Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.6
4.1
Journals
Remote Sensing
Special Issues
Supporting Earth Observation and Human–Environment Interaction with Global Geospatial Information
remotesensing-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Supporting Earth Observation and Human–Environment Interaction with Global Geospatial Information

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 6248

Special Issue Editors

Dr. Hua Liu

E-Mail Website
Guest Editor
Department of Political Science & Geography, Old Dominion University, Norfolk, VA 23529, USA
Interests: remote sensing; GIS; urban environmental changes; public health
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hone-Jay Chu

E-Mail Website
Guest Editor
Department of Geomatics, National Cheng Kung University, No.1, University Road, Tainan City 701, Taiwan
Interests: space-time insights and data mining from remote sensing; big data; open data for environmental management and social sensing; environmental resilience; water and air quality mapping; groundwater; land cover and land use change
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Global geospatial information focuses on spatial information collections on a global scale. It not only supports investigations of nature phenomena and processes across the Earth surface, but also helps to monitor human–environment interactions in societies. With the blooming of advanced remote sensing technologies, such as muti-platform earth observations, artificial intelligence (AI) technologies, image fusion, and open-source big data, it is important to track how these fast-growing technologies can help to construct global geospatial information in supporting Earth observation and human–environment interaction at a global scale. Urban hazards and human health, as well as climate resilience, are crucial to human societies. This interdisciplinary research regarding global geospatial information warrants further exploration.

The Special Issue is focused on methods and applications of global geospatial information. While all relevant manuscripts are welcome, the Special Issue is especially interested in original work addressing the following topics:

  1. Muti-platform earth observations in supporting earth observation and/or human–environment interaction on a global scale;
  2. AI for global geospatial information;
  3. Image fusion in global earth observation and human–environment interaction;
  4. Open-source big data in global earth observation and/or human–environment interaction;
  5. Monitoring urban hazards and human health with global geospatial information;
  6. Anthropogenic influences on natural environments across the Earth surface;
  7. Climate resilience with global geospatial information;
  8. Climate change and global change monitoring.

Dr. Hua Liu
Dr. Hone-Jay Chu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • muti-platform earth observations
  • AI
  • big data
  • remote sensing
  • image fusion
  • hazards
  • human health
  • anthropogenic influences
  • climate resilience
  • global change
  • climate change

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

38 pages, 11590 KB  
Article
Validation of the MODIS Clumping Index: A Case Study in Saihanba National Forest Park
by Siyang Yin, Ziti Jiao, Yadong Dong, Lei Cui, Anxin Ding, Feng Qiu, Qian Zhang, Yongguang Zhang, Xiaoning Zhang, Jing Guo, Rui Xie, Yidong Tong, Zidong Zhu, Sijie Li, Chenxia Wang and Jiyou Jiao
Remote Sens. 2025, 17(22), 3770; https://doi.org/10.3390/rs17223770 - 20 Nov 2025
Viewed by 371
Abstract
The clumping index (CI) describes the level of foliage grouping relative to the random distribution within the canopy. It plays a vital role in the derivation of other important parameters (e.g., the leaf area index, (LAI)) that are usually employed in hydrological, ecological [...] Read more.
The clumping index (CI) describes the level of foliage grouping relative to the random distribution within the canopy. It plays a vital role in the derivation of other important parameters (e.g., the leaf area index, (LAI)) that are usually employed in hydrological, ecological and climatological modeling. In recent years, several satellite-based CI products have been developed using multi-angle reflectance data. However, these products have been validated through the use of a “point-to-point” comparison, which rarely involves a quantitative analysis of spatial representativeness for field-measured CIs in most cases. In this study, we developed a methodological framework to validate the MODIS CI at three different data scales on the basis of intense field measurements, high-resolution unmanned aerial vehicle (UAV) observations and Landsat 8 data. This framework was used to understand the impacts of the scale issue and subpixel variance of the CI in the validation of the MODIS CI for a case study of 12 gridded 500 m pixels in Saihanba National Forest Park, Hebei, China. The results revealed that the MODIS CIs in the study area were in good agreement with the upscaled field CIs (R = 0.75, RMSE = 0.05, bias = 0.02) and UAV CIs. Through a comparison of the observed CIs along the 30 m transects with the 500 m MODIS CIs, we gained insight into the uncertainty caused by the direct “point-to-pixel” evaluation method, which ranged from −0.21~+0.27 for the 10th and 90th percentiles of the observed-MODIS CI error distribution for the twelve pixels. Moreover, semivariogram analysis revealed that the representativeness assessments based on high-resolution albedo and CI maps could reflect the spatial heterogeneity within pixels, whereas the CI map provided more information on the variation in vegetation structures. The average observational footprint needed for a spatially representative sample is approximately 209 m according to an analysis of the high-resolution CI map. The uncertainty of mismatched MODIS land cover types can lead to a deviation of 0.33 in CI estimates, and compared with the CLX method, the scaled-up CI method based on simple arithmetic averages tends to overestimate CIs. In summary, various validation efforts in this case study reveal that the accuracy of the MODIS CIs is generally reliable and in good agreement with that of the upscaled field CIs and UAV CIs; however, with the development of surface process modeling and remote sensing technology, substantial measurements of field CIs in conjunction with high-resolution remotely sensed CI maps derived from single-angle advanced methods are urgently needed for further validation and potential applications. Certainly, such a validation effort will help to improve the understanding of MODIS CI products, which, in turn, will further support the methods and applications of global geospatial information. Full article
(This article belongs to the Special Issue Supporting Earth Observation and Human–Environment Interaction with Global Geospatial Information)
►▼ Show Figures

Figure 1

25 pages, 5657 KB  
Article
Elevation-Dependent Trends in Himalayan Snow Cover (2004–2024) Based on MODIS Terra Observations
by Ghania Tauqir, Wei Zhao, Mengjiao Xu and Dongjie Fu
Remote Sens. 2025, 17(18), 3175; https://doi.org/10.3390/rs17183175 - 12 Sep 2025
Viewed by 1922
Abstract
Snow cover in the Himalayas plays a vital role in regulating elevation-dependent climate processes and sustaining downstream hydrology. However, its altitude-specific dynamics and implications for snow mass balance remain underexplored. Using the MOD09A1 dataset (2004–2024), this study conducts a pixel-based, elevation-stratified analysis with [...] Read more.
Snow cover in the Himalayas plays a vital role in regulating elevation-dependent climate processes and sustaining downstream hydrology. However, its altitude-specific dynamics and implications for snow mass balance remain underexplored. Using the MOD09A1 dataset (2004–2024), this study conducts a pixel-based, elevation-stratified analysis with advanced spectral filtering and gap-filling techniques to enhance snow cover detection in complex terrain. The mean SCA was ~2.10 × 105 km2, with sub-regional contributions from WH: 8.59 × 104 km2, CH: 9.55 × 104 km2, and EH: 2.99 × 104 km2, indicating distinct spatiotemporal variability. Correlation analysis revealed that SCA in WH and CH is mainly precipitation-driven (r = +0.70 and r = +0.91), whereas EH is temperature-dominant (r = −0.65), reflecting strong climatic control. Altitudinal and zonal snow cover changes were assessed using Equilibrium Line Altitude–AAR and AABR methods for mass balance estimation. Regional trends showed a positive mass balance of 0.0389 at 4105 m in WH, with increasing SCA around 4516.12 ± 531.94 m; CH exhibited a negative balance (−0.0268 at 4989 m), with declines at higher altitudes; and EH demonstrated a negative balance (−0.015 at 4378 m), with notable SCA reduction. Mann–Kendall and Kendall Tau tests validated these trends, highlighting spatially heterogeneous snow-cover dynamics and their implications for Himalayan snow-mass balance. Full article
(This article belongs to the Special Issue Supporting Earth Observation and Human–Environment Interaction with Global Geospatial Information)
►▼ Show Figures

Figure 1

21 pages, 6504 KB  
Article
Drought Amplifies the Suppressive Effect of Afforestation on Net Primary Productivity in Semi-Arid Ecosystems: A Case Study of the Yellow River Basin
by Futao Wang, Ziqi Zhang, Mingxuan Du, Jianzhong Lu and Xiaoling Chen
Remote Sens. 2025, 17(12), 2100; https://doi.org/10.3390/rs17122100 - 19 Jun 2025
Cited by 2 | Viewed by 1162
Abstract
As a critical ecologicalbarrier in the semi-arid to semi-humid transition zone of northern China, the interaction between afforestation and climatic stressors in the Yellow River Basin constitutes a pivotal scientific challenge for regional sustainable development. However, the synthesis effects of afforestation and climate [...] Read more.
As a critical ecologicalbarrier in the semi-arid to semi-humid transition zone of northern China, the interaction between afforestation and climatic stressors in the Yellow River Basin constitutes a pivotal scientific challenge for regional sustainable development. However, the synthesis effects of afforestation and climate on primary productivity require further investigation. Integrating multi-source remote sensing data (2000–2020), meteorological observations with the Standardized Precipitation Evapotranspiration Index (SPEI) and an improved CASA model, this study systematically investigates spatiotemporal patterns of vegetation net primary productivity (NPP) responses to extreme drought events while quantifying vegetation coverage’s regulatory effects on ecosystem drought sensitivity. Among drought events identified using a three-dimensional clustering algorithm, high-intensity droughts caused an average NPP loss of 23.2 gC·m−2 across the basin. Notably, artificial irrigation practices in the Hetao irrigation district significantly mitigated NPP reduction to −9.03 gC·m−2. Large-scale afforestation projects increased the NDVI at a rate of 3.45 × 10−4 month−1, with a contribution rate of 78%, but soil moisture competition from high-density vegetation reduced carbon-sink benefits. However, mixed forest structural optimization in the Three-North Shelterbelt Forest Program core area achieved local carbon-sink gains, demonstrating that vegetation configuration alleviates water competition pressure. Drought amplified the suppressive effect of afforestation through stomatal conductance-photosynthesis coupling mechanisms, causing additional NPP losses of 7.45–31.00 gC·m−2, yet the April–July 2008 event exhibited reversed suppression effects due to immature artificial communities during the 2000–2004 baseline period. Our work elucidates nonlinear vegetation-climate interactions affecting carbon sequestration in semi-arid ecosystems, providing critical insights for optimizing ecological restoration strategies and climate-adaptive management in the Yellow River Basin. Full article
(This article belongs to the Special Issue Supporting Earth Observation and Human–Environment Interaction with Global Geospatial Information)
►▼ Show Figures

Graphical abstract

18 pages, 39280 KB  
Article
Rapid Mapping of Rainfall-Induced Landslide Using Multi-Temporal Satellite Data
by Mohammad Adil Aman, Hone-Jay Chu, Sumriti Ranjan Patra and Vaibhav Kumar
Remote Sens. 2025, 17(8), 1407; https://doi.org/10.3390/rs17081407 - 15 Apr 2025
Viewed by 1828
Abstract
In subtropical regions, typhoons and tropical storms can generate massive rainstorms resulting in thousands of landslides, often termed as Multiple-Occurrence of Regional Landslide Events (MORLE). Understanding the hazards, their location, and their triggering mechanism can help to mitigate exposure and potential impacts. Extreme [...] Read more.
In subtropical regions, typhoons and tropical storms can generate massive rainstorms resulting in thousands of landslides, often termed as Multiple-Occurrence of Regional Landslide Events (MORLE). Understanding the hazards, their location, and their triggering mechanism can help to mitigate exposure and potential impacts. Extreme rainfall events and earthquakes frequently trigger destructive landslides that cause extensive economic loss, numerous fatalities, and significant damage to natural resources. However, inventories of rainfall-induced landslides suggest that they occur frequently under climate change. This study proposed a semi-automated time series algorithm that integrates Sentinel-2 and Integrated Multi-satellite Retrievals for Global Precipitation Measurements (GPM-IMERG) data to detect rainfall-induced landslides. Pixel-wise NDVI time series data are analyzed to detect change points, which are typically associated with vegetation loss due to landslides. These NDVI abrupt changes are further correlated with the extreme rainfall events in the GPM-IMERG dataset, within a defined time window, to detect RIL. The algorithm is tested and evaluated eight previously published landslide inventories, including both those manually mapped and those derived from high-resolution satellite data. The landslide detection yielded an overall F1-score of 0.82 and a mean producer accuracy of 87%, demonstrating a substantial improvement when utilizing moderate-resolution satellite data. This study highlights the combination of using optical images and rainfall time series data to detect landslides in remote areas that are often inaccessible to field monitoring. Full article
(This article belongs to the Special Issue Supporting Earth Observation and Human–Environment Interaction with Global Geospatial Information)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop