scholarly journals Mapping Land Cover Based on Time Series Synthetic Aperture Radar (SAR) Data in Klaten, Indonesia

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Vidya Nahdhiyatul Fikriyah
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Cover ◽  
Synthetic Aperture Radar ◽  
Agricultural Land ◽  
Unsupervised Classification ◽  
Synthetic Aperture ◽  
Optical Remote Sensing ◽  
Tropical Regions ◽  
Aperture Radar

<p><em>Information </em><em>on </em><em>the existing land cover is important for land management and planning because it can represent the intensity, location, and pattern of human activities. However, mapping land cover in tropical regions is not easy when using optical remote sensing due to the scarcity of cloud-free images. Therefore, the objective of this study is to map the land cover in Klaten Regency using a time-series Sentinel-1 data. Sentinel-1 data is one of remote sensing image</em><em>s</em><em> with Synthetic Aperture Radar (SAR) system which is well known by its capabilit</em><em>y</em><em> of cloud penetration and all-weather observation. A time-series Sentinel-1 data of both polarisations, VV and VH were automatically classified using an unsupervised classification technique, ISODATA. The results show that the land cover classifications obtained overall accuracies of 79</em><em>.</em><em>26% and 73</em><em>.</em><em>79</em><em>% </em><em>for VV and VH respectively. It is also found that Klaten is still dominated by the vegetated land (agriculture and non-agricultural land).</em><em> T</em><em>hese results suggest the opportunity of mapping land cover using SAR multi temporal data. </em></p><p><strong><em> </em></strong></p><p><strong><em> Keywords</em></strong><em>: </em><em>Land cover; Synthetic Aperture Radar; Time series; Sentinel-1; Klaten</em><em></em></p>

scholarly journals Combined Use of Optical and Synthetic Aperture Radar Data for REDD+ Applications in Malawi

Land ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 116 ◽  
Author(s):  
Manuela Hirschmugl ◽  
Carina Sobe ◽  
Janik Deutscher ◽  
Mathias Schardt
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Synthetic Aperture Radar ◽  
Data Availability ◽  
Synthetic Aperture ◽  
Optical Data ◽  
Series Data ◽  
Forest Mapping ◽  
Sar Data ◽  
Aperture Radar

Recent developments in satellite data availability allow tropical forest monitoring to expand in two ways: (1) dense time series foster the development of new methods for mapping and monitoring dry tropical forests and (2) the combination of optical data and synthetic aperture radar (SAR) data reduces the problems resulting from frequent cloud cover and yields additional information. This paper covers both issues by analyzing the possibilities of using optical (Sentinel-2) and SAR (Sentinel-1) time series data for forest and land cover mapping for REDD+ (Reducing Emissions from Deforestation and Forest Degradation) applications in Malawi. The challenge is to combine these different data sources in order to make optimal use of their complementary information content. We compare the results of using different input data sets as well as of two methods for data combination. Results show that time-series of optical data lead to better results than mono-temporal optical data (+8% overall accuracy for forest mapping). Combination of optical and SAR data leads to further improvements: +5% in overall accuracy for land cover and +1.5% for forest mapping. With respect to the tested combination methods, the data-based combination performs slightly better (+1% overall accuracy) than the result-based Bayesian combination.

scholarly journals Tropical Peatland Identification using L-Band Full Polarimetric Synthetic Aperture Radar (SAR) Imagery (Study Case: Siak Regency, Riau Province)

2019 ◽  
Vol 26 (2) ◽  
pp. 63
Author(s):  
Desti Ayunda ◽  
Ketut Wikantika ◽  
Dandy A. Novresiandi ◽  
Agung B. Harto ◽  
Riantini Virtriana ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Vegetation Index ◽  
Synthetic Aperture ◽  
Optical Remote Sensing ◽  
Error Matrix ◽  
Tropical Peatland ◽  
Average Value ◽  
Sar Imagery ◽  
Aperture Radar

From previous research reported that tropical peatland is one of terrestrial carbon storage in Earth, and has contribution to climate change. Synthetic Aperture Radar (SAR) is one of remote sensing technology which is more efcient than optical remote sensing. Its ability to penetrate cloud makes it useful to monitor tropical environment. This research is conducted in a tropical peatland in Siak Regency, Riau Province. This research was conducted to identify tropical peatland in Siak Regency using polarimetric decomposition, unsupervised classifcation ISODATA, and Radar Vegetation Index (RVI) from SAR data that had been geometrically and radiometrically corrected. Polarimetric decomposition Freeman-Durden was performed to analyze radar backscattering mechanism in tropical peatland, which shows that volume and surface scattering was dominant because of the presence of vegetation and open area. Unsupervised classifcation ISODATA was then performed to extract “shrub class”. By assessing its accuracy, the class that represents shrub class in reference map was selected as the selected “shrub class”. RVI then was calculated using a certain formula. Spatial analysis was then conducted to acquire certain information that average value of RVI in tropical peatland tend to be higher than in non-tropical peatland. By integrating selected “shrub class” and RVI, peat classes were extracted. The best peat class was selected by comparing with peatland referenced map which is acquired from the Indonesian Agency for Agricultural Resources and Development (IAARD) using error matrix. In this research, the best peat class yielded 73.5 percent of Producer’s Accuracy (PA), 81.6 percent of User’s Accuracy (UA), 66.1 percent of Overall Accuracy (OA), and 0.1079 of Kappa coefcient (Ks).

scholarly journals Fault-Based Geological Lineaments Extraction Using Remote Sensing and GIS—A Review

Geosciences ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 183
Author(s):  
Hemayatullah Ahmadi ◽  
Emrah Pekkan
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Thermal Emission ◽  
Active Faults ◽  
Remote Sensing Data ◽  
Shuttle Radar Topography Mission ◽  
Synthetic Aperture ◽  
Optical Remote Sensing ◽  
Remotely Sensed Data ◽  
Aperture Radar

Geological lineaments are the earth’s linear features indicating significant tectonic units in the crust associated with the formation of minerals, active faults, groundwater controls, earthquakes, and geomorphology. This study aims to provide a systematic review of the state-of-the-art remote sensing techniques and data sets employed for geological lineament analysis. The critical challenges of this approach and the diverse data verification and validation techniques will be presented. Thus, this review spanned academic articles published since 1975, including expert reports and theses. Landsat series, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Sentinel 2 are the prevalent optical remote sensing data widely used for lineament detection. Moreover, Shuttle Radar Topography Mission (SRTM) derived Digital Elevation Model (DEM), Synthetic-aperture radar (SAR), Interferometric synthetic aperture radar (InSAR), and Sentinel 1 are the typical radar remotely sensed data which are widely used for the detection of geological lineaments. The geological lineaments acquired via GIS techniques are not consistent even though a variety of manual, semi-automated, and automated techniques are applied. Therefore, a single method may not provide an accurate lineament distribution and may include artifacts requiring integration of multiple algorithms, e.g., manual and automated algorithms.

scholarly journals Urban Sensing with Spaceborne Interferometric Synthetic Aperture Radar

2021 ◽  
pp. 345-365
Author(s):  
Hongyu Liang ◽  
Wenbin Xu ◽  
Xiaoli Ding ◽  
Lei Zhang ◽  
Songbo Wu
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Optical Remote Sensing ◽  
Microwave Radar ◽  
Urban Remote Sensing ◽  
Multi Temporal ◽  
Urban Sensing ◽  
Interferometric Sar ◽  
Aperture Radar

AbstractSynthetic aperture radar (SAR) and interferometric SAR (InSAR) are state-of-the-art radar remote sensing technologies and are very useful for urban remote sensing. The technologies have some very special characteristics compared to optical remote sensing and are especially advantageous in cloudy regions due to the ability of the microwave radar signals used by the current SAR sensors to penetrate clouds. This chapter introduces the basic concepts of SAR, differential InSAR, and multi-temporal InSAR, and their typical applications in urban remote sensing. Examples of applying the various InSAR techniques in generating DEMs and monitoring ground and infrastructure deformation are given. The capabilities and limitations of InSAR techniques in urban remote sensing are briefly discussed.

scholarly journals Multi-Temporal Dual- and Quad-Polarimetric Synthetic Aperture Radar Data for Crop-Type Mapping

2019 ◽  
Vol 11 (13) ◽  
pp. 1518 ◽  
Author(s):  
Rubén Valcarce-Diñeiro ◽  
Benjamín Arias-Pérez ◽  
Juan M. Lopez-Sanchez ◽  
Nilda Sánchez
Keyword(s):  
Land Cover ◽  
Synthetic Aperture Radar ◽  
Environmental Sustainability ◽  
Agricultural Land ◽  
Radar Data ◽  
Synthetic Aperture ◽  
Agricultural Policies ◽  
Synthetic Aperture Radar Data ◽  
Monitoring Effort ◽  
Aperture Radar

Land-cover monitoring is one of the core applications of remote sensing. Monitoring and mapping changes in the distribution of agricultural land covers provide a reliable source of information that helps environmental sustainability and supports agricultural policies. Synthetic Aperture Radar (SAR) can contribute considerably to this monitoring effort. The first objective of this research is to extend the use of time series of polarimetric data for land-cover classification using a decision tree classification algorithm. With this aim, RADARSAT-2 (quad-pol) and Sentinel-1 (dual-pol) data were acquired over an area of 600 km2 in central Spain. Ten polarimetric observables were derived from both datasets and seven scenarios were created with different sets of observables to evaluate a multitemporal parcel-based approach for classifying eleven land-cover types, most of which were agricultural crops. The study demonstrates that good overall accuracies, greater than 83%, were achieved for all of the different proposed scenarios and the scenario with all RADARSAT-2 polarimetric observables was the best option (89.1%). Very high accuracies were also obtained when dual-pol data from RADARSAT-2 or Sentinel-1 were used to classify the data, with overall accuracies of 87.1% and 86%, respectively. In terms of individual crop accuracy, rapeseed achieved at least 95% of a producer’s accuracy for all scenarios and that was followed by the spring cereals (wheat and barley), which achieved high producer’s accuracies (79.9%-95.3%) and user’s accuracies (85.5% and 93.7%).

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