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Applied Sciences
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Advanced Studies in Land Cover Change and Geographic Data Fusion
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Advanced Studies in Land Cover Change and Geographic Data Fusion

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: 30 December 2025 | Viewed by 881

Special Issue Editors

Prof. Dr. Jingxiong Zhang

E-Mail Website
Guest Editor
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
Interests: geostatistics; remote rensing; digital terrain analysis; vegetation mapping; land cover
Special Issues, Collections and Topics in MDPI journals
Dr. Ludovit Kovanic

E-Mail Website
Guest Editor
Faculty of Mining, Ecology, Process Control and Geotechnologies, Institute of Geodesy, Cartography and GIS, Technical University of Košice, Park Komenskeho 19, 040 01 Košice, Slovakia
Interests: geohazards; geodesy; 3D mapping; engineering geodesy; spatial analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Land cover change tops research agendas in fields of remote sensing, geoinformatics, geography, ecology, and environmental science. Multi-source data are necessarily fused to enable accurate, high-resolution, and (near) real-time change monitoring and analyses. Spatial statistics in general and Bayesian inference in particular, especially accurate analytical approximation provided by INLA and SPDE, will have greater roles in these endeavors. This is because these new developments in Bayesian spatio-temporal statistical methods will be required to support multi- and cross-resolution data fusion, data and model integration, enhanced change monitoring and analyses, and uncertainty characterization. Spatio-temporal data assimilation will also find impetus for applications in land change analytics. Ultimately, the aforementioned methods and technical thrusts constitute the backbones of the emerging digital twins of the environment.

This Special Issue aims to publish papers which study the emerging important methodology and pursue technical advancements in change monitoring, spatio-temporal data fusion, data–model integration, change analyses, and uncertainty quantification and propagation. Topics of interest include, but are not limited to, the following:

  • Land cover change typology;
  • From change detection to change monitoring;
  • Multi-source data technology and data streaming;
  • Applied Bayesian statistics in geospatial domain;
  • Data support and cross-resolution data modeling;
  • Bayesian spatio-temporal data fusion;
  • Bayesian downscaling;
  • Land cover change as surface time series;
  • Bayesian spatio-temporal data assimilation;
  • New landscapes for machine learning;
  • Uncertainty quantification and propagation;
  • Statistical methodology towards building up digital twins of the environment;
  • Case studies in natural resources and the environment.

Prof. Dr. Jingxiong Zhang
Dr. Ludovit Kovanic
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Applied Sciences 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 2400 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

  • land cover change
  • applied Bayesian statistics
  • spatio-temporal data fusion
  • remote sensing
  • geoinformatics

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Published Papers (1 paper)

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Research

24 pages, 14940 KiB  
Article
Predicting Non-Point Source Pollution in Henan Province Using the Diffuse Pollution Estimation with Remote Sensing Model with Enhanced Sensitivity Analysis
by Weiqiang Chen, Yue Wan, Yulong Guo, Guangxing Ji and Lingfei Shi
Appl. Sci. 2025, 15(5), 2261; https://doi.org/10.3390/app15052261 - 20 Feb 2025
Viewed by 393
Abstract
Non-point source pollution (NPSP) originates from domestic agricultural pollutants and deforestation. Agricultural NPSP discharges into rivers and oceans through precipitation and soil runoff. Awareness and research regarding NPSP and its harmful effects on human health and the environment are increasing. The Diffuse Pollution [...] Read more.
Non-point source pollution (NPSP) originates from domestic agricultural pollutants and deforestation. Agricultural NPSP discharges into rivers and oceans through precipitation and soil runoff. Awareness and research regarding NPSP and its harmful effects on human health and the environment are increasing. The Diffuse Pollution Estimation with Remote Sensing (DPeRS) model, a distributed NPSP model proposed by Chinese researchers, seeks to predict agricultural NPSP and includes modules estimating nitrogen and phosphorus balance, vegetation coverage, dissolved pollution, and absorbed pollution. By applying the DPeRS model, the present work aims to predict the distribution of all nitrogen and phosphorus pollutants in Henan Province, China in 2021. We used statistical yearbook, remotely sensed, and hydrological data as input. To facilitate uncertainty characterization in pollution predictions, we performed sensitivity analysis, which identified the model input variables that contributed most to uncertainty in model output. Specifically, we used ArcGIS for processing data for nitrogen and phosphorus balance equations, an ENVI 5.3 software system for deriving vegetation cover, and the RUSLE soil erosion model for predicting absorption pollution. Dissolved pollution was estimated using a unified approach to estimating agricultural runoff, urban runoff, rural resident, and livestock pollutants. Absorbed pollution was estimated by considering the soil erosion model and precipitation. Moreover, Sobol’s method was applied for sensitivity analysis. We found that regardless of the accumulation of nitrogen or phosphorus, indicators of the dissolved pollution of Zhoukou were relatively high. Sensitivity analysis of the models for estimating dissolved pollution and absorbed pollution revealed that the top four influential variables for dissolved pollution were standard runoff coefficient ε0, natural factor correction coefficient Ni, the newly produced TN pollutants per area QiN, and runoff coefficient ε. For absorbed pollution, influential variables were rainfall erosion factor R, water and soil conservation factor P, slope degree factor S, and slope length factor L. The total discharges of Henan Province were 9546.4649 t, 1061.8940 t, 6031.4577 t, and 3587.6113 t for TN, TP, NH4+-N, and COD, respectively, in 2021. This paper provides a valuable reference for understanding the status of NPSP in Henan province. The DPeRS approach presented in this paper provides strong support for policymakers in the field of environmental management in China. This study confirmed that the DPeRS model can be feasibly applied to larger areas for NPSP prediction enhanced with sensitivity analysis due to its fast computation and reliance on accessible and simple data sources. Full article
(This article belongs to the Special Issue Advanced Studies in Land Cover Change and Geographic Data Fusion)
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