Suppr超能文献

利用谷歌地球引擎云计算平台上的 Sentinel-1 和 Sentinel-2 数据,以 10 米的空间分辨率生成伊朗湿地分布图。

Iranian wetland inventory map at a spatial resolution of 10 m using Sentinel-1 and Sentinel-2 data on the Google Earth Engine cloud computing platform.

机构信息

Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John's, Canada.

School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran.

出版信息

Environ Monit Assess. 2023 Apr 13;195(5):558. doi: 10.1007/s10661-023-11202-z.

Abstract

Detailed wetland inventories and information about the spatial arrangement and the extent of wetland types across the Earth's surface are crucially important for resource assessment and sustainable management. In addition, it is crucial to update these inventories due to the highly dynamic characteristics of the wetlands. Remote sensing technologies capturing high-resolution and multi-temporal views of landscapes are incredibly beneficial in wetland mapping compared to traditional methods. Taking advantage of the Google Earth Engine's computational power and multi-source earth observation data from Sentinel-1 multi-spectral sensor and Sentinel-2 radar, we generated a 10 m nationwide wetlands inventory map for Iran. The whole country is mapped using an object-based image processing framework, containing SNIC superpixel segmentation and a Random Forest classifier that was performed for four different ecological zones of Iran separately. Reference data was provided by different sources and through both field and office-based methods. Almost 70% of this data was used for the training stage and the other 30% for evaluation. The whole map overall accuracy was 96.39% and the producer's accuracy for wetland classes ranged from nearly 65 to 99%. It is estimated that 22,384 km of Iran are covered with water bodies and wetland classes, and emergent and shrub-dominated are the most common wetland classes in Iran. Considering the water crisis that has been started in Iran, the resulting ever-demanding map of Iranian wetland sites offers remarkable information about wetland boundaries and spatial distribution of wetland species, and therefore it is helpful for both governmental and commercial sectors.

摘要

详细的湿地清单以及有关地球表面湿地类型的空间排列和范围的信息对于资源评估和可持续管理至关重要。此外,由于湿地具有高度动态的特征,因此更新这些清单至关重要。与传统方法相比,捕获景观高分辨率和多时相视图的遥感技术在湿地制图方面具有巨大优势。利用 Google Earth Engine 的计算能力以及 Sentinel-1 多光谱传感器和 Sentinel-2 雷达的多源地球观测数据,我们为伊朗生成了一张 10 米分辨率的全国湿地清单图。整个国家都采用基于对象的图像处理框架进行映射,其中包含 SNIC 超像素分割和随机森林分类器,分别针对伊朗的四个不同生态区进行了分类。参考数据来自不同的来源,通过现场和办公室方法提供。该数据的近 70%用于训练阶段,其余 30%用于评估。整个地图的总体准确率为 96.39%,湿地类别的生产者准确率在近 65%至 99%之间。据估计,伊朗有 22384 公里的水体和湿地覆盖,其中挺水和灌木为主的湿地是伊朗最常见的湿地类型。考虑到伊朗已经开始出现的水资源危机,由此产生的对伊朗湿地位置的需求不断增加的地图提供了有关湿地边界和湿地物种空间分布的重要信息,因此对政府和商业部门都有帮助。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验