Remote sensing and GIS techniques for prediction of land use land cover change effects on soil erosion in the high basin of the Oum Er Rbia River (Morocco)

Mapping soil suitability using phenological information derived from MODIS time series data in a semi-arid region: A case study of Khouribga, Morocco

To address the increasing global demand for food, it is crucial to implement sustainable agricultural practices, which include effective soil management techniques for enhancing productivity and environmental conditions. In this regard, a study was conducted to assess the efficacy of utilizing phenological metrics derived from satellite data in order to map and identify suitable agricultural soil within a semi-arid region. Two distinct methodologies were compared: one based on physicochemical soil parameters and the other utilizing the phenological response of vegetation through the application of the Normalized Difference Vegetation Index (NDVI) Modis-time series. The study findings indicated that the NDVI-based approach successfully identified a specific class of soil suitability for agriculture (referred to as S1) that could not be effectively mapped using the multi-criteria analysis (MCAD) method relying on soil physicochemical parameters. This S1 class of soil suitability accounted for approximately 5 % of the total study area. These outcomes suggest that phenological-based approaches offer greater potential for spatio-temporal monitoring of soil suitability status compared to MCAD, which heavily relies on discrete observations and necessitates frequent updates of soil parameters. The approach developed to map the soil-suitability is a valuable tool for sustainable agricultural development, and it can play an effective role in ensuring food security and conducting a land agriculture assessment.

Impact of Accessibility to Cities at Multiple Administrative Levels on Soil Conservation: A Case Study of Hunan Province

The development of traffic infrastructure involves massive land use changes along the transportation routes and stimulates urban sprawl at transfer nodes, leading to a degradation in ecosystem services, including soil conservation. For developing countries, especially for China, it is very important to differentiate the influences between different standards of traffic infrastructure associated with the different administrative levels of the regions where they are constructed on soil conservation. In this study, we attempt to analyze the differences in the influence of accessibility at different levels on soil conservation, for the case study area in Hunan province in China. The results indicate that: (1) traffic conditions in Hunan province have witnessed continuous improvement, and the time taken to access mega-cities, prefecture-level cities, and county-level cities from various regions has been significantly reduced. (2) The total annual soil conservation in Hunan province is maintained at approximately 2.93 × 109 t. However, the spatial heterogeneity shows severe degradation in regions with lower accessibility, and weak enhancement in regions with higher accessibility. (3) A negative spatial autocorrelationship exists between accessibility and soil conservation at all levels, with the increase of administrative rank of the destination making it more obvious and intense, along with an increased tendency for the spatial distribution to concentrate. (4) Building more railways and highways from prefecture-level cities with LH clusters nearby as transfer nodes, instead of the construction of national roads and provincial roads that diverge from these railways and highways, will help limit the massive expansion of construction land and soil erosion within prefecture-level cities, rather than spreading to towns of LH clusters. This research provides an important scientific basis for future regional planning and traffic infrastructure construction, and also a reference for traffic infrastructure development in other geographically similar regions on a synchronous development stage in the world.

Modeling and Mapping of Soil Water Erosion Risks in the Srou Basin (Middle Atlas, Morocco) Using the EPM Model, GIS and Magnetic Susceptibility

The Oued Srou watershed located in the Middle Atlas Mountain of Morocco has been a subject of serious soil erosion problems due to the combination of natural factors and anthropic activities. Therefore, soil erosion hazard assessment and mapping can be handy to initiate remedial measures in the area. In this study, the improved Erosion Potential Model (EPM) integrated with GIS and remote sensing techniques is employed to map and assess the vulnerability of the Oued Srou watershed to the water erosion phenomenon and its impact on the silting of the Ahmed El Hansali dam. The results of the EPM model showed that the maximum annual soil loss rates were in the range of 5-652 m3/km2/year, with an average of 49 m3/km2/year. The delivery coefficient ratio showed that about 34433 t/year of the sediments reach the outlet of the watershed. The correlation analysis between all erosion factors revealed the following order of their importance in the water erosion control: soil sensitivity to erosion, soil protection, slope, erosive state, temperature, and rainfall. The magnetic susceptibility provided results on the evolution of soils; it showed that the most degraded soils had a high erosion rate. Generally, the stable soils not eroded showed an upward increase of magnetic susceptibility values in soil profiles; the evolution of magnetic susceptibility of degraded soils is disturbed. The magnetic susceptibility has also made it possible to highlight the source zones of sediments that reach the outlet of the watershed.

Estimating the Soil Erosion Response to Land-Use Land-Cover Change Using GIS-Based RUSLE and Remote Sensing: A Case Study of Miyun Reservoir, North China

Soil erosion by water is a major cause of land degradation. Agricultural practices and many other ecological environmental problems contribute to land degradation worldwide, especially in arid and semi-arid areas. Miyun County, which is located in a mountainous region of North China, is an important natural ecological zone and surface source of drinking water for Beijing and is very vulnerable to soil erosion due to its thin soil layer and human activities. Landsat images from 2003 and 2013 were used to analyze the land-use and land-cover change (LULCC) over this period. The revised universal soil loss equation (RUSLE) model integrated with Geographic Information System (GIS) was used to quantify soil loss and to map erosion risk. In addition, the response of soil erosion to LULCC was evaluated. The results showed that the areas under cropland, forest, and water bodies increased over the study period by 66.03, 243.44, and 9.01 km2, respectively. The increase in forested land indicated that the improved ground vegetation cover was due to the implementation of active ecological measures. Between 2003 and 2013, light soil erosion increased by 587.46 km2, and extremely severe soil erosion increased by 9.57 km2. The extents of slight, moderate, severe, and very severe soil erosion, however, decreased by 8.02, 445.21, 142.69, and 1.11 km2, respectively. A total of 57.5% of land with moderate soil erosion has been converted to light soil erosion, which could be highly beneficial for the improvement of vegetation control of soil and water losses. In terms of area, forestland exhibited the greatest increase, while moderate soil erosion exhibited the greatest decrease over the study period. Land-use change led to an alteration in the intensity of soil erosion due to changes or loss of vegetation. The conversion from high intensity soil erosion to low intensity was attributed to the implementation of ecological environmental protection. The results generated from this study may be useful for planners and land-use managers to make appropriate decisions for soil conservation.

Application of Deep Learning Architectures for Satellite Image Time Series Prediction: A Review

Satellite image time series (SITS) is a sequence of satellite images that record a given area at several consecutive times. The aim of such sequences is to use not only spatial information but also the temporal dimension of the data, which is used for multiple real-world applications, such as classification, segmentation, anomaly detection, and prediction. Several traditional machine learning algorithms have been developed and successfully applied to time series for predictions. However, these methods have limitations in some situations, thus deep learning (DL) techniques have been introduced to achieve the best performance. Reviews of machine learning and DL methods for time series prediction problems have been conducted in previous studies. However, to the best of our knowledge, none of these surveys have addressed the specific case of works using DL techniques and satellite images as datasets for predictions. Therefore, this paper concentrates on the DL applications for SITS prediction, giving an overview of the main elements used to design and evaluate the predictive models, namely the architectures, data, optimization functions, and evaluation metrics. The reviewed DL-based models are divided into three categories, namely recurrent neural network-based models, hybrid models, and feed-forward-based models (convolutional neural networks and multi-layer perceptron). The main characteristics of satellite images and the major existing applications in the field of SITS prediction are also presented in this article. These applications include weather forecasting, precipitation nowcasting, spatio-temporal analysis, and missing data reconstruction. Finally, current limitations and proposed workable solutions related to the use of DL for SITS prediction are also highlighted.

Possibility of using multiscale normalized difference vegetation index data for the assessment of total suspended solids (TSS) concentrations in surface water: A specific case of scale issues in remote sensing

The degradation of watersheds creates immense pressure on water quality, especially in arid and semiarid regions. Total suspended solids (TSS) provide essential information to water environmental quality assessments. However, the calibration of direct retrieval models requires complicated preparations and further increases uncertainties. Here, we hypothesized that a common remote sensing index (NDVI, normalized difference vegetation index) could be used to estimate TSS concentrations in water due to the effects of canopy cover. To address this hypothesis, we collected 65 water samples from the Ebinur Lake Watershed, northwest China, to investigate the potential relationships between TSS concentrations and Sentinel-2-based NDVI at various scales (100, 200, 300, 400, and 500 m). Subsequently, we established a classical measurement error (CME) model for the estimation of TSS concentrations. The results showed that TSS concentration is negatively related to the NDVI value at all buffer distances. The 300 m scale mean NDVI value showed the most effective explanation of the variations in TSS concentrations (R² = 0.83, P-value < 0.001), which indicated that the TSS concentration can be assessed by NDVI. The CME model showed that NDVI values played an important role in the assessment of TSS concentrations in surface water. Furthermore, the results of both leave-one-out cross-validation and the accuracy measure suggested that this specific method is satisfactory. Compared with previous statistical and field monitoring results, the proposed method is promising for cost-effective monitoring of TSS concentrations in water, especially in data-poor watersheds. This specific method may provide the basis for the conservation and management of nonpoint source pollution in arid regions.

Soil loss and sediment yield in a perennial catchment in southwest Piauí, Brazil

Soil and water are vital natural resources. However, due to their indiscriminate use, these resources are being seriously threatened. Therefore, it is essential to manage them in a sustainable way and leave them for future generations. Population and agricultural areas have expanded, deforesting native landscapes for cultivation and pastures. As a result, soil loss from agricultural areas is increasing the amount of sediment transport in water courses. The objective of this study was to quantify soil loss and sediment yield from the Corrente dos Matões sub-basin (CMSB). These measurements are essential to quantify the environmental impact of advancing agricultural frontiers. The Universal Soil Loss Equation (USLE) was applied due to its wide use, compatibility with GIS, and data availability. The suspended sediment transport was calculated by collecting samples with DH-48 sampler. From the application of USLE, the average soil erosion contributed very little to sediment delivery in the watercourse, with a magnitude of only 0.37 t ha^-1 year^-1. The highest soil loss was associated with greater slope and was observed in areas with agriculture or under the absence of vegetation cover. The low transport of suspended sediments in CMSB is due to the existence of preserved sites. About 99% of the sub-basin had a low degree of erosion. The high degree of soil loss was attributed to the cliffs and the development of agricultural activities leaving the soil uncovered. This work will help identify the most susceptible areas to water erosion for optimizing the allocation of financial resources for the preservation of this natural resource.

Extraction of Land Information, Future Landscape Changes and Seismic Hazard Assessment: A Case Study of Tabriz, Iran

Exact land cover inventory data should be extracted for future landscape prediction and seismic hazard assessment. This paper presents a comprehensive study towards the sustainable development of Tabriz City (NW Iran) including land cover change detection, future potential landscape, seismic hazard assessment and municipal performance evaluation. Landsat data using maximum likelihood (ML) and Markov chain algorithms were used to evaluate changes in land cover in the study area. The urbanization pattern taking place in the city was also studied via synthetic aperture radar (SAR) data of Sentinel-1 ground range detected (GRD) and single look complex (SLC). The age of buildings was extracted by using built-up areas of all classified maps. The logistic regression (LR) model was used for creating a seismic hazard assessment map. From the results, it can be concluded that the land cover (especially built-up areas) has seen considerable changes from 1989 to 2020. The overall accuracy (OA) values of the produced maps for the years 1989, 2005, 2011 and 2020 are 96%, 96%, 93% and 94%, respectively. The future potential landscape of the city showed that the land cover prediction by using the Markov chain model provided a promising finding. Four images of 1989, 2005, 2011 and 2020, were employed for built-up areas’ land information trends, from which it was indicated that most of the built-up areas had been constructed before 2011. The seismic hazard assessment map indicated that municipal zones of 1 and 9 were the least susceptible areas to an earthquake; conversely, municipal zones of 4, 6, 7 and 8 were located in the most susceptible regions to an earthquake in the future. More findings showed that municipal zones 1 and 4 demonstrated the best and worst performance among all zones, respectively.

Survey on Land Use/Land Cover (LU/LC) change analysis in remote sensing and GIS environment: Techniques and Challenges

The surface of the earth is rapidly changing every day due to certain natural reasons and other impacts by society. Over the last few decades, the hottest topics in the field of remote sensing and GIS (geographic information system) environments have evolved from observing the nature of the earth. Owing to the enlargement of several worldwide modifications related to the nature of the earth, land use/land cover (LU/LC) change is considered as the matter of utmost importance in the natural atmosphere, and it has also become an interesting area to be studied by the researchers. As there is a lack of review articles in the land use/land cover change analysis process, we presented a comprehensive review which may help the researchers to proceed further. This paper deals with the most frequent methods used by researchers on various processes like pre-processing, classification, and prediction of time series satellite images for analyzing the LU/LC changes using satellite images. The generic flow of the LU/LC change analysis process and the challenges faced during each process by the researchers are discussed. Varied resolutions of the environmental image captured by remote sensing satellites for analyzing the LU/LC changes are discussed. Various LU/LC classes depending on change in the earth's surface are also studied and the constraint used in each application is stated. The importance of this review lies in the motivation for future researchers to work on the LU/LC change analysis problem effectively.

Impact of Extreme Drought Climate on Water Security in North Borneo: Case Study of Sabah

For countries in Southeast Asia that mainly rely on surface water as their water resource, changes in weather patterns and hydrological systems due to climate change will cause severely decreased water resource availability. Warm weather triggers more water use and exacerbates the extraction of water resources, which will change the operation patterns of water usage and increase demand, resulting in water scarcity. The occurrence of prolonged drought upsets the balance between water supply and demand, significantly increasing the vulnerability of regions to damaging impacts. The objectives of this study are to identify trends and determine the impacts of extreme drought events on water levels for the major important water dams in the northern part of Borneo, and to assess the risk of water insecurity for the dams. In this context, remote sensing images are used to determine the degree of risk of water insecurity in the regions. Statistical methods are used in the analysis of daily water levels and rainfall data. The findings show that water levels in dams on the North and Northeast Coasts of Borneo are greatly affected by the extreme drought climate caused by the Northeast Monsoon, with mild to the high risk recorded in terms of water insecurity, with only two of the water dams being water-secure. This study shows how climate change has affected water availability throughout the regions.

Assessing Regional Scale Water Balances through Remote Sensing Techniques: A Case Study of Boufakrane River Watershed, Meknes Region, Morocco

This paper aims to develop a method to assess regional water balances using remote sensing techniques. The Boufakrane river watershed in Meknes Region (Morocco), which is characterized by both a strong urbanization and a rural land use change, is taken as a study case. Firstly, changes in land cover were mapped by classifying remote sensing images (Thematic Mapper, Enhanced Thematic Mapper Plus and Operational Land Imager) at a medium scale resolution for the years 1990, 2003 and 2018. By means of supervised classification procedures the following land cover categories could be mapped: forests, bare soil, arboriculture, arable land and urban area. For each of these categories a water balance was developed for the different time periods, taking into account changing management and consumption patterns. Finally, the land cover maps were combined with the land cover specific water balances resulting in a total water balance for the selected catchment. The procedure was validated by comparing the assessments with data from water supply stations and the number of licensed ground water extraction pumps. In terms of land use/land cover changes (LULCC), the results showed that urban areas, natural vegetation, arboriculture and cereals increased by 183.74%, 12.55%, 34.99 and 48.77% respectively while forests and bare soils decreased by 78.65% and 16.78% respectively. On the other hand, water consumption has been increased significantly due to the Meknes city growth, the arboriculture expansion and the new crops’ introduction in the arable areas. The increased water consumption by human activities is largely due to reduced water losses through evapotranspiration because of deforestation. Since the major part of the forest in the catchment has disappeared, a further increase of the water consumption by human activities can no longer be offset by deforestation.