Identifying the greenhouses by Google Earth Engine to promote the reuse of fragmented land in urban fringe

Yang, Gan; Ru, Xu; Chen, Yi; Wu, Zhaoping; Du, Yuanyuan; Liu, Shun; Qu, Zelong; Guo, Kejian; Peng, Changhui; Chang, Jie; Ge, Ying

doi:10.1016/j.scs.2021.102743

Cited by 18 publications

(7 citation statements)

References 52 publications

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“…In particular, the location of service city functional components can be accurately obtained through electronic maps. WTPs have detailed lists and location data on the government websites, and GHs can be accurately identified in Google Earth images due to their unique appearance 31 . Therefore, these three types of raw data are listed as the main sources of location data for functional components.…”

Section: Methodsmentioning

confidence: 99%

“…Accurate WTP latitude and longitude data were obtained by using the WTP name and address to query the AutoNavi map coordinate picking system [the WTP name and address were acquired from the Ministry of Ecology and Environment of the People’s Republic of China ( www.mee.gov.cn ), China Environment Network ( www.cenews.com.cn ) and Beijing Municipal Ecology and Environment Bureau (sthjj.beijing.gov.cn)]. GH latitude and longitude data were determined via a method commonly used in community ecology, which has previously been reported 31 . Briefly, ArcGIS 10.3 software was employed to generate grids covering the entire city (the size of each grid was 0.5 × 0.5 km), and these grids were then converted into the keyhole markup language (KML) format and imported into Google Earth for GH visual interpretation.…”

Section: Methodsmentioning

confidence: 99%

“…The relationship between the number of city functional component types and sampling area has also been demonstrated to be consistent with the saturation curve relationship between species and area in ecology 29 . Recent studies have found that the distribution of city functional components along urban–rural gradients is similar to that of biological species along ecological gradients, exhibiting monotonic or unimodal shapes 11 , 30 , 31 . In fact, existing studies have used gradient analysis to study the distribution of city functional components.…”

Section: Introductionmentioning

confidence: 99%

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Studying the distribution patterns, dynamics and influencing factors of city functional components by gradient analysis

Liu

Yang

et al. 2021

Sci Rep

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Understanding the spatial distribution characteristics and formation mechanism of urban facilities (city functional components) constitutes the basis of urban layout optimization. Currently, research on the overall distribution of the various types of city functional components is lacking. In this study, by applying the gradient analysis method common in ecology, we considered 13 types of city functional components (80,214 individuals in total) in large, medium and small Chinese cities (9 cities in total) to carry out quantitative analysis of the distribution of components along urban–rural gradients through density distribution curves. The results indicated that: (1) a higher density of city functional components near the city centre revealed an obvious aggregated distribution; (2) the spatial distribution dynamics of city functional components were related to the city size, providing a reference for the rational distribution of components in cities of different sizes; (3) the distribution of city functional components was affected by their ecosystem services. This study offers a new perspective for the application of ecological methods in the examination of the distribution of city functional components.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Studying the distribution patterns, dynamics and influencing factors of city functional components by gradient analysis

Liu

Yang

et al. 2021

Sci Rep

Self Cite

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“…In the global context, most of the research on green isolation areas was devoted to the historical evolution and implementation effectiveness. Despite the fact that some scholars carried out research on the role of land ownership [17,18] and market transaction standard system [19] from the dimension of application, there is still a lack of research on the EEH in general. At present, there are some mature cases of green isolation areas abroad-London, Moscow, Frankfurt, Seoul, and Tokyo, etc.…”

Section: Research On the Development Of Green Isolation Areasmentioning

confidence: 99%

Analysis of Harmonious Development of Eco‐Economic System in the Second Green Isolation Area in Beijing

Cao

Zhai

2022

Discrete Dynamics in Nature and Society

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The construction of green isolation areas is an important means to prevent the sprawl of central urban areas, guide the rational layout of urban green spaces, and protect the ecological environment. However, with the continuous construction advancement of green isolation areas in Beijing, the problems of mutual restriction between ecology and economy are increasingly prominent, but few studies are devoted to such problems. Taking the second green isolation area of Beijing in 2000, 2010, and 2020 as the research objects, the kilometer grids were taken as the basic research units, and the land cover data and GDP data were placed in the unified spatial coordinate system by GIS software; also, the equivalent factor method was adopted to calculate the ecological service value (ESV) of the research region, and the eco-economic harmony degree (EEH) was calculated using the ratio of ESPpr to GDPpr. This research innovatively used the kilometer grid as the basic research unit to study the second green isolation area in Beijing and compared the harmony degree between the second green isolation area and the overall ecological, economic system of Beijing, so as to dynamically compare the ecological environment changes in the second green isolation area during the study period and the degree of mutual influence in the process of economic development. The study found that the construction of the second green isolation area improved the harmony of the eco-economic system in a certain area, but it failed to effectively curb the spread of construction land in the urban area of Beijing. The research results can provide scientific guidance for the coordinated planning of the ecological economy in the second green isolation area.

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“…They report very high accuracies of up to 98%. Most recently, Yang et al (2021) published a manual approach to identify greenhouses to study urban fringe based on imagery downloaded from Google Earth.…”

Section: Introductionmentioning

confidence: 99%

Detection of Plastic Greenhouses Using High Resolution Rgb Remote Sensing Data and Convolutional Neural Network

Jakab

Leeuwen

Tobak

2021

Journal of Environmental Geography

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Agricultural production in greenhouses shows a rapid growth in many parts of the world. This form of intensive farming requires a large amount of water and fertilizers, and can have a severe impact on the environment. The number of greenhouses and their location is important for applications like spatial planning, environmental protection, agricultural statistics and taxation. Therefore, with this study we aim to develop a methodology to detect plastic greenhouses in remote sensing data using machine learning algorithms. This research presents the results of the use of a convolutional neural network for automatic object detection of plastic greenhouses in high resolution remotely sensed data within a GIS environment with a graphical interface to advanced algorithms. The convolutional neural network is trained with manually digitized greenhouses and RGB images downloaded from Google Earth. The ArcGIS Pro geographic information system provides access to many of the most advanced python-based machine learning environments like Keras – TensorFlow, PyTorch, fastai and Scikit-learn. These libraries can be accessed via a graphical interface within the GIS environment. Our research evaluated the results of training and inference of three different convolutional neural networks. Experiments were executed with many settings for the backbone models and hyperparameters. The performance of the three models in terms of detection accuracy and time required for training was compared. The model based on the VGG_11 backbone model (with dropout) resulted in an average accuracy of 79.2% with a relatively short training time of 90 minutes, the much more complex DenseNet121 model was trained in 16.5 hours and showed a result of 79.1%, while the ResNet18 based model showed an average accuracy of 83.1% with a training time of 3.5 hours.

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Identifying the greenhouses by Google Earth Engine to promote the reuse of fragmented land in urban fringe

Cited by 18 publications

References 52 publications

Studying the distribution patterns, dynamics and influencing factors of city functional components by gradient analysis

Studying the distribution patterns, dynamics and influencing factors of city functional components by gradient analysis

Analysis of Harmonious Development of Eco‐Economic System in the Second Green Isolation Area in Beijing

Detection of Plastic Greenhouses Using High Resolution Rgb Remote Sensing Data and Convolutional Neural Network

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