Eslami, Asgar, Anvari, Sedigheh, Karimi, Neahmat, Mohamadi, Sedigheh (2022). Application of pixel-based and object-based approaches for LULC mapping in Jiroft region, S.E. Iran. , 10(1), 71-83.
Asgar Eslami; Sedigheh Anvari; Neahmat Karimi; Sedigheh Mohamadi. "Application of pixel-based and object-based approaches for LULC mapping in Jiroft region, S.E. Iran". , 10, 1, 2022, 71-83.
Eslami, Asgar, Anvari, Sedigheh, Karimi, Neahmat, Mohamadi, Sedigheh (2022). 'Application of pixel-based and object-based approaches for LULC mapping in Jiroft region, S.E. Iran', , 10(1), pp. 71-83.
Eslami, Asgar, Anvari, Sedigheh, Karimi, Neahmat, Mohamadi, Sedigheh Application of pixel-based and object-based approaches for LULC mapping in Jiroft region, S.E. Iran. , 2022; 10(1): 71-83.

Application of pixel-based and object-based approaches for LULC mapping in Jiroft region, S.E. Iran

ECOPERSIA
Article 7, Volume 10, Issue 1 - Serial Number 39, 2022, Pages 71-83    PDF (2.53 M)
Document Type: Original Research
Authors
Asgar Eslami1; Sedigheh Anvari* 2; Neahmat Karimi3; Sedigheh Mohamadi4
1Graduate University of Advanced Technology
2Department of Ecology, Institute of Science and High Technology and Environmental Science, Graduate University of Advanced Technology, Kerman, Iran
3Water Research Institute of Ministry of Energy
4Graduate University of Advanced Technology, Kerman
Abstract
Aims Due to increase of demand for industrial and agricultural products, many tropical regions of Iran have experienced landscape changes. The present study aims to detect the land use/land cover (LULC) using some pixel/object-based approaches.



Method This research was conducted in Jiroft area using some pixel-based and object-based image analysing methods (PBIA and OBIA respectively). To this end, at the first phase, the LULC maps were extracted using PBIA for September, 2020. The PBIA are including as Mahalanobis distance (MD), maximum likelihood (ML), neural network (NN), support vector machine (SVM). At the second phase, the LULC was produced using OBIA approach, encompassing the multi-resolution method and decision tree (DT) technique, for segmentation and classification respectively. Using a hybrid methodology, the high-resolution images of Worldview-2 were segmented. The segmented objects were later combined with the 7-month time series of NDVI, to find the necessary thresholds for DT.



Findings Results of the LULC maps demonstrated that the kappa coefficient and overall accuracy for ISODATA, MD, ML, NN, and SVM methods were calculated to be (51%, 66%), (81%, 86%), (88%, 91%), (90%, 93%) and (88% and 92%), respectively. The outcomes of the second phase for mapping the LULC showed the OBIA achieved a high overall accuracy of about 96%.

Conclusion among the PBIA techniques and regarding both accuracy and execution time, the ML was the best. Although both PBIA and OBIA approaches are applicable in mapping LULC, the OBIA significantly outperformed the PBIA classifiers by higher overall accuracy and Kappa statistics
Keywords
Jiroft; Land cover/Land use; pixel/object-based classifiers
References
Statistics
Article View: 7,404
PDF Download: 11,177
Statistics
Number of Journals45
Number of Issues2,171
Number of Articles24,674
Article View24,449,190
PDF Download17,555,703