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Prediction and Selection of Appropriate Landscape Metrics and Optimal Scale Ranges Based on Multi-Scale Interaction Analysis

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  • Gang Fu

    (State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
    College of Water Sciences, Beijing Normal University, Beijing 100875, China
    State Environment Protection Key Laboratory of Regional Eco-Processand Function Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China)

  • Wei Wang

    (State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
    State Environment Protection Key Laboratory of Regional Eco-Processand Function Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China)

  • Junsheng Li

    (State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
    College of Water Sciences, Beijing Normal University, Beijing 100875, China
    State Environment Protection Key Laboratory of Regional Eco-Processand Function Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China)

  • Nengwen Xiao

    (State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
    State Environment Protection Key Laboratory of Regional Eco-Processand Function Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China)

  • Yue Qi

    (State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
    State Environment Protection Key Laboratory of Regional Eco-Processand Function Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China)

Abstract

Landscape metrics are widely used in landscape planning and land use management. Understanding how landscape metrics respond with scales can provide more accurate prediction information; however, ignoring the interference of multi-scale interaction may lead to a severe systemic bias. In this study, we quantitatively analyzed the scaling sensitivity of metrics based on multi-scale interaction and predict their optimal scale ranges. Using a big data method, the multivariate adaptive regression splines model (MARS), and the partial dependence model (PHP), we studied the scaling relationships of metrics to changing scales. The results show that multi-scale interaction commonly exists in most landscape metric scaling responses, making a significant contribution. In general, the scaling effects of the three scales (i.e., spatial extent, spatial resolution, and classification of land use) are often in a different direction, and spatial resolution is the primary driving scale in isolation. The findings show that only a few metrics are highly sensitive to the three scales throughout the whole scale spectrum, while the other metrics are limited within a certain threshold range. This study confirms that the scaling-sensitive scalograms can be used as an application guideline for selecting appropriate landscape metrics and optimal scale ranges.

Suggested Citation

  • Gang Fu & Wei Wang & Junsheng Li & Nengwen Xiao & Yue Qi, 2021. "Prediction and Selection of Appropriate Landscape Metrics and Optimal Scale Ranges Based on Multi-Scale Interaction Analysis," Land, MDPI, vol. 10(11), pages 1-21, November.
    • Handle: RePEc:gam:jlands:v:10:y:2021:i:11:p:1192-:d:672846
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    Citations

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    Cited by:

    1. Hebing Zhang & Qingqing Yan & Fangfang Xie & Shouchen Ma, 2023. "Evaluation and Prediction of Landscape Ecological Security Based on a CA-Markov Model in Overlapped Area of Crop and Coal Production," Land, MDPI, vol. 12(1), pages 1-18, January.
    2. Lei Shi & Xu He & Bo Hu & Jiuwei Li & Lei Yu, 2024. "Evaluation of Rice–Crayfish Field Fragmentation Based on Landscape Indices: A Case Study of Qianjiang City, China," Land, MDPI, vol. 13(7), pages 1-17, July.
    3. Karolina Zięba-Kulawik & Piotr Wężyk, 2022. "Monitoring 3D Changes in Urban Forests Using Landscape Metrics Analyses Based on Multi-Temporal Remote Sensing Data," Land, MDPI, vol. 11(6), pages 1-19, June.
    4. Shangkun Yu & Yi Miao & Mengcheng Li & Xiaoming Ding & Chengxin Wang & Wangsheng Dou, 2022. "Theoretical Development Model for Rural Settlements against Rural Shrinkage: An Empirical Study on Pingyin County, China," Land, MDPI, vol. 11(8), pages 1-20, August.
    5. Sonali Sharma & Pawan Kumar Joshi & Christine Fürst, 2022. "Exploring Multiscale Influence of Urban Growth on Landscape Patterns of Two Emerging Urban Centers in the Western Himalaya," Land, MDPI, vol. 11(12), pages 1-23, December.

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