Iron tailings are crystalline powders predominantly composed of iron (Fe) and silicon dioxide (SiO₂). Spatially characterizing of the physical and chemical properties of iron tailings is greatly important for optimal utilization and proper disposal of tailings. Visible–near infrared–shortwave infrared (VIS–NIR–SWIR; 350–2500 nm) spectroscopy offers a rapid, non-destructive, and cost-effective method for quantitatively analyzing tailings properties. The main objective of this study was to map the spatial distribution of total Fe (TFe) and SiO₂ content in a tailings dam through the use of laboratory spectra and GF-5 hyperspectral imagery based a calibration transfer model approach. A total of 77 samples were collected from the surface of targeting field and scanned by a laboratory VIS–NIR–SWIR reflectance spectrometer. The competitive adaptive re-weighted sampling (CARS) algorithm was applied to select important spectral features. Subsequently, different spectral indices were calculated to enhance the prediction performance of the calibration models. Rulefit and random forest (RF) algorithms were used to calibrate spectral information with associated tailing properties. The results showed that the Rulefit algorithm with selected feature bands and calculated spectral indices yielded the highest estimation accuracy for TFe (R² = 0.86, RMSE = 1.30%, LCCC = 0.87 and bias = -0.45) and SiO₂ (R² = 0.74, RMSE = 2.00%, LCCC = 0.84 and bias = 0.38). The direct standardization (DS) algorithm was applied to correct GF-5 hyperspectral images and enhance the efficiency of calibration model transfer process. Finally, the Rulefit models were transferred to corrected GF-5 hyperspectral images for mapping the spatial distribution of TFe and SiO₂ contents. Our results demonstrated the possibility of successful transfer of laboratory spectral-based model to the GF-5 hyperspectral imagery for mapping spatial distribution of tailing compositions. This finding can be applied for efficiently recovering valuable metals and minimizing environmental risks. 

How to cite: Bao, N., Lei, H., Cao, Y., Gholizadeh, A., Saberioon, M., and Peng, Y.: TFe and SiO2 Spatial Mapping Enhancement in Iron Tailings: Efficiency of a Calibration Transfer Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-78, https://doi.org/10.5194/egusphere-egu24-78, 2024.

The Serra da Canastra National Park (SCNP) is an important conservation unit of the Cerrado biome (Brazilian savanna) located in a mountainous area in southeastern Brazil, where the headwaters of the São Francisco River, one of the longest rivers in the country, are located. In this area, large forest fires have occurred annually, destroying native vegetation and fauna. Due to the large expanse of the Serra da Canastra National Park, firefighting in this location is a very difficult and time-consuming task. This article presents a methodological procedure for mapping soil surface moisture in areas that have shown high fire frequencies over a timespan longer than 20 years in the national park using images from the OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) instruments onboard the Landsat-8 satellite. A soil surface moisture map was generated based on the drying of the vegetation using the temperature index calculated from a scatterplot of surface temperatures and vegetation index values in each pixel image. The vegetation index values were calculated from the normalized difference vegetation index (NDVI) algorithm using the red and near-infrared spectral bands of the OLI. The soil surface temperature was estimated using data from the TIRS sensor. We observed that areas with minimal moisture occurred on convex slopes, while areas with maximal moisture occurred on concave slopes. In addition, we found that areas with minimal moisture were more frequently located on north- and northeast-facing slopes, while areas with maximal moisture were more frequently found on south- and southwest-facing slopes. The accuracy of the soil surface moisture map was evaluated based on relative volumetric soil moisture data collected in the field using an instantaneous electronic soil moisture meter. The results of this research may contribute to the monitoring and forecasting of other areas highly susceptible to the occurrence of fire and to the planning of firefighting actions in the Serra da Canastra National Park.

How to cite: César Ferreira, M. and Carneiro Valente, D.: Estimating soil surface moisture in areas with high fire incidence in Serra da Canastra National Park, Brazil, using OLI and TIRS landsat-8 sensor data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-310, https://doi.org/10.5194/egusphere-egu24-310, 2024.

Satellite remote sensing technology, with its ability to record spatial and temporal land surface conditions, has been extensively and effectively utilized in evaluating mining environments. Western China, characterized by arid ecosystems, is a significant mineral resource area, boasting at least ten super-large mineral bases, including coal, non-ferrous metal ores, and metal mines. Surface mining activities, marked by large spatial and temporal scales, can exacerbate the fragility and changes to the ecological environment in vulnerable areas. Consequently, it is crucial to assess and comprehend the spatial and temporal impacts of mining on arid ecological systems for green mine construction and mine reclamation. This study focuses on three typical open-pit mines in the arid regions of Xinjiang, China (site Ⅰ: Jinbao Iron open-pit mine, site Ⅱ: Heishan coal open-pit mine, site Ⅲ: Wulagen Lead Zinc open-pit mine). The primary objective was to develop a remote sensing index (Mined Land Ecological Status Index, MLESI) that considers biological factors such as dryness, bare soil flatness, land surface temperature, and slope to assess the ecological status in arid mining areas. Subsequently, Principal Component Analysis was employed to couple these four factors to construct MLESI. The efficacy of MLESI was compared with the Remote Sensing Ecological Index (RSEI) and the Land Surface Ecological Status Composition Index (LSESCI) in different landforms in arid mining areas characterized by bare soil and rock. The spatial and temporal changes in mining effects from 2005 to 2020 were analyzed using the Sen+Mann-Kendall method based on Landsat time series images. The results indicated that the average Pearson correlation coefficient (r) between MLESI and each factor exceeded 0.65. The heat factor had the highest correlation coefficient of 0.8 with MLESI for mine site Ⅰ, while the dryness factor had the highest correlation of 0.82 with MLESI for mine site Ⅱ. The slope factor had the highest correlation coefficient of 0.82 for mine site Ⅲ. For mine sites Ⅰ and Ⅱ, the LSESCI overestimated the areas of poor ecological status, identifying most of the natural land as poor. RSEI was unable to reveal the changes in ecological status correlating with landform variety. In general, MLESI was not only highly effective in characterizing the mining area from natural bare soil and rock lands but also in indicating ecological changes along the mining direction with landform changes. The ecological status in mine site I deteriorated by 54.84% since 2015 due to extensive surface mining activities. For mine site II, the ecological status gradually declined from an MLESI value of 0.68 in 2005 to 0.38 in 2020, with a total of 2.36 km2 area experiencing significant changes to poor ecological status over 15 years. For mine site Ⅲ, the ecological status improved due to land reclamation, reaching the highest MLESI value of 0.77 in 2017. An area of 0.95 km2 experienced significant changes to good ecological status from 2008 to 2020. Therefore, the proposed MLESI outperformed RSEI and LSEISCI in monitoring different mine ecological statuses in a typical arid ecosystem.

How to cite: Meng, D., Bao, N., Yang, T., and Li, Q.: A remote sensing index for assessing long-term ecological impact in arid mined land, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-319, https://doi.org/10.5194/egusphere-egu24-319, 2024.

The precise mapping of coastal wetlands holds great significance in the context of monitoring carbon sequestration and storage within coastal ecosystems, particularly in light of climate change and human-induced activities. Time series and multi-source remote sensing data offers distinct advantage in the spatial and temporal mapping of land use, particularly in wetland systems, encompassing various types of vegetation. The primary aim of this study was to delineate the spatial distribution of land use within the Liao River Delta (LRD) wetland. This was achieved by employing a stacking ensemble model that integrated time series GaoFen-1 (GF-1) optical imagery, GaoFen-3 (GF-3) synthetic aperture radar (SAR) imagery, and Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) imagery. The first step involved the application of the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) to fuse the GF-1 NDVI and MODIS NDVI datasets, resulting in the production of time series NDVI data. Subsequently, the parameters pertaining to vegetation phenology were obtained by employing the threshold method on time series NDVI data. We compiled feature datasets that encompassed GF-1 spectral bands, spectral indices, phenological parameters, and GF-3 SAR features. In order to mitigate data redundancy, the Recursive Feature Elimination and Cross-Validation (RFECV) model was employed to identify and select significant features. Finally, the stacking ensemble model was constructed by combining five base models [K-Nearest Neighbors (KNN), Random Forest (RF), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM)] to perform wetland classification. The findings showed the following: (1) ESTARFM was able to successfully fuse GF-1 NDVI and MODIS NDVI data, resulting in a spatiotemporal fusion with a coefficient of determination (R²) of 0.85 and a root mean square error (RMSE) of 0.07. (2) In the process of wetland classification, the RFECV algorithm was employed to select relevant features. Specifically, 75 spectral band features, 89 spectral index features, 13 SAR features, and 7 phenological parameters were identified as significant for this task. (3) A stacking ensemble model was constructed using the aforementioned multi-source features. This model exhibited a robust and consistent performance in wetland classification, achieving the highest overall accuracy of 94.33%. Notably, this accuracy improvement ranged from approximately 0.09% to 10.02% when compared to the individual base models. Thus, the present study has the potential to be utilized in the context of fine-scale wetland monitoring, thereby offering valuable assistance to the field of wetland environmental research.

How to cite: Liu, Y., Bao, N., Qian, H., and Meng, D.: Spatiotemporal fusion of multi-source Chinese Gaofen remote sensing data for mapping costal wetland using stacking ensemble classification method, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-484, https://doi.org/10.5194/egusphere-egu24-484, 2024.

Hyperspectral sensors have become indispensable tools in remote sensing applications, playing a pivotal role in precision agriculture, mineral alteration mapping, land cover classification, and autonomous satellite wildfire detection. Their high spectral resolution and comprehensive spectral information about ground objects underscore their significance. However, despite the wealth of hyperspectral data sources, challenges in data processing have impeded their quantitative application on the Earth's surface. Atmospheric correction, a critical procedure for deriving surface reflectance that accurately captures surface properties, is essential to overcoming these challenges.

In this context, we present an adaptive and automated atmospheric correction scheme specifically designed for hyperspectral data. This approach involves the inversion of atmospheric parameters, including aerosol optical depth (AOD) and precipitable water vapor content (PWV), utilizing the inherent hyperspectral information in the image. Importantly, the atmospheric correction is performed without the need for simultaneous atmospheric and surface observations. AOD retrievals exhibit excellent consistency with ground measurements (RMSE < 0.05, R² > 0.78), and PWV retrievals are also accurate, with an RMSE less than 0.17 g/cm² and an R² greater than 0.94. The acquired surface reflectance demonstrates a remarkable resemblance in terms of shape (spectral angle < 2.2°) and magnitude (RMSE < 0.02) compared to in-situ measurements.

The ZY-1 02D Satellite (ZY02D), led by the Ministry of Natural Resources of China, is purposefully designed for monitoring natural resources. The Advanced HyperSpectral Imager (AHSI) aboard ZY02D covers a wide area of 60 km * 60 km with a medium-to-high resolution of 30 m and a spectral range spanning from 400 to 2500 nm, featuring 166 channels. To monitor mineral resources in Guangdong Province, China, ZY02D AHSI images are acquired. After atmospheric correction using our method, the resulting surface reflectance spectral curve is utilized to identify mineral areas. The similarity between satellite-based and in-situ measured surface reflectance is assessed by spectral angle and Euclidean distance to identify potential mineral areas. The absorption characteristics of minerals are extracted from the satellite-based surface reflectance to enhance the results. Through comparison with mining rights maps, more than 80% of mining area maps are successfully identified. Moreover, it facilitates the monitoring of unauthorized mining activities, thereby improving the enforcement efficiency of government agencies.

How to cite: Jiang, Q., He, T., and Li, W.: Atmospheric correction method and application of satellite hyperspectral data: A case study in mineral resource monitoring, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4282, https://doi.org/10.5194/egusphere-egu24-4282, 2024.

Laser heterodyne spectroscopy detection technology boasts exceptional advantages such as high spectral resolution and high signal-to-noise ratio (SNR). It excels at capturing spectral line broadening information of upper atmospheric molecules. This presents substantial research value in the realms of greenhouse gas profile measurement and the assessment of laser propagation effects in the atmosphere. This paper delves into the investigation of the processing method for heterodyne signals, adopting a non-modulated signal processing approach to construct a near-infrared non-modulated fiber laser heterodyne radiometer. This innovative design significantly enhances the device's response speed and SNR. The radiometer achieves a spectral resolution of 0.006 cm^-1 and an SNR of 300. This facilitates the acquisition of vertical profile distribution and column concentration of CH₄ by measuring the absorption line of atmospheric CH₄. Comparative tests reveal compelling advantages of the non-modulated device, with the modulated device collecting data 6 times in 6 minutes, yielding an SNR of 58. In contrast, the non-modulated device demonstrates superior efficiency by collecting data 6000 times in 2 minutes, resulting in a remarkable SNR of 103. The inversion results of CH₄ column concentration from the laser heterodyne radiometer were compared with those from the Fourier transform spectrometer (EM27/SUN), with average concentrations of 1.88×10^-6 and 1.93×10^-6, exhibiting an overall deviation of approximately 2.6%. The non-modulated laser heterodyne radiometer provides a new reference for the rapid, accurate and high spectral resolution measurement of greenhouse gas concentration.

How to cite: Huang, J., Zhu, W., Huang, Y., Cao, Z., Lu, X., and Meng, Y.: Measurement of Atmospheric CH4 by 1.6 μm High Resolution Non-Modulated Laser Heterodyne Radiometer (NM-LHR) , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4513, https://doi.org/10.5194/egusphere-egu24-4513, 2024.

In line with Saudi Vision 2030 and the ambitions of the Saudi Green Initiative to plant 10 billion trees and protect 30% of Saudi Arabia’s land and sea areas, large parts of Saudi Arabia are being transitioned into nature reserves and undergoing regreening activities, while also fencing off large areas for vegetation restoration and protection. To effectively manage the regreening and restoration initiatives, it is imperative to frequently monitor biomass changes over time and quantify the accumulation of biomass to determine if the restoration and tree-planting initiatives have the intended outcomes. However, landscape responses to regreening and vegetation restoration are poorly understood in hyper-arid rangelands. Environmental processes within different habitat zones might differ, which further complicates biomass monitoring. Here, we present a framework that is currently being applied across multiple hyper-arid rangelands in Saudi Arabia to estimate biomass within newly established nature reserves. The initial work focused on developing a scaling approach between ground, unmanned aerial vehicle (UAV), and satellite image data, including PlanetScope and Sentinel-2 imagery. Field sites were identified using Google Earth imagery and a number of criteria, including a 5-year Sentinel-2 NDVI time-series to identify greening events, terrain characteristics based on DEM data, and differences in vegetation functional types (annual and perennial grass/herbs/forbs, shrubs and trees), soil types, and habitats. Field-based measurements at selected sites focused on determining biomass of different vegetation functional types, using a double-sampling approach, including a limited number of destructive samples, and a large number of biomass estimates based on the structural and dimensional characteristics of the destructive samples. UAV-based light detection and ranging (LiDAR) and multispectral data were obtained for each site to estimate biomass from the field-based samples using different machine and deep learning approaches (random forest, support vector machines, vision transformer, UNet with attention encoder). It was found that LiDAR data provided useful information on vegetation height and volume, which improved the biomass estimates, whereas the multispectral data enabled discrimination between photosynthetic and non-photosynthetic vegetation components. Based on the UAV-derived estimates of biomass, a scaling approach was applied to estimate biomass from both PlanetScope and Sentinel-2 data, with results demonstrating that the higher spatial resolution of the PlanetScope data improved the accuracy due to the very sparse vegetation in most parts of the hyper-arid rangelands of this study. While hyper-arid rangelands are generally underrepresented in research studies worldwide, this research provide a viable framework for assessing vegetation dynamics of biomass both seasonally and annually.

How to cite: Johansen, K., Galvis, J. R., Cheng, H., Almashharawi, S., and McCabe, M.: A Framework for Monitoring Above Ground Biomass of Hyper-Arid Rangelands in the Middle East, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5257, https://doi.org/10.5194/egusphere-egu24-5257, 2024.

The geostationary satellites operated by the Korea Aerospace Research Institute (KARI), known as the Cheonlian satellite series, include three satellites dedicated to Earth observation missions. Cheonlian 1 (Communication Ocean and Meteorological Satellite; COMS), with a decade-long operation, utilized two onboard instruments (MI, GOCI) for Earth observation missions, accumulating approximately 600,000 images over 10 years. The Earth observation images from Cheonlian 1 can be obtained free of charge through Korean data utilization organizations (National Meteorological Satellite Center, Korea Ocean Satellite Center). The KARI ground station holds complete data related to satellite control and operation, including the Earth observation images from Cheonlian 1. After completing its Earth observation mission, Cheonlian 1 is currently operating only its communication payload. The plan is to continue operations until the launch of Cheonlian 3 in 2027, which will take over communication payload duties. Cheonlian 2A (Geo-KOMPSAT-2A; GK-2A), launched in December 2018, succeeded Cheonlian 1 in meteorological payload Earth observation tasks. Equipped with the Advanced Meteorological Imager (AMI), developed by HARRIS, Cheonlian 2A observes the globe with 16 optical bands, focusing on the Korean Peninsula. Real-time distribution of global observation images in UHRIT, HRIT, and LRIT formats is available. Cheonlian 2B (Geo-KOMPSAT-2B; GK-2B), operated since February 2020, simultaneously operates GOCI-Ⅱ and GEMS instruments for Earth observation missions. These two instruments observe only during daytime unlike meteorological payload. GOCI-Ⅱ performs Earth observation missions with a more diverse set of optical bands compared to GOCI on Cheonlian 1. Utilizing hyperspectral sensors, GEMS observes the Earth in five different areas, enabling more accurate observation of atmospheric characteristics such as aerosols. This paper provides a detailed overview of the observation areas, data types, and validation materials of Earth observation images from KARI's geostationary satellites. Additionally, it will be introduced operational issues related to Earth observation image preprocessing system of currently operational Cheonlian 2A/2B satellites. Operational issues of image preprocessing systems include various events such as failure of image reception due to environmental factors such as Sun and radio interference. Image processing failures, such as software errors, are also part of these problems. We also discuss payload anomalies that occurred during the post-launch stabilization phase. KARI is committed to continuous research and development for the stable operation of geostationary satellites, thereby ensuring the best performance for Earth observation missions.

How to cite: Park, E.-B. and Lee, S.-C.: Introduction to Earth Observation Images and Preprocessing Operational Issues of KARI's Geostationary Satellites, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7524, https://doi.org/10.5194/egusphere-egu24-7524, 2024.

Turkey is ranked 4^th worldwide in terms of installed geothermal power plant (GPP) capacity (IRENA, 2023). On the other hand, there are some discussions regarding their impacts on valuable economic agricultural products like figs, olives, and grapes in intense GPP regions. Satellite-based NDVI and LAI data were used in several studies for agricultural vegetation monitoring, management and yield prediction in addition to forest vegetation. FAO (2023) utilizes monthly NDVI anomaly maps to investigate density and health of agricultural vegetation, especially for March-October covering vegetation period and phenological stages of viticulture in the Northern Hemisphere.

MODIS instruments on Terra and Aqua continuously collect NDVI and LAI data every 1-2 days in 36 spectral channels with global coverage. However, they have different spatial and temporal resolutions. Both have been used in several studies regarding vineyards. This study aims to observe changes in satellite-based NDVI and LAI for vineyards around GPPs in Turkey using several statistical analysis methods. Within this scope, the vineyard NDVI and LAI time series were obtained from 250m spatial, 16 days temporal resolutions NDVI and 500m spatial, 4 days temporal resolutions LAI between 2002-2023. All vineyard areas in the study region (AA) were defined using CORINE Land Cover (CLC) data. Three possible impacted (IA1-IA3) and two non-impacted vineyard areas (NIA1-NIA2) were selected considering prevailing wind directions and wind speeds, the capacity of GPPs, and the possible impact distances of emissions from the plants: one of which is the all-impacted area (AIA) consisting of IA1-IA3 within close proximity to GPPs and two of which are NIA1-NIA2. The correlations of NDVI and LAI were analyzed by the areas and each other. According to preliminary results, the 22-year annual profile of NDVI and LAI for AA were between 0.34-0.53 and 0.50-1.07, respectively. Moreover, the NDVI and LAI values vary from 0.26 to 0.63 and 0.30 to 1.48, respectively, between March and October in each year similar to the values found in the literature for the vegetation period for vineyards. The highest NDVI and LAI values were observed in NIA1 and NIA2. The lowest NDVI and LAI values were observed in IA2 and IA3. R (Spearman's correlation coefficients) values for AA are higher between AIA and IA1-IA3 compared to NIA1-NIA2. The lower R values are observed between NIA1 and all other areas.  

The trend, seasonal, and residual components in the NDVI and LAI time series were decomposed with seasonal and trend decomposition using LOESS. Moreover, the observations of NDVI and LAI changes were evaluated with meteorological and GPP-related parameters at phenological stages taking into consideration the pre-and post-GPP installations. The results demonstrate the patterns and changes for vineyards based on seasonality and other reasons, and give insight on whether there are any impacts by GPPs.

Keywords: NDVI, LAI, Vineyards, Geothermal Power Plants

References

Food and Agriculture Organization of the United Nations (FAO) (2023). Earth Observation. https://www.fao.org/home/en/

International Renewable Energy Agency (IRENA) (2023). Renewable Capacity Statistics 2023. https://www.irena.org

How to cite: Aydin, M., Leuchner, M., and Kaynak, B.: Observations of Changes in Vineyards Using Long-Term MODIS NDVI and LAI around Geothermal Power Plants, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11264, https://doi.org/10.5194/egusphere-egu24-11264, 2024.

Surface expression of seismic rupture is a distinctive feature of large earthquakes, and the length of the surface rupture varies from several to several hundred kilometers depending on the magnitude of the earthquake. The structure and mechanical properties of fault zones strongly influence the behavior of earthquake ruptures, and detailed mapping and documentation of fault geometry such as fault bends, steps, branches, and their related segment geometry play a crucial role in determining the propagation and path of earthquake ruptures. The most precise method to map earthquake surface ruptures in detail might be manual mapping in the field by an expert; however, it takes a long time to analyze a vast area, and not everyone has access to the necessary expertise. As an alternative, rupture mapping based on remote sensing has been proposed to supplement the limitations of these field surveys.

In this study, we propose a robust model for automatic detection and analysis of morphological features of surface ruptures using deep learning based on previous research results. Additionally, a skeletonization technique was developed for morphological analysis of detected fractures, and fractures of complex structures on the ground were objectified as individual fractures. Finally, the geometric quantitative characteristics of individualized fractures, including crack location, length, width, and direction, were automatically extracted. By comparing the detection results of the proposed model with the ground truth confirmed by the expert using a line map, the reliability of the entire model could be confirmed.

To examine the applicability of the proposed model, the detection performance of various surface ruptures in localized areas was evaluated, and key characteristics, including rupture direction and pattern for extensive surface ruptures, were clearly identified. The satellite image-based surface destruction detection model proposed in this study can be used as a useful tool for field investigation and earthquake-related basic data collection by automatically detecting various surface destruction and deformations caused by earthquakes. Therefore, the proposed model, which enables fast and accurate fracture and fault mapping and quantitative analysis using high-resolution satellite data, is expected to be utilized as an important integrated solution. It would also be extremely beneficial in characterizing and comprehending the structural, geometrical complexity, and mechanical properties of fault zones.

How to cite: Choi, Y.: Earthquake surface ruptures mornitoring based on deep learning and remote sensing, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14501, https://doi.org/10.5194/egusphere-egu24-14501, 2024.

Mediterranean temporary ponds on the Southwest Portuguese coast are areas of significant biodiversity and are recognized as priority habitats by the Habitat Directive (92/43/EEC). The fauna and flora species that inhabit these ponds are well adapted to the variability of the Mediterranean climate, exhibiting a high resistance to drought and the ability to survive without water for months. Among them, the Triops vicentinus species stands out for its uniqueness, considered a living fossil that has persisted since the time of the dinosaurs.

These temporary ponds are situated in shallow depressions where rainwater accumulates for seasonal periods. They face increasing threats due to anthropogenic pressures, primarily arising from intensive farming techniques, real estate development linked to tourism, and a general lack of awareness among the population regarding the significant biological and ecological value of this habitat. The area was the focus of the LIFE + project titled 'Conservation of Temporary Ponds in the Southwest Portuguese Coast of Portugal' conducted between 2013 and 2018. During this initiative, georeferenced mapping of the ponds and associated biodiversity was produced. However, the geographical evolution of these ponds has not been monitored since then.

This study aims to map the ponds and track their evolution between 2018 and the present. To map the water bodies, we use Sentinel-1 and 2 datasets along with the Sentinel application platform SNAP. The surface water extraction method relies on the Normalized Difference Water Index and a supervised classification algorithm. The applied methodology has proven to be efficient in detecting and interpreting the dynamics of water bodies. The results of this investigation enhance our understanding of the uncertainties associated with the applications of Sentinel-2 and Sentinel-1 for monitoring temporary ponds and contribute to our knowledge of the current status of this habitat.

This work was funded by the Portuguese Fundação para a Ciência e a Tecnologia (FCT) I.P./MCTES through national funds (PIDDAC) –

UIDB/50019/2020 (https://doi.org/10.54499/UIDB/50019/2020),

UIDP/50019/2020 (https://doi.org/10.54499/UIDP/50019/2020) and

LA/P/0068/2020(https://doi.org/10.54499/LA/P/0068/2020).

How to cite: Gennari, G., Neves, M. C., and Monteiro, J. P.: Mapping Mediterranean temporary ponds in SW Portugal using remote sensing, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8553, https://doi.org/10.5194/egusphere-egu24-8553, 2024.

Benggang is an erosion landform developed on the deep weathered crust in the low mountain and hilly areas of southern China, characterized by wide distribution and high erosion intensity. Similar erosional landforms globally include the lavakas landform in Madagascar, vocorocas in Brazil, and the "collapse" landform in Japan. However, benggang exhibits distinctive development characteristics that differ from these landforms, thus being considered a geomorphic feature unique to southern China. Investigations into benggang reveal that there are approximately 239000 benggangs in southern China, with a total area of 1220 km². Since 1949, benggang erosion has caused the destruction of 360,000 hm² of arable land, damage to over 500,000 houses, and siltation in around 9,000 reservoirs. This has severely hindered residents' lives and economic development, earning it the moniker "ecological ulcer." Previous studies on benggang have primarily focused on the scale of land blocks or sample plots, identifying elevation, slope, and aspect as key factors influencing benggang erosion. Regional-scale studies have found that geological and pedogenic factors, prolonged and intense rainfall, and vegetation cover significantly impact benggang development. However, research on the critical conditions for benggang formation at the small watershed scale is limited. Therefore, this study focuses on the Yuankengshui watershed in Wuhua County, Guangdong Province. Based on drone photogrammetry technology, digital orthophoto images and digital surface models were obtained, and the boundaries of 296 benggangs distributed in the small watersheds were delineated, including the collapsed walls, alluvial bodies, channels and other parts of each benggang. Then, machine learning and multiple linear regression methods are used to analyze the impact of factors such as altitude, slope, aspect, temperature, rainfall, and vegetation cover on the probability of benggang distribution. Results show that benggang is mainly distributed in the 210-230m altitude range, 23-26 ° slope range, 20-60m slope length range, and most of them are located in the sunny slope. Rainstorm is the main reason for the intensified erosion of benggang. Based on the above research, we have established an information based benggang risk prediction model to explore potential benggang occurrence areas and provide reference for future benggang erosion prevention and control.

How to cite: ou, H., yuan, Z., and yang, X.: Benggang erosion: Critical condition and risk prediction in small watershed, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15824, https://doi.org/10.5194/egusphere-egu24-15824, 2024.