Polarimetric decomposition methods for LULC mapping using ALOS L-band PolSAR data in Western parts of Mizoram, Northeast India

Research Article Polarimetric decomposition methods for LULC mapping using ALOS L‑band PolSAR data in Western parts of Mizoram, Northeast India Bikash Ranjan Parida1 · Shyama Prasad Mandal1 Received: 22 January 2020 / Accepted: 1 May 2020 © Springer Nature Switzerland AG 2020 Abstract The rapid advancement of remote sensing and availability of polarimetric SAR (PolSAR) data have facilitated to monitor the land use land cover (LULC) dynamics. In the recent past, polarimetric decomposition theorems are applied widely to perform LULC classification with the help of machine learning techniques. In this study, we utilized ALOS PALSAR-1 L-band quad polarimetric data for performing polarimetric decomposition, textural information extraction, and to generate LULC maps over the western part of Mizoram state, northeast India. The study area comprises three districts, namely Mamit, Lunglei, and Lawngtlai. We adopted two representative full-polarimetric decomposition models: classical model-based Freeman–Durden and Yamaguchi decomposition. These methods decompose the coherency matrix of PolSAR images into surface, double-bounce, and volume scattering. Textural measures, such as variance, contrast, entropy, homogeneity, dissimilarity, and uniformity are also retrieved using grey-level co-occurrence matrix (GLCM) for LULC classification. For LULC classification, we employed a support vector machine classifier and calculated the area statistics of LULC. The outcomes were checked with the help of confusion matrix derived for six classes, such as built-up, deciduous forest, evergreen forest, scrubland, bareland, and waterbody. Each LULC class is separated using the scattering properties of PolSAR images. Results exhibited that Yamaguchi four-component decomposition (overall accuracy 90% and kappa coefficient 0.88) gives relatively better LULC classification results than the Freeman–Durden three-component decomposition (overall accuracy 87% and kappa coefficient 0.84). Use of textural images of GLCM has supported the classification accuracy at par with the Yamaguchi model. Integration of polarimetric information offers a new dimension in LULC classification and produces high accuracy maps. This approach overcomes the limitations of optical data in cloud covering areas, and furthermore, it provides better classification accuracy. Keywords L-band SAR · Polarimetric SAR · Polarimetric decompositions · GLCM · Support vector machines · LULC mapping 1 Introduction The alteration of terrestrial surface by human activities is typically known for Land use land cover (LULC) change. Over the last few decades, the LULC is changing rapidly around the globe [1]. It is widely documented that the alterations of LULC have caused severe environmental problems, such as floods, landslides, deforestation, loss of biodiversity, and urbanization, among others [2–7] due to mismanagement of agriculture, forest, urban, wetland, and forest. So, LULC maps are very essential for understanding any unprecedented changes in agriculture [8–10], forest ecosystems [7, 11], biodiversity/ecological process [6], environmental process, and hazard assessment [4]. LULC change information is essential for providing vital input to * Bikash Ranjan Parida, | 1Department of Geoinformatics, School of Natural Resource Management, Central University of Jharkhand, Ranchi 835205, India. SN Applied Sciences (2020) 2:1049 | https://doi.org/10.1007/s42452-020-2866-1 Vol.:(0123456789) Research Article SN Applied Sciences (2020) 2:1049 decision-making bodies of natural resources management including town planning. LULC maps can be created through field surveys or remote sensing techniques. As field surveys are comprehensive, costly and cumbersome, typically remote sensing techniques were preferred in the recent years. In the case of remote sensing, digital image processing techniques play an important role in LULC classification due to availability of various multi-spectral satellite images (Landsat, SPOT (Satellite Pour l’Observation de la Terre), IRS (Indian Remote Sensing satellites), ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), Sentinel-2A) including the synthetic Aperture Radar (SAR) images, such as ENVISAT (Environmental Satellite), ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array type L-band Synthetic Aperture Radar), RADARSAT (Radar Satellite), TSX, (TerraSAR-X), and TDX (TanDEM-X). Classification of remotely sensed data produces thematic maps; however, it is very challenging to achieve accuracy of classified LULC map. The precision and accuracy of an imagery classification depends upon many factors, such as complexity of the landscape, selected remote sensing data, spatial resolution, atmospheric condition, adopted image processing techniques, and classification approaches. All these factors can affect the effectiveness and accuracy of a LULC map [12]. Furthermore, the classification methods employed in mapping LULC are perhaps the most important one [12] and pose a challenge to the research community. Numerous techniques have been developed over the years to produce LULC maps using the satellite images. The commonly used techniques are image classification [13, 14], principal component analysis [15], fuzzy classification [16], artificial neural network [17], machine/deep learning [18, 19], and object-based classification [20]. Most of these supervised classification methods involve training and human supervision. Further, most of these techniques are employed with optical satellite images that have inherent limitations of clouds, but these techniques are rarely used with the full-polarimetric SAR data. With the recent advancement of availability of full-polarimetric SAR data with multi-bands (X, C, S, L bands), there are approaches, namely polarimetric decomposition theorems which applied widely to perform LULC classification by using the machine learning classification methods [21–23]. Polarimetric decomposition theorems are developed either based on eigenvalue decomposition or physical modelbased decomposition. Most common statistics and physical model-based decomposition methods developed for PolSAR data are Cloude–Pottier (H/A/α), Huynen, Cameron, Freeman–Durden, and Yamaguchi decompositions, which are being utilized to retrieve various maps, such as LULC maps, forest density maps, and crop type’s maps, among Vol:.(1234567890) | https://doi.org/10.1007/s42452-020-2866-1 others. [22–24]. Based on the coherency and covariance matrix, Moriyama decomposition, Krogager decomposition, Van Zyl decomposition, and Touzi decomposition were also developed. Polarimetric SAR (PolSAR) images were analysed using decomposition theorems [21], which also increase the accuracy of LULC. PolSAR was intensively used as a monitoring tool for crops that grew during the rainy season [25]. Studies have also suggested that a combined approach of using optical and radar images improves the LULC ma (...truncated)