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Special Issue "Applications of GIS and Remote Sensing in Soil Environment Monitoring"

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

Deadline for manuscript submissions: 15 January 2023 | Viewed by 3294

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
Dr. Blaž Repe
E-Mail Website
Guest Editor
Department of Geography, University of Ljubljana, Ljubljana, Slovenia
Interests: geoinformatics (GIS); geography; cartography; soil science
Dr. Mario Elia
E-Mail Website
Guest Editor
Department of Agricultural and Environmental Science, University of Bari, 70126 Bari, Italy
Interests: biosystems engineering; environmental science; theoretical production ecology; cartography; geoinformatics (GIS); geostatistics
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 the 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, and earthquakes);
  • Modeling 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

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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 2000 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

Published Papers (5 papers)

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Research

Article
Mapping of Land Degradation Vulnerability in the Semi-Arid Watershed of Rajasthan, India
Sustainability 2022, 14(16), 10198; https://doi.org/10.3390/su141610198 - 17 Aug 2022
Viewed by 355
Abstract
Global soils are under extreme pressure from various threats due to population expansion, economic development, and climate change. Mapping of land degradation vulnerability (LDV) using geospatial techniques play a significant role and has great importance, especially in semi-arid climates for the management of [...] Read more.
Global soils are under extreme pressure from various threats due to population expansion, economic development, and climate change. Mapping of land degradation vulnerability (LDV) using geospatial techniques play a significant role and has great importance, especially in semi-arid climates for the management of natural resources in a sustainable manner. The present study was conducted to assess the spatial distribution of land degradation hotspots based on some important parameters such as land use/land cover (LULC), Normalized Difference Vegetation Index (NDVI), terrain characteristics (Topographic Wetness Index and Multi-Resolution Index of Valley Bottom Flatness), climatic parameters (land surface temperature and mean annual rainfall), and pedological attributes (soil texture and soil organic carbon) by using Analytical Hierarchical Process (AHP) and GIS techniques in the semi-arid region of the Bundi district, Rajasthan, India. Land surface temperature (LST) and NDVI products were derived from time-series Moderate-Resolution Imaging Spectroradiometer (MODIS) datasets, rainfall data products from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), terrain characteristics from Shuttle Radar Topography Mission (SRTM), LULC from Landsat 9, and pedological variables from legacy soil datasets. Weights derived for thematic layers from the AHP in the studied area were as follows: LULC (0.38) > NDVI (0.23) > ST (0.15) > LST (0.08) > TWI (0.06) > MAR (0.05) > SOC (0.03) > MRVBF (0.02). The consistency ratio (CR) for all studied parameters was <0.10, indicating the high accuracy of the AHP. The results show that about 20.52% and 23.54% of study area was under moderate and high to very high vulnerability of land degradation, respectively. Validation of LDV zones with the help of ultra-high-resolution Google Earth imageries indicates good agreement with the model outputs. The research aids in a better understanding of the influence of land degradation on long-term land management and development at the watershed level. Full article
(This article belongs to the Special Issue Applications of GIS and Remote Sensing in Soil Environment Monitoring)
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Article
Mapping Soil Properties at a Regional Scale: Assessing Deterministic vs. Geostatistical Interpolation Methods at Different Soil Depths
Sustainability 2022, 14(16), 10049; https://doi.org/10.3390/su141610049 - 13 Aug 2022
Viewed by 403
Abstract
To determine which interpolation technique is the most suitable for each case study is an essential task for a correct soil mapping, particularly in studies performed at a regional scale. So, our main goal was to identify the most accurate method for mapping [...] Read more.
To determine which interpolation technique is the most suitable for each case study is an essential task for a correct soil mapping, particularly in studies performed at a regional scale. So, our main goal was to identify the most accurate method for mapping 12 soil variables at three different depth intervals: 0–5, 5–10 and >10 cm. For doing that, we have compared nine interpolation methods (deterministic and geostatistical), drawing soil maps of the Spanish region of Extremadura (41,635 km2 in size) from more than 400 sampling sites in total (e.g., more than 500 for pH for the depth of 0–5 cm). We used the coefficient of determination (R2), the mean error (ME) and the root mean square error (RMSE) as statistical parameters to assess the accuracy of each interpolation method. The results indicated that the most accurate method varied depending on the property and depth of study. In soil properties such as clay, EBK (Empirical Bayesian Kriging) was the most accurate for 0–5 cm layer (R2 = 0.767 and RMSE = 3.318). However, for 5–10 cm in depth, it was the IDW (Inverse Distance Weighted) method with R2 and RMSE values of 0.689 and 5.131, respectively. In other properties such as pH, the CRS (Completely Regularized Spline) method was the best for 0–5 cm in depth (R2 = 0.834 and RMSE = 0.333), while EBK was the best for predicting values below 10 cm (R2 = 0.825 and RMSE = 0.399). According to our findings, we concluded that it is necessary to choose the most accurate interpolation method for a proper soil mapping. Full article
(This article belongs to the Special Issue Applications of GIS and Remote Sensing in Soil Environment Monitoring)
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Article
Assessing Landslide Susceptibility by Coupling Spatial Data Analysis and Logistic Model
Sustainability 2022, 14(14), 8426; https://doi.org/10.3390/su14148426 - 09 Jul 2022
Viewed by 495
Abstract
Landslides represent one of the most critical issues for landscape managers. They can cause injuries and loss of human life and damage properties and infrastructure. The spatial and temporal distribution of these detrimental events makes them almost unpredictable. Studies on landslide susceptibility assessment [...] Read more.
Landslides represent one of the most critical issues for landscape managers. They can cause injuries and loss of human life and damage properties and infrastructure. The spatial and temporal distribution of these detrimental events makes them almost unpredictable. Studies on landslide susceptibility assessment can significantly contribute to prioritizing critical risk zones. Further, landslide prevention and mitigation and the relative importance of the affecting drivers acquire even more significance in areas characterized by seismicity. This study aimed to investigate the relationship between a set of environmental variables and the occurrence of landslide events in an area of the Apulia Region (Italy). Logistic regression was applied to a landslide-prone area in the Apulia Region (Italy) to identify the main causative factors using a large dataset of environmental predictors (47). The results of this case study show that the logistic regression achieved a good performance, with an AUC (Area Under Curve) >70%. Therefore, the model developed would be a useful tool to define and assess areas for landslide occurrence and contribute to implementing risk mitigation strategy and land use policy. Full article
(This article belongs to the Special Issue Applications of GIS and Remote Sensing in Soil Environment Monitoring)
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Article
Soil Order-Land Use Index Using Field-Satellite Spectroradiometry in the Ecuadorian Andean Territory for Modeling Soil Quality
Sustainability 2022, 14(12), 7426; https://doi.org/10.3390/su14127426 - 17 Jun 2022
Viewed by 597
Abstract
Land use conversion is the main cause for soil degradation, influencing the sustainability of agricultural activities in the Ecuadorian Andean region. The possibility to identify the quality based on the spectral properties allows remote sensing methods to offer an alternative form of monitoring [...] Read more.
Land use conversion is the main cause for soil degradation, influencing the sustainability of agricultural activities in the Ecuadorian Andean region. The possibility to identify the quality based on the spectral properties allows remote sensing methods to offer an alternative form of monitoring the environment. This study used laboratory spectroscopy and multi-spectral images (Sentinel 2) with environmental covariates (physicochemical parameters) to find an affordable method that can be used to present spatial prediction models as a tool for the evaluation of the quality of Andean soils. The models were developed using statistical techniques of logistic regression and linear discriminant analysis to generate an index based on soil order and three indexes based on the combination of soil order and land use. This combined approach offers an effective method, relative to traditional laboratory methods, to derive estimates of the content and composition of soil constituents, such as electrical conductivity (CE), organic matter (OM), pH, and soil moisture (HU). For Mollisol index.3 with Páramo land use, a value of organic matter (OM) ≥8.6% was obtained, whereas for Mollisol index.4 with Shrub land use, OM was ≥6.1%. These results reveal good predictive (estimation) capabilities for these soil order–land use groups. This provides a new way to monitor soil quality using remote sensing techniques, opening promising prospects for operational applications in land use planning. Full article
(This article belongs to the Special Issue Applications of GIS and Remote Sensing in Soil Environment Monitoring)
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Article
Assessing Soil Erosion by Monitoring Hilly Lakes Silting
Sustainability 2022, 14(9), 5649; https://doi.org/10.3390/su14095649 - 07 May 2022
Viewed by 594
Abstract
Soil erosion continues to be a threat to soil quality, impacting crop production and ecosystem services delivery. The quantitative assessment of soil erosion, both by water and by wind, is mostly carried out by modeling the phenomenon via remote sensing approaches. Several empirical [...] Read more.
Soil erosion continues to be a threat to soil quality, impacting crop production and ecosystem services delivery. The quantitative assessment of soil erosion, both by water and by wind, is mostly carried out by modeling the phenomenon via remote sensing approaches. Several empirical and process-based physical models are used for erosion estimation worldwide, including USLE (or RUSLE), MMF, WEPP, PESERA, SWAT, etc. Furthermore, the amount of sediment produced by erosion phenomena is obtained by direct measurements carried out in experimental sites. Data collection for this purpose is very complex and expensive; in fact, we have few cases of measures distributed at the basin scale to monitor this phenomenon. In this work, we propose a methodology based on an expeditious way to monitor the volume of hilly lakes with GPS, sonar sensor and aquatic drone. The volume is obtained by means of an automatic GIS procedure based on the measurements of lake depth and surface area. Hilly lakes can be considered as sediment containers. Time-lapse measurements make it possible to estimate the silting rate of the lake. The volume of 12 hilly lakes in Tuscany was measured in 2010 and 2018, and the results in terms of silting rate were compared with the estimates of soil loss obtained by RUSLE and MMF. The analyses show that all the lakes measured are subject to silting phenomena. The sediment estimated by the measurements corresponds well to the amount of soil loss estimated with the models used. The relationships found are significant and promising for a distributed application of the methodology, which allows rapid estimation of erosion phenomena. Substantial differences in the proposed comparison (mainly found in two cases) can be justified by particular conditions found on site, which are difficult to predict from the models. The proposed approach allows for a monitoring of basin-scale erosion, which can be extended to larger domains which have hilly lakes, such as, for example, the Tuscany region, where there are more than 10,000 lakes. Full article
(This article belongs to the Special Issue Applications of GIS and Remote Sensing in Soil Environment Monitoring)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Assessing landslide susceptibility by coupling spatial data analysis and logistic model

Authors: Antonio Ganga1, Mario Elia 2*, Ersilia D’Ambrosio2, Simona Tripaldi3, Gian Franco Capra1, Francesco Gentile2, Giovanni Sanesi2

Affiliation:

1 Dipartimento di Architettura, Design e Urbanistica, Università degli Studi di Sassari, Viale Piandanna n 4, 07100, Sassari, Italy

2 Department of Agricultural and Environmental Sciences, University of Bari A. Moro, Bari, Italy

3 Department of Earth and Geo-environmental Sciences, University of Bari Aldo Moro, Bari, Italy *

Correspondence. Department of Agricultural and Environmental Sciences, University of Bari Aldo Moro, Via Amendola 165/A, 70126 Bari, Italy. E-mail address: [email protected]

Abstract: Landslides represent one of the most critical issues for landscape managers, being a phenomenon that could generate injuries and loss of human life, properties, and infrastructure. The spatial and temporal distribution of these detrimental events makes it almost unpredictable. Studies on landslide susceptibility assessment can significantly contribute to prioritizing critical risk zones. Further, landslide prevention and mitigation and the relative importance of the affecting drivers acquire even more significance in areas characterized by seismicity. This study aimed to investigate the relationship between a set of environmental variables and the occurrence of landslide events in an area of the Apulia Region (Italy). The logistic regression was applied to a landslide-prone area in the Apulia Region (Italy), to identify the main causative factors using a large dataset of environmental predictors (47). The results show as in this case study the logistic regression achieved a good performance, (AUC>70%), therefore, the model developed would be a useful tool to define and assess areas for landslide occurrence and contribute to implementing risk mitigation strategy and land use policy.

Keywords: Landslide, Logistic regression, Environmental Hazard, Risk Assessment

Title: An Integrated Environmental Monitoring Model for Holm Oak Decline Risk Assessment in a Mediterranean Coastal Forest
Authors: Francesca Angius 1, Bruno Scanu 1, Ludmila Roder 2, Andrea Brandano 1, Gian Franco Capra 2 and Antonio Ganga2,*
Affiliation: 1 Department of Agricultural Sciences, University of Sassari, Viale Italia 39A, 07100 Sassari, Italy; 2 Department of Architecture, Design and Urban Planning, University of Sassari * Correspondence: [email protected];
Abstract: Abstract: In recent years, the decline of holm oak forests in the Mediterranean area is assuming worrying dimensions. This phenomenon is complex and correlated to several factors that directly or indirectly interact with tree pathogens, such as Phytophthora species. Therefore, phytosanitary monitoring has become a priority to protect these forests, especially in coastal and more densely urbanized areas. This work aims to propose and validate an integrated monitoring model developed starting from soil data collected during field activities, remote sensing elaboration, Phytophthora presence and distribution and phytosanitary data conditions of a holm oak trees on Caprera Island, Italy. The spatial analysis elaborations showed the evolution of the phenomenon correlated with some morphological and pedological parameters. Further, the multivariate statistical analysis reveals the relationship between environmental drivers and the distribution of holm oak decline caused by Phytophthora species. An integrated approach is revealing fundamental to improve the monitoring and management of oak decline, nowadays considered nearly irreversible.

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