### Incorporating legacy soil data to minimize errors in salinity change detection: a case study of Darab Plain, Iran

Mojtaba Pakparvar UGent, Donald Gabriëls UGent and Wim Cornelis UGent (2012) 33(19). p.6215-6238
abstract
The results of a 1990 soil survey of a salinized region in Darab Plain, southern Iran, were combined with soil sampling data taken in 2002 from the same locations and employed as a basis for salinity change detection in the region. New preprocessing of satellite imagery was used, along with statistical analysis of the digital number (DN)−salinity relationship, in order to determine salinization of the area. Removal of outliers on the basis of interfering land uses improved the correlations. Nonlinear regression (NLR) in the form y = a + bxα provided a suitable predictor of salinity (y, dS m−1) for both 1990 and 2002 based on DNs (x). Among the 12 tested methods of salinity classification in this study, the six salinity class method with intervals 0–4, 4–10, 10–32, 32–64, 64–80 and >80 dSm–1 was selected. A series of accuracy assessments through a trial-and-error procedure was the basis of the selection of the best method and led to a final accuracy of 91%. About 42% of the lands located on ‘no saline’ and ‘low salinity’ classes in 1990 had changed to the ‘medium’, ‘very high’ and ‘new agricultural land’ classes in 2002.
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
CHINA, SALT-AFFECTED SOILS, GIS
journal title
INTERNATIONAL JOURNAL OF REMOTE SENSING
Int. J. Remote Sens.
volume
33
issue
19
pages
6215 - 6238
Web of Science type
Article
Web of Science id
000303587600014
JCR category
IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY
JCR impact factor
1.138 (2012)
JCR rank
10/23 (2012)
JCR quartile
2 (2012)
ISSN
0143-1161
DOI
10.1080/01431161.2012.676688
language
English
UGent publication?
yes
classification
A1
I have transferred the copyright for this publication to the publisher
id
2117798
handle
http://hdl.handle.net/1854/LU-2117798
date created
2012-05-29 15:54:10
date last changed
2012-09-28 14:43:55
@article{2117798,
abstract     = {The results of a 1990 soil survey of a salinized region in Darab Plain, southern Iran, were combined with soil sampling data taken in 2002 from the same locations and employed as a basis for salinity change detection in the region. New preprocessing of satellite imagery was used, along with statistical analysis of the digital number (DN)\ensuremath{-}salinity relationship, in order to determine salinization of the area. Removal of outliers on the basis of interfering land uses improved the correlations.
Nonlinear regression (NLR) in the form y = a + bx\ensuremath{\alpha} provided a suitable predictor of salinity (y, dS m\ensuremath{-}1) for both 1990 and 2002 based on DNs (x). Among the 12 tested methods of salinity classification in this study, the six salinity class method with intervals 0--4, 4--10, 10--32, 32--64, 64--80 and {\textrangle}80 dSm--1 was selected. A series of accuracy assessments through a trial-and-error procedure was the basis of the selection of the best method and led to a final accuracy of 91\%. About 42\% of the lands located on {\textquoteleft}no saline{\textquoteright} and {\textquoteleft}low salinity{\textquoteright} classes in 1990 had changed to the {\textquoteleft}medium{\textquoteright}, {\textquoteleft}very high{\textquoteright} and {\textquoteleft}new agricultural land{\textquoteright} classes in 2002.},
author       = {Pakparvar, Mojtaba and Gabri{\"e}ls, Donald and Aarabi, Kazem and Edraki, Masoud and Raes, Dirk and Cornelis, Wim},
issn         = {0143-1161},
journal      = {INTERNATIONAL JOURNAL OF REMOTE SENSING},
keyword      = {CHINA,SALT-AFFECTED SOILS,GIS},
language     = {eng},
number       = {19},
pages        = {6215--6238},
title        = {Incorporating legacy soil data to minimize errors in salinity change detection: a case study of Darab Plain, Iran},
url          = {http://dx.doi.org/10.1080/01431161.2012.676688},
volume       = {33},
year         = {2012},
}


Chicago
Pakparvar, Mojtaba, Donald Gabriëls, Kazem Aarabi, Masoud Edraki, Dirk Raes, and Wim Cornelis. 2012. “Incorporating Legacy Soil Data to Minimize Errors in Salinity Change Detection: a Case Study of Darab Plain, Iran.” International Journal of Remote Sensing 33 (19): 6215–6238.
APA
Pakparvar, M., Gabriëls, D., Aarabi, K., Edraki, M., Raes, D., & Cornelis, W. (2012). Incorporating legacy soil data to minimize errors in salinity change detection: a case study of Darab Plain, Iran. INTERNATIONAL JOURNAL OF REMOTE SENSING, 33(19), 6215–6238.
Vancouver
1.
Pakparvar M, Gabriëls D, Aarabi K, Edraki M, Raes D, Cornelis W. Incorporating legacy soil data to minimize errors in salinity change detection: a case study of Darab Plain, Iran. INTERNATIONAL JOURNAL OF REMOTE SENSING. 2012;33(19):6215–38.
MLA
Pakparvar, Mojtaba, Donald Gabriëls, Kazem Aarabi, et al. “Incorporating Legacy Soil Data to Minimize Errors in Salinity Change Detection: a Case Study of Darab Plain, Iran.” INTERNATIONAL JOURNAL OF REMOTE SENSING 33.19 (2012): 6215–6238. Print.