Fig 4 - uploaded by Chuma BASIMINE Géant
Content may be subject to copyright.
Distribution of width (a), length (b) and area (c) of the gullies. Values were separated from one land use and land cover to another; the period from 2011 to 2021 was selected. The nonparametric Kruskal-Wallis test was performed at 5% probability threshold ( p > 0.05, non significant (ns); p < 0.05, significant ( * * ); p < 0.01, highly significant ( * * * )).
Context in source publication
Context 1
... Kruskal-Wallis test revealed that all changes in the morphological parameters of the gully network were significant only in the bare land and building class ( p < 0.05) ( Fig. 4 ) and the distributions of area, length and width values were significantly ( p < 0.05) different between LULC classes in 2015 only. It should be noted that the averages for all morphological parameters were calculated without considering the largest gully in the Wayimirya tributary valley ( Fig. 5 (b)). ...
Citations
... Soil erosion in drylands under climate change is one of human society's most important challenges due to its negative effects on environmental quality and food security (Sui et al., 2022;Turner et al., 2022). Unfortunately, this situation is prevalent in most African countries, which have limited adaptive capacity to the effects of climate change (Mahamba et al., 2023;Welborn, 2018). Moreover, by 2050, Africa is projected to be the most vulnerable region to the impacts of climate change and variability (Hoch et al., 2021). ...
Soil erosion is considered one of the most prevalent natural hazards in semiarid regions, leading to the instability of ecosystems and human life. The main purpose of this research was to investigate and analyze soil erosion susceptibility maps in the Medjerda basin in northern Africa. This study utilizes four ensemble models based on the analytical hierarchy process (AHP) multicriteria decision-making analysis, namely, deep learning neural network AHP (DLNN-AHP), frequency ratio AHP (FR-AHP), Monte Carlo AHP (MC-AHP), and fuzzy AHP (F-AHP). Eight predictor variables were considered as inputs to the model, namely, the slope degree, digital elevation model (DEM), topographic wetness index (TWI), distance to river (DFR), distance to road (DFRD), normalized difference vegetation index (NDVI), rainfall erosivity (R), factor and soil erodibility factor (K). Soil erosion inventory maps were developed from field surveys and satellite images. The dataset was randomly divided into 70% for training and 30% for testing. The performances of the utilized models were compared using a receiver operating characteristic (ROC) curve. The results highlighted that all the models utilized exhibited good performance, with DLNN-AHP (93.1%) exhibiting slight superiority, followed by FR-AHP (90.9%), F-AHP (88.9%), and MC-AHP (88.5%). Among the influencing factors, the distance to the river and rainfall erosivity had the most significant impacts on the incidence of soil erosion. Moreover, the current findings revealed that 38.3% of the study area is extremely highly susceptible to soil erosion. The results of this study can aid in developing decision-support tools for planners and managers aiming to mitigate the adverse effects of soil erosion.
... The use of remote sensing can help in valuation analyses, as was the case with the use of Google Earth images in this study (Mahamba et al 2023). The analysis of spectral images allows the identification of land cover changes (Pani 2017). ...
This study carries out the first evaluation of the impacts of ravines and gullies in urban areas in Brazil considering environmental damage, such as costs related to land restoration and erosion control, infrastructure destruction, economic losses and income losses related to property and urban land taxes. In this study, the city of Bauru, Brazil, has been selected as study site, where three areas were chosen due to the large impact that ravines and gullies have caused over the past two decades. Our analysis indicates that the total damage exceeds US 13.3 million. Furthermore, according to our analysis, urban areas affected by ravines and gullies represent problems similar to brownfields. The assessment of the impacts and challenges associated with urban ravines and gullies can help promote accountability by those responsible for their initiation and may contribute to decreasing the development of new eroded areas.
... Various techniques and tools such as Google Earth Engine, data mining, field-based monitoring, laboratory simulation, remote sensing images, sediment fingerprinting, unmanned aerial vehicle, and machine learning (ML) (Xu et al., 2022;Zhang et al., 2023aZhang et al., , 2023bĐokić et al., 2023;Zhang et al., 2023aZhang et al., , 2023bWang et al., 2022;Titti et al., 2022;Saha et al., 2021a;Mahamba et al., 2023;Yibeltal et al., 2023), have been applied to study gully erosion. ML models, as a branch of data science, have been frequently employed for prediction and mapping purposes in various scientific fields, including watershed management, soil, and geomorphology. ...
Gully erosion possess a serious hazard to critical resources such as soil, water, and vegetation cover within watersheds. Therefore, spatial maps of gully erosion hazards can be instrumental in mitigating its negative consequences. Among the various methods used to explore and map gully erosion, advanced learning techniques, especially deep learning (DL) models, are highly capable of spatial mapping and can provide accurate predictions for generating spatial maps of gully erosion at different scales (e.g., local, regional, continental, and global). In this paper, we applied two DL models, namely a simple recurrent neural network (RNN) and a gated recurrent unit (GRU), to map land susceptibility to gully erosion in the Shamil-Minab plain, Hormozgan province, southern Iran. To address the inherent black box nature of DL models, we applied three novel interpretability methods consisting of SHaply Additive explanation (SHAP), ceteris paribus and partial dependence (CP-PD) profiles and permutation feature importance (PFI). Using the Boruta algorithm, we identified seven important features that control gully erosion: soil bulk density, clay content, elevation, land use type, vegetation cover, sand content, and silt content. These features, along with an inventory map of gully erosion (based on a 70 % training dataset and 30 % test dataset), were used to generate spatial maps of gully erosion using DL models. According to the Kolmogorov-Smirnov (KS) statistic performance assessment measure, the simple RNN model (with KS = 91.6) outperformed the GRU model (with KS = 66.6). Based on the results from the simple RNN model, 7.4 %, 14.5 %, 18.9 %, 31.2 % and 28 % of total area of the plain were classified as very-low, low, moderate, high and very-high hazard classes, respectively. According to SHAP plots, CP-PD profiles, and PFI measures, soil silt content, vegetation cover (NDVI) and land use type had the highest impact on the model's output. Overall, the DL modelling techniques and interpretation methods used in this study proved to be helpful in generating spatial maps of soil erosion hazard, especially gully erosion. Their interpretability can support watershed sustainable management.
Since 2012, the “Mountain Excavation and City Construction” (MECC) project has been implemented extensively on the Loess Plateau of China, transforming gullies into flat land for urban sprawl by leveling loess hilltops to fill in valleys. However, this unprecedented human activity has caused widespread controversy over its unknown potential ecological impacts. Quantitative assessment of the impacts of the MECC project on the vegetation is key to ecological management and restoration. Taking the largest MECC project area on the Loess Plateau, Yan'an New District (YND), as the study area, this study investigated the spatiotemporal pattern of vegetation dynamics before and after the implementation of the MECC project using a multitemporal normalized difference vegetation index (NDVI) time series from 2009 to 2023 and explored the response of vegetation dynamics to the large-scale MECC project. The results showed that the vegetation dynamics in the YND exhibited significant spatial and temporal heterogeneity due to the MECC project, with the vegetation in the project-affected areas showing rapid damage followed by slow recovery. Vegetation damage occurred only in the project-affected area, and 84 % of these areas began recovery within 10 years, indicating the limited impact of the large-scale MECC project on the regional vegetation. The strong correlation between vegetation dynamics and the MECC project suggested that the destruction and recovery of vegetation in the project-affected areas was mainly under anthropogenic control, which highlights the importance of targeted ecological policies. Specifically, the MECC project induced local anthropogenic damage to the plant population structure during the land creation period, but regeneration and rational allocation of the vegetation were achieved through urbanization, gradually forming a new balanced ecological environment. These findings will contribute to a full understanding of the response of vegetation to such large-scale engineering activities and help local governments adopt projects or policies that facilitate vegetation recovery.
Soil erosion is a concern in many parts of the world, causing environmental and social impacts. Aiming at obtaining indicators of the recovery of brownfields created by gullies in urban areas, this study adapts the Tailored Improvement of Brownfield Regeneration in Europe (TIMBRE) for the analysis and classification of areas affected by gullies in the city of Bauru, Brazil. The TIMBRE methodology assists in the decision-making of priority areas for remediation and their reinsertion in urban spaces. The inventory of areas affected by gullies was compiled based on the analysis of two image sets (2004 and 2020) available on Google Earth. For the classification of brownfields, three classes were considered: Class 1 - local potential for business development, Class 2 - attractiveness and marketing, and Class 3 – environmental risks. These results demonstrate a correlation between gullies and urban expansion. The inventory identified 175 gullies in the municipality's urban perimeter in 2004, which affected an area of over 64 ha. In 2020, the number of gullies increased to 189, but the affected area decreased to 62 ha due to the recovery of some large gullies. The proposed methodology identified the area of Quinta da Bela Olinda as the one with the highest scores in all three classifications. Quinta da Bela Olinda is the location that has a local potential for business development, as it is the most attractive brownfield, as well as the area with the highest environmental risk. This area should, thus, be prioritized by public management for remediation. In conclusion, the proposed method of analysis can be transferred to other areas with adaptations in the criteria used and, therefore, may facilitate the management of urban areas affected by gullies in other places around the world.
