Evaluating landscape connectivity in fragmented habitats: Cantabrian capercaillie (Tetrao urogallus cantabricus) in northern Spain

Velázquez, Juan J. L.; Gutiérrez, José Félix Pérez; Hernando, Ana; García‐Abril, Antonio

doi:10.1016/j.foreco.2016.12.006

Cited by 35 publications

(23 citation statements)

References 31 publications

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“…Landscape connectivity is defined as the continuity of corridors, networks, or spatial matrices that quantitatively characterize the relationships between ecosystems [51]. Morphological spatial pattern analysis was adopted to identify and extract the core area, and the probability of connectivity (PC) index (Equation (3)) was selected to evaluate the landscape connectivity of the core area [52]. Repeated experiments show that 500 m is a more suitable fixed distance for connectivity analysis using the PC index.…”

Section: Identification Of Ecological Sourcesmentioning

confidence: 99%

Simulation of Dynamic Urban Expansion under Ecological Constraints Using a Long Short Term Memory Network Model and Cellular Automata

Liu

Xiao

et al. 2021

Remote Sensing

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Rapid urban expansion has seriously threatened ecological security and the natural environment on a global scale, thus, the simulation of dynamic urban expansion is a hot topic in current research. Existing urban expansion simulation models focus on the mining of spatial neighborhood features among driving factors, however, they ignore the over-fitting, gradient explosion, and vanishing problems caused by the long-term dependence of time series data, which results in limited model accuracy. In this study, we proposed a new dynamic urban expansion simulation model. Considering the long-time dependence issue, long short term memory (LSTM) was employed to automatically extract the transformation rules through memory units and provide the optimal attribute features for cellular automata (CA). This study selected Lanzhou, which is a semi-arid region in Northwest China, as an example to confirm the validity of the model performance using data from 2000 to 2020. The results revealed that the overall accuracy of the model was 91.01%, which was higher than that of the traditional artificial neural network (ANN)-CA and recurrent neural network (RNN)-CA models. The LSTM-CA framework resolved existing problems with the traditional algorithm, while it significantly reduced complexity and improved simulation accuracy. In addition, we predicted urban expansion to 2030 based on natural expansion (NE) and ecological constraint (EC) scenarios, and found that EC was an effective control strategy. This study provides a certain theoretical basis and reference value toward the realization of new urbanization and ecologically sound civil construction, in the context of territorial spatial planning and healthy/sustainable urban development.

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Section: Identification Of Ecological Sourcesmentioning

confidence: 99%

Simulation of Dynamic Urban Expansion under Ecological Constraints Using a Long Short Term Memory Network Model and Cellular Automata

Liu

Xiao

et al. 2021

Remote Sensing

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“…This index is calculated from the results obtained in the MSPA analysis, which is performed for a 1 pixel dispersion band. Taking into account the classifications of the 7 components of the MSPA, only the nodes and bridges are considered in the PC analysis, since only these can contribute to the improvement of connectivity (Saura et al, 2011;Velázquez et al, 2017).When analyzing the importance of the nodes and bridges, the PC-infinite data for each component was obtained, so that the importance of connectivity of the selected buildings for the location of the green roofs could be analyzed. Finally, we proceeded to reclassify PC data based on the maximum value obtained, on a scale of 1 to 100, to facilitate the comparison of the data.…”

Section: Tablementioning

confidence: 99%

Planning and selection of green roofs in large urban areas. Application to Madrid metropolitan area

Velázquez

Anza

Gutiérrez

et al. 2019

Urban Forestry & Urban Greening

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Due to the numerous environmental problems facing today's society, and especially urban areas, green roofs are presented as an adequate technique to fight the consequences of pollution, traffic and lack of green areas. These green structures help to reduce the effects of Urban Heat Island, to decrease noise and atmospheric pollution, to protect homes from isolation and cold; they also capture rainwater and improve biodiversity. A new methodology is presented to select the best location of green roofs in large cities. In the first phase, this methodology helps to determine the most suitable neighborhoods, analyzing four main variables of interest in urban environs: pollution, traffic, green areas and population. In order to benefit a greater number of inhabitants, the neighborhoods with the worst air quality, more traffic, less green áreas and higher population density, are selected. In the second phase, we used LIDAR technology to identify available roofs for the installation of the green roofs according to the height and roof typology of the buildings. To select the optimal roofs, connectivity analysis techniques were used. The results show that the most conflictive neighborhoods from the environmental point of view are those located in the city center, so they result the ideal places for the location of green roofs. In general, all neighborhoods except one presented high connectivity values. This methodology helps to improve the connectivity of the green spaces of Madrid, favoring the dispersion of plant and animal species, air quality and promoting sustainable and quality urban development.

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“…By combining the analyses of MSPA (focusing on structural analysis) and dPC (taking account of structural connectivity) in this study, we identified 10 cores with higher connectivity value as hubs [39]. The 10 core patches were identified by quantitatively examining their role in maintaining landscape connectivity rather than by simply selecting the ten largest patches in the landscape.…”

Section: Identifying the Current Status Of Ugimentioning

confidence: 99%

Evaluating Spatial Priority of Urban Green Infrastructure for Urban Sustainability in Areas of Rapid Urbanization: A Case Study of Pukou in China

Wei

Qian

et al. 2018

Sustainability

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Rapid urbanization in recent decades highlights the limitations on the sustainable development of cities due to the fragmentation of restricted urban green land. The aim of this paper is to formulate a workable framework for planning and managing urban green infrastructure (UGI) for urban sustainability. This study provides a new method for modeling and analyzing UGI based on a case study of the Pukou District in Nanjing, which is a typical developing area in China. We adopt the morphological spatial pattern analysis (MSPA) method and combine it with the landscape connectivity index to identify the UGI hubs and links. In addition, the least-cost path model is employed to construct the potential UGI network in this case. We further integrate the spatial syntax model into landscape ecological principles to evaluate the spatial priority of the UGI network. The results showed that the framework proposed in this study is suitable for the green infrastructure network construction by combining the MSPA, landscape connectivity, and the space syntax methods. This framework can be used to better understand the spatial distribution and priority of the green infrastructure network for achieving urban sustainability in China.

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Evaluating landscape connectivity in fragmented habitats: Cantabrian capercaillie (Tetrao urogallus cantabricus) in northern Spain

Cited by 35 publications

References 31 publications

Simulation of Dynamic Urban Expansion under Ecological Constraints Using a Long Short Term Memory Network Model and Cellular Automata

Simulation of Dynamic Urban Expansion under Ecological Constraints Using a Long Short Term Memory Network Model and Cellular Automata

Planning and selection of green roofs in large urban areas. Application to Madrid metropolitan area

Evaluating Spatial Priority of Urban Green Infrastructure for Urban Sustainability in Areas of Rapid Urbanization: A Case Study of Pukou in China

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