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Mapping and Area Estimation of Mango Orchards of Lucknow Region by Applying Knowledge Based Decision Tree to Landsat 8 OLI Satellite Images

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Title Mapping and Area Estimation of Mango Orchards of Lucknow Region by Applying Knowledge Based Decision Tree to Landsat 8 OLI Satellite Images
Not Available
 
Creator Harish ChandraVerma,, Tasneem Ahmed and Shailendra Rajan
 
Subject Mango, crop cover, classification, decision tree, satellite images
 
Description Not Available
Mango is a very important fruit which is liked by
majority of the population due to its nutritional value and
excellent taste. India is the largest producer of mango in the
world. Accurate information is required for policy decision
making in terms of providing subsidy, area expansion, and crop
insurance planning. Hence, this type of information may be
retrieve through satellite images by using the image classification
techniques, which are playing a crucial role in crop cover
classification, yield prediction and crop monitoring etc.
Classification of optical satellite images is still a challenging task
due to effect of changing atmospheric conditions such as cloud,
snow, haze, dust, fog, and rain etc. In this paper, knowledge based
decision tree classification (DTC) has been proposed to classify
the mango orchards of Lucknow district using multi-temporal
Landsat 8 operational land imager (OLI) images from year 2015
to 2017 and further mango orchard area were also estimated. In
order to develop the DTC, separability analysis for various land
cover classes was carried out on different vegetation indices
namely, normalized difference vegetation index (NDVI), modified
normalized difference water index (MNDWI), and soil adjusted
vegetation index (SAVI). In order to analyze the performance of
DTC, most commonly used satellite image classifiers such as
unsupervised classifier (i.e. ISODATA) and supervised classifier
(i.e. Maximum Likelihood) have been used and it is observed that
the proposed DTC outperformed these traditional classifiers. Also,
accuracy assessment has been carried out to measure the
performance of proposed DTC and it is observed that all of the
three images from 2015 to 2017 are classified with high overall
accuracy, which is ranging from 70.66% to 86.69%. Kappa
Coefficient (KC) for all the three images ranged from 0.65 to 0.83,
which indicates that classified images are highly acceptable for
area estimation.
Not Available
 
Date 2021-08-13T06:14:13Z
2021-08-13T06:14:13Z
2020-01
 
Type Research Paper
 
Identifier 1. Verma, Harish Chandra, Ahmed Tasneem and Rajan Shailendra(2020). Mapping and Area Estimation of Mango Orchards of Lucknow Region by Applying Knowledge Based Decision Tree to Landsat 8 OLI Satellite Images, International Journal of Innovative Technology and Exploring Engineering, 9(3): pp. 3627-3635
2278-3075
http://krishi.icar.gov.in/jspui/handle/123456789/56228
 
Language English
 
Relation Not Available;
 
Publisher Blue Eyes Intelligence Engineering & Sciences Publication