Prof. Qingyan Meng Profile

Prof. Qingyan Meng

at Institute of Remote Sensing and Digital Earth

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Publications (3)

Proceedings Article | 24 October 2017 Paper

Assessing the impacts of grain sizes on landscape pattern of urban green space

Yunxiao Sun, Qingyan Meng, Zhenhui Sun, Jiahui Zhang, Linlin Zhang

Proceedings Volume 10462, 104623J (2017) https://doi.org/10.1117/12.2285177

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As an important part of the city, urban green space (UGS) plays an essential role in enhancing human well-being by virtue of multiple environmental, social and economic benefits. Study on landscape pattern of UGS is a focal point and hotspot in landscape ecology. The latest studies demonstrated that landscape metrics provides an effective method in quantifying UGS pattern. However, the study of the scale effect of landscape metrics should be strengthened. The objective of scale related research in UGS is to determine the appropriate scale in the measurement and evaluation of UGS and to find the underlying mechanisms by use of the selected scales.

This study aims to identify the scale characteristics and scale domain of UGS pattern, and provide basic information for pattern analysis and scaling in UGS research. In this paper, taking the central urban area of Székesfehérvár in Hungary as an example, we firstly extracted UGS from WordView-2 multi-spectral image (2m), then obtained a series of grain sizes by upscaling, and finally calculated and analyzed the characteristics of different landscape metrics with varying grain sizes. In this study, both the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Green Index (NDGI) were used to ensure the accuracy of the green space extraction in high spatial resolution image. On the basis of green space extraction, the green space patterns at different grain sizes were obtain by the assembly of grid cells. A total of 20 grain sizes were selected in this paper, ranging from 2 m to 40 m with a step size of 2 m. Landscape metrics both under class and landscape levels, including Patch Density (PD), Percentage of Landscape (PLAND), Mean Perimeter-Area Fractal Dimension (FRAC_MN), Division Index (DIVISION), Cohesion Index (COHESION), and Shannon’s Evenness Index (SHEI) were calculated.

The results demonstrated that with the increase of grain size, the landscape metrics under class level and landscape level were significantly affected by the grain size, and there was obvious critical grain size. On the whole, 16 m is the critical grain size of the green space pattern, and the suitable grain size for landscape metrics calculation of UGS ranges from 2 m to 16 m. The responding curves were varied by landscape metrics. Some metrics had clear changing trend and obvious turning grain size, while the others also had obvious turning grain size, but without clear changing trend. According to scale inflexions and responding curves discussed in the paper, scale domains of landscape metrics were confirmed. Generally, from 2 m to 16 m was the scale domain of UGS pattern, which means that related ecological model of UGS can be scaled across this scale extent by ordinary transformation. The study of impacts of changing scale on UGS can provide a reference for understanding the ecological benefits of UGS and optimizing the green space pattern.

Proceedings Article | 8 September 2015 Paper

Extracting information on urban impervious surface from GF-1 data in Tianjin City of China

Bin Li, Qingyan Meng, Jun Wu, Xingfa Gu

Proceedings Volume 9607, 96072G (2015) https://doi.org/10.1117/12.2189288

KEYWORDS: Remote sensing, Satellites, Reflectivity, Satellite imaging, Earth observing sensors, Spectral models, Image resolution, Image processing, Data modeling, Cameras

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Proceedings Article | 8 November 2014 Paper

Fractional vegetation cover estimation over large regions using GF-1 satellite data

Yulin Zhan, Qingyan Meng, Chunmei Wang, Juan Li, Ke Zhou, Dachong Li

Proceedings Volume 9260, 92604B (2014) https://doi.org/10.1117/12.2069845

KEYWORDS: Vegetation, Data modeling, Remote sensing, Satellites, Shape memory alloys, Spectral models, Cameras, Environmental sensing, Satellite imaging, Imaging systems

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