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Front and back yard green analysis with subpixel vegetation fractions from earth observation data in a city

机译:城市地球观测数据的亚像素植被分数前后院子绿色分析

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This paper introduces a novel approach to green space availability in cities that includes the thus-far mostly neglected urban front and backyard green space around residential buildings on privately owned ground. To quantify the full spatial scope of urban green space, we calculated subpixel vegetation fractions from RapidEye remote-sensing data for the entire city with a spectral unmixing technique that enabled us to model the extent of urban vegetation with a high degree of confidence (MAE 7%, R2 0.92). We then applied a new 'urban front and back yard green space derivation algorithm', namely, a masking of the fractional vegetation data using GIS vector data of land cover, in order to delineate the front and backyard greenspace of residential houses in a city with an accuracy of 96%. Combining these two approaches, we can calculate the area of urban front and back yard green space for the entire city (including different residential structure types) and compare this data to the area of public (parks, urban forests) and semi-public (allotment gardens) green spaces that have been used for prevailing per capita green space availability analyses. The new method is exemplified at the city of Leipzig, Germany, which provides different residential structures concerning house types and the surrounding green that are characteristic of many European cities. Key findings include that the total amount of urban front and back yard green space is almost 2000 ha, which is similar to 40% of the amount of public green space (4768 ha). In 15 out of the 63 total districts, there is more front and backyard than public green space, which highlights the importance of these urban front and back yard green space for the analysis of urban livelihoods and a tool for detailed ecosystem services-oriented urban planning.
机译:本文介绍了一个新的城市绿色空间可用性的方法,这些方法包括在私人地面上的住宅建筑周围的大多数忽视城市前部和后院绿地。为了量化城市绿地的全部空间范围,我们用谱解密技术计算了从剑剑遥感数据的亚像素植被分数,使我们能够以高度的信心模拟城市植被的范围(MAE 7 %,R2 0.92)。然后我们应用了一个新的“城市前部和后院绿色空间推导算法”,即使用陆地覆盖的GIS矢量数据掩盖分数植被数据,以便描绘在一个城市的住宅房屋的前院和后院壁空间准确性为96%。结合这两种方法,我们可以为整个城市(包括不同的住宅结构类型)来计算城市前部和后院绿色空间区域,并将这些数据与公共区域(公园,城市森林)和半公共(分配)进行比较花园)绿色空间已用于普遍的人均绿色空间空间可用性分析。新方法举例说明德国莱比锡市,提供了有关房屋类型的不同住宅结构以及许多欧洲城市特征的周围绿色。主要发现包括城市前院和后院绿地的总量近2000公顷,类似于公共绿地量的40%(4768公顷)。在总区的63个中,有更多的前院和后院,而不是公共绿地,这突出了这些城市前院和后院绿色空间的重要性,为城市生计分析和一个详细的生态系统服务为导向的城市规划工具。

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