Yu Hao, Li Shicheng, Liang Zhimin, Xu Shengnan, Yang Xin, Li Xiaoyan
Modern Industry College, Jilin Jianzhu University, Changchun 130118, China.
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China.
Sensors (Basel). 2024 Oct 16;24(20):6664. doi: 10.3390/s24206664.
Wetlands play a vital role in regulating the global carbon cycle, providing biodiversity, and reducing flood risks. These functions maintain ecological balance and ensure human well-being. Timely, accurate monitoring of wetlands is essential, not only for conservation efforts, but also for achieving Sustainable Development Goals (SDGs). In this study, we combined Sentinel-1/2 images, terrain data, and field observation data collected in 2020 to better understand wetland distribution. A total of 22 feature variables were extracted from multi-source data, including spectral bands, spectral indices (especially red edge indices), terrain features, and radar features. To avoid high correlations between variables and reduce data redundancy, we selected a subset of features based on recursive feature elimination (RFE) and Pearson correlation analysis methods. We adopted the random forest (RF) method to construct six wetland delineation schemes and incorporated multiple types of characteristic variables. These variables were based on remote sensing image pixels and objects. Combining red-edge features, terrain data, and radar data significantly improved the accuracy of land cover information extracted in low-mountain and hilly areas. Moreover, the accuracy of object-oriented schemes surpassed that of pixel-level methods when applied to wetland classification. Among the three pixel-based schemes, the addition of terrain and radar data increased the overall classification accuracy by 7.26%. In the object-based schemes, the inclusion of radar and terrain data improved classification accuracy by 4.34%. The object-based classification method achieved the best results for swamps, water bodies, and built-up land, with relative accuracies of 96.00%, 90.91%, and 96.67%, respectively. Even higher accuracies were observed in the pixel-based schemes for marshes, forests, and bare land, with relative accuracies of 98.67%, 97.53%, and 80.00%, respectively. This study's methodology can provide valuable reference information for wetland data extraction research and can be applied to a wide range of future research studies.
湿地在调节全球碳循环、提供生物多样性和降低洪水风险方面发挥着至关重要的作用。这些功能维持着生态平衡并保障人类福祉。及时、准确地监测湿地至关重要，这不仅对于保护工作而言，而且对于实现可持续发展目标（SDGs）也是如此。在本研究中，我们结合了哨兵 - 1/2 影像、地形数据以及 2020 年收集的实地观测数据，以更好地了解湿地分布情况。总共从多源数据中提取了 22 个特征变量，包括光谱波段、光谱指数（特别是红边指数）、地形特征和雷达特征。为避免变量之间的高相关性并减少数据冗余，我们基于递归特征消除（RFE）和皮尔逊相关分析方法选择了一个特征子集。我们采用随机森林（RF）方法构建了六种湿地划定方案，并纳入了多种类型的特征变量。这些变量基于遥感影像像素和对象。结合红边特征、地形数据和雷达数据显著提高了低山丘陵地区土地覆盖信息提取的准确性。此外，在应用于湿地分类时，面向对象方案的准确性超过了像素级方法。在三种基于像素的方案中，添加地形和雷达数据使总体分类精度提高了 7.26%。在基于对象的方案中，纳入雷达和地形数据使分类精度提高了 4.34%。基于对象的分类方法在沼泽、水体和建设用地方面取得了最佳结果，相对精度分别为 96.00%、90.91% 和 96.67%。在基于像素的方案中，对于湿地、森林和裸地观察到了更高的精度，相对精度分别为 98.67%、97.53% 和 80.00%。本研究的方法可以为湿地数据提取研究提供有价值的参考信息，并可应用于广泛的未来研究。
