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

Deadline for manuscript submissions: closed (15 July 2023) | Viewed by 38611

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Special Issue Editors

Dr. Antonio Ganga

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Guest Editor

Department of Architecture Design and Planning, University of Sassari, 08100 Sassari, Italy
Interests: soil science; regional planning; remote sensing; geostatistics
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Dr. Blaž Repe

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Department of Geography, University of Ljubljana, Ljubljana, Slovenia
Interests: geoinformatics (GIS); geography; cartography; soil science
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Dr. Mario Elia

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Department of Agricultural and Environmental Science, University of Bari, Bari, Italy
Interests: forest ecology; fire and fuel management; geospatial modelling
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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

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Keywords

GIS
soil science
spatial analysis
soil properties detection
geostatistics
remote sensing

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Published Papers (11 papers)

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Editorial

Jump to: Research, Review

2 pages, 191 KiB

Open AccessEditorial

Applications of GIS and Remote Sensing in Soil Environment Monitoring

by Antonio Ganga, Mario Elia and Blaž Repe

Sustainability 2023, 15(18), 13705; https://doi.org/10.3390/su151813705 - 14 Sep 2023

Cited by 1 | Viewed by 1594

Abstract

Monitoring plays an essential role in the efficient and sustainable management of the environment [...] Full article

(This article belongs to the Special Issue Applications of GIS and Remote Sensing in Soil Environment Monitoring)

Research

Jump to: Editorial, Review

19 pages, 9477 KiB

Open AccessArticle

Spatial Mapping of Soil Salinity Using Machine Learning and Remote Sensing in Kot Addu, Pakistan

by Yasin ul Haq, Muhammad Shahbaz, H. M. Shahzad Asif, Ali Al-Laith and Wesam H. Alsabban

Sustainability 2023, 15(17), 12943; https://doi.org/10.3390/su151712943 - 28 Aug 2023

Cited by 10 | Viewed by 1990

Abstract

The accumulation of salt through natural causes and human artifice, such as saline inundation or mineral weathering, is marked as salinization, but the hindrance toward spatial mapping of soil salinity has somewhat remained a consistent riddle despite decades of efforts. The purpose of the current study is the spatial mapping of soil salinity in Kot Addu (situated in the south of the Punjab province, Pakistan) using Landsat 8 data in five advanced machine learning regression models, i.e., Random Forest Regressor, AdaBoost Regressor, Decision Tree Regressor, Partial Least Squares Regression and Ridge Regressor. For this purpose, spectral data were obtained between 20 and 27 of January 2017 and a field survey was carried out to gather a total of fifty-five soil samples. To evaluate and compare the model’s performances, the coefficient of determination (R²), Mean Squared Error (MSE), Mean Absolute Error (MAE) and the Root-Mean-Squared Error (RMSE) were used. Spectral data of band values, salinity indices and vegetation indices were employed to study the salinity of soil. The results revealed that the Random Forest Regressor outperformed the other models in terms of prediction, achieving an R² of 0.94, MAE of 1.42 dS/m, MSE of 3.58 dS/m and RMSE of 1.89 dS/m when using the Differential Vegetation Index (DVI). Alternatively, when using the Soil Adjusted Vegetation Index (SAVI), the Random Forest Regressor achieved an R² of 0.93, MAE of 1.46 dS/m, MSE of 3.90 dS/m and RMSE of 1.97 dS/m. Hence, remote sensing technology with machine learning models is an efficient method for the assessment of soil salinity at local scales. This study will contribute to mitigating osmotic stress and minimizing the risk of soil erosion by providing early warnings regarding soil salinity. Additionally, it will assist agriculture officers in estimating soil salinity levels within a shorter time frame and at a reduced cost, enabling effective resource allocation. Full article

(This article belongs to the Special Issue Applications of GIS and Remote Sensing in Soil Environment Monitoring)

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15 pages, 1813 KiB

Open AccessArticle

Impact of Agricultural Land Use Types on Soil Moisture Retention of Loamy Soils

by Szabolcs Czigány, Noémi Sarkadi, Dénes Lóczy, Anikó Cséplő, Richárd Balogh, Szabolcs Ákos Fábián, Rok Ciglič, Mateja Ferk, Gábor Pirisi, Marcell Imre, Gábor Nagy and Ervin Pirkhoffer

Sustainability 2023, 15(6), 4925; https://doi.org/10.3390/su15064925 - 9 Mar 2023

Cited by 3 | Viewed by 3283

Abstract

Increasingly severe hydrological extremes are predicted for the Pannonian Basin as one of the consequences of climate change. The challenges of extreme droughts require the adaptation of agriculture especially during the intense growth phase of crops. For dryland farming, the selections of the optimal land use type and sustainable agricultural land management are potential adaptation tools for facing the challenges posed by increased aridity. To this end, it is indispensable to understand soil moisture (SM) dynamics under different land use types over drought-affected periods. Within the framework of a Slovenian–Hungarian project, soil moisture, matric potential and rainfall time series have been collected at three pilot sites of different land use types (pasture, orchards and a ploughland) in SW Hungary since September 2018. Experiments were carried out in soils of silt, silt loam and clay loam texture. In the summers (June 1 to August 31) of 2019 and 2022, we identified normal and dry conditions, respectively, with regard to differences in water balance. Our results demonstrated that soil moisture is closely controlled by land use. Marked differences of the moisture regime were revealed among the three land use types based on statistical analyses. Soils under pasture had the most balanced regime, whereas ploughland soils indicated the highest amplitude of moisture dynamics. The orchard, however, showed responses to weather conditions in sharp contrast with the other two sites. Our results are applicable for loamy soils under humid and subhumid temperate climates and for periods of extreme droughts, a condition which is expected to be the norm for the future. Full article

(This article belongs to the Special Issue Applications of GIS and Remote Sensing in Soil Environment Monitoring)

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15 pages, 15432 KiB

Open AccessArticle

GIS-Based Soil Erosion Risk Assessment in the Watersheds of Bukidnon, Philippines Using the RUSLE Model

by Indie G. Dapin and Victor B. Ella

Sustainability 2023, 15(4), 3325; https://doi.org/10.3390/su15043325 - 11 Feb 2023

Cited by 5 | Viewed by 8624

Abstract

The sustainability of watersheds for supplying water and for carbon sequestration and other environmental services depends to a large extent on their susceptibility to soil erosion, particularly under changing climate. This study aimed to assess the risk of soil erosion in the watersheds in Bukidnon, Philippines, determine the spatial distribution of soil loss based on recent land cover maps, and predict soil loss under various rainfall scenarios based on recently reported climate change projections. The soil erosion risk assessment and soil loss prediction made use of GIS and the RUSLE model, while the rainfall scenarios were formulated based on PAGASA’s prediction of drier years for Bukidnon in the early-future to late-future. Results showed that a general increase in soil loss was observed in 2015, over the period from 2010 to 2020, although some watershed clusters also showed a declining trend of soil erosion, particularly the Agusan-Cugman and Maridugao watershed clusters. Nearly 60% of Bukidnon has high to very severe soil loss rates. Under extreme rainfall change scenario with 12.61% less annual rainfall, the soil loss changes were only +1.37% and −2.87% in the category of none-to-slight and very severe, respectively. Results showed that a decrease in rainfall would have little effect on resolving the excessive soil erosion problem in Bukidnon. Results of this study suggest that having more vegetative land cover and employing soil conservation measures may prove to be effective in minimizing the risk of soil erosion in the watersheds. This study provides valuable information to enhance the sustainability of the watersheds. The erosion-prone areas identified will help decision-makers identify priority areas for soil conservation and environmental protection. Full article

(This article belongs to the Special Issue Applications of GIS and Remote Sensing in Soil Environment Monitoring)

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12 pages, 3952 KiB

Open AccessArticle

A Model between Cohesion and Its Inter-Controlled Factors of Fine-Grained Sediments in Beichuan Debris Flow, Sichuan Province, China

by Qinjun Wang, Jingjing Xie, Jingyi Yang, Peng Liu, Dingkun Chang and Wentao Xu

Sustainability 2022, 14(19), 12832; https://doi.org/10.3390/su141912832 - 8 Oct 2022

Cited by 6 | Viewed by 1531

Abstract

Cohesion is the attraction between adjacent particles within the same material, which is the main inter-controlled factor of fine-grained sediment stability, and thus plays an important role in debris flow hazard early warning. However, there is no quantitative model of cohesion and its inter-controlled factors, including effective internal friction angle, permeability coefficient and density. Therefore, establishing a quantitative model of cohesion and its inter-controlled factors is of considerable significance in debris flow hazard early warning. Taking Beichuan county in southwestern China as the study area, we carried out a series of experiments on cohesion and its inter-controlled factors. Using the value of cohesion as the dependent variable and values of normalized density, normalized logarithm of permeability coefficient and normalized effective internal friction angle as the independent variables, we established a quantitative model of cohesion and its inter-controlled factors by the least-squares multivariate statistical method. Fitting of the model showed that its determination coefficient (R²) was 0.61, indicating that the corresponding correlation coefficient (R) was 0.78. Furthermore, t-tests of the model showed that except for the p value of density, which was 0.05, those of other factors were less than 0.01, indicating that cohesion was significantly correlated to its inter-controlled factors, providing a scientific basis for debris flow hazard early warning. Full article

(This article belongs to the Special Issue Applications of GIS and Remote Sensing in Soil Environment Monitoring)

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16 pages, 4051 KiB

Open AccessArticle

Mapping of Land Degradation Vulnerability in the Semi-Arid Watershed of Rajasthan, India

by Lal Chand Malav, Brijesh Yadav, Bhagwati L. Tailor, Sarthak Pattanayak, Shruti V. Singh, Nirmal Kumar, Gangalakunta P. O. Reddy, Banshi L. Mina, Brahma S. Dwivedi and Prakash Kumar Jha

Sustainability 2022, 14(16), 10198; https://doi.org/10.3390/su141610198 - 17 Aug 2022

Cited by 11 | Viewed by 3328

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 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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20 pages, 9432 KiB

Open AccessArticle

Mapping Soil Properties at a Regional Scale: Assessing Deterministic vs. Geostatistical Interpolation Methods at Different Soil Depths

by Jesús Barrena-González, Joaquín Francisco Lavado Contador and Manuel Pulido Fernández

Sustainability 2022, 14(16), 10049; https://doi.org/10.3390/su141610049 - 13 Aug 2022

Cited by 15 | Viewed by 2462

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 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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13 pages, 1787 KiB

Open AccessArticle

Assessing Landslide Susceptibility by Coupling Spatial Data Analysis and Logistic Model

by Antonio Ganga, Mario Elia, Ersilia D’Ambrosio, Simona Tripaldi, Gian Franco Capra, Francesco Gentile and Giovanni Sanesi

Sustainability 2022, 14(14), 8426; https://doi.org/10.3390/su14148426 - 9 Jul 2022

Cited by 9 | Viewed by 2293

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 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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28 pages, 6987 KiB

Open AccessArticle

Soil Order-Land Use Index Using Field-Satellite Spectroradiometry in the Ecuadorian Andean Territory for Modeling Soil Quality

by Susana Arciniegas-Ortega, Iñigo Molina and Cesar Garcia-Aranda

Sustainability 2022, 14(12), 7426; https://doi.org/10.3390/su14127426 - 17 Jun 2022

Cited by 3 | Viewed by 2577

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 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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20 pages, 10808 KiB

Open AccessEditor’s ChoiceArticle

Assessing Soil Erosion by Monitoring Hilly Lakes Silting

by Yamuna Giambastiani, Riccardo Giusti, Lorenzo Gardin, Stefano Cecchi, Maurizio Iannuccilli, Stefano Romanelli, Lorenzo Bottai, Alberto Ortolani and Bernardo Gozzini

Sustainability 2022, 14(9), 5649; https://doi.org/10.3390/su14095649 - 7 May 2022

Cited by 9 | Viewed by 2493

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 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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Review

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25 pages, 12069 KiB

Open AccessReview

Current Status and Development Trend of Soil Salinity Monitoring Research in China

by Yingxuan Ma and Nigara Tashpolat

Sustainability 2023, 15(7), 5874; https://doi.org/10.3390/su15075874 - 28 Mar 2023

Cited by 22 | Viewed by 4995

Abstract

Soil salinization is a resource and ecological problem that currently exists on a large scale in all countries of the world. This problem is seriously restricting the development of agricultural production, the sustainable use of land resources, and the stability of the ecological environment. Salinized soils in China are characterized by extensive land area, complex saline species, and prominent salinization problems. Therefore, strengthening the management and utilization of salinized soils, monitoring and identifying accurate salinization information, and mastering the degree of regional salinization are important goals that researchers have been trying to explore and overcome. Based on a large amount of soil salinization research, this paper reviews the developmental history of saline soil management research in China, discusses the research progress of soil salinization monitoring, and summarizes the main modeling methods for remote sensing monitoring of saline soils. Additionally, this paper also proposes and analyzes the limitations of China’s soil salinity monitoring research and its future development trend, taking into account the real needs and frontier hotspots of the country in related research. This is of great practical significance to comprehensively grasp the current situation of salinization research, further clarify and sort out research ideas of salinization monitoring, enrich the remote sensing monitoring methods of saline soils, and solve practical problems of soil salinization in China. Full article

(This article belongs to the Special Issue Applications of GIS and Remote Sensing in Soil Environment Monitoring)

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