作者:Wu, JJ (Wu, Junjun)[ 1 ] ; Gao, ZH (Gao, Zhihai)[ 2 ] ; Liu, QH (Liu, Qinhuo)[ 1 ] ; Li, ZY (Li, Zengyuan)[ 2 ] ; Zhong, B (Zhong, Bo)[ 1 ]

GEODERMA

卷: 316  页: 89-99

DOI: 10.1016/j.geoderma.2017.12.015

出版年: APR 15 2018

摘要

Sandification is becoming a serious threat to the sustainability of human habitation. The potential of remote sensing in sandy land detection has been previously demonstrated, but transitional sandy land is difficult to detect because of vegetation cover. The aim of this study, therefore, was to propose methods for sandy land detection based on mixed pixel decomposition and soil particle composition to determine the effects of vegetation coverage and transitional sandy land, using Zhenglan Banner of China as the study area and GF-1 multispectral images as the main data. Results showed that the pixel purity index (PPI) is a viable indicator for pure endmember extraction for sandy land detection via remote sensing. A linear spectral unmixing (LSU) model was established to distinguish sandy land coverage from vegetation, alkaline land, etc. Results showed that without considering the vegetation proportion, when the endmember proportion of sandy land accounted for > 50% of the total (except for the vegetation), a pixel would be detected as sandy land, its extraction accuracy was verified to be 86.42% by field data. The results derived from soil particle composition showed that silt was the best indicator for sandy land detection, and clay was secondary to it. Through Partial Least Squares Analysis (PLSA), the percentage of silt content was determined as the dependent variable; 8676 and 8774 were selected as independent variables to establish the inversion model according to the model effect weights and VIP value. The minimum Prediction Residual Error Sum of Squares (PRESS) was 0.824 tested by leave one out cross validation. The threshold of silt content was determined finally as 3.5%, namely, when the silt content was < 3.5%, the pixel would be classified as sandy land, its extraction accuracy was 80.86% that was verified using the same field data.

通讯作者地址: Li, ZY (通讯作者)

Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China.

地址:

[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China

[ 2 ] Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China