Search Results (103)

Image analysis is a valuable approach in a wide array of environmental applications. Mapping land cover categories depicted from satellite images enables the monitoring of landscape dynamics. Such a technique plays a key role for land management and predictive ecosystem modelling. Satellite-based mapping of environmental dynamics enables us to define factors that trigger these processes and are crucial for our understanding of Earth system processes. In this study, a reclassification scheme of image analysis was developed for mapping the adjusted categorisation of land cover types using multispectral remote sensing datasets and Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS) software. The data included four Landsat 8–9 satellite images on 2015, 2019, 2021 and 2023. The sequence of time series was used to determine land cover dynamics. The classification scheme consisting of 17 initial land cover classes was employed by logical workflow to extract 10 key land cover types of the coastal areas of Bab-el-Mandeb Strait, southern Red Sea. Special attention is placed to identify changes in the land categories regarding the thermal saline lake, Lake Assal, with fluctuating salinity and water levels. The methodology included the use of machine learning (ML) image analysis GRASS GIS modules ‘r.reclass’ for the reclassification of a raster map based on category values. Other modules included ‘r.random’, ‘r.learn.train’ and ‘r.learn.predict’ for gradient boosting ML classifier and ‘i.cluster’ and ‘i.maxlik’ for clustering and maximum-likelihood discriminant analysis. To reveal changes in the land cover categories around the Lake of Assal, this study uses ML and reclassification methods for image analysis. Auxiliary modules included ‘i.group’, ‘r.import’ and other GRASS GIS scripting techniques applied to Landsat image processing and for the identification of land cover variables. The results of image processing demonstrated annual fluctuations in the landscapes around the saline lake and changes in semi-arid and desert land cover types over Djibouti. The increase in the extent of semi-desert areas and the decrease in natural vegetation proved the processes of desertification of the arid environment in Djibouti caused by climate effects. The developed land cover maps provided information for assessing spatial–temporal changes in Djibouti. The proposed ML-based methodology using GRASS GIS can be employed for integrating techniques of image analysis for land management in other arid regions of Africa. Full article

Land desertification represents a significant and sensitive global ecological issue. In the Inner Mongolia region of China, soil desertification and salinization are widespread, resulting from the combined effects of extreme drought conditions and human activities. Using Gaofen 5B AHSI imagery as our data source, we collected spectral data for seven distinct land cover types: lush vegetation, yellow sand, white sand, saline soil, saline shell, saline soil with saline vegetation, and sandy soil. We applied Particle Swarm Optimization (PSO) to fine-tune the Wavelet Packet (WP) decomposition levels, thresholds, and wavelet basis function, ensuring optimal spectral decomposition and reconstruction. Subsequently, PSO was deployed to optimize key hyperparameters of the Random Forest algorithm and compare its performance with the ResNet-Transformer model. Our results indicate that PSO effectively automates the search for optimal WP decomposition parameters, preserving essential spectral information while efficiently reducing high-frequency spectral noise. The Genetic Algorithm (GA) was also found to be effective in extracting feature bands relevant to land desertification, which enhances the classification accuracy of the model. Among all the models, integrating wavelet packet denoising, genetic algorithm feature selection, the first-order differential (FD), and the hybrid architecture of the ResNet-Transformer, the WP-GA-FD-ResNet-Transformer model achieved the highest accuracy in extracting soil sandification and salinization, with Kappa coefficients and validation set accuracies of 0.9746 and 97.82%, respectively. This study contributes to the field by advancing hyperspectral desertification monitoring techniques and suggests that the approach could be valuable for broader ecological conservation and land management efforts. Full article

Guangxi is among China’s regions most severely affected by karst rocky desertification (KRD). Over the past two decades, global climate change and human activities have jointly led to significant changes in the extent and intensity of KRD in Guangxi. Given this context, it is crucial to comprehensively analyze the spatiotemporal evolution of KRD in Guangxi and its driving forces. This study proposed a novel three-dimensional feature space model for monitoring KRD in Guangxi. We then applied transition matrices, dynamic degree indices, and landscape metrics to analyze the spatiotemporal evolution of KRD. We also proposed a Spatiotemporal Interaction Intensity Index (STII) to quantify mutual influences among KRD patches. Finally, we used GeoDetector to analyze the driving factors of KRD. The results indicate the following: (1) The three-dimensional model showed high applicability for large-scale KRD monitoring, with an overall accuracy of 92.86%. (2) KRD in Guangxi exhibited an overall recovery–deterioration–recovery trend from 2000 to 2023. The main recovery phases were 2005–2015 and 2020–2023. During these phases, both severe and moderate KRD showed strong signals of recovery, including significant declines in area, number of patches, and Landscape Shape Index, along with persistently low STII values. In contrast, from 2015 to 2020, KRD predominantly deteriorated, primarily characterized by transitions from no KRD to potential KRD and from potential KRD to light KRD. (3) For severe KRD patches, the intensity of interaction required from neighboring patches to promote recovery exceeded that which led to deterioration, indicating the difficulty of reversing severe KRD. (4) Slope, land use, and elevation were the main drivers of KRD in Guangxi from 2000 to 2023. Erosive rainfall exhibited a higher explanatory power for KRD than average precipitation. Two-factor interactions significantly enhanced the driving forces of KRD. These findings provide a scientific basis for KRD management. Full article

The São Francisco River provides water for agriculture, urban areas, and hydroelectric power generation, benefiting millions of people in Brazil. Its Basin supports various species, some of which are endemic and rely on its unique habitats for survival. Currently, monitoring maximum air temperature in the São Francisco River Basin is limited due to sparse weather stations. This study proposes three linear regression models to estimate maximum air temperature using satellite-derived land surface temperature from the Aqua’s moderate resolution imaging spectroradiometer across the Basin’s three main biomes: Caatinga, Cerrado, and Mata Atlântica. With over 94,000 paired observations of ground and satellite data, the models showed good performance, accounting for 46% to 54% of temperature variation. Cross-validation confirmed reliable estimates with errors below 2.7 °C. The findings demonstrate that satellite data can improve air temperature monitoring in areas with limited ground observations and suggest that the proposed biome-specific models could assist in environmental management and water resource planning in the São Francisco River Basin. This includes providing more informed policies for climate adaptation and sustainable development or analyzing variations in maximum air temperature in arid and semi-arid regions to contribute to desertification mitigation strategies in the São Francisco River Basin. Full article

The desertified ecological restoration vegetation of Wuzhumuqin grassland plays an important role in the ecological restoration and protection of the region. However, there are few studies on the monitoring of the changes in ecological restoration vegetation in grassland sandy land in the past. In order to improve the low efficiency of ecological restoration vegetation monitoring, this study used Gaofen-6 (GF-6) remote sensing data to calculate the kernel Normalized Difference Vegetation Index (kNDVI) and vegetation coverage of ecological restoration vegetation and analyze their spatial and temporal trends. At the same time, a transform three-branch network structure based on deep learning is proposed to extract visual features. The kernel Normalized Difference Vegetation Index-position-temporal awareness transformer (kNDVI-PT-Former) model monitoring method based on two-phase remote sensing image features combined with kNDVI for spatio-temporal feature extraction can accurately obtain the vegetation changes in desertification ecological restoration in Wuzhumuqin grassland. The results show that the kNDVI of the study area shows an increasing trend from 2019 to 2024. The kNDVI value is 0.4086 in 2019 and 0.4927 in 2024. From the perspective of the change trend of vegetation coverage, the overall vegetation coverage of the Wuzhumuqin desertification restoration study area showed a gradual increase trend from 2019 to 2024, and the vegetation coverage increased by 19% in 2024 compared with 2019. The transformation of vegetation coverage from low level to high level in the study area is more prominent. Based on the self-built monitoring dataset of more than 5.2 million pairs of grassland vegetation changes, through model comparison and analysis, the kNDVI-PT-Former model obtains that the Class Pixel Accuracy (CPA) is 0.7295, the Intersection over Union (IoU) is 0.7228, and the overall monitoring accuracy of the model is improved by 11%. Furthermore, the stability of the model’s performance was confirmed through evaluation with five-fold cross-validation. Full article

Over the past four decades, Wadi Fatimah in western Saudi Arabia has undergone significant environmental changes that have contributed to desertification. High-resolution spatial and temporal analyses are essential for monitoring the extent of desertification and understanding its driving factors. This study aimed to assess the spatial distribution of desertification in Wadi Fatimah using satellite and climate data. Landsat imagery from 1984 to 2022 was employed to derive land surface temperature (LST) and assess vegetation trends using the Normalized Difference Vegetation Index (NDVI). Climate variables, including precipitation and evapotranspiration (ET), were sourced from the gridded TerraClimate dataset (1980–2022). LST estimates were validated using MOD11A2 products (2001–2022), while TerraClimate precipitation data were evaluated against observations from four local rain gauge stations: Wadi Muharam, Al-Seal Al-Kabeer, Makkah, and Baharah Al-Jadeedah. A Desertification Index (DI) was developed based on four variables: NDVI, LST, precipitation, and ET. Five regression models—ridge, lasso, elastic net, polynomial regression (degree 2), and random forest regression—were applied to evaluate the predictive capacity of these variables in explaining desertification dynamics. Among these, Random Forest and Polynomial Regression demonstrated superior predictive performance. The classification accuracy of the desertification map showed high overall accuracy and a strong Kappa coefficient. Results revealed extensive land degradation in the central and lower sub-basins of Wadi Fatimah, driven by both climatic stressors and anthropogenic pressures. LST exhibited a clear upward trend between 1984 and 2022, especially in the lower sub-basin. Precipitation and ET analysis confirmed the region’s arid climate, characterized by limited rainfall and high ET, which exacerbate vegetation stress and soil moisture deficits. Validation of LST with MOD11A2 data showed reasonable agreement, with RMSE values ranging from 2 °C to 6 °C and strong correlation coefficients across most years. Precipitation validation revealed low correlation at Al-Seal Al-Kabeer, moderate at Baharah Al-Jadeedah, and high correlations at Wadi Muharam and Makkah stations. These results highlight the importance of developing robust validation methods for gridded climate datasets, especially in data-sparse regions. Promoting sustainable land management and implementing targeted interventions are vital to mitigating desertification and preserving the environmental integrity of Wadi Fatimah. Full article

Land desertification is becoming increasingly significant for the Mediterranean basin, particularly due to the rising pressures on agricultural land. Regarding the olive grove sector, intensive farming methods can have detrimental effects on the provision of various agroecosystem services. Conversely, agroecological approaches, such as reduced tillage/no tillage and the use of cover crops, can help mitigate soil degradation and enhance soil arthropod biodiversity. Herein, an experiment was conducted in a hilly olive grove in southern Peloponnese, a key olive production area in Greece. Different soil treatments were implemented across nine plots (three plots per treatment), including the following: (i) the use of a cover crop mixture (Pisum sativum, Vicia faba, Hordeum vulgare), (ii) herbicide application, and (iii) spontaneous vegetation (control). A comprehensive survey was performed at the plot level for monitoring carbon sequestration and ground-dwelling arthropod diversity. The results indicated that cover crops had a positive impact on soil fertility and structure, leading to an increase in total biomass production per plot, while also contributing to the preservation of key soil arthropod populations when compared to treatments that resulted in bare soil. The findings from this in situ study are meant to be integrated into the frames of a long-term monitoring process in order to be used for climate change mitigation and biodiversity management models, enhancing the resilience and regeneration of degraded land. Full article

Soil salinization is a critical factor affecting land desertification and limiting agricultural development in arid regions, and the rapid acquisition of salinized soil information is crucial for prevention and mitigation efforts. In this study, we selected the Yutian Oasis in Xinjiang, China as the study area and utilized Gaofen-3 synthetic aperture radar (SAR) remote sensing data and field measurements to analyze the correlations between the salinized soil properties and 36 polarimetric radar feature components. Based on the analysis results, two components with the highest correlation, namely, Yamaguchi4_vol (p < 0.01) and Freeman3_vol (p < 0.01), were selected to construct a two-dimensional feature space, named Yamaguchi4_vol-Freeman3_vol. Based on this feature space, a radar salinization monitoring index (RSMI) model was developed. The results indicate that the RSMI exhibited a strong correlation with the surface soil salinity, with a correlation coefficient of 0.85. The simulated values obtained using the RSMI model were well-fitted to the measured soil electrical conductivity (EC) values, achieving an R² value of 0.72 and a root mean square error (RMSE) of 7.28 dS/m. To assess the model’s generalizability, we applied the RSMI to RADARSAT-2 SAR data from the environmentally similar Weiku Oasis. The validation results showed comparable accuracy (R² = 0.70, RMSE = 9.29 dS/m), demonstrating the model’s robustness for soil salinity retrieval across different arid regions. This model offers a rapid and reliable approach for quantitative monitoring and assessment of soil salinization in arid regions using fully polarimetric radar remote sensing. Furthermore, it lays the groundwork for further exploring the application potential of Gaofen-3 satellite data and expanding its utility in soil salinization monitoring. Full article

Desertification sensitivity in semi-arid urbanizing regions remains a critical challenge for sustainable land management. This study analyzes the spatiotemporal dynamics (2018–2022) of desertification sensitivity in the Beijing–Tianjin–Hebei core region using the Normalized Difference Vegetation Index (NDVI), soil texture, the Digital Elevation Model (DEM), and nighttime light data. Using a GIS-based model, we found a decline in overall desertification sensitivity, with vegetation degradation (post-2020) emerging as a key factor. Key recommendations include optimizing urban spatial patterns via ecological red lines, prioritizing vegetation restoration in high-sensitivity zones, and establishing dynamic remote sensing-based monitoring systems. These strategies aim to coordinate urban growth with ecological resilience, offering actionable pathways for semi-arid regions facing similar pressures. Future work should integrate socioeconomic drivers to refine adaptive governance frameworks. Full article

Soil salinization significantly impacts global agricultural productivity, contributing to desertification and land degradation; thus, rapid regional monitoring of soil salinization is crucial for agricultural production and sustainable management. With advancements in artificial intelligence, the efficiency and precision of deep learning classification models applied to remote sensing imagery have been demonstrated. Given the limited feature learning capability of traditional machine learning, this study introduces an innovative deep fusion U-Net model called MSA-U-Net (Multi-Source Attention U-Net) incorporating a Convolutional Block Attention Module (CBAM) within the skip connections to improve feature extraction and fusion. A salinized soil classification dataset was developed by combining spectral indices obtained from Landsat-8 Operational Land Imager (OLI) data and polarimetric scattering features extracted from RADARSAT-2 data using polarization target decomposition. To select optimal features, the Boruta algorithm was employed to rank features, selecting the top eight features to construct a multispectral (MS) dataset, a synthetic aperture radar (SAR) dataset, and an MS + SAR dataset. Furthermore, Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), and deep learning methods including U-Net and MSA-U-Net were employed to identify the different degrees of salinized soil. The results indicated that the MS + SAR dataset outperformed the MS dataset, with the inclusion of the SAR band resulting in an Overall Accuracy (OA) increase of 1.94–7.77%. Moreover, the MS + SAR MSA-U-Net, in comparison to traditional machine learning methods and the baseline model, improved the OA and Kappa coefficient by 8.24% to 12.55% and 0.08 to 0.15, respectively. The results demonstrate that the MSA-U-Net outperformed traditional models, indicating the potential of integrating multi-source data with deep learning techniques for monitoring soil salinity. Full article

In desertified regions, monitoring vegetation phenology and elucidating its relationship with climatic factors are of crucial significance for understanding how desertification responds to climate change. This study aimed to extract the spatial-temporal evolution of land surface phenology metrics from 2001 to 2020 using MODIS NDVI products (NASA, Greenbelt, MD, USA) and explore the potential impacts of climate change on land surface phenology through partial least squares regression analysis. The key results are as follows: Firstly, regionally the annual mean start of the growing season (SOS) ranged from day of year (DOY) 130 to 170, the annual mean end of the growing season (EOS) fell within DOY 270 to 310, and the annual mean length of the growing season (LOS) was between 120 and 180 days. Most of the desertified areas demonstrated a tendency towards an earlier SOS, a delayed EOS, and a prolonged LOS, although a small portion exhibited the opposite trends. Secondly, precipitation prior to the SOS period significantly influenced the advancement of SOS, while precipitation during the growing season had a marked impact on EOS delay. Thirdly, high temperatures in both the pre-SOS and growing seasons led to moisture deficits for vegetation growth, which was unfavorable for both SOS advancement and EOS delay. The influence of temperature on SOS and EOS was mainly manifested during the months when SOS and EOS occurred, with the minimum temperature having a more prominent effect than the average and maximum temperatures. Additionally, the wind in the pre-SOS period was found to adversely impact SOS advancement, potentially due to severe wind erosion in desertified areas during spring. The findings of this study reveal that the delayed spring phenology, precipitated by the occurrence of a warm and dry spring in semi-arid desertified areas of northern China, has the potential to heighten the risk of desertification. Full article

Desertification remains a critical global issue driven by climate change, unsustainable land use, and socio-economic pressures. This bibliometric review provides an in-depth analysis of desertification research from 1974 to 2024 using the Scopus database to identify trends, key players, and methodological advances. Publications grew exponentially during this period, from 21 in 1974 to a peak of 186 in 2023, demonstrating growing academic and policy attention. The analysis found that 4178 authors contributed to 2004 peer-reviewed articles, with China emerging as a leading research hub, contributing 386 publications and leading efforts in environmental restoration projects such as the Great Green Wall. Advanced methodologies, including remote sensing and geographic information systems (GIS), have facilitated large-scale monitoring, despite challenges such as data inconsistencies and limited resolution. Institutions such as Guizhou Normal University and Lanzhou University have led the global research effort, publishing 316 and 124 publications, respectively. Influential journals, including Land Degradation and Development and the Journal of Arid Environments, have played a key role in shaping the discourse. Historical analysis has highlighted the persistent threat of desertification to human societies, exemplified by the decline of civilizations such as the Sumerian and Khorezmian. Despite significant progress, regional differences in research attention persist, with Central Asia receiving limited attention despite its vulnerability. This review highlights the need for standardized methodologies, interdisciplinary approaches, and enhanced international collaboration. By leveraging advanced technologies and sustainable land management practices, the global community can mitigate the environmental and socio-economic impacts of desertification, promoting the resilience of ecosystems and communities while moving toward land degradation neutrality. Full article

Land desertification management in the Mu Us Desert has received widespread attention. Assessing land desertification sensitivity is crucial for desertification monitoring and management. This study constructed a comprehensive evaluation index system using four factors: dryness index, the number of windy and sandy days in the winter and spring, soil texture, and vegetation cover. Land sand sensitivity was divided into five grades, and multi-source data from the Ecological Functional Reserve of the Mu Us Desert from 2002 to 2022 were used to study spatial distribution and dynamic changes. The results show the following: (1) the overall land desertification sensitivity in the Mu Us Desert Ecological Functional Reserve decreased from 2002 to 2022, with the proportion of highly sensitive land decreasing from 92.39% to 82.75%, and the proportion of medium-, medium–low-, and low-sensitivity areas increasing from 0.63% to 1.70%. (2) Low-sensitivity areas were concentrated in Jingbian County, Hengshan District, and southern Uxin Banner. Southeast Otog Banner and northern Jingbian County saw the most significant decreases in land desertification sensitivity since 2002. (3) The four selected factors interacted, with increased vegetation cover being the most crucial factor. This study provides a reference for future ecological restoration in the Mu Us Desert area. Full article

Desertification is one of the most severe environmental issues facing the world today, and effective desertification monitoring is critical for understanding its dynamics and developing prevention and control strategies. Although numerous studies on desertification monitoring using remote sensing have been conducted, there remain differences in indicator selection, and a unified monitoring system has yet to be established. In this study, we constructed the Desertification Composite Index (DCI) using Landsat satellite images, integrating six remote sensing indicators reflecting the natural and ecological characteristics of desertified areas. We also incorporated 383 UAV imagery datasets to accurately identify and analyze the spatial and temporal distributions of desertification in the Ring-Tarim Basin from 1990 to 2020 and subsequently assess its spatiotemporal trends. The results show the following: (1) The constructed DCI was used to identify desertification in 2020, achieving an overall accuracy of 0.86 and a Kappa coefficient of 0.8, indicating that the DCI is suitable for extracting regional desertification information. (2) From 1990 to 2020, the area of desertification decreased significantly, with an average annual reduction rate of −0.0022 ha/a, indicating continuous ecological improvement. Despite localized deterioration, the overall trend was one of “general improvement and local containment.” (3) GeoDetector-based analysis showed that cultivated land area and land use type were the primary single-factor drivers of desertification. The interaction between cultivated land and vegetation type exhibited a synergistic effect as a two-factor driver. (4) Desertification in the Ring-Tarim Basin is primarily influenced by human activities. Appropriate management and intervention measures, efficient and intensive cropland management, and rational land use planning can help develop effective strategies to combat desertification. Full article

The research investigates the advantages of real-time soil quality monitoring for various land management applications. We emphasize the crucial role of soil modeling and mapping by visualizing and understanding aridity trends across different regions. The primary objective is to develop an innovative soil monitoring system utilizing Internet of Things (IoT) technology. This system, equipped with intelligent sensors, will operate autonomously, collecting real-time data to identify key trends in soil conditions. Our system employs smart soil sensors to measure macronutrient values up to a depth of 80 cm. These sensors will transmit data wirelessly. Laboratory research involved a two-month evaluation of the system’s performance across three distinct soil types collected from diverse geographical locations. Analysis of the three soil types yielded a model accuracy estimate of 0.01. A strong positive linear correlation (0.92) between moisture and macronutrients has been observed in two out of the three soil types. The results, particularly related to soil moisture, were averaged over the testing period. While precipitation values were not directly integrated into the modeling framework, they were calculated in l/m² to ensure accurate real-time estimates. The need for such advanced monitoring systems is critical for optimizing key soil macronutrients and enabling spatiotemporal mapping. This information is essential for developing effective strategies to mitigate soil aridification and prevent desertification. Full article