Climate effects on an inland alpine lake in Xinjiang, China over the past 40 years

Inland lakes are important water resources in arid and semiarid regions. Understanding climate effects on these lakes is critical to accurately evaluate the dynamic changes of water resources. This study focused on the changes in Sayram Lake of Xinjiang, China, and addressed the effects of climate fluctuations on the inland lake based on long-term sequenced remote sensing images and meteorological data from the past 40 years. A geographic information system (GIS) method was used to obtain the hypsometry of the basin area of Sayram Lake, and estimation methods for evaporation from rising temperature and water levels from increasing precipitation were proposed. Results showed that: (1) Areal values of Sayram Lake have increased over the past 40 years. (2) Both temperature and precipitation have increased with average increases of more than 1.8℃ and 82 mm, respectively. Variation of the water levels in the lake was consistent with local climate changes, and the areal values show linear relationships with local temperature and precipitation data. (3) According to the hypsometry data of the basin area, the estimated lake water levels increased by 2.8 m, and the water volume increased by 12.9×10 8 m3 over the past 40 years. The increasing area of Sayram Lake correlated with local and regional climatic changes because it is hardly affected by human activities.

COVID-19: Challenges to GIS with Big Data

The outbreak of the 2019 novel coronavirus disease(COVID-19)has caused more than 100,000 people infected and thousands of deaths.Currently,the number of infections and deaths is still increasing rapidly.COVID-19 seriously threatens human health,production,life,social functioning and international relations.In the fight against COVID-19,Geographic Information Systems(GIS)and big data technologies have played an important role in many aspects,including the rapid aggregation of multi-source big data,rapid visualization of epidemic information,spatial tracking of confirmed cases,prediction of regional transmission,spatial segmentation of the epidemic risk and prevention level,balancing and management of the supply and demand of material resources,and socialemotional guidance and panic elimination,which provided solid spatial information support for decision-making,measures formulation,and effectiveness assessment of COVID-19 prevention and control.GIS has developed and matured relatively quickly and has a complete technological route for data preparation,platform construction,model construction,and map production.However,for the struggle against the widespread epidemic,the main challenge is finding strategies to adjust traditional technical methods and improve speed and accuracy of information provision for social management.At the data level,in the era of big data,data no longer come mainly from the government but are gathered from more diverse enterprises.As a result,the use of GIS faces difficulties in data acquisition and the integration of heterogeneous data,which requires governments,businesses,and academic institutions to jointly promote the formulation of relevant policies.At the technical level,spatial analysis methods for big data are in the ascendancy.Currently and for a long time in the future,the development of GIS should be strengthened to form a data-driven system for rapid knowledge acquisition,which signifies ts that GIS should be used to reinforce the social operation parameterization of models and methods,especially when providing support for social management.

Analysis and comparison of spatial interpolation methods for temperature data in Xinjiang Uygur Autonomous Region, China

Spatial interpolation methods are frequently used to estimate values of meteorological data in locations where they are not measured. However, very little research has been investigated the relative performance of different interpolation methods in meteorological data of Xinjiang Uygur Autonomous Region (Xinjiang). Actually, it has importantly practical significance to as far as possibly improve the accuracy of interpolation results for meteorological data, especially in mountainous Xinjiang. There- fore, this paper focuses on the performance of different spatial interpolation methods for monthly temperature data in Xinjiang. The daily observed data of temperature are collected from 38 meteorological stations for the period 1960- 2004. Inverse distance weighting (IDW), ordinary kriging (OK), temperature lapse rate method (TLR) and multiple linear regressions (MLR) are selected as interpolated methods. Two rasterized methods, multiple regression plus space residual error and directly interpolated observed temperature (DIOT) data, are used to analyze and compare the performance of these interpolation methods respectively. Moreover, cross-validation is used to evaluate the performance of different spatial interpolation methods. The results are as follows: 1) The method of DIOT is unsuitable for the study area in this paper. 2) It is important to process the observed data by local regression model before the spatial interpolation. 3) The MLR-IDW is the optimum spatial interpolation method for the monthly mean temperature based on cross-validation. For the authors, the reliability of results and the influence of measurement accuracy, density, distribution and spatial variability on the accuracy of the interpolation methods will be tested and analyzed in the future.

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.

A four-band index for both liquid and solid water and its applications in the Aral Sea Basin

Various investigations have been conducted to analyze the water-coverage area of the Aral Sea and the Aral Sea Basin(ASB). However, the investigations incorporated considerable uncertainty and the used water indices had misclassification problem, which made different research groups present different results. Thus we first ascertain the boundaries of the ASB, the Syr and Amu river basins as well as their upper, middle and lower reaches. Then a four-band index for both liquid and solid water(ILSW) is proposed to address the misclassification problems of the classic water indices. ILSW is calculated by using the reflectance values of the green, red, near infrared, and thermal infrared bands, which combines the normalized difference water index(NDWI) and land surface temperature(LST) together. Validation results show that the ILSW water index has the highest accuracy by far in the Aral Sea Basin. Our results indicate that annual average decline of the water-coverage area was 963 km^(2) in the southern Aral Sea, whereas the northern Aral Sea has experienced little change. In the meanwhile, permanent ice and snow in upper reach of ASB has retreated considerably. Annual retreating rates of the permanent ice and snow were respectively 6233and 3841 km^(2) in upper reaches of Amu river basin(UARB) and Syr river basin(USRB). One of major reasons is that climate has become warmer in ASB. The climate change has caused serious water deficit problem. The water deficit had an increasing trend since the 1990s and its increasing rates was 3.778 billion m^(3) yearly on average. The total water deficit was 76.967 billion m^(3) on average in the whole area of ASB in the 2010s. However, up reaches of Syr river basin(USRB), a component area of ASB, had water surplus of 25.461 billion m^(3). These conclusions are useful for setting out a sustainable development strategy in ASB.

An adaptive representation model for geoscience knowledge graphs considering complex spatiotemporal features and relationships

Geoscience knowledge graph(GKG)can organize various geoscience knowledge into a machine understandable and computable semantic network and is an effective way to organize geoscience knowledge and provide knowledge-related services.As a result,it has gained significant attention and become a frontier in geoscience.Geoscience knowledge is derived from many disciplines and has complex spatiotemporal features and relationships of multiple scales,granularities,and dimensions.Therefore,establishing a GKG representation model conforming to the characteristics of geoscience knowledge is the basis and premise for the construction and application of GKG.However,existing knowledge graph representation models leverage fixed tuples that are limited in fully representing complex spatiotemporal features and relationships.To address this issue,this paper first systematically analyzes the categorization and spatiotemporal features and relationships of geoscience knowledge.On this basis,an adaptive representation model for GKG is proposed by considering the complex spatiotemporal features and relationships.Under the constraint of a unified spatiotemporal ontology,this model adopts different tuples to adaptively represent different types of geoscience knowledge according to their spatiotemporal correlation.This model can efficiently represent geoscience knowledge,thereby avoiding the isolation of the spatiotemporal feature representation and improving the accuracy and efficiency of geoscience knowledge retrieval.It can further enable the alignment,transformation,computation,and reasoning of spatiotemporal information through a spatiotemporal ontology.

Knowledge System,Ontology,and Knowledge Graph of the Deep-Time Digital Earth(DDE): Progress and Perspective

THE USE OF KNOWLEDGE GRAPH IN NATURAL SCIENCE Knowledge graph is a field of Artificial Intelligence(AI)that aims to represent knowledge in the form of graphs,consisting of nodes and edges which represent entities and relationships between nodes respectively(Aidan et al.,2022).Although the knowledge graph was popularized recently due to use of this idea in Google’s search engine in 2012(Amit,2012),its root can be traced back to the emergence of the Semantic Web as well as earlier works in ontology(Aggarwal,2021).

真实土地覆被变化的局地温度效应呈现出显著的纬度差异和不对称性

局地温度对土地覆被变化(Land cover change,LCC)的生物物理响应是目前研究的热点问题.以往基于观测的工作主要使用空间代替时间的假设分析潜在影响,并聚焦于毁林/造林活动,而实际发生的所有类型的LCC对温度的真实影响仍未被充分探索.本研究采用空间结合时间的策略,同时利用海量1 km分辨率的卫星观测,实现了这一评估.结果表明,2006~2015年,全球发生LCC的区域平均温度增加0.08 K,但在不同纬度呈现显著差异,温度变化从-0.05 K到0.18 K;耕地扩张主导了北半球中纬度地区夏季的降温作用,而森林相关变化造成了其他区域的升温作用;这些温度效应占同期整体升温的比值最高可达44.6%,表明LCC对局地温度的影响不可忽略.另外,真实影响存在明显的不对称性特征:产生增温效应的LCC比降温效应的LCC具有更大的发生面积和作用强度,即使是在相同区域两种覆被类型之间的相互转换,增温LCC通常也具有比其反转的降温LCC更大的作用强度,这是由转换程度和驱动变量的不对称变化共同造成.这些新的发现来源于对真实发生LCC的估算,因此可为土地管理和气候缓解政策提供更现实的指导.

地理综合研究方法的发展与思考

地理系统是一个复杂巨系统,地理综合研究是整体性认知与理解地理系统的重要途径.随着地理学的发展演进,地理综合成为新时期地理学创新与突破的根本任务;发展地理综合的理论方法,成为防止地理学空心化,建立地理学核心理论的重要途径.然而,传统地理综合方法多采用"先分后综"的思路,导致"易分难综",地理综合方法体系亟待进一步探索.本文总结了现有地理综合相关研究及方法,分析了阻碍地理综合研究方法发展的根结所在,提出了发展基于地理模态的地理综合研究方法新构想.地理模态作为地理系统规律性认知的描述形式与表达机制,是比地理要素与地理过程更高层次的抽象载体;以多层次地理模态的有机聚合、有序迭代形成地理系统的整体性、系统性认知,有望指导后期地理系统的综合研究与模型重构.基于地理模态的地理综合研究方法将试图打通片段地理认知到综合地理认知的双向通道,构建地理数据到地理规律再到综合地理认知的模型方法,推动地理综合研究在理论和方法上的协同演进,促进地理综合研究的发展.

河口最大浑浊带研究的回顾与展望

最大浑浊带是河口近岸泥沙含量显著高于上下游,并在一定范围内有规律迁移的浑浊水体,是河流入海泥沙输移过程中的特有现象.它显著体现了河流、海洋及物质能量的耦合作用,其发展动态、研究进展备受关注.本文通过回顾国内外河口最大浑浊带80多年来的研究现状和历程,总结经验、展望未来.既有研究已对河口最大浑浊带的泥沙含量、物质来源及组成等基本特征进行了较为全面的总结,认识到不同类型河口最大浑浊带的形成发育存在不同的主导动力过程和主控影响因素;此外,河口最大浑浊带在中短尺度上的时空变化也得到了比较充分的分析和讨论.相关研究在多方面都取得了瞩目的成果和进展,具有重要的科学价值与现实意义.但是,分析认为仍亟待继续开展并进一步加强最大浑浊带的精准定量识别方法研究、空间立体分布整体特性研究,特别是人类活动加剧和全球自然环境显著变化影响下的长期时空动态特征研究,以促进对河口最大浑浊带现象及其时空演变规律和趋势更加全面、更加深刻的认识和理解,助力社会经济和自然环境健康可持续发展.

Geoscience knowledge graph in the big data era

Since the beginning of the 21 st century,the geoscience research has been entering a significant transitional period with the establishment of a new knowledge system as the core and with the drive of big data as the means.It is a revolutionary leap in the research of geoscience knowledge discovery from the traditional encyclopedic discipline knowledge system to the computer-understandable and operable knowledge graph.Based on adopting the graph pattern of general knowledge representation,the geoscience knowledge graph expands the unique spatiotemporal features to the Geoscience knowledge,and integrates geoscience knowledge elements,such as map,text,and number,to establish an all-domain geoscience knowledge representation model.A federated,crowd intelligence-based collaborative method of constructing the geoscience knowledge graph is developed here,which realizes the construction of high-quality professional knowledge graph in collaboration with global geo-scientists.We also develop a method for constructing a dynamic knowledge graph of multi-modal geoscience data based on in-depth text analysis,which extracts geoscience knowledge from massive geoscience literature to construct the latest and most complete dynamic geoscience knowledge graph.A comprehensive and systematic geoscience knowledge graph can not only deepen the existing geoscience big data analysis,but also advance the construction of the high-precision geological time scale driven by big data,the compilation of intelligent maps driven by rules and data,and the geoscience knowledge evolution and reasoning analysis,among others.It will further expand the new directions of geoscience research driven by both data and knowledge,break new ground where geoscience,information science,and data science converge,realize the original innovation of the geoscience research and achieve major theoretical breakthroughs in the spatiotemporal big data research.

Geographic modeling and simulation systems for geographic research in the new era: Some thoughts on their development and construction

Regionality,comprehensiveness,and complexity are regarded as the basic characteristics of geography.The exploration of their core connotations is an essential way to achieve breakthroughs in geography in the new era.This paper focuses on the important method in geographic research:Geographic modeling and simulation.First,we clarify the research requirements of the said three characteristics of geography and its potential to address geo-problems in the new era.Then,the supporting capabilities of the existing geographic modeling and simulation systems for geographic research are summarized from three perspectives:Model resources,modeling processes,and operational architecture.Finally,we discern avenues for future research of geographic modeling and simulation systems for the study of regional,comprehensive and complex characteristics of geography.Based on these analyses,we propose implementation architecture of geographic modeling and simulation systems and discuss the module composition and functional realization,which could provide theoretical and technical support for geographic modeling and simulation systems to better serve the development of geography in the new era.

A method of characterizing land-cover swap changes in the arid zone of China

网区域变化分析能戏剧性地低估陆地盖子的全部的变化，严重甚至有时曲解生态系统的生态的过程，特别在干旱或半干旱的地区。在这份报纸，索引的一间套房被介绍描绘陆地封面交换那可以严重在干旱或半干旱的地区损坏生态系统，基于从遥远地察觉到的图象提取的交换变化区域。首先，交换全部的区域的百分比并且交换全部的变化的紧张被用来决定陆地封面的地位在一个区域交换变化。然后，统治了交换范畴和单个交换变化紧张因为一个陆地封面范畴被用来决定标记的陆地封面交换变化范畴。最后，交换变化模式和 Pielous 索引被用来决定主导的范畴的陆地封面交换变化过程。案例研究用这条途径被进行，基于在 1980 年代的二张陆地封面地图并且 2000 在 Naiman Qi， Tongliao 城市，内部蒙古，中国。这研究证明途径罐头清楚地确定严厉和陆地封面交换变化的标记的类并且与生态系统的生态的进程揭示他们的关系。这些结果显示途径能给深卓见进交换变化，它能对陆地封面政策做和管理很珍贵。

The Generic Annular Bucket Histogram for Estimating the Selectivity of Spatial Selection and Spatial Join

Selectivity estimation is crucial for query optimizers choosing an optimal spatial execution plan in a spatial database management system.This paper presents an Annular Bucket spatial histogram(AB histogram)that can estimate the selectivity in finer spatial selection and spatial join operations even when the spatial query has more operators or more joins.The AB histogram is represented as a set of bucket-range,bucket-count value pairs.The bucket-range often covers an annular region like a sin-gle-cell-sized photo frame.The bucket-count is the number of objects whose Minimum Bounding Rectangles(MBRs)fall between outer rectangle and inner rectangle of the bucket-range.Assuming that all MBRs in each a bucket distribute evenly,for every buck-et,we can obtain serial probabilities that satisfy a certain spatial selection or join conditions from the operations' semantics and the spatial relations between every bucket-range and query ranges.Thus,according to some probability theories,spatial selection or join selectivity can be estimated by the every bucket-count and its probabilities.This paper also shows a way to generate an updated AB histogram from an original AB histogram and those probabilities.Our tests show that the AB histogram not only supports the selectivity estimation of spatial selection or spatial join with "disjoint","intersect","within","contains",and "overlap" operators but also provides an approach to generate a reliable updated histogram whose spatial distribution is close to the distribution of ac-tual query result.

Numerical modeling and prediction of future response of permafrost to different climate change scenarios on the Qinghai-Tibet Plateau

Projecting the future distribution of permafrost under different climate change scenarios is essential,especially for the Qinghai–Tibet Plateau(QTP).The altitude-response model is used to estimate future permafrost changes on the QTP for the four RCPs(RCP2.6,RCP4.5,RCP6.0,and RCP8.5).The simulation results show the following:(1)from now until 2070,the permafrost will experience different degrees of significant degradation under the four RCP scenarios.This will affect 25.68%,40.54%,45.95%,and 62.84%of the current permafrost area,respectively.(2)The permafrost changes occur at different rates during the periods 2030–2050 and 2050–2070 for the four different RCPs.(1)In RCP2.6,the permafrost area decreases a little during the period 2030–2050 but shows a small increase from 2050 to 2070.(2)In RCP4.5,the rate of permafrost loss during the period 2030–2050(about 12.73%)is higher than between 2050 and 2070(about 8.33%).(3)In RCP6.0,the permafrost loss rate for the period 2030–2050(about 16.52%)is similar to that for 2050–2070(about 16.67%).(4)In RCP8.5,there is a significant discrepancy in the rate of permafrost decrease for the periods 2030–2050 and 2050–2070:the rate is only about 3.70%for the first period but about 29.49%during the second.

Geospatial assessment of rooftop solar photovoltaic potential using multi-source remote sensing data

Rooftop solar photovoltaics (PV) play increasing role in the global sustainable energy transition. This raises the challenge of accurate and high-resolution geospatial assessment of PV technical potential in policymaking and power system planning. To address the challenge, we propose a general framework that combines multi-resource satellite images and deep learning models to provide estimates of rooftop PV power generation. We apply deep learning based inversion model to estimate hourly solar radiation based on geostationary satellite images, and automatic segmentation model to extract building footprint from high-resolution satellite images. The framework enables precise survey of available rooftop resources and detailed simulation of power generation on an hourly basis with a spatial resolution of 100 m. The case study in Jiangsu Province demonstrates that the framework is applicable for large areas and scalable between precise locations and arbitrary regions across multiple temporal scales. Our estimates show that rooftop resources across the province have a potential installed capacity of 245.17 GW, corresponding to an annual power generation of 290.66 TWh. This highlights the huge space for carbon emissions reduction through developing rooftop PVs.