Abstract
Classification for land cover mapping is of great importance for accurate analysis and temporal monitoring of natural resources. In this study, the classification process was carried out using four synthetic aperture radar (SAR) and optical satellite images obtained in different seasons at equal intervals within a year. In addition to combining optical and SAR data for classification, single optical and SAR images have been classified separately. Thus, the effect of combining SAR and optical images on classification accuracy was examined. Moreover, the normalized difference vegetation index (NDVI), which is a vegetation index, was added to the image data, and the seasonal effect on accuracy was examined for the region with dense vegetation. In classification, three different object-oriented classification algorithms, support vector machines (SVM), random forest algorithm (RF), and k-nearest neighbors algorithm (kNN), were used. Finally, the number of training samples used for classification was increased, and its effect on accuracy was revealed in the study. The lowest overall classification accuracy was found to be 40.46% with classification using single SAR images, while the highest classification accuracy was found to be 95.12% as a result of the classification of the image obtained by combined SAR and optical satellite images. Furthermore, an additional testing area was considered to validate the method, and consistent results were obtained in that area as well. As a result, monitoring of the natural resources with high accuracy has been discussed, considering the data sources, machine learning methods, and the seasonal effects.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Availability of data and materials
The datasets used during the current study are available from the corresponding author on reasonable request.
References
Abbas AW, Minallh N, Ahmad N et al (2016) K-means and ISODATA clustering algorithms for landcover classification using remote sensing. Sindh Univ Res J 48(2):315–318
Abdi AM (2020) Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data. Gisci Remote Sens 57(1):1–20. https://doi.org/10.1080/15481603.2019.1650447
Abdikan S, Sanli FB, Ustuner M, Calò F (2016) Land cover mapping using Sentinel-1 SAR data. Int Arch Photogramm Remote Sens Spatial Inf Sci 41:757–761. https://doi.org/10.5194/isprs-archives-XLI-B7-757-2016
Abdollahi A, Pradhan B (2021a) Integrating semantic edges and segmentation information for building extraction from aerial images using UNet. Mach Learn Appl 6:100194. https://doi.org/10.1016/j.mlwa.2021.100194
Abdollahi A, Pradhan B (2021b) Urban vegetation mapping from aerial imagery using Explainable AI (XAI). Sensors 21(14):4738. https://doi.org/10.3390/s21144738
Abdollahi A, Pradhan B, Shukla N, Chakraborty S, Alamri A (2020b) Deep learning approaches applied to remote sensing datasets for road extraction: a state-of-the-art review. Remote Sens 12(9):1444. https://doi.org/10.3390/rs12091444
Abdollahi A, Pradhan B, Sharma G, Maulud KNA, Alamri A (2021a) Improving road semantic segmentation using generative adversarial network. IEEE Access 9:64381–64392. https://doi.org/10.1109/ACCESS.2021.3075951
Abdollahi A, Pradhan B, Shukla N, Chakraborty S, Alamri A (2021b) Multi-object segmentation in complex urban scenes from high-resolution remote sensing data. Remote Sens 13(18):3710. https://doi.org/10.3390/rs13183710
Abdollahi A, Pradhan B, Alamri A (2020a) An ensemble architecture of deep convolutional Segnet and Unet networks for building semantic segmentation from high-resolution aerial images. Geocarto Int:1-16 https://doi.org/10.1080/10106049.2020a.1856199
Açıksarı E, Akçay Ö (2018) İkili PolSAR görüntülerinin çok zamanlı ayrışma ve kontrolsüz sınıflandırma analizi. Çanakkale Onsekiz Mart Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 4(2):69–87 https://doi.org/10.28979/comufbed.418458 (In Turkish)
Adagbasa EG, Adelabu SA, Okello TW (2022) Application of deep learning with stratified K-fold for vegetation species discrimation in a protected mountainous region using Sentinel-2 image. Geocarto Int 37(1):142–162. https://doi.org/10.1080/10106049.2019.1704070
Adrian J, Sagan V, Maimaitijiang M (2021) Sentinel SAR-optical fusion for crop type mapping using deep learning and Google Earth Engine. ISPRS J Photogramm Remote Sens 175:215–235. https://doi.org/10.1016/j.isprsjprs.2021.02.018
Ali I, Cao S, Naeimi V, Paulik C, Wagner W (2018) Methods to remove the border noise from Sentinel-1 synthetic aperture radar data: implications and importance for time-series analysis. IEEE J Sel Top Appl Earth Obs Remote Sens 11(3):777–786. https://doi.org/10.1109/jstars.2017.2787650
Al-Najjar HA, Kalantar B, Pradhan B, Saeidi V, Halin AA, Ueda N, Mansor S (2019) Land cover classification from fused DSM and UAV images using convolutional neural networks. Remote Sens 11(12):1461. https://doi.org/10.3390/rs11121461
Arango RB, Díaz I, Campos A, Canas ER, Combarro EF (2016) Automatic arable land detection with supervised machine learning. Earth Sci Inform 9(4):535–545. https://doi.org/10.1007/s12145-016-0270-6
Baatz M, Schäpe A (2000) Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation. In: Strobl J et al (eds) Angewandte geographische informationsverarbeitung. Wichmann-Verlag, Heidelberg, pp 12–23
Balzter H, Cole B, Thiel C, Schmullius C (2015) Mapping CORINE land cover from Sentinel-1A SAR and SRTM digital elevation model data using random forests. Remote Sens 7(11):14876–14898. https://doi.org/10.3390/rs71114876
Belgiu M, Csillik O (2018) Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis. Remote Sens Environ 204:509–523. https://doi.org/10.1016/j.rse.2017.10.005
Benz UC, Hofmann P, Willhauck G, Lingenfelder I, Heynen M (2004) Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information ISPRS J Photogramm Remote Sens 58(3–4):239–258. https://doi.org/10.1016/j.isprsjprs.2003.10.002
Betrie GD, Tesfamariam S, Morin KA, Sadiq R (2013) Predicting copper concentrations in acid mine drainage: a comparative analysis of five machine learning techniques. Environ Monit Assess 185(5):4171–4182. https://doi.org/10.1007/s10661-012-2859-7
Bioresita F, Puissant A, Stumpf A, Malet JP (2019) Fusion of Sentinel-1 and Sentinel-2 image time series for permanent and temporary surface water mapping. Int J Remote Sens 40(23):9026–9049. https://doi.org/10.1080/01431161.2019.1624869
Blaschke T, Burnett C, Pekkarinen A (2004) Image segmentation methods for object-based analysis and classification. In: Jong SMD, Meer FDV (ed) Remote sensing image analysis: Including the spatial domain, Springer, Dordrecht, pp 211–236 https://doi.org/10.1007/978-1-4020-2560-0_12
Blaschke T, Lang S, Hay G (2008) Object-based image analysis: spatial concepts for knowledge-driven remote sensing applications. Springer-Verlag, Berlin. https://doi.org/10.1007/978-3-540-77058-9
Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. Proceedings of the fifth annual workshop on Computational learning theory, Pittsburgh, pp 144–152 https://doi.org/10.1145/130385.130401
Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324
Calò F, Abdikan S, Gorım T, Pepe A, Kilic H, Sanli FB (2015) The space-borne SBAS-DInSAR technique as a supporting tool for sustainable urban policies: the case of Istanbul Megacity, Turkey. Remote Sens 7(12):16519–16536. https://doi.org/10.3390/rs71215842
Cavallaro G, Riedel M, Richerzhagen M, Benediktsson JA, Plaza A (2015) On understanding big data impacts in remotely sensed image classification using support vector machine methods. IEEE J Sel Top Appl Earth Obs Remote Sens 8(10):4634–4646. https://doi.org/10.1109/JSTARS.2015.2458855
Chaudhary A, Kolhe S, Kamal R (2016) An improved random forest classifier for multi-class classification. Inf Process in Agric 3(4):215–222. https://doi.org/10.1016/j.inpa.2016.08.002
Chen KS, Huang WP, Tsay DH, Amar F (1996) Classification of multifrequency polarimetric SAR imagery using a dynamic learning neural network. IEEE T Geosci Remote 34(3):814–820. https://doi.org/10.1109/36.499786
Clerici N, ValbuenaCalderón CA, Posada JM (2017) Fusion of Sentinel-1A and Sentinel-2A data for land cover mapping: a case study in the lower Magdalena region, Colombia. J Maps 13(2):718–726. https://doi.org/10.1080/17445647.2017.1372316
Comert R, Matci DK, Avdan U (2019) Object based burned area mapping with random forest algorithm. Int J Eng Geosci 4(2):78–87. https://doi.org/10.26833/ijeg.455595
Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37(1):35–46. https://doi.org/10.1016/0034-4257(91)90048-B
Congalton RG, Green K (2019) Assessing the accuracy of remotely sensed data: principles and practices. CRC press, Boca Raton, FL. https://doi.org/10.1201/9780429052729
Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE T Inform Theory 13(1):21–27. https://doi.org/10.1109/TIT.1967.1053964
da Silva CA, Nanni MR, Teodoro PE, Silva GFC (2017) Vegetation indices for discrimination of soybean areas: a new approach. Agron J 109(4):1331–1343. https://doi.org/10.2134/agronj2017.01.0003
Dostálová A, Hollaus M, Milenković M, Wagner W (2016) Forest area derivation from Sentinel-1 data. ISPRS Ann Photogramm Remote Sens Spat Inf Sci 3(7):227–233. https://doi.org/10.5194/isprsannals-III-7-227-2016
Duro DC, Franklin SE, Dubé MG (2012) A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sens Environ 118:259–272. https://doi.org/10.1016/j.rse.2011.11.020
El Naqa I, Murphy MJ (2015) What is machine learning? In: Machine learning in radiation oncology. Springer, Cham, pp 3–11 https://doi.org/10.1007/978-3-319-18305-3_1
Environmental and Social Impact Assessment for the Eurasia Tunnel Project Istanbul, Turkey. 2011, Volume I. Project No. P0106067: Draft report http://www.eib.org/attachments/pipeline/20090678_nts_en.pdf Accessed 24 August 2021
European Space Agency (ESA) https://sentinel.esa.int/web/sentinel/home. Accessed 20 August 2021
Fauvel M, Chanussot J, Benediktsson JA (2009) Kernel principal component analysis for the classification of hyperspectral remote sensing data over urban areas. EURASIP J Adv Sig Pr 2009:1–14. https://doi.org/10.1155/2009/783194
Freixenet J, Muñoz X, Raba D, Martí J, Cufí X (2002) Yet another survey on image segmentation: region and boundary information integration. In: Heyden A, Sparr G, Nielsen M, Johansen P (ed) European conference on computer vision. Springer, Berlin, Heidelberg, pp 408–422 https://doi.org/10.1007/3-540-47977-5_27
Gaber A, Soliman F, Koch M, El-Baz F (2015) Using full-polarimetric SAR data to characterize the surface sediments in desert areas: a case study in El-Gallaba Plain, Egypt. Remote Sens Environ 162:11–28. https://doi.org/10.1016/j.rse.2015.01.024
Gharbi M, Chen J, Barron JT, Hasinoff SW, Durand F (2017) Deep bilateral learning for real-time image enhancement. ACM T Graphic 36(4):1–12. https://doi.org/10.1145/3072959.3073592
Ghosh A, Mishra NS, Ghosh S (2011) Fuzzy clustering algorithms for unsupervised change detection in remote sensing images. Inf Sci 181(4):699–715. https://doi.org/10.1016/j.ins.2010.10.016
Gibril MBA, Bakar SA, Yao K, Idrees MO, Pradhan B (2017) Fusion of RADARSAT-2 and multispectral optical remote sensing data for LULC extraction in a tropical agricultural area. Geocarto Int. 32(7):735–748. https://doi.org/10.1080/10106049.2016.1170893
Goldewijk KK (2001) Estimating global land use change over the past 300 years: the HYDE database. Global Biogeochem Cy 15(2):417–443. https://doi.org/10.1029/1999GB001232
Gordon LJ, Steffen W, Jönsson BF, Folke C, Falkenmark M, Johannessen Å (2005) Human modification of global water vapor flows from the land surface. P Natl Acad Sci USA 102(21):7612–7617. https://doi.org/10.1073/pnas.0500208102
Hansen MC, Loveland TR (2012) A review of large area monitoring of land cover change using Landsat data. Remote Sens Environ 122:66–74. https://doi.org/10.1016/j.rse.2011.08.024
Hara Y, Atkins RG, Yueh SH, Shin RT, Kong JA (1994) Application of neural networks to radar image classification. IEEE T Geosci Remote 32(1):100–109. https://doi.org/10.1109/36.285193
Helber P, Bischke B, Dengel A, Borth D (2019) Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification. IEEE J Sel Top Appl Earth Obs Remote Sens 12(7):2217–2226. https://doi.org/10.1109/JSTARS.2019.2918242
Hengl T, Walsh MG, Sanderman J, Wheeler I, Harrison SP, Prentice IC (2018) Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potential. PeerJ 6:e5457. https://doi.org/10.7717/peerj.5457
Hornacek M, Wagner W, Sabel D et al (2012) Potential for high resolution systematic global surface soil moisture retrieval via change detection using Sentinel-1. IEEE J Sel Top Appl Earth Obs Remote Sens 5(4):1303–1311. https://doi.org/10.1109/JSTARS.2012.2190136
Hossain MD, Chen D (2019) Segmentation for object-based image analysis (OBIA): a review of algorithms and challenges from remote sensing perspective. ISPRS J Photogramm Remote Sens 150:115–134. https://doi.org/10.1016/j.isprsjprs.2019.02.009
Huang C, Davis LS, Townshend JRG (2002) An assessment of support vector machines for land cover classification. Int J Remote Sens 23(4):725–749. https://doi.org/10.1080/01431160110040323
Hudait M, Patel PP (2022) Crop-type mapping and acreage estimation in smallholding plots using Sentinel-2 images and machine learning algorithms: some comparisons. Egypt J Remote Sens Space Sci 25(1):147–156. https://doi.org/10.1016/j.ejrs.2022.01.004
Immitzer M, Vuolo F, Atzberger C (2016) First experience with Sentinel-2 data for crop and tree species classifications in central Europe. Remote Sens 8(3):166. https://doi.org/10.3390/rs8030166
Jhonnerie R, Siregar VP, Nababan B, Prasetyo LB, Wouthuyzen S (2015) Random forest classification for mangrove land cover mapping using Landsat 5 TM and ALOS PALSAR imageries. Procedia Environ Sci 24:215–221. https://doi.org/10.1016/j.proenv.2015.03.028
Jia K, Liang S, Zhang L, Wei X, Yao Y, Xie X (2014) Forest cover classification using Landsat ETM+ data and time series MODIS NDVI data. Int J Appl Earth Obs 33:32–38. https://doi.org/10.1016/j.jag.2014.04.015
Joshi N, Baumann M, Ehammer A et al (2016) A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sens 8(1):70. https://doi.org/10.3390/rs8010070
Jozdani SE, Johnson BA, Chen D (2019) Comparing deep neural networks, ensemble classifiers, and support vector machine algorithms for object-based urban land use/land cover classification. Remote Sens 11(14):1713. https://doi.org/10.3390/rs11141713
Kavzoglu T, Tonbul H (2017) A comparative study of segmentation quality for multi-resolution segmentation and watershed transform. In: 2017 IEEE 8th International Conference on Recent Advances in Space Technologies (RAST) pp 113–117. https://doi.org/10.1109/RAST.2017.8002984
Kennedy RE, Townsend PA, Gross JE et al (2009) Remote sensing change detection tools for natural resource managers: understanding concepts and tradeoffs in the design of landscape monitoring projects. Remote Sens Environ 113(7):1382–1396. https://doi.org/10.1016/j.rse.2008.07.018
Kuemmerle T, Erb K, Meyfroidt P et al (2013) Challenges and opportunities in mapping land use intensity globally. Curr Opin Env Sust 5(5):484–493. https://doi.org/10.1016/j.cosust.2013.06.002
Lambin EF, Turner BL, Geist HJ et al (2001) The causes of land-use and land-cover change: moving beyond the myths. Global Environ Chang 11(4):261–269. https://doi.org/10.1016/S0959-3780(01)00007-3
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444. https://doi.org/10.1038/nature14539
Lee JS (1986) Speckle suppression and analysis for synthetic aperture radar images. Opt Eng 25(5):255636. https://doi.org/10.1117/12.7973877
Lee TM, Yeh HC (2009) Applying remote sensing techniques to monitor shifting wetland vegetation: a case study of Danshui River estuary mangrove communities. Taiwan Ecol Eng 35(4):487–496. https://doi.org/10.1016/j.ecoleng.2008.01.007
Liaw A, Wiener M (2002) Classification and regression by random Forest. R News 2(3):18–22
Liu J, Heiskanen J, Aynekulu E, Pellikka PKE (2015) Seasonal variation of land cover classification accuracy of Landsat 8 images in Burkina Faso. Int Arch Photogramm Remote Sens Spatial Inf Sci 40(7):455. https://doi.org/10.5194/isprsarchives-XL-7-W3-455-2015
López E, Bocco G, Mendoza M, Duhau E (2001) Predicting land-cover and land-use change in the urban fringe: a case in Morelia city. Mexico Landsc Urban Plan 55(4):271–285. https://doi.org/10.1016/S0169-2046(01)00160-8
Maktav D, Erbek FS (2005) Analysis of urban growth using multi-temporal satellite data in Istanbul. Turkey Int J Remote Sens 26(4):797–810. https://doi.org/10.1080/01431160512331316784
Mathur A, Foody GM (2008) Crop classification by support vector machine with intelligently selected training data for an operational application. Int J Remote Sens 29(8):2227–2240. https://doi.org/10.1080/01431160701395203
Maxwell AE, Warner TA, Strager MP, Conley JF, Sharp AL (2015) Assessing machine-learning algorithms and image-and lidar-derived variables for GEOBIA classification of mining and mine reclamation. Int J Remote Sens. 36(4):954–978. https://doi.org/10.1080/01431161.2014.1001086
Maxwell AE, Warner TA, Fang F (2018) Implementation of machine-learning classification in remote sensing: an applied review. Int J Remote Sens 39(9):2784–2817. https://doi.org/10.1080/01431161.2018.1433343
Meroni M, d’Andrimont R, Vrieling A et al (2021) Comparing land surface phenology of major European crops as derived from SAR and multispectral data of Sentinel-1 and-2. Remote Sens Environ 253:112232. https://doi.org/10.1016/j.rse.2020.112232
Mohajane M, Costache R, Karimi F et al (2021) Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area. Ecol Indic 129:107869. https://doi.org/10.1016/j.ecolind.2021.107869
Mojaddadi H, Pradhan B, Nampak H, Ahmad N, Ghazali AHB (2017) Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS. Geomat Nat Haz Risk 8(2):1080–1102. https://doi.org/10.1080/19475705.2017.1294113
Moran EF, Brondizio E (1998) Land-use change after deforestation in Amazonia. People and pixels: linking remote sensing and social science, National Academy Press, Washington DC, pp 94–120
Mountrakis G, Im J, Ogole C (2011) Support vector machines in remote sensing: a review. ISPRS J Photogramm Remote Sens 66(3):247–259. https://doi.org/10.1016/j.isprsjprs.2010.11.001
Muller-Wilm U, Louis J, Richter R, Gascon F, Niezette M (2013) Sentinel-2 level 2A prototype processor: architecture, algorithms and first results. Proceedings of the ESA Living Planet Symposium, Edinburgh, UK, pp 9–13
Muro J, Canty M, Conradsen K et al (2016) Short-term change detection in wetlands using Sentinel-1 time series. Remote Sens 8(10):795. https://doi.org/10.3390/rs8100795
Mustak SK, Uday G, Ramesh B, Praveen B (2019) Evaluation of the performance of SAR and SAR-optical fused dataset for crop discrimination. Int Arch Photogramm Remote Sens Spatial Inf Sci XLII-3/W6(3):563–571
Naghibi SA, Vafakhah M, Hashemi H, Pradhan B, Alavi SJ (2020) Water resources management through flood spreading project suitability mapping using frequency ratio, k-nearest neighbours, and random forest algorithms. Nat Resour Res 29(3):1915–1933. https://doi.org/10.1007/s11053-019-09530-4
Noi TP, Kappas M (2018) Comparison of random forest, k-Nearest neighbor, and support vector machine classifiers for land cover classification using Sentinel-2 imagery. Sensors 18(1):18. https://doi.org/10.3390/s18010018
Oguro Y, Suga Y, Takeuchi S et al (2001) Comparison of SAR and optical sensor data for monitoring of rice plant around Hiroshima. Adv Space Res 28(1):195–200. https://doi.org/10.1016/S0273-1177(01)00345-3
Olofsson P, Foody GM, Herold M, Stehman SV, Woodcock CE, Wulder MA (2014) Good practices for estimating area and assessing accuracy of land change. Remote Sens Environ 148:42–57. https://doi.org/10.1016/j.rse.2014.02.015
Pereira LDO, Freitas CDC, Sant´ Anna SJS, Lu D, Moran EF, (2013) Optical and radar data integration for land use and land cover mapping in the Brazilian Amazon. GISci Remote Sens. 50(3):301–321. https://doi.org/10.1080/15481603.2013.805589
Pesaresi S, Mancini A, Casavecchia S (2020) Recognition and characterization of forest plant communities through remote-sensing NDVI time series. Diversity 12(8):313. https://doi.org/10.3390/d12080313
Pettorelli N, Laurance WF, O’Brien TG, Wegmann M, Nagendra H, Turner W (2014) Satellite remote sensing for applied ecologists: opportunities and challenges. J Appl Ecol 51(4):839–848. https://doi.org/10.1111/1365-2664.12261
Pradhan B, Al-Najjar HA, Sameen MI, Tsang I, Alamri A (2020) Unseen land cover classification from high-resolution orthophotos using integration of zero-shot learning and convolutional neural networks. Remote Sens 12(10):1676. https://doi.org/10.3390/rs12101676
Puletti N, Chianucci F, Castaldi C (2018) Use of Sentinel-2 for forest classification in Mediterranean environments. Ann Silvic Res 42:32–38. https://doi.org/10.12899/asr-1463
Rana VK, Suryanarayana TMV (2020) Performance evaluation of MLE, RF and SVM classification algorithms for watershed scale land use/land cover mapping using sentinel 2 bands. Remote Sens Appl Soc Environ 19:100351. https://doi.org/10.1016/j.rsase.2020.100351
Rodriguez-Galiano VF, Ghimire B, Rogan J, Chica-Olmo M, Rigol-Sanchez JP (2012) An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J Photogramm Remote Sens 67:93–104. https://doi.org/10.1016/j.isprsjprs.2011.11.002
Schiewe J (2002) Segmentation of high-resolution remotely sensed data-concepts, applications and problems. Int Arch Photogramm Remote Sens Spatial Inf Sci 34(4):380–385
Schmitt M, Tupin F, Zhu XX (2017) Fusion of SAR and optical remote sensing data—challenges and recent trends. 2017 IEEE Int Geosc Remote Sens Symp:5458–5461 https://doi.org/10.1109/IGARSS.2017.8128239
Stumpf A, Kerle N (2011) Object-oriented mapping of landslides using random forests. Remote Sens Environ 115(10):2564–2577. https://doi.org/10.1016/j.rse.2011.05.013
Su T, Liu T, Zhang S, Qu Z, Li R (2020) Machine learning-assisted region merging for remote sensing image segmentation. ISPRS J Photogramm Remote Sens 168:89–123. https://doi.org/10.1016/j.isprsjprs.2020.07.017
Sun C, Bian Y, Zhou T, Pan J (2019) Using of multi-source and multi-temporal remote sensing data improves crop-type mapping in the subtropical agriculture region. Sensors 19(10):2401. https://doi.org/10.3390/s19102401
Talukdar S, Singha P, Mahato S et al (2020) Land-use land-cover classification by machine learning classifiers for satellite observations—a review. Remote Sens 12(7):1135. https://doi.org/10.3390/rs12071135
Taufik A, Ahmad SSS, Ahmad A (2016) Classification of Landsat 8 satellite data using NDVI thresholds. J Telecommun Electron Comput Eng 8(4):37–40
Tehrany MS, Pradhan B, Jebuv MN (2014) A comparative assessment between object and pixel-based classification approaches for land use/land cover mapping using SPOT 5 imagery. Geocarto Int. 29(4):351–369. https://doi.org/10.1080/10106049.2013.768300
Teluguntla P, Thenkabail PS, Oliphant A et al (2018) A 30-m Landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform. ISPRS J Photogramm Remote Sens 144:325–340. https://doi.org/10.1016/j.isprsjprs.2018.07.017
Topaloğlu RH, Sertel E, Musaoğlu N (2016) Assessment of classification accuracies of Sentinel-2 and Landsat-8 data for land cover/use mapping. Int Arch Photogramm Remote Sens Spatial Inf Sci 41(8):1055–1059. https://doi.org/10.5194/isprsarchives-XLI-B8-1055-2016
Torres R, Snoeij P, Geudtner D et al (2012) GMES Sentinel-1 mission. Remote Sens Environ 120:9–24. https://doi.org/10.1016/j.rse.2011.05.028
Turkish Statistical Institute. Available online: http://www.turkstat.gov.tr Accessed 20 August 2021
Ustuner M, Sanli FB, Dixon B (2015) Application of support vector machines for landuse classification using high-resolution rapideye images: a sensitivity analysis. Eur J Remote Sens 48(1):403–422. https://doi.org/10.5721/EuJRS20154823
Van Beijma S, Comber A, Lamb A (2014) Random forest classification of salt marsh vegetation habitats using quad-polarimetric airborne SAR, elevation and optical RS data. Remote Sens Environ 149:118–129. https://doi.org/10.1016/j.rse.2014.04.010
Vanhellemont Q, Ruddick K (2016) Acolite for Sentinel-2: aquatic applications of MSI imagery. Proceedings of the 2016 ESA Living Planet Symposium, Prague, Czech Republic, pp 9–13
Wang J, Xiao X, Liu L et al (2020) Mapping sugarcane plantation dynamics in Guangxi, China, by time series Sentinel-1, Sentinel-2 and Landsat images. Remote Sens Environ 247:111951. https://doi.org/10.1016/j.rse.2020.111951
Wang P, Fan E, Wang P (2021) Comparative analysis of image classification algorithms based on traditional machine learning and deep learning. Pattern Recogn Lett 141:61–67. https://doi.org/10.1016/j.patrec.2020.07.042
Washaya P, Balz T, Mohamadi B (2018) Coherence change-detection with sentinel-1 for natural and anthropogenic disaster monitoring in urban areas. Remote Sens 10(7):1026. https://doi.org/10.3390/rs10071026
Weih RC, Riggan ND (2010) Object-based classification vs. pixel-based classification: comparative importance of multi-resolution imagery. Int Arch Photogramm Remote Sens Spatial Inf Sci 38(4):C7
Whiteside TG, Boggs GS, Maier SW (2011) Comparing object-based and pixel-based classifications for mapping savannas. Int J Appl Earth Obs Geoinf 13(6):884–893. https://doi.org/10.1016/j.jag.2011.06.008
Willhauck G, Schneider T, De Kok R, Ammer U (2000) Comparison of object oriented classification techniques and standard image analysis for the use of change detection between SPOT multispectral satellite images and aerial photos. Proceedings of XIX ISPRS Congress, Amsterdam 33:35–42
Wu M, Chen L (2015) Image recognition based on deep learning. In: 2015 IEEE Chinese Automation Congress pp 542–546 https://doi.org/10.1109/CAC.2015.7382560
Xing X, Yan C, Jia Y et al (2020) An effective high spatiotemporal resolution NDVI fusion model based on histogram clustering. Remote Sens 12(22):3774. https://doi.org/10.3390/rs12223774
Xu X, Scanlon BR, Schilling K, Sun A (2013) Relative importance of climate and land surface changes on hydrologic changes in the US Midwest since the 1930s: implications for biofuel production. J Hydrol 497:110–120. https://doi.org/10.1016/j.jhydrol.2013.05.041
Yan L, Roy DP, Zhang H, Li J, Huang H (2016) An automated approach for sub-pixel registration of Landsat-8 operational land imager (OLI) and Sentinel-2 multi spectral instrument (MSI) imagery. Remote Sens 8(6):520. https://doi.org/10.3390/rs8060520
Zhang Y, Zhang J, Zhang X, Wu H, Guo M (2015) Land cover classification from polarimetric SAR data based on image segmentation and decision trees. Can J Remote Sens 41(1):40–50. https://doi.org/10.1080/07038992.2015.1032901
Zhang C, Sargent I, Pan X et al (2018) An object-based convolutional neural network (OCNN) for urban land use classification. Remote Sens Environ 216:57–70. https://doi.org/10.1016/j.rse.2018.06.034
Zhao ZQ, Zheng P, Xu ST, Wu X (2019) Object detection with deep learning: a review. IEEE T Neur Net Lear 30(11):3212–3232. https://doi.org/10.1109/TNNLS.2018.2876865
Zhou J, Civco DL, Silander JA (1998) A wavelet transform method to merge Landsat TM and SPOT panchromatic data. Int J Remote Sens 19(4):743–757. https://doi.org/10.1080/014311698215973
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of interest
The authors declare that they have no competing interests.
Additional information
Responsible Editor: Biswajeet Pradhan
Rights and permissions
About this article
Cite this article
Polat, A.B., Akcay, O. & Balik Sanli, F. Monitoring seasonal effects in vegetation areas with Sentinel-1 SAR and Sentinel-2 optic satellite images. Arab J Geosci 15, 670 (2022). https://doi.org/10.1007/s12517-022-09947-x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12517-022-09947-x