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Land and Water Degradation in Catchments: The Role of Remote Sensing for Assessment and Management

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Water Management".

Deadline for manuscript submissions: closed (26 March 2023) | Viewed by 18926

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Prof. Dr. Fernando António Leal Pacheco

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DG-CQVR-UTAD – Department of Geology, Chemistry Research Centre, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
Interests: groundwater management; groundwater contamination risk; water–rock interactions; groundwater flow modeling; groundwater–surface water interactions; land degradation and surface water quality; spatial decision support systems in public water supply planning; conjunctive use of water resources; water security
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Prof. Dr. Luís Filipe Sanches Fernandes

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CITAB—Centre for the Research and Technology of Agro-Environment and Biological Sciences, Universidade de Trás-os-Montes e Alto Douro, 5001-801 Vila Real, Portugal
Interests: flood-detention basins; rainwater harvesting for drought effects attenuation; hydrologic modeling at the catchment scale; water resources management; quality data; integrated monitoring of climate and environmental impacts; sustainability in agri-food and forestry ecosystems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Many landscapes are shaped by multiple uses and occupations in the rural and urban space, which frequently induce significant perturbations in soil and water characteristics, ultimately causing degradation. The return to sustainability, or at least neutrality, requires the implementation of adequate land uses and correct management practices in the watershed. The catchment links soil to water degradation because it is the place where weather and hydrologic processes generate and transport loose materials and contaminants from the lithosphere into the hydrosphere. Considering the evolution of geographic information systems and the appearance of big data, particularly related to satellite images with progressively higher spatial and time resolutions, remote sensing research and applications are currently becoming topical in environmental science. Examples include the use of satellite and drone images to detect land degradation caused by intensive livestock pasturing. The results from remote sensing assessments are expected to generate valuable insights for the scientific community, but also to trigger the implementation of politics and the development of metrics that can be used by judicial, political, and administrative authorities in the governance of soil and water.

The purpose of this Special Issue is, therefore, to bring scientists into a discussion on remote sensing applications and their potential use in sustainable watershed management.

Thank you very much for your contributions.

Prof. Dr. Fernando A.L. Pacheco
Prof. Dr. Luís Filipe Sanches Fernandes
Guest Editors

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Keywords

Soil degradation
water pollution
watershed, remote sensing
management

Published Papers (7 papers)

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Research

21 pages, 5867 KiB

Open AccessArticle

The Accuracy of Land Use and Cover Mapping across Time in Environmental Disaster Zones: The Case of the B1 Tailings Dam Rupture in Brumadinho, Brazil

by Carlos Roberto Mangussi Filho, Renato Farias do Valle Junior, Maytê Maria Abreu Pires de Melo Silva, Rafaella Gouveia Mendes, Glauco de Souza Rolim, Teresa Cristina Tarlé Pissarra, Marília Carvalho de Melo, Carlos Alberto Valera, Fernando António Leal Pacheco and Luís Filipe Sanches Fernandes

Sustainability 2023, 15(8), 6949; https://doi.org/10.3390/su15086949 - 20 Apr 2023

Cited by 4 | Viewed by 1687

Abstract

The rupture of a tailings dam causes several social, economic, and environmental impacts because people can die, the devastation caused by the debris and mud waves is expressive and the released substances may be toxic to the ecosystem and humans. There were two major dam failures in the Minas Gerais state, Brazil, in the last decade. The first was in 2015 in the city of Mariana and the second was in 2019 in the municipality of Brumadinho. The extent of land use and cover changes derived from those collapses were an expression of their impacts. Thus, knowing the changes to land use and cover after these disasters is essential to help repair or mitigate environmental degradation. This study aimed to diagnose the changes to land cover that occurred after the failure of dam B1 in Brumadinho that affected the Ferro-Carvão stream watershed. In addition to the environmental objective, there was the intention of investigating the impact of image preparation, as well as the spatial and spectral resolution on the classification’s accuracy. To accomplish the goals, visible and near-infrared bands from Landsat (30 m), Sentinel-2 (10 m), and PlanetScope Dove (4.77 m) images collected between 2018 and 2021 were processed on the Google Earth Engine platform. The Pixel Reduction to Median tool was used to prepare the record of images, and then the random forest algorithm was used to detect the changes in land cover caused by the tailings dam failure under the different spatial and spectral resolutions and to provide the corresponding measures of accuracy. The results showed that the spatial resolution of the images affects the accuracy, but also that the selected algorithm and images were all capable of accurately classifying land use and cover in the Ferro-Carvão watershed and their changes over time. After the failure, mining/tailings areas increased in the impacted zone of the Ferro-Carvão stream, while native forest, pasture, and agricultural lands declined, exposing the environmental deterioration. The environment recovered in subsequent years (2020–2021) due to tailings removal and mobilization. Full article

(This article belongs to the Special Issue Land and Water Degradation in Catchments: The Role of Remote Sensing for Assessment and Management)

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20 pages, 2273 KiB

Open AccessArticle

Assessment of the Uncertainty Associated with Statistical Modeling of Precipitation Extremes for Hydrologic Engineering Applications in Amman, Jordan

by Mohamad Najib Ibrahim

Sustainability 2022, 14(24), 17052; https://doi.org/10.3390/su142417052 - 19 Dec 2022

Cited by 2 | Viewed by 1441

Abstract

Estimates of extreme precipitation are commonly associated with different sources of uncertainty. One of the primary sources of uncertainty in the statistical modeling of precipitation extremes comes from extreme data series (i.e., sampling uncertainty). Therefore, this research aimed to quantify the sampling uncertainty in terms of confidence intervals. In addition, this article examined how the data record length affects predicted extreme precipitation estimates and data set statistics. A nonparametric bootstrap resample was utilized to quantify the precipitation quantile sampling distribution at a particular non exceedance probability. This sampling distribution can provide a point estimation of the precipitation quantile and the confidence interval at a particular non exceedance probability. It has been shown that the different types of probability distributions fit the extreme precipitation data series of various weather stations. Therefore, the uncertainty analysis should be conducted using the best-fit probability distribution for extreme precipitation data series rather than a predefined single probability distribution for all stations based on modern extreme value theory. According to the 95% confidence intervals, precipitation quantiles are subject to significant uncertainty and the band of the uncertainty intervals increases with the return period. These uncertainty bounds need to be integrated into any frequency analysis from historical data. The average, standard deviation, skewness and kurtosis are highly affected by the data record length. Thus, a longer record length is desirable to decrease the sampling uncertainty and, therefore, decrease the error in the predicted quantile values. Moreover, the results suggest that a series of at least 40 years of data records is needed to obtain reasonably accurate estimates of the distribution parameters and the precipitation quantiles for 100 years return periods and higher. Using only 20 to 25 years of data to obtain estimates of the higher return period quantile is risky, since it created high sampling variability relative to the full data length. Full article

(This article belongs to the Special Issue Land and Water Degradation in Catchments: The Role of Remote Sensing for Assessment and Management)

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13 pages, 3163 KiB

Open AccessArticle

Classifying Vegetation Types in Mountainous Areas with Fused High Spatial Resolution Images: The Case of Huaguo Mountain, Jiangsu, China

by Dan Chen, Xianyun Fei, Zhen Wang, Yajun Gao, Xiaowei Shen, Tingting Han and Yuanzhi Zhang

Sustainability 2022, 14(20), 13390; https://doi.org/10.3390/su142013390 - 17 Oct 2022

Cited by 3 | Viewed by 1866

Abstract

This study tested image fusion quality aiming at vegetation classification in the Kongquegou scenic location on the southern slope of Huaguo Mountain in Lianyungang, Jiangsu Province, China. Four fusion algorithms were used to fuse WorldView-2 multispectral and panchromatic images: GS (Gram-Schmidt) transform, Ehlers, Wavelet transform, and Modified IHS. The fusion effect was evaluated through visual comparison, quantitative index analysis, and vegetation classification accuracy. The study result revealed that GS and Wavelet transformation produced higher spectral fidelity and better-quality fusion images, followed by Modified IHS and Ehlers. In terms of vegetation classification, for the Wavelet transform, both spectral information and adding spatial structure provided higher accuracy and displayed suitability for vegetation classification in the selected area. Meanwhile, although the spectral features obtained better classification accuracy using the Modified IHS, adding spatial structure to the classification process produced less improvement and a lower robustness effect. The GS transform yielded better spectral fidelity but relatively low vegetation classification accuracy using spectral features only and combined spectral features and spatial structure. Lastly, the Ehlers method’s vegetation classification results were similar to those of the GS transform image fusion method. Additionally, the accuracy was significantly improved in the fused images compared to the multispectral image. Overall, Wavelet transforms showed the best vegetation classification results in the study area among the four fusion algorithms. Full article

(This article belongs to the Special Issue Land and Water Degradation in Catchments: The Role of Remote Sensing for Assessment and Management)

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19 pages, 6841 KiB

Open AccessArticle

Forecasting of Streamflow and Comparison of Artificial Intelligence Methods: A Case Study for Meram Stream in Konya, Turkey

by Mustafa Najat Asaad, Şule Eryürük and Kağan Eryürük

Sustainability 2022, 14(10), 6319; https://doi.org/10.3390/su14106319 - 22 May 2022

Cited by 8 | Viewed by 2594

Abstract

The planning and management of water resources are affected by streamflow. The analysis of the sustainability of water resources has used well-grounded methods such as artificial neural networks, used for streamflow forecasting by researchers in recent years. The main aim of this study is to evaluate the performance of various methods for long-term forecasting from the data of the mean monthly streamflows between 1981 and 2017 from the Kucukmuhsine station on the Meram Stream in the Turkish province of Konya. For that reason, the multilayer perceptron (MLP), long short-term memory (LSTM), and adaptive neuro-fuzzy inference system (ANFIS) artificial intelligence techniques were employed in this study for the long-term forecasting of streamflow for 12 months, 24 months, and 36 months. The mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²) were used to evaluate the performance of the models developed to make predictions using the data from 1981 to 2017, and the Mann-Whitney test was applied to examine the differences between the actual data from 2018 to 2020 and each model’s forecasted results for those three years. The LSTM model showed superiority based on the values of R² (calculated as 0.730) and RMSE (lowest value of 0.510), whereas the MLP yielded better prediction accuracy as reflected by the value of MAE (lowest value of 0.519). The ANFIS model did not have the best prediction ability for any of the criteria. In accordance with the Mann-Whitney test results, LSTM and MLP indicated no significant difference between the actual data from 2018 to 2020 and the forecasted values; whereas, there was a significant difference for the ANFIS model at a confidence level of 95%. The results showed that the LSTM model had a better prediction performance, surpassing the MLP and ANFIS models, when comparing mean monthly streamflow forecasts. Full article

(This article belongs to the Special Issue Land and Water Degradation in Catchments: The Role of Remote Sensing for Assessment and Management)

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22 pages, 17105 KiB

Open AccessArticle

Assessment of Water-Induced Soil Erosion as a Threat to Natura 2000 Protected Areas in Crete Island, Greece

by Stefanos Stefanidis, Vasileios Alexandridis and Kaushik Ghosal

Sustainability 2022, 14(5), 2738; https://doi.org/10.3390/su14052738 - 25 Feb 2022

Cited by 31 | Viewed by 3234

Abstract

Water erosion is a major threat to biodiversity, according to the European Commission’s Soil Thematic Strategy, as it negatively affects soil structure, soil fertility and water availability for plants. The island of Crete (Southern Greece) has been characterized as a biodiversity hotspot including several Natura 2000 (N2K)-protected areas. The aim of this study was to model the soil loss rate in Crete regarding species richness, habitat types and their conservation status, as well as the MAES (Mapping and Assessment of Ecosystem and their Services) ecosystem types. To this end, the RUSLE soil erosion prediction model was implemented, using freely available geospatial data and cloud-computing processes. The estimated average soil loss in the study area was 6.15 t ha⁻¹ y⁻¹, while there was no significant difference between the terrestrial N2K (6.06 t ha⁻¹ y⁻¹) and non-N2K (6.19 t ha⁻¹ y⁻¹) areas. Notably, the natural habitats of principal importance for the conservation of biodiversity (referred to as “priority” areas), according to Annex I to Directive 92/43/EEC, are threatened by soil erosion with an estimated mean annual soil loss equal to 8.58 t ha⁻¹ y⁻¹. It is also notable that grasslands, heathland and shrubs and sparsely vegetated areas experienced the highest erosion rates among the identified MAES ecosystem types. The results showed that soil erosion is a serious threat to biodiversity in N2K-protected areas. Therefore, there is a need for systematic spatiotemporal monitoring and the implementation of erosion mitigation measures. Full article

(This article belongs to the Special Issue Land and Water Degradation in Catchments: The Role of Remote Sensing for Assessment and Management)

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19 pages, 10484 KiB

Open AccessEditor’s ChoiceArticle

Quantitative Assessment of Environmental Sensitivity to Desertification Using the Modified MEDALUS Model in a Semiarid Area

by Sayed Fakhreddin Afzali, Ali Khanamani, Ehsan Kamali Maskooni and Ronny Berndtsson

Sustainability 2021, 13(14), 7817; https://doi.org/10.3390/su13147817 - 13 Jul 2021

Cited by 17 | Viewed by 2894

Abstract

Iran is mainly located in the arid and semiarid climate zone and seriously affected by desertification. This is a severe environmental problem, which results in a persistent loss of ecosystem services that are fundamental to sustaining life. Process understanding of this phenomenon through the evaluation of important drivers is, however, a challenging work. The main purpose of this study was to perform a quantitative evaluation of the current desertification status in the Segzi Plain, Isfahan Province, Iran, through the modified Mediterranean Desertification and Land Use (MEDALUS) model and GIS. In this regard, five main indicators including soil, groundwater, vegetation cover, climate, and erosion were selected for estimating the environmental sensitivity to desertification. Each of these qualitative indicators is driven by human interference and climate. After statistical analysis and a normality test for each indicator data, spatial distribution maps were established. Then, the maps were scored in the MEDALUS approach, and the current desertification status in the study area from the geometric mean of all five quality indicators was created. Based on the results of the modified MEDALUS model, about 23.5% of the total area can be classified as high risk to desertification and 76.5% classified as very high risk to desertification. The results indicate that climate, vegetation, and groundwater quality are the most important drivers for desertification in the study area. Erosion (wind and water) and soil indices have minimal importance. Full article

(This article belongs to the Special Issue Land and Water Degradation in Catchments: The Role of Remote Sensing for Assessment and Management)

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13 pages, 4302 KiB

Open AccessArticle

Identification of Heracleum sosnowskyi-Invaded Land Using Earth Remote Sensing Data

by Jūratė Sužiedelytė Visockienė, Eglė Tumelienė and Vida Maliene

Sustainability 2020, 12(3), 759; https://doi.org/10.3390/su12030759 - 21 Jan 2020

Cited by 12 | Viewed by 2761

Abstract

H. sosnowskyi (Heracleum sosnowskyi) is a plant that is widespread both in Lithuania and other countries and causes abundant problems. The damage caused by the population of the plant is many-sided: it menaces the biodiversity of the land, poses risk to human health, and causes considerable economic losses. In order to find effective and complex measures against this invasive plant, it is very important to identify places and areas where H. sosnowskyi grows, carry out a detailed analysis, and monitor its spread to avoid leaving this process to chance. In this paper, the remote sensing methodology was proposed to identify territories covered with H. sosnowskyi plants (land classification). Two categories of land cover classification were used: supervised (human-guided) and unsupervised (calculated by software). In the application of the supervised method, the average wavelength of the spectrum of H. sosnowskyi was calculated for the classification of the RGB image and according to this, the unsupervised classification by the program was accomplished. The combination of both classification methods, performed in steps, allowed obtaining better results than using one. The application of authors’ proposed methodology was demonstrated in a Lithuanian case study discussed in this paper. Full article

(This article belongs to the Special Issue Land and Water Degradation in Catchments: The Role of Remote Sensing for Assessment and Management)

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