scholarly journals UTILIZATION OF MULTI TEMPORAL SAR DATA FOR FOREST MAPPING MODEL DEVELOPMENT

2014 ◽  
Vol 10 (1) ◽  
Author(s):  
Bambang Trisakti ◽  
Rossi Hamzah
Keyword(s):  
Model Development ◽  
Training Sample ◽  
Water Area ◽  
Tropical Region ◽  
Threshold Method ◽  
Alos Palsar ◽  
Forest Mapping ◽  
Forest Classification ◽  
Multi Temporal ◽  
Sar Data

Utilization  of  optical  satellite  data  in  tropical  region  was  limited to  free  cloud  cover. Therefore, Synthetic  Aperture  Radar  (SAR)  becomes  an  alternative  solution  for  forest  mapping  in Indonesia due to its capability to penetrate cloud. The objective of this research was to develop a forestmapping model based on multi temporal SAR data. Multi temporal ALOS PALSAR data for 2007 and 2008  were  used  for  forest  mapping,  and  one  year  mosaic  LANDSAT  data  in  2008  was  used  as references  data  to  obtain  training  sample  and  to  verify  the  final  forest  classification.  PALSAR processing was done using gamma naught conversion and Lee filtering. Samples were made in forest and  water  area, and  the  statistical  values  of the  each  object  were  calculated.  Some  thresholds  were determined  based  on  the  average  and  standard  deviation,  and  the  best  threshold  was  selected  to classify forest and water in 2008. It was assumed that forest could not change in 1-2 years period. The classification of forest, water, and the change were combined to produce final forest in 2008, and then it was visually verified with mosaic LANDSAT in 2008. The result showed that forest, water, and the change  could  be  well  classified  using  threshold  method.  The  forest  derived  from  PALSAR  was visually  consistent  with  forest  appearance  in  LANDSAT  and  forest  produced  from  INCAS.  It  has better performance than forest derived from INCAS for separating oil palm plantation from the forest.

scholarly journals MODELLING ABOVE GROUND BIOMASS OF MANGROVE FOREST USING SENTINEL-1 IMAGERY

2018 ◽  
Vol IV-3 ◽  
pp. 13-20 ◽  
Author(s):  
Reginald Jay Labadisos Argamosa ◽  
Ariel Conferido Blanco ◽  
Alvin Balidoy Baloloy ◽  
Christian Gumbao Candido ◽  
John Bart Lovern Caboboy Dumalag ◽  
...  
Keyword(s):  
Random Forest ◽  
Principal Components ◽  
Mangrove Forest ◽  
Above Ground Biomass ◽  
Alos Palsar ◽  
Forest Mapping ◽  
Ground Biomass ◽  
Sar Data ◽  
Volumetric Objects ◽  
Occurrence Matrix

Many studies have been conducted in the estimation of forest above ground biomass (AGB) using features from synthetic aperture radar (SAR). Specifically, L-band ALOS/PALSAR (wavelength ~23&amp;thinsp;cm) data is often used. However, few studies have been made on the use of shorter wavelengths (e.g., C-band, 3.75&amp;thinsp;cm to 7.5&amp;thinsp;cm) for forest mapping especially in tropical forests since higher attenuation is observed for volumetric objects where energy propagated is absorbed. This study aims to model AGB estimates of mangrove forest using information derived from Sentinel-1 C-band SAR data. Combinations of polarisations (VV, VH), its derivatives, grey level co-occurrence matrix (GLCM), and its principal components were used as features for modelling AGB. Five models were tested with varying combinations of features; a) sigma nought polarisations and its derivatives; b) GLCM textures; c) the first five principal components; d) combination of models a&amp;minus;c; and e) the identified important features by Random Forest variable importance algorithm. Random Forest was used as regressor to compute for the AGB estimates to avoid over fitting caused by the introduction of too many features in the model. Model e obtained the highest r<sup>2</sup> of 0.79 and an RMSE of 0.44&amp;thinsp;Mg using only four features, namely, &amp;sigma;<sup>&amp;deg;</sup><sub><i>VH</i></sub> GLCM variance, &amp;sigma;<sup>&amp;deg;</sup><sub><i>VH</i></sub> GLCM contrast, PC1, and PC2. This study shows that Sentinel-1 C-band SAR data could be used to produce acceptable AGB estimates in mangrove forest to compensate for the unavailability of longer wavelength SAR.

scholarly journals Study of Discrimination Between Plantation and Dense Scrub Based on Backscattering Behavior of C Band SAR Data

2014 ◽  
Vol XL-8 ◽  
pp. 755-760 ◽  
Author(s):  
M. Iyyappan ◽  
S. S. Ramakrishnan ◽  
K. Srinivasa Raju
Keyword(s):  
Classification Accuracy ◽  
Confusion Matrix ◽  
Training Sample ◽  
Support Vector ◽  
Dual Polarization ◽  
Envisat Asar ◽  
Band Combination ◽  
Training Samples ◽  
Multi Temporal ◽  
Sar Data

The study about on landuse and landcover classification using multi polarization and multi temporal C-band Synthetic Aperture Radar (SAR) data of recently launched multi-mode of RISAT-1 (Radar Imaging Satellite) by Indian Space Research Organization (ISRO) and European satellite, Envisat ASAR data. The backscattering coefficient were extracted for various land features from Cband SAR data. The training sample collecting from satellite optical imagery of study and field visit for verification. The training samples are used for the supervised classification technique of maximum Likelihood (ML) algorithms, Neural Network (NN) and Support Vector Machine (SVM) algorithms were applied for fourteen different polarizations combination of multi temporal and multiple polarizations. The previous study was carried only four band combination of RISAT 1 data, the continuation of work both SAR data were used in this study. The Classification results are verified with confusion matrix. The pixel based classification gives the good results in the dual polarization of CRS &ndash; HH and HV of RISAT &minus;1 compared to dual polarization Envisat ASAR data. Meanwhile the quad Polarization combination of Envisat ASAR data got better classification accuracy. The SVM classifiers has given better classification results for all band combination followed by ML and NN. The Scrub are better identified in EnviSat ASAR &ndash; VV & VH Polarization and Plantation are better identified in EnviSat ASAR &ndash; HH, HH-HV & HV Polarization. The classification accuracy of both Scrub and Plantation is about 80 % in EnviSat ASAR &ndash; HH, VH & VV Polarization combination.

Forest mapping with multi-temporal dual polarization ALOS PALSAR data

2008 ◽  
Author(s):  
Feilong Ling ◽  
Zengyuan Li ◽  
Erxue Chen ◽  
Qinmin Wang
Keyword(s):  
Dual Polarization ◽  
Alos Palsar ◽  
Forest Mapping ◽  
Multi Temporal

scholarly journals Retrieval and Multi-scale Validation of Soil Moisture from Multi-temporal SAR Data in a Semi-Arid Tropical Region

2015 ◽  
Vol 7 (6) ◽  
pp. 8128-8153 ◽  
Author(s):  
Sat Tomer ◽  
Ahmad Al Bitar ◽  
Muddu Sekhar ◽  
Mehrez Zribi ◽  
S. Bandyopadhyay ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Scale Validation ◽  
Tropical Region ◽  
Multi Scale ◽  
Multi Temporal ◽  
Sar Data ◽  
Semi Arid

scholarly journals Multi-Temporal Loess Landslide Inventory Mapping with C-, X- and L-Band SAR Datasets—A Case Study of Heifangtai Loess Landslides, China

2018 ◽  
Vol 10 (11) ◽  
pp. 1756 ◽  
Author(s):  
Xiaojie Liu ◽  
Chaoying Zhao ◽  
Qin Zhang ◽  
Jianbing Peng ◽  
Wu Zhu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Landslide Inventory ◽  
Synthetic Aperture ◽  
Small Scale ◽  
Alos Palsar ◽  
Multi Temporal ◽  
Loess Landslides ◽  
L Band ◽  
Sar Data

The Interferometric Synthetic Aperture Radar (InSAR) technique is a well-developed remote sensing tool which has been widely used in the investigation of landslides. Average deformation rates are calculated by weighted averaging (stacking) of the interferograms to detect small-scale loess landslides. Heifangtai loess terrace, Gansu province China, is taken as a test area. Aiming to generate multi-temporal landslide inventory maps and to analyze the landslide evolution features from December 2006 to November 2017, a large number of Synthetic Aperture Radar (SAR) datasets acquired by L-band ascending ALOS/PALSAR, L-band ascending and descending ALOS/PALSAR-2, X-band ascending and descending TerraSAR-X and C-band descending Sentinel-1A/B images covering different evolution stages of Heifangtai terrace are fully exploited. Firstly, the surface deformation of Heifangtai terrace is calculated for independent SAR data using the InSAR technique. Subsequently, InSAR-derived deformation maps, SAR intensity images and a DEM gradient map are jointly used to detect potential loess landslides by setting the appropriate thresholds. More than 40 active loess landslides are identified and mapped. The accuracy of the landslide identification results is verified by comparison with published literatures, the results of geological field surveys and remote sensing images. Furthermore, the spatiotemporal evolution characteristics of the landslides during the last 11 years are revealed for the first time. Finally, strengths and limitations of different wavelength SAR data, and the effects of track direction, geometric distortions of SAR images and the differences in local incidence angle between two adjacent satellite tracks in terms of small-scale loess landslides identification, are analyzed and summarized, and some suggestions are given to guide the future identification of small-scale loess landslides with the InSAR technique.

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