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

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,...

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Autores principales:	Gang Fu, Wei Wang, Junsheng Li, Nengwen Xiao, Yue Qi
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	landscape patterns landscape indicator LULC spatial heterogeneity scale transfer scaling sensitivity Agriculture S
Acceso en línea:	https://doaj.org/article/ed06da89ff9543f5b1d2cf200d82755d
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spelling	oai:doaj.org-article:ed06da89ff9543f5b1d2cf200d82755d2021-11-25T18:09:34ZPrediction and Selection of Appropriate Landscape Metrics and Optimal Scale Ranges Based on Multi-Scale Interaction Analysis10.3390/land101111922073-445Xhttps://doaj.org/article/ed06da89ff9543f5b1d2cf200d82755d2021-11-01T00:00:00Zhttps://www.mdpi.com/2073-445X/10/11/1192https://doaj.org/toc/2073-445XLandscape 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.Gang FuWei WangJunsheng LiNengwen XiaoYue QiMDPI AGarticlelandscape patternslandscape indicatorLULCspatial heterogeneityscale transferscaling sensitivityAgricultureSENLand, Vol 10, Iss 1192, p 1192 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	landscape patterns landscape indicator LULC spatial heterogeneity scale transfer scaling sensitivity Agriculture S
spellingShingle	landscape patterns landscape indicator LULC spatial heterogeneity scale transfer scaling sensitivity Agriculture S Gang Fu Wei Wang Junsheng Li Nengwen Xiao Yue Qi Prediction and Selection of Appropriate Landscape Metrics and Optimal Scale Ranges Based on Multi-Scale Interaction Analysis
description	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.
format	article
author	Gang Fu Wei Wang Junsheng Li Nengwen Xiao Yue Qi
author_facet	Gang Fu Wei Wang Junsheng Li Nengwen Xiao Yue Qi
author_sort	Gang Fu
title	Prediction and Selection of Appropriate Landscape Metrics and Optimal Scale Ranges Based on Multi-Scale Interaction Analysis
title_short	Prediction and Selection of Appropriate Landscape Metrics and Optimal Scale Ranges Based on Multi-Scale Interaction Analysis
title_full	Prediction and Selection of Appropriate Landscape Metrics and Optimal Scale Ranges Based on Multi-Scale Interaction Analysis
title_fullStr	Prediction and Selection of Appropriate Landscape Metrics and Optimal Scale Ranges Based on Multi-Scale Interaction Analysis
title_full_unstemmed	Prediction and Selection of Appropriate Landscape Metrics and Optimal Scale Ranges Based on Multi-Scale Interaction Analysis
title_sort	prediction and selection of appropriate landscape metrics and optimal scale ranges based on multi-scale interaction analysis
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ed06da89ff9543f5b1d2cf200d82755d
work_keys_str_mv	AT gangfu predictionandselectionofappropriatelandscapemetricsandoptimalscalerangesbasedonmultiscaleinteractionanalysis AT weiwang predictionandselectionofappropriatelandscapemetricsandoptimalscalerangesbasedonmultiscaleinteractionanalysis AT junshengli predictionandselectionofappropriatelandscapemetricsandoptimalscalerangesbasedonmultiscaleinteractionanalysis AT nengwenxiao predictionandselectionofappropriatelandscapemetricsandoptimalscalerangesbasedonmultiscaleinteractionanalysis AT yueqi predictionandselectionofappropriatelandscapemetricsandoptimalscalerangesbasedonmultiscaleinteractionanalysis
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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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