The rapid development of urban agglomerations has endangered the eco-environment. It is critical to research the coupling coordination between the eco-environment and urbanization to promote regional sustainable development. The aim of this study is to investigate the coupling coordination degree (CCD) between the eco-environment and urbanization in the MRYRUA and to identify the key factors influencing the CCD. Based on the remote sensing perspective, this study used the remote sensing ecological index (RSEI) and comprehensive nighttime light index (CNLI) to measure the eco-environment and urbanization level of the middle reaches of the Yangtze River urban agglomeration (MRYRUA). Then, based on the CCD model, the relationship between the eco-environment and urbanization was evaluated. The geographical detector was utilized to analyze the key factors. The results showed the following: (1) both the eco-environment and urbanization of the MRYRUA show a fluctuating upward trend, and the change rate of urbanization is greater than that of the eco-environment. (2) The overall coupling level of the MRYRUA changes from moderate unbalanced coupling to moderate balanced coupling, and the distribution of the CCD presents spatial imbalance. The coupling level of the central region is higher than that of the peripheral cities, and the coupling level of the provincial capital cities is higher than that of other cities. (3) The explanatory power of socioeconomic factors in the CCD is higher than that of natural factors in the single-factor detection; urban green space (UGS) plays the most significant role in the interaction detection. (4) RSEI has a negative correlation with the CCD, while the CNLI has a positive correlation with the CCD. The development of urbanization has played a decisive role in the CCD. The results provide a scientific foundation on which the MRYRUA can achieve sustainable urban development. 

Coupling coordination between the ecological environment and urbanization in the middle reaches of the Yangtze River urban agglomeration

The technological progress in the field of artificial intelligence (AI) has brought new opportunities and challenges to the intelligent development and innovative research in geospatial related fields. Geospatial artificial intelligence (GeoAI) refers to the interdisciplinary research direction that combines geography, earth science and artificial intelligence, and seeks to solve major scientific and engineering problems in human-environmental interaction systems through the research and development of spatial intelligence in machines to improve the dynamic perception, intelligent reasoning and knowledge discovery of geographic phenomena and earth science processes. This paper briefly summarizes the historical origins of GeoAI development, introduces spatially explicit and implicit AI models, reviews recent GeoAI research and applications(including spatial representation learning, spatiotemporal prediction and spatial interpolation, monitoring of geographic resources and environment, cartography, and geo-text data semantic analysis), and identifies several potential research challenges and directions for the future development of GeoAI.

A Review of Recent Researches and Reflections on Geospatial Artificial Intelligence

Abstract About 30 years have passed since Michael F. Goodchild proposed the term geographical information science (GIScience) in 1992. In the past 30 years, GIScience has made great progress in expanding research findings and perfecting theories and methods. To understand the development progress of GIScience, this research conducts a bibliometric analysis of 9400 publications between 1991 and 2020 in 10 international refereed journals and 2 international conferences of GIScience. We analyze the publication statistics and trends in GIScience from two aspects of journals/conferences and countries/territories. Based on the community detection of the citation network, we extract 15 research themes and show their leading authors and highly cited articles. Furthermore, the change of publication number in different themes over time can indicate the evolution of some research focuses in GIScience. The results demonstrate that the publication proportions of some themes grow rapidly, such as “moving object,” “volunteered geographic information,” and “geographically weight regression,” while the publication proportions of some themes are decreasing, such as “digital elevation model,” “planning support system,” and “ontology.” In the discussion, the journal distribution of papers on different themes is discussed. Moreover, we suggest a few research directions that are worthy of attention in the future.

Research themes of geographical information science during 1991–2020: a retrospective bibliometric analysis

Crops and ecosystems constantly change, and risks are derived from heavy rains, hurricanes, droughts, human activities, climate change, etc. This has caused additional damages with economic and social impacts. Natural phenomena have caused the loss of crop areas, which endangers food security, destruction of the habitat of species of flora and fauna, and flooding of populations, among others. To help in the solution, it is necessary to develop strategies that maximize agricultural production as well as reduce land wear, environmental impact, and contamination of water resources. The generation of crop and land-use maps is advantageous for identifying suitable crop areas and collecting precise information about the produce. In this work, a strategy is proposed to identify and map sorghum and corn crops as well as land use and land cover. Our approach uses Sentinel-2 satellite images, spectral indices for the phenological detection of vegetation and water bodies, and automatic learning methods: support vector machine, random forest, and classification and regression trees. The study area is a tropical agricultural area with water bodies located in southeastern Mexico. The study was carried out from 2017 to 2019, and considering the climate and growing seasons of the site, two seasons were created for each year. Land use was identified as: water bodies, land in recovery, urban areas, sandy areas, and tropical rainforest. The results in overall accuracy were: 0.99% for the support vector machine, 0.95% for the random forest, and 0.92% for classification and regression trees. The kappa index was: 0.99% for the support vector machine, 0.97% for the random forest, and 0.94% for classification and regression trees. The support vector machine obtained the lowest percentage of false positives and margin of error. It also acquired better results in the classification of soil types and identification of crops.

/pdf/mapping-of-land-cover-with-optical-images-supervised-2jqh3f70.pdf

Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine

Temporal and spatial variations of eco-environment in Association of Southeast Asian Nations from 2000 to 2021 based on information granulation

Geographic information science (GIScience) and remote sensing have long provided essential data and methodological 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 effects 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 applications. 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.

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

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