Integration of ecological knowledge with Google Earth Engine for diverse wetland sampling in global mapping

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal Pub Date : 2024-11-01 DOI:10.1016/j.jag.2024.104249

Xuanlin Huo, Zhenguo Niu, Linsong Liu, Yuhang Jing

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引用次数: 0

Abstract

Accurate wetland extraction using remote sensing technology poses significant challenges due to the complex hydrological dynamics, diverse landscapes, and varied wetland types. Constructing a reliable sample set is a critical first step in overcoming these challenges for large-scale wetland mapping. To meet the demand for global wetland mapping, this study (1) proposes a multi-level wetland classification system suitable for remote sensing, incorporating the soil moisture, vegetation cover and temporal dynamic characteristics of wetlands; (2) introduces a theoretically plausible wetland sample identification method based on the ecological, geographical and temporal dynamic characteristics of wetland ecosystems; (3) develops an approach that combines the Inundation-Frequency and Ecological Remote Sensing Indicators for global wetland sampling based on global climatic zones. The global wetland sample set was finally produced with 64,486 samples. The dataset revealed that seasonal marsh, swamp, mangrove, floodplain, salt marsh, tidal flat and permanent marsh accounted for 22.99%, 20.05%, 18.06%, 14.58%, 12.38%, 10.62% and 1.29% of the total sample set, respectively. Furthermore, the water body sample set comprised 13,402 samples, distributed among permanent (45.50%), seasonal (31.35%) and temporary (23.15%) water bodies. The proposed knowledge-based method, which makes use of big earth-observing data and the Google Earth Engine platform, has been demonstrated to have the capability to generate reliable wetland samples with a high degree of accuracy. This represents the first effort to create a global wetland sample set, which has the potential to offer critical support for comprehensive wetland mapping initiatives

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将生态知识与谷歌地球引擎相结合，在全球制图中进行多样化湿地取样

由于复杂的水文动态、多样的地貌和不同的湿地类型，利用遥感技术进行精确的湿地提取面临着巨大的挑战。构建可靠的样本集是克服这些挑战、绘制大规模湿地地图的关键第一步。为了满足全球湿地绘图的需求，本研究（1）结合湿地的土壤水分、植被覆盖和时间动态特征，提出了适合遥感的多级湿地分类系统；（2）根据湿地生态系统的生态、地理和时间动态特征，提出了理论上可行的湿地样本识别方法；（3）基于全球气候带，开发了一种结合淹没频率和生态遥感指标的全球湿地取样方法。全球湿地样本集最终产生了 64 486 个样本。数据集显示，季节性沼泽、沼泽、红树林、洪泛平原、盐沼、滩涂和永久性沼泽分别占样本集总数的 22.99%、20.05%、18.06%、14.58%、12.38%、10.62% 和 1.29%。此外，水体样本集包括 13 402 个样本，分布于永久性水体（45.50%）、季节性水体（31.35%）和临时性水体（23.15%）。所提出的基于知识的方法利用了地球观测大数据和谷歌地球引擎平台，已被证明有能力生成高精度的可靠湿地样本。这是创建全球湿地样本集的首次努力，有可能为全面的湿地绘图计划提供重要支持。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International journal of applied earth observation and geoinformation : ITC journal Global and Planetary Change, Management, Monitoring, Policy and Law, Earth-Surface Processes, Computers in Earth Sciences

CiteScore

12.00

自引率

0.00%

发文量

审稿时长

77 days

期刊介绍： The International Journal of Applied Earth Observation and Geoinformation publishes original papers that utilize earth observation data for natural resource and environmental inventory and management. These data primarily originate from remote sensing platforms, including satellites and aircraft, supplemented by surface and subsurface measurements. Addressing natural resources such as forests, agricultural land, soils, and water, as well as environmental concerns like biodiversity, land degradation, and hazards, the journal explores conceptual and data-driven approaches. It covers geoinformation themes like capturing, databasing, visualization, interpretation, data quality, and spatial uncertainty.