Sentinel-1A (53.52%) in EXP2-2 and Sentinel-2 (86.53%) in EXP5-2. In all seven experimental groups, the rectangular tile classification model was found to improve overall accuracy (OA) by 1.20% to 13.99% compared to the traditional global classification model. This paper presents novel perspectives and guidance for improving the accuracy of remote sensing mapping for land cover classification in arid zones with highly diverse landscapes. The study presents a flexible and scalable model within the Google Earth Engine framework, which can be readily customized and implemented in various geographical locations and time periods.
摘要：
干旱区草地是陆地生态系统的重要组成部分，在生态系统保护和水土流失防治中发挥着重要作用。然而,准确绘制干旱区草地空间信息是一个巨大的挑战。干旱区遥感草地测绘的精度受到高度多样性景观引起的光谱变化的影响。在这项研究中,我们探索了矩形瓷砖分类模型的潜力，使用随机森林算法和Sentinel-1A（合成孔径雷达图像）和Sentinel-2（光学图像）的集成图像构建，为了提高鄂尔多斯半干旱干旱区草地制图的准确性，中国。每月Sentinel-1A中值图像被合成，和四个MODIS植被指数均值曲线(NDVI，MSAVI，NDWI和NDBI）用于确定Sentinel-2图像的最佳合成时间窗口。七个实验组,包括基于矩形瓦片分类模型和传统全局分类模型的14种实验方案，是设计的。通过应用矩形瓦片分类模型和Sentinel集成图像，我们成功地识别和提取了草原。结果表明，植被指数特征和纹理特征的整合提高了草地制图的准确性。EXP7-2的Sentinel整合图像的总体准确率为88.23%，比EXP2-2中的Sentinel-1A(53.52%)和EXP5-2中的Sentinel-2(86.53%)的精度更高。在所有七个实验组中，与传统的全局分类模型相比，矩形瓷砖分类模型的总体准确性（OA）提高了1.20％至13.99％。本文提出了新颖的观点和指导，以提高遥感制图对景观高度多样化的干旱区土地覆盖分类的准确性。该研究在GoogleEarthEngine框架内提出了一个灵活且可扩展的模型，这可以很容易地定制和实现在不同的地理位置和时间段。
