干旱区生态环境知识资源中心(Arid): Remotely sensed data capacities to assess soil degradation

Arid

DOI	10.1016/j.ejrs.2015.12.001
	Remotely sensed data capacities to assess soil degradation
	Rayegani, Behzad; Barati, Susan; Sohrabi, Tayebeh Alsadat; Sonboli, Bahareh
通讯作者	Rayegani, B
来源期刊	EGYPTIAN JOURNAL OF REMOTE SENSING AND SPACE SCIENCES
ISSN	1110-9823
EISSN	2090-2476
出版年	2016
卷号	19 期号:2 页码:207-222
英文摘要	This research has tried to take advantage of the two-field based methodology in order to assess remote sensing data capacities for modeling soil degradation. Based on the findings of our investigation, preprocessing analysis types have not shown significant effects on the accuracy of the model. Conversely, type of indicators and indices of the used field based model has a large impact on the accuracy of the model. In addition, using some remote sensed indices such as iron oxide index and ferrous minerals index can help to improve modeling accuracy of some field indices of soil condition assessment. According to the results, the model capacities can significantly be improved by using time-series remotely sensed data compared with using single date data. In addition, if artificial neural networks are used on single remotely sensed data instead of multivariate linear regression, accuracy of the model can be increased dramatically because it helps the model to take the nonlinear form. However, if time series of remotely sensed data are used, the accuracy of the artificial neural network modeling is not much different from the accuracy of the regression model. It turned out to be contrary to what is thought, but according to our results, increasing the number of inputs to artificial neural network modeling in practice reduces the actual accuracy of the model. (C) 2015 National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
英文关键词	Iranian Model of Desertification Potential Assessment LADA methodology of land degradation Remote sensed time series Artificial neural networks
类型	Article
语种	英语
开放获取类型	DOAJ Gold
收录类别	ESCI
WOS记录号	WOS:000390912900004
WOS关键词	LAND DEGRADATION ; RISK-ASSESSMENT ; NILE DELTA ; DESERTIFICATION ; GIS ; SALINITY ; SPECTROSCOPY ; RESILIENCE ; INDICATORS ; IMAGERY
WOS类目	Environmental Sciences ; Remote Sensing
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/331703
作者单位	[Rayegani, Behzad; Sonboli, Bahareh] Univ Environm, Dept Environm Sci & Nat Resources, Karaj, Iran; [Barati, Susan] Isfahan Univ Technol, Fac Nat Resources, Esfahan, Iran; [Sohrabi, Tayebeh Alsadat] Kashan Univ, Fac Nat Resources & Earth Sci, Kashan, Iran
推荐引用方式 GB/T 7714	Rayegani, Behzad,Barati, Susan,Sohrabi, Tayebeh Alsadat,et al. Remotely sensed data capacities to assess soil degradation[J],2016,19(2):207-222.
APA	Rayegani, Behzad,Barati, Susan,Sohrabi, Tayebeh Alsadat,&Sonboli, Bahareh.(2016).Remotely sensed data capacities to assess soil degradation.EGYPTIAN JOURNAL OF REMOTE SENSING AND SPACE SCIENCES,19(2),207-222.
MLA	Rayegani, Behzad,et al."Remotely sensed data capacities to assess soil degradation".EGYPTIAN JOURNAL OF REMOTE SENSING AND SPACE SCIENCES 19.2(2016):207-222.