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Advanced Studies in Land Cover Change and Geographic Data Fusion

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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

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Research

24 pages, 14940 KiB

Open AccessArticle

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 448

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 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

ε_{0}

, natural factor correction coefficient

N_{i}

, the newly produced TN pollutants per area

Q_{i N}

, 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,

{N H}_{4}^{+}

-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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