Fusing multidimensional hierarchical information into finer spatial landscape metrics

Abstract One of the core issues of ecology is to understand the effects of landscape patterns on ecological processes. For this, we need to accurately capture changes in the fine landscape structures to avoid losing information about spatial heterogeneity. The landscape pattern indicators (LPIs) can...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Gang Fu, Nengwen Xiao, Yue Qi, Wei Wang, Junsheng Li, Caiyun Zhao, Ming Cao, Juyi Xia
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	hierarchy theory information entropy information volume landscape ecology spatial heterogeneity Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/efcebffb2f924fd381f79268892e464a
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:efcebffb2f924fd381f79268892e464a
record_format	dspace
spelling	oai:doaj.org-article:efcebffb2f924fd381f79268892e464a2021-11-08T17:10:41ZFusing multidimensional hierarchical information into finer spatial landscape metrics2045-775810.1002/ece3.8206https://doaj.org/article/efcebffb2f924fd381f79268892e464a2021-11-01T00:00:00Zhttps://doi.org/10.1002/ece3.8206https://doaj.org/toc/2045-7758Abstract One of the core issues of ecology is to understand the effects of landscape patterns on ecological processes. For this, we need to accurately capture changes in the fine landscape structures to avoid losing information about spatial heterogeneity. The landscape pattern indicators (LPIs) can characterize the spatial structures and give some information about landscape patterns. However, researches on LPIs had mainly focused on the horizontal structure of landscape patterns, while few studies addressed vertical relationships between the levels of hierarchical landscape structures. Thus, the ignorance of the vertical hierarchical relationships may cause serious biases and reduce LPIs' representational ability and accuracy. The hierarchy theory about the landscape pattern structures could notably reduce the loss of hierarchical information, and the information entropy could quantitatively describe the vertical status of landscape units. Therefore, we established a new multidimensional fusion method of LPIs based on hierarchy theory and information entropy. Here, we created a general fusion formula for commonly used simple LPIs based on two‐grade land use data (whose land use classification system contains two grades/levels) and derived 3 fusion landscape pattern indicators (FLIs) with a case study. The results show that the information about fine spatial structure is captured by the fusion method. The regions with the most differences between the FLIs and the traditional LPIs are those with the largest vertical structure such as the ecological ecotones, where vertical structure was ignored before. The FLIs have a finer spatial representational ability and accuracy, not only retaining the main trend information of first‐grade land use data, but also containing the internal detail information of second‐grade land use data. Capturing finer spatial information of landscape patterns should encourage the application of fusion method, which should be suitable for more LPIs or more dimensional data. And the increased accuracy of FLIs will improve ecological models that rely on finer spatial information.Gang FuNengwen XiaoYue QiWei WangJunsheng LiCaiyun ZhaoMing CaoJuyi XiaWileyarticlehierarchy theoryinformation entropyinformation volumelandscape ecologyspatial heterogeneityEcologyQH540-549.5ENEcology and Evolution, Vol 11, Iss 21, Pp 15225-15236 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	hierarchy theory information entropy information volume landscape ecology spatial heterogeneity Ecology QH540-549.5
spellingShingle	hierarchy theory information entropy information volume landscape ecology spatial heterogeneity Ecology QH540-549.5 Gang Fu Nengwen Xiao Yue Qi Wei Wang Junsheng Li Caiyun Zhao Ming Cao Juyi Xia Fusing multidimensional hierarchical information into finer spatial landscape metrics
description	Abstract One of the core issues of ecology is to understand the effects of landscape patterns on ecological processes. For this, we need to accurately capture changes in the fine landscape structures to avoid losing information about spatial heterogeneity. The landscape pattern indicators (LPIs) can characterize the spatial structures and give some information about landscape patterns. However, researches on LPIs had mainly focused on the horizontal structure of landscape patterns, while few studies addressed vertical relationships between the levels of hierarchical landscape structures. Thus, the ignorance of the vertical hierarchical relationships may cause serious biases and reduce LPIs' representational ability and accuracy. The hierarchy theory about the landscape pattern structures could notably reduce the loss of hierarchical information, and the information entropy could quantitatively describe the vertical status of landscape units. Therefore, we established a new multidimensional fusion method of LPIs based on hierarchy theory and information entropy. Here, we created a general fusion formula for commonly used simple LPIs based on two‐grade land use data (whose land use classification system contains two grades/levels) and derived 3 fusion landscape pattern indicators (FLIs) with a case study. The results show that the information about fine spatial structure is captured by the fusion method. The regions with the most differences between the FLIs and the traditional LPIs are those with the largest vertical structure such as the ecological ecotones, where vertical structure was ignored before. The FLIs have a finer spatial representational ability and accuracy, not only retaining the main trend information of first‐grade land use data, but also containing the internal detail information of second‐grade land use data. Capturing finer spatial information of landscape patterns should encourage the application of fusion method, which should be suitable for more LPIs or more dimensional data. And the increased accuracy of FLIs will improve ecological models that rely on finer spatial information.
format	article
author	Gang Fu Nengwen Xiao Yue Qi Wei Wang Junsheng Li Caiyun Zhao Ming Cao Juyi Xia
author_facet	Gang Fu Nengwen Xiao Yue Qi Wei Wang Junsheng Li Caiyun Zhao Ming Cao Juyi Xia
author_sort	Gang Fu
title	Fusing multidimensional hierarchical information into finer spatial landscape metrics
title_short	Fusing multidimensional hierarchical information into finer spatial landscape metrics
title_full	Fusing multidimensional hierarchical information into finer spatial landscape metrics
title_fullStr	Fusing multidimensional hierarchical information into finer spatial landscape metrics
title_full_unstemmed	Fusing multidimensional hierarchical information into finer spatial landscape metrics
title_sort	fusing multidimensional hierarchical information into finer spatial landscape metrics
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/efcebffb2f924fd381f79268892e464a
work_keys_str_mv	AT gangfu fusingmultidimensionalhierarchicalinformationintofinerspatiallandscapemetrics AT nengwenxiao fusingmultidimensionalhierarchicalinformationintofinerspatiallandscapemetrics AT yueqi fusingmultidimensionalhierarchicalinformationintofinerspatiallandscapemetrics AT weiwang fusingmultidimensionalhierarchicalinformationintofinerspatiallandscapemetrics AT junshengli fusingmultidimensionalhierarchicalinformationintofinerspatiallandscapemetrics AT caiyunzhao fusingmultidimensionalhierarchicalinformationintofinerspatiallandscapemetrics AT mingcao fusingmultidimensionalhierarchicalinformationintofinerspatiallandscapemetrics AT juyixia fusingmultidimensionalhierarchicalinformationintofinerspatiallandscapemetrics
_version_	1718441530462568448

Fusing multidimensional hierarchical information into finer spatial landscape metrics

Ejemplares similares