Volume 8, Issue 2 (2020)
ECOPERSIA 2020, 8(2): 97-107 |
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Mirzaei S, Vafakhah M, Pradhan B, Alavi S. An Improved Land Use Classification Scheme Using Multi-Seasonal Satellite Images and Secondary Data. ECOPERSIA 2020; 8 (2) :97-107
URL: http://ecopersia.modares.ac.ir/article-24-37060-en.html
URL: http://ecopersia.modares.ac.ir/article-24-37060-en.html
1- Watershed Management Engineering Department, Natural Resources Faculty, Tarbiat Modares University, Nur, Iran
2- The Centre for Advanced Modelling & Geospatial Information Systems (CAMGIS), Engineering & Information Technology Faculty, University of Technology Sydney, Ultimo, Australia ,vafakhah@modares.ac.ir
3- Energy & Mineral Resources Engineering Department, Sejong University, Seoul, South Korea
4- Forestry Department, Natural Resources Faculty, Tarbiat Modares University, Nur, Iran
2- The Centre for Advanced Modelling & Geospatial Information Systems (CAMGIS), Engineering & Information Technology Faculty, University of Technology Sydney, Ultimo, Australia ,
3- Energy & Mineral Resources Engineering Department, Sejong University, Seoul, South Korea
4- Forestry Department, Natural Resources Faculty, Tarbiat Modares University, Nur, Iran
Abstract: (3387 Views)
Aims: Generally, optical satellite images are used to produce a land use map. Due to spectral mixing, these data can affect the accuracy of land use classifications, especially in areas with diverse vegetation.
Materials & Methods: In the present study, in order to achieve the correct land use classification in a mountainous-forested basin, four Landsat 8 thermal images were used with a few additional information (Normalized Difference Vegetation Index (NDVI), Digital Elevation Model (DEM), slope angle and slope aspect) along with optical data and data of multi-temporal images.
Findings: Results showed that thermal data, slope angle and DEM have a significant role in increasing the accuracy of land use classification, so that they increase the overall accuracy by about 3-10% from late spring to the beginning of autumn. Among the data used, slope angle and elevation data have a significant role in increasing the accuracy of agricultural classes. The total accuracy and Kappa coefficient in land use maps obtained from monotemporal images in the wet season (late spring; 83.93 and 0.82) and early summer (83.79 and 0.81)) are more than the dry season (late summer; 81.25 and 0.79) and early autumn).
Conclusion: Generally, the highest total accuracy among monotemporal images generated from optical data is about 83.95%, while the application of thermal and additional data along with optical data and the combination of monotemporal images of the wet season, the accuracy of the information multitemporal increased to 91.60% of the land use map.
Materials & Methods: In the present study, in order to achieve the correct land use classification in a mountainous-forested basin, four Landsat 8 thermal images were used with a few additional information (Normalized Difference Vegetation Index (NDVI), Digital Elevation Model (DEM), slope angle and slope aspect) along with optical data and data of multi-temporal images.
Findings: Results showed that thermal data, slope angle and DEM have a significant role in increasing the accuracy of land use classification, so that they increase the overall accuracy by about 3-10% from late spring to the beginning of autumn. Among the data used, slope angle and elevation data have a significant role in increasing the accuracy of agricultural classes. The total accuracy and Kappa coefficient in land use maps obtained from monotemporal images in the wet season (late spring; 83.93 and 0.82) and early summer (83.79 and 0.81)) are more than the dry season (late summer; 81.25 and 0.79) and early autumn).
Conclusion: Generally, the highest total accuracy among monotemporal images generated from optical data is about 83.95%, while the application of thermal and additional data along with optical data and the combination of monotemporal images of the wet season, the accuracy of the information multitemporal increased to 91.60% of the land use map.
Article Type: Original Research |
Subject:
Ecosystem Management, Monitoring, Policy and Law
Received: 2019/10/8 | Accepted: 2019/12/7 | Published: 2020/05/19
Received: 2019/10/8 | Accepted: 2019/12/7 | Published: 2020/05/19
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