通过整合大量无人机图像和卫星数据,高分辨率绘制中国草地冠层覆盖图

IF 10.6 1区 地球科学 Q1 GEOGRAPHY, PHYSICAL ISPRS Journal of Photogrammetry and Remote Sensing Pub Date : 2024-09-11 DOI:10.1016/j.isprsjprs.2024.09.004
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引用次数: 0

摘要

冠层覆盖是评估草地健康和生态系统服务的重要指标。然而,由于野外测量的空间覆盖范围有限以及野外测量与卫星图像之间的尺度不匹配,在大空间尺度上实现对草地冠层覆盖的精确高分辨率估算仍具有挑战性。在本研究中,我们利用无人机图像和多源遥感数据的整合,提出了一种基于回归的方法来估算大尺度草地冠层覆盖率,从而解决了这些难题。具体而言,我们在全国 1,255 个地点收集了 90,000 多张 10 × 10 米的无人机图像。所有无人机图像瓦片都被划分为草地和非草地像素,以生成地面实况的冠层覆盖估算值。然后,将这些估算值与卫星图像衍生特征进行时间对齐,建立随机森林回归模型,绘制中国草地冠层覆盖分布图。我们的研究结果表明,单一分类模型可以有效区分无人机图像中的草地和非草地像素,这些图像跨越了不同的草地类型和大的空间尺度,其中多层感知器的分类精度优于Canopeo、支持向量机、随机森林和金字塔场景解析网络。大量无人机图像的整合成功解决了传统地面测量与卫星图像之间的尺度不匹配问题,为提高制图精度做出了重大贡献。生成的 2021 年中国全国冠层覆盖图呈现出由西北向东南递增的空间格局,平均值为 56%,标准偏差为 26%。此外,它还表现出很高的精度,决定系数为 0.89,均方根误差为 12.38%。所绘制的中国高分辨率冠层覆盖图在推进我们对草原生态系统过程的理解和倡导草原资源的可持续管理方面具有巨大潜力。
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High-resolution mapping of grassland canopy cover in China through the integration of extensive drone imagery and satellite data

Canopy cover is a crucial indicator for assessing grassland health and ecosystem services. However, achieving accurate high-resolution estimates of grassland canopy cover at a large spatial scale remains challenging due to the limited spatial coverage of field measurements and the scale mismatch between field measurements and satellite imagery. In this study, we addressed these challenges by proposing a regression-based approach to estimate large-scale grassland canopy cover, leveraging the integration of drone imagery and multisource remote sensing data. Specifically, over 90,000 10 × 10 m drone image tiles were collected at 1,255 sites across China. All drone image tiles were classified into grass and non-grass pixels to generate ground-truth canopy cover estimates. These estimates were then temporally aligned with satellite imagery-derived features to build a random forest regression model to map the grassland canopy cover distribution of China. Our results revealed that a single classification model can effectively distinguish between grass and non-grass pixels in drone images collected across diverse grassland types and large spatial scales, with multilayer perceptron demonstrating superior classification accuracy compared to Canopeo, support vector machine, random forest, and pyramid scene parsing network. The integration of extensive drone imagery successfully addressed the scale-mismatch issue between traditional ground measurements and satellite imagery, contributing significantly to enhancing mapping accuracy. The national canopy cover map of China generated for the year 2021 exhibited a spatial pattern of increasing canopy cover from northwest to southeast, with an average value of 56 % and a standard deviation of 26 %. Moreover, it demonstrated high accuracy, with a coefficient of determination of 0.89 and a root-mean-squared error of 12.38 %. The resulting high-resolution canopy cover map of China holds great potential in advancing our comprehension of grassland ecosystem processes and advocating for the sustainable management of grassland resources.

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来源期刊
ISPRS Journal of Photogrammetry and Remote Sensing
ISPRS Journal of Photogrammetry and Remote Sensing 工程技术-成像科学与照相技术
CiteScore
21.00
自引率
6.30%
发文量
273
审稿时长
40 days
期刊介绍: The ISPRS Journal of Photogrammetry and Remote Sensing (P&RS) serves as the official journal of the International Society for Photogrammetry and Remote Sensing (ISPRS). It acts as a platform for scientists and professionals worldwide who are involved in various disciplines that utilize photogrammetry, remote sensing, spatial information systems, computer vision, and related fields. The journal aims to facilitate communication and dissemination of advancements in these disciplines, while also acting as a comprehensive source of reference and archive. P&RS endeavors to publish high-quality, peer-reviewed research papers that are preferably original and have not been published before. These papers can cover scientific/research, technological development, or application/practical aspects. Additionally, the journal welcomes papers that are based on presentations from ISPRS meetings, as long as they are considered significant contributions to the aforementioned fields. In particular, P&RS encourages the submission of papers that are of broad scientific interest, showcase innovative applications (especially in emerging fields), have an interdisciplinary focus, discuss topics that have received limited attention in P&RS or related journals, or explore new directions in scientific or professional realms. It is preferred that theoretical papers include practical applications, while papers focusing on systems and applications should include a theoretical background.
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