Mapping upland crop–rice cropping systems for targeted sustainable intensification in South China

Upland crop-rice cropping systems(UCR)facilitate sustainable agricultural intensification.Accurate UCR cultivation mapping is needed to ensure food security,sustainable water management,and rural revitalization.However,datasets describing cropping systems are limited in spatial coverage and crop types.Mapping UCR is more challenging than crop identification and most existing approaches rely heavily on accurate phenology calendars and representative training samples,which limits its applications over large regions.We describe a novel algorithm(RRSS)for automatic mapping of upland crop-rice cropping systems using Sentinel-1 Synthetic Aperture Radar(SAR)and Sentinel-2 Multispectral Instrument(MSI)data.One indicator,the VV backscatter range,was proposed to discriminate UCR and another two indicators were designed by coupling greenness and pigment indices to further discriminate tobacco or oilseed UCR.The RRSS algorithm was applied to South China characterized by complex smallholder rice cropping systems and diverse topographic conditions.This study developed 10-m UCR maps of a major rice bowl in South China,the Xiang-Gan-Min(XGM)region.The performance of the RRSS algorithm was validated based on 5197 ground-truth reference sites,with an overall accuracy of 91.92%.There were7348 km^(2) areas of UCR,roughly one-half of them located in plains.The UCR was represented mainly by oilseed-UCR and tobacco-UCR,which contributed respectively 69%and 15%of UCR area.UCR patterns accounted for only one-tenth of rice production,which can be tripled by intensification from single rice cropping.Application to complex and fragmented subtropical regions suggested the spatiotemporal robustness of the RRSS algorithm,which could be further applied to generate 10-m UCR datasets for application at national or global scales.

Riding towards a sustainable future:an evaluation of bike sharing’s environmental benefits in Xiamen Island,China

In the pursuit of sustainable urbanization,Bike-Sharing Services(BSS)emerge as a pivotal instrument for promoting green,low-carbon transit.While BSS is often commended for its environmental benefits,we offer a more nuanced analysis that elucidates previously neglected aspects.Through the Dominant Travel Distance Model(DTDM),we evaluate the potential of BSS to replace other transportation modes for specific journey based on travel distance.Utilizing multiscale geographically weighted regression(MGWR),we illuminate the relationship between BSS’s environmental benefits and built-environment attributes.The life cycle analysis(LCA)quantifies greenhouse gas(GHG)emissions from production to operation,providing a deeper understanding of BSS’s environmental benefits.Notably,our study focuses on Xiamen Island,a Chinese“Type II large-sized city”(1–3 million population),contrasting with the predominantly studied“super large-sized cities”(over 10 million population).Our findings highlight:(1)A single BSS trip in Xiamen Island reduces GHG emissions by an average of 19.97 g CO_(2)-eq,accumulating monthly savings of 144.477 t CO_(2)-eq.(2)Areas in the southwest,northeast,and southeast of Xiamen Island,characterized by high population densities,register significant BSS environmental benefits.(3)At a global level,the stepwise regression model identifies five key built environment factors influencing BSS’s GHG mitigation.(4)Regionally,MGWR enhances model precision,indicating that these five factors function at diverse spatial scales,affecting BSS’s environmental benefits variably.

From Digitalized to Intelligentized Surveying and Mapping: Fundamental Issues and Research Agenda

Nowadays Surveying and Mapping(S&M)production and services are facing some serious challenges such as real-timization of data acquisition,automation of information processing,and intellectualization of service applications.The main reason is that current digitalized S&M technologies,which involve complex algorithms and models as the core,are incapable of completely describing and representing the diverse,multi-dimensional and dynamic real world,as well as addressing high-dimensional and nonlinear spatial problems using simple algorithms and models.In order to address these challenges,it is necessary to explore the use of natural intelligence in S&M,and to develop intelligentized S&M technologies,which are knowledge-guided and algorithm-based.This paper first discusses the basic concepts and ideas of intelligentized S&M,and then analyzes and defines its fundamental issues in the analysis and modeling of natural intelligence in S&M,the construction and realization of hybrid intelligent computing paradigm,and the mechanism and path of empowering production.Further research directions are then proposed in the four areas,including knowledge systems,technologies and methodologies,application systems,and instruments and equipments of intelligentized S&M.Finally,some institutional issues related to promoting scientific research and engineering applications in this area are discussed.

Bundle adjustment for data processing of theodolite industrial surveying system

The photogrammetric bundle adjustment was used in data processing of electronic theodolite industrial surveying system by converting angular observations into virtual photo coordinates. The developed algorithm has ability of precision estimation and data snooping, do not need initial values of exterior orientation elements and object point coordinates. The form of control condition for the system is quite flexible. Neither centering nor leveling is the theodolite needed and the lay out of theodolite position is flexible when the system is used for precise survey. Experiments carried out in test field verify the validity of the data processing method. [

Application of GNSS-PPP on Dynamic Deformation Monitoring of Offshore Platforms

The real-time dynamic deformation monitoring of offshore platforms under environmental excitation is crucial to their safe operation.Although Global Navigation Satellite System-Precise Point Positioning(GNSS-PPP)has been considered for this purpose,its monitoring accuracy is relatively low.Moreover,the influence of background noise on the dynamic monitoring accuracy of GNSS-PPP remains unclear.Hence,it is imperative to further validate the feasibility of GNSS-PPP for deformation monitoring of offshore platforms.To address these concerns,vibration table tests with different amplitudes and frequencies are conducted.The results demonstrate that GNSS-PPP can effectively monitor horizontal vibration displacement as low as±30 mm,which is consistent with GNSS-RTK.Furthermore,the spectral characteristic of background noise in GNSS-PPP is similar to that of GNSS-RTK(Real Time Kinematic).Building on this observation,an improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)has been proposed to de-noise the data and enhance the dynamic monitoring accuracy of GNSS-PPP.Field monitoring application research is also undertaken,successfully extracting and analyzing the dynamic deformation of an offshore platform structure under environmental excitation using GNSS-PPP monitoring in conjunction with improved CEEMDAN de-noising.By comparing the de-noised dynamic deformation trajectories of the offshore platform during different periods,it is observed that the platform exhibits reversible alternating vibration responses under environmental excitation,with more pronounced displacement deformation in the direction of load action.The research results confirm the feasibility and potential of GNSS-PPP for dynamic deformation monitoring of offshore platforms.

Hybrid pedestrian positioning system using wearable inertial sensors and ultrasonic ranging

Pedestrian positioning system(PPS)using wearable inertial sensors has wide applications towards various emerging fields such as smart healthcare,emergency rescue,soldier positioning,etc.The performance of traditional PPS is limited by the cumulative error of inertial sensors,complex motion modes of pedestrians,and the low robustness of the multi-sensor collaboration structure.This paper presents a hybrid pedestrian positioning system using the combination of wearable inertial sensors and ultrasonic ranging(H-PPS).A robust two nodes integration structure is developed to adaptively combine the motion data acquired from the single waist-mounted and foot-mounted node,and enhanced by a novel ellipsoid constraint model.In addition,a deep-learning-based walking speed estimator is proposed by considering all the motion features provided by different nodes,which effectively reduces the cumulative error originating from inertial sensors.Finally,a comprehensive data and model dual-driven model is presented to effectively combine the motion data provided by different sensor nodes and walking speed estimator,and multi-level constraints are extracted to further improve the performance of the overall system.Experimental results indicate that the proposed H-PPS significantly improves the performance of the single PPS and outperforms existing algorithms in accuracy index under complex indoor scenarios.

A detailed rock density model of the Hong Kong territories

We used the geological map and published rock density measurements to compile the digital rock density model for the Hong Kong territories.We then estimated the average density for the whole territory.According to our result,the rock density values in Hong Kong vary from 2101 to 2681 kg·m^(-3).These density values are typically smaller than the average density of 2670 kg·m^(-3),often adopted to represent the average density of the upper continental crust in physical geodesy and gravimetric geophysics applications.This finding reflects that the geological configuration in Hong Kong is mainly formed by light volcanic formations and lava flows with overlying sedimentary deposits at many locations,while the percentage of heavier metamorphic rocks is very low(less than 1%).This product will improve the accuracy of a detailed geoid model and orthometric heights.

Influence of Human Activity Intensity on Habitat Quality in Hainan Tropical Rainforest National Park,China

Due to long-term human activity interference,the Hainan Tropical Rainforest National Park(HTRNP)of China has experienced ecological problems such as habitat fragmentation and biodiversity loss,and with the expanding scope and intensity of human activity impact,the regional ecological security is facing serious challenges.A scientific assessment of the interrelationship between human activity intensity and habitat quality in the HTRNP is a prerequisite for achieving effective management of ecological disturbances caused by human activities and can also provide scientific strategies for the sustainable development of the region.Based on the land use change data in 2000,2010,and 2020,the spatial and temporal variations and the relationship between habitat quality(HQ)and human activity intensity(HAI)in the HTRNP were explored using the integrated valuation of ecosystem services and trade-offs(InVEST)model.System dynamics and land use simulation models were also combined to conduct multi-scenario simulations of their relationships.The results showed that during 2000–2020,the habitat quality of the HTRNP improved,the intensity of human activities decreased each year,and there was a negative correlation between the two.Second,the system dynamic model could be well coupled with the land use simulation model by combining socio-economic and natural factors.The simulation scenarios of the coupling model showed that the harmonious development(HD)scenario is effective in curbing the increasing trend of human activity intensity and decreasing trend of habitat quality,with a weaker trade-off between the two compared with the baseline development(BD)and investment priority oriented(IPO)scenarios.To maintain the authenticity and integrity of the HTRNP,effective measures such as ecological corridor construction,ecological restoration,and the implementation of ecological compensation policies need to be strengthened.

Low-cost cloud computing solution for geo-information processing

Cloud computing has emerged as a leading computing paradigm,with an increasing number of geographic information(geo-information) processing tasks now running on clouds.For this reason,geographic information system/remote sensing(GIS/RS) researchers rent more public clouds or establish more private clouds.However,a large proportion of these clouds are found to be underutilized,since users do not deal with big data every day.The low usage of cloud resources violates the original intention of cloud computing,which is to save resources by improving usage.In this work,a low-cost cloud computing solution was proposed for geo-information processing,especially for temporary processing tasks.The proposed solution adopted a hosted architecture and can be realized based on ordinary computers in a common GIS/RS laboratory.The usefulness and effectiveness of the proposed solution was demonstrated by using big data simplification as a case study.Compared to commercial public clouds and dedicated private clouds,the proposed solution is more low-cost and resource-saving,and is more suitable for GIS/RS applications.

A Survey of Mobile Cloud Computing

Mobile Cloud Computing （MCC） is emerging as one of the most important branches of cloud computing. In this paper, MCC is defined as cloud computing extended by mobility, and a new ad-hoc infrastructure based on mobile devices. It provides mobile users with data storage and processing services on a cloud computing platform. Because mobile cloud computing is still in its infancy we aim to clarify confusion that has arisen from different views. Existing works are reviewed, and an overview of recent advances in mobile cloud computing is provided. We investigate representative infrastructures of mobile cloud computing and analyze key components. Moreover, emerging MCC models and services are discussed, and challenging issues are identified that will need to be addressed in future work.

Dynamic model for landsliding monitoring under rigid body assumption

Based on the assumption that the slope bodies are rigid, the dynamic model of the landsiding (forward model) was put forward. According to the dynamic model, the system equations of Kalman filter were constituted. The mechanical status of a slope was hence combined with the monitoring data by Kalman filter. The model uncertainties or model errors could also be considered through some fictitious observation equations. Different from existed methods, the presented method can make use for not only the statistic information contained in the data but also the information provided by the mechanical and geological aspect of slopes. At last a numerical example was given out to show the feasibility of the method. [

Rapid report of June 1,2022 M_(W) 5.9 Lushan earthquake,China with geodetic and teleseismic data

Timely response to earthquake characterization can facilitate earthquake emergency rescue and further scientific investigations.On June 1,2022,M_(W) 5.9 earthquake occurred in the southern area of the Longmenshan fault zone.This event also happened at the south end of the Dayi seismic gap and is the largest earthquake that has occurred in this seismic gap since the 1970 M 6.2 event.The slip-distribution model constrained by the seismic waveforms suggests a thrust-dominated faulting mechanism.The main slip occurs at a depth of~14 km,and the cumulative energy is released in the first 6 s.The variations of Coulomb stress caused by the mainshock show a positive change in the southwest area of the Dayi seismic gap,indicating possible activation of future earthquakes.In addition,we emphasize the importance of rapid estimation of deformation for near-field hazard delineation,especially when interferometric radar fails to image coseismic deformation in a high relief terrain.

深圳市GPS测图一体化系统的研制

介绍深圳市GPS测图一体化系统研制思路、系统设计原则、技术路线及关键技术,并简要介绍GPS测图一体化系统的使用情况。使用情况表明:该系统能够满足深圳市基本比例尺地形图测绘的技术要求,且使用该系统能够带来明显的社会经济效益。

Improved Goldstein filter for InSAR noise reduction based on local SNR

尽管基于本地 signal-to-noise (SNR ) 的修改 Goldstein 过滤器被证明了比有更多的全面过滤器参数的古典 Goldstein 和 Baran 过滤器优异，它的改编不在阶段噪音的减小总是是足够的。在这个工作，本地基于 SNR 的 Goldstein 过滤器进一步在本地 SNR 的定义和过滤补丁尺寸的改编与改进被开发。为阻止过多的过滤引起的阶段信号的损失，而且，过滤操作的一次重复也在这个新算法被介绍。评估建议算法的表演，模仿的数字举起模型(DEM ) interferogram 和跨越 L 就鸟座地震的真实 SAR 变丑 interferogram 被执行。量的结果从模仿并且真实数据表明多达 79.5% 噪音能被新过滤器减少，在以前的本地基于 SNR 的 Goldstein 过滤器上显示 9%32% 改进。这证明新过滤器是不仅与足够的改编，而且罐头装备了没有阶段信号的牺牲，压制阶段噪音。

Time-Frequency Characteristics of the Relationships Between Tropical Indo-Pacific SSTs

In this study, several advanced analysis methods are applied to understand the relationships between the Nino-3.4 sea surface temperatures （SST） and the SSTs related to the tropical Indian Ocean Dipole （IOD）. By analyzing a long data record, the authors focus on the time-frequency characteristics of these relationships, and of the structure of IOD. They also focus on the seasonal dependence of those characteristics in both time and frequency domains. Among the Nino-3.4 SST, IOD, and SSTs over the tropical western Indian Ocean （WIO） and eastern Indian Ocean （EIO）, the WIO SST has the strongest annual and semiannual oscillations. While the Nino-3.4 SST has large inter-annual variability that is only second to its annual variability, the IOD is characterized by the largest semiannual oscillation, which is even stronger than its annual oscillation. The IOD is strongly and stably related to the EIO SST in a wide range of frequency bands and in all seasons. However, it is less significantly related to the WIO SST in the boreal winter and spring. There exists a generally weak and unstable relationship between the WIO and EIO SSTs, especially in the biennial and higher frequency bands. The relationship is especially weak in summer and fall, when IOD is apparent, but appears highly positive in winter and spring, when the IOD is unimportantly weak and even disappears. This feature reflects a caution in the definition and application of IOD. The Nino-3.4 SST has a strong positive relationship with the WIO SST in all seasons, mainly in the biennial and longer frequency bands. However, it shows no significant relationship with the EIO SST in summer and fall, and with IOD in winter and spring.

Event-based incremental updating of spatio-temporal database

Based on the relationship among the geographic events, spatial changes and the database operations, a new automatic (semi-automatic) incremental updating approach of spatio-temporal database (STDB) named as (event-based) incremental updating (E-BIU) is proposed in this paper. At first, the relationship among the events, spatial changes and the database operations is analyzed, then a total architecture of E-BIU implementation is designed, which includes an event queue, three managers and two sets of rules, each component is presented in detail. The process of the E-BIU of master STDB is described successively. An example of building’s incremental updating is given to illustrate this approach at the end. The result shows that E-BIU is an efficient automatic updating approach for master STDB.

Quantitative study of atmospheric effects in spaceborne InSAR measurements

Atmospheric effects on interferometric synthetic aperture radar(InSAR) measurements are quantitatively studied based on a tandem pair of SAR data and a month-long continuous GPS tracking data obtained at six stations. Differential atmospheric signals extracted from the SAR data for two selected areas show apparent power law characteristics. The RMS values of the signals are 2.04 and 3.66 rad respectively for the two areas. These differential delays can potentially cause in the two areas peak-to-peak deformation errors of 3.64 and 6.52cm, respectively, at the 95% confidence level and Gaussian distribution. The respective potential peak-to-peak DEM errors are 123 and 221 m. The GPS tropospheric total zenith delays estimate indicates that a peak-to-peak error of about 7.8cm can potentially be caused in a SAR interferogram with only 1 d interval at the 95% confidence level. The error increases to about 9.6cm for 10 d interval. The potential peak-to-peak DEM and deformation errors estimated from GPS total zenith delay measurements are however quite similar to those estimated from InSAR data. This provides us with a useful tool to pre-estimate the potential atmospheric effects in a SAR interferogram before we order the SAR images. Nevertheless, the results reveal that even in a small area the atmospheric delays can obscure centimetre level ground displacements and introduce a few hundred meters of errors to derived DEM.

Analysis of the Quality of Daily DEM Generation with Geosynchronous InSAR

Up-to-date digital elevation model(DEM)products are essential in many fields such as hazards mitigation and urban management.Airborne and low-earth-orbit(LEO)space-borne interferometric synthetic aperture radar(InSAR)has been proven to be a valuable tool for DEM generation.However,given the limitations of cost and satellite repeat cycles,it is difficult to generate or update DEMs very frequently(e.g.,on a daily basis)for a very large area(e.g.,continental scale or greater).Geosynchronous synthetic aperture radar(GEOSAR)satellites fly in geostationary earth orbits,allowing them to observe the same ground area with a very short revisit time(daily or shorter).This offers great potential for the daily DEM generation that is desirable yet thus far impossible with space-borne sensors.In this work,we systematically analyze the quality of daily GEOSAR DEM.The results indicate that the accuracy of a daily GEOSAR DEM is generally much lower than what can be achieved with typical LEO synthetic aperture radar(SAR)sensors;therefore,it is important to develop techniques to mitigate the effects of errors in GEOSAR DEM generation.

Ground-based/UAV-LiDAR data fusion for quantitative structure modeling and tree parameter retrieval in subtropical planted forest

Light detection and ranging(LiDAR)has contributed immensely to forest mapping and 3D tree modelling.From the perspective of data acquisition,the integration of LiDAR data from different platforms would enrich forest information at the tree and plot levels.This research develops a general framework to integrate ground-based and UAV-LiDAR(ULS)data to better estimate tree parameters based on quantitative structure modelling(QSM).This is accomplished in three sequential steps.First,the ground-based/ULS LiDAR data were co-registered based on the local density peaks of the clustered canopy.Next,redundancy and noise were removed for the ground-based/ULS LiDAR data fusion.Finally,tree modeling and biophysical parameter retrieval were based on QSM.Experiments were performed for Backpack/Handheld/UAV-based multi-platform mobile LiDAR data of a subtropical forest,including poplar and dawn redwood species.Generally,ground-based/ULS LiDAR data fusion outperforms ground-based LiDAR with respect to tree parameter estimation compared to field data.The fusion-derived tree height,tree volume,and crown volume significantly improved by up to 9.01%,5.28%,and 18.61%,respectively,in terms of rRMSE.By contrast,the diameter at breast height(DBH)is the parameter that has the least benefits from fusion,and rRMSE remains approximately the same,because stems are already well sampled from ground data.Additionally,particularly for dense forests,the fusion-derived tree parameters were improved compared to those derived from ground-based LiDAR.Ground-based LiDAR can potentially be used to estimate tree parameters in low-stand-density forests,whereby the improvement owing to fusion is not significant.

On stress-forecasting strategy of earthquakes from stress buildup,stress shadow and stress transfer(SSS) based on numerical approach

Global Positioning System （GPS） and Interferometric Synthetic Aperture Radar （InSAR）, used for monitoring crust deformation, are found to be very promising in earthquake prediction subject to stress-forecasting. However, it is recognized that unless we can give reasonable explanations of these curious precursory phenomena that continue to be serendipitously observed from time to time, such high technology of GPS or InSAR is difficult to be efficiently used. Therefore, a proper model revealing the relation between earthquake evolution and stress variation, such as the phenomena of stress buildup, stress shadow and stress transfer （SSS）, is crucial to the GPS or InSAR based earthquake prediction. Here we address this question through a numerical approach of earthquake development using an intuitive physical model with a map-like configuration of discontinuous fault system. The simulation provides a physical basis for the principle of stress-forecasting of earthquakes based on SSS and for the application of GPS or InSAR in earthquake prediction. The observed SSS associated phenomena with images of stress distribution during the failure process can be continuously simulated. It is shown that the SSS are better indicators of earthquake precursors than that of seismic foreshocks, suggesting a predictability of earthquakes based on stress-forecasting strategy.