DocumentCode :
3461
Title :
Quantifying Contribution of Land Use Types to Nighttime Light Using an Unmixing Model
Author :
Xi Li ; Linlin Ge ; Xiaoling Chen
Author_Institution :
State Key Lab. of Inf. Eng. in Surveying, Wuhan Univ., Wuhan, China
Volume :
11
Issue :
10
fYear :
2014
fDate :
Oct. 2014
Firstpage :
1667
Lastpage :
1671
Abstract :
In this study, a model is developed to quantify the land use contribution to nighttime light using coarse-resolution nighttime light imagery and fine-resolution land use data. We assumed that the nighttime light of a region can be represented by a linear combination of land use areas, with its nighttime light intensity (NLI) as coefficient. Based on an unmixing strategy, the NLI of each land use type was estimated. The Berlin City and MA State were used as study areas. For the Berlin City, we made use of nighttime light imagery from the Suomi National Polar-orbiting Partnership and the land use maps with 52 classes as data sets for analysis, and we used a nighttime aerial photograph to derive reference data. For the MA State, we made use of nighttime light imagery from the Defense Meteorological Satellite Program´s Operational Linescan System and the land use maps with 33 classes as data sets for analysis, and we used a nighttime photograph from the International Space Station to derive reference data. The reference NLI data were correlated with the estimated NLI data, and the R2 values of Berlin and Massachusetts were 0.7277 and 0.7982, respectively, proving that the proposed model is effective.
Keywords :
geophysical image processing; image representation; image resolution; land use; photography; Berlin City; Defense Meteorological Satellite Program; International Space Station; MA State; Massachusetts; NLI; Suomi National Polar-orbiting partnership; coarse-resolution nighttime light imagery; fine-resolution land use data; image representation; land use contribution quantify; nighttime aerial photograph; nighttime light intensity; operational linescan system; unmixing model; Cities and towns; Data models; Image resolution; Remote sensing; Satellites; Sociology; Statistics; Land use; nighttime light; nonnegative least square (NLS); remote sensing; unmixing;
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing Letters, IEEE
Publisher :
ieee
ISSN :
1545-598X
Type :
jour
DOI :
10.1109/LGRS.2014.2304496
Filename :
6747967
Link To Document :
بازگشت