Evolution of Subkilometer-scale Impact Craters on the Lunar Maria as Constrained from Mini-RF Data and Topographic Degradation Model

Physical properties(e.g.,ejecta size and distribution)of impact craters are crucial and essential to understanding the ejecta excavation and deposition process,estimating rock breakdown rate,and revealing their evolution characteristics.However,whether these physical properties are scale-dependent and how they evolve in different radial regions needs further studies.In this study,we first investigated the physical properties and evolution of subkilometer(D≤800 m)craters on lunar maria based on the radar circular polarization ratio(CPR).In addition,we estimated the periods over which rocks and blocky ejecta are exposed and buried in the shallow subsurface layer(termed as exposure time)in different radial regions and assessed the retention time and degradation states for potential radar anomalous craters.We found that in the central region of craters,the largest median CPR occurs after an 80 Myr delay following crater formation.In the rim region,there is no obvious CPR peak in the first100 Ma,whereas in the upper wall region,an evident CPR peak occurs beyond 100 Ma and could last over one billion years.In addition,the probable exposure time of rocks and blocky ejecta is estimated to be~2.0 Gyr(central region),~2.7 Gyr(upper wall region),~2.1 Gyr(rim region),and~0.6 Gyr(continuous ejecta blanket region).We also propose that the retention time of radar anomalous craters depends on the crater size,whereas their degraded states are independent of crater size.

GIScience and remote sensing in natural resource and environmental research:Status quo and future perspectives

Geographic information science(GIScience)and remote sensing have long provided essential data and method-ological support for natural resource challenges and environmental problems research.With increasing advances in information technology,natural resource and environmental science research faces the dual challenges of data and computational intensiveness.Therefore,the role of remote sensing and GIScience in the fields of natural resources and environmental science in this new information era is a key concern of researchers.This study clarifies the definition and frameworks of these two disciplines and discusses their role in natural resource and environmental research.GIScience is the discipline that studies the abstract and formal expressions of the basic concepts and laws of geography,and its research framework mainly consists of geo-modeling,geo-analysis,and geo-computation.Remote sensing is a comprehensive technology that deals with the mechanisms of human ef-fects on the natural ecological environment system by observing the earth surface system.Its main areas include sensors and platforms,information processing and interpretation,and natural resource and environmental appli-cations.GIScience and remote sensing provide data and methodological support for resource and environmental science research.They play essential roles in promoting the development of resource and environmental science and other related technologies.This paper provides forecasts of ten future directions for GIScience and eight future directions for remote sensing,which aim to solve issues related to natural resources and the environment.

Short-term displacement prediction for newly established monitoring slopes based on transfer learning

This study makes a significant progress in addressing the challenges of short-term slope displacement prediction in the Universal Landslide Monitoring Program,an unprecedented disaster mitigation program in China,where lots of newly established monitoring slopes lack sufficient historical deformation data,making it difficult to extract deformation patterns and provide effective predictions which plays a crucial role in the early warning and forecasting of landslide hazards.A slope displacement prediction method based on transfer learning is therefore proposed.Initially,the method transfers the deformation patterns learned from slopes with relatively rich deformation data by a pre-trained model based on a multi-slope integrated dataset to newly established monitoring slopes with limited or even no useful data,thus enabling rapid and efficient predictions for these slopes.Subsequently,as time goes on and monitoring data accumulates,fine-tuning of the pre-trained model for individual slopes can further improve prediction accuracy,enabling continuous optimization of prediction results.A case study indicates that,after being trained on a multi-slope integrated dataset,the TCN-Transformer model can efficiently serve as a pretrained model for displacement prediction at newly established monitoring slopes.The three-day average RMSE is significantly reduced by 34.6%compared to models trained only on individual slope data,and it also successfully predicts the majority of deformation peaks.The fine-tuned model based on accumulated data on the target newly established monitoring slope further reduced the three-day RMSE by 37.2%,demonstrating a considerable predictive accuracy.In conclusion,taking advantage of transfer learning,the proposed slope displacement prediction method effectively utilizes the available data,which enables the rapid deployment and continual refinement of displacement predictions on newly established monitoring slopes.

New insights into multiple provenances evolution of the Jurassic from heavy minerals characteristics in southern Junggar Basin, NW China

The method of random forest was used to classify the heavy mineral assemblages of 2 418 Jurassic samples in the southern Junggar Basin, and determine the distribution of the heavy mineral assemblages from the same provenance systems. Based on the analysis of heavy minerals assemblages, ZTR index, sedimentary characteristics, U-Pb zircon ages, whole-rock geochemical and paleocurrent direction analysis, the study reveals that five important provenances were providing sediments to the southern Junggar Basin in the Jurassic period: The North Tianshan(NTS), Central Tianshan(CTS), Bogda Mountains, Zhayier Mountains and Kalamaili Mountains. During the Early Jurassic, NTS-CTS, Kalamaili Mountains and Zhayier Mountains are primary provenances, Bogda Mountains started to uplift and supply clastic materials in the Middle Jurassic. There are three sedimentary area in the Jurassic of southern Junggar Basin: the western part, the central part and the eastern part. In the western part, the clastic materials of the Early Jurassic was mainly from NTS blocks and Zhayier Mountains, and the sediments were dominantly derived from the Zhayier Mountains during the Middle–Late Jurassic. In the central part, the main provenance of the Early Jurassic switched from NTS to CTS. In the Xishanyao Formation, the main source went back to NTS again. The NTS was the primary provenance during the sedimentary periods of Toutunhe Formation and Qigu Formation. In the eastern part, the contribution of CTS and Kalamaili Mountains were considered as major provenances in the Early Jurassic-Xishanyao Formation, small proportion of sediments were from NTS. The Bogda mountains uplifted and started to provide sediments to the Junggar Basin in the sedimentary period of Xishanyao Formation, and became the major source during the Toutunhe Formation period, with small amount of sediments from CTS. The provenance from CTS was hindered during the sedimentary period of Qigu Formation owing to the uplifting of the Bogda mountains, and the sediments were mainly from the Bogda mountains and NTS.

SHARAD study on the structures and permittivity of a buried impact crater in Martian Central Elysium Planitia

The Elysium Planitia,located in the transition zone between the northern and southern hemispheres,is one of the key areas for studying the stratigraphic structure and geological history of Mars.Previous studies have shown that this plain has undergone complex surface modification processes including fluvial and volcanic processes,and systematic progress has been made in the study of macro-geological processes.However,there are relatively few studies on the regional structure of the plain,which restricts our understanding of the regional geological processes.A buried impact crater in the central part of the Elysium Planitia could have recorded the surface modification process since the formation of the impact crater,however,it is difficult to distinguish the subsurface stratigraphy due to the weak orbital radar reflection signal.In this study,we denoised Shallow Radar data and obtained a radargram with clear subsurface reflectors.We estimated the permittivity of subsurface materials via a multilayer reflection model.The results show that two subsurface reflectors divide the structure of the buried impact crater into three layers(overlying layer,underlying layer,and bottom layer).The shallow subsurface reflector covers almost the whole impact crater,while the deep subsurface reflector covers only the southwest part of the impact crater.Combining the permittivity inversion results with the geological background of lava activity in the Elysium Planitia area,we argue that the overlying layer may be a mixture of regolith and lava flow with low density,while the underlying layer and bottom layer are dense lava flows.The reflector between the underlying layer and bottom layer is probably a thin deposit derived from weathering between two lava activities,and its possible formation mechanism is as follows:the crater rim and peripheral ejecta has undergone relatively strong wind erosion and the eroded material was transport to the southwestern part of the impact crater,forming continuous thin deposits,between the emplacements of two lava flows.This is consistent with the wind erosion environment prevailing at low latitudes in the Late Amazonian of Mars.This study uses processed orbital radar data,dielectric property inversion,and geological structure interpretation of regional buried impact craters as a local example to demonstrate how radar data can be used to understand regional depositional processes,and it serves as a reference for studying the geological history of similar regional structures on Mars.

Tracking the Moisture Sources of an Extreme Precipitation Event in Shandong,China in July 2007:A Computational Analysis

This paper utilizes a modified Water Accounting Model （WAM） to track the moisture sources of an extreme precipitation event in Shandong during 18-20 July 2007. It is found that different methods in dealing with the residual of the water budget always produce different results in moisture recycling calculations. In addition, results from the backward tracking without the residual are in complete agreement with those from the forward tracking with the residual, and vice versa, implying a mathematical consistency. We thus analyze and derive the conditions under which the two tracking approaches equate with each other. We applied the backward tracking to the Shandong extreme rainfall case and obtained quantitative estimates of moisture contributions of three selected regions away from the rainfall area. The results indicate that the spatial pattern rather than numerical value of the recycling moisture is more reliable in tracking the moisture sources. The moisture of this Shandong rainfall event comes mostly from the nearby upwind area in Southwest China, which is of the terrestrial origin; while the moisture originating from the neighboring West Pacific contributes little to this event.

Nonscalability of Fractal Dimension to Quantify Canopy Structural Complexity from Individual Trees to Forest Stands

Canopy structural complexity is a critical emergent forest attribute,and light detection and ranging(lidar)-based fractal dimension has been recognized as its powerful measure at the individual tree level.However,the current lidar-based estimation method is highly sensitive to data characteristics,and its scalability from individual trees to forest stands remains unclear.This study proposed an improved method to estimate fractal dimension from lidar data by considering Shannon entropy,and evaluated its scalability from individual trees to forest stands through mathematical derivations.Moreover,a total of 280 forest stand scenes simulated from the terrestrial lidar data of 115 trees spanning large variability in canopy structural complexity were used to evaluate the robustness of the proposed method and the scalability of fractal dimension.The results show that the proposed method can significantly improve the robustness of lidar-derived fractal dimensions.Both mathematical derivations and experimental analyses demonstrate that the fractal dimension of a forest stand is equal to that of the tree with the largest fractal dimension in it,manifesting its nonscalability from individual trees to forest stands.The nonscalability of fractal dimension reveals its limited capability in canopy structural complexity quantification and indicates that the power-law scaling theory of a forest stand underlying fractal geometry is determined by its dominant tree instead of the entire community.Nevertheless,we believe that fractal dimension is still a useful indicator of canopy structural complexity at the individual tree level and might be used along with other stand-level indexes to reflect the“tree-to-stand”correlation of canopy structural complexity.

Canopy structure:An intermediate factor regulating grassland diversity-function relationships under human disturbances

Grasslands are one of the largest coupled human-nature terrestrial ecosystems on Earth,and severe anthropogenic-induced grassland ecosystem function declines have been reported recently.Understanding factors influencing grassland ecosystem functions is critical for making sustainable management policies.Canopy structure is an important factor influencing plant growth through mediating within-canopy microclimate(e.g.,light,water,and wind),and it is found coordinating tightly with plant species diversity to influence forest ecosystem functions.However,the role of canopy structure in regulating grassland ecosystem functions along with plant species diversity has been rarely investigated.Here,we investigated this problem by collecting field data from 170 field plots distributed along an over 2000 km transect across the northern agro-pastoral ecotone of China.Aboveground net primary productivity(ANPP)and resilience,two indicators of grassland ecosystem functions,were measured from field data and satellite remote sensing data.Terrestrial laser scanning data were collected to measure canopy structure(represented by mean height and canopy cover).Our results showed that plant species diversity was positively correlated to canopy structural traits,and negatively correlated to human activity intensity.Canopy structure was a significant indicator for ANPP and resilience,but their correlations were inconsistent under different human activity intensity levels.Compared to plant species diversity,canopy structural traits were better indicators for grassland ecosystem functions,especially for ANPP.Through structure equation modeling analyses,we found that plant species diversity did not have a direct influence on ANPP under human disturbances.Instead,it had a strong indirect effect on ANPP by altering canopy structural traits.As to resilience,plant species diversity had both a direct positive contribution and an indirect contribution through mediating canopy cover.This study highlights that canopy structure is an important intermediate factor regulating grassland diversity-function relationships under human disturbances,which should be included in future grassland monitoring and management.

Personalized tourist route recommendation model with a trajectory understanding via neural networks

Travel recommendations form a major part of tourism service. Traditional collaborative filtering and Markov model are not appropriate for expressing the trajectory features,for travel preferences of tourists are dynamic and affected by previous behaviors. Inspired by the success of deep learning in sequence learning,a personalized recurrent neural network (P-RecN) is proposed for tourist route recommendation. It is data-driven and adaptively learns the unknown mapping of historical trajectory input to recommended route output. Specifically,a trajectory encoding module is designed to mine the semantic information of trajectory data,and LSTM neural networks are used to capture the sequence travel patterns of tourists. In particular,a temporal attention mechanism is integrated to emphasize the main behavioral intention of tourists. We retrieve a geotagged photo dataset in Shanghai,and evaluate our model in terms of accuracy and ranking ability. Experimental results illustrated that P-RecN outperforms other baseline approaches and can effectively understand the travel patterns of tourists.

Recognizing mixed urban functions from human activities using representation learning methods

When various urban functions are integrated into one location,they form a mixture of functions.The emerging big data promote an alternative way to identify mixed functions.However,current methods are largely unable to extract deep features in these data,resulting in low accuracy.In this study,we focused on recognizing mixed urban functions from the perspective of human activities,which are essential indicators of functional areas in a city.We proposed a framework to comprehensively extract deep features of human activities in big data,including activity dynamics,mobility interactions,and activity semantics,through representation learning methods.Then,integrating these features,we employed fuzzy clustering to identify the mixture of urban functions.We conducted a case study using taxiflow and social media data in Beijing,China,in whichfive urban functions and their correlations with land use were recognized.The mixture degree of urban functions in each location was revealed,which had a negative correlation with taxi trip distance.The results confirmed the advantages of our method in understanding mixed urban functions by employing various representation learning methods to comprehensively depict human activities.This study has important implications for urban planners in understanding urban systems and developing better strategies.

Optimizing segmented trajectory data storage with HBase for improved spatio-temporal query efficiency

The surging accumulation of trajectory data has yielded invaluable insights into urban systems,but it has also presented challenges for data storage and management systems.In response,specialized storage systems based on non-relational databases have been developed to support large data quantities in distributed approaches.However,these systems often utilize storage by point or storage by trajectory methods,both of which have drawbacks.In this study,we evaluate the effectiveness of segmented trajectory data storage with HBase optimizations for spatio-temporal queries.We develop a prototype system that includes trajectory segmentation,serialization,and spatio-temporal indexing and apply it to taxi trajectory data in Beijing.Ourfindings indicate that the segmented system provides enhanced query speed and reduced memory usage compared to the Geomesa system.

嫦娥五号月壤中首次发现蒸发沉积成因的蓝辉铜矿

由于缺乏磁场和大气的保护,月球表面持续受到陨石和微陨石的轰击.撞击引起的气化沉积作用是月表典型的改造过程,该过程往往伴随独特矿物相的产生(如纳米金属铁以及铁硅化合物).研究团队通过对嫦娥五号月壤进行细致的扫描电子显微镜观察,在纯铁颗粒以及玻璃质表面发现了具有典型气相沉积特征的含铜组分,进一步通过透射电子显微镜以及电子能量损失谱确认了这些含铜沉积物为蓝辉铜矿这是首次在月壤中发现此类矿物.本研究提供了月表硫化物发生气化沉积的直接证据,揭示了月表撞击过程引起的气相组分迁移及其对月表物质的显著改造效应.同时,嫦娥五号月壤中蓝辉铜矿的发现进一步拓宽了人们对月表复杂矿物组成的认识.

Building a model-based personalised recommendation approach for tourist attractions from geotagged social media data

When travelling,people are accustomed to taking and uploading photos on social media websites,which has led to the accumulation of huge numbers of geotagged photos.Combined with multisource information(e.g.weather,transportation,or textual information),these geotagged photos could help us in constructing user preference profiles at a high level of detail.Therefore,using these geotagged photos,we built a personalised recommendation system to provide attraction recommendations that match a user’s preferences.Specifically,we retrieved a geotagged photo collection from the public API for Flickr(Flickr.com)and fetched a large amount of other contextual information to rebuild a user’s travel history.We then created a model-based recommendation method with a two-stage architecture that consists of candidate generation(the matching process)and candidate ranking.In the matching process,we used a support vector machine model that was modified for multiclass classification to generate the candidate list.In addition,we used a gradient boosting regression tree to score each candidate and rerank the list.Finally,we evaluated our recommendation results with respect to accuracy and ranking ability.Compared with widely used memory-based methods,our proposed method performs significantly better in the cold-start situation and when mining‘long-tail’data.

Core or edge? Revisiting GIScience from the geography-discipline perspective

As an inter-disciplinary area between geography and computer sciences, geographical information science(GIScience) inherits the spatial analysis tradition of the former. Together with other branches of information geography, it seeks the balance between universality and particularity of geographical laws by combining methods from neighboring disciplines(such as big data and artificial intelligence) with the special nature of geographical spaces. Meanwhile, at the core position of the geography discipline, GIScience makes geography stronger from two directions: "strengthening the theoretical foundation" and "improving technology and promoting the practical applications".

A framework for mixed-use decomposition based on temporal activity signatures extracted from big geo-data

Mixed use has been extensively applied as an urban planning principle and hinders the study of single urban functions.To address this problem,it is worth decomposing the mixed use.Inspired by the concept of spectral unmixing in remote sensing applications,this paper proposes a framework for mixed-use decomposition based on big geo-data.Mixeduse decomposition in terms of human activities differs from traditional land use research,and it is more reasonable to infer the actual urban function of land.The framework consists of four steps,namely temporal activity signature extraction,urban function base curve extraction,mixeduse decomposition,and result validation.First,the temporal activity signatures(TASs)of each zone are extracted as the proxy of human activity patterns.Second,the diurnal TASs of routine activities are extracted as urban function base curves(i.e.endmembers).Third,a linear decomposition model is used to decompose the mixed use and obtain multiple results(urban function composition,dynamic activity proportions,and the mixing index).Finally,result validation strategies are concluded.This framework offers method extensibility and has few requirements for the input data.It is validated by means of a case study of Beijing,based on a social media check-in dataset.

iTourSPOT:a context-aware framework for next POI recommendation in location-based social networks

The rising prosperity of Location-based Social Networks(LBSNs)witnessed an explosion in the availability of geo-tagged social media data,which enables tremendous location-aware online services,especially next point of interest(POI)recommendation.However,previous next POI recommendation studies usually adopt fix-length time windows for user check-in sequence modeling,leading to a limited capacity in capturing fine-grained user temporal preferences that easily change over time.Besides,existing methods often directly leverage multi-modal contexts as auxiliary to alleviate the data sparsity issue,which fails to fully exploit the sequential patterns of contextual information for inferring user interest drift.To address the above challenges,we propose a novel framework named iTourSPOT which extends traditional collaborative filtering methods with a context-aware POI embedding architecture.For enhancing temporal interests modeling capacity,we associate the context feature extraction with varying-length sessions and incorporate check-in frequencies of POIs as prior knowledge to instruct the session representation learning of our model.Moreover,a collaborative sequence transduction model is designed for joint context sequence modeling and session-based POI recommendation.Experimental results on a real-world geo-tagged photo dataset clearly demonstrate the effectiveness of the proposed framework when compared with state-of-the-art baseline methods,especially in both sparse and cold-start scenarios.

Latent spatio-temporal activity structures: a new approach to inferring intra-urban functional regions via social media check-in data

This article introduces a novel low rank approximation (LRA)-based model to detect the functional regions with the data from about 15 million social media check-in records during a year-long period in Shanghai, China. We identified a series of latent structures, named latent spatio-temporal activity structures. While interpreting these structures, we can obtain a series of underlying associations between the spatial and temporal activity patterns. Moreover, we can not only reproduce the observed data with a lower dimensional representative, but also project spatio-temporal activity patterns in the same coordinate system. With the K-means clustering algorithm, five significant types of clusters that are directly annotated with a combination of temporal activities can be obtained, providing a clear picture of the correlation between the groups of regions and different activities at different times during a day. Besides the commercial and transportation dominant areas, we also detected two kinds of residential areas, the developed residential areas and the developing residential areas.We further interpret the spatial distribution of these clusters using urban form analytics. The results are highly consistent with the government planning in the same periods, indicating that our model is applicable to infer the functional regions from social media check-in data and can benefit a wide range of fields, such as urban planning, public services, and location-based recommender systems.