干旱区生态环境知识资源中心(Arid): Selecting environmental factors to predict spatial distribution of soil organic carbon stocks, northwestern Iran

Arid

DOI	10.1007/s10661-021-09502-3
	Selecting environmental factors to predict spatial distribution of soil organic carbon stocks, northwestern Iran
	Aqdam, Kamal Khosravi; Mahabadi, Nafiseh Yaghmaeian; Ramezanpour, Hassan; Rezapour, Salar; Mosleh, Zohreh
通讯作者	Aqdam, KK ; Mahabadi, NY (corresponding author), Univ Guilan, Fac Agr Sci, Dept Soil Sci, Rasht, Iran.
来源期刊	ENVIRONMENTAL MONITORING AND ASSESSMENT
ISSN	0167-6369
EISSN	1573-2959
出版年	2021
卷号	193 期号:11
英文摘要	Knowledge of environmental factors controlling soil organic carbon (SOC) stocks can help predict spatial distribution SOC stocks. So, this study was carried out to select the best environmental factors to model and estimate the spatial distribution of SOC stocks in northwestern Iran. Soil sampling was performed at 210 points by multiple conditioned Latin Hypercube method (cLHm) and SOC stocks were measured. Also, environmental factors, including terrain attributes, moisture index, and normalized difference vegetation index (NDVI), were calculated. SOC stocks were modeled using random forest (RF) and partial least squares regression (PLSR) models. Modeling SOC stocks by RF model showed that the efficient factors for estimating the SOC stocks were slope height (slph), terrain surface texture (texture), standardized height (standh), elevation, relative slope position (rsp), and normalized height (normalh). Also, the PLSR model selected standardized height (standh), relative slope position (rsp), slope, and channel network base level (chnl base) to model SOC stocks. In both RF and PLSR methods, the standh and rsp factors were suitable parameters for estimating the SOC stocks. Predicting the spatial distribution of SOC stocks using environmental factors showed that the R-2 values for RF and PLSR models were 0.81 and 0.40, respectively. The result of this study showed that in areas with complex land features, terrain attributes can be good predictors for estimating SOC stocks. These predictors allow more accurate estimates of SOC stocks and contribute considerably to the effective application of land management strategies in arid and semiarid area.
英文关键词	Moisture index Partial least squares regression Random forest Terrain attributes
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000706760000003
WOS关键词	SEQUESTRATION ; TOPOGRAPHY ; MOUNTAINS ; PATTERNS ; REGION ; BASIN
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/363131
作者单位	[Aqdam, Kamal Khosravi; Mahabadi, Nafiseh Yaghmaeian; Ramezanpour, Hassan] Univ Guilan, Fac Agr Sci, Dept Soil Sci, Rasht, Iran; [Rezapour, Salar] Urmia Univ, Fac Agr, Dept Soil Sci, Orumiyeh, Iran; [Mosleh, Zohreh] Agr Res Educ & Extens Org AREEO, Soil & Water Res Inst, Karaj, Iran
推荐引用方式 GB/T 7714	Aqdam, Kamal Khosravi,Mahabadi, Nafiseh Yaghmaeian,Ramezanpour, Hassan,et al. Selecting environmental factors to predict spatial distribution of soil organic carbon stocks, northwestern Iran[J],2021,193(11).
APA	Aqdam, Kamal Khosravi,Mahabadi, Nafiseh Yaghmaeian,Ramezanpour, Hassan,Rezapour, Salar,&Mosleh, Zohreh.(2021).Selecting environmental factors to predict spatial distribution of soil organic carbon stocks, northwestern Iran.ENVIRONMENTAL MONITORING AND ASSESSMENT,193(11).
MLA	Aqdam, Kamal Khosravi,et al."Selecting environmental factors to predict spatial distribution of soil organic carbon stocks, northwestern Iran".ENVIRONMENTAL MONITORING AND ASSESSMENT 193.11(2021).