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Search Results: 1 - 10 of 137 matches for " Alos Palsar Backscatter "
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Estimation of Above Ground Biomass in Forests Using Alos Palsar Data in Kericho and Aberdare Ranges  [PDF]
Eunice Wamuyu Maina, Patroba Achola Odera, Mwangi James Kinyanjui
Open Journal of Forestry (OJF) , 2017, DOI: 10.4236/ojf.2017.72006
Abstract: Above Ground Biomass is one of the six pools identified in the inventory of forest resources and estimation of greenhouse gas emissions and sinks from the forestry sector. The pool varies by management practices in different agro-ecological or agro-climatic zones in forests. The quantification of above ground biomass (AGB) hence carbon sequestration in forests has been very difficult due to the immense costs required. This research was done to estimate AGB using ALOS PALSAR L band data (HH, HV polarisation) acquired in 2009 in relation with ground measurements data in Kericho and Aberdares ranges in Kenya. Tree data information was obtained from ground measurement of DBH and tree heights in 100 circular plots of 15 m radius, by use of random sampling technique. ALOS PALSAR image is advantageous for its active microwave sensor using L-band frequency to achieve cloud free imageries, and the ability of long wavelength cross-polarization to estimate AGB accurately for tropical forests. The variations result between Natural and plantation forest for measured and estimated biomass in Kericho HV band regression value was 0.880 and HH band was 0.520. In Aberdare ranges HV regression value of 0.708 and HH band regression value of 0.511 for measured and estimated biomass respectively. The variations can be explained by the influence of different management regimes induced human disturbances, forest stand age, density, species composition, and trees diameter distribution. However, further research is required to investigate how strong these factors affect relationship between AGB and Alos Palsar backscatters.
Nation-Wide Clear-Cut Mapping in Sweden Using ALOS PALSAR Strip Images
Maurizio Santoro,Andreas Pantze,Johan E. S. Fransson,Jonas Dahlgren,Anders Persson
Remote Sensing , 2012, DOI: 10.3390/rs4061693
Abstract: Advanced Land Observing Satellite (ALOS) Phased Array L-band type Synthetic Aperture Radar (PALSAR) backscatter images with 50 m pixel size (strip images) at HV-polarization were used to map clear-cuts at a regional and national level in Sweden. For a set of 31 clear-cuts, on average 59.9% of the pixels within each clear-cut were correctly detected. When compared with a one-pixel edge-eroded version of the reference dataset, the accuracy increased to 88.9%. With respect to statistics from the Swedish Forest Agency, county-wise clear-felled areas were underestimated by the ALOS PALSAR dataset (between 25% and 60%) due to the coarse resolution. When compared with statistics from the Swedish National Forest Inventory, the discrepancies were larger, partly due to the estimation errors from the plot-wise forest inventory data. In Sweden, for the time frame of 2008–2010, the total area felled was estimated to be 140,618 ha, 172,532 ha and 194,586 ha using data from ALOS PALSAR, the Swedish Forest Agency and the Swedish National Forest Inventory, respectively. ALOS PALSAR strip images at HV-polarization appear suitable for detection of clear-felled areas at a national level; nonetheless, the pixel size of 50 m is a limiting factor for accurate delineation of clear-felled areas.
The Application of ALOS PALSAR Data on Mangrove Forest Extraction
ALOS PALSAR数据在漳江口红树林提取中的应用

XIAO Wei-shan,WANG Xiao-qin,LING Fei-long,

遥感技术与应用 , 2010,
Abstract: Mangrove forest is an important vegatation type on biodiversity conservation and wetland ecologi-cal protection. Acquisition changes on mangrove area timely is an urgent need for protection. In this paper, taking Fujian Zhangjiang Estuary National Nature Reserve as study area,multi-temporal ALOS PALSAR data acquired in 2007 are processed. The temporal change characteristics and depolarization characteristics on L band HH,HV channel are analyzed. On L band HH、HV channel, backscatter information does not change significantly over time, which is similar to local forests, but depolarization ability between mangrove forests and local forests is obviously different. Compared to temporal information, polarization information is more important for mangrove forests extraction. Based on object-oriented approach classification ,we pro-posed a method for mangrove forests extraction by using HV, HH polarization and their ratio from a mono temporal data. The results show that mangrove can be extracted with high accuracy.
南京林业大学学报(自然科学版) , 2016, DOI: 10.3969/j.issn.1000-2006.2016.05.017
Abstract: 为了提高森林蓄积量估测精度,以福建省三明市将乐县国有林场中杉木林作为试验区,选择资源3号卫星多光谱高分辨率影像及Alos Palsar影像为数据源,将相关性较高的极化雷达参数与最优窗口下的纹理参数相结合,协同两种遥感数据反演蓄积量。利用灰度共生矩阵分别提取高分辨率影像在3×3、5×5、7×7、9×9和11×11的5组窗口大小下8种纹理特征信息,提取Alos Palsar影像双极化方式下后向散射系数并进行比值运算。采用多元逐步回归分析方法,分别利用5组纹理特征信息反演杉木林蓄积量,找出最优窗口; 检测不同极化方式下后向散射系数与蓄积量之间相关性。结果表明,单数据源反演蓄积量模型中,5×5窗口反演效果最好,模型复相关系数R=0.869,均方根误差σRMSE=23.38 m3/hm2,蓄积量总体的估测精度为80.32%; 多数据源反演蓄积量模型中,两种极化方式下的后向散射系数比值与高分影像纹理特征参数结合后,反演模型的效果更好,模型中R=0.901,σRMSE=22.32 m3/hm2,蓄积量总体估测精度达到85.42%。研究表明,基于多数据源数据的森林蓄积量反演精度更高,结果更准确。
In order to improve the precision of forest volume estimation, the Chinese fir(Cunninghamia lanceolata)stands of state-owned forest farm in Jiangle County,Sanming City,Fujian Province were selected as the study object, and the high resolution images of ZY-3 and the images of Alos Palsar were selected as the remotely sensed data sources. The polarization radar parameters with high correlation and the texture parameters of the optimal window were combined for the volume inversion. Eight texture features of ZY-3 high resolution image were extracted by the gray level co-occurrence matrix under 5 kinds of window sizes including 3×3,5×5,7×7,9×9 and 11×11 pixels. Meanwhile, the backscatter coefficients in HH and HV polarization modes were derived from Alos Palsar images. Furthermore, ratio of the two backscatter coefficients above was computed. The texture features from 5 different windows were used as independent variable in the inversion of forest volume respectively by using stepwise regression analysis to find the optimal window. Then, the correlation between backscatter coefficients from different polarization modes and the forest volume was computed. The results showed that for the inversion model based on ZY-3 images, the optimal window was the size of 5×5, the value of multiple correlation coefficient reached to 0.869, with a root mean square error 23.38 m3/hm2 and a total estimation accuracy of 80.32%. While for the inversion model integrating the ratio of backscatter coefficients from Palsar with the texture features of optimal window from ZY-3, the value of multiple correlation coefficient reached to 0.901, with the root mean square error 22.32 m3/hm2 and the estimation accuracy of total forest volume 85.42%.The results suggests using bi-source remote sensing data can produce a higher precision of volume estimation on average
Identification of Paddy Planted Area Using ALOS PALSAR Data  [PDF]
Rizatus Shofiyati, Ishak Hanafiah Ismullah, Dan Dudung Muhally Hakim
Journal of Geographic Information System (JGIS) , 2011, DOI: 10.4236/jgis.2011.34033
Abstract: Agricultural land has a strategic function as the primary food provider for the people of Indonesia. Various methods of agricultural production estimation, particularly food crops, provide different information. It can be a source of error in decision making. Satellite data, provides information periodically, wide coverage area, can be used as a source of information on the condition of agricultural lands and even remote areas. The advantages of SAR data that does not depend on sunlight and can penetrate of clouds and fog can fill the lack of optical data. ALOS PALSAR data has been used for analysis and ALOS AVNIR-2 is for checking of land cover visually, with acquisition date on 10 May 2007. Sampling of each rice crop growth period used several of rice field conditions in each period, on one scene data. Results showed a possibility to use soil moisture conditions derived from ALOS PALSAR for estimating rice planting area. On a scatter diagram between backscatter of ALOS PALSAR and near infrared of ALOS AVNIR-2 showed a specific pattern for each growing period of paddy. The results of the analysis produce distribution maps of the rice planting area Subang area, West Java Province. However, validation of the method used remains to be done. Remote sensing results of this study are expected to provide better information and can contribute in the planning of higher quality agricultural land.
ICESat GLAS Elevation Changes and ALOS PALSAR InSAR Line-of-Sight Changes on the Continuous Permafrost Zone of the North Slope, Alaska  [PDF]
Reginald R. Muskett
International Journal of Geosciences (IJG) , 2015, DOI: 10.4236/ijg.2015.610086
Abstract: Measuring centimeter-scale and smaller surface changes by satellite-based systems on the periglacial terrains and permafrost zones of the northern hemisphere is an ongoing challenge. We are investigating this challenge by using data from the NASA Ice, Cloud, and land Elevation Satellite Geoscience Laser Altimeter System (ICESat GLAS) and the JAXA Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) on the continuous permafrost zone of the North Slope, Alaska. Using the ICESat GLAS exact-repeat profiles in the analysis of ALOS PALSAR InSAR Line-Of-Sight (LOS) changes, we find evidence of volume scattering over much of the tundra vegetation covered active-layer and surface scattering from river channel/banks (deposition and erosion), from rock outcropping bluffs and ridges. Pingos, ice-cored mounds common to permafrost terrains can be used as benchmarks for assessment of LOS changes. For successful InSAR processing, topographic and tropospheric phase cannot be assumed negligible and must be removed. The presence of significant troposphere phase in short-period repeat interferograms renders stacking ill suited for the task of deriving verifiable centimeter-scale surface deformation phase and reliable LOS changes.
Simple Relationship Analysis between L-Band Backscattering Intensity and the Stand Characteristics of Sugi (Cryptomeria japonica) and Hinoki (Chamaecyparis obtusa) Trees  [PDF]
Kotaro Iizuka, Ryutaro Tateishi
Advances in Remote Sensing (ARS) , 2014, DOI: 10.4236/ars.2014.34015
Abstract: In this study, we have performed an analysis between the L-band backscattering intensity derived from the slope corrected ALOS PALSAR remote sensing data and the in-situ stand biophysical parameter of Sugi (Cryptomeria japonica) and Hinoki (Chamaecyparis obtusa) trees at the forests of Chiba Prefecture, Japan. Diameter at breast height (DBH), tree height, and stem volume were statistically compared with the slope corrected sigma naught backscattering in an empirical approach. It was found that the relationship between the backscattering and the stand characteristics was strongly dependent on species showing different trends between the Sugi and Hinoki trees. The Hinoki trees showed an increasing backscattering with increasing parameters (higher DBH, higher Tree height and higher stem volume), as it was mentioned on various researches, while the Sugi tree showed and decreasing backscattering with increasing parameters. We have also found for the Sugi trees that the backscattering is affected strongly by the number of stems. We have assumed that this is because of the characteristics of the Sugi trees which have high moisture content in the heartwood of the stem, compared with other tree species in Japan. The results pave the way to the possibility for estimating biophysical parameters within the forests of Japan by considering such trends and at highly rugged areas by using slope corrected imagery of the SAR data.
Comparison of Simulated Backscattering Signal and ALOS PALSAR Backscattering over Arid Environment Using Experimental Measurement  [PDF]
Saeid Gharechelou, Ryutaro Tateishi, Josaphat Tetuko Sri Sumantyo
Advances in Remote Sensing (ARS) , 2015, DOI: 10.4236/ars.2015.43018
Abstract: The purpose of this paper is to simulate the backscattered signal by experimental data and field working then, comparing with the backscattered signal from actual L-band SAR data over arid to semi-arid environments. The experimental data included the laboratory-measured dielectric constant of soil samples and the roughness parameter. A backscattering model used to simulate the backscattering coefficient in sparse vegetation land cover. The backscattering coefficient (σ0) simulated using the AIEM (advanced integral equation model) based on the experimental data. The roughness data were considered by the field observation, chain method measuring and photogrammetry simulation technique by stereo image of ground real photography. The simulated backscattering coefficients were compared with the real extracted backscattering coefficient (σ0) from the ALOS PALSAR single and dual polarization mode data. The most problem in backscattering simulation was the vegetation water content. Therefore, the water-cloud model using the water index result of optical data applied on the simulated backscatter model for enhancement the backscattering heterogeneity from vegetation water contents due to the mix pixel of vegetation in spars vegetation. At the results the AIEM model overestimated the backscattering simulation, it might be cause of high sensitivity of this model to roughness. The ALOS PALSAR HV polarization mode is more sensitive than the HH mode to vegetation water content. The water-cloud model could improve the result and the correlation function of the samples was increased but, the difficulties were the input the A and B parameters to model.
Evaluating the Extraction Approaches of Flood Extended Area by Using ALOS-2/PALSAR-2 Images as a Rapid Response to Flood Disaster  [PDF]
A. Besse Rimba, Fusanori Miura
Journal of Geoscience and Environment Protection (GEP) , 2017, DOI: 10.4236/gep.2017.51003
Abstract: Flash floods are recurrent events around the Japan region almost every year. Torrential rain occurred around Kanto and Tohoku area due to typhoon No. 18 in September 2015. Overflowing of the Kinugawa River led to river bank collapse. Thus, the flood extended into Joso City, Ibaraki Prefecture, Japan. ALOS-2/PALSAR-2 was the fastest satellite to record this flood disaster area. A quick method to extract the flood inundation area by utilizing the ALOS-2/ PALSAR-2 image as a rapid response to the flood disaster is required. This study evaluated three methods to extract the flood immediately after the flood occurring. This study compared the extraction approaches of flooded area by unsupervised classification, supervised classification and binary/threshold of backscattering value of flood. The results show that unsupervised classification and supervised classification are overestimated. This study recommends the binarization of the backscattering value to extract the extended flood area. This method is a straight forward approach and generates a similar distribution with the field survey by using the aerial photo with high accuracy (94% of kappa coefficient). We utilized slope map which derived from DEM data to eliminate the overestimated area due to shadowing effect in SAR images.
Assessment of Supervised Classifiers for Land Cover Categorization Based on Integration of ALOS PALSAR and Landsat Data  [PDF]
Dorothea Deus
Advances in Remote Sensing (ARS) , 2018, DOI: 10.4236/ars.2018.72004
Abstract: Many supervised classification algorithms have been proposed, however, they are rarely evaluated for specific application. This research examines the performance of machine learning classifiers support vector machine (SVM), neural network (NN), Random Forest (RF) against maximum classifier (MLC) (traditional supervised classifier) in forest resources and land cover categorization, based on combination of Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) and Landsat Thematic Mapper (TM) data, in Northern Tanzania. Various data categories based on Landsat TM surface reflectance, ALOS PALSAR backscattering and their derivatives were generated for various classification scenarios. Then a separate and joint processing of Landsat and ALOS PALSAR data were executed using SVM, NN, RF and ML classifiers. The overall classification accuracy (OA), kappa coefficient (KC) and F1 score index values were computed. The result proves the robustness of SVM and RF in classification of forest resource and land cover using mere Landsat data and integration of Landsat and PALSAR (average OA = 92% and F1 = 0.7 to 1). A two sample t-statistics was utilized to evaluate the performance of the classifiers using different data categories. SVM and RF indicate there is no significance difference at 5% significance level. SVM and RF show a significant difference when compared to NN and ML. Generally, the study suggests that parametric classifiers indicate better performance compared to parametric classifier.
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