Quantifying spatiotemporal pattern of urban greenspace: new insights from high resolution data

Abstract

Context Urban greenspace provides myriad ecosystem services. To fully understand the ecological services provided by urban greenspace, it is crucial to first accurately quantify the composition and configuration, and change of urban greenspace. Objectives (1) Quantify the dynamics of greenspace in highly urbanized areas. (2) Compare and evaluate the efficacy of medium and high resolution image data on quantifying urban greenspace dynamics. Methods Using two very well-developed urban districts of Beijing, we compare and contrast the changes in urban greenspace from 2005 to 2009 based on two different datasets, the most commonly used Landsat TM data with 30 m resolution, and 2.5 m high spatial resolution imagery.Results We found urban greenspace in the two welldeveloped urban districts of Beijing to be very dynamic, in contrast to findings from previous research that greenspace in inner cities tends to remain largely unchanged. Such dynamics, however, could only be revealed by high spatial resolution imagery because medium resolution data, such as TM data greatly underestimated the percent cover of greenspace. The underestimate neglects smaller elements of greenspace as well as changes in configuration of larger patches, limiting the ability of TM data to detect such changes. Conclusions Our results underscore the importance and necessity of using high spatial resolution data to adequately quantify the distribution of urban greenspace and its change. Results from this study have important implications for urban greenspace management and planning. In addition, the dynamics revealed support emerging conceptions of urban areas as hierarchical patch mosaics.

A new approach for land cover classification and change analysis: Integrating backdating and an object-based method

“…It is one of the most important data sources for LULC monitoring at the global, national and regional scale. However, because of the limitation of spatial resolution of 30 m, relatively small but abundant changes in LULC, such as changes in urban greenspace in the inner cities, cannot be detected by TM data (Qian, Zhou, Yu, & Pickett, 2015). Imagery with higher spatial resolutions is needed to quantify such changes at very fine scales.…”

Patch size of trees affects its cooling effectiveness: A perspective from shading and transpiration processes

“…Identifying such optimal patch size can have significant practical implications for urban greenspace management and design. This is because land in cities that can be used for greening is very limited (Qian et al, 2015b), and therefore knowledge on enhancing the cooling effects of greenspace by for example, optimizing their spatial configuration in addition to adding more greenspace is highly desirable. Unfortunately, we could not identify this optimal size in this study because we were not able to choose adequate number of patches of woodland with various sizes that met the experimental conditions.…”

Impacts of Street-Visible Greenery on Housing Prices: Evidence from a Hedonic Price Model and a Massive Street View Image Dataset in Beijing

“…Planting grass, shrubs, and trees along both sides of roads can reduce noise pollution, purify the air, beautify the streetscape, and offer inhabitants ecological, entertainment, and recreational functions [1,2]. Street greenery is a public resource that provides residents aesthetic value and ecological benefits in addition to other, unconscious benefits.…”

“…In Los Angeles, and in Ramsey and Dakota counties in Minnesota, US, and Brisbane, Australia, some studies have also shown that street trees can increase neighbouring housing prices [29][30][31]. These studies took Earth observation approaches for extracting street tree cover data, for instance, using visual interpretation or object-oriented segmentation methods to recognize and quantify street canopy cover or other green vegetation cover in remote sensing or airborne images [2,32].…”