An improved quality assessment framework to better inform large-scale forest restoration management

Dynamic monitoring of forest ecosystem quality is necessary for restoration program evaluation but remains challenging for very large-scale programs. Current evaluation methods employ regional forest quality indicators that compare the quality status of targeted forests with benchmarks from remnant...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Zhaowei Ding, Ruonan Li, Patrick O'Connor, Hua Zheng, Binbin Huang, Lingqiao Kong, Yi Xiao, Weihua Xu, Zhiyun Ouyang
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Regional forest ecosystem quality assessment Forest site classification Classification effectiveness validation Remote sensing method Forestry restoration Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/e920cf6998b04d55b0254755f2c9fc28
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:e920cf6998b04d55b0254755f2c9fc28
record_format	dspace
spelling	oai:doaj.org-article:e920cf6998b04d55b0254755f2c9fc282021-12-01T04:44:29ZAn improved quality assessment framework to better inform large-scale forest restoration management1470-160X10.1016/j.ecolind.2021.107370https://doaj.org/article/e920cf6998b04d55b0254755f2c9fc282021-04-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21000352https://doaj.org/toc/1470-160XDynamic monitoring of forest ecosystem quality is necessary for restoration program evaluation but remains challenging for very large-scale programs. Current evaluation methods employ regional forest quality indicators that compare the quality status of targeted forests with benchmarks from remnant old-growth forest communities, however data availability usually limits the application of available methods to small scales. We constructed an improved framework, integrating forest site classification selection and local remnant old-growth forest community delimitation, to quantify and map forest quality using environmental data and remote sensing (RS) based approaches. A classification strength model was introduced to improve the accuracy of forest site classification. The remote-sensing-based method integrates species composition and forest biological productivity characteristics recognition to develop a practical tool for large-scale remnant old-growth forest community delimitation. The new assessment framework was tested across the entire spatially heterogeneous Yangtze River Basin, the largest watershed in China and showed high accuracy in forest quality assessment based on observed field data validation. The forest site classification was selected by considering spatial heterogeneities in climate, topography and soil type, with 37 forest sites classified. The native forest community groups with less human disturbance in each forest site used for forest quality baseline estimation were also selected as forests with top 10% of biomass in protected areas. The case study demonstrated that forest areas of low and poor quality accounted for 34.46% of the total forest area in 2015. Between 2000 and 2015, 55.72% of forest areas experienced increases in quality level, and 7.07% experienced decreases. The improved forest quality assessment framework enhances the scope and accuracy of forest restoration information and can be applied as an evaluation tool for forest restoration management.Zhaowei DingRuonan LiPatrick O'ConnorHua ZhengBinbin HuangLingqiao KongYi XiaoWeihua XuZhiyun OuyangElsevierarticleRegional forest ecosystem quality assessmentForest site classificationClassification effectiveness validationRemote sensing methodForestry restorationEcologyQH540-549.5ENEcological Indicators, Vol 123, Iss , Pp 107370- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Regional forest ecosystem quality assessment Forest site classification Classification effectiveness validation Remote sensing method Forestry restoration Ecology QH540-549.5
spellingShingle	Regional forest ecosystem quality assessment Forest site classification Classification effectiveness validation Remote sensing method Forestry restoration Ecology QH540-549.5 Zhaowei Ding Ruonan Li Patrick O'Connor Hua Zheng Binbin Huang Lingqiao Kong Yi Xiao Weihua Xu Zhiyun Ouyang An improved quality assessment framework to better inform large-scale forest restoration management
description	Dynamic monitoring of forest ecosystem quality is necessary for restoration program evaluation but remains challenging for very large-scale programs. Current evaluation methods employ regional forest quality indicators that compare the quality status of targeted forests with benchmarks from remnant old-growth forest communities, however data availability usually limits the application of available methods to small scales. We constructed an improved framework, integrating forest site classification selection and local remnant old-growth forest community delimitation, to quantify and map forest quality using environmental data and remote sensing (RS) based approaches. A classification strength model was introduced to improve the accuracy of forest site classification. The remote-sensing-based method integrates species composition and forest biological productivity characteristics recognition to develop a practical tool for large-scale remnant old-growth forest community delimitation. The new assessment framework was tested across the entire spatially heterogeneous Yangtze River Basin, the largest watershed in China and showed high accuracy in forest quality assessment based on observed field data validation. The forest site classification was selected by considering spatial heterogeneities in climate, topography and soil type, with 37 forest sites classified. The native forest community groups with less human disturbance in each forest site used for forest quality baseline estimation were also selected as forests with top 10% of biomass in protected areas. The case study demonstrated that forest areas of low and poor quality accounted for 34.46% of the total forest area in 2015. Between 2000 and 2015, 55.72% of forest areas experienced increases in quality level, and 7.07% experienced decreases. The improved forest quality assessment framework enhances the scope and accuracy of forest restoration information and can be applied as an evaluation tool for forest restoration management.
format	article
author	Zhaowei Ding Ruonan Li Patrick O'Connor Hua Zheng Binbin Huang Lingqiao Kong Yi Xiao Weihua Xu Zhiyun Ouyang
author_facet	Zhaowei Ding Ruonan Li Patrick O'Connor Hua Zheng Binbin Huang Lingqiao Kong Yi Xiao Weihua Xu Zhiyun Ouyang
author_sort	Zhaowei Ding
title	An improved quality assessment framework to better inform large-scale forest restoration management
title_short	An improved quality assessment framework to better inform large-scale forest restoration management
title_full	An improved quality assessment framework to better inform large-scale forest restoration management
title_fullStr	An improved quality assessment framework to better inform large-scale forest restoration management
title_full_unstemmed	An improved quality assessment framework to better inform large-scale forest restoration management
title_sort	improved quality assessment framework to better inform large-scale forest restoration management
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/e920cf6998b04d55b0254755f2c9fc28
work_keys_str_mv	AT zhaoweiding animprovedqualityassessmentframeworktobetterinformlargescaleforestrestorationmanagement AT ruonanli animprovedqualityassessmentframeworktobetterinformlargescaleforestrestorationmanagement AT patrickoconnor animprovedqualityassessmentframeworktobetterinformlargescaleforestrestorationmanagement AT huazheng animprovedqualityassessmentframeworktobetterinformlargescaleforestrestorationmanagement AT binbinhuang animprovedqualityassessmentframeworktobetterinformlargescaleforestrestorationmanagement AT lingqiaokong animprovedqualityassessmentframeworktobetterinformlargescaleforestrestorationmanagement AT yixiao animprovedqualityassessmentframeworktobetterinformlargescaleforestrestorationmanagement AT weihuaxu animprovedqualityassessmentframeworktobetterinformlargescaleforestrestorationmanagement AT zhiyunouyang animprovedqualityassessmentframeworktobetterinformlargescaleforestrestorationmanagement AT zhaoweiding improvedqualityassessmentframeworktobetterinformlargescaleforestrestorationmanagement AT ruonanli improvedqualityassessmentframeworktobetterinformlargescaleforestrestorationmanagement AT patrickoconnor improvedqualityassessmentframeworktobetterinformlargescaleforestrestorationmanagement AT huazheng improvedqualityassessmentframeworktobetterinformlargescaleforestrestorationmanagement AT binbinhuang improvedqualityassessmentframeworktobetterinformlargescaleforestrestorationmanagement AT lingqiaokong improvedqualityassessmentframeworktobetterinformlargescaleforestrestorationmanagement AT yixiao improvedqualityassessmentframeworktobetterinformlargescaleforestrestorationmanagement AT weihuaxu improvedqualityassessmentframeworktobetterinformlargescaleforestrestorationmanagement AT zhiyunouyang improvedqualityassessmentframeworktobetterinformlargescaleforestrestorationmanagement
_version_	1718405798525140992

An improved quality assessment framework to better inform large-scale forest restoration management

Ejemplares similares