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Mansourmoghaddam, M., Rousta, I., Ghafarian Malamiri, H.R.. (1401). Evaluation of the classification accuracy of NDVI index in the preparation of land cover map. , 27(2), 329-341. doi: 10.22059/jdesert.2022.90834
M. Mansourmoghaddam; I. Rousta; H.R. Ghafarian Malamiri. "Evaluation of the classification accuracy of NDVI index in the preparation of land cover map". , 27, 2, 1401, 329-341. doi: 10.22059/jdesert.2022.90834
Mansourmoghaddam, M., Rousta, I., Ghafarian Malamiri, H.R.. (1401). 'Evaluation of the classification accuracy of NDVI index in the preparation of land cover map', , 27(2), pp. 329-341. doi: 10.22059/jdesert.2022.90834
Mansourmoghaddam, M., Rousta, I., Ghafarian Malamiri, H.R.. Evaluation of the classification accuracy of NDVI index in the preparation of land cover map. , 1401; 27(2): 329-341. doi: 10.22059/jdesert.2022.90834

Evaluation of the classification accuracy of NDVI index in the preparation of land cover map

Desert
دوره 27، شماره 2، اسفند 2022، صفحه 329-341    اصل مقاله (1.07 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jdesert.2022.90834
نویسندگان
M. Mansourmoghaddam1؛ I. Rousta2؛ H.R. Ghafarian Malamiri* 2
1Center for Remote Sensing and GIS studies, Shahid Beheshti University, Tehran, Iran
2Department of Geography, Yazd University, Yazd, Iran
چکیده
The preparation of land cover maps provides the possibility of studying the impact of land surface changes on sustainable development and is significant for a wide range of important issues at the global level. The current research aims to facilitate the preparation of land cover maps using the classification of Normalized Difference Vegetation Index (NDVI) values ​​and prepare land cover maps from it. For this purpose, first, two complete consecutive Landsat-8 scenes of parts of Iran and Turkmenistan were selected for August 30, 2021. Then the images were classified using supervised classification algorithms including Neural Network Classification (NNC), maximum Likelihood Classification (MLC), Support Vector Machine (SVM), Minimum Distance (MinD) and Mahalanobis Distance (MahD). In the next step, to perform an evaluation, by using a thousand ROI for a test, the overall accuracy, kappa coefficient, user accuracy and producer accuracy of the map produced by each of the algorithms were calculated. Then, using the most optimal algorithm, the threshold of NDVI image values ​​was extracted in order to classify it and the obtained map was re-evaluated for accuracy. Among the evaluated algorithms, the MLC algorithm had the most optimal performance with a kappa coefficient of 0.75 and overall accuracy of 80.86%. The results of evaluating the accuracy of the NDVI Based land cover Classification (NBC) index also indicated that this map has extracted the land cover map with an overall accuracy of 83% and a Kappa coefficient of 0.77. This index showed good performance in the classification of Bare Land Class (BLC), Water Area Class (WAC) and Salt Marsh Class (SMC) with user accuracy and producer accuracy above 94%. This is while the Agricultural Land Class (ALC) and Vegetation Class (VC) were classified by this index with producer accuracy of above 73% and user accuracy of 69% and 97%, respectively. The results of this research indicate the acceptable accuracy of NDVI index values ​​for the production of natural land cover maps and can be used in order to prepare these maps for geographic monitoring and achieving sustainable environmental development.
کلیدواژه‌ها
Landsat 8؛ Threshold؛ Normalized Difference Vegetation Index؛ Satellite Image Classification
مراجع
References 
 
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