Araştırma Makalesi
BibTex RIS Kaynak Göster

Comparison of Different Classification Approaches for Land Cover Classification using Multispectral and Fusion Satellite Data: A Case Study in Ören Forest Planning Unit

Yıl 2021, Cilt: 23 Sayı: 1, 306 - 322, 15.04.2021
https://doi.org/10.24011/barofd.882471

Öz

In this study, the success of different satellite images and classification approaches in land cover (LC) classification were compared. A total of six satellite images, including two passive (Landsat 8 OLI (L8) and Sentinel-2 (S2)) satellite images and four fused satellite images from active (Sentinel-1(S1)-VH and VV polarization) and passive satellite images (L8-S1-VH, L8-S1-VV, S2-S1-VH and S2-S1-VV) were used in the classification in the study. For this purpose, L8, S2, L8-S1-VH, L8-S1-VV, S2-S1-VH and S2-S1-VV satellite images were classified according to three ((Maximum Likelihood Classification (MLC), Support Vector Machine (SVM) and Artificial Neural Networks (ANN)) different image classification approaches using the forest cover types map as gorund data. The results obtained from classification methods were evaluated based on overall accuracies (OA) and kappa coefficients (KC). When the classification successes obtained from the three classification methods are evaluated, it was observed that the KC ranged from 0.66 to 0.95 and the OA ranged from 76.82% to 96.67. The results indicated that the highest OA was displayed by MLC (ranged 85.33% to 96.67%), closely followed by SVM (ranged 80.11% to 91.93%), and finally ANN (ranged 76.82% to 89.92%). In addition, a comparison of classification performance using three utilized classification algorithms was performed. The S1-VH; S1-VV and, S2 and L8 fused images classified with an MLC algorithm produce the most accurate LC map, indicating an OA of 92.00%, 94.00%, 96.67%, 93.33% and a KC of 0.90, 0.93, 0.95, 0.92 for S2 and L8, respectively. Thus, it can be concluded that fused of satellite images improve the accuracies of LC classification.

Teşekkür

I would like to thank to for support to the Head of Forest Management and Planning Department, General Directorate of Forestry, Republic of Turkey.

Kaynakça

  • Abdikan, S. (2018). Exploring image fusion of ALOS/PALSAR data and Landsat data to differentiate forest area. Geocarto International, 33(1), 21-37.
  • Anonymous (2018). Ankara Regional Directorate of Forestry, Eskipazar Forest Managemet Enterprise, Ören Forest managemet planning unit ecosystem based multiple use forest management planning. P:247.
  • Bagan, H., Kinoshita, T., Yamagata, Y. (2012). Combination of AVNIR-2, PALSAR, and polarimetric parameters for land cover classification. IEEE Transactions on Geoscience and Remote Sensing, 50(4), 1318–1328.
  • Ban, Y., Peng, G., Giri, C. (2015). Global land cover mapping using Earth observation satellite data: Recent progresses and challenges. ISPRS Journal of Photogrammetry and Remote Sensing, 10.1016/j.isprsjprs.2015.01.001.
  • Bulut, S., Günlü, A. (2016). Comparison of different supervised classification algorithms for land use classes. Kastamonu University, Journal of Forestry Faculty, 16 (2), 528-535.
  • Burkhard, B., Kroll, F., Nedkov, S., Müller, F. (2012). Mapping ecosystem service supply, demand and budgets. Ecol. Indic. 21, 17–29.
  • Büyüksalih, İ. (2016). Landsat images classification and change analysis of land cover/use in Istanbul. International Journal of Environment and Geoinformatics, 3(2), 56-65.
  • Camargo, F.F., Sano, E.E., Almedia, M., Mura, J.C., Almedia, T. (2019). 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, 11, 1600.
  • Clerici, N., Calderon, C.A.V., Posada, J.M. (2017). Fusion of Sentinel-1A and Sentinel-2A data for land cover mapping: a case study in the lower Magdalena region, Colombia. Journal of Maps, 13(2), 718–726.
  • Deus, D. (2016). Integration of ALOS PALSAR and Landsat data for land cover and forest mapping in Northern Tanzania. Land, 5, 43.
  • Dixon, B., Candade, N. (2008). Multispectral landuse classification using neural networks and support vector machines: one or the other, or both?. International Journal of Remote Sensing, 29(4), 1185-1206.
  • Elatawneh, A., Kalaitzidis, C., Petropoulos, G.P., Schneider, T. (2014). Evaluation of diverse classification approaches for land use/cover mapping in a mediterranean region utilizing hyperion data. International Journal of Digital Earth, 7( 3),194–216.
  • Erasmi, S., Twele, A. (2009). Regional land over mapping in the humid tropics using combined optical and SAR satellite data – a case study from Central Sulawesi, Indonesia. International Journal of Remote Sensing, 30, (10), 2465–2478.
  • Fonteh, M.L., Theophile, F., Cornelius, M.L., Main, R., Ramoelo, A., Cho, M.A. (2016). Assessing the utility of Sentinel-1 C band synthetic aperture radar imagery for land use land cover classification in a tropical coastal systems when compared with Landsat 8. Journal of Geographic Information System, 8, 495-505.
  • Fry, J.A., Xian, G., Jin, S., Dewitz, J.A., Homer, C.G., Yang, L., Barnes, C.A., Herold, N.D., Wickham, J.D. (2012). Completion of the 2006 national land cover database for the conterminous United States. Photogramm. Eng. Remote Sens., 77, 858–864.
  • Gebhardt, S., Wehrmann, T., Ruiz, M.A.M., Maeda, P., Bishop, J., Schramm, M., Kopeinig, R., Cartus, O., Kellndorfer, J., Ressl, R., Santos, L.A., Schmidt, M. (2014). MAD-MEX: Automatic wall-to-wall land cover monitoring for the Mexican REDD-MRV program using all Landsat data. Remote Sens., 6, 3923-3943.
  • Gomez, C., Mangeas, M., Petit, M., Corbane, C., Hamon, P. (2010). Use of high-resolution satellite imagery in an integrated model to predict the distribution of shade coffee tree hybrid zones. Remote Sens. Environ., 114, 2731–2744.
  • Guidici, D., Clark, M.L. (2017). One-dimensional convolutional neural network land-cover classification of multi-seasonal hyperspectral imagery in the San Francisco Bay area, California. Remote Sens., 9, 629.
  • Günlü, A., Sivrikaya, F., Başkent, E.Z., Keleş, S., Çakır, G., Kadıoğulları, A.İ. (2008). Estimation of stand type parameters and land cover using Landsat-7 ETM image: a case study from Turkey. Sensors, 8, 2509-2525.
  • Han, N., Wang, K., Yu, L., Zhang, X. (2012). Integration of texture and landscape features into object-based classification for delineating Torreya using IKONOS imagery. Int. J. Remote Sens., 33(7), 2003–2033.
  • Hong, G., Zhang, A., Zhou, F., Brisco, B. (2014). Integration of optical and synthetic aperture radar (SAR) images to differentiate grassland and alfalfa in Prairie area. Int. J. Appl. Earth Obs. Geoinf., 28 (1), 12–19.
  • Hussain, S., Mubeen, M., Ahmad, A., Akram, W., Hammad, H.M., Ali, M., Masood, N., Amin, A., Farid, H.U., Sultana, S.R., Fahad, S., Wang, D., Nasim, W. (2020). Using GIS tools to detect the land use/land cover changes during forty years in Lodhran district of Pakistan. Environmental Science and Pollution Research, https://doi.org/10.1007/s11356-019-06072-3.
  • Hütt, C., Koppe, W., Miao, Y., Bareth, G. (2016). Best accuracy Land use/Land cover (LULC) classification to derive crop types using multitemporal, multisensor, and multi-polarization SAR satellite images. Remote Sens., 8, 684.
  • Hyde, P., Dubayah, R., Walker, W., Blair, J.B., Hofton, M., Hunsaker, C. (2006). Mapping forest structure for wildlife habitat analysis using multi-sensor (LiDAR, SAR/inSAR, ETM+, QuickBird) synergy. Remote Sens. Environ., 102, 63–73.
  • Jia, K., Wu, B.F., Tian, Y.C., Zeng, Y., Li, Q.Z. (2011). Vegetation classification method with biochemical composition estimated from remote sensing data. Int J Remote Sens., 32(24), 9307–9325.
  • Jia, K., Wei, X., Gu, X., Yao, Y., Xie, X., Li, B. (2014). Land cover classification using Landsat 8 operational land imager data in Beijing, China. Geocarto International, 29 (8), 941–951.
  • Juliev, M., Pulatov, A., Fuchs, S., Hübl, J. (2019). Analysis of land use land cover change detection of Bostanlik District, Uzbekistan. Pol. J. Environ. Stud., 28(5), 3235-3242.
  • Khan, A., Govil, H., Kumar, G., Dave, R. (2020). Synergistic use of Sentinel-1 and Sentinel-2 for improved LULC mapping with special reference to bad land class: a case study for Yamuna River floodplain, India. Spat. Inf. Res., https://doi.org/10.1007/s41324-020-00325-x.
  • Khatami, R., Mountrakis, G., Stehman, S.V.A. (2016). 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.
  • Kimes, D.S., Nelson, R.F., Manry, M.T., Fung, A.K. (1998). Attributes of neural networks for extracting continuous vegetation variables from optical and radar measurements. Int. J. Remote Sens., 19, 2639–2663.
  • Knorn, J., Rabe, A., Radeloff, V.C., Kuemmerle, T., Kozak, J., Hostert, P. (2009). Land cover mapping of large areas using chain classification of neighboring Landsat satellite images. Remote. Sens. Environ., 113, 957–964.
  • Kuemmerle, T., Erb, K., Meyfroidt, P., Müller, D., Verburg, P. H., Estel, S., Reenberg, A. (2013). Challenges and opportunities in mapping land use intensity globally. Current Opinion in Environmental Sustainability, 5(5), 484–493.
  • Kulkarni, A.D., Lowe B. (2016). Random forest algorithm for land cover classification. International Journal on Recent and Innovation Trends in Computing and Communication, 4(3), 58-63.
  • Kussul, N., Lemoine, G., Gallego, F.J., Skakun, S.V., Lavreniuk, M., Shelestov, A.Y. (2016). Parcel-based crop classification in Ukraine using Landsat-8 data and Sentinel-1A data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(6), 2500–2508.
  • Lillesand, T., Kiefer, R., Chipman, J. (2004). Remote Sensing and Image Interpretation. 5th Edn., John Wiley and Sons, New York, USA.
  • Lu, D., Weng, Q. A. (2007). Survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28, 823- 870.
  • Lu, D., Batistella, M., Li, G., Moran, E., Hetrick, S., Freitas, C.D.C., Futra, L.V., Anna, J.S.S. (2012). Land use/cover classification in the Brazilian Amazon using satellite images. Brasília, 47(9), 1185-1208.
  • Lu, D., Li, G., Moran, E., Kuang, W. (2014). A comparative analysis of approaches for successional vegetation classification in the Brazilian Amazon. GIScience & Remote Sensing, 51(6), 695–709.
  • Mann, D., Joshi, P.K. (2017). Evaluation of image classification algorithms on hyperion and aster data for land cover classification. Proceedings of the National Academy of Sciences, India - Section A 87(1). https://doi.org/10.1007/s40010-017-0454-6.
  • Miettinen, J., Liew, S.C. (2011). Separability of insular South east Asian woody plantation species in the 50 m resolution Alos Palsar mosaic product. Remote Sensing Letters, 2, 299–307.
  • Mohajane, M., Essahlaoui, A., Oudija, F., Hafyani, M.E., Hmaidi, A.E., Ouali, A.E., Randazzo, G., Teodora, C. (2018). Land use/Land cover (LULC) using Landsat data series (MSS, TM, ETM+ and OLI) in Azrou Forest, in the Central Middle Atlas of Morocco. Environments, 5, 131.
  • Morgan, R.S., Rahim, I.S., El-Hady, M.A. (2015). A Comparison of classification techniques for the land use/ land cover classification. Global Advanced Research Journal of Agricultural Science, 4(11), 810-818.
  • Mountrakis, G., Im, J., Ogole, C. (2011). Support vector machines in remote sensing: a review. ISPRS J Photogramm Remote Sens 66:247–259
  • Mushore, T.D., Mutanga, O., Odindi, J., Dube, T. (2017). Assessing the potential of integrated Landsat 8 thermal bands, with the traditional reflective bands and derived vegetation indices in classifying urban landscapes. Geocarto InternatIonal, 32(8), 886–899.
  • Muthukumarasamy, I., Shanmugam, R.S., Usha, T. (2019). Incorporation of textural information with SAR and optical imagery for improved land cover mapping. Environmental Earth Sciences, 78, 643.
  • Noi, P.T, 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, 18.
  • Pacifici, F., Frate, F.D., Emery, W.J., Gamba, P., Chanossot, J. (2008). Urban mapping using coarse sar and optical data: outcome of the 2007 GRSS Data Fusion Contest. IEEE Geoscıence And Remote Sensıng Letters, 5(3), 331-335.
  • Pal, M., Mather, P. M. (2004). Assessment of the effectiveness of support vector machines for hyperspectral data. Future Generation Computer Systems, 20(7), 1215-1225.
  • Pradhan, R., Pradhan, M.P., Bhusan, A., Pradhan, R.K., Ghose, M.K. (2010). Land-cover classification and mapping for eastern Himalayan State Sikkim. Journal of Computing, 2(3), 166-170.
  • Rao, K.V.R., Kumar, R. (2017). Land cover classification using Sentinel-1 SAR data. International Journal for Research in Applied Science & Engineering Technology. 5(12), 1054-1060.
  • Ridwan, I., Bisri, M., Yusran, F.H., Hakim, L., Kadir, S. (2017). Identification of characteristics of land cover in mangkauk catchment area Using support vector machine (SVM) and artificial neural network (ANN). American Journal of Applied Sciences, 14 (7), 726-736.
  • Sameen, M.I., Nahhas, F.H., Buraihi, F.H., Pradhan, B., Shariff, A.R.B.M. (2016). A refined classification approach by integrating Landsat Operational Land Imager (OLI) and Radarsat-2 imagery for land-use and land-cover mapping in a tropical area. Internatıonal Journal of Remote Sensıng, 37(10), 2358–2375.
  • Shi D., Yang X. (2015). Support vector machines for land cover mapping from remote sensor imagery. In: Li J., Yang X. (eds) Monitoring and Modeling of Global Changes: A Geomatics Perspective. Springer Remote Sensing/Photogrammetry. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9813-6_13
  • Shivakumar, B.R., Rajashekararadhya, S.V. (2018). Investigation on land cover mapping capability of maximum likelihood classifier: a case study on North Canara, India. Procedia Computer Science, 143, 579–586.
  • Sirro, L., Häme, T., Rauste, Y., Kilpi, J., Hämäläinen, J., Gunia, K., Jong, B.D., Pellat, F.P. (2018). Potential of different optical and SAR data in forest and land cover classification to support REDD+ MRV. Remote Sens., 10, 942.
  • Soria-Ruiz, J., Fernandez-Ordonez, Y., Woodhouse, L. (2010). Land-cover classification using radar and optical images: a case study in Central Mexico. International Journal of Remote Sensing, 31(2), 3291-3305.
  • Srivastava, P.K., Han, D., Rico-Ramirez, M.A., Bray, M., Islam, T. (2012). Selection of classification techniques for land use/land cover change investigation. Advances in Space Research, 50, 1250–1265.
  • Steinhausen, M., Wagner, P.D., Narasimhan, B., Waske, B. (2018). Combining Sentinel-1 and Sentinel-2 data for improved land use and land cover mapping of monsoon regions. International Journal of Applied Earth Observation and Geoinformation, 73, 595-604
  • Sukawattanavijit, C., Chen J. (2015). Fusion of Radarsat-2 imagery with Landsat-8 multispectral data for improving land cover classification performance using SVM. IEEE 5th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR), Singapore, pp. 567-572.
  • Szuster, B.W., Chen, Q., Borger, M. (2011). A comparison of classification techniques to support land cover and land use analysis in tropical coastal zones. Applied Geography, 31, 525-532.
  • Şerifoğlu Yılmaz, Ç., Güngör, O., Kahraman, H.T. (2018). Land cover mapping with advanced classification algorithms, Nature Sciences (NWSANS), 13(3), 41-50.
  • Tavares, P.A., Beltrão, N.E.S., Guimarães, U.S., Teodoro, A.C. (2019). Integration of Sentinel-1 and Sentinel-2 for classification and LULC mapping in the urban area of Belém, Eastern Brazilian Amazon. Sensors, 19, 1140.
  • Topaloglu, H.R., Sertel, E., Musaoglu, N. (2016). Assessment of classification accuracies of Sentinel-2 and Landsat-8 data for land cover/use mapping. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences; ISPRS: Prague, Czech Republic, Volume XLI-B8, pp. 1055–1059.
  • Ullah, S., Tahir, A.A., Akbar, T.A., Hassan, Q.K., Dewan, A., Khan, A.J., Khan, M. (2019). Remote sensing-based quantification of the relationships between land use land cover changes and surface temperature over the lower Himalayan region. Sustainability, 11, 5492.
  • Van-Beijma, S., Comber, A., Lamb, A. (2014). Random forest classification of salt marsh vegetation habitats using quad-polarimetric airborne SAR, elevation and optical remote sensing data. Remote Sens., Environ. 149, 118–129.
  • Verma, P., Raghubanshi, A., Srivastava, P.K., Raghubanshi, A.S. (2020). Appraisal of kappa-based metrics and disagreement indices of accuracy assessment for parametric and nonparametric techniques used in LULC classification and change detection. Model. Earth Syst. Environ., https://doi.org/10.1007/s40808-020-00740-x.
  • Waser, L.T., Ginzler, C., Kuechler, M., Baltsavias, E., Hurni, L. (2011). Semi-automatic classification of tree species in different forest ecosystems by spectral and geometric vaiables derived from Airborne Digital Sensor (ADS40) and RC30 data. Remote Sens. Environ., 115,76–85.
  • Wei, L., Xiangyong, L., Juan D., Yao, L. (2016). Land cover classification of Radarsat-2 SAR data using convolutional neural network. Wuhan University Journal of Natural Sciences, 21(2), 151-158.
  • Xie, Z., Chen, Y., Lu, D., Li, G., Chen, E. (2019). Classification of land cover, forest, and tree species classes with ZiYuan-3 multispectral and stereo data. Remote Sens., 11, 164.
  • Yousefi, S., Mirzaee, S., Tazeh, M., Pourghasemi, H., Karimi, H. (2015). Comparison of different algorithms for land use mapping in dry climate using satellite images: a case study of the Central regions of Iran. Desert, 20(1),1-10.
  • Yuan, H., Wiele, F.V.D., Khorram, S. (2009). An Automated artificial neural network system for land use/land cover classification from Landsat TM imagery. Remote Sens., 1, 243-265.
  • Zakeri, H., Yamazaki, F., Liu W. (2017). Texture Analysis and land cover classification of Tehran using polarimetric synthetic aperture radar imagery. Appl. Sci., 7, 452.
  • Zhang, H., Xu, R. (2018). Exploring the optimal integration levels between SAR and optical data for better urban land cover mapping in the Pearl River Delta. Int. J. Appl. Earth Obs. Geoinf, 64, 87–95.

Multispektral ve Birleştirilmiş Uydu Görüntüleri Kullanılarak Arazi Örtüsü Sınıflandırılmasında Farklı Sınıflandırma Yaklaşımlarının Karşılaştırılması: Ören Orman İşletme Şefliği Örneği

Yıl 2021, Cilt: 23 Sayı: 1, 306 - 322, 15.04.2021
https://doi.org/10.24011/barofd.882471

Öz

Bu çalışmada, arazi örtüsünün sınıflandırılmasında farklı uydu görüntüleri ve sınıflandırma yaklaşımlarının başarıları karşılaştırılmıştır. Çalışmada iki pasif (Landsat 8 OLI (L8) ve Sentinel-2 (S2)) uydu görüntüsü ile birlikte aktif (Sentinel-1 (S1)-VH ve VV polarizasyonlu) ve pasif uydu görüntülerinin birleştirilmesiyle elde edilmiş (L8-S1-VH, L8-S1-VV, S2-S1-VH ve S2-S1-VV) dört uydu görüntüsü olmak üzere toplam altı uydu görüntüsü sınıflandırmada kullanılmıştır. Bu amaçla, L8, S2, L8-S1-VH, L8-S1-VV, S2-S1-VH ve S2-S1-VV uydu görüntüleri yersel veri olarak meşcere tipleri haritası kullanılarak üç farklı görüntü sınıflandırma ((maksimum olasılık sınıflandırması (MOS), Destek Vektör Makineleri (DVM) ve Yapay Sinir Ağları (YSA)) yaklaşımına göre sınıflandrılmıştır.Üç sınıflandırma metodundan elde edilen sınıflandırma başarıları değerlendirildiğinde, Kappa Katsayı (KK)’nın 0,66 ile 0,95, Genel Doğruluğun (GD) ise %76,82 ile 96,67 arasında değiştiği görülmüştür. En yüksek GD'nın MOS ile (%85,33 ile %96,67 arasında), sonra DVM (%80,11 ile %91,93 arasında) ve son olarak YSA (%76,82 ile %89,92 arasında) olduğunu görülmüştür. Bununla birlikte, kullanılan üç sınıflandırma yaklaşımının başarıları karşılaştırılmıştır. Birleştirilmiş S2-S1-VH, S2-S1-VV, L8-S1-VH ve L8-S1-VV uydu görüntüleri ile MOS sınıflandırma yaklaşımında sırasıyla en iyi GD (92.00%, 94.00%, 96.67%, 93.33) ve KK (0.90, 0.93, 0.95, 0.92) değerleri sırasıyla elde edilmiştir. Elde edilen sonuçlar değerlendirildiğinde, birleştirilmiş uydu görüntülerinin kullanılması arazi örtüsü sınıflandırmasının başarısını artırdığı görülmüştür.

Kaynakça

  • Abdikan, S. (2018). Exploring image fusion of ALOS/PALSAR data and Landsat data to differentiate forest area. Geocarto International, 33(1), 21-37.
  • Anonymous (2018). Ankara Regional Directorate of Forestry, Eskipazar Forest Managemet Enterprise, Ören Forest managemet planning unit ecosystem based multiple use forest management planning. P:247.
  • Bagan, H., Kinoshita, T., Yamagata, Y. (2012). Combination of AVNIR-2, PALSAR, and polarimetric parameters for land cover classification. IEEE Transactions on Geoscience and Remote Sensing, 50(4), 1318–1328.
  • Ban, Y., Peng, G., Giri, C. (2015). Global land cover mapping using Earth observation satellite data: Recent progresses and challenges. ISPRS Journal of Photogrammetry and Remote Sensing, 10.1016/j.isprsjprs.2015.01.001.
  • Bulut, S., Günlü, A. (2016). Comparison of different supervised classification algorithms for land use classes. Kastamonu University, Journal of Forestry Faculty, 16 (2), 528-535.
  • Burkhard, B., Kroll, F., Nedkov, S., Müller, F. (2012). Mapping ecosystem service supply, demand and budgets. Ecol. Indic. 21, 17–29.
  • Büyüksalih, İ. (2016). Landsat images classification and change analysis of land cover/use in Istanbul. International Journal of Environment and Geoinformatics, 3(2), 56-65.
  • Camargo, F.F., Sano, E.E., Almedia, M., Mura, J.C., Almedia, T. (2019). 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, 11, 1600.
  • Clerici, N., Calderon, C.A.V., Posada, J.M. (2017). Fusion of Sentinel-1A and Sentinel-2A data for land cover mapping: a case study in the lower Magdalena region, Colombia. Journal of Maps, 13(2), 718–726.
  • Deus, D. (2016). Integration of ALOS PALSAR and Landsat data for land cover and forest mapping in Northern Tanzania. Land, 5, 43.
  • Dixon, B., Candade, N. (2008). Multispectral landuse classification using neural networks and support vector machines: one or the other, or both?. International Journal of Remote Sensing, 29(4), 1185-1206.
  • Elatawneh, A., Kalaitzidis, C., Petropoulos, G.P., Schneider, T. (2014). Evaluation of diverse classification approaches for land use/cover mapping in a mediterranean region utilizing hyperion data. International Journal of Digital Earth, 7( 3),194–216.
  • Erasmi, S., Twele, A. (2009). Regional land over mapping in the humid tropics using combined optical and SAR satellite data – a case study from Central Sulawesi, Indonesia. International Journal of Remote Sensing, 30, (10), 2465–2478.
  • Fonteh, M.L., Theophile, F., Cornelius, M.L., Main, R., Ramoelo, A., Cho, M.A. (2016). Assessing the utility of Sentinel-1 C band synthetic aperture radar imagery for land use land cover classification in a tropical coastal systems when compared with Landsat 8. Journal of Geographic Information System, 8, 495-505.
  • Fry, J.A., Xian, G., Jin, S., Dewitz, J.A., Homer, C.G., Yang, L., Barnes, C.A., Herold, N.D., Wickham, J.D. (2012). Completion of the 2006 national land cover database for the conterminous United States. Photogramm. Eng. Remote Sens., 77, 858–864.
  • Gebhardt, S., Wehrmann, T., Ruiz, M.A.M., Maeda, P., Bishop, J., Schramm, M., Kopeinig, R., Cartus, O., Kellndorfer, J., Ressl, R., Santos, L.A., Schmidt, M. (2014). MAD-MEX: Automatic wall-to-wall land cover monitoring for the Mexican REDD-MRV program using all Landsat data. Remote Sens., 6, 3923-3943.
  • Gomez, C., Mangeas, M., Petit, M., Corbane, C., Hamon, P. (2010). Use of high-resolution satellite imagery in an integrated model to predict the distribution of shade coffee tree hybrid zones. Remote Sens. Environ., 114, 2731–2744.
  • Guidici, D., Clark, M.L. (2017). One-dimensional convolutional neural network land-cover classification of multi-seasonal hyperspectral imagery in the San Francisco Bay area, California. Remote Sens., 9, 629.
  • Günlü, A., Sivrikaya, F., Başkent, E.Z., Keleş, S., Çakır, G., Kadıoğulları, A.İ. (2008). Estimation of stand type parameters and land cover using Landsat-7 ETM image: a case study from Turkey. Sensors, 8, 2509-2525.
  • Han, N., Wang, K., Yu, L., Zhang, X. (2012). Integration of texture and landscape features into object-based classification for delineating Torreya using IKONOS imagery. Int. J. Remote Sens., 33(7), 2003–2033.
  • Hong, G., Zhang, A., Zhou, F., Brisco, B. (2014). Integration of optical and synthetic aperture radar (SAR) images to differentiate grassland and alfalfa in Prairie area. Int. J. Appl. Earth Obs. Geoinf., 28 (1), 12–19.
  • Hussain, S., Mubeen, M., Ahmad, A., Akram, W., Hammad, H.M., Ali, M., Masood, N., Amin, A., Farid, H.U., Sultana, S.R., Fahad, S., Wang, D., Nasim, W. (2020). Using GIS tools to detect the land use/land cover changes during forty years in Lodhran district of Pakistan. Environmental Science and Pollution Research, https://doi.org/10.1007/s11356-019-06072-3.
  • Hütt, C., Koppe, W., Miao, Y., Bareth, G. (2016). Best accuracy Land use/Land cover (LULC) classification to derive crop types using multitemporal, multisensor, and multi-polarization SAR satellite images. Remote Sens., 8, 684.
  • Hyde, P., Dubayah, R., Walker, W., Blair, J.B., Hofton, M., Hunsaker, C. (2006). Mapping forest structure for wildlife habitat analysis using multi-sensor (LiDAR, SAR/inSAR, ETM+, QuickBird) synergy. Remote Sens. Environ., 102, 63–73.
  • Jia, K., Wu, B.F., Tian, Y.C., Zeng, Y., Li, Q.Z. (2011). Vegetation classification method with biochemical composition estimated from remote sensing data. Int J Remote Sens., 32(24), 9307–9325.
  • Jia, K., Wei, X., Gu, X., Yao, Y., Xie, X., Li, B. (2014). Land cover classification using Landsat 8 operational land imager data in Beijing, China. Geocarto International, 29 (8), 941–951.
  • Juliev, M., Pulatov, A., Fuchs, S., Hübl, J. (2019). Analysis of land use land cover change detection of Bostanlik District, Uzbekistan. Pol. J. Environ. Stud., 28(5), 3235-3242.
  • Khan, A., Govil, H., Kumar, G., Dave, R. (2020). Synergistic use of Sentinel-1 and Sentinel-2 for improved LULC mapping with special reference to bad land class: a case study for Yamuna River floodplain, India. Spat. Inf. Res., https://doi.org/10.1007/s41324-020-00325-x.
  • Khatami, R., Mountrakis, G., Stehman, S.V.A. (2016). 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.
  • Kimes, D.S., Nelson, R.F., Manry, M.T., Fung, A.K. (1998). Attributes of neural networks for extracting continuous vegetation variables from optical and radar measurements. Int. J. Remote Sens., 19, 2639–2663.
  • Knorn, J., Rabe, A., Radeloff, V.C., Kuemmerle, T., Kozak, J., Hostert, P. (2009). Land cover mapping of large areas using chain classification of neighboring Landsat satellite images. Remote. Sens. Environ., 113, 957–964.
  • Kuemmerle, T., Erb, K., Meyfroidt, P., Müller, D., Verburg, P. H., Estel, S., Reenberg, A. (2013). Challenges and opportunities in mapping land use intensity globally. Current Opinion in Environmental Sustainability, 5(5), 484–493.
  • Kulkarni, A.D., Lowe B. (2016). Random forest algorithm for land cover classification. International Journal on Recent and Innovation Trends in Computing and Communication, 4(3), 58-63.
  • Kussul, N., Lemoine, G., Gallego, F.J., Skakun, S.V., Lavreniuk, M., Shelestov, A.Y. (2016). Parcel-based crop classification in Ukraine using Landsat-8 data and Sentinel-1A data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(6), 2500–2508.
  • Lillesand, T., Kiefer, R., Chipman, J. (2004). Remote Sensing and Image Interpretation. 5th Edn., John Wiley and Sons, New York, USA.
  • Lu, D., Weng, Q. A. (2007). Survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28, 823- 870.
  • Lu, D., Batistella, M., Li, G., Moran, E., Hetrick, S., Freitas, C.D.C., Futra, L.V., Anna, J.S.S. (2012). Land use/cover classification in the Brazilian Amazon using satellite images. Brasília, 47(9), 1185-1208.
  • Lu, D., Li, G., Moran, E., Kuang, W. (2014). A comparative analysis of approaches for successional vegetation classification in the Brazilian Amazon. GIScience & Remote Sensing, 51(6), 695–709.
  • Mann, D., Joshi, P.K. (2017). Evaluation of image classification algorithms on hyperion and aster data for land cover classification. Proceedings of the National Academy of Sciences, India - Section A 87(1). https://doi.org/10.1007/s40010-017-0454-6.
  • Miettinen, J., Liew, S.C. (2011). Separability of insular South east Asian woody plantation species in the 50 m resolution Alos Palsar mosaic product. Remote Sensing Letters, 2, 299–307.
  • Mohajane, M., Essahlaoui, A., Oudija, F., Hafyani, M.E., Hmaidi, A.E., Ouali, A.E., Randazzo, G., Teodora, C. (2018). Land use/Land cover (LULC) using Landsat data series (MSS, TM, ETM+ and OLI) in Azrou Forest, in the Central Middle Atlas of Morocco. Environments, 5, 131.
  • Morgan, R.S., Rahim, I.S., El-Hady, M.A. (2015). A Comparison of classification techniques for the land use/ land cover classification. Global Advanced Research Journal of Agricultural Science, 4(11), 810-818.
  • Mountrakis, G., Im, J., Ogole, C. (2011). Support vector machines in remote sensing: a review. ISPRS J Photogramm Remote Sens 66:247–259
  • Mushore, T.D., Mutanga, O., Odindi, J., Dube, T. (2017). Assessing the potential of integrated Landsat 8 thermal bands, with the traditional reflective bands and derived vegetation indices in classifying urban landscapes. Geocarto InternatIonal, 32(8), 886–899.
  • Muthukumarasamy, I., Shanmugam, R.S., Usha, T. (2019). Incorporation of textural information with SAR and optical imagery for improved land cover mapping. Environmental Earth Sciences, 78, 643.
  • Noi, P.T, 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, 18.
  • Pacifici, F., Frate, F.D., Emery, W.J., Gamba, P., Chanossot, J. (2008). Urban mapping using coarse sar and optical data: outcome of the 2007 GRSS Data Fusion Contest. IEEE Geoscıence And Remote Sensıng Letters, 5(3), 331-335.
  • Pal, M., Mather, P. M. (2004). Assessment of the effectiveness of support vector machines for hyperspectral data. Future Generation Computer Systems, 20(7), 1215-1225.
  • Pradhan, R., Pradhan, M.P., Bhusan, A., Pradhan, R.K., Ghose, M.K. (2010). Land-cover classification and mapping for eastern Himalayan State Sikkim. Journal of Computing, 2(3), 166-170.
  • Rao, K.V.R., Kumar, R. (2017). Land cover classification using Sentinel-1 SAR data. International Journal for Research in Applied Science & Engineering Technology. 5(12), 1054-1060.
  • Ridwan, I., Bisri, M., Yusran, F.H., Hakim, L., Kadir, S. (2017). Identification of characteristics of land cover in mangkauk catchment area Using support vector machine (SVM) and artificial neural network (ANN). American Journal of Applied Sciences, 14 (7), 726-736.
  • Sameen, M.I., Nahhas, F.H., Buraihi, F.H., Pradhan, B., Shariff, A.R.B.M. (2016). A refined classification approach by integrating Landsat Operational Land Imager (OLI) and Radarsat-2 imagery for land-use and land-cover mapping in a tropical area. Internatıonal Journal of Remote Sensıng, 37(10), 2358–2375.
  • Shi D., Yang X. (2015). Support vector machines for land cover mapping from remote sensor imagery. In: Li J., Yang X. (eds) Monitoring and Modeling of Global Changes: A Geomatics Perspective. Springer Remote Sensing/Photogrammetry. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9813-6_13
  • Shivakumar, B.R., Rajashekararadhya, S.V. (2018). Investigation on land cover mapping capability of maximum likelihood classifier: a case study on North Canara, India. Procedia Computer Science, 143, 579–586.
  • Sirro, L., Häme, T., Rauste, Y., Kilpi, J., Hämäläinen, J., Gunia, K., Jong, B.D., Pellat, F.P. (2018). Potential of different optical and SAR data in forest and land cover classification to support REDD+ MRV. Remote Sens., 10, 942.
  • Soria-Ruiz, J., Fernandez-Ordonez, Y., Woodhouse, L. (2010). Land-cover classification using radar and optical images: a case study in Central Mexico. International Journal of Remote Sensing, 31(2), 3291-3305.
  • Srivastava, P.K., Han, D., Rico-Ramirez, M.A., Bray, M., Islam, T. (2012). Selection of classification techniques for land use/land cover change investigation. Advances in Space Research, 50, 1250–1265.
  • Steinhausen, M., Wagner, P.D., Narasimhan, B., Waske, B. (2018). Combining Sentinel-1 and Sentinel-2 data for improved land use and land cover mapping of monsoon regions. International Journal of Applied Earth Observation and Geoinformation, 73, 595-604
  • Sukawattanavijit, C., Chen J. (2015). Fusion of Radarsat-2 imagery with Landsat-8 multispectral data for improving land cover classification performance using SVM. IEEE 5th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR), Singapore, pp. 567-572.
  • Szuster, B.W., Chen, Q., Borger, M. (2011). A comparison of classification techniques to support land cover and land use analysis in tropical coastal zones. Applied Geography, 31, 525-532.
  • Şerifoğlu Yılmaz, Ç., Güngör, O., Kahraman, H.T. (2018). Land cover mapping with advanced classification algorithms, Nature Sciences (NWSANS), 13(3), 41-50.
  • Tavares, P.A., Beltrão, N.E.S., Guimarães, U.S., Teodoro, A.C. (2019). Integration of Sentinel-1 and Sentinel-2 for classification and LULC mapping in the urban area of Belém, Eastern Brazilian Amazon. Sensors, 19, 1140.
  • Topaloglu, H.R., Sertel, E., Musaoglu, N. (2016). Assessment of classification accuracies of Sentinel-2 and Landsat-8 data for land cover/use mapping. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences; ISPRS: Prague, Czech Republic, Volume XLI-B8, pp. 1055–1059.
  • Ullah, S., Tahir, A.A., Akbar, T.A., Hassan, Q.K., Dewan, A., Khan, A.J., Khan, M. (2019). Remote sensing-based quantification of the relationships between land use land cover changes and surface temperature over the lower Himalayan region. Sustainability, 11, 5492.
  • Van-Beijma, S., Comber, A., Lamb, A. (2014). Random forest classification of salt marsh vegetation habitats using quad-polarimetric airborne SAR, elevation and optical remote sensing data. Remote Sens., Environ. 149, 118–129.
  • Verma, P., Raghubanshi, A., Srivastava, P.K., Raghubanshi, A.S. (2020). Appraisal of kappa-based metrics and disagreement indices of accuracy assessment for parametric and nonparametric techniques used in LULC classification and change detection. Model. Earth Syst. Environ., https://doi.org/10.1007/s40808-020-00740-x.
  • Waser, L.T., Ginzler, C., Kuechler, M., Baltsavias, E., Hurni, L. (2011). Semi-automatic classification of tree species in different forest ecosystems by spectral and geometric vaiables derived from Airborne Digital Sensor (ADS40) and RC30 data. Remote Sens. Environ., 115,76–85.
  • Wei, L., Xiangyong, L., Juan D., Yao, L. (2016). Land cover classification of Radarsat-2 SAR data using convolutional neural network. Wuhan University Journal of Natural Sciences, 21(2), 151-158.
  • Xie, Z., Chen, Y., Lu, D., Li, G., Chen, E. (2019). Classification of land cover, forest, and tree species classes with ZiYuan-3 multispectral and stereo data. Remote Sens., 11, 164.
  • Yousefi, S., Mirzaee, S., Tazeh, M., Pourghasemi, H., Karimi, H. (2015). Comparison of different algorithms for land use mapping in dry climate using satellite images: a case study of the Central regions of Iran. Desert, 20(1),1-10.
  • Yuan, H., Wiele, F.V.D., Khorram, S. (2009). An Automated artificial neural network system for land use/land cover classification from Landsat TM imagery. Remote Sens., 1, 243-265.
  • Zakeri, H., Yamazaki, F., Liu W. (2017). Texture Analysis and land cover classification of Tehran using polarimetric synthetic aperture radar imagery. Appl. Sci., 7, 452.
  • Zhang, H., Xu, R. (2018). Exploring the optimal integration levels between SAR and optical data for better urban land cover mapping in the Pearl River Delta. Int. J. Appl. Earth Obs. Geoinf, 64, 87–95.
Toplam 73 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Orman Endüstri Mühendisliği
Bölüm Biodiversity, Environmental Management and Policy, Sustainable Forestry
Yazarlar

Alkan Günlü 0000-0003-4759-3125

Yayımlanma Tarihi 15 Nisan 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 23 Sayı: 1

Kaynak Göster

APA Günlü, A. (2021). Comparison of Different Classification Approaches for Land Cover Classification using Multispectral and Fusion Satellite Data: A Case Study in Ören Forest Planning Unit. Bartın Orman Fakültesi Dergisi, 23(1), 306-322. https://doi.org/10.24011/barofd.882471


Bartin Orman Fakultesi Dergisi Editorship,

Bartin University, Faculty of Forestry, Dean Floor No:106, Agdaci District, 74100 Bartin-Turkey.

Tel: +90 (378) 223 5094, Fax: +90 (378) 223 5062,

E-mail: bofdergi@gmail.com