Landscape generator: Method to generate landscape configurations for spatial plan-making

Slager, Ctj Kymo; Vries, de B Bauke

doi:10.1016/j.compenvurbsys.2013.01.007

Cited by 19 publications

(13 citation statements)

References 17 publications

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“…LG is based on a multiobjective optimization that “searches” for a landscape that best meets the composition and configuration objectives, which are specified by a user‐defined set of class‐ and patch‐level landscape metrics (Slager and de Vries ). To reach the objectives, LG iteratively swaps pairs of raster cells and, after every swap, evaluates whether the new landscape pattern is closer to the objectives.…”

Section: Methodsmentioning

confidence: 99%

“…To reach the objectives, LG iteratively swaps pairs of raster cells and, after every swap, evaluates whether the new landscape pattern is closer to the objectives. For a more detailed description of the optimization algorithm implemented in LG, we refer to Slager () and Slager and de Vries ().…”

Section: Methodsmentioning

confidence: 99%

“…To generate a landscape, LG requires a landscape raster file and a list of input settings (Slager and de Vries ). The landscape raster can be a random percolation map in which each raster cell is randomly assigned a land‐use category proportionate to the probability of occurrence of each land‐use type.…”

Section: Methodsmentioning

confidence: 99%

“…Including these basic metrics as input parameters of neutral landscape models would thus improve their ability to capture the patterns characteristic of real landscapes. In summary, in order for neutral landscape models to more accurately reproduce the patterns found in real landscapes, they should ideally be able to generate landscapes with several land‐use classes using a set of easily calculable class‐ and patch‐level landscape metrics as input parameters; an idea recently followed by Slager and de Vries ().…”

Section: Introductionmentioning

confidence: 99%

“…The neutral landscape model “Landscape Generator” (LG) developed by Slager and de Vries () allows users to specify many of the above mentioned class‐ and patch‐level landscape metrics to control the pattern of the generated landscape. LG has been developed for participatory spatial planning and has been demonstrated on the planning of, for instance, residential areas at a relatively small scale (i.e., parcel level; Slager ; Slager and de Vries ). Here, we demonstrate how LG can also be used at larger scales to generate landscapes for ecological simulation studies.…”

Section: Introductionmentioning

confidence: 99%

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An improved neutral landscape model for recreating real landscapes and generating landscape series for spatial ecological simulations

Strien

Slager

Vries

et al. 2016

Ecology and Evolution

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Many studies have assessed the effect of landscape patterns on spatial ecological processes by simulating these processes in computer‐generated landscapes with varying composition and configuration. To generate such landscapes, various neutral landscape models have been developed. However, the limited set of landscape‐level pattern variables included in these models is often inadequate to generate landscapes that reflect real landscapes. In order to achieve more flexibility and variability in the generated landscapes patterns, a more complete set of class‐ and patch‐level pattern variables should be implemented in these models. These enhancements have been implemented in Landscape Generator (LG), which is a software that uses optimization algorithms to generate landscapes that match user‐defined target values. Developed for participatory spatial planning at small scale, we enhanced the usability of LG and demonstrated how it can be used for larger scale ecological studies. First, we used LG to recreate landscape patterns from a real landscape (i.e., a mountainous region in Switzerland). Second, we generated landscape series with incrementally changing pattern variables, which could be used in ecological simulation studies. We found that LG was able to recreate landscape patterns that approximate those of real landscapes. Furthermore, we successfully generated landscape series that would not have been possible with traditional neutral landscape models. LG is a promising novel approach for generating neutral landscapes and enables testing of new hypotheses regarding the influence of landscape patterns on ecological processes. LG is freely available online.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An improved neutral landscape model for recreating real landscapes and generating landscape series for spatial ecological simulations

Strien

Slager

Vries

et al. 2016

Ecology and Evolution

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References

2019

Geographical Modeling

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Artificial Intelligence in Landscape Ecology: Recent Advances, Perspectives, and Opportunities

Frazier,

Song

2024

Curr Landscape Ecol Rep

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Purpose of Review Artificial intelligence (AI) is disrupting science and discovery across disciplines, offering new modes of inquiry that are changing how questions are asked and answered and upsetting established norms. In this paper, we review the state of the art of AI in landscape ecology and offer six areas of opportunity for landscape ecologists to capitalize on AI tools moving forward. These areas include geospatial AI (GeoAI), geometric AI, Explainable AI (xAI), generative AI (GenAI), Natural Language Processing (NLP), and robotics. Recent Findings Landscape ecology has a long history of using AI, notably machine learning methods for image classification tasks, agent-based modeling, and species distribution modeling but also knowledge representation and automated reasoning for landscape generation and spatial planning. Methods have become more diverse and complex in recent years, with a new generation of AI-based tools rapidly emerging. These new tools have potential to improve how landscape ecologists map, measure, and model landscape patterns and processes as well as improve the explainability of model outputs. Summary There are many untapped opportunities for landscape ecologists to leverage emerging AI-based tools in research and practice including generating virtual landscapes for simulating processes such as wildfires and leveraging natural language processing to generate new insights from text data. Regardless of the application, researchers using AI tools must also consider the ethical implications of data and algorithmic biases and critically assess how these methods can be used responsibly.

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Landscape generator: Method to generate landscape configurations for spatial plan-making

Cited by 19 publications

References 17 publications

An improved neutral landscape model for recreating real landscapes and generating landscape series for spatial ecological simulations

An improved neutral landscape model for recreating real landscapes and generating landscape series for spatial ecological simulations

References

Artificial Intelligence in Landscape Ecology: Recent Advances, Perspectives, and Opportunities

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