PolSAR-Decomposition-Based Extended Water Cloud Modeling for Forest Aboveground Biomass Estimation

Kumar, Shashi; Garg, Rahul; Govil, Himanshu; Kushwaha, S. P. S.

doi:10.3390/rs11192287

Cited by 22 publications

(12 citation statements)

References 59 publications

(72 reference statements)

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“…Useful enrichment of the available feature space has been demonstrated using multitemporal datasets [18][19][20][21], incorporating texture measures [14,22] and field-derived or satellite-derived three-dimensional information [23][24][25][26][27][28][29][30]. Other approaches are based on the use of ultra-high-resolution VNIR imagery (usually not free of cost) [31,32], radar imagery [1,[33][34][35][36][37][38][39][40][41][42][43][44][45][46], or combinations of VNIR and radar imagery [47][48][49][50][51][52][53]. We should also note approaches based on the direct use of spaceborne laser profiling [54] and those that explicitly incorporate a landscape characterisation, derived from satellite data, into a VNIR [55] or radar [56] analysis.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification

et al. 2021

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Growing stock volume (GSV) is a fundamental parameter of forests, closely related to the above-ground biomass and hence to carbon storage. Estimation of GSV at regional to global scales depends on the use of satellite remote sensing data, although accuracies are generally lower over the sparse boreal forest. This is especially true of boreal forest in Russia, for which knowledge of GSV is currently poor despite its global importance. Here we develop a new empirical method in which the primary remote sensing data source is a single summer Sentinel-2 MSI image, augmented by land-cover classification based on the same MSI image trained using MODIS-derived data. In our work the method is calibrated and validated using an extensive set of field measurements from two contrasting regions of the Russian arctic. Results show that GSV can be estimated with an RMS uncertainty of approximately 35–55%, comparable to other spaceborne estimates of low-GSV forest areas, with 70% spatial correspondence between our GSV maps and existing products derived from MODIS data. Our empirical approach requires somewhat laborious data collection when used for upscaling from field data, but could also be used to downscale global data.

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Section: Introductionmentioning

confidence: 99%

Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification

et al. 2021

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“…In [1,2], methods were developed for determining above-ground biomass, the first being based on water cloud modelling (WCM) from quad-polarised L-band (PALSAR) data on the Advanced Land Observing Satellite-2 (ALOS-2), and the second from dual-polarised X-band TerraSAR-X and quad-pol-InSAR C-band data from RADARSAT-2 data. The concept of water cloud modelling (WCM) was developed in [3], in which a vegetation canopy was represented by a water cloud, based on a number of parameters which were obtained through regression analysis of the radar cross section data for ground truth data based on four crop types.…”

Section: Overview Of Contributionsmentioning

confidence: 99%

“…The authors in [1] discuss the options for the decomposition of quad-pol SAR data and the retrieval of scattering components of forest vegetation. The shift in the polarization orientation angle (POA) affects the PolSAR-based scattering information, which is dependent on the Faraday rotation angle (FRA) and structural properties of the scatterer.…”

Section: Overview Of Contributionsmentioning

confidence: 99%

Editorial for Special Issue “Applications of Synthetic Aperture Radar (SAR) for Land Cover Analysis”

Trinder¹

2020

Remote Sensing

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Synthetic aperture radar (SAR) imaging systems derive microwave data, from space or airborne (piloted and remote piloted), that provide opportunities for the interpretation of many characteristics of the terrain surface. The increasing number of satellites equipped with SAR data acquisition systems that are being launched with a range of wavelengths, polarizations, and operating characteristics are enabling a better understanding of the earth’s environment, for such activities as vegetation analysis, forest inventories, land subsidence, and urban analysis. In addition, airborne systems for remote piloted systems and ground-based systems are available. This Special Issue presents six quality scientific papers on typical applications of SAR technologies. They include methods for the determination of above ground biomass (AGB), crop mapping using data from an advanced X-band system developed in Japan, analysis of natural and human-induced slow-rate ground deformations in the region of Campania, in Italy, the location of landslides caused by natural phenomena based on SAR images derived from the Japanese high-resolution Advanced Land Observing Satellite-2 (ALOS-2), and monitoring the size of refugee camps and their environmental impacts caused by the displacement of people from Myanmar to the Cox’s Bazar District, around Kutupalong, in Bangladesh. The paper concludes with some comments on the future directions of developments in SAR systems.

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“…Synthetic aperture Radar remote sensing has the advantages of scattering-based object characterization to identify structural and electrical properties and has several other advantages in data acquisition in the darkened environment and active imaging that can acquire data for SAR-based earth observation at night time too 21 . Polarimetric Synthetic Aperture Radar (PolSAR) is an advanced technique of SAR remote sensing in which fully polarimetric properties of the transmitted and received electromagnetic waves are used to characterize the scattering behaviour of different objects/scatterers within a SAR resolution cell 22 . SAR backscatter is a coherent sum of scattering contributed by all the scatterers within a resolution cell, and the information retrieval of different scatterers within a resolution cell is not possible with single or dual-polarised data.…”

Section: Introductionmentioning

confidence: 99%

Semantic segmentation of PolSAR image data using advanced deep learning model

Garg

Kumar

Bansal

et al. 2021

Sci Rep

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Urban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.

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PolSAR-Decomposition-Based Extended Water Cloud Modeling for Forest Aboveground Biomass Estimation

Cited by 22 publications

References 59 publications

Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification

Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification

Editorial for Special Issue “Applications of Synthetic Aperture Radar (SAR) for Land Cover Analysis”

Semantic segmentation of PolSAR image data using advanced deep learning model

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