Green and brown infrastructures support a landscape-level implementation of ecological engineering

Mander, Ülo; Kull, Ain; Uuemaa, Evelyn; Mõisja, Kiira; Külvik, Mart; Kikas, Tambet; Raet, Janar; Tournebize, Julien; Sepp, Kalev

doi:10.1016/j.ecoleng.2018.05.019

Cited by 23 publications

(13 citation statements)

References 34 publications

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“…The ecological planning of landscape refers to the planning measures taken to ensure the ecological balance of landscape based on the principles of ecology and the laws of society and nature. Therefore, the ecological planning of landscape can improve the status of the ecological environment, ensure the good development of the ecological environment, and also make people feel the importance of environmental protection [ 4 ]. A high-order wavelet neural network (WNN) algorithm combining wavelet analysis and artificial neural network is proposed, and a landscape ecological evaluation model based on high-order wavelet neural network algorithm is constructed, so as to evaluate the landscape ecology and provide reference data for landscape ecological planning.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Analysis of Landscape Ecological Planning Based on the High‐Order Multiwavelet Neural Network Algorithm

Yu¹,

2021

Computational Intelligence and Neuroscience

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Landscape architecture has both natural and social properties, which is the embodiment of people protecting the natural environment. Since the industrial revolution, the modern industry has developed rapidly. It has increased the living standard of people and consumed a lot of natural resources such as forest and energy. The ecological environment has been greatly damaged, and the landscape of gardens has been affected. Therefore, it is of great significance to find a method to evaluate the landscape ecology and plan the landscape ecology. This paper proposes a new high-order wavelet neural network algorithm combining wavelet analysis and artificial neural network. A model of ecological evaluation of landscape based on high-order wavelet neural network algorithm is proposed to evaluate the landscape ecology and provide reference data for the ecological planning of the landscape. The results show that the training times of the wavelet neural network to achieve the target accuracy are 3600 times less than those of the BP neural network. The MSE and MAE of the WNN are 0.0639 and 0.1501, respectively. The average error of the model to the comprehensive evaluation index of the landscape ecology is 0.005. The accuracy of the model to evaluate the sustainability of landscape land resources is 98.67%. The above results show that the model based on the wavelet neural network can effectively and accurately complete the evaluation of landscape ecology and then provide a decision-making basis for landscape ecological planning, which is of high practicability.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Analysis of Landscape Ecological Planning Based on the High‐Order Multiwavelet Neural Network Algorithm

Yu¹,

2021

Computational Intelligence and Neuroscience

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“…Wetlands can be a significant source of methane and may make up to 30% of global methane emissions (Bridgham et al 2013). The presence of SOC may lead to methane production if soils remain inundated (Martins et al 2017;McNicol et al 2017;Mander et al 2018). Methane emissions could be controlled by reducing the amendment application rate (Lou et al 2007;Winton & Richardson 2015).…”

Section: Discussionmentioning

confidence: 99%

The role of organic amendments in wetland restorations

Scott

Baldwin

Ballantine

et al. 2020

Restoration Ecology

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At the present rate of loss (since 1990), half of the remaining wetlands worldwide will be developed within ~140 years, underscoring the importance of improving the creation and restoration of wetlands. Organic amendments are sometimes used during wetland creation. To evaluate the effectiveness of adding organic amendments we used a combined numerical method to assign “scores” on five categories of evaluation metrics: plant growth, soil properties, carbon accrual, denitrification, and anaerobic processes (e.g. redox potential). We found that amendments identified as “topsoil” scored measurably higher and had consistently more positive values with fewer negative results compared to amendments identified as “allochthonous organic matter” (alOM). Organic amendments had about the same effect on soils with low soil organic carbon (<2.5%) compared to soils richer in organic carbon. Organic amendments are not uniformly effective, and in some cases may have negative side effects. For example, alOM often resulted in a loss of plant diversity. These outcomes along with site conditions should be evaluated before using organic amendments.

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“…Consequently, identifying, promoting and preserving a strategically planned GI network (Kopecká et al, 2017) is nowadays the focus of many studies which aim to develop a methodology that helps to identify, map and assess existing GI (e.g. Dennis et al, 2018;Liquete et al, 2015;Mander et al, 2018).…”

Section: Methodological Issuesmentioning

confidence: 99%

Mapping Green Infrastructure Elements Based on Available Data, A Case Study of the Czech Republic

Skokanová

González

Slach

2020

Journal of Landscape Ecology

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The contribution of Green Infrastructure (GI) in vital areas such as regional development, climate change, agriculture, forestry and environmental protection is already acknowledged and listed as one of the priorities in several key EU policies, and is the basis for the EU Green Infrastructure Strategy. However, the concept is not yet fully integrated into actual planning strategies, in this respect the Czech Republic is no different. A major problem in doing so is a lack of guidelines/ how to identify and map GI using available sources and thus keeping costs down. The existing data sources for land cover or land use often do not fulfil the requirements in terms of thematic coverage, resolution or accuracy. In order to work around such limitations, we analyse the existing land use/land cover data in the Czech Republic from the GI perspective. Furthermore, we propose a GI mapping methodology based on the diagnosis of three mapping approaches at the regional level, grounded on the utilisation and processing of different data sets. We compared GI maps based on European data from CORINE Land Cover Database, Czech national database called Consolidated Layer of Ecosystems (CLE) and combination of Czech national and regional data and manual vectorization. Our results show that CORINE based GI map is suitable for transnational scale but unsuitable for regional scale. The CLE based GI map is good for both national and regional scale but its information on GI in urban areas is lacking. The detailed regional GI map is good for regional and to some degree even for local scale but its creation is time consuming. However, careful combination of existing national and regional data can provide good outcome in creating GI map usable for territorial planning.

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Green and brown infrastructures support a landscape-level implementation of ecological engineering

Cited by 23 publications

References 34 publications

[Retracted] Analysis of Landscape Ecological Planning Based on the High‐Order Multiwavelet Neural Network Algorithm

[Retracted] Analysis of Landscape Ecological Planning Based on the High‐Order Multiwavelet Neural Network Algorithm

The role of organic amendments in wetland restorations

Mapping Green Infrastructure Elements Based on Available Data, A Case Study of the Czech Republic

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