Effects of vegetation and terrain changes on spatial heterogeneity of soil C-N-P in the coastal zone protected forests at northern China.

Coastal zone Geostatistics Soil organic carbon Soil total nitrogen Soil total phosphorus Spatial variability

Journal

Journal of environmental management
ISSN: 1095-8630
Titre abrégé: J Environ Manage
Pays: England
ID NLM: 0401664

Informations de publication

Date de publication:
01 Sep 2022
Historique:
received: 17 01 2022
revised: 30 05 2022
accepted: 30 05 2022
entrez: 25 6 2022
pubmed: 26 6 2022
medline: 29 6 2022
Statut: ppublish

Résumé

Soil organic carbon (SOC), total nitrogen (TN) and total phosphorus (TP) are important indicators reflecting soil quality, and they can be used to effectively evaluate the effect of soil remediation. Many studies have evaluated the content of SOC, TN and TP in different ecosystems. However, after constructing protected forests for ecological restoration in the ecologically fragile coastal zone, the spatial distribution and influencing mechanism of SOC, TN and TP content is still uncertain. In this study, the spatial heterogeneity and influencing factors of SOC, TN and TP in surface (0-20 cm) soil were analyzed by traditional analysis and geostatistics. A total of 39 soil samples were collected under the coastal zone protected forest types including Quercus acutissima Carruth (QAC), Pinus thunbergii Parl (PTP), mixed PTP and QAC (QP) and Castanea mollissima BL (CMB) in the coastal zone protected forests in northern China. The results show that SOC, TN and TP content were defined as moderate variation, and they also show significant changes under different protected forest types (P < 0.05). The semivariance results indicate that SOC, TN and TP all exhibited strong spatial dependence class, with Range of 224 m, 229 m and 282 m respectively, which were more than the sampling scale of 200 m. The spatial prediction results showed that SOC, TN and TP content all appear in large areas of extremely low value in CMB, and its cross validation results showed that using vegetation and terrain factors as covariates in the spatial prediction of SOC, TN and TP can improve the prediction accuracy. The results of correlation analysis showed that the influencing factor for SOC and TN, and TP were NDVI and topographical changes, respectively. In general, vegetation and terrain factors as auxiliary factors can improved the accuracy of soil C-N-P spatial distribution prediction after afforestation in coastal zone.

Identifiants

pubmed: 35751271
pii: S0301-4797(22)01045-3
doi: 10.1016/j.jenvman.2022.115472
pii:
doi:

Substances chimiques

Soil 0
Phosphorus 27YLU75U4W
Carbon 7440-44-0
Nitrogen N762921K75

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

115472

Informations de copyright

Copyright © 2022 Elsevier Ltd. All rights reserved.

Auteurs

Zixu Zhang (Z)

Mountain Tai Forest Ecosystem Research Station of State Forestry and Grassland Administration, Forestry College, Shandong Agricultural University, Tai'an, Shandong, 271018, China.

Ming Hao (M)

Mountain Tai Forest Ecosystem Research Station of State Forestry and Grassland Administration, Forestry College, Shandong Agricultural University, Tai'an, Shandong, 271018, China.

Yongqiang Li (Y)

National Engineering Research Center for Efficient Utilization of Soil and Fertilizer Resources, Shandong Agricultural University, Taian, Shandong, 271018, China. Electronic address: lyqlinda@163.com.

Ziqing Shao (Z)

Mountain Tai Forest Ecosystem Research Station of State Forestry and Grassland Administration, Forestry College, Shandong Agricultural University, Tai'an, Shandong, 271018, China.

Qinghui Yu (Q)

Mountain Tai Forest Ecosystem Research Station of State Forestry and Grassland Administration, Forestry College, Shandong Agricultural University, Tai'an, Shandong, 271018, China.

Yuan He (Y)

Mountain Tai Forest Ecosystem Research Station of State Forestry and Grassland Administration, Forestry College, Shandong Agricultural University, Tai'an, Shandong, 271018, China.

Peng Gao (P)

Mountain Tai Forest Ecosystem Research Station of State Forestry and Grassland Administration, Forestry College, Shandong Agricultural University, Tai'an, Shandong, 271018, China. Electronic address: gaopengy@163.com.

Jingwei Xu (J)

Shandong Academy of Forestry, Ji'nan, Shandong, 250014, China.

Xingjian Dun (X)

Shandong Academy of Forestry, Ji'nan, Shandong, 250014, China.

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