规模很重要:空间分辨率如何影响基于遥感的城市绿地绘图?

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引用次数: 0

摘要

城市绿地(UGS)具有固碳、制氧、增湿、降噪和吸收污染等生态和生境效益。根据遥感图像绘制的城市绿地地图是城市规划和碳封存评估的基础数据。然而,遥感图像的空间分辨率和城市结构模式对 UGS 地图的绘制有很大影响,因此要获得准确的 UGS 地图非常困难。为了研究空间分辨率对 UGS 测绘的影响,本研究使用了五种不同的空间分辨率数据集:高分 2 号(1 米、4 米)、哨兵 2 号(10 米)和 Landsat8 号(15 米、30 米)。采用随机森林、LightGBM 和支持向量机绘制 UGS 地图,并比较了不同空间分辨率的 UGS 地图的准确性。随后,从总体和城市功能区两个角度分析了 UGS 地图不确定性的空间分布模式。此外,还考虑了城市功能区的不同景观格局,对 UGS 测绘进行了不确定性分析。结果表明:(1) 不同空间分辨率的 UGS 地图存在差异。空间分辨率越高,不确定性越大。中、粗空间分辨率图像不能充分捕捉城市绿地的细粒度分布。(2) 不同空间分辨率的 UGS 测绘的不确定性在空间分布上基本一致。从功能分区的角度来看,非自然区绿地绘图的准确性对空间分辨率非常敏感。(3) UGS 斑块的分布模式影响 UGS 测绘的精度。基于 UGS 景观格局指数,通过多元线性回归、随机森林和 LightGBM 模型,可以降低中、粗空间分辨率下 UGS 测绘的不确定性。本研究全面揭示了多空间分辨率遥感影像在不同城市功能区和景观格局指数下绘制 UGS 的不确定性,首次尝试提出了基于景观格局指数的 UGS 面积修正方法。该研究成果将有助于不同空间分辨率遥感数据在城市地区的应用。
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Scale matters: How spatial resolution impacts remote sensing based urban green space mapping?
Urban green spaces (UGS) provide ecological and habitat benefits such as carbon sequestration, oxygen production, humidity increase, noise reduction, and pollution absorption. UGS maps derived from remote sensing images serve as the fundamental data for urban planning and carbon sequestration assessments. However, the spatial resolution of remote sensing image and the pattern of urban structures significantly influence UGS mapping, making it challenging to obtain accurate UGS maps. To investigate the impact of spatial resolution on UGS mapping, this study utilized five different spatial resolution datasets: Gaofen2 (1 m, 4 m), Sentinel2 (10 m), and Landsat8 (15 m, 30 m). Random forest, LightGBM, and support vector machine were employed to map UGS, and the accuracies of UGS maps at different spatial resolutions were compared. Subsequently, the spatial distribution patterns of uncertainties in UGS maps were analyzed from both overall and urban functional zone perspectives. Furthermore, the uncertainty analysis of UGS mapping was conducted considering different landscape patterns in urban functional zones. The results indicate: (1) UGS map varies at different spatial resolution. Higher uncertainties associated with coarser spatial resolutions. Medium and coarse spatial resolution images inadequately capture the fine-grained distribution of urban green spaces. (2) Uncertainty in UGS mapping at different spatial resolutions is generally consistent in spatial distribution. From a functional zoning perspective, the accuracy of green space mapping over non-natural zones is sensitive to spatial resolution. (3) The distribution pattern of UGS patches affects the accuracy of UGS mapping. Uncertainty can be reduced in UGS mapping at medium and coarse spatial resolutions based on UGS landscape pattern indices by multiple linear regression, random forest and LightGBM model. This study comprehensively reveals that uncertainties in mapping UGS from multi-spatial resolution remote sensing images vary across urban functional zones and landscape pattern indices, and it is the first attempt to propose methods for UGS area correction based on landscape pattern indices. The results of this study will facilitate the application of remote sensing data at different spatial resolutions in urban areas.
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来源期刊
International journal of applied earth observation and geoinformation : ITC journal
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
0
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.
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