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Applications of GIS and Remote Sensing in Soil Environment Monitoring 2nd Edition

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Soil Conservation and Sustainability".

Deadline for manuscript submissions: 20 November 2024 | Viewed by 4109

Special Issue Editors

Dr. Antonio Ganga

E-Mail Website
Guest Editor
Department of Architecture Design and Planning, University of Sassari, 08100 Sassari, Italy
Interests: soil science; regional planning; remote sensing; geostatistics
Special Issues, Collections and Topics in MDPI journals
Dr. Blaž Repe

E-Mail Website
Guest Editor
Department of Geography, University of Ljubljana, Ljubljana, Slovenia
Interests: geoinformatics (GIS); geography; cartography; soil science
Special Issues, Collections and Topics in MDPI journals
Dr. Mario Elia

E-Mail Website
Guest Editor
Department of Agricultural and Environmental Science, University of Bari, Bari, Italy
Interests: forest ecology; fire and fuel management; geospatial modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The monitoring of environmental features is a key issue in the sustainable management of land resources, where the increasing availability of temporal and spatial soil data plays a fundamental role. Remote sensing and GIS (geographic information system) applications enable efficient handling of these data with the aim of developing predictive and sound models to reduce land degradation and soil erosion. There is a need to further improve studies of soil dynamics in different environmental biosystems to gain more insights into erosion processes and the effectiveness of conservation measures that contribute to the currently perceived new and modern sustainable practices.

In addition, new insights and perspective studies on soil dynamics offer great potential to better understand how soil erosion is related to natural disasters such as landslides, floods, slope instability, biodiversity loss, and climate change.

The aim of this Special Issue is to contribute to a better description of the most popular research direction in spatial data analysis of soil, focusing on the following topics:

  • Applications of remote sensing and GIS to detect and monitor soil properties;
  • Land use and sustainable soil management practices;
  • Linking soil erosion and natural disasters (e.g., landslides, floods, earthquakes);
  • Modelling sediment transport in rivers;
  • Transport of river sediments modeling;
  • Monitoring and assessment of soil erosion in agriculture and forestry.

Dr. Antonio Ganga
Dr. Blaž Repe
Dr. Mario Elia
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. Sustainability 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

  • GIS
  • soil science
  • spatial analysis
  • soil properties detection
  • geostatistics
  • remote sensing

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Published Papers (4 papers)

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Research

17 pages, 4828 KiB  
Article
Modeling of Soil Cation Exchange Capacity Based on Chemometrics, Various Spectral Transformations, and Multivariate Approaches in Some Soils of Arid Zones
by Abdel-rahman A. Mustafa, Elsayed A. Abdelsamie, Elsayed Said Mohamed, Nazih Y. Rebouh and Mohamed S. Shokr
Sustainability 2024, 16(16), 7002; https://doi.org/10.3390/su16167002 - 15 Aug 2024
Viewed by 585
Abstract
Cation exchange capacity is a crucial metric for managing soil fertility and promoting agricultural sustainability. An alternative technique for the non-destructive assessment of important soil parameters is reflectance spectroscopy. The main focus of this paper is on how to analyze and predict the [...] Read more.
Cation exchange capacity is a crucial metric for managing soil fertility and promoting agricultural sustainability. An alternative technique for the non-destructive assessment of important soil parameters is reflectance spectroscopy. The main focus of this paper is on how to analyze and predict the content of various soil cation exchange capacities (CEC) in arid conditions (Sohag governorate, Egypt) at a low cost using laboratory analysis of CEC, visible near-infrared and shortwave infrared (Vis-NIR) spectroscopy, partial least-squares regression (PLSR), and Ordinary Kriging (OK). Utilizing reflectance spectroscopy with a spectral resolution of 10 nm and laboratory studies with a spectral range of 350 to 2500 nm, 104 surface soil samples were collected to a depth of 30 cm in the Sohag governorate, Egypt (which is part of the dry region of North Africa), in order to accomplish this goal. The association between the spectroradiometer and CEC averaged values was modeled using PLSR in order to map the predicted value using Ordinary Kriging (OK). Thirty-one soil samples were selected for validation. The predictive validity of the cross-validated models was evaluated using the coefficient of determination (R2), root mean square error (RMSE), residual prediction deviation (RPD), and ratio of performance to interquartile distance (RPIQ). The results indicate that ten transformation methods yielded calibration models that met the study’s requirements, with R2 > 0.6, RPQ > 2.5, and RIQP > 4.05. For evaluating CEC in Vis-NIR spectra, the most efficient transformation and calibration model was the reciprocal of Log R transformation (R2 = 0.98, RMSE = 0.40, RPD = 6.99, and RIQP = 9.22). This implies that combining the reciprocal of Log R with PLSR yields the optimal model for predicting CEC values. The CEC values were best fitted by four models: spherical, exponential, Gaussian, and circular. The methodology used here does offer a “quick”, inexpensive tool that can be broadly and quickly used, and it can be readily implemented again in comparable conditions in arid regions. Full article
(This article belongs to the Special Issue Applications of GIS and Remote Sensing in Soil Environment Monitoring 2nd Edition)
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20 pages, 19235 KiB  
Article
Utilizing Machine Learning Algorithms for the Development of Gully Erosion Susceptibility Maps: Evidence from the Chotanagpur Plateau Region, India
by Md Hasanuzzaman, Pravat Kumar Shit, Saeed Alqadhi, Hussein Almohamad, Fahdah Falah ben Hasher, Hazem Ghassan Abdo and Javed Mallick
Sustainability 2024, 16(15), 6569; https://doi.org/10.3390/su16156569 - 31 Jul 2024
Viewed by 639
Abstract
Gully erosion is a serious environmental threat, compromising soil health, damaging agricultural lands, and destroying vital infrastructure. Pinpointing regions prone to gully erosion demands careful selection of an appropriate machine learning algorithm. This choice is crucial, as the complex interplay of various environmental [...] Read more.
Gully erosion is a serious environmental threat, compromising soil health, damaging agricultural lands, and destroying vital infrastructure. Pinpointing regions prone to gully erosion demands careful selection of an appropriate machine learning algorithm. This choice is crucial, as the complex interplay of various environmental factors contributing to gully formation requires a nuanced analytical approach. To develop the most accurate Gully Erosion Susceptibility Map (GESM) for India’s Raiboni River basin, researchers harnessed the power of two cutting-edge machine learning algorithm: Extreme Gradient Boosting (XGBoost) and Random Forest (RF). For a comprehensive analysis, this study integrated 24 potential control factors. We meticulously investigated a dataset of 200 samples, ensuring an even balance between non-gullied and gullied locations. To assess multicollinearity among the 24 variables, we employed two techniques: the Information Gain Ratio (IGR) test and Variance Inflation Factors (VIF). Elevation, land use, river proximity, and rainfall most influenced the basin’s GESM. Rigorous tests validated XGBoost and RF model performance. XGBoost surpassed RF (ROC 86% vs. 83.1%). Quantile classification yielded a GESM with five levels: very high to very low. Our findings reveal that roughly 12% of the basin area is severely affected by gully erosion. These findings underscore the critical need for targeted interventions in these highly susceptible areas. Furthermore, our analysis of gully characteristics unveiled a predominance of V-shaped gullies, likely in an active developmental stage, supported by an average Shape Index (SI) value of 0.26 and a mean Erosivness Index (EI) of 0.33. This research demonstrates the potential of machine learning to pinpoint areas susceptible to gully erosion. By providing these valuable insights, policymakers can make informed decisions regarding sustainable land management practices. Full article
(This article belongs to the Special Issue Applications of GIS and Remote Sensing in Soil Environment Monitoring 2nd Edition)
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17 pages, 26369 KiB  
Article
Changes in Land Use and Cover and Their Environmental Impacts in the Cerrado of Mato Grosso Do Sul, Brazil
by Melina Fushimi, Gabriela Narcizo de Lima and Viviane Capoane
Sustainability 2024, 16(10), 4266; https://doi.org/10.3390/su16104266 - 18 May 2024
Viewed by 1186
Abstract
In Brazilian regional landscapes, the Cerrado has one of the richest flora among the savannas in the world, with a high level of endemism; however, many plant species are threatened with extinction as a consequence of spatio-temporal changes in land use and cover. [...] Read more.
In Brazilian regional landscapes, the Cerrado has one of the richest flora among the savannas in the world, with a high level of endemism; however, many plant species are threatened with extinction as a consequence of spatio-temporal changes in land use and cover. This study aimed to analyze changes in land use and cover in the upper course of the Ceroula stream basin, located in the Cerrado of Mato Grosso do Sul, Brazil, based on maps of land use and cover in 1985 and 2022, the normalized difference vegetation index (NDVI), precipitation data, and fieldwork. The results indicated that in 1985, forest vegetation was replaced by pasture, and in 2022, in addition to pasture, there was the introduction of soybean monoculture with corn in the off-season, influenced by the international commodities market. These land use and cover alterations, without adequate management and in the absence of conservation practices, led to environmental impacts, such as accelerated linear erosive processes (rill, ravine, and gully). The results may help provide important insights into the dynamics of land use and cover, the consequences of the lack of conservation practices, and the environmental impacts in the Cerrado of Mato Grosso do Sul, contributing to better understanding of the environmental challenges faced in the region and the need to provide subsidies for the development of sustainable management strategies. Full article
(This article belongs to the Special Issue Applications of GIS and Remote Sensing in Soil Environment Monitoring 2nd Edition)
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18 pages, 6400 KiB  
Article
Vegetation–Lake–Sand Landscape of Northeast China Sandy Land between 1980 and 2022: Pattern, Evolution, and Driving Forces
by Weiyi Lu, Geer Teni and Huishi Du
Sustainability 2024, 16(8), 3382; https://doi.org/10.3390/su16083382 - 18 Apr 2024
Viewed by 1020
Abstract
Northeast China’s sandy region is an arid and semi-arid zone highly susceptible to climate change. Investigating the long-term changes in the Northeast China sandy land (Northeast China sandy land, DBSL) landscape can provide an important basis for the ecological restoration of this region. [...] Read more.
Northeast China’s sandy region is an arid and semi-arid zone highly susceptible to climate change. Investigating the long-term changes in the Northeast China sandy land (Northeast China sandy land, DBSL) landscape can provide an important basis for the ecological restoration of this region. This study analyzed long-term remote sensing data of the DBSL from 1980 to 2022 and explored the spatial pattern, evolution, and driving mechanisms. In 2022, vegetation was mainly distributed in the northwest, center, and southwest, covering a total area of 30,508.82 km2. Areas with high and medium vegetation cover showed strong aggregation characteristics and were mainly distributed in the southwest, whereas those with low vegetation coverage were highly dispersed and widely distributed in the central region. Lakes were widely distributed in the northwest and central regions, with a total area of 2736.43 km2. In the last 42 years, the vegetation cover decreased by 24.48%. Areas with high and medium vegetation coverage decreased in size, and those with low vegetation coverage first increased and then decreased, with overall decreases of 35.35%, 19.16%, and 6.88%, respectively. The overall area of the DBSL showed various degrees of degradation. Shrinking and dry lakes were concentrated in the sandy hinterland. The lake landscape changed significantly from 1990 to 2010, with a decrease in lake area of 27.41%. In contrast, the sandy area increased by 25.65%, indicating a high degree of desertification. However, from 2005 to 2022, desertification decelerated. The most important factors driving the evolution of the DBSL were socio-economic factors. The increase in human disturbance will have a certain impact on the landscape changes in the region in the short term. The national policy of returning farmland to fields and grasslands will affect the increase of vegetation and lake landscape area in the short term, and the sand area and excessive animal husbandry will be reduced. This study provides a scientific basis for ecological restoration and sustainable development in Northeast China. Full article
(This article belongs to the Special Issue Applications of GIS and Remote Sensing in Soil Environment Monitoring 2nd Edition)
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