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Editorial

Advancing Multi-Scale Geographic Environmental Monitoring: A Synthesis of Cutting-Edge Research and Scalable Solutions

by
Jingzhe Wang
1,2,
Yangyi Wu
3,4,
Yinghui Zhang
5,
Ivan Lizaga
6 and
Zipeng Zhang
7,*
1
School of Artificial Intelligence, Shenzhen Polytechnic University, Shenzhen 518055, China
2
Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area, Shenzhen Polytechnic University, Shenzhen 518055, China
3
School of Urban Design, Wuhan University, Wuhan 430072, China
4
Hubei Habitat Environment Research Centre of Engineering and Technology, Wuhan 430072, China
5
MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong Key Laboratory of Urban Informatics & Guangdong–Hong Kong-Macau Joint Laboratory for Smart Cities & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China
6
Instituto Pirenaico de Ecología (IPE-CSIC), Spanish National Research Council, Avenida-Montañana 1005, 50059 Zaragoza, Spain
7
College of Geographical and Remote Sciences, Xinjiang University, Urumqi 830017, China
*
Author to whom correspondence should be addressed.
Land 2025, 14(5), 1059; https://doi.org/10.3390/land14051059
Submission received: 29 April 2025 / Revised: 9 May 2025 / Accepted: 9 May 2025 / Published: 13 May 2025
(This article belongs to the Topic Advances in Multi-Scale Geographic Environmental Monitoring: Theory, Methodology and Applications)
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Versions Notes

1. Introduction

The geographic environment is a complex concept that encompasses various natural elements of the Earth’s surface and human activities [1,2]. Natural conditions such as climate, land, and rivers are fundamental to the emergence, survival, and development of humanity [3,4]. Conversely, human activities also significantly impact the geographic environment [5,6]. The interaction and mutual influence between natural conditions and human activities constitute the geographic environment of the Earth [7,8]. However, previous research on geographic environmental monitoring has often focused on specific objects and spatial and/or temporal scales, making it difficult to comprehensively understand the distinctive characteristics, shifts, and interconnections within the geographic environment.
Currently, field surveys, monitoring stations, sensor networks, multi-source remote sensing (satellite, airborne, and ground-based), geospatial big data, and especially the development of remote sensing technology and geographic environmental monitoring networks enable multidimensional and multi-scale observation of geographic environmental conditions over extended periods and high frequencies, obtaining data from global to local and from macro- to micro-scales [9,10,11,12]. Integrated data analysis from different scales can lead to a better understanding of the geographic environment’s overall characteristics and changing patterns [13,14,15,16].
The diversification of methods for geographic environmental monitoring has expanded the depth, breadth, and accuracy of geographic process simulation and analyses [17,18]. Geographic environmental monitoring has a wide range of objectives and scientific application scenarios, such as ecosystem services [19], natural resource distribution [20], water resource management [21], climate change research [22], disaster monitoring [23], and environmental protection [24]. In summary, studies on multi-scale geographic environmental elements are of great significance for deepening our understanding of the complexity of the Earth’s systems, predicting environmental changes, supporting sustainable development, and promoting interdisciplinary communication and collaboration [11,25,26,27,28].
Drawing on these research contexts, several studies were collected and compiled. The purpose of this Topic is to present the latest research related to multi-scale geographic environmental monitoring with a focus on sustainable development. The contributions incorporate diverse and creative applications of theories and methodologies across multiple fields, including geography, hydrology, remote sensing, ecology, modeling, and management.

2. Overviews of Topic Papers

After continuous invitation and promotion, a total of 44 papers were published and underwent a thorough peer review process, with at least two rounds of reviews. Following the guidelines and requirements of the journals (Climate, Drones, Forests, Land, Remote Sensing, and ISPRS International Journal of Geo-Information (IJGI)), all submitted papers were reviewed by at least two experts. These papers contributed to the Topic, entitled “Advances in Multi-Scale Geographic Environmental Monitoring: Theory, Methodology and Applications”, in various ways, providing deep insights into relevant issues and presenting novel approaches, techniques, and applications. The articles cover a wide range of issues, such as water resource management, land use and land cover, vegetation change, snow cover analysis, water body extraction, and other fields. Generally, these papers can be categorized into several groups, covering studies on spatiotemporal analysis of geographic environmental elements, assessment of ecosystem services, fusion and scale conversion of multi-source heterogeneous data products, scale effects of spatial heterogeneity of geographic ecological elements, and other related themes.
With regard to paper distribution by journal, “Land” has the largest number, with 16 papers, followed by “Remote Sensing”, with 14 papers. Both “Climate” and “Drones” received the lowest number, with two papers each (Figure 1).
In terms of research objectives and scopes, most of the papers focus on natural phenomena, hydrogeology, the atmosphere, land use and land cover, and vegetation. These studies further establish connections with human activities, such as carbon emissions, agriculture, ecosystem services, the three life spaces (production, living, and ecological spaces), the urban heat island effect, and even the European Land Account System. Moreover, the scope of the studies covers a diverse range of geographical regions, including river basins, border regions, desert–oasis regions, the Qinghai–Tibet Plateau, some provincial regions, and even national-scale applications.
The studies included in this Topic employ a variety of approaches and data origins. These investigations involve the construction and application of remote sensing-based integrated learning conceptual models [29], soil column experiments conducted in laboratories [30], spatiotemporal variation and decoupling analyses [31], and quality comparisons of remote sensing-derived products [32].
As for data sources, platforms such as satellites and unmanned aerial vehicles were utilized [33,34]. Many studies used diverse and multi-source data. For instance, Chen et al. [35] compared the performance of Landsat-8 and Landsat-9 satellites in water body extraction. Other studies rely on multi-source data to achieve a comprehensive understanding [36].
In general, this Topic encompasses multidisciplinary research findings. These studies present a series of cutting-edge data processing techniques, innovative models, and effective strategies, which have significantly propelled the development of geographical environmental monitoring. Despite notable progress, current methodologies demonstrate persistent constraints and unexplored potential. Subsequent research should prioritize resolving these research gaps to catalyze innovative applications of geospatial technologies in geographic environmental monitoring.

3. Future Perspectives: Scalable Solutions

3.1. Advancing Multi-Scale Theoretical Frameworks and Dynamic Modeling

The cross-scale interaction mechanisms of geographic environmental elements (e.g., climate–ecosystem–human activity cascading effects) require systematic elucidation [37,38]. Future research must transcend single-scale limitations by developing dynamic coupled models that integrate global macro-scale patterns with local micro-scale processes. For instance, satellite remote sensing-derived forest cover changes can be dynamically linked to soil moisture variations captured by ground-based sensors to reveal nonlinear threshold effects (e.g., ecosystem collapse tipping points). Concurrently, establishing a unified multi-scale classification framework is critical to resolving scale compatibility challenges between natural elements (e.g., watershed hydrology) and anthropogenic factors (e.g., urban sprawl), thereby providing theoretical support for multi-source data integration [39,40].

3.2. Intelligent Collaborative Monitoring Technology Systems

To address the fusion bottlenecks of multi-source heterogeneous data (remote sensing, sensor networks, and social media), an integrated “space–air–ground” sensing network should be prioritized [41,42]. This system leverages satellites for broad coverage, drones for flexible gap-filling, and ground sensors for high-precision calibration, collectively enhancing spatiotemporal resolution (e.g., hourly disaster monitoring). Adaptive scale conversion algorithms, such as deep learning-based super-resolution reconstruction techniques, can downscale 1 km resolution precipitation data to 100 m for agricultural irrigation needs while incorporating uncertainty quantification methods (e.g., Bayesian neural networks) to mitigate error propagation risks. Furthermore, edge computing combined with cloud platforms enables real-time data processing, shifting monitoring paradigms from post-event analysis to early warning [43].

3.3. Precision Decision-Making and Sustainable Governance

Translating multi-scale monitoring into actionable policies necessitates cross-scale evaluation indicator systems. For example, coupling regional carbon sequestration potential (macro-scale) with community green space distribution (micro-scale) can inform spatial planning and carbon neutrality strategies [44]. Interactive decision-support platforms visualizing multi-tiered data (e.g., urban heat island simulations under global climate change scenarios) will foster collaborative governance among governments, industries, and the public. Enhancing coupled modeling of extreme events and human activities (e.g., flood-inundation models integrated with population-mobility big data) strengthens urban resilience planning, forming a closed-loop “monitoring–simulation–decision” framework [45,46].

3.4. Mitigating Uncertainties and Enhancing Data Credibility

Scale dependency and data heterogeneity in environmental monitoring introduce uncertainties [47]. Addressing these uncertainties requires error source attribution frameworks to quantify contributions from sensor noise, model parameter sensitivity, and scale conversion biases. Hybrid uncertainty analysis approaches (e.g., integrating fuzzy logic with Monte Carlo simulations) can improve data product reliability [48]. Promoting global standardized validation networks—such as shared ground-truth datasets for vegetation indices—and establishing data quality certification protocols will enhance the international comparability of research outcomes [49].

3.5. Cross-Disciplinary Innovation Ecosystems

The multi-scale complexity of geographic environments demands interdisciplinary collaboration. Integrated platforms bridging Earth system science, data science, and social science could link remote sensing data with socioeconomic statistics to elucidate connections between ecological conservation and poverty alleviation [50,51]. FAIR principle-driven data sharing (Findable, Accessible, Interoperable, Reusable) and open-source tools (e.g., Google Earth Engine plugin libraries) will democratize multi-scale analytics. Citizen science crowdsourcing models leveraging smartphone geolocation and social media imagery can supplement traditional monitoring gaps (e.g., urban microclimates, species distribution), fostering professional–public collaborative monitoring paradigms [52].
Future advancements in multi-scale geographic environmental monitoring will hinge on the synergistic evolution of theory, technology, and applications. Dynamic models must unravel cross-scale patterns in complex systems, while intelligent technologies must enable precise data acquisition and fusion—ultimately serving global sustainability and human well-being. This trajectory demands concurrent breakthroughs in technological innovation (e.g., Earth observation, digital twins), methodological advancements (e.g., causal inference integrated with machine learning), and global cooperation mechanisms. By bridging Earth system science with societal needs, these efforts will establish a robust foundation for addressing planetary-scale environmental challenges while empowering localized, evidence-based decision-making.
As representatives of all authors, the Guest Editors hope that this Topic supports ongoing research on multi-scale geographic environmental monitoring, including theory, methodology, and applications. In particular, we believe that this Topic can contribute to vital holistic geographic environmental science and provide policy- and decision-makers with a comprehensive view of spatial patterns across ecosystems.

Author Contributions

Conceptualization, J.W., Y.W. and Z.Z.; writing—original draft preparation, J.W., Y.W., Y.Z., I.L. and Z.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This work was jointly supported by the National Natural Science Foundation of China (42401065 and 42201347), Guangdong Basic and Applied Basic Research Foundation (2023A1515011273), the Specific Innovation Program of the Department of Education of Guangdong Province (2023KTSCX315), and Shenzhen Polytechnic University Research Fund (6025310064K).

Data Availability Statement

Not applicable.

Acknowledgments

As the Guest Editors, we would like to thank all of the authors who submitted their work to this Topic. Special thanks to all anonymous reviewers involved in the Topic who helped the authors to improve their manuscripts. Thanks also to the editorial staff of Climate, Drones, Forests, Land, Remote Sensing, and ISPRS International Journal of Geo-Information (IJGI) for supporting the idea of this Topic: Advances in Multi-Scale Geographic Environmental Monitoring: Theory, Methodology and Applications.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Land 14 01059 g001
Figure 1. Cross-journal distribution of published papers in this Topic.
Figure 1. Cross-journal distribution of published papers in this Topic.
Land 14 01059 g001
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MDPI and ACS Style

Wang, J.; Wu, Y.; Zhang, Y.; Lizaga, I.; Zhang, Z. Advancing Multi-Scale Geographic Environmental Monitoring: A Synthesis of Cutting-Edge Research and Scalable Solutions. Land 2025, 14, 1059. https://doi.org/10.3390/land14051059

AMA Style

Wang J, Wu Y, Zhang Y, Lizaga I, Zhang Z. Advancing Multi-Scale Geographic Environmental Monitoring: A Synthesis of Cutting-Edge Research and Scalable Solutions. Land. 2025; 14(5):1059. https://doi.org/10.3390/land14051059

Chicago/Turabian Style

Wang, Jingzhe, Yangyi Wu, Yinghui Zhang, Ivan Lizaga, and Zipeng Zhang. 2025. "Advancing Multi-Scale Geographic Environmental Monitoring: A Synthesis of Cutting-Edge Research and Scalable Solutions" Land 14, no. 5: 1059. https://doi.org/10.3390/land14051059

APA Style

Wang, J., Wu, Y., Zhang, Y., Lizaga, I., & Zhang, Z. (2025). Advancing Multi-Scale Geographic Environmental Monitoring: A Synthesis of Cutting-Edge Research and Scalable Solutions. Land, 14(5), 1059. https://doi.org/10.3390/land14051059

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