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Veerabhadraswamy, Naveen
- Fusion of Complementary Information of SAR and Optical Data for Forest Cover Mapping using Random Forest Algorithm
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Authors
Affiliations
1 University of Agricultural and Horticultural Sciences, Shivamogga College of Forestry, Ponnampet, Kodagu 571 216, IN
1 University of Agricultural and Horticultural Sciences, Shivamogga College of Forestry, Ponnampet, Kodagu 571 216, IN
Source
Current Science, Vol 120, No 1 (2021), Pagination: 193-199Abstract
We developed a methodological framework for accurate forest cover mapping of Shivamogga taluk, Karnataka, India using multi-sensor remote sensing data. For this, we used Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical data. These datasets were fused using principal component analysis technique, and forest and non-forest areas were classified using a random forest (RF) algorithm. Backscatter analysis was performed to understand the variation in γ 0 values between forest and non-forest sample points. The average γ 0 values of forest were higher than the non-forest samples in VH and VV polarizations. The average γ 0 backscatter difference between forest and non-forest samples was 8.50 dB in VH and 5.64 dB in VV polarization. The highest classification accuracy of 92.25% was achieved with the multi-sensor fused data compared to the single-sensor SAR (78.75%) and optical (83.10%) data. This study demonstrates that RF classification of multi-sensor data fusion improves the classification accuracy by 13.50% and 9.15%, compared to SAR and optical data.Keywords
Forest Cover, Mapping, Multi-sensor Data Fusion, Principal Component Analysis, Remote Sensing, Random Forest Algorithm.Full Text
References
- Devaney, J., Barrett, B., Barrett, F., Redmond, J. and O’Halloran, J., Forest cover estimation in Ireland using radar remote sensing: a comparative analysis of forest cover assessment methodologies. PLoS ONE, 2015, 10(8), 1–27.
- Reddy, C. S., Jha, C. S. and Dadhwal, V. K., Assessment and monitoring of long-term forest cover changes (1920–2013) in Western Ghats biodiversity hotspot. J. Earth Syst. Sci., 2016, 125, 103–114.
- Kellndorfer, J., Cartus, O., Bishop, J., Walker, W. and Holecz, F., Large scale mapping of forests and land cover with synthetic aperture radar data. In Land Applications of Radar Remote Sensing (eds Holecz, F. et al.), Intech Open, Rijeka, Croatia, 2016, pp.59–94.
- Reddy, C. S., Jha, C. S. and Dadhwal, V. K., Earth observations based conservation prioritization in Western Ghats, India. J. Geol. Soc. India, 2018, 92, 562–567.
- Chakraborty, K., Sivasankar, T., Lone, J. M., Sarma, K. K. and Raju, P. L. N., Status and opportunities for forest resources management using geospatial technologies in northeast India. In Spatial Information Science for Natural Resource Management (eds Singh, S. K., Kanga, S. and Mishra, V. N.), IGI Global, Hershey, Pennsylvania, USA, 2020, pp. 206–224.
- Mitchell, A. L., Rosenqvist, A. and Mora, B., Current remote sensing approaches to monitoring forest degradation in support of countries measurement, reporting and verification (MRV) systems for REDD+. Carbon Balance Manage., 2017, 12(9), 1–22.
- Roy, P. S. and Joshi, P. K., Forest cover assessment in north-east India – the potential of temporal wide swath satellite sensor data (IRS-1C WiFS). Int. J. Remote Sensing, 2002, 23(22), 4881–4896.
- Roy, P. S. et al., Development of decadal (1985–1995–2005) land use and land cover database for India. Remote Sensing, 2015, 7, 2401–2430.
- Wang, Y. et al., Mapping tropical disturbed forests using multidecadal 30 m optical satellite imagery. Remote Sensing Environ., 2019, 221, 474–488.
- Ju, J. and Roy, D. P., The availability of cloud-free Landsat ETM+ data over the conterminous United States and globally. Remote Sensing Environ., 2008, 112, 1196–1211.
- Wagner, P. D., Kumar, S. and Schneider, K., An assessment of land use change impacts on the water resources of the Mula and Mutha rivers catchment upstream of Pune, India. Hydrol. Earth Syst. Sci., 2013, 17, 2233–2246.
- Ghassemian, H., A review of remote sensing image fusion methods. Inf. Fusion, 2016, 32, 75–89.
- Abdikan, S., Sanli, F. B., Ustuner, M. and Calò, F., Land cover mapping using Sentinel-1 SAR data. Int. Arch. Photogramm., Remote Sensing Spat. Inf. Sci., 2016, 757–761.
- Touzi, R., Landry, R. and Charbonneau, F. J., Forest type discrimination using calibrated C-band polarimetric SAR data. Can. J. Remote Sensing, 2004, 30(3), 543–551.
- Masjedi, A., Valadan Zoej, M. J. and Maghsoudi, Y., Classification of polarimetric SAR images based on modeling contextual information and using texture features. IEEE Trans. Geosci. Remote Sensing, 2016, 54(2), 932–943.
- Yu, Y., Li, M. and Fu, Y., Forest type identification by random forest classification combined with SPOT and multitemporal SAR data. J. For. Res., 2017, 29, 1407–1414.
- Navale, A. and Haldar, D., Evaluation of machine learning algorithms to Sentinel SAR data. Spat. Inf. Res., 2020, 28, 345– 355.
- Ngo, K. D., Lechner, A. M. and Vu, T. T., Land cover mapping of the Mekong Delta to support natural resource management with multi-temporal Sentinel-1A synthetic aperture radar imagery. Remote Sensing Appl.: Soc. Environ., 2020, 17, 1–14.
- Dostálová, A., Hollaus, M., Milenković, M. and Wagner, W., Forest area derivation from Sentinel-1 data. ISPRS Ann. Photogramm. Remote Sensing Spat. Inf. Sci., 2016, III(7), 227–233.
- Moreira, A., Prats-Iraola, P., Younis, M., Krieger, G., Hajnsek, I. and Papathanassiou, K. P., A tutorial on synthetic aperture radar. IEEE Geosci. Remote Sensing Mag., 2013, 1(1), 6–43.
- Kulkarni, S. C. and Rege, P. P., Pixel level fusion techniques for SAR and optical images: a review. Inf. Fusion, 2020, 59, 13– 29.
- Schmitt, M. and Zhu, X. X., Data fusion and remote sensing: an ever-growing relationship. IEEE Geosci. Remote Sensing Mag., 2016, 4(4), 6–23.
- Singh, R. and Gupta, R., Improvement of classification accuracy using image fusion techniques. In International Conference on Computational Intelligence and Applications, IEEE, Jeju, South Korea, 2016, pp. 36–40.
- Clerici, N., Valbuena Calderón, C. A. and Posada, J. M., Fusion of Sentinel-1A and Sentinel-2A data for land cover mapping: a case study in the lower Magdalena region, Colombia. J. Maps, 2017, 13(2), 718–726.
- Gaetano, R., Cozzolino, D., D’Amiano, L., Verdoliva, L. and Poggi, G., Fusion of SAR–optical data for land cover monitoring. In 2017 IEEE International Geoscience and Remote Sensing Symposium, Texas, USA, 2017, pp. 5470–5473.
- Yuhendra, Y. E. and Na’am, J., Optical SAR fusion of Sentinel-2 images for mapping high resolution land cover. In 2018 International Conference on System Science and Engineering, New Taipei, Taiwan, 2018, pp. 1–4.
- Fortin, J. A., Cardille, J. A. and Perez, E., Multi-sensor detection of forest-cover change across 45 years in Mato Grosso, Brazil. Remote Sensing Environ., 2020, 238, 1–14.
- Steinhausen, M. J., Wagner, P. D., Narasimhan, B. and Waske, B., Combining Sentinel-1 and Sentinel-2 data for improved land use and land cover mapping of monsoon regions. Int. J. Appl. Earth Obs. Geoinf., 2018, 73, 595–604.
- Wegner, J. D., Thiele, A. and Soergel, U., Fusion of optical and InSAR features for building recognition in urban areas. In Int. Arch. Photogramm. Remote Sensing (eds Stilla, U., Rottensteiner, F. and Paparoditis, N.), Paris, France, 2009, pp. 169–174.
- Zhang, H., Zhang, Y. and Lin, H., Urban land cover mapping using random forest combined with optical and SAR data. In 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 2012, pp. 6809–6812.
- Kasapoğlu, G. N., Anfinsen, S. N. and Eltoft, T., Fusion of optical and multifrequency POLSAR data for forest classification. In 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 2012, pp. 3355–3358.
- Laurin, G. V. et al., Optical and SAR sensor synergies for forest and land cover mapping in a tropical site in West Africa. Int. J. Appl. Earth Obs. Geoinf., 2013, 21, 7–16.
- Hütt, C., Koppe, W., Miao, Y. and Bareth, G., Best accuracy land use/land cover (LULC) classification to derive crop types using multitemporal, multisensor, and multi-polarization SAR satellite images. Remote Sensing, 2016, 8, 1–15.
- Estornell, J., Martí-Gavliá, J. M., Sebastiá, M. T. and Mengual, J., Principal component analysis applied to remote sensing. Model. Sci. Educ. Learn., 2013, 6(7), 83–89.
- Kuplich, T. M., Freitas, C. C. and Soares, J. V., The study of ERS1 SAR and Landsat TM synergism for land use classification. Int. J. Remote Sensing, 2000, 21, 2101–2111.
- Belgiu, M. and Drăgu, L., Random forest in remote sensing: a review of applications and future directions. ISPRS J. Photogramm. Remote Sensing, 2016, 114, 24–31.
- Gómez, C., White, J. C. and Wulder, M. A., Optical remotely sensed time series data for land cover classification: a review. ISPRS J. Photogramm. Remote Sensing, 2016, 116, 55–72.
- Imangholiloo, M., Rasinmäki, J., Rauste, Y. and Holopainen, M., Utilizing Sentinel-1A radar images for large-area land cover mapping with machine-learning methods. Can. J. Remote Sensing, 2019, 45(2), 163–175.
- ISFR, India State of Forest Report 2019, Forest Survey of India, Dehradun, Ministry of Environment, Forest and Climate Change, Government of India, 2019.
- Rajashekar, G. et al., Remote sensing in forest mapping, monitoring and measurement. J. Gov. – Spl. Issue Environ., 2019, 18, 27– 54.
- Small, D., Flattening gamma: radiometric terrain correction for SAR imagery. IEEE Trans. Geosci. Remote Sensing, 2011, 49(8), 3081–3093.
- Haralick, R. M., Shanmugam, K. and Dinstein, I., Textural features for image classification. IEEE Trans. Syst. Man Cybern., 1973, SMC-3, 610–621.
- Mishra, V. N., Prasad, R., Rai, P. K., Vishwakarma, A. K. and Arora, A., Performance evaluation of textural features in improving land use/land cover classification accuracy of heterogeneous landscape using multi-sensor remote sensing data. Earth Sci. Inform., 2019, 12, 71–86.
- Zhang, J., Multi-source remote sensing data fusion: status and trends. Int. J. Image Data Fusion, 2010, 1(1), 5–24.
- Braun, A. and Hochschild, V., Combined use of SAR and optical data for environmental assessments around refugee camps in semiarid landscapes. Int. Arch. Photogramm., Remote Sensing Spat. Inform. Sci., 2015, 777–782.
- Breiman, L., Random forests. Mach. Learn., 2001, 45, 5–32.
- Horning, N., Random forests: an algorithm for image classification and generation of continuous fields data sets. In International Conference on Geoinformatics for Spatial Infrastructure Development in Earth and Allied Sciences, Hanoi, Vietnam, 2010, pp. 1–6.
- Congalton, R. G. and Green, K., Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, CRC Press/ Taylor & Francis, Boca Raton, FL, USA, 2019, 3rd edn.
- Anderson, F. et al., SAR Handbook: Comprehensive Methodologies for Forest Monitoring and Biomass Estimation, NASA, USA, 2019.
- Whittle, M., Quegan, S., Uryu, Y., Stüewe, M. and Yulianto, K., Detection of tropical deforestation using ALOS-PALSAR: a Sumatran case study. Remote Sensing Environ., 2012, 124, 83–98.
- Zou, B., Li, W., Xin, Y. and Zhang, L., Discrimination of forests and man-made targets in SAR images based on spectrum analysis. In Proceedings of SPIE 10988, Automatic Target Recognition XXIX, Baltimore, Maryland, USA, 2019, pp. 1–9.
- Mercier, A. et al., Evaluation of Sentinel-1 and 2 time series for land cover classification of forest – agriculture Mosaics in temperate and tropical landscapes. Remote Sensing, 2019, 11, 1–20.
- Pohl, C. and Van Genderen, J. L., Multisensor image fusion in remote sensing: concepts, methods and applications. Int. J. Remote Sensing, 1998, 19, 823–854.
- Hong, G., Zhang, A., Zhou, F. and Brisco, B., Integration of optical and synthetic aperture radar (SAR) images to differentiate grassland and alfalfa in Prairie area. Int. J. Appl. Earth Obs. Geoinf., 2014, 28, 12–19.
- Stefanski, J. et al., Mapping land management regimes in western Ukraine using optical and SAR data. Remote Sensing, 2014, 6, 5279–5305.