Journal of Geo-information Science >
Using Decision Tree Model to Extract Paddy Rice Information from Multi-temporal TM Images
Received date: 2012-12-26
Revised date: 2013-03-11
Online published: 2013-06-17
Remote sensing images have been widely used in extracting and studying paddy rice information and its spatial distribution, which is highly important in land cover analysis, grain structural adjustment and prices making. But studies using multi-temporal TM images to extract small-scale rice information were few. Based on that rice favors growing on wet land, this paper selected three bands of TM images, the short-wave infrared band (1.55-1.75um) which can reflect plant water content and soil moisture, the red band (0.62-0.69um) and the near infrared band (0.76-0.96um), both of which can reflect vegetation coverage and growing condition, to compute the NDVI and LSWI of rice paddles in the three different periods: transplanting stage, heading stage, and maturing stage. Given the two indices demonstrate different characteristics during rice's different growing stages, the paper developed a corresponding time-series-based decision tree model for rice information extraction. A case study using this model was performed in the southern Luannan County, Tangshan City. Been field validated, the experiment results showed the effectiveness of the decision model in distinguishing rice paddles from water area, corn land, farm land and other similar land features, and the producer accuracy and the user accuracy are 95.18% and 98.84%, which outperform the single-temporal results by 6.78% and 7.54% respectively.
SHU Liang, BENG Bo, SU Fen-Zhen, DU Yun-Yan, SU Wei-Guang . Using Decision Tree Model to Extract Paddy Rice Information from Multi-temporal TM Images[J]. Journal of Geo-information Science, 2013 , 15(3) : 446 -451 . DOI: 10.3724/SP.J.1047.2013.00446
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