On-orbit Geometric Calibration and Preliminary Accuracy Evaluation of GF-14 Satellite

GF-14 satellite is a new generation of sub-meter stereo surveying and mapping satellite in China,carrying dual-line array stereo mapping cameras to achieve 1∶10000 scale topographic mapping without Ground Control Points(GCPs).In fact,space-based high-precision mapping without GCPs is a challenging task that depends on the close cooperation of several payloads and links,of which on-orbit geometric calibration is one of the most critical links.In this paper,the on-orbit geometric calibration of the dual-line array cameras of GF-14 satellite was performed using the control points collected in the high-precision digital calibration field,and the calibration parameters of the dual-line array cameras were solved as a whole by alternate iterations of forward and backward intersection.On this basis,the location accuracy of the stereo images using the calibration parameters was preliminarily evaluated by using several test fields around the world.The evaluation result shows that the direct forward intersection accuracy of GF-14 satellite images without GCPs after on-orbit geometric calibration reaches 2.34 meters(RMS)in plane and 1.97 meters(RMS)in elevation.

Accuracy evaluation of two precipitation datasets over upper reach of Heihe River Basin, northwestern China

As an important forcing data for hydrologic models, precipitation has significant effects on model simulation. The China Meteorological Forcing Dataset （ITP） and Global Land Data Assimilation System （GLDAS） precipitation data are the two commonly used data sources in the Heihe River Basin （HRB）. This paper focused on evaluating the accuracy of these two precipitation datasets. A set of metrics were developed to characterize the trend, magnitude, annual allocation, event matching, frequency, and spatial distribution of the two datasets. Meanwhile, such accuracy evaluation was performed at various scales, i.e., daily, monthly, and yearly. By comparing with observations, this study concluded that： first, both ITP and GLDAS precipitation data well represented the trends at corresponding sites, and GLDAS underestimated precipita- tion in most regions except the east tributary headwater region; second, unusual annual precipitation distribution was observed in both datasets with overestimation of precipitation in May through September and GLDAS appeared to be much severe; third, the ITP data seriously over-predicted the precipitation events; fourth, the ITP data have better spatial distribution than GLDAS in the upper reach area of HRB. Overall, we recommended ITP precipitation data for the land surface study in the uooer reach of HRB.

Quantifying the agreement and accuracy characteristics of four satellite-based LULC products for cropland classification in China

Various land use and land cover(LULC)products have been produced over the past decade with the development of remote sensing technology.Despite the differences in LULC classification schemes,there is a lack of research on assessing the accuracy of their application to croplands in a unified framework.Thus,this study evaluated the spatial and area accuracies of cropland classification for four commonly used global LULC products(i.e.,MCD12Q1V6,GlobCover2009,FROM-GLC and GlobeLand30)based on the harmonised FAO criterion,and quantified the relationships between four factors(i.e.,slope,elevation,field size and crop system)and cropland classification agreement.The validation results indicated that MCD12Q1 and GlobeLand30 performed well in cropland classification regarding spatial consistency,with overall accuracies of 94.90 and 93.52%,respectively.The FROMGLC showed the worst performance,with an overall accuracy of 83.17%.Overlaying the cropland generated by the four global LULC products,we found the proportions of complete agreement and disagreement were 15.51 and 44.72% for the cropland classification,respectively.High consistency was mainly observed in the Northeast China Plain,the Huang-Huai-Hai Plain and the northern part of the Middle-lower Yangtze Plain,China.In contrast,low consistency was detected primarily on the eastern edge of the northern and semiarid region,the Yunnan-Guizhou Plateau and southern China.Field size was the most important factor for mapping cropland.For area accuracy,compared with China Statistical Yearbook data at the provincial scale,the accuracies of different products in descending order were:GlobeLand30,FROM-GLC,MCD12Q1,and GlobCover2009.The cropland classification schemes mainly caused large area deviations among the four products,and they also resulted in the different ranks of spatial accuracy and area accuracy among the four products.Our results can provide valuable suggestions for selecting cropland products at the national or provincial scale and help cropland mapping and reconstruction,which is essential for food security and crop management,so they can also contribute to achieving the Sustainable Development Goals issued by the United Nations.

Ephemeral gully recognition and accuracy evaluation using deep learning in the hilly and gully region of the Loess Plateau in China

Ephemeral gullies are widely distributed in the hilly and gully region of the Loess Plateau and play a unique role in the slope gully erosion system.Rapid and accurate identification of ephemeral gullies impacts the distribution law and development trend of soil erosion on the Loess Plateau.Deep learning algorithms can quickly and accurately process large data samples that recognize ephemeral gullies from remote sensing images.Here,we investigated ephemeral gullies in the Zhoutungou watershed in the hilly and gully region of the Loess Plateau in China using satellite and unmanned aerial vehicle images and combined a deep learning image semantic segmentation model to realize automatic recognition and feature extraction.Using Accuracy,Precision,Recall,F1value,and AUC,we compared the ephemeral gully recognition results and accuracy evaluation of U-Net,R2U-Net,and SegNet image semantic segmentation models.The SegNet model was ranked first,followed by the R2U-Net and U-Net models,for ephemeral gully recognition in the hilly and gully region of the Loess Plateau.The ephemeral gully length and width between predicted and measured values had RMSE values of 6.78 m and 0.50 m,respectively,indicating that the model has an excellent recognition effect.This study identified a fast and accurate method for ephemeral gully recognition in the hilly and gully region of the Loess Plateau based on remote sensing images to provide an academic reference and practical guidance for soil erosion monitoring and slope and gully management in the Loess Plateau region.

Accuracy Comparison of Gridded Historical Cultivated Land Data in Jiangsu and Anhui Provinces

The spatial resolution of source data, the impact factor selection on the grid model and the size of the grid might be the main limitations of global land datasets applied on a regional scale. Quantitative studies of the impacts of rasterization on data accuracy can help improve data resolution and regional data accuracy. Through a case study of cropland data for Jiangsu and Anhui provinces in China, this research compared data accuracy with different data sources, rasterization methods, and grid sizes. First, we investigated the influence of different data sources on gridded data accuracy. The temporal trends of the History Database of the Global Environment （HYDE）, Chinese Historical Cropland Data （CHCD）, and Suwan Cropland Data （SWCD） datasets were more similar. However, differ- ent spatial resolutions of cropland source data in the CHCD and SWCD datasets revealed an average difference of 16.61% when provin- cial and county data were downscaled to a 10 x 10 km2 grid for comparison. Second, the influence of selection of the potential arable land reclamation rate and temperature factors, as well as the different processing methods for water factors, on accuracy of gridded datasets was investigated. Applying the reclamation rate of potential cropland to grid-processing increased the diversity of spatial distri- bution but resulted in only a slightly greater standard deviation, which increased by 4.05. Temperature factors only produced relative disparities within 10% and absolute disparities within 2 km2 over more than 90% of grid cells. For the different processing methods for water factors, the HYDE dataset distributed 70% more cropland in grid cells along riverbanks, at the abandoned Yellow River Estuary （located in Binhai County, Yancheng City, Jiangsu Province）, and around Hongze Lake, than did the SWCD dataset. Finally, we ex- plored the influence of different grid sizes. Absolute accuracy disparities by unit area for the year 2000 were within 0.1 km2 at a 1 km2 grid size, a 25% improvement over the 10 km2 grid size. Compared to the outcomes of other similar studies, this demonstrates that some model hypotheses and grid-processing methods in international land datasets are truly incongruent with actual land reclamation proc- esses, at least in China. Combining the model-based methods with historical empirical data may be a better way to improve the accuracy of regional scale datasets. Exploring methods for the above aspects improved the accuracy of historical crop/and gridded datasets for finer regional scales.

The improved artificial bee colony algorithm for mixed additive and multiplicative random error model and the bootstrap method for its precision estimation

To solve the complex weight matrix derivative problem when using the weighted least squares method to estimate the parameters of the mixed additive and multiplicative random error model(MAM error model),we use an improved artificial bee colony algorithm without derivative and the bootstrap method to estimate the parameters and evaluate the accuracy of MAM error model.The improved artificial bee colony algorithm can update individuals in multiple dimensions and improve the cooperation ability between individuals by constructing a new search equation based on the idea of quasi-affine transformation.The experimental results show that based on the weighted least squares criterion,the algorithm can get the results consistent with the weighted least squares method without multiple formula derivation.The parameter estimation and accuracy evaluation method based on the bootstrap method can get better parameter estimation and more reasonable accuracy information than existing methods,which provides a new idea for the theory of parameter estimation and accuracy evaluation of the MAM error model.

A High-Precision US-Guided Robot-Assisted HIFU Treatment System for Breast Cancer

Breast cancer is the most commonly diagnosed cancer in women. A strong treatment candidate is high- intensity focused ultrasound （HIFU）, a non-invasive therapeutic method that has already demonstrated its promise. To improve the precision and lower the cost of HIFU treatment, our group has developed an ultrasound （US）-guided, five-degree-of-freedom （DOF）, robot-assisted HIFU system. We constructed a fully functional prototype enabling easy three-dimensional （3D） US image reconstruction, target seg-mentation, treatment path generation, and automatic HIFU irradiation. The position was calibrated using a wire phantom and the coagulated area was assessed on heterogeneous tissue phantoms. Under the US guidance, the centroids of the HIFU-ahlated area deviated by less than 2 mm from the planned treatment region. The overshoot around the planned region was well below the tolerance of clinical usage. Our system is considered to he sufficiently accurate for breast cancer treatment.

Constrained geometry analysis to resolve 3-D deformations from three ground-based radars

When multiple ground-based radars(GB-rads)are utilized together to resolve three-dimensional(3-D)deformations,the resolving accuracy is related with the measurement geometry constructed by these radars.This paper focuses on constrained geometry analysis to resolve 3-D deformations from three GB-rads.The geometric dilution of precision(GDOP)is utilized to evaluate 3-D deformation accuracy of a single target,and its theoretical equation is derived by building a simplified 3-D coordinate system.Then for a 3-D scene,its optimal accuracy problem is converted into determining the minimum value of an objective function with a boundary constraint.The genetic algorithm is utilized to solve this constrained optimization problem.Numerical simulations are made to validate the correctness of the theoretical analysis results.

A Review on the Study of "Repeating Earthquakes" by Cross-correlation of Seismic Waveforms

"Repeating earthquakes",identified by cross-correlation of seismic waveforms,are found to be much more abundant in the nature than conventionally expected. In recent years, with the development of digital seismic networks, waveform cross correlation and "repeating earthquakes"have caused much attention to the measuring the variation of crustal medium properties and estimation of location accuracy and fault slip rate at depth or earthquake recurrence intervals. Moreover, as a useful tool, the "repeating earthquake" approach has also been used in the assessment of the accuracy of seismic phase picking,hypocenter location,fault structure and physics of earthquake sources,as well as the study of earthquake prediction. In this paper, we summarized the latest research and applications of "repeating earthquakes".

Accuracy and spatio-temporal variation of high resolution satellite rainfall estimate over the Ganjiang River Basin

Based on spatial interpolation rainfall of the ground gauge measurement,we proposed a method to comprehensively evaluate and compare the accuracy of satellite rainfall estimates (SREs) at three spatial scales:0.25°×0.25° grid scale,sub-catchment scale and the whole basin scale.Using this method,we evaluated the accuracy of six high-resolution monthly SREs (TRMM 3B42 V6,3B42RT V6,CMORPH,GSMaP MWR+,GSMaP MVK+ and PERSIANN) and revealed the spatio-temporal variation of the SRE accuracy based on spatial interpolated rainfall from a dense network of 325 gauges during 2003-2009 over the Ganjiang River Basin in the Southeast China.The results showed that ground gauge-calibrated 3B42 had the highest accuracy with slight overestimation,whereas the other five uncalibrated SREs had severe underestimation.The accuracy of the six SREs in wet seasons was remarkably higher than that in the dry seasons.When the time scale was expanded,the accuracy of SRE,particularly 3B42,increased.Furthermore,the accuracy of SREs was relatively low in the western mountains and northern piedmont areas,while it was relatively high in the central and southeastern hills and basins of the Ganjiang River Basin.When the space scale was expanded,the accuracy of the six SREs gradually increased.This study provided an example for of SRE accuracy validation in other regions,and a direct basis for further study of SRE-based hydrological process.

Evaluating the accuracy of Chinese pasture data in global historical land use datasets

Global historical land use datasets are widely used in global environmental change studies; however, uncertainties in the included pasture data have not been evaluated. In this study, using trend, relative difference ratio(RDR), and spatial comparisons, we evaluated the accuracy of China pasture data in the HYDE(versions 3.1 and 3.2), SAGE, and PJ datasets using historical document-based reconstructions, including the land use datasets for China covering 1935–1997(hereafter Ge-dataset)and Northeast China covering the 1700 s–2000(hereafter Ye-dataset), and the satellite-based China's Land-Use/cover Datasets(CLUDs) for 1980–2015. Four important results were obtained.(1) China pasture area in the HYDE, SAGE, and PJ datasets and grassland area in the Ge-dataset, Ye-dataset, and CLUDs show both disparate trends and large differences in absolute values.(2)Spatially, 50.9%, 52.8%, and 63.0% pasture/grassland grids in 2000 had RDRs greater than 60% between HYDE3.1, HYDE3.2,and SAGE datasets, and CLUDs, respectively. The percentage of grids with RDRs less than 20% were 24.9%, 26.7%, and 16.0%,respectively.(3) Based on HYDE3.2, the spatial distribution of pasture in Northeast China over the last 300 year has expanded,which is in contrast to the results from the Ye-dataset, which shows the spatial distribution of grassland shrinking because of human reclamation activities.(4) The large contrasts between the global datasets and Chinese native datasets are due to differences in pasture/grassland definitions, land use practices, and spatial reconstruction methods.

Quality of terrestrial data derived from UAV photogrammetry:A case study of Hetao irrigation district in northern China

Most crops in northern China are irrigated,but the topography affects the water use,soil erosion,runoff and yields.Technologies for collecting high-resolution topographic data are essential for adequately assessing these effects.Ground surveys and techniques of light detection and ranging have good accuracy,but data acquisition can be time-consuming and expensive for large catchments.Recent rapid technological development has provided new,flexible,high-resolution methods for collecting topographic data,such as photogrammetry using unmanned aerial vehicles(UAVs).The accuracy of UAV photogrammetry for generating high-resolution Digital Elevation Model(DEM)and for determining the width of irrigation channels,however,has not been assessed.A fixed-wing UAV was used for collecting high-resolution(0.15 m)topographic data for the Hetao irrigation district,the third largest irrigation district in China.112 ground checkpoints(GCPs)were surveyed by using a real-time kinematic global positioning system to evaluate the accuracy of the DEMs and channel widths.A comparison of manually measured channel widths with the widths derived from the DEMs indicated that the DEM-derived widths had vertical and horizontal root mean square errors of 13.0 and 7.9 cm,respectively.UAV photogrammetric data can thus be used for land surveying,digital mapping,calculating channel capacity,monitoring crops,and predicting yields,with the advantages of economy,speed and ease.

A global land cover map produced through integrating multi-source datasets

In the past decades,global land cover datasets have been produced but also been criticized for their low accuracies,which have been affecting the applications of these datasets.Producing a new global dataset requires a tremendous amount of efforts;however,it is also possible to improve the accuracy of global land cover mapping by fusing the existing datasets.A decision-fuse method was developed based on fuzzy logic to quantify the consistencies and uncertainties of the existing datasets and then aggregated to provide the most certain estimation.The method was applied to produce a 1-km global land cover map(SYNLCover)by integrating five global land cover datasets and three global datasets of tree cover and croplands.Efforts were carried out to assess the quality:1)inter-comparison of the datasets revealed that the SYNLCover dataset had higher consistency than these input global land cover datasets,suggesting that the data fusion method reduced the disagreement among the input datasets;2)quality assessment using the human-interpreted reference dataset reported the highest accuracy in the fused SYNLCover dataset,which had an overall accuracy of 71.1%,in contrast to the overall accuracy between 48.6%and 68.9%for the other global land cover datasets.

Urban Geospatial Information Acquisition Mobile Mapping System based on close-range photogrammetry and IGS site calibration

The measurement accuracy of the Mobile Mapping System (MMS) is the main problem, which restricts its development and application, so how to calibrate the MMS to improve its measure-ment accuracy has always been a research hotspot in the industry. This paper proposes a position and attitude calibration method with error correction based on the combination of the feature point and feature surface. First, the initial value of the spatial position relation-ship between each sensor of MMS is obtained by close-range photogrammetry. Second, the optimal solution for error correction is calculated by feature points in global coordinates jointly measured with International GNSS Service (IGS) stations. Then, the final transformation para-meters are solved by combining the initial values obtained originally, thereby realizing the rapid calibration of the MMS. Finally, it analyzed the RMSE of MMS point cloud after calibration, and the results demonstrate the feasibility of the calibration approach proposed by this method. Under the condition of a single measurement sensor accuracy is low, the plane and elevation absolute accuracy of the point cloud after calibration can reach 0.043 m and 0.072 m, respectively, and the relative accuracy is smaller than 0.02 m. It meets the precision require-ments of data acquisition for MMS. It is of great significance for promoting the development of MMS technology and the application of some novel techniques in the future, such as auton-omous driving, digital twin city, urban brain et al.

A comparison of APACHE II,BISAP,Ranson’s score and modified CTSI in predicting the severity of acute pancreatitis based on the 2012 revised Atlanta Classification

Objective:Our aim was to prospectively compare the Accuracy of Acute Physiology and Chronic Health Evaluation(APACHE)II,Bedside Index of Severity in Acute Pancreatitis(BISAP),Ranson’s score and modified Computed Tomography Severity Index(CTSI)in predicting the severity of acute pancreatitis based on Atlanta 2012 definitions in a tertiary care hospital in northern India.Methods:Fifty patients with acute pancreatitis admitted to our hospital during the period of March 2015 to September 2016 were included in the study.APACHE II,BISAP and Ranson’s score were calculated for all the cases.Modified CTSI was also determined based on a pancreatic protocol contrast enhanced computerized tomography(CT).Optimal cut-offs for these scoring systems and the area under the curve(AUC)were evaluated based on the receiver operating characteristics(ROC)curve and these scoring systems were compared prospectively.Results:Of the 50 cases,14 were graded as severe acute pancreatitis.Pancreatic necrosis was present in 15 patients,while 14 developed persistent organ failure and 14 needed intensive care unit(ICU)admission.The AUC for modified CTSI was consistently the highest for predicting severe acute pancreatitis(0.919),pancreatic necrosis(0.993),organ failure(0.893)and ICU admission(0.993).APACHE II was the second most accurate in predicting severe acute pancreatitis(AUC 0.834)and organ failure(0.831).APACHE II had a high sensitivity for predicting pancreatic necrosis(93.33%),organ failure(92.86%)and ICU admission(92.31%),and also had a high negative predictive value for predicting pancreatic necrosis(96.15%),organ failure(96.15%)and ICU admission(95.83%).Conclusion:APACHE II is a useful prognostic scoring system for predicting the severity of acute pancreatitis and can be a crucial aid in determining the group of patients that have a high chance of need for tertiary care during the course of their illness and therefore need early resuscitation and prompt referral,especially in resource-limited developing countries.