Quantity assessment of urban green, blue, and grey spaces in Poland

Suligowski, Roman; Ciupa, Tadeusz; Cudny, Waldemar

doi:10.1016/j.ufug.2021.127276

Cited by 15 publications

(5 citation statements)

References 99 publications

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“…While an FNN's neurons only transfer information between layers, an RNN allows information to be transferred between neurons by introducing a circular structure [15][16][17]. In this way, RNN can memorize the last input as a "storage" and provide a reference for the following step [18][19][20][21][22]. Unlike an FNN, which can only map a hidden layer-to-output layer input, an RNN can map all information to an individual output neuron with a better prediction.…”

Section: Rnn and Lstm Algorithmmentioning

confidence: 99%

Artificial Intelligence and Urban Green Space Facilities Optimization Using the LSTM Model: Evidence from China

Yu,

Guan,

Zhu

et al. 2023

Sustainability

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Urban road green belts, an essential component of Urban Green Space (UGS) planning, are vital in improving the urban environment and protecting public health. This work chooses Long Short-Term Memory (LSTM) to optimize UGS planning and design methods in urban road green belts. Consequently, sensitivity-based self-organizing LSTM shows a Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) of 1.75, 1.12, and 6.06, respectively. These values are superior to those of LSTM, XGBoost, and SVR. Furthermore, we configure three typical plant community models using the improved LSTM model and found that different plant community configurations have distinct effects on reducing PM 2.5 concentrations. The experimental results show that other plant community configuration models have specific effects on reducing PM 2.5 concentrations, and the multi-layered green space with high canopy density in the community has a better impact on PM 2.5 reduction than the single-layer green space model with low canopy density. We also assess the reduction function of green road spaces on PM 2.5, which revealed that under zero pollution or slight pollution (PM 2.5 < 100 μg.m−3), the green space significantly reduces PM 2.5. In UGS planning, the proposed model can help reveal UGS spatial morphology indicators that significantly impact PM 2.5 reduction, thereby facilitating the formulation of appropriate green space planning strategies. The finding will provide primary data for selecting urban road green space plant configuration.

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Section: Rnn and Lstm Algorithmmentioning

confidence: 99%

Artificial Intelligence and Urban Green Space Facilities Optimization Using the LSTM Model: Evidence from China

Yu,

Guan,

Zhu

et al. 2023

Sustainability

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“…Patches, as the most basic unit of the landscape, provide habitats for many organisms. Based on their different compositions, landscape patches can be divided into green, blue, and gray patches [23]. In Shaanxi Province, the green patches are mainly green spaces composed of farmland, forest land, and grassland; the blue patches are strip-like patches composed of rivers or lakes; and the gray patches are mainly composed of artificially constructed hardened surfaces, such as urban construction land and rural residential areas.…”

Section: Identification Of Ecological Sourcesmentioning

confidence: 99%

Establishing and Optimizing the Ecological Security Pattern in Shaanxi Province (China) for Ecological Restoration of Land Space

Zhang

Cao

et al. 2022

Forests

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Land space underpins an ecological civilization. A thorough grasp of regional natural resources and subsequent optimization of the ecological security pattern are both essential for the comprehensive development and overall planning of the management of natural resources. In this study, we established and optimized the ecological security pattern in Shaanxi Province, China. Landscape patches distinguished by high importance of ecosystem services (carbon fixation and oxygen release, water conservation, habitat maintenance, and soil conservation) and high sensitivity of ecological environment to human interference (ecological sensitivity index) were extracted as ecological sources. An ecological resistance surface was built based on the following resistance factors: land-use type, topographic position index, and soil erosion intensity. A minimum cumulative resistance model was used to identify ecological corridors. Our results showed that those ecological sources with high to extreme ecological importance and sensitivity together covered an area of 67,457 km2 (32.8% of the total land area of Shaanxi Province). A total of 32 ecological nodes were identified at the center of important ecological sources to serve as main areas for implementing ecological protection projects. In addition, 72 ecological corridors were identified, towards which efforts should be targeted to maintain their functions for the inter-connection and serial connection of ecological nodes and source patches. The ecological corridors create favorable habitats for wildlife and superior spaces for ecological migration based on the blue and green linear corridor system. According to these findings, we propose establishing an ecological security pattern featuring “two barriers, three belts, and multiple corridors” in Shaanxi Province and that the green ecological security barriers should be strengthened based on their natural background and resource endowments.

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“…Hence, conducting long-term studies with more detailed information is essential in this field. In terms of primary data, previous research on UBS landscapes has primarily relied on traditional data sources, such as historical maps and aerial images with large spatial resolutions and limited information [31,32]. These sources only support studies at a patch scale [33,34].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Analysis and Multifactor Driving Mechanisms of Urban Blue Space in Beijing Using Remote Sensing

Chen,

Zhen,

et al. 2023

Preprint

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With the rapid development in Beijing, there is a critical need to explore the circumstance and reveal the mechanisms of precious urban natural resources. In this context, urban blue space has attracted more and more attention by driving microcirculation, cooling heat islands, and relaxing residential. We extracted the UBS at Beijing using remote sensing, explored the spatial and temporal development in the last two decades via USDA methods, uncovered the full spectrum of landscape patterns from an ecological perspective, and simulated the mechanisms of the UBS area and the landscape quantitatively. We found that: (1) The UBS area in Beijing increased with fluctuation from 2000 to 2020. (2) The spatial clustering has distributed stable with some subtle changes. (3)The ecological circumstance has improved in the last 21 years in Beijing, with the increasing habitat diversity and richness, while the inferior landscape fragmentation has indicated some severe challenges. (4) Natural factors impact urban blue space areas more than social ones, while both similarly influence the UBS landscape. (5) Vegetation circumstances and precipitation are the most important natural factors on both area and landscape of UBS, and population and artificial surface are the most important social factors.

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Quantity assessment of urban green, blue, and grey spaces in Poland

Cited by 15 publications

References 99 publications

Artificial Intelligence and Urban Green Space Facilities Optimization Using the LSTM Model: Evidence from China

Artificial Intelligence and Urban Green Space Facilities Optimization Using the LSTM Model: Evidence from China

Establishing and Optimizing the Ecological Security Pattern in Shaanxi Province (China) for Ecological Restoration of Land Space

Spatiotemporal Analysis and Multifactor Driving Mechanisms of Urban Blue Space in Beijing Using Remote Sensing

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