Georeferencing Multi-source Geospatial Data Using Multi-temporal TerraSAR-X Imagery: a Case Study in Qixing Farm, Northeast China

Zhao, Quanying; Hütt, Christoph; Lenz-Wiedemann, Victoria I. S.; Miao, Yuxin; Yuan, Fei; Zhang, Fusuo; Bareth, Georg

doi:10.1127/pfg/2015/0262

Cited by 5 publications

(6 citation statements)

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“…Concerning the second error, Rodriguez et al [48] state the absolute error of the SRTM to be below 10 m. The resulting low positional error of the images is required for the combined pixel-based processing of the scenes. For a more detailed analysis of this aspect, see [17], where a subset of the same dataset was used to create a spatial reference for various other datasets.…”

Section: Preprocessing Of the Remote Sensing Datamentioning

confidence: 99%

Best Accuracy Land Use/Land Cover (LULC) Classification to Derive Crop Types Using Multitemporal, Multisensor, and Multi-Polarization SAR Satellite Images

et al. 2016

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When using microwave remote sensing for land use/land cover (LULC) classifications, there are a wide variety of imaging parameters to choose from, such as wavelength, imaging mode, incidence angle, spatial resolution, and coverage. There is still a need for further study of the combination, comparison, and quantification of the potential of multiple diverse radar images for LULC classifications. Our study site, the Qixing farm in Heilongjiang province, China, is especially suitable to demonstrate this. As in most rice growing regions, there is a high cloud cover during the growing season, making LULC from optical images unreliable. From the study year 2009, we obtained nine TerraSAR-X, two Radarsat-2, one Envisat-ASAR, and an optical FORMOSAT-2 image, which is mainly used for comparison, but also for a combination. To evaluate the potential of the input images and derive LULC with the highest possible precision, two classifiers were used: the well-established Maximum Likelihood classifier, which was optimized to find those input bands, yielding the highest precision, and the random forest classifier. The resulting highly accurate LULC-maps for the whole farm with a spatial resolution as high as 8 m demonstrate the beneficial use of a combination of x-and c-band microwave data, the potential of multitemporal very high resolution multi-polarization TerraSAR-X data, and the profitable integration and comparison of microwave and optical remote sensing images for LULC classifications.

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Section: Preprocessing Of the Remote Sensing Datamentioning

confidence: 99%

Best Accuracy Land Use/Land Cover (LULC) Classification to Derive Crop Types Using Multitemporal, Multisensor, and Multi-Polarization SAR Satellite Images

et al. 2016

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“…The precision of geometric correction depends on the number, distribution, and accuracy of the Ground Control Points (GCPs) [57]. To avoid labor intensive work of in-situ GCP collection, Zhao et al [58] developed a geometric correction method for georeferencing multi-source geodata by using TerraSAR-X data as a reference. The same method was applied in this study.…”

Section: Satellite Image Pre-processingmentioning

confidence: 99%

“…The positional error (PE) was less than 6 m for all three images. More details about the method can be found in Zhao et al [58]. The mean reflectance and VIs for each sample plot were calculated using the "Zonal Analysis" tool in ERDAS IMAGINE 2013.…”

Section: Satellite Image Pre-processingmentioning

confidence: 99%

Investigating Within-Field Variability of Rice from High Resolution Satellite Imagery in Qixing Farm County, Northeast China

Zhao

Lenz-Wiedemann

Yuan

et al. 2015

IJGI

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Rice is a primary staple food for the world population and there is a strong need to map its cultivation area and monitor its crop status on regional scales. This study was conducted in the Qixing Farm County of the Sanjiang Plain, Northeast China. First, the rice cultivation areas were identified by integrating the remote sensing (RS) classification maps from three dates and the Geographic Information System (GIS) data obtained from a local agency. Specifically, three FORMOSAT-2 (FS-2) images captured during the growing season in 2009 and a GIS topographic map were combined using a knowledge-based classification method. A highly accurate classification map (overall accuracy = 91.6%) was generated based on this Multi-Data-Approach (MDA). Secondly, measured agronomic variables that include biomass, leaf area index (LAI), plant nitrogen (N) concentration and plant N uptake were correlated with the date-specific FS-2 image spectra using stepwise multiple linear regression models. The best model validation results with a relative error 237(RE) of 8.9% were found in the biomass regression model at the phenological stage of heading. The best index of agreement (IA) value of 0.85 with an RE of 13.6% was found in the LAI model, also at the heading stage. For plant N uptake estimation, the most accurate model was again achieved at the heading stage with an RE of 11% and an IA value of 0.77; however, for plant N concentration estimation, the model performance was best at the booting stage. Finally, the regression models were applied to the identified rice areas to map the within-field variability of the four agronomic variables at different growth stages for the Qixing Farm County. The results provide detailed spatial information on the within-field variability on a regional scale, which is critical for effective field management in precision agriculture.

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“…The primary purpose of developing this system is to precisely control the sensors’ position and orientation. In remote sensing, POS information is crucial auxiliary information for image georeferencing and rectification [ 18 , 29 ]. A five DOF system including horizontal, vertical, pan, tilt, roll was built.…”

Section: Methodsmentioning

confidence: 99%

Development of a Near Ground Remote Sensing System

Zhang

Xiao

Zhuang

et al. 2016

Sensors

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Unmanned Aerial Vehicles (UAVs) have shown great potential in agriculture and are increasingly being developed for agricultural use. There are still a lot of experiments that need to be done to improve their performance and explore new uses, but experiments using UAVs are limited by many conditions like weather and location and the time it takes to prepare for a flight. To promote UAV remote sensing, a near ground remote sensing platform was developed. This platform consists of three major parts: (1) mechanical structures like a horizontal rail, vertical cylinder, and three axes gimbal; (2) power supply and control parts; (3) onboard application components. This platform covers five degrees of freedom (DOFs): horizontal, vertical, pitch, roll, yaw. A stm32 ARM single chip was used as the controller of the whole platform and another stm32 MCU was used to stabilize the gimbal. The gimbal stabilizer communicates with the main controller via a CAN bus. A multispectral camera was mounted on the gimbal. Software written in C++ language was developed as the graphical user interface. Operating parameters were set via this software and the working status was displayed in this software. To test how well the system works, a laser distance meter was used to measure the slide rail’s repeat accuracy. A 3-axis vibration analyzer was used to test the system stability. Test results show that the horizontal repeat accuracy was less than 2 mm; vertical repeat accuracy was less than 1 mm; vibration was less than 2 g and remained at an acceptable level. This system has high accuracy and stability and can therefore be used for various near ground remote sensing studies.

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Georeferencing Multi-source Geospatial Data Using Multi-temporal TerraSAR-X Imagery: a Case Study in Qixing Farm, Northeast China

Cited by 5 publications

References 0 publications

Best Accuracy Land Use/Land Cover (LULC) Classification to Derive Crop Types Using Multitemporal, Multisensor, and Multi-Polarization SAR Satellite Images

Best Accuracy Land Use/Land Cover (LULC) Classification to Derive Crop Types Using Multitemporal, Multisensor, and Multi-Polarization SAR Satellite Images

Investigating Within-Field Variability of Rice from High Resolution Satellite Imagery in Qixing Farm County, Northeast China

Development of a Near Ground Remote Sensing System

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