Understandings of Urban Sustainability in China:a Bibliometric Analysis Using Chinese Literatures

Cities hold a critical responsibility for achieving the Sustainable Development Goals(SDGs)due to their high population density,extensive resource consumption,and significant economic contributions.To examine the present state of understandings regarding urban sustainability(SDG 11:Sustainable Cities and Communities)within Chinese research communities,this study collected 15950 papers from 1994 to 2022 on the 12 indicators of SDG 11,from the China National Knowledge Infrastructure(CNKI),a hub of Chinese academic papers,that directly relate to policymaking.Significant research topics on SDG 11 were identified for each indicator using bibliometrics analysis approaches.The high-frequency keywords and clusters of keywords over the last three decades reveal that existing studies primarily concentrated on the physical aspects,such as transportation and environment,while there is a lack of consideration of societal aspects.This indicates a limited and biased understanding of the urban sustainability within the Chinese academic community.Hence,it is crucial to prioritize the societal aspects in order to develop a research agenda that further advances urban sustainability.

Improved SMB speckle filtering of polarimetric SAR data with synergistic use of orientation angle compensation and spatial majority rule

The scattering-model-based(SMB)speckle filtering for polarimetric SAR(Pol SAR)data is reasonably effective in preserving dominant scattering mechanisms.However,the efficiency strongly depends on the accuracies of both the decomposition and classification of the scattering properties.In addition,a relatively weak speckle reduction particularly in distributed media was reported in the related literatures.In this work,an improved SMB filtering strategy is proposed considering the aforementioned deficiencies.First,the orientation angle compensation is incorporated into the SMB filtering process to remedy the overestimation of the volume scattering contribution in the Freeman-Durden decomposition.In addition,an algorithm to select the homogenous pixels is developed based on the spatial majority rule for adaptive speckle reduction.We demonstrate the superiority of the proposed methods in terms of scattering property preservation and speckle noise reduction using L-band Pol SAR data sets of San Francisco that were acquired by the NASA/JPL airborne SAR(AIRSAR)system.

High resolution crop intensity mapping using harmonized Landsat-8 and Sentinel-2 data

An increase in crop intensity could improve crop yield but may also lead to a series of environmental problems, such as depletion of ground water and increased soil salinity. The generation of high resolution(30 m) crop intensity maps is an important method used to monitor these changes, but this is challenging because the temporal resolution of the 30-m image time series is low due to the long satellite revisit period and high cloud coverage. The recently launched Sentinel-2 satellite could provide optical images at 10–60 m resolution and thus improve the temporal resolution of the 30-m image time series. This study used harmonized Landsat Sentinel-2(HLS) data to identify crop intensity. The sixth polynomial function was used to fit the normalized difference vegetation index(NDVI) and enhanced vegetation index(EVI) curves. Then, 15-day NDVI and EVI time series were then generated from the fitted curves and used to generate the extent of croplands. Lastly, the first derivative of the fitted VI curves were used to calculate the VI peaks;spurious peaks were removed using artificially defined thresholds and crop intensity was generated by counting the number of remaining VI peaks. The proposed methods were tested in four study regions, with results showing that 15-day time series generated from the fitted curves could accurately identify cropland extent. Overall accuracy of cropland identification was higher than 95%. In addition, both the harmonized NDVI and EVI time series identified crop intensity accurately as the overall accuracies, producer’s accuracies and user’s accuracies of non-cropland, single crop cycle and double crop cycle were higher than 85%. NDVI outperformed EVI as identifying double crop cycle fields more accurately.

DAPR-tree: a distributed spatial data indexing scheme with data access patterns to support Digital Earth initiatives

This paper proposes a novel data indexing scheme,the distributed access pattern R-tree(DAPR-tree),for spatial data retrieval in a distributed computing environment.As compared to traditional distributed indexing schemes,the DAPR-tree introduces the data access patterns during the indexing utilization stage so that a more balanced indexing structure can be provided for spatial applications(e.g.Digital Earth data warehouse).In this new indexing scheme,(a)an indexing penalty matrix is proposed by considering the balance of data number,topology and access load between different indexing nodes;(b)an‘access possibility’element is integrated to a classic‘Master-Client’structure for a distributed indexing environment;and(c)indexing algorithm for the DAPR-tree is provided for index implementations.By using a duplication of official GEOSS Clearinghouse system as a case study,the DAPR-tree was evaluated in a number of scenarios.The results show that our indexing schemes generally outperform(around 9%)traditional distributed indices with the utilization of data access patterns.Finally,we discuss the applicability of the DARP-tree and document DARP-tree shortcomings to encourage researchers pursuing related topics in Big Data indexing for Digital Earth and other geospatial initiatives.

What about people in pedestrian navigation?

Pedestrian navigation has become an important theoretical and practical research topic in many disciplines such as cartography,geographical information science,global and indoor positioning,spatial behavior,psychology,sociology,and neuroscience.Many research studies view pedestrian navigation using process-oriented and goal-directed approaches.However,this paper revisits people’s needs in pedestrian navigation and classifies their needs as three layers:physical sense layer,physiological safety layer,and mental satisfaction layer according to Maslow’s theory.This paper introduces a people-centric framework for pedestrian navigation theory based on these three layers and discusses theoretical challenges for meeting each layer of people’s needs.These challenging theories may represent promising and valuable research and promote usage of pedestrian navigation systems or devices in the future.

From earth observation to human observation:Geocomputation for social science

It is possible to obtain vast amounts of spatiotemporal data related to human activities to support the study of human behavior and social evolution.In this context,geography,with the human-nature relationship as its core,is undergoing a transition from strictly earth observations to the observation of human activities.Geocomputation for social science is one manifestation thereof.Geocomputation for social science is an interdisciplinary approach combining remote sensing techniques,social science,and big data computation.Driven by the availability of spatially and temporally expansive big data,geocomputation for social science uses spatiotemporal statistical analyses to detect and analyze the interactions between human behavior,the natural environment,and social activities;Remote sensing(RS)observations are used as primary data.Geocomputation for social science can be used to investigate major social issues and to assess the impact of major natural and societal events,and will surely be an area of focused development in geography in the near future.We briefly review the background of geocomputation in the social sciences,discuss its definition and disciplinary characteristics,and highlight the main research foci.Several key technologies and applications are also illustrated with relevant case studies of the Syrian Civil War,typhoon transits,and traffic patterns.