Jiawei Zou, Hao Li, Chao Ding, Suhong Liu, Qingdong Shi
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The NDVI (Normalized Difference Vegetation Index) time series was then reconstructed using the preferred filtering method to obtain phenological parameter features, and the random forest model was input by combining the phenological parameter, spectral index, textural, and backscattering features. An active learning method was employed to optimize the model and obtain the best model for extracting P. euphratica. Finally, the map of natural P. euphratica forests with a resolution of 10 m in the mainstream of the Tarim River was obtained. The overall accuracy, producer’s accuracy, user’s accuracy, kappa coefficient, and F1-score of the map were 0.96, 0.98, 0.95, 0.93, and 0.96, respectively. The comparison experiments showed that simultaneously adding backscattering and textural features improved the P. euphratica extraction accuracy, while textural features alone resulted in a poor extraction effect. The method developed in this study fully considered the prior and posteriori information and determined the feature set suitable for the P. euphratica identification task, which can be used to quickly obtain accurate large-area distribution data of P. euphratica. 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引用次数: 0
摘要
胡杨是沙漠河岸地区独特的建群树种,对维持绿洲生态系统的稳定至关重要。塔里木河流域拥有世界上分布最密集的胡杨林种群,获得塔里木河主流地区的准确分布数据将为胡杨林的保护和恢复提供重要支持。我们基于谷歌地球引擎云平台和随机森林算法,提出了一种利用 Sentinel-1 和 2 以及 Landsat-8 图像自动提取 P. euphratica 的新方法。为了节省计算资源,我们根据先验知识创建了一个 P. euphratica 潜在分布区的掩膜。然后使用优选滤波方法重建归一化植被指数(NDVI)时间序列,以获得物候参数特征,并结合物候参数、光谱指数、纹理和反向散射特征输入随机森林模型。采用主动学习方法对模型进行优化,获得提取极乐鸟的最佳模型。最后,得到了塔里木河主流地区分辨率为 10 米的天然欧鼠李森林分布图。该地图的总体准确度、生产者准确度、用户准确度、卡帕系数和 F1 分数分别为 0.96、0.98、0.95、0.93 和 0.96。对比实验表明,同时添加反向散射特征和纹理特征提高了极乐鸟的提取精度,而单独添加纹理特征则提取效果不佳。本研究建立的方法充分考虑了先验信息和后验信息,确定了适合于极乐鸟识别任务的特征集,可用于快速获取准确的极乐鸟大面积分布数据。该方法还可为识别其他典型沙漠植被提供参考。
Mapping Natural Populus euphratica Forests in the Mainstream of the Tarim River Using Spaceborne Imagery and Google Earth Engine
Populus euphratica is a unique constructive tree species within riparian desert areas that is essential for maintaining oasis ecosystem stability. The Tarim River Basin contains the most densely distributed population of P. euphratica forests in the world, and obtaining accurate distribution data in the mainstream of the Tarim River would provide important support for its protection and restoration. We propose a new method for automatically extracting P. euphratica using Sentinel-1 and 2 and Landsat-8 images based on the Google Earth Engine cloud platform and the random forest algorithm. A mask of the potential distribution area of P. euphratica was created based on prior knowledge to save computational resources. The NDVI (Normalized Difference Vegetation Index) time series was then reconstructed using the preferred filtering method to obtain phenological parameter features, and the random forest model was input by combining the phenological parameter, spectral index, textural, and backscattering features. An active learning method was employed to optimize the model and obtain the best model for extracting P. euphratica. Finally, the map of natural P. euphratica forests with a resolution of 10 m in the mainstream of the Tarim River was obtained. The overall accuracy, producer’s accuracy, user’s accuracy, kappa coefficient, and F1-score of the map were 0.96, 0.98, 0.95, 0.93, and 0.96, respectively. The comparison experiments showed that simultaneously adding backscattering and textural features improved the P. euphratica extraction accuracy, while textural features alone resulted in a poor extraction effect. The method developed in this study fully considered the prior and posteriori information and determined the feature set suitable for the P. euphratica identification task, which can be used to quickly obtain accurate large-area distribution data of P. euphratica. The method can also provide a reference for identifying other typical desert vegetation.
期刊介绍:
Remote Sensing (ISSN 2072-4292) publishes regular research papers, reviews, letters and communications covering all aspects of the remote sensing process, from instrument design and signal processing to the retrieval of geophysical parameters and their application in geosciences. Our aim is to encourage scientists to publish experimental, theoretical and computational results in as much detail as possible so that results can be easily reproduced. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.