Evaluation of Desertification in the Middle Moulouya Basin (North-East Morocco) Using Sentinel-2 Images and Spectral Index Techniques

Lamaamri, Mohamed; Lghabi, Nezha; Ghazi, Abdelkhalek; Harchaoui, Noureddyne El; Adnan, Mohammed Sarfaraz Gani; Islam, Mohammad Shakiul

doi:10.1007/s41748-022-00327-9

Cited by 9 publications

(5 citation statements)

References 71 publications

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“…The SAVI is more sensitive to vegetation, allowing us to observe areas of potential soil degradation [ 54 ]. The MSAVI is commonly used to detect sparsely vegetated areas where soil background influences are important, minimizing external influences and enhancing vegetation signals [ 55 ]. A low MSAVI means sparse vegetation, indicating desertification [ 56 ].…”

Section: Methodsmentioning

confidence: 99%

Karst vegetation coverage detection using UAV multispectral vegetation indices and machine learning algorithm

et al. 2023

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Background Karst vegetation is of great significance for ecological restoration in karst areas. Vegetation Indices (VIs) are mainly related to plant yield which is helpful to understand the status of ecological restoration in karst areas. Recently, karst vegetation surveys have gradually shifted from field surveys to remote sensing-based methods. Coupled with the machine learning methods, the Unmanned Aerial Vehicle (UAV) multispectral remote sensing data can effectively improve the detection accuracy of vegetation and extract the important spectrum features. Results In this study, UAV multispectral image data at flight altitudes of 100 m, 200 m, and 400 m were collected to be applied for vegetation detection in a karst area. The resulting ground resolutions of the 100 m, 200 m, and 400 m data are 5.29, 10.58, and 21.16 cm/pixel, respectively. Four machine learning models, including Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Deep Learning (DL), were compared to test the performance of vegetation coverage detection. 5 spectral values (Red, Green, Blue, NIR, Red edge) and 16 VIs were selected to perform variable importance analysis on the best detection models. The results show that the best model for each flight altitude has the highest accuracy in detecting its training data (over 90%), and the GBM model constructed based on all data at all flight altitudes yields the best detection performance covering all data, with an overall accuracy of 95.66%. The variables that were significantly correlated and not correlated with the best model were the Modified Soil Adjusted Vegetation Index (MSAVI) and the Modified Anthocyanin Content Index (MACI), respectively. Finally, the best model was used to invert the complete UAV images at different flight altitudes. Conclusions In general, the GBM_all model constructed based on UAV imaging with all flight altitudes was feasible to accurately detect karst vegetation coverage. The prediction models constructed based on data from different flight altitudes had a certain similarity in the distribution of vegetation index importance. Combined with the method of visual interpretation, the karst green vegetation predicted by the best model was in good agreement with the ground truth, and other land types including hay, rock, and soil were well predicted. This study provided a methodological reference for the detection of karst vegetation coverage in eastern China.

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Section: Methodsmentioning

confidence: 99%

Karst vegetation coverage detection using UAV multispectral vegetation indices and machine learning algorithm

et al. 2023

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“…As shown in Figure 5, according to the research conclusions of Verstrate and Pinty [32,33], if the SWCI-KNDVI feature space was divided in the vertical direction of the desertification change trend, different desertification lands could be effectively distinguished. According to this principle, 2000 sample points in the study area were randomly selected, and the SWCI and KNDVI values of the sample points were extracted to construct the point-line regression equation (Equation (11)). According to the coefficients a, SWCI, and KNDVI in the equation, the point-line desertification difference index model (Equation ( 12)) was constructed as follows:…”

Section: Construction Of Desertification Remote-sensing Monitoring In...mentioning

confidence: 99%

“…Gao et al [10] utilized four vegetation indices, namely, the NDVI, the enhanced vegetation index (EVI), the ratio vegetation index (RVI), and the modified soil adjusted vegetation index (MSAVI), to establish four feature space index models with land surface temperature (Ts) and found that the MSAVI-Ts feature space index model performed the best in desertification extraction under different vegetation cover and soil backgrounds. Mohamed et al [11] quantitatively assessed desertification in the central region of the Moulouya basin in northeastern Morocco based on Sentinel-2 satellite imagery data and found that the feature space index model of NDVI-Albedo and MSAVI-Albedo showed the best correlations. Vani et al [12] constructed a feature space model using the NDVI and LST to calculate the soil moisture index and then combined the crop condition classification map with SMI to evaluate the drought conditions of four different types in India in 2016.…”

Section: Introductionmentioning

confidence: 99%

A New Large-Scale Monitoring Index of Desertification Based on Kernel Normalized Difference Vegetation Index and Feature Space Model

Guo,

Zhang,

et al. 2024

Remote Sensing

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As a new vegetation monitoring index, the KNDVI has certain advantages in characterizing the evolutionary process of regional desertification. However, there are few reports on desertification monitoring based on KNDVI and feature space models. In this study, seven feature parameters, including the kernel normalized difference vegetation index (KNDVI) and Albedo, were introduced to construct different models for desertification remote-sensing monitoring. The optimal desertification remote-sensing monitoring index model was determined with the measured data; then, the spatiotemporal evolution pattern of desertification in Gulang County from 2013 to 2023 was analyzed and revealed. The main conclusions were as follows: (1) Compared with the NDVI and MSAVI, the KNDVI showed more advantages in the characterization of the desertification evolution process. (2) The point–line pattern KNDVI-Albedo remote-sensing index model had the highest monitoring accuracy, reaching 94.93%, while the point–line pattern NDVI-TGSI remote-sensing monitoring index had the lowest accuracy of 54.38%. (3) From 2013 to 2023, the overall desertification situation in Gulang County showed a trend of improvement with a pattern of “firstly aggravation and then alleviation.” Additionally, the gravity center of desertification in Gulang County first shifted to the southeast and then to the northeast, indicating that the northeast’s aggravating rate of desertification was higher than in the southwest during the period. (4) From 2013 to 2023, the area of stable desertification in Gulang County was the largest, followed by the slightly weakened zone, and the most significant transition area was that of extreme desertification to severe desertification. The research results provide important decision support for the precise monitoring and governance of regional desertification.

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“…However, due to the complex causes of the evolution of desertification, a single index cannot fully reflect desertification [26]. The Albedo-NDVI model, based on the negative correlation between Albedo and NDVI, provides an effective method for quantitative analysis of desertification [27], and this method has been used in many desertified lands, such as the Moulouya basin [28], central Mexico [29], the Mongolian plateau [30], and the Yellow River in China [31]. There are few studies on the evolution of the desertification of the Mu Us Sandy Land, and short-time-scale desertification research has practical significance for assessing the desertification control effect.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Spatial-Temporal Changes and Driving Forces of Desertification in the Mu Us Sandy Land from 1991 to 2021

Yang

Liu

et al. 2023

Sustainability

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Desertification is one of the most critical environmental and socioeconomic issues in the world today. Located in the transitional region between the desert and the Loess Plateau, the Mu Us Sandy Land is one of the nine most environmentally sensitive areas in the world. Remote sensing provides an effective technical method for desertification monitoring. In order to analyze the spatiotemporal distribution of desertification in the Mu Us Sandy Land from 1991 to 2021, the “MSAVI-Albedo” model was employed to extract desertification data in 1991, 2002, 2009 and 2021. The clustering characteristics of desertification were analyzed based on Moran’s I statistic. Subsequently, the driving forces in desertification changes were investigated using a geographical detector to analyze the influence of soil, meteorology, and topography on desertification. Additionally, the impact of meteorological and human factors on desertification change in the Mu Us Sandy Land was assessed. From 1991 to 2021, the degree of desertification of the Mu Us Sandy Land showed an overall decreasing trend, and the percentage of land classified as undergoing extremely severe, severe, moderate and mild desertification was improved by 86.11%, 81.82%, 52.5% and 37.42%, respectively. The proportion of land classified as undergoing extremely severe desertification decreased from 29.22% to 5.62%, and the proportion of land undergoing no desertification increased from 4.16% to 18.33%. At the same time, the desertification center shifted westward, and the desertification distribution showed a clustering trend. It is known that different factors affect the formation and distribution of desertification in the Mu Us Sandy Land in the following order: soil, meteorology, and topography. Over the past 30 years, the mean annual temperature and annual precipitation increased at rates of 0.01871 °C/a and 1.0374 mm/a, respectively, while the mean annual wind speed decreased at a rate of 0.00945 m/s·a. These changes provided more favorable natural conditions for vegetation growth and sand fixation. Human factors, such as economic development, agriculture and animal husbandry practices, and the policy of returning farmland to forest (grassland) also had a significant impact on the desertification process, leading to a year-by-year improvement in the ecological environment of the Mu Us Sandy Land.

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Evaluation of Desertification in the Middle Moulouya Basin (North-East Morocco) Using Sentinel-2 Images and Spectral Index Techniques

Cited by 9 publications

References 71 publications

Karst vegetation coverage detection using UAV multispectral vegetation indices and machine learning algorithm

Karst vegetation coverage detection using UAV multispectral vegetation indices and machine learning algorithm

A New Large-Scale Monitoring Index of Desertification Based on Kernel Normalized Difference Vegetation Index and Feature Space Model

Analysis of Spatial-Temporal Changes and Driving Forces of Desertification in the Mu Us Sandy Land from 1991 to 2021

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