Environmental Monitoring of Land Use/ Land Cover by Integrating Remote Sensing and Machine Learning Algorithms


  • Firas Aljanabi Soil Science and Plant Nutrition Department, College of Agriculture, Selcuk University, Konya, Turkey Author https://orcid.org/0000-0002-5122-3699
  • Mert Dedeoğlu Soil Science and Plant Nutrition Department, College of Agriculture, Selcuk University, Konya, Turkey Author https://orcid.org/0000-0001-8611-3724
  • Cevdet Şeker Soil Science and Plant Nutrition Department, College of Agriculture, Selcuk University, Konya, Turkey Author https://orcid.org/0000-0002-8760-6990




Geographic information system, Remote sensing, Land use/ land cover, Machine learning, Random trees, Support vector machine


Evaluation of the land use/ land cover (LULC) case over large regions is very important in a variety of domains, including natural resources such as soil, water, etc., and climate change risks and LULC change has emerged as a high anxiety for the environment. Therefore, we tested and compared the performance of three classification algorithms: Support Vector Machines (SVM), Random Trees (RT), and Maximum Likelihood (MaxL) to derive and extract LULC information for the district of Sarayönü/ Konya across five distinct classes: water, plantation, grassland, built-up, and bare land. Two remote sensing indices, the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI), were used as supplementary inputs for the classification of LULC. To evaluate the performance of the algorithms, a confusion matrix was employed. The average overall accuracy of support vector machines, random trees, and maximum likelihood algorithms was found 85.60%, 79.20%, and 74.80%, respectively, and 82.00%, 74.00%, and 68.50% for the Kappa coefficient. These results indicate that the support vector machines algorithm outperforms other algorithms in terms of accuracy. As a result of the research, it was determined that classification algorithms integrated with remote sensing in LULC change monitoring/determination could produce accurate classification maps that can be used as base data. This is due to the ability of machine learning algorithms to learn complex patterns, adapt to diverse data, and continuously improve, making them achieve higher accuracy compared to traditional classifiers. Therefore, their use was recommended for decision-makers.


K. N. Loukika, V. R. Keesara, and V. Sridhar, "Analysis of Land Use and Land Cover Using Machine Learning Algorithms on Google Earth Engine for Munneru River Basin, India," Sustainability, vol. 13, no. 24, p. 13758, 2021, doi: https://doi.org/10.3390/su132413758.

S. Talukdar and S. Pal, "Effects of damming on the hydrological regime of Punarbhaba river basin wetlands," Ecological Engineering, vol. 135, pp. 61-74, 2019, doi: https://doi.org/10.1016/j.ecoleng.2019.05.014.

Y. Zhang, T. Ge, W. Tian, and Y.-A. Liou, "Debris flow susceptibility mapping using machine-learning techniques in Shigatse area, China," Remote Sensing, vol. 11, no. 23, p. 2801, 2019, doi: https://doi.org/10.3390/rs11232801.

J.-F. Mas, R. Lemoine-Rodríguez, R. González-López, J. López-Sánchez, A. Piña-Garduño, and E. Herrera-Flores, "Land use/land cover change detection combining automatic processing and visual interpretation," European Journal of Remote Sensing, vol. 50, no. 1, pp. 626-635, 2017, doi: https://doi.org/10.1080/22797254.2017.1387505.

D. Dutta, A. Rahman, S. Paul, and A. Kundu, "Changing pattern of the urban landscape and its effect on land surface temperature in and around Delhi," Environmental monitoring and assessment, vol. 191, pp. 1-15, 2019, doi: https://doi.org/10.1007/s10661-019-7645-3.

S. A. Al-Tamimi, "The Role of Urban Sprawl on Agricultural Uses of Land Surrounding the City of Baghdad," Journal of Engineering and Sustainable Development, vol. 18, no. 6, pp. 19-44, 2014. [Online]. Available: https://jeasd.uomustansiriyah.edu.iq/index.php/jeasd/article/view/874.

A. D. Abed, "Use of Geographic Information Systems in Controlling Land Use (Study Area -Samarra City)," Journal of Engineering and Sustainable Development, vol. 23, no. 5, 2019, doi: https://doi.org/10.31272/jeasd.23.5.16.

Shahfahad, B. Kumari, M. Tayyab, H. T. Hang, M. F. Khan, and A. Rahman, "Assessment of public open spaces (POS) and landscape quality based on per capita POS index in Delhi, India," SN Applied Sciences, vol. 1, pp. 1-13, 2019, doi: https://doi.org/10.1007/s42452-019-0372-0.

A. Rahman et al., "Performance of different machine learning algorithms on satellite image classification in rural and urban setup," Remote Sensing Applications: Society and Environment, vol. 20, p. 100410, 2020, doi: https://doi.org/10.1016/j.rsase.2020.100410.

V. Sridhar and K. A. Anderson, "Human-induced modifications to land surface fluxes and their implications on water management under past and future climate change conditions," Agricultural and Forest Meteorology, vol. 234-235, pp. 66-79, 2017, doi: https://doi.org/10.1016/j.agrformet.2016.12.009.

A. M. Abdi, "Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data," GIScience & Remote Sensing, vol. 57, no. 1, pp. 1-20, 2020, doi: https://doi.org/10.1080/15481603.2019.1650447.

S. Talukdar et al., "Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review," Remote Sensing, vol. 12, no. 7, p. 1135, 2020, doi: https://doi.org/10.3390/rs12071135.

A. Schneider, "Monitoring land cover change in urban and peri-urban areas using dense time stacks of Landsat satellite data and a data mining approach," Remote Sensing of Environment, vol. 124, pp. 689-704, 2012, doi: https://doi.org/10.1016/j.rse.2012.06.006.

D. ARIKAN and F. YILDIZ, "Sentinel-2 Uydu Görüntülerinde Destek Vektör Makinesi ve Rastgele Orman Algoritmaları Kullanılarak Piksel Tabanlı Arazi Sınıflandırması," Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol. 6, no. 2, pp. 1243-1260, 2023. [Online]. Available: https://dergipark.org.tr/en/pub/okufbed/issue/78780/1123426.

Y. Zhang, G. Wang, F.-l. Chung, and S. Wang, "Support vector machines with the known feature-evolution priors," Knowledge-Based Systems, vol. 223, p. 107048, 2021, doi: https://doi.org/10.1016/j.knosys.2021.107048.

L. Breiman, "Random forests," Machine learning, vol. 45, pp. 5-32, 2001, doi: https://doi.org/10.1023/a:1010933404324.

S. A. Woznicki, J. Baynes, S. Panlasigui, M. Mehaffey, and A. Neale, "Development of a spatially complete floodplain map of the conterminous United States using random forest," Science of the total environment, vol. 647, pp. 942-953, 2019, doi: https://doi.org/10.1016/j.scitotenv.2018.07.353.

M. A. Z. Aguilera, "Classification Of Land-Cover Through Machine Learning Algorithms For Fusion Of Sentinel-2a And Planetscope Imagery," in 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS), 2020: IEEE, pp. 246-253, doi: https://doi.org/10.1109/lagirs48042.2020.9165632.

E. A. Freeman, G. G. Moisen, J. W. Coulston, and B. T. Wilson, "Random forests and stochastic gradient boosting for predicting tree canopy cover: comparing tuning processes and model performance," Canadian Journal of Forest Research, vol. 46, no. 3, pp. 323-339, 2016, doi: https://doi.org/10.1139/cjfr-2014-0562.

Q. Guo et al., "Urban tree classification based on object-oriented approach and random forest algorithm using unmanned aerial vehicle (uav) multispectral imagery," Remote Sensing, vol. 14, no. 16, p. 3885, 2022, doi: https://doi.org/10.3390/rs14163885.

S. a. Ibrahim, "Improving land use/cover classification accuracy from random forest feature importance selection based on synergistic use of sentinel data and digital elevation model in agriculturally dominated landscape," Agriculture, vol. 13, no. 1, p. 98, 2022, doi: https://doi.org/10.3390/agriculture13010098.

J. Svoboda, P. Štych, J. Laštovička, D. Paluba, and N. Kobliuk, "Random Forest Classification of Land Use, Land-Use Change and Forestry (LULUCF) Using Sentinel-2 Data—A Case Study of Czechia," Remote Sensing, vol. 14, no. 5, p. 1189, 2022, doi: https://doi.org/10.3390/rs14051189.

P. Prasad, V. J. Loveson, P. Chandra, and M. Kotha, "Evaluation and comparison of the earth observing sensors in land cover/land use studies using machine learning algorithms," Ecological Informatics, vol. 68, p. 101522, 2022, doi: https://doi.org/10.1016/j.ecoinf.2021.101522.

I. Alimuddin, "The application of Sentinel 2B satellite imagery using Supervised Image Classification of Maximum Likelihood Algorithm in Landcover Updating of The Mamminasata Metropolitan Area, South Sulawesi," in IOP Conference Series: Earth and Environmental Science, 2019, vol. 280, no. 1: IOP Publishing, p. 012033, doi: https://doi.org/10.1088/1755-1315/280/1/012033.

M. F. Baig, M. R. U. Mustafa, H. binti Takaijudin, and M. T. Zeshan, "Comparative analysis of support vector machine and maximum likelihood classifications using satellite images of Selangor, Malaysia," in 2021 Third International Sustainability and Resilience Conference: Climate Change, 2021: IEEE, pp. 405-409, doi: https://doi.org/10.1109/IEEECONF53624.2021.9668109.

H. M. Salih and N. M. Salih, "The implementation of image classification and analysis of Mrsd using three different classifiers: a Case Study of Newcastle-UK," in Published in the Journal of the Second Engineering Scientific Conference of the College of Engineering/University of Diyala. For the period from, 2015, vol. 16, pp. 521-537. [Online]. Available: https://djes.info/index.php/djes/article/view/346.

R. Sivagami, R. Krishankumar, and K. Ravichandran, "A Comparative Analysis of Supervised Learning Techniques for Pixel Classification in Remote Sensing Images," in 2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 2018: IEEE, pp. 1-4, doi: https://doi.org/10.1109/wispnet.2018.8538518.


M. ALTUN and M. TÜRKER, "Çoklu zamanlı Sentinel-2 görüntülerinden tarımsal ürün tespiti: Mardin–Kızıltepe örneği," Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, vol. 21, no. 4, pp. 881-899, 2021, doi: https://doi.org/10.35414/akufemubid.890436.

M. K. Sbahi, A. R. T. Ziboon, and K. I. Hassoon, "Evaluation of the Efficiency of Agricultural Production in the Pivotal Farms Utilizing Remote Sensing Techniques," Journal of Engineering and Sustainable Development, vol. 23, no. 4, pp. 86-99, 2019, doi: https://doi.org/10.31272/jeasd.23.4.6.

M. Dedeoğlu, L. Başayiğit, M. Yüksel, and F. Kaya, "Assessment of the vegetation indices on Sentinel-2A images for predicting the soil productivity potential in Bursa, Turkey," Environmental Monitoring and Assessment, vol. 192, pp. 1-16, 2020, doi: https://doi.org/10.1007/s10661-019-7989-8.

M. Gomroki, M. Hasanlou, and P. Reinartz, "STCD-EffV2T Unet: Semi Transfer Learning EfficientNetV2 T-Unet Network for Urban/Land Cover Change Detection Using Sentinel-2 Satellite Images," Remote Sensing, vol. 15, no. 5, p. 1232, 2023, doi: https://doi.org/10.3390/rs15051232.

E. Tunca and E. KÖKSAL, "Sentinel 2 Uydu Görüntülerinden Bitki Türlerinin Makine Öğrenmesi ile Belirlenmesi," ÇOMÜ Ziraat Fakültesi Dergisi, vol. 9, no. 1, pp. 189-200, 2021, doi: https://doi.org/10.33202/comuagri.842202.

R. G. Congalton and K. Green, Assessing the accuracy of remotely sensed data: principles and practices. CRC Press, 2019.

C. Cortes and V. Vapnik, "Support-vector networks," in machine learning, vol. 20, 1995, pp. 273–297.

V. N. Vapnik, "Estimation of dependences based on empirical data. 1982," NY: Springer-Verlag, 1995, doi: https://doi.org/10.1007/0-387-34239-7.

P. Addesso, R. Conte, M. Longo, R. Restaino, and G. Vivone, "SVM-based cloud detection aided by contextual information," in 2012 Tyrrhenian Workshop on Advances in Radar and Remote Sensing (TyWRRS), 2012: IEEE, pp. 214-221, doi: https://doi.org/10.1109/tywrrs.2012.6381132.

I. Lizarazo, "SVM‐based segmentation and classification of remotely sensed data," International Journal of Remote Sensing, vol. 29, no. 24, pp. 7277-7283, 2008, doi: https://doi.org/10.1080/01431160802326081.

G. Mountrakis, J. Im, and C. Ogole, "Support vector machines in remote sensing: A review," ISPRS journal of photogrammetry and remote sensing, vol. 66, no. 3, pp. 247-259, 2011, doi: https://doi.org/10.1016/j.isprsjprs.2010.11.001.

K. M. Buddhiraju and I. A. Rizvi, "Comparison of CBF, ANN and SVM classifiers for object based classification of high-resolution satellite images," in 2010 IEEE international geoscience and remote sensing symposium, 2010: IEEE, pp. 40-43, doi: https://doi.org/10.1109/igarss.2010.5652033.

P. Mather and B. Tso, Classification methods for remotely sensed data. CRC Press, 2016.

M. Denil, D. Matheson, and N. De Freitas, "Narrowing the gap: Random forests in theory and in practice," in International conference on machine learning, 2014: PMLR, pp. 665-673. [Online]. Available: https://proceedings.mlr.press/v32/denil14.html.

E. Adam, O. Mutanga, J. Odindi, and E. M. Abdel-Rahman, "Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers," International Journal of Remote Sensing, vol. 35, no. 10, pp. 3440-3458, 2014, doi: https://doi.org/10.1080/01431161.2014.903435.

F. F. Camargo, E. E. Sano, C. M. Almeida, J. C. Mura, and T. Almeida, "A comparative assessment of machine-learning techniques for land use and land cover classification of the Brazilian tropical savanna using ALOS-2/PALSAR-2 polarimetric images," Remote Sensing, vol. 11, no. 13, p. 1600, 2019, doi: https://doi.org/10.3390/rs11131600.

L. Ma, M. Li, X. Ma, L. Cheng, P. Du, and Y. Liu, "A review of supervised object-based land-cover image classification," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 130, pp. 277-293, 2017, doi: https://doi.org/10.1016/j.isprsjprs.2017.06.001.

M. Belgiu and L. Drăguţ, "Random forest in remote sensing: A review of applications and future directions," ISPRS journal of photogrammetry and remote sensing, vol. 114, pp. 24-31, 2016, doi: https://doi.org/10.1016/j.isprsjprs.2016.01.011.

A. Liaw and M. Wiener, "Classification and regression by randomForest," R News, vol. 2, no. 3, pp. 18-22, 2002. [Online]. Available: https://journal.r-project.org/articles/RN-2002-022/RN-2002-022.pdf.

Q. Feng, J. Gong, J. Liu, and Y. Li, "Flood mapping based on multiple endmember spectral mixture analysis and random forest classifier—The case of Yuyao, China," Remote Sensing, vol. 7, no. 9, pp. 12539-12562, 2015, doi: https://doi.org/10.3390/rs70912539.

N. Currit, "Development of a remotely sensed, historical land-cover change database for rural Chihuahua, Mexico," International Journal of Applied Earth Observation and Geoinformation, vol. 7, no. 3, pp. 232-247, 2005, doi: https://doi.org/10.1016/j.jag.2005.05.001.

B. A. Osunmadewa, W. Z. Gebrehiwot, E. Csaplovics, and O. C. Adeofun, "Spatio-temporal monitoring of vegetation phenology in the dry sub-humid region of Nigeria using time series of AVHRR NDVI and TAMSAT datasets," Open Geosciences, vol. 10, no. 1, pp. 1-11, 2018, doi: https://doi.org/10.1515/geo-2018-0001.

M. Pal and P. M. Mather, "An assessment of the effectiveness of decision tree methods for land cover classification," Remote sensing of environment, vol. 86, no. 4, pp. 554-565, 2003, doi: https://doi.org/10.1016/s0034-4257(03)00132-9.

C. Smith and N. Brown, "Erdas field guide. revised and expanded," ed: ERDAS®, Inc Atlanta, 1999.

G. James, D. Witten, T. Hastie, and R. Tibshirani, An introduction to statistical learning. Springer, 2013.

S. Myeong, D. J. Nowak, P. F. Hopkins, and R. H. Brock, "Urban cover mapping using digital, high-spatial resolution aerial imagery," Urban ecosystems, vol. 5, pp. 243-256, 2001. [Online]. Available: https://link.springer.com/article/10.1023/A:1025687711588.

R. G. Congalton, "A review of assessing the accuracy of classifications of remotely sensed data," Remote sensing of environment, vol. 37, no. 1, pp. 35-46, 1991, doi: https://doi.org/10.1016/0034-4257(91)90048-b.

D. Lu and Q. Weng, "A survey of image classification methods and techniques for improving classification performance," International Journal of Remote sensing, vol. 28, no. 5, pp. 823-870, 2007, doi: https://doi.org/10.2139/ssrn.3349696.

H. He and E. A. Garcia, "Learning from imbalanced data," IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 9, pp. 1263-1284, 2009, doi: https://doi.org/10.1109/tkde.2008.239.

B. Shivakumar and S. Rajashekararadhya, "Investigation on land cover mapping capability of maximum likelihood classifier: a case study on North Canara, India," Procedia computer science, vol. 143, pp. 579-586, 2018, doi: https://doi.org/10.1016/j.procs.2018.10.434.

O. Buck, V. E. G. Millán, A. Klink, and K. Pakzad, "Using information layers for mapping grassland habitat distribution at local to regional scales," International Journal of Applied Earth Observation and Geoinformation, vol. 37, pp. 83-89, 2015, doi: https://doi.org/10.1016/j.jag.2014.10.012.

Y. Zhao and Z. Zhu, "ASI: An artificial surface Index for Landsat 8 imagery," International Journal of Applied Earth Observation and Geoinformation, vol. 107, p. 102703, 2022, doi: https://doi.org/10.1016/j.jag.2022.102703.

A. Abdi, "Decadal land-use/land-cover and land surface temperature change in Dubai and implications on the urban heat island effect: A preliminary assessment," 2019, doi: https://doi.org/10.31223/osf.io/w79ea.


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Environmental Monitoring of Land Use/ Land Cover by Integrating Remote Sensing and Machine Learning Algorithms. (2024). Journal of Engineering and Sustainable Development, 28(4), 455-466. https://doi.org/10.31272/jeasd.28.4.4

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