scholarly journals Assessing Impacts of Urban Form on Landscape Structure of Urban Green Spaces in China Using Landsat Images Based on Google Earth Engine

2018 ◽  
Vol 10 (10) ◽  
pp. 1569 ◽  
Author(s):  
Conghong Huang ◽  
Jun Yang ◽  
Peng Jiang
Keyword(s):  
Land Cover ◽  
Landscape Structure ◽  
Urban Form ◽  
Google Earth ◽  
Green Spaces ◽  
Road Density ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Google Earth Engine ◽  
Land Cover Maps

The structure of urban green spaces (UGS) plays an important role in determining the ecosystem services that they support. Knowledge of factors shaping landscape structure of UGS is imperative for planning and management of UGS. In this study, we assessed the influence of urban form on the structure of UGS in 262 cities in China based on remote sensing data. We produced land cover maps for 262 cities in 2015 using 6673 scenes of Landsat ETM+/OLI images based on the Google Earth Engine platform. We analyzed the impact of urban form on landscape structure of UGS in these cities using boosted regression tree analysis with the landscape and urban form metrics derived from the land cover maps as response and prediction variables, respectively. The results showed that the three urban form metrics—perimeter area ratio, road density, and compound terrain complexity index—were all significantly correlated with selected landscape metrics of UGS. Cities with high road density had less UGS area and the UGS in those cities was more fragmented. Cities with complex built-up boundaries tended to have more fragmented UGS. Cities with high terrain complexity had more UGS but the UGS were more fragmented. Our results for the first time revealed the importance of urban form on shaping landscape structure of UGS in 262 cities at a national scale.

scholarly journals Rapid Mapping and Annual Dynamic Evaluation of Quality of Urban Green Spaces on Google Earth Engine

2021 ◽  
Vol 10 (10) ◽  
pp. 670
Author(s):  
Qiang Chen ◽  
Cuiping Zhong ◽  
Changfeng Jing ◽  
Yuanyuan Li ◽  
Beilei Cao ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Urban Area ◽  
Google Earth ◽  
Green Spaces ◽  
Dynamic Evaluation ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Rapid Mapping ◽  
Google Earth Engine

In order to achieve the United Nations 2030 Sustainable Development Goals (SDGs) related to green spaces, monitoring dynamic urban green spaces (UGSs) in cities around the world is crucial. Continuous dynamic UGS mapping is challenged by large computation, time consumption, and energy consumption requirements. Therefore, a fast and automated workflow is needed to produce a high-precision UGS map. In this study, we proposed an automatic workflow to produce up-to-date UGS maps using Otsu’s algorithm, a Random Forest (RF) classifier, and the migrating training samples method in the Google Earth Engine (GEE) platform. We took the central urban area of Beijing, China, as the study area to validate this method, and we rapidly obtained an annual UGS map of the central urban area of Beijing from 2016 to 2020. The accuracy assessment results showed that the average overall accuracy (OA) and kappa coefficient (KC) were 96.47% and 94.25%, respectively. Additionally, we used six indicators to measure quality and temporal changes in the UGS spatial distribution between 2016 and 2020. In particular, we evaluated the quality of UGS using the urban greenness index (UGI) and Shannon’s diversity index (SHDI) at the pixel level. The experimental results indicate the following: (1) The UGSs in the center of Beijing increased by 48.62 km2 from 2016 to 2020, and the increase was mainly focused in Chaoyang, Fengtai, and Shijingshan Districts. (2) The average proportion of relatively high and above levels (UGI > 0.5) in six districts increased by 2.71% in the study area from 2016 to 2020, and this proportion peaked at 36.04% in 2018. However, our result revealed that the increase was non-linear during this assessment period. (3) Although there was no significant increase or decrease in SHDI values in the study area, the distribution of the SHDI displayed a noticeable fluctuation in the northwest, southwest, and northeast regions of the study area between 2016 and 2020. Furthermore, we discussed and analyzed the influence of population on the spatial distribution of UGSs. We found that three of the five cold spots were located in the east and southeast of Haidian District. Therefore, the proposed workflow could provide rapid mapping and dynamic evaluation of the quality of UGS.

scholarly journals Analyzing the Level of Accessibility of Public Urban Green Spaces to Different Socially Vulnerable Groups of People

2018 ◽  
Vol 10 (11) ◽  
pp. 3917 ◽  
Author(s):  
K Rahman ◽  
Dunfu Zhang
Keyword(s):  
Green Space ◽  
Public Spaces ◽  
Google Earth ◽  
High Density ◽  
Green Spaces ◽  
Vulnerable Groups ◽  
Geographical Data ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Socially Vulnerable Groups

This study estimates the factors affecting socially vulnerable groups’ demand for and accessibility levels to green public spaces in Dhaka City, Bangladesh. Dhaka is a high-density city with one of the lowest levels of green space per capita in the world. Dhaka has just 8.5% of tree-covered lands, while an ideal city requires at least 20% of green space. Urban public green space provides a healthy environment to city dwellers as well as ecological soundness. This study aims to examine the effects of population density and size of a community area (Thana) on the social demand for and accessibility to green parks. To determine the socially vulnerable group demand index, this study used demographic data from the National Population and Housing Census 2011 conducted by the Bangladesh Bureau of Statistics. This study used geographical data extracted from Google Earth Pro to measure accessibility levels, and additionally analyzed geographical data with ArcGIS 10.0 and Google Earth Pro. We drew radius circles using Free Map Tools to measure time-distance weighted scores from community areas to urban green spaces. The results show that the large population size of socially vulnerable groups creates very high demand at the score of 0.61 for urban green public parks and small-sized, high-density community areas generate very good accessibility at 2.01% to green public spaces. These findings are highly useful to policymakers, urban planners, landscape engineers, and city governments to make a compact city sustainable, inclusive, and resilient. Moreover, the notion of a “smart city” might be a smart solution in order to manage Dhaka Megacity sustainably in this modern technological age.

scholarly journals Land Cover Classification using Google Earth Engine and Random Forest Classifier—The Role of Image Composition

2020 ◽  
Vol 12 (15) ◽  
pp. 2411 ◽  
Author(s):  
Thanh Noi Phan ◽  
Verena Kuch ◽  
Lukas W. Lehnert
Keyword(s):  
Time Series ◽  
Random Forest ◽  
Land Cover ◽  
Land Cover Classification ◽  
High Accuracy ◽  
Google Earth ◽  
Temporal Aggregation ◽  
Google Earth Engine ◽  
Time Series Images ◽  
Land Cover Maps

Land cover information plays a vital role in many aspects of life, from scientific and economic to political. Accurate information about land cover affects the accuracy of all subsequent applications, therefore accurate and timely land cover information is in high demand. In land cover classification studies over the past decade, higher accuracies were produced when using time series satellite images than when using single date images. Recently, the availability of the Google Earth Engine (GEE), a cloud-based computing platform, has gained the attention of remote sensing based applications where temporal aggregation methods derived from time series images are widely applied (i.e., the use the metrics such as mean or median), instead of time series images. In GEE, many studies simply select as many images as possible to fill gaps without concerning how different year/season images might affect the classification accuracy. This study aims to analyze the effect of different composition methods, as well as different input images, on the classification results. We use Landsat 8 surface reflectance (L8sr) data with eight different combination strategies to produce and evaluate land cover maps for a study area in Mongolia. We implemented the experiment on the GEE platform with a widely applied algorithm, the Random Forest (RF) classifier. Our results show that all the eight datasets produced moderately to highly accurate land cover maps, with overall accuracy over 84.31%. Among the eight datasets, two time series datasets of summer scenes (images from 1 June to 30 September) produced the highest accuracy (89.80% and 89.70%), followed by the median composite of the same input images (88.74%). The difference between these three classifications was not significant based on the McNemar test (p > 0.05). However, significant difference (p < 0.05) was observed for all other pairs involving one of these three datasets. The results indicate that temporal aggregation (e.g., median) is a promising method, which not only significantly reduces data volume (resulting in an easier and faster analysis) but also produces an equally high accuracy as time series data. The spatial consistency among the classification results was relatively low compared to the general high accuracy, showing that the selection of the dataset used in any classification on GEE is an important and crucial step, because the input images for the composition play an essential role in land cover classification, particularly with snowy, cloudy and expansive areas like Mongolia.

scholarly journals Quantification and monetary valuation of urban ecosystem services in Munich, Germany

2015 ◽  
Vol 59 (3) ◽  
Author(s):  
Tim Aevermann ◽  
Jürgen Schmude
Keyword(s):  
Ecosystem Services ◽  
Land Cover ◽  
Urban Areas ◽  
Green Space ◽  
Local Population ◽  
Urban Green Space ◽  
Green Spaces ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Runoff Reduction

AbstractUrban green spaces provide ecosystem services that can be used by the local population. The valuation of these ecosystem services in urban areas gives information for stakeholders and decision-makers to improve urban planning processes. In addition, this information can be used to provide a better understanding of how urban green spaces should be managed. In this study, we quantify and monetize four ecosystem services (carbon sequestration and storage, air pollution removal, runoff reduction, groundwater recharge) provided by the urban green space of Schlosspark Nymphenburg in Munich, Germany. We classify four different land cover types (tree, grass/herbaceous, water, impervious) to calculate different amounts of ecosystem services according to the land cover type. In addition, we quantify the maintenance costs which this urban green space causes to the city of Munich. The interpretation, communication and risks of valuations studies are discussed.

scholarly journals Balancing Urban Green Space and Residential Infill Development: A Spatial Multi-Criteria Approach Based on Practitioner Engagement

2018 ◽  
Vol 20 (03) ◽  
pp. 1840004 ◽  
Author(s):  
Maija Tiitu ◽  
Arto Viinikka ◽  
Leena Kopperoinen ◽  
Davide Geneletti
Keyword(s):  
Urban Form ◽  
Green Space ◽  
Green Spaces ◽  
Infill Development ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Future Developments ◽  
Wide Range ◽  
The Impact ◽  
The City

The objectives in consolidating the urban form and preserving green spaces are often in conflict in growing cities. The usability of spatial multi-criteria decision analysis (SMCDA) was tested as a tool for integrating residential infill development and urban green spaces in the City of Järvenpää, Finland. In collaboration with local practitioners, this study focused on the benefits and challenges of SMCDA. The results were based on two workshops with the practitioners along with comprehensive GIS analyses based on a wide range of available data. The practitioners saw SMCDA as a useful method to bring together a variety of factors related to infill development. They highlighted the importance of the method’s transparency, emphasising the comprehensive explanation of each step of the method. Better understanding of the impact of individual criteria weightings on the results was mentioned as one of the key future developments of the method.

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