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Comparative analysis of sensors and classification algorithms for land cover classification in Islamabad, Pakistan

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Abstract

Land use and land cover (LULC) classification is essential for environmental monitoring and sustainable land management. The selection of satellite sensors and classification algorithms influences the accuracy of LULC classification. This study evaluates the performance of three satellite sensors, GF-6 (GF-6), S2 (S2), and L9(L9), and three machine learning classifiers, Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), in classifying LULC in Islamabad, Pakistan. The satellite data with high-to-course spatial resolution data was utilized, and a comprehensive pre-processing workflow ensured high-quality imagery. The results indicate that XGBoost, paired with GF-6, achieved the highest overall classification accuracy (94.24%) and kappa coefficient (0.9279), outperforming RF and SVM. S2 combined with XGBoost also showed superior performance (92.89%) compared to other sensor-algorithm combinations. The study reveals that high spatial resolution (GF-6) significantly improves LULC classification, particularly in detecting forest and urban areas. Feature importance analysis identified GF-6 Red and NIR bands as the most significant predictors, especially for vegetation-related classes. The findings underscore the importance of selecting the appropriate sensor and classifier for specific LULC tasks, with XGBoost and high-resolution sensors like GF-6 providing the most accurate results. This study contributes to the growing body of research on LULC classification and offers valuable insights for urban planning and environmental monitoring. 

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References

  • Abbas F, Cai Z, Shoaib M, Iqbal J, Ismail M, Alrefaei ARIFULLAHAF, and Mohammed Fahad Albeshr (2024). Uncertainty Analysis of Predictive Models for Water Quality Index: Comparative Analysis of XGBoost, Random Forest, SVM, KNN, Gradient Boosting,Decision Tree Algorithms

  • Abdi A (2019) Assessing machine learning models for land cover classification using Sentinel-2 data in boreal landscapes. J Geoinformatics 57(4):1-20

    Google Scholar 

  • Abdi AM (2020) Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2data. GIScience & Remote Sens 57(1):1–20

  • Abedi R, Costache R (2022) Flash-flood susceptibility mapping based on XGBoost, random forest, and boosted regression trees. Geocarto International. Highlights XGBoost’s adaptability to area size changes. providing stable Precision-Recall trade-offs

    Google Scholar 

  • Aboelnour M, Engel BA (2018) Application of Remote Sensing techniques and Geographic Information Systems to analyze Land Surface temperature in response to Land Use/Land Cover Change in Greater Cairo Region, Egypt. J Geographic Inform Syst 10(1):57–88

    Article  Google Scholar 

  • Adelabu S, and Timothy Dube (2015) Employing Ground and Satellite-based QuickBird Data and Random Forest To Discriminate Five Tree Species in a southern African Woodland. Geocarto Int 30(4):457–471

    Article  Google Scholar 

  • Akram M, Hayat U, Shi J, Anees SA (2022) Association of the Female Flight Ability of Asian Spongy Moths (Lymantria dispar asiatica) with locality, age and mating: a Case Study from China. Forests 13(8):1158. https://doi.org/10.3390/f13081158

    Article  Google Scholar 

  • Aldiansyah S, Saputra R (2023) Comparison of RF, SVM, and CART using Sentinel-2 and Landsat-8 data for land cover classification. Remote Sensing Applications

  • Alshari EA, Bharti WG (2021) Development of Classification System for LULC Using Remote Sensing and GIS. Global Transitions Proceedings 2(1):8–17

  • Altarez P, Rodríguez S, Gutierrez M (2023) LULC classification with deep learning models: an ANOVA-based performance analysis. J Geospatial Res

  • Amini M, Moradi M, Karimi H (2022) Improvements in LULC classification accuracy using landsat data and spectral indices: a machine learning approach. Remote Sens Appl

  • Andreevich UV, Reza SSO, Stepanovich TI, Amirhossein A, Meng Z, Anees SA, Petrovich CV (2020) Are there differences in the response of natural stand and plantation biomass to changes in temperature and precipitation? A case for two-needled pines in Eurasia. J Resour Ecol 11(4):331. https://doi.org/10.5814/j.issn.1674-764x.2020.04.001

    Article  Google Scholar 

  • Anees SA, Zhang X, Khan KA, Abbas M, Ghramh HA, Ahmad Z (2022a) Estimation of fractional vegetation cover dynamics and its drivers based on multi-sensor data in Dera Ismail Khan, Pakistan. J King Saud University-Science 34(6):102217. https://doi.org/10.1016/j.jksus.2022.102217

    Article  Google Scholar 

  • Anees SA, Zhang X, Shakeel M, Al-Kahtani MA, Khan KA, Akram M, Ghramh HA (2022b) Estimation of fractional vegetation cover dynamics based on satellite remote sensing in Pakistan: a comprehensive study on the FVC and its drivers. J King Saud University-Science 34(3):101848. https://doi.org/10.1016/j.jksus.2022.101848

    Article  Google Scholar 

  • Anees SA, Mehmood K, Khan WR, Sajjad M, Alahmadi TA, Alharbi SA, Luo M (2024a) Integration of machine learning and remote sensing for above ground biomass estimation through Landsat-9 and field data in temperate forests of the himalayan region. Ecol Inf 82(8):102732. https://doi.org/10.1016/j.ecoinf.2024.102732

    Article  Google Scholar 

  • Anees SA, Mehmood K, Raza SIH, Pfautsch S, Shah M, Jamjareegulgarn P, Shahzad F, Alarfaj AA, Alharbi SA, Khan WR, Dube T (2024b) Spatiotemporal analysis of surface Urban Heat Island intensity and the role of vegetation in six major Pakistani cities. Ecological Informatics, p.102986. https://doi.org/10.1016/j.ecoinf.2024.102986

  • Anees A S.A., Mehmood K, Rehman A, Rehman NU, Muhammad S, Shahzad F, Hussain K, Luo M, Alarfaj A.A., Alharbi S.A., Khan W.R. (2024c) Unveiling Fractional Vegetation Cover dynamics: a spatiotemporal analysis using MODIS NDVI and Machine Learning. Environ Sustain Indic 24(8):100485. https://doi.org/10.1016/j.indic.2024.100485

    Article  Google Scholar 

  • Anees SA, Yang X, Mehmood K (2024d) The stoichiometric characteristics and the relationship with hydraulic and morphological traits of the Faxon fir in the subalpine coniferous forest of Southwest China. Ecol Ind 159:111636. https://doi.org/10.1016/j.ecolind.2024.111636

    Article  Google Scholar 

  • Aslam MS, Huanxue P, Sohail S, Majeed MT, Rahman SU, Anees SA (2022) Assessment of major food crops production-based environmental efficiency in China, India, and Pakistan. Environmental Science and Pollution Research, pp 1–10. https://doi.org/10.1007/s11356-021-16161-x

    Book  Google Scholar 

  • Astola H, Häme T, Sirro L, Molinier M, Kilpi J (2019) Comparison of S2 and landsat 8 imagery for Forest Variable Prediction in Boreal Region. Remote Sens Environ 223:257–273

    Article  Google Scholar 

  • Badshah MT, Ahmad A, Muneer MA, Rehman AU, Wang J, Khan M, Muhammad B, Amir M, Meng J (2017) Evaluation of the forest structure, Diversity and Biomass Carbon Potential in the Southwest Region of Guangxi, China. Appl Ecol Environ Res 18:447–467

    Article  Google Scholar 

  • Badshah M, Tariq K, Hussain AU, Rehman K, Mehmood B, Muhammad R, Wiarta RF, Silamon Muhammad Anas Khan, and Jinghui Meng. 2024. The role of Random Forest and Markov Chain Models in understanding Metropolitan Urban Growth Trajectory. Front Forests Global Change 7:1345047

    Article  Google Scholar 

  • Balha S, Kumar R, Shankar V (2021) Comparative analysis of pixel- and object-based classification using multisource satellite data. Int J Remote Sens Appl

  • Bao F, Cheng T, Li Y, Gu X, Guo H, Wu Y, Wang Y, and Jinhui Gao (2019) Retrieval of Black Carbon Aerosol Surface Concentration using Satellite Remote sensing observations. Remote Sens Environ 226:93–108

    Article  Google Scholar 

  • Belgiu M, and Lucian Drăgu (2016) Random Forest in Remote sensing: a review of applications and future directions. ISPRS J Photogrammetry Remote Sens

  • Bivand R, Hauke J, and Tomasz Kossowski (2013) Computing the J Acobian in G Aussian spatial autoregressive models: an Illustrated comparison of available methods. Geographical Anal 45(2):150–179

    Article  Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  Google Scholar 

  • Briassoulis H (2020) Analysis of Land Use Change. Theoretical and Modeling Approaches.

    Google Scholar 

  • Brodleyf CE (1997) Decision Tree Classification of Land Cover from Remotely.Pdf.

  • Butt A, Shabbir R, Ahmad SS, Aziz N (2015) Land Use Change Mapping and Analysis using remote sensing and GIS: a case study of Simly Watershed, Islamabad, Pakistan. Egypt J Remote Sens Space Sci 18(2):251–259

    Google Scholar 

  • Carlson TN, Traci Arthur S (2000) The impact of Land Use - Land Cover Changes due to urbanization on Surface Microclimate and Hydrology: A Satellite Perspective. Glob Planet Change. https://doi.org/10.1016/S0921-8181(00)00021-7

    Article  Google Scholar 

  • Chen X, Xie D, Zhang Z, Sharma RP, Chen Q, Liu Q, Fu L (2023) Compatible Biomass Model with Measurement Error using Airborne LiDAR Data. Remote Sens 15(14):3546. https://doi.org/10.3390/rs15143546

    Article  Google Scholar 

  • Chen J, Song Y, Li D, Lin X, Zhou S, Xu W (2024) Specular removal of Industrial Metal objects without changing lighting configuration. IEEE Trans Industr Inf 20(3):3144–3153. https://doi.org/10.1109/TII.2023.3297613

    Article  Google Scholar 

  • Comber A, and Michael Wulder (2019) Considering spatiotemporal processes in Big Data Analysis: insights from Remote Sensing of Land Cover and Land Use. Trans GIS 1–13. https://doi.org/10.1111/tgis.12559

  • Coomes OT, Eric F, Lambin BL, Turner HJ, Geist SB, Agbola A, Angelsen C, Folke JW, Bruce, Oliver T, Coomes R, Dirzo PS, George K, Homewood J, Imbernon R, Leemans X, Li EF, Moran M, Mortimore PS, Ramakrishnan JF, Richards C, Vogel, and Jianchu Xu (2001) The causes of Land-Use and Land-Cover Change: moving beyond the myths the causes of Land-Use and Land-Cover Change : moving beyond the myths Helle Sk a. Glob Environ Change 11(December):261–269. https://doi.org/10.1016/S0959-3780(01)00007-3

    Article  Google Scholar 

  • Dhakal S, Singh R, Khan M (2022) Land cover classification using Sentinel-2 and Landsat-9: a comparative analysis. Int J Appl Remote Sens

  • Ding Z-D, Sun Z, Xie Y-H, Qiao J-J, Liang R-T, Chen X, Hussain K, and Yu-Jun Sun (2024) Optimizing Crown Density and volume estimation across Two Coniferous Forest Types in Southern China via Boruta and Cubist methods. J Plant Ecol 17(5):rtae039.

    Article  Google Scholar 

  • Ebrahimy H, Karimi A, Zarei F (2021) Improving classification accuracy in complex environments using RF and data balancing techniques. Geocarto International

    Google Scholar 

  • Estoque RC, and Yuji Murayama (2015) Classification and change detection of built-up lands from Landsat-7 ETM + and Landsat-8 OLI/TIRS imageries: a comparative Assessment of various spectral indices. Ecol Ind 56:205–217

    Article  Google Scholar 

  • Fang Y, Zhang H, Mao Q, Li Z (2018) Land Cover classification with Gf-3 polarimetric synthetic aperture Radar Data by Random Forest Classifier and fast Super-pixel Segmentation. Sensors 18(7):2014

    Article  Google Scholar 

  • Faruque M, Omer, Md Afser Jani Rabby, Md Alamgir Hossain, Md Rashidul Islam, Md Mamun Ur Rashid, and, Muyeen SM (2022) A Comparative Analysis to Forecast Carbon Dioxide Emissions. Energy Reports 8:8046–60

  • Fernández-Urrutia G, Montesinos P, Ruiz A (2023) Remote sensing techniques for paddy crop classification: a systematic review. Remote Sensing in Agriculture

    Google Scholar 

  • Friedl MA, Brodley CE (1997) Decision Tree classification of Land Cover from remotely sensed data. Remote Sens Environ 61(3):399–409

    Article  Google Scholar 

  • Gao H, Guo J, Guo P, and Xiuwan Chen (2021) Classification of very-high-spatial-resolution aerial images based on Multiscale Features with limited semantic information. Remote Sens 13(3):364

    Article  CAS  Google Scholar 

  • Gašparović M, and Tomislav Jogun (2018) The effect of fusing S2 bands on land-cover classification. Int J Remote Sens 39(3):822–841. https://doi.org/10.1080/01431161.2017.1392640

    Article  Google Scholar 

  • Gašparović M, and Dino Dobrinić (2020) Comparative Assessment of Machine Learning Methods for Urban Vegetation Mapping using Multitemporal Sentinel-1 imagery. Remote Sens 12(12):1952

    Article  Google Scholar 

  • Gašparović M, Zrinjski M (2019a) and Marina Gudelj. Automatic Cost-Effective Method for Land Cover Classification (ALCC). Computers, Environment and Urban Systems 76(December 2018):1–10. https://doi.org/10.1016/j.compenvurbsys.2019.03.001

  • Gašparović M, Zrinjski M (2019b) and Marina Gudelj. Automatic Cost-Effective Method for Land Cover Classification (ALCC). Computers, Environment and Urban Systems 76(December 2018):1–10. https://doi.org/10.1016/j.compenvurbsys.2019.03.001

  • Georganos S, Grippa T, Vanhuysse S, Lennert M, Shimoni M, and Eleonore Wolff (2018) Very high-resolution object-based land use–land cover urban classification using Extreme Gradient Boosting. IEEE Geosci Remote Sens Lett 15(4):607–611

    Article  Google Scholar 

  • Ghayour L, Ahmad T, Khalil M (2021) Comparing machine learning methods for Sentinel-2 and Landsat-8 land use classification. Remote Sens Lett

  • Haider K, Khokhar MF, Chishtie F, RazzaqKhan W, Hakeem KR (2017) Identification and future description of warming signatures over Pakistan with special emphasis on evolution of CO 2 levels and temperature during the first decade of the twenty-first century, vol 24. Environmental Science and Pollution Research, pp 7617–7629

    Google Scholar 

  • Hamedianfar A, Gibril MBA, Hosseinpoor M (2022) Synergistic use of particle swarm optimization, artificial neural networks, and XGBoost for LULC mapping. Remote Sens Appl

  • Han H, Feng Z, Du W, Guo S, Wang P, and Tongyu Xu (2024) Remote sensing image classification based on multi-spectral Cross-sensor Super-resolution Combined with texture features: a Case Study in the Liaohe Planting Area. IEEE Access

  • Hasan S, Shamim L, Zhen MG, Miah T, Ahamed, and Abdus Samie (2020) Impact of Land Use Change on Ecosystem services: a review. Environ Dev 34:100527

    Article  Google Scholar 

  • Hosseiny H, Mohammadzadeh A, Mirik M (2022) Evaluation of XGBoost performance for Sentinel-2-based urban land use classification. Int J Appl Earth Obs Geoinf 105:102623

    Google Scholar 

  • Hussain K, Mehmood K, Anees SA, Ding Z, Muhammad S, Badshah T, Shahzad F, Haidar I, Wahab A, Ali J, Ansari MJ (2024a) Assessing Forest Fragmentation due to Land use Changes from 1992 to 2023: A Spatio-Temporal Analysis Using Remote Sensing Data. Heliyon. https://doi.org/10.1016/j.heliyon.2024.e34710

  • Hussain K, Mehmood K, Yujun S, Badshah T, Anees SA, Shahzad F, Nooruddin, Ali J, Bilal M (2024b) Analysing LULC transformations using remote sensing data: insights from a multilayer perceptron neural network approach. Ann GIS 1–27. https://doi.org/10.1080/19475683.2024.2343399

  • Jallat H, Khokhar MF, Kudus KA, Nazre M, Saqib NU, Tahir U, Khan WR (2021) Monitoring carbon stock and land-use change in 5000-year-old juniper forest stand of Ziarat, Balochistan, through a synergistic approach. Forests 12(1):51

    Article  Google Scholar 

  • Jansen LJM, Antonio DG (2002) Parametric Land Cover and Land-Use classifications as Tools for Environmental Change Detection. Agric Ecosyst Environ 91(1–3):89–100. https://doi.org/10.1016/S0167-8809(01)00243-2

    Article  Google Scholar 

  • Jiang L, Lai Y, Guo R, Li X, Hong W, Tang X (2024) Measuring the impact of government intervention on the spatial variation of market-oriented urban redevelopment activities in Shenzhen, China. Cities 147:104834. https://doi.org/10.1016/j.cities.2024.104834

    Article  Google Scholar 

  • Jombo S, and Samuel Adelabu (2023) Evaluating Landsat-8, L9and S2 Imageries in Land Use and Land Cover (LULC) classification in a heterogeneous urban area. GeoJournal 88(Suppl 1):377–399

    Article  Google Scholar 

  • Jombo S, Adam E, Byrne MJ, and Solomon W. Newete (2020) Evaluating the capability of Worldview-2 imagery for mapping alien Tree species in a heterogeneous Urban Environment. Cogent Social Sci 6(1):1754146

    Google Scholar 

  • Jozdani SE, Forootan E, Safari A (2019) Ensemble methods for heterogeneous urban land cover classification: comparing RF, SVM, and XGBoost. Urban Remote Sens J 7(3):205–218

    Google Scholar 

  • Khatami R, Mountrakis G, Stehman SV (2016) A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: general guidelines for practitioners and future research. Remote Sens Environ 177:89–100

  • Kamali Maskooni S et al (2020) Advanced machine learning algorithms for assessing groundwater potential and evaluating model performance through ANOVA. J Hydrol

  • Kavidya P, Bilucan F (2023) Effects of auxiliary and ancillary data on LULC classification in a heterogeneous environment using optimized random forest algorithm. International Journal of Remote Sensing Applications. Random Forest’s ability to adapt to different area sizes is emphasized, but performance dips with increasing complexity in larger heterogeneous areas

  • Keshtkar E, Abdolshahi R, Sasanfar H, Zand E, Beffa R, Dayan FE, and Per Kudsk (2019) Assessing fitness costs from a herbicide-resistance management perspective: a review and insight. Weed Sci 67(2):137–148

    Article  Google Scholar 

  • Khaldi M, Farouk A, Zhou Q (2024) High-resolution LULC classification using GF-6 sensor data and XGBoost algorithm: a case study. Remote Sens Applications: Soc Environ 25:100429

    Google Scholar 

  • Khan WR, Rasheed F, Zulkifli SZ, Kasim MRBM, Zimmer M, Pazi AM, Kamrudin NA, Zafar Z, Faridah-Hanum I, Nazre M (2020) Phytoextraction potential of Rhizophora apiculata: a case study in Matang mangrove forest reserve, Malaysia, vol 13. Tropical Conservation Science, p 1940082920947344

    Google Scholar 

  • Khan WR, Nazre M, Akram S, Anees SA, Mehmood K, Ibrahim FH, Zhu X (2024) Assessing the Productivity of the Matang Mangrove Forest Reserve: review of one of the best-managed Mangrove forests. Forests 15(5):747. https://doi.org/10.3390/f15050747

    Article  Google Scholar 

  • Kindu M, Schneider T, Teketay D, Knoke T (2013) Land Use/Land Cover Change Analysis using object-based classification Approach in Munessa-Shashemene Landscape of the Ethiopian highlands. Remote Sens 5(5):2411–2435

    Article  Google Scholar 

  • Koranteng A, Frimpong BF, Adu-Poku I, Asamoah JN, Tomasz Z-N (2023) Assessment of Past and Future Land Use/Land Cover dynamics of the Old Kumasi Metropolitan Assembly and Atwima Nwabiagya Municipal Area, Ghana. J Geoscience Environ Prot 11(3):44–69

    Article  Google Scholar 

  • Kuang W, Wang J, Li X (2024) Vegetation indices from Sentinel-2 and Landsat-9 for pine wilt disease detection. Ecological Informatics

  • Kundu S, Rana NK, and Susanta Mahato (2024) Unravelling Blue Landscape Fragmentation Effects on Ecosystem Services in Urban agglomerations. Sustainable Cities Soc 102:105192

    Article  Google Scholar 

  • Lassalle G, Ferreira MP Laura Elena Cué La Rosa, Rebecca Del’papa Moreira Scafutto, and Carlos Roberto De Souza Filho. 2023. Advances in Multi-and Hyperspectral Remote Sensing of Mangrove species: a synthesis and study case on Airborne and Multisource Spaceborne Imagery. ISPRS J Photogrammetry Remote Sens 195:298–312

    Article  Google Scholar 

  • Li DR (2022) China’s High-Resolution Earth Observation System (CHEOS): advances and perspectives. ISPRS Annals Photogrammetry Remote Sens Spat Inform Sci 3:583–590

    Google Scholar 

  • Li T, Xin S, Xi Y, Tarkoma S, Hui P, Li Y (2022) Predicting Multi-level Socioeconomic Indicators from Structural Urban Imagery. Paper presented at the CIKM ‘22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, New York, NY, USAfrom https://doi.org/10.1145/3511808.3557153

  • Liu C, Zhang Q, Luo H, Qi S, Tao S, Xu H, and Yuan Yao (2019) An efficient Approach to capture continuous impervious Surface dynamics using spatial-temporal rules and dense Landsat Time Series Stacks. Remote Sens Environ 229:114-132. https://doi.org/10.1016/j.rse.2019.04.025

    Article  Google Scholar 

  • Liu J, Liu W, Allechy FB, Zheng Z, Liu R, Kouadio KL (2024) Machine learning-based techniques for land subsidence simulation in an urban area. J Environ Manage 352:120078. https://doi.org/10.1016/j.jenvman.2024.120078

    Article  Google Scholar 

  • Luo M, Anees SA, Huang Q, Qin X, Qin Z, Fan J, Han G, Zhang L, Shafri HZM (2024) Mach Learn Models Forests 15(6):975. https://doi.org/10.3390/f15060975. Improving Forest Above-Ground Biomass Estimation by Integrating Individual

  • Masek JG, Wulder MA, Markham B, McCorkel J, Crawford CJ, Storey J, Jenstrom DT (2020) Landsat 9: empowering Open Science and Applications through Continuity. Remote Sens Environ 248:111968

    Article  Google Scholar 

  • Matyukira D, Mucherera B, Ndlovu T (2023) Comparative performance of RF and XGBoost for urban land cover mapping with GF-6 data. Geospatial J Afr 15(2):115–129

    Google Scholar 

  • Mehmood K, Anees SA, Luo M, Akram M, Zubair M, Khan KA, Khan WR (2024a) Assessing Chilgoza Pine (Pinus gerardiana) Forest Fire Severity: remote sensing analysis, correlations, and Predictive modeling for enhanced management strategies. Trees, Forests and People, p 100521. https://doi.org/10.1016/j.tfp.2024.100521

    Book  Google Scholar 

  • Mehmood K, Anees SA, Muhammad S, Hussain K, Shahzad F, Liu Q, Ansari MJ, Alharbi SA, Khan WR (2024b) Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables. Sci Rep 14(1):11775. https://doi.org/10.1038/s41598-024-62464-7

    Article  CAS  Google Scholar 

  • Mehmood K, Anees SA, Rehman A, Rehman NU, Muhammad S, Shahzad F, Liu Q, Alharbi SA, Alfarraj S, Ansari MJ, Khan WR (2024c) c Assessment of Climatic Influences on Net Primary Productivity along Elevation Gradients in Temperate Ecoregions. Trees, Forests and People, p.100657

  • Mehmood K, Anees SA, Rehman A, Tariq A, Liu Q, Muhammad S, Rabbi F, Pan SA, Hatamleh WA (2024d) Assessing forest cover changes and fragmentation in the Himalayan Temperate Region: implications for forest conservation and management. J Forestry Res 35(1):82. https://doi.org/10.1007/s11676-024-01734-6

    Article  Google Scholar 

  • Mehmood K, Anees SA, Rehman A, Tariq A, Zubair M, Liu Q, Rabbi F, Khan KA, Luo M (2024e) Exploring spatiotemporal dynamics of NDVI and climate-driven responses in ecosystems: insights for sustainable management and climate resilience. Ecol Inf 102532. https://doi.org/10.1016/j.ecoinf.2024.102532

  • Mengesha TE, Desta LT, Gamba P, Ayehu GT (2024) Multi-temporal passive and active remote sensing for agricultural mapping and acreage estimation in the context of small farmholds in Ethiopia. Land 13(3):335

    Article  Google Scholar 

  • Mountrakis G, Im J, and Caesar Ogole (2011) Support Vector machines in Remote sensing: a review. ISPRS J Photogrammetry Remote Sens 66(3):247–259

    Article  Google Scholar 

  • Muhammad B, Ilahi T, Ullah S, Wu X, Siddique MA, Khan MA, Badshah MT, Jia Z (2020) Litter decomposition and soil nutrients Prince Rupprecht’s (Larix Principis-Rupprechtii) Plantations Area in Saihanba, Northern China. Appl Ecol Environ Res 18(3):4417-4434

    Article  Google Scholar 

  • Naeem S, Cao C, Qazi WA, Zamani M, Wei C, Acharya BK, Asid UR (2018) Studying the Association between Green Space Characteristics and Land Surface Temperature for Sustainable Urban Environments: an analysis of Beijing and Islamabad. ISPRS Int J Geo-Information 7(2):38

    Article  Google Scholar 

  • Nti Asamoah J, Osei EM, Jnr. AS, Amoah, Acquah PC (2018a) Assessment of a landsat 8 image of Atiwa District in Ghana using ECHO, Random Forest, Minimum Distance and Maximum Likelihood classification algorithms. Int Coference Appl Sci Technol 183–193

  • Nti Asamoah J, Osei EM, Jnr. AS, Amoah, Acquah PC (2018b) Assessment of a landsat 8 image of Atiwa District in Ghana using ECHO, Random Forest, Minimum Distance and Maximum Likelihood classification algorithms. Int Coference Appl Sci Technol 183–193

  • Odindi JO, Bangamwabo V, and Onisimo Mutanga (2015) Assessing TheValue OfUrbanGreen spaces InMitigatingMulti-SeasonalUrban Heat UsingMODISLand SurfaceTemperature (LST) AndLandsat 8 Data. Int J Environ Res 9(1):9–18

    Google Scholar 

  • Ouma YO, Maina J, Gitau C (2022) Comparative study of RF, SVM, CART, and GTB for urban land cover classification. Urban Remote Sensing Journal

    Google Scholar 

  • Orieschnig CA, Belaud G, Venot JP, Massuel S, Ogilvie A (2021) Input imagery, classifiers, and cloud computing: insights from multi temporal LULC mapping in the Cambodian Mekong Delta. Eur J Remote Sens 54(1):398–341

  • Pal M, and Paul M. Mather (2003) An Assessment of the effectiveness of decision tree methods for land cover classification. Remote Sens Environ 86(4):554–565. https://doi.org/10.1016/S0034-4257(03)00132-9

    Article  Google Scholar 

  • Pal M, Mather PM (2005) Support Vector machines for classification in Remote Sensing. Int J Remote Sens 26(5):1007–1011. https://doi.org/10.1080/01431160512331314083

    Article  Google Scholar 

  • Palanisamy PA, Jain K, Bonafoni S (2023) Machine learning classifier evaluation for different input combinations: a case study with landsat 9 and Sentinel-2 data. Remote Sens 15(13):3241

    Article  Google Scholar 

  • Pan SA, Anees SA, Li X, Yang X, Duan X, Li Z (2023) Spatial and temporal patterns of non-structural carbohydrates in Faxon Fir (Abies Fargesii var. Faxoniana), Subalpine Mountains of Southwest China. Forests 14(7):1438. https://doi.org/10.3390/f14071438

    Article  Google Scholar 

  • Parracciani F, Valenti S, Mancini L (2024) Exploring LULC classification improvements in Google Earth Engine with ANOVA. J Environ Monit Modelling

  • Pham Q, Bao SA, Ali F, Parvin V, Van On Lariyah Mohd Sidek, Bojan Đurin, Vlado Cetl, Sanja Šamanović, and Nguyen Nguyet Minh. 2024. Multi-spectral remote sensing and GIS-Based analysis for Decadal Land Use Land Cover Changes and Future Prediction using Random Forest Tree and Artificial neural network. Adv Space Res 74(1):17–47

    Google Scholar 

  • Qian Y, Zhou W, Yan J, Li W, and Lijian Han (2015) Comparing machine learning classifiers for object-based land cover classification using very high Resolution Imagery. Remote Sens 7(1):153–168. https://doi.org/10.3390/rs70100153

    Article  Google Scholar 

  • Rahman A, Zahid M (2020) Machine learning algorithms in satellite image classification for rural and urban setups. J Spat Sci

  • Rash A, Mustafa Y, Hamad R (2023) Quantitative assessment of land use/land cover changes in a developing region using machine learning algorithms. Geospatial Analysis Journal. Demonstrates how area size affects RF and SVM’s performance, with XGBoost remaining the most adaptable

  • Rebinth, Anisha S, Mohan Kumar T, Kumanan, Varaprasad G (2021) Glaucoma Image Classification Using Entropy Feature and Maximum Likelihood Classifier. P. 042075 in Journal of Physics: Conference Series. Vol. 1964. IOP Publishing

  • Rehman AU, Zhang L, Sajjad MM, Raziq A (2024) Multi-temporal Sentinel-1 and S2 data for orchards discrimination in Khairpur District, Pakistan using spectral separability analysis and machine learning classification. Remote Sens 16(4):686

    Article  Google Scholar 

  • Rodriguez-Galiano VF, Chica-Olmo M, Abarca-Hernandez F, Atkinson PM, Jeganathan C (2012a) Random Forest Classification of Mediterranean Land Cover using Multi-seasonal Imagery and Multi-seasonal texture. Remote Sens Environ 121:93–107. https://doi.org/10.1016/j.rse.2011.12.003

    Article  Google Scholar 

  • Rodriguez-Galiano V, Francisco B, Ghimire J, Rogan M, Chica-Olmo, and Juan Pedro Rigol-Sanchez (2012b) An Assessment of the effectiveness of a Random Forest Classifier for Land-Cover classification. ISPRS J Photogrammetry Remote Sens 67:93–104

    Article  Google Scholar 

  • Rogan J, and Dong Mei Chen (2004) Remote Sensing Technology for Mapping and Monitoring Land-Cover and Land-Use Change. Progress Plann

  • Sahin EK (2020) Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest. Natural hazards. Discusses area size impacts, with XGBoost showing fewer performance drops than RF and SVM

  • Shahzad F, Mehmood K, Hussain K, Haidar I, Anees SA, Muhammad S, Ali J, Adnan M, Wang Z, Feng Z (2024) Comparing machine learning algorithms to predict vegetation fire detections in Pakistan. Fire Ecol 20(1):1–20. https://doi.org/10.1186/s42408-024-00289-5

    Article  Google Scholar 

  • Shalaby A, and Ryutaro Tateishi (2007) Remote sensing and GIS for Mapping and Monitoring Land Cover and Land-Use changes in the Northwestern Coastal Zone of Egypt. Appl Geogr. https://doi.org/10.1016/j.apgeog.2006.09.004

    Article  Google Scholar 

  • Shao Z, Ahmad MN, and Akib Javed (2024) Comparison of Random Forest and XGBoost Classifiers Using Integrated Optical and SAR Features for Mapping Urban Impervious Surface. Remote Sens 16(4):665

    Article  Google Scholar 

  • Shobairi SOR, Lin H, Usoltsev VA, Osmirko AA, Tsepordey IS, Ye Z, Anees SA (2022) A comparative pattern for Populus spp. and Betula Spp. Stand Biomass in Eurasian Climate gradients. Croatian J for Engineering: J Theory Application Forestry Eng 43(2):457–467. https://doi.org/10.5552/crojfe.2022.1340

    Article  Google Scholar 

  • Sohn Y, and N. Sanjay Rebello (2002) Supervised and unsupervised Spectral Angle Classifiers. Photogram Eng Remote Sens 68(12):1271–1280

    Google Scholar 

  • Sun L, Wang Q, Chen Y, Zheng Y, Wu Z, Fu L, Jeon B (2023) CRNet: Channel-enhanced remodeling-based network for salient object detection in Optical Remote sensing images. IEEE Trans Geosci Remote Sens 61:1–14. https://doi.org/10.1109/TGRS.2023.3305021

    Article  Google Scholar 

  • Swetanisha R, Kumar A, Verma P (2022) Comparison of RF and SVM for mixed land cover classification in heterogeneous urban landscapes. Int J Remote Sens 43(8):2990–3008

    Google Scholar 

  • Thanh Noi P, and Martin Kappas (2017) Comparison of Random Forest, k-Nearest neighbor, and support Vector Machine classifiers for Land Cover classification using S2 imagery. Sensors 18(1):18

    Article  Google Scholar 

  • Treitz P, and John Rogan (2004) Remote sensing for Mapping and Monitoring Land-Cover and Land-Use Change-an introduction. Progress Plann 61(4):269–279. https://doi.org/10.1016/S0305-9006(03)00064-3

    Article  Google Scholar 

  • Tu C, Li P, Li Z, Wang H, Yin S, Li D, Zhu Q, Chang M, Liu J, Wang G (2021) Synergetic classification of Coastal wetlands over the Yellow River Delta with GF-3 full-polarization SAR and Zhuhai-1 OHS Hyperspectral Remote sensing. Remote Sens 13(21):4444

    Article  Google Scholar 

  • Tu B, Ren Q, Li J, Cao Z, Chen Y, Plaza A (2024) NCGLF2: Network combining global and local features for fusion of multisource remote sensing data. Inform Fusion 104:102192. https://doi.org/10.1016/j.inffus.2023.102192

    Article  Google Scholar 

  • Ur Rehman, Arif S, Ullah M, Shafique MS, Khan MT, Badshah, Qi-jing L (2021) Combining Landsat-8 spectral bands with ancillary variables for land cover classification in mountainous terrains of Northern Pakistan. J Mt Sci 18(9):2388–2401

    Article  Google Scholar 

  • Usoltsev VA, Chen B, Shobairi SOR, Tsepordey IS, Chasovskikh VP, Anees SA (2020) Patterns for Populus spp. stand biomass in gradients of winter temperature and precipitation of Eurasia. Forests 11(9):906. https://doi.org/10.3390/f11090906

    Article  Google Scholar 

  • Usoltsev VA, Lin H, Shobairi SOR, Tsepordey IS, Ye Z, Anees SA (2022) The principle of space-for-time substitution in predicting Betula Spp. Biomass change related to climate shifts. Appl Ecol Environ Res 20(4):3683–3698. https://doi.org/10.15666/aeer/2004_36833698

    Article  Google Scholar 

  • Vandewalle J (1999) SpringerLink - neural Processing letters, 9, number 3. Springerlink Com 9(3):293–300. https://doi.org/10.1023/A:1018628609742

    Article  Google Scholar 

  • Varga B, Szabó M, Tóth P (2021) Validating land cover classifications through ANOVA: machine learning applications with Sentinel-2 data. Earth Science Informatics

    Google Scholar 

  • Wang Y, Liu C, Zhang X (2024) Enhancing land cover segmentation with multi-source satellite data fusion. J Environ Monit

  • Weng Q (2002) Land Use Change Analysis in the Zhujiang Delta of China using Satellite Remote Sensing, GIS and stochastic modelling. J Environ Manage 64(3):273–284. https://doi.org/10.1006/jema.2001.0509

    Article  Google Scholar 

  • Weng Q, Xuefei Hu, and Hua Liu (2009) Estimating impervious surfaces using Linear Spectral Mixture Analysis with Multitemporal ASTER images. Int J Remote Sens 30(18):4807–4830

    Article  Google Scholar 

  • Xi Y, Li T, Wang H, Li Y, Tarkoma S, Hui P (2022) Beyond the First Law of Geography: Learning Representations of Satellite Imagery by Leveraging Point-of-Interests. Paper presented at the WWW ‘22, New York, NY, USAfrom https://doi.org/10.1145/3485447.3512149

  • Xie D, Huang H, Feng L, Sharma RP, Chen Q, Liu Q, Fu L (2023) Aboveground biomass prediction of arid shrub-dominated community based on Airborne LiDAR through Parametric and nonparametric methods. Remote Sens 15(13):3344. https://doi.org/10.3390/rs15133344

    Article  Google Scholar 

  • Xu H, Li Q, Chen J (2022) Highlight removal from a single Grayscale Image using attentive GAN. Appl Artif Intell 36(1):1988441. https://doi.org/10.1080/08839514.2021.1988441

    Article  Google Scholar 

  • Yan F, Wang X, Huang C, Zhang J, Su F, Zhao Y, Lyne V (2023) Sea Reclamation in Mainland China: process, pattern, and management. Land Use Policy 127:106555. https://doi.org/10.1016/j.landusepol.2023.106555

    Article  Google Scholar 

  • Yan P, Zhao J, Hou R, Duan X, Cai S, Wang X (2024) Clustered remote sensing target distribution detection aided by density-based spatial analysis. Int J Appl Earth Obs Geoinf 132:104019. https://doi.org/10.1016/j.jag.2024.104019

    Article  Google Scholar 

  • Yang C, Li R, and Zongyao Sha (2020) Exploring the Dynamics of Urban Greenness Space and their driving factors using geographically weighted regression: a Case Study in Wuhan Metropolis, China. Land 9(12):500

    Article  Google Scholar 

  • Yu J, Liu Y, Ren Y, Ma H, Wang D, Jing Y, and Linjun Yu (2020) Application study on double-constrained change detection for Land Use/Land Cover based on GF-6 WFV Imageries. Remote Sens 12(18):2943

    Article  Google Scholar 

  • Yu S, Guan D, Gu Z, Guo J, Liu Z, Liu Y (2024) Radar Target Complex High-Resolution Range Profile Modulation by External Time Coding Metasurface. IEEE Trans Microwave Theory Tech 72(10):6083–6093. https://doi.org/10.1109/TMTT.2024.3385421

    Article  Google Scholar 

  • Zeller U, Starik N, Göttert T (2017) Biodiversity, Land Use and Ecosystem Services—An Organismic and Comparative Approach to different geographical regions. Global Ecol Conserv 10:114–125

    Article  Google Scholar 

  • Zhang J (2010) Multi-source remote Sensing Data Fusion: Status and Trends. Int J Image Data Fusion 1(1):5–24

    Article  Google Scholar 

  • Zhang T, Su J, Xu Z, Luo Y, Li J (2021) Sentinel-2 satellite imagery for urban land cover classification by optimized random forest classifier. Appl Sci 11(2):543

    Article  Google Scholar 

  • Zhang B, Zhu H, Song W, Zhu J, Dai J, Zhang J, Li C (2024) A Multi-baseline Forest Height Estimation Method combining Analytic and geometric expression of the RVoG Model. Forests 15(9):1496. https://doi.org/10.3390/f15091496

    Article  Google Scholar 

  • Zhao Y, Wang X, Huang Z (2024) Multi-function Radar modeling: a review. IEEE Sens J 24(20):31658–31680. https://doi.org/10.1109/JSEN.2024.3436877

    Article  Google Scholar 

  • Zhou G, Li H, Song R, Wang Q, Xu J, Song B (2022a) Orthorectification of Fisheye Image under Equidistant Projection Model. Remote Sens 14(17):4175. https://doi.org/10.3390/rs14174175

    Article  Google Scholar 

  • Zhou G, Liu W, Zhu Q, Lu Y, Liu Y (2022b) ECA-MobileNetV3(large) + SegNet Model for Binary Sugarcane classification of remotely sensed images. IEEE Trans Geosci Remote Sens 60. https://doi.org/10.1109/TGRS.2022.3215802

  • Zhou G, Zhou X, Li W, Zhao D, Song B, Xu C, Zou L (2022c) Development of a Lightweight single-Band Bathymetric LiDAR. Remote Sens 14(22):5880. https://doi.org/10.3390/rs14225880

    Article  Google Scholar 

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Acknowledgements

We are grateful to the State Forestry and Grassland Administration Key Laboratory of Forest Resources and Environmental Management, Beijing Forestry University, Beijing (100083), P. R. China, for providing assistance and platforms for this research. The Geofen 6 imagery data has been taken from Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.

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Author notes

  1. Khadim Hussain and Tariq Badshah contributed equally to this work.

Authors and Affiliations

  1. State Forestry and Grassland Administration Key Laboratory of Forest Resources and Environmental Management, Beijing Forestry University, Beijing, 100083, P. R. China

    Khadim Hussain, Tariq Badshah & Sun Yujun

  2. Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing, 100083, P. R. China

    Kaleem Mehmood

  3. Institute of Forest Sciences, University of Swat, Swat, Pakistan

    Kaleem Mehmood

  4. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China

    Arif ur Rahman

  5. Mapping and 3S Technology Center, Beijing Forestry University, Beijing, 100083, China

    Fahad Shahzad

  6. Department of Forestry, The University of Agriculture, Dera Ismail Khan, 29050, Pakistan

    Shoaib Ahmad Anees

  7. Department of Forestry Science and Biodiversity, Faculty of Forestry and Environment, Universiti Putra Malaysia, Serdang, 43400, Malaysia

    Waseem Razzaq Khan

  8. Advanced Master in Sustainable Blue Economy, Applied Geophysics - OGS, National Institute of Oceanography, University of Trieste, Trieste, 34127, Italy

    Khadim Hussain, Tariq Badshah, Kaleem Mehmood, Arif ur Rahman, Fahad Shahzad, Shoaib Ahmad Anees, Waseem Razzaq Khan & Sun Yujun

  9. Institut Ekosains Borneo (IEB), Universiti Putra Malaysia Bintulu Campus, Sarawak, 97008, Malaysia

    Khadim Hussain, Tariq Badshah, Kaleem Mehmood, Arif ur Rahman, Fahad Shahzad, Shoaib Ahmad Anees, Waseem Razzaq Khan & Sun Yujun

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  1. Khadim Hussain
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Contributions

Khadim Hussain: conceptualization, methodology, software, formal analysis, visualization, data curation, writing—original draft, investigation, validation, writing—review and editing. Tariq Baadshah: conceptualization, methodology, software, formal analysis, visualization, data curation, writing—original draft, investigation, validation, writing—review and editing. Kaleem Mehmood: visualization, writing—review and editing, Arif Ur Rahman: writing—review and editing, Fahad Shahzad: writing—review and editing, Shoaib Ahmad Anees: formal analysis, visualization, validation, investigation, writing—review and editing, Waseem Razzaq Khan: formal analysis, investigation, writing—review and editing, Sun Yujun: writing—review and editing, Supervision.

Corresponding authors

Correspondence to Kaleem Mehmood or Sun Yujun.

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Communicated by: Hassan Babaie

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Hussain, K., Badshah, T., Mehmood, K. et al. Comparative analysis of sensors and classification algorithms for land cover classification in Islamabad, Pakistan. Earth Sci Inform 18, 212 (2025). https://doi.org/10.1007/s12145-025-01720-4

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