Zi Wan

Analysis of ALOS PALSAR InSAR data for mapping water level changes in Yellow River Delta wetlands

Wetlands play a key role in regional and global environments and are critically linked to many major issues such as climate change, water quality, hydrological and carbon cycles, and wildlife habitat and biodiversity. It is very important to measure water level changes and consequently water storage capacity changes in wetlands to enable wetland protection and reconstruction. In this study, HH polarization L-band synthetic aperture radar (SAR) data were used in conjunction with synchronous field measurements and investigations to investigate the potential to detect water level changes under different types of wetlands. After evaluating factors that influence interferometric coherence, the framework for measuring water level changes using interferometric synthetic aperture radar (InSAR) phase information is presented in this article. Additionally, the SAR data obtained are used to investigate InSAR-derived water level changes in Yellow River Delta wetlands. The results show that InSAR technology has great potential for application in mapping water level changes in coastal wetlands, and InSAR-derived water level changes can supply unprecedented spatial details.

Interpretation of buildings in high resolution sar images based on electromagnetic method

High resolution SAR (Synthetic Aperture Radar) will provide an innovative tool for urban area applications. Nevertheless, interpretation of SAR image in urban area is far from solved by the increase of spatial resolution. It is usually difficult to establish a determined relationship between scattering centers in high resolution SAR images and the basic units of building targets. In order to thoroughly understand the backscattering behaviour of building targets in high resolution SAR images, a method of high resolution SAR imaging simulation based on electromagnetic model is proposed in this paper, which mainly includes 3-D model establishment for the building target, triangular facets partition, RCS prediction for each facet, and SAR imaging through coherent superposition of echo from each triangular facet. Through comparison of the simulated and the original SAR image, building scatter mechanisms in high resolution SAR images can be well interpreted.

Ortho-rectification of high-resolution SAR image in mountain area by DEM

Because of the side-looking imaging characteristics, the quality of Synthetic Aperture Radar (SAR) image is badly affected by variable terrain. Such terrain can introduce large displacements in the SAR image geometry that inhibits the collocation of SAR-derived quantities with geographically referenced information acquired from other sources. So it is necessary to eliminate such inherent geometric distortions by generating a radar ortho-imagery that corresponds to a well defined map projection. In this paper, aiming at the newest high-resolution SAR data-RADARSAT-2 and TerraSAR-X, an effective ortho-rectification method was studied in detail and the result showed this method could achieve high geo-location accuracy.

Forest mapping using bi-aspect polarimetric SAR data in southwest China

Synthetic aperture radar (SAR) provides a powerful tool for forestry inventory because of its all-weather and all-day capabilities. In this paper forest mapping method using bi-aspect polarimetric SAR data acquired from ascending and descending path has been studied. Zhazuo forest farm in Guizhou province was selected as test site and an 8-temporal field experiment was designed to obtain bio-physical parameters and spatial structure parameters of the 12 sample plots. Then the Michigan Microwave Canopy Scattering model (MIMICS) was employed to analyze the seasonal variation of these 4 types of managed forests. Using polarimetric Radarsat 2 data, scattering mechanisms of each forest type were determined and polarimetric variables were extracted and analyzed for forest discrimination. Considering the inherent geometric distortion of SAR imaging in hilly areas, a geometric correction strategy using bi-aspect SAR images and high resolution DEM was proposed. Then support vector machines method was adopted for classification of the whole test area. Experiments show that the bi-aspect geometric strategy is useful for hilly areas especially for shadow elimination in SAR image, and polarimetric SAR data is helpful to forest mapping.

Band Selection for Forest Stock Volume Estimation in Southern China

When forest stock volume is quantitative estimated using SPOT-5, QuickBird and ALOS optical data with linear regression model, the optimal ratio of remote sensing band is chosen from the above three types of optical remote sensing data respectively, which is a significant part. In this study, the experiments are taken in Zhazuo Forest of Xiuwen County of Guizhou Province. Comprehensive utilization of the three optical data, the selected ratio of band is confirmed according with characteristics of the forest region. Optimization of the ratio of band remote sensing method used is the criteria of mean residual sum of square called RMSq. In this paper the multicollinearity which commonly exsits between ratio of the original band is analyzed and studied to get rid of its unfavorable influence in this paper. By means of the criteria of mean residual sum of square, the ratio of remote sensing band which determines the impact of forest stock volume estimation is confirmed finally. Conclusions are as follows: Compared with the selected band, multiple-correlation has been greatly reduced. The optimal ratio of remote sensing band such as SP4, SP2-3/2+3, SP 1-4/1+4, SP1*3/2 has an important role on the interpretation of forest stock volume estimation.

Forest mapping using multi-temporal polarimetric SAR data in southwest China

In the southwest of China, Synthetic aperture radar (SAR) is anticipated to provide an important tool for forestry inventory because of its all weather capabilities. In this paper, Zhazuo area in Guizhou Province of southwest China, with typical Karst landform, was selected as the test site, and six RADARSAT-2 polarimetric images were used for experiments. Methods for forest mapping based on polarimetric decomposition and multi-temporal polarimetric SAR data fusion were proposed. Experiments showed that polarimetric signatures of forest were significantly different with other targets, and fusion of multi-temporal RADARSAT-2 images can effectively improve image quality and enhance forest and deforestation information.

Analyzing the “Ear” feature of a dried up lake based on volume scattering modelling

This paper presents the volume scattering mechanism of subsurface and analyzes the fundamental cause of “Ear” feature of Lop Nur on SAR images. ALOS PALSAR image acquired in 2007 was collected, and the Cloude decomposition method was conducted on the calibrated image to extract the volume scattering component. Two field investigations were conducted in 2006 and 2008 respectively, and lots of precise soil samples were gathered for the parameters analysis. Validation of the model is also conducted based on the surveying parameter and the image processing results, and the fitting effect is relatively good. According to the analysis on the parameters of the model, it is found that salinity at subsurface is the fundamental reason to “Ear” feature, which is strongly correlated with Lop Nur evolution.

Quantitative Estimation of Forest Stock Volume Based on Multi-Source Data

Using SPOT-5, QuickBird, ALOS optical data with linear regression model, forest stock volume is quantitative estimated in Zhazuo Forest of Xiuwen County of Guizhou Province. The optimal ratio of band is chosen from the above three types of optical remote sensing data respectively by the criteria of mean residual sum of square called RMSq. Combined with GIS factors (tree species, land type, altitude, slope and slope aspect, forest age group, canopy density), the linear estimation model is confirmed. Four experiments are carried out using the three data respectively and multiple-source data. In this study, compared with the others, regression prediction model using ALOS data fits best. In this experiment, its root mean square error equals 0.623929, the total forecast relative error of the regression model equals 0.082878, and prediction error equals 0.75233. The ALOS ratio wave bands which finally enter the regression equation are AL1-AL2 /AL1+AL2, AL3/AL2, AL3-AL2/AL3+AL2. The use of multi-source data is also worthy of consideration.

New ortho-rectification method using two-looking direction RADARSAT-2 imageries

Synthetic Aperture Radar images reveal geometric and radiometric distortions that are caused by terrain undulations. It is necessary to eliminate the distortions before the measurement of geophysical and biophysical parameters from the SAR images. The traditional method based on a single SAR image can not totally eliminate the geometric and radiometric distortions that are caused by variable terrain. In this paper a new intact operation system based on two-looking direction RADARSAT-2 images is put forward which can eliminate the geometric and radiometric distortions. This new operation system consists of 4 major steps and is applied to the test area under investigation.