Enhancing the Accuracy of Land Use/Cover Map Using Some Spectral Indices in Sarab County–East Azerbaijan

آب و خاک

Volume 38, Issue 5 - Serial Number 97, November and December 2024, Pages 605-591   

Document Type: Research Article

DOI: 10.22067/jsw.2024.88043.1410

Authors

A. Sarabchi; H. Rezaei^* ; F. Shahbazi

Soil Science Department, Faculty of Agriculture, University of Tabriz, Tabriz

Abstract

Introduction
High-resolution satellite imagery data is widely utilized for Land Use/Land Cover (LULC) mapping. Analyzing the patterns of LULC and the data derived from changes in land use caters to the increasing societal demands, improving convenience, and fostering a deeper comprehension of the interaction between human activities and environmental factors. Although numerous studies have focused on remote sensing for LULC‎ mapping, there is a pressing need to improve the quality of LULC maps to achieve sustainable land management, especially in light of recent advancements made. This study was carried out in an area covering approximately 8000 hectares, characterized by diverse conditions in LULC, geomorphology and pedology. The objective was to investigate the potential for achieving maximum differentiation and accurate mapping of land features related to LULC. Additionally, the study assessed the impact of various spectral indices on enhancing the results from the classification of Landsat 8 imagery, while also evaluating the efficacy of support vector machine (SVM) and maximum likelihood algorithms in producing maps with satisfactory accuracy and precision.
 
Materials and Methods
As an initial step, LULC features were identified through fieldwork, and their geographic coordinates were recorded using GPS. These features included various types of LULC, soil surface characteristics, and landform types. Following the fieldwork, 12 types of LULC units were identified. Subsequently, the LULC pattern in the study area was classified using the RGB+NIR+SWIR1 bands of Landsat 8, employing both SVM and maximum likelihood classifiers. To assess the impact of various spectral indices on improving the accuracy of the LULC maps, a set of vegetation indices (NDVI, SAVI, LAI, EVI, and EVI2), bare soil indices (BSI, BSI3, MNDSI, NBLI, DBSI, and MBI), and integrated indices (TLIVI, ATLIVI, and LST), and digital elevation model of study area were successively incorporated into the classification algorithms. Finally, the outcomes from the two classification algorithms were compared, taking into account the influence of the applied indexes. The classification process continued with the selected classifier and indices until reaching the maximum overall accuracy and kappa coefficient.
 
Results and Discussion
Field observations revealed that the study area could be categorized into 12 primary LULC units, including irrigated farms, flow farming, dry farming, traditional gardens (with no evident order observed among planted trees), modern gardens (featuring regular rows where soil reflectance is visible between tree rows), grasslands, degraded grasslands, highland pastures (covered by Astragalus spp., dominantly), lowland pastures (covered by halophyte plants), salt domes (with no or very poor vegetation), outwash areas (River channel with many waterways), and resistant areas. The results of image classification indicated that the performance of the SVM algorithm across different band combinations is superior to that of the maximum likelihood method. Using SVM resulted in an increase in overall accuracy and Kappa coefficient by 3-8% and 0.03-0.08, respectively. For the map generated using RGB+NIR+SWIR1 bands and employing SVM, overall accuracy and Kappa coefficient were determined to be 76.6% and 0.72, respectively. Among the vegetation indices used in the SVM algorithm, LAI had the most significant impact, increasing the classification accuracy by 2.64%. Among the soil indices, BSI and MBI indices demonstrated the best performance; with BSI increasing the classification accuracy by 1.95% and MBI by 1.64%. Among the integrated indices, LST and ALTIVI enhanced the classification accuracy by 2.75% and 2.35%, respectively. It should be noted that the inclusion of the digital elevation model did not significantly improve the classification accuracy when using the support vector machine algorithm; in fact, it led to a decrease in accuracy when applied to the maximum likelihood classification. The probable reason for this issue is the different nature of DEM data compared to the other input data, as well as the limitations of parametric statistical approaches to effectively integrating data from diverse sources. Finally, the classification process was executed using the three visible bands, NIR, and SWIR1, in conjunction with selected indices (LAI, BSI, MBI, LST, and ALTIVI). Results indicated that using these spectral indices significantly improved classification accuracy, particularly for the DF, DGL, MG, O, and IF land cover/use classes. The calculated accuracies for these classes increased by 11.62%, 18.57%, 20.06%, 29.39%, and 33.19% respectively. Consequently, the accuracy of the classification and the Kappa coefficient (using support vector machine algorithm) increased to 85.24% and 0.82, respectively.
 
Conclusion
In this research, we aimed to accurately map various land use/land covers by utilizing Landsat 8 imagery and incorporating three group of spectral indexes. Despite spectral interferences and overlaps among various phenomena related to LULC, the utilization of different spectral indices resulted in significant differentiation among LULC classes. Finally, considering the limitations of modelling in ENVI software, it is recommended to investigate the effectiveness of other models for classification in more specialized software, such as R.

Keywords

Land sustainable management; Maximum likelihood; Support vector machine

References

1. Al-doski, J., Mansorl Sh.B., & Shafri, H.Z.M. (2013). Image classification in remote sensing. Journal of Environment and Earth Science, 3(10), 141-47. https://doi.org/10.1088/1755-1315/540/1/012022
2. Amirantekabi, S., Javan, F., & Hasani Moghaddam, H. (2017). Detection of land use changes and its influencing factors using Artificial Neural Network (Case study: Talesh County). Journal of GIS & RS Application in Planning, 8(3), 1-11. (In Persian with English abstract)
3. Amiri, F. (2023). A review of remote sensing vegetation indices in the land cover assessment. Water and Soil Management and Modeling, 3(2), 297-318. (In Persian with English abstract). https://doi.org/10.22098/MMWS.2023.12207.1212
4. Amiri, F., & Nateghi, S. (2023). Lands cover classification of Bushehr Province using landsat-8 and MODIS images. Water and Soil Management and Modelling, 3(2), 143-156. (In Persian with English abstract). https://doi.org/10.22098/mmws.2022.11372.1124
5. Beg, A.A.F., & Reddy, Y.S. (2010). Estimation of urban heat island using Landsat ETM+ imagery at Chennai city – a case study. International Journal of Earth Sciences and Engineering, 3(3), 332-340.
6. Banaei, M.H. (1998). Soil Moisture and Temperature Regime Map of Iran. Soil and Water Research Institute, Ministry of Agriculture, Iran.
7. Dehghani, T., Ahmadpari, H., & Amini, A. (2022). Assessment of land use changes using multispectral satellite images and artificial neural network. Water and Soil Management and Modeling, 3(2), 18-35. (In Persian with English abstract). https://doi.org/10.22098/mmws.2022.11279.1114
8. Diek, S., Fornallaz, F., Schaepman, M.E., & De Jong, R. (2017). Barest pixel composite for agricultural areas using landsat time series. Remote Sensing, 9(12), 1245. https://doi.org/10.3390/rs9121245
9. Esfandeh, S., Danehkar, A., Salmanmahiny, A., Sadeghi, S.M.M., & Marcu, M.V. (2021). Climate change risk of urban growth and land use/land cover conversion: An in-depth review of the recent research in Iran. Sustainability, 14(1), 338. https://doi.org/10.3390/su14010338
10. Ghoodjani, A. (2016). Advanced statistical methods and applications. Jameh Negar Publication. (In Persian)
11. Gitelson, A.A., Merzlyak, M.N., & Chivkunova, O.B. (2001). Optical properties and nondestructive estimation of anthocyanin content in plant leaves. Photochemistry and Photobiology, 74(1), 38-45.
12. Hasani Moghaddam, H., Adli Atiq, R., Gholami, J., Abasi Ghadim, A., & Zeaiean Firouz Abadi, P. (2018). Performance analysis of support vector machine, neural network and maximum likelihood in land use/cover mapping and GIS (A Case Study: Namin County).1-13. In 2nd International Conference on New Horizons in the Engineering Science, 9 August 2018. Yildiz Technical University, Istanbul, Turkey.
13. Hosseini, S.B., Saremi, A., Noori Gheydari, M.H., Sedghi, H., & Firoozfar, A.R. (2020).Land use classification and determining the pattern of changes for 2014-2017, using OLI sensor’s data. Journal of Water and Soil, 34(1), 55-71. (In Persian with English abstract). https://doi.org/10.22067/jsw.v34i2.74878
14. Huete, A.R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295-309.
15. Huete, A.R., Liu, H.Q., Batchily, K., & van Leeuwen, W.J.D. (1997). A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing Environment, 59, 440-451.
16. (2021). Country Climate Analysis. In: Islamic Republic of Iran Meteorological Organization, Sarab center. Data sheet.
17. Jamalabad, M.S., & Abkar, A.A. (2004). Forest canopy density monitoring, using satellite images.1-6. In XXth Proceedings of the International Society for Photogrammetry and Remote Sensing (ISPRS), 12-23 July 2004. ISPRS Congress: Istanbul, Turkey.
18. Jensen, J. (2005). Introductory Digital Image Processing. Prentice Hall.
19. Koroleva, P.V., Rukhovich, D.I., Rukhovich, A.D., Rukhovich, D.D., Kulyanitsa, A.L., Trubnikov, A.V., Kalinina, N.V., & Simakova, M.S. (2018). Characterization of soil types and subtypes in n-dimensional space of multitemporal (empirical) soil line. Eurasian Soil Science, 51(9), 1021-33. https://doi.org/10.1134/S1064229318090065
20. Li, H., Wang, C., Zhong, C., Su, A., Xiong, C., Wang, J., & Liu, J. (2017). Mapping urban bare land automatically from landsat imagery with a simple index. Remote Sensing, 9, 700. https://doi.org/10.3390/rs9030249
21. Nguyen, C.T., Chidthaisong, A., Diem, P.K., & Huo, L. (2021). A modified bare soil index to identify bare land features during agricultural fallow-period in Southeast Asia using landsat 8. Land, 10(3), 231. https://doi.org/10.3390/land10030231
22. Niazi, Y., Ekhtesasi, M.R., Maleki Nejad, H., Morshedi, J., & Hoseyni, S.Z. (2011). Comparison between two classification methods of maximum likelihood and artificial neural network for providing land use maps, Case study: Ilam dam area. Geoghraphy and Development, 8(20), 119-132. (In Persian with English abstract). https://doi.org/10.22111/gdij.2010.633
23. Nouri, A., Omidvar, J., Modaresi, F., Davari, K., Nouri, S., & Asadi, A. (2024). Estimating the changes in the agricultural lands using satellite images, Case study: Fariman dam downstream basin. Journal of Water and Soil, 37(6), 829-840. (In Persian with English abstract). https://doi.org/10.22067/jsw.2023.83163.1301
24. Ojaghi, S., Ebadi, H., & Ahmadi, F. (2015). Using artificial neural network for classification of high resolution remotely sensed images and assessment of its performance compared with statistical methods. American Journal of Engineering, Technology and Society, 2(1), 1-8.
25. Piyoosh, A.K., & Ghosh, S.K. (2018). Development of a modified bare soil and urban index for landsat 8 satellite data. Geocarto International, 33(4), 423-442. https://doi.org/10.1080/10106049.2016.1273401
26. Qian, X., & Zhang, L. (2022). An integration method to improve the quality of global land cover. Advances in Space Research, 69(3), 1427-38. https://doi.org/10.1016/j.asr.2021.11.002
27. Radoux, J., Chomé, G., Jacques, D.C., Waldner, F., Bellemans, N., Matton, N., & Defourny, P. (2016). Sentinel-2’s potential for sub-pixel landscape feature detection. Remote Sensing, 8(6), 488. https://doi.org/10.3390/rs8060488
28. Rasul, A., Balzter, H., Ibrahim, G.R.F., Hameed, H.M., Wheeler, J., Adamu, B., Ibrahim, S., & Najmaddin, P.M. (2018). Applying built-up and bare-soil indices from landsat 8 to cities in dry climates. Land, 7, 81. https://doi.org/10.3390/land7030081
29. Rouse, J.J., Haas, R.H., Deering, D., Schell, J., & Harlan, J.C. (1974). Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. NASA Special Publication 351, 309.
30. Rujoiu-Mare, M.R., & Mihai, B.A. (2016). Mapping land cover using remote sensing data and GIS techniques: A case study of Prahova Subcarpathians. Procedia Environmental Sciences, 32, 244-55. https://doi.org/10.1016/j.proenv.2016.03.029
31. Saraskanrood, S., Khodabandelo, B., Naseri, A., & Moradi, A. (2019). Extracting land use map based on a comparison between pixel-based and object-oriented classification methods, Case study: Zanjan city. Scientific Research Quarterly of Geographical Data, 28(110), 195-208. (In Persian with English abstract). https://doi.org/10.22131/sepehr.2019.36623
32. Sinha, S., Sharma, L.K., & Nathawat, M.S. (2015). Improved land-use/land cover classification of semi-arid deciduous forest landscape using thermal remote sensing. The Egyptian Journal of Remote Sensing and Space Science, 18(2), 217-33. http://dx.doi.org/10.1016/j.ejrs.2015.09.005
33. Yadav, P., Kapoor, M., & Sarma, K. (2012). Land use land cover mapping, change detection and conflict analysis of Nagzira-Navegaon corridor, central India using geospatial technology. International Journal of Remote Sensing and GIS, 1(2), 90-98.
34. Yao, Y., Yan, X., Luo, P., Liang, Y., Ren, Sh., Hu, Y., Han, J., & Guan, Q. (2022). Classifying land-use patterns by integrating time-series electricity data and high-spatial resolution remote sensing imagery. International Journal of Applied Earth Observation and Geoinformation, 106(102664), 1-11. https://doi.org/10.1016/j.jag.2021.102664
35. Yousefi, S., Tazeh, M., Mirzaee, S., Moradi, H., & Tavangar, S. (2011). Comparison of different classification algorithms in satellite imagery to produce land. Journal of Applied RS & GIS Techniques in Natural Resource Science, 2(2), 15-24. (In Persian with English abstract)
36. Zinck, J.A., Metternicht, G., Bocco, G., & Del Valle, H.F. (2016). Geopedology (An Integration of Geomorphology and Pedology for Soil and Landscape Studies). Springer Cham.