Volume 12, Issue 3 (2010)
JAST 2010, 12(3): 309-320 |
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Mobasheri M R, Chahardoli M, Farajzadeh M. Introducing PASAVI and PANDVI Methods for Sugarcane Physiological Date Estimation, Using ASTER Images. JAST 2010; 12 (3) :309-320
URL: http://jast.modares.ac.ir/article-23-10262-en.html
URL: http://jast.modares.ac.ir/article-23-10262-en.html
1- Remote Sensing Engineering, Khajeh Nasir-o-din Toosi University of Technology, Tehran, Islamic Republic of Iran.
2- Islamic Azad University, Malayer Branch, Islamic Republic of Iran
3- Department of Remote Sensing and GIS, Tarbiat Modares University, Tehran, Islamic Republic of Iran
2- Islamic Azad University, Malayer Branch, Islamic Republic of Iran
3- Department of Remote Sensing and GIS, Tarbiat Modares University, Tehran, Islamic Republic of Iran
Abstract: (5929 Views)
Ecological studies based on field data have shown that vegetation phenology follows a relatively well-defined temporal pattern. This pattern, that is reflecting the cumulative temperature from the date of the beginning of the growth, can be represented by the use of a suitable model. Due to the spatial, temporal, and ecological complexity of these processes a simple method to monitor phenological behavior of the vegetation canopies through remote sensing has proven elusive. Employing ASTER images from different seasons, might make it possible to produce an algorithm for sugarcane phenological date estimation and as well to monitor different stages of the plant growth from cultivation to harvest. For this, a parameter, namely Physiological Date is employed. Based on the field collected data and selected ASTER Images, 133 Regions Of Interest (ROI) having different Phenological Dates (PD) in units of Degree-Days (DDs) were supplied. One hundred of these samples were taken for modeling and another 33 for testing the models. Such indices as NDVI and SAVI along with PDs for the ROIs were calculated. The correlation between these indices and PDs was investigated. This ended up with the introduction of two models of PANDVI and PASAVI respectively based on the use of NDVI and SAVI indices for PD assessment. PANDVI model showed a better correlation with the field recorded data although either of the models can be well enough predictive.
Received: 2010/02/28 | Accepted: 2010/02/28 | Published: 2010/02/28
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