The prime mission of the Earth Surface Mineral Dust Source Investigation (EMIT) was to characterize the mineral composition of the Earth’s arid land regions, deliver new constraints on the radiative forcing impacts of mineral dust aerosols in the Earth System today, and assess potential changes in the future. To achieve this objective, a high signal-to-noise ratio imaging spectrometer measuring the visible to short wavelength infrared (VSWIR) was developed and then launched to the International Space Station (ISS) on the 14th of July 2022. Having measured more than 100 billion spectra across six continents, EMIT reported prime mission success on the 26th of August 2024. Also in 2024, the EMIT mission was extended and the target observation areas expanded to include biodiversity, terrestrial ecology, corals, volcanos, coastal and inland waters, mid/low latitude snow/ice, and new geology regions across the six continents observable from the ISS. In addition to new science, these extended mission observations support a broad set of new measurement and monitoring applications related to agriculture, forestry, critical minerals, water quality, wildfire fuels and burn severity, water resources, surface plastics, and more. In support of these objectives, >120,000 EMIT VSWIR imaging spectroscopy scenes have been measured and are currently available as radiance and reflectance along with a suite of mineralogy products. A new fractional cover product with photosynthetic vegetation, non-photosynthetic vegetation, soil, snow/ice, water, and char is in development. All EMIT data and products are freely available from the NASA Land Process Distributed Active Archive Center (LP DAAC). EMIT measurements and products include uncertainty estimates, and the project algorithms are available on GitHub. This contribution on remote sensing environmental monitoring presents new results from EMIT observations along with an overview of the measurements, products, and plans for the ongoing mission. EMIT observations also support preparatory activities for NASA’s Surface Geology and Biology Decadal Survey mission with a next-generation VSWIR imaging spectrometer that is part of the NASA Earth System Observatory and a companion mission to ESA’s Copernicus Hyperspectral Imaging Mission for the Environment (CHIME).
How to cite: Green, R., Thompson, D., Brodrick, P., Chadwick, D., and Thorpe, A.: New Environmental Measurement and Monitoring with 120,000 EMIT Imaging Spectroscopy Scenes Acquired Across Six Continents from the International Space Station., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13838, https://doi.org/10.5194/egusphere-egu25-13838, 2025.
Acid mine drainage (AMD) is a severe environmental issue associated with the generation of finely milled rock waste containing high concentrations of sulfide minerals and potentially toxic elements (PTEs) during mining activities. The formation of secondary minerals, such as sulfates and iron oxyhydroxides, results from sulfide oxidation and subsequent acid neutralization by carbonate and silicate minerals, making them key indicators of AMD. Efficient identification of these minerals is crucial for monitoring their impact on soils.
This study compares the capabilities of Landsat-09, ASTER, and Sentinel-2 satellite images in identifying Jarosite, Goethite, Ferrihydrite, Anhydrite, and Gypsum (associated with AMD) using the "Spectral Angle Mapping" (SAM) technique. SAM is a spectral analysis method that classifies materials based on the angle between spectral vectors corresponding to their spectral signatures.
The evaluated satellite images were selected based on their spatial, spectral, and temporal resolution. Their strengths and limitations in detecting the selected secondary minerals were assessed using the SAM technique in ENVI software. The algorithm was trained with spectral signatures ranging from 0.4 to 2.5 micrometers, obtained from the USGS and ASTER spectral libraries. Landsat-09 offers moderate resolution and global coverage; ASTER excels in shortwave infrared capabilities but lacks recent satellite imagery for current analyses; and Sentinel-2 combines high resolution with a broad spectral range and biweekly temporal resolution, with continuous image acquisition to date.
The results show significant differences in each sensor's ability to identify the minerals of interest. Sentinel-2 demonstrated high accuracy due to its spatial resolution and specific spectral bands. Conversely, ASTER was unable to precisely delineate pixels associated with the requested minerals. Lastly, Landsat-09 showed limitations in mineral identification using this technique due to the sensor’s spatial resolution. This study highlights that spatial resolution is the most critical factor in selecting satellite imagery for SAM applications. Thus Sentinel-2, with the highest spatial resolution (10 m) achieved superior results in identifying AMD-related minerals.
This study provides guidance for selecting satellite sensors based on spatial and spectral resolutions in studies aimed at mineral identification using SAM. It contributes to the development of more efficient strategies for environmental management, mineral exploration, and energy resource studies, among other applications.
How to cite: Almazan Valencia, J. T., Archundia Peralta, D., and Ramírez Serrato, N. L.: Evaluation of different types of satellite images for the identification of minerals formed by Acid Mine Drainage using Spectral Angle Mapping (SAM), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7570, https://doi.org/10.5194/egusphere-egu25-7570, 2025.
Satellite remote sensing techniques have emerged as crucial tools in monitoring and analysing Earth's surface, enabling insights into high-risk natural phenomena and enhancing decision-making processes. The PARACELSO project (funded by ASI – Italian Space Agency) aims to leverage satellites observations and innovative data analysis approaches to improve the mapping and surveillance practices of the Po River Basin Authority with respect to the dynamics of rivers, landslides and rock glaciers. As of landslides, the project aims to introduce the usage of techniques such as interferogram stacking and offset tracking (OT) for detecting and monitoring large-scale slope movements characterized by displacement rates higher than these allowing the application of multi-interferometric techniques. In such framework, and with reference to moderate velocity active earthslides and earthflows in the northern Apennines of Italy, this presentation deals with the application of OT algorithms implemented in Python (such as Normalized Cross-Correlation, Phase Correlation and Optical Flow), to imagery from the Sentinel 2 (multispectral), Prisma (hyperspectral) and Cosmo-SkyMed (X-band SAR) missions. Results obtained so far, validated by ground-based evidences and monitoring, confirm that offset tracking can become a powerful tool for leveraging satellite data for characterizing landslide dynamics over both short and extended periods of time. Furthermore, they evidence some limitations and the need for an optimization of data pre-processing routines (e.g., co-registration and terrain correction) and of the OT algorithms (so to reduce computing times). On such basis, it is concluded that using OT algorithms with satellite imagery can effectively allow the extraction of relevant motion distribution at the slope scale for specific landslides and, possibly, allow the identification of unrecognized active phenomena over quite large areas, so to advance the possibility of slope movements detection for hazard and risk management both for researchers and decision-makers.
How to cite: Critelli, V., Tondo, M., Fabbiani, C., Mulas, M., Lelli, F., Simonelli, T., and Corsini, A.: Advancements in landslide monitoring by leveraging satellites observations using Offset Tracking algorithms, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11514, https://doi.org/10.5194/egusphere-egu25-11514, 2025.
Land degradation is a key threat to the productivity of agroecosystems which are increasingly pressured by climate change and growing global populations. Land degradation can impact arable land through a range of pathways including physical processes such as salinization and erosion, loss or inhibition of biological function in soils through chemical or physical deterioration, and through climatic shifts such as aridification which are projected to worsen in coming decades. Land degradation affects a large but uncertain portion of agricultural land globally and the implication of degradation for food production is highly variable. Despite the widely recognized prevalence of cropland degradation and its potential impacts, tools for measuring and monitoring productivity losses over long time periods and large spatial scales are lacking. Many global maps of land degradation rely on outdated statistics, manual surveys, and overly basic image analysis and computational approaches. Existing land degradation assessments also lack the granularity required for decision-making at regional and local levels. The robust spatial and temporal availability of remote sensing imagery presents a unique opportunity to monitor the long-term trends in productivity of cropland through measurements of vegetation greenness as a proxy for yield. In this work, we present a novel methodology for detecting long-term changes in cropland productivity that is globally scalable and robust to changes in land use and management. Using the entire MODIS imagery record (2000-2024), we use a Discrete Wavelet Transform to decompose EVI signals and isolate the long-term trend in vegetation greenness at 250m resolution for a test region covering Argentinian croplands. We find that large areas of maize and soy cropland in Argentina have a negative trend in long-term greenness, with subtle but important long-term declines in productivity that may be attributable to degradation. These declines appear more pronounced in older croplands than in newer croplands suggesting a potential cause in soil health related changes. The approach presented is globally applicable and advances the use of earth observation technology to measure land degradation and monitor land use change.
How to cite: Ippolito, T., Neff, J., Campos, A., and Romero, D.: Remote Sensing-based Detection of Cropland Degradation Signals Using Discrete Wavelet Decomposition Analysis – A Case Study in Argentina , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13096, https://doi.org/10.5194/egusphere-egu25-13096, 2025.
Precise and dynamic cropland maps are essential for research and practical applications, such as soil fertility assessment and crop production monitoring. In Africa, continued population growth and increasing land-use pressures make the need for reliable land cover information greater than ever. Earth observation missions provide timely, large-scale data, and recent efforts have produced high-resolution (30m or better) global and continental cropland/land use land cover (LULC) maps. However, low consensus among these maps for cropland predictions in Africa largely limits their downstream local applicability despite reported high accuracy.
Here, we conducted a case study in the Copperbelt region (DRC), where most cropland is managed by smallholders within fragmented landscapes. Our objectives were to: (i) map cropland dynamics from 2000 to 2023; (ii) evaluate the accuracy of both static maps and dynamic changes (cropland gain and loss); and (iii) compare the performance of our maps with five existing high-resolution (10/30m) cropland/LULC products. We used the Landsat Analysis Ready Data (ARD, 30m resolution) to derive eight annualized NDVI time series (aggregated every three years from 2000 to 2023) as input data. A binary random forest classifier was trained on over 6000 cropland and 12000 non-cropland reference samples collected from 2000 to 2023. Independent validation for the static map in 2020 showed an overall accuracy (OA) of 91.2%, outperforming all existing maps (OA: 60.2%–83.2%). While effective at identifying large cropland fields, most existing maps overlooked small, fragmented fields, leading to an underestimation of cropland area up to 91%. Based on our predicted maps, cropland area increased by 20% from 2000 to 2023. Two drastic short-term changes were observed: a surge from 2017 to 2020 (+57%) and a decrease from 2020 to 2023 (-37%), reflecting intense deforestation and urban expansion in the two periods. However, accuracy for detecting cropland gain (71.9%) and loss (53.3%) was limited, likely due to the 30m resolution being insufficient to separate smaller fields, particularly near suburban built-up areas where cropland is often interspersed with single houses.
In conclusion, existing global and continental cropland/LULC maps remain inadequate for regional use in Africa, where fragmented cropland is prevalent. Improving these maps requires region-specific training samples, particularly from smallholder farms. Moreover, detecting cropland changes remains challenging, and higher-resolution imagery may present an opportunity to better monitor the dynamic landscapes.
How to cite: Ou, X., Shi, P., Bazirake Mujinya, B., and Van Oost, K.: Historical mapping of fragmented cropland in Africa: a case study in the Copperbelt region, DRC (2000-2023), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11516, https://doi.org/10.5194/egusphere-egu25-11516, 2025.
Assessment of shoreline evolution is key for many coastal management and practical applications. Coastal zones are dynamic by nature, with this dynamism subject to numerous studies often focusing on the identification of ‘coastlines’/’shorelines’. Where this coastline (or shoreline) location is, and how it changes, is a fundamental variable in understanding coastal environments. For example, beaches are highly mobile and their interaction with coastal dunes has been key for the development of classical conceptual models. A number of remote sensing methods and algorithms have been designed to extract instantaneous coastlines from satellite imagery. These represent a snapshot of the position of the coastline at a particular time. However, coastlines do not exist over a time period, as they dissolve in a ‘buffer zone’, an interphase between land and ocean where water and sand are constantly mixing. This buffer zone can vary in size depending on the location and the time of the year. Drawing on the experience gathered by the use of variance images from Argus video monitoring systems, we present an alternative approach for coastline and shore buffer zone detection using medium resolution satellite imagery and Google Earth Engine. This method takes advantage of the increasing availability of satellite data and focuses on collections of satellite images acquired over time periods, instead of single-date images. It aims to minimise user input by applying image compositing and segmentation, with shore buffer zones identified using variance image composites. The approach is tested in beaches with diverse hydro and morphodynamic characteristics. This method obtains medium to long term information of coastal dynamics including average coastline locations and extent of shore buffer zones.
How to cite: Martín-Gallego, P., Montes, J., Delgado-Fernandez, I., Del Río, L., and Marston, C.: A tool to extract coastlines and shore buffer zones using satellite imagery and Google Earth Engine, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12963, https://doi.org/10.5194/egusphere-egu25-12963, 2025.
Scanning Array for hyper-Multispectral RAdiowave Imaging (SAMRAI) is a passive interferometric radiometer. In this respect, it is similar to MIRAS on board the SMOS satellite launched by ESA, but it realizes ultra-wideband (1-41 GHz) and high-frequency-resolution (27 MHz) microwave spectrum measurement. We believe that SAMRAI is the world's first microwave hyperspectral radiometer.
JAXA will continue to operate the satellite-borne microwave radiometer AMSR series for more than 30 years, including AMSR3 currently under development. The design has remained largely unchanged for 30 years, and various issues are becoming apparent. In particular, the radio frequency interference (RFI) contaminating the natural-origin signals is a serious problem, and we believe that microwave hyperspectral measurement is essential for identifying and isolating RFI signals. This was a big motivation for developing SAMRAI. In addition, microwave hyperspectral measurement must have new possibilities, such as making it possible to measure the frequency characteristics of the emissivity of the Earth surface.
Development of the satellite-borne SAMRAI is progressing toward launch in 2027. On the other hand, as the microwave spectrum from the Earth surface will be observed from a satellite for the first time in the world, pre-launch calibration and validation activities are more important than ever in order to generate geophysical data promptly after the satellite is launched. From this perspective, we have developed a ground-based microwave spectral radiometer using part of the SAMRAI receiver system. This ground-based microwave spectral radiometer is much smaller than SAMRAI and can be easily taken out to various environments for observation, and like SAMRAI, it is capable of measuring microwave spectra at 27 MHz intervals from 1 GHz to 41 GHz.
Here, we presents the technical detail of this ground-based microwave spectral radiometer and its performance confirmation results of the observation experiment in addition to the current status of the satellite-mounted SAMRAI development.
How to cite: Maeda, T., Kobayashi, Y., Tat Trung, N., Takei, Y., Yano, T., and Tomii, N.: Development of a ground-based microwave spectral radiometer - a downsized version of the satellite-mounted SAMRAI, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14722, https://doi.org/10.5194/egusphere-egu25-14722, 2025.
Earth's surface, our primary habitat, provides essential ecosystem and social services, such as carbon sequestration and food production. Numerous studies reveal that global changes are destabilizing the Earth's surface, as evidenced by extreme events' increasing frequency and complexity. These events trigger substantial losses across various sectors, including the economy and public health, necessitating accurate detection. Currently, large-scale monitoring of phenomena like vegetation disturbances and wildfires is achieved using remote sensing, with detection accuracy expected to improve through advanced machine learning techniques. However, these approaches primarily provide specific, event-based information, detecting only predefined types of events. Macroscopic mapping, which involves identifying these instabilities without relying on specific event types, remains unresolved despite its value for comprehensive detection and broader understanding.
To fill this gap, we propose a novel method for detecting unstable surfaces, termed Surface Anomalies (SAs). We hypothesize that a surface's evolution is influenced by its initial state and environmental factors within a given geographical region, including climate and land use. Consequently, homogeneous surfaces with similar initial states under comparable environmental conditions are expected to follow similar evolutionary trajectories. Building upon this hypothesis, SAs are defined as surfaces with evolutionary trajectories that deviate from their homogeneous counterparts. Compared to event-based definitions, our conceptual framework accommodates instabilities that are not predefined yet are nonetheless important, providing more comprehensive detection. Compared to algebraic change or anomaly detection methods, our definition offers a more precise characterization of SAs and has the potential to reduce irrelevant detections.
We operationalize this conceptual framework using remote sensing imagery in a two-stage process. In the first phase, we model the normal evolutionary patterns of a region. This involves acquiring a pair of baseline images where each pixel represents a surface, spectral values represent surface states, and differences between images represent evolutionary trajectories. We apply K-Means clustering with a sufficiently large number of cluster centers to segment the imagery, with each cluster corresponding to a type of homogeneous surface. For each homogeneous surface, we fit a Gaussian Mixture Model to the distribution of evolutionary trajectories, representing normalcy. In the detection phase, we acquire new image pairs from nearby locations and calculate the probability that their evolutionary trajectories fit within the GMM of the corresponding homogeneous surface model. Lower probabilities indicate higher instability. This probabilistic approach allows us to detect surface anomalies by identifying deviations from normal evolutionary patterns.
We evaluated our method's effectiveness by comparing it with traditional non-event-based approaches such as algebraic change detection and change vector analysis. This comparison was performed on a dataset encompassing various types of SAs, including wildfires, floods, volcanic activities, deforestation, and bark beetle infestations. Our method's results indicate significant improvements, substantially reducing false alarms and omissions. In summary, our method for detecting SAs from a macroscopic perspective has the potential to enhance our understanding of how Earth's surface responds to global change.
How to cite: Xu, J., Yan, K., and Wang, Q.: Dynamic Modeling Reveals Earth Surface Anomalies: An Innovative Conceptual Framework and Detection Method, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15649, https://doi.org/10.5194/egusphere-egu25-15649, 2025.
Elevated flare is the utter most significant exhaust source in chemical plants and as well as the best way to centralized disposal of combustible gases at high altitude. It is considerable to monitor the concentration of the exhaust plume of flares, however, flare research over the past decade has increasingly illustrated that there is likely no one effective method can accomplish the task. Passive Fourier Transform Infrared Spectroscopy (FTIR) remote sensing system is widely used in the field of hazardous chemicals park monitoring and warning, gas distribution monitoring at the scene of the explosion in the way of non-contact long-distance remote sensing. Compared to active FTIR absorption spectrometry, it is easier to install because the hot gas just has to be in the field of view of the telescope of the spectrometer and once there is equivalent radiation bright temperature difference between the measured plume and the background, IR radiation emitted by exhausts or gas plumes is detected and remote sensing by passive FTIR spectrometry allows the retrieval of column densities or concentrations of molecules in gas plumes such as exhaust plumes of aircraft, smoke stacks and flares. In the paper, the exhaust plumes of two elevated flare in a chemical plant in Shanghai is measured by passive FTIR remote sensing system and quantitative the concentrations of CO, N2O, HCN, NH3, C3H3N, C3H6 and C2H4 in the plumes. The theory and process relate with radiometric calibration and calculation of transmittance is presented as well as the factors caused the error of the concentrations of target gases is analysed. The passive FTIR remote sensing system makes up the inability of measure the exhaust plume of flares and provide the efficient and powerful date for estimate/evaluate the combustion process and efficiency of flare, building the list of emission index and so on
How to cite: Han, X. and Li, X.: Remote Sensing for Flaming Plume of Elevated Flares with Passive Fourier Transform Infrared Spectroscopy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2387, https://doi.org/10.5194/egusphere-egu25-2387, 2025.
Between 2019 and 2022, multiple fires affected Stromboli Island, causing significant environmental damage and highlighting the need for effective preventive measures. In 2019, two fires were ignited by eruptive activity, while in 2022, human actions were responsible for a major wildfire. These events underline the complexity of fire dynamics in volcanic environments, where topography, wind, and vegetation flammability play critical roles in fire propagation. Furthermore, the aftermath of these fires has triggered secondary hazards, such as floods and debris flows following heavy rains, which further exacerbated the environmental and societal impact on the island.
In this work we leverage a cellular automata-based numerical model specifically designed for volcanic-induced fires, integrating factors such as wind, topography, and vegetation characteristics to simulate the fire evolution as well as the interaction with possible mitigations measurements. We conducted fire spreading numerical simulations exploring different configurations of firebreak lines, including the strategic use of hiking trails as potential barriers to fire spread. This model also benefits from the integration of Geographic Information Systems (GIS), enhancing its ability to classify soil types and map burnt areas with good spatial accuracy.
The simulations demonstrate how the proposed model can be used to create fire hazard scenarios and evaluate the effectiveness of various mitigation strategies. For instance, our results for the 2019 and 2022 fires exhibit high spatial accuracy, with Brier scores of 0.188±0.002 and 0.073±0.001, respectively. These findings underscore the utility of numerical modeling not only for understanding fire dynamics but also for planning preventive actions to mitigate wildfire risks and reduce the cascading effects of secondary hazards, ultimately contributing to more effective fire and disaster management on Stromboli and other volcanoes with similar environmental conditions.
How to cite: Guardo, R., Bilotta, G., Ganci, G., Zuccarello, F., Andronico, D., and Cappello, A.: Stromboli Fires: What could have happened?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5752, https://doi.org/10.5194/egusphere-egu25-5752, 2025.
Land Surface Temperature (LST) estimation is an important part of climate research, helping understand surface heat and environmental changes. This study introduces a simple and innovative way to estimate LST using machine learning and data collected by unmanned aerial vehicle (UAV). The UAV used RGB and near-infrared (NIR) sensors, which are commonly available and affordable.
The research took place in the Šumava Mountains of the Czech Republic, an area with unique landscapes and sensitive ecosystems. The UAV surveys used two types of cameras: one combined RGB and NIR sensors to capture visual and near-infrared data, and the other was a thermal camera to measure ground temperature. The thermal images provided the training data for machine learning models, which were designed to estimate LST using only RGB and NIR data. To test and validate the model, an integrated approach is used: sensors installed in different land cover types, direct measurements of air temperature from ground stations and medium-resolution satellites with a thermal band. This correlation with reference temperature sources ensures the model reflects real thermal conditions rather than relative differences alone. This method can be very useful when thermal cameras are not available, as they are often expensive and need careful calibration.
The models created in this study showed good accuracy, with strong agreement between the predicted and actual LST values but it is still necessary to check the LST directly. Incorporating reference temperature values enhances the model’s accuracy and applicability, allowing for consistent results. This means the models can reliably predict LST using just RGB and NIR data. This approach offers a practical alternative to traditional thermal measurements, which are more costly and harder to use for large-scale or frequent studies. One key advantage of this method is its affordability and ease of use. RGB and NIR sensors are much more accessible than thermal cameras, making it possible for researchers with limited budgets to monitor LST effectively.
This study offers a novel method for estimating LST by combining UAV technology, RGB and NIR sensors, and machine learning. The results show that the proposed approach is reliable and applicable for environmental and climate research. By integrating reference temperature sources, this study overcomes the challenges of relative-only measurements, providing reliable LST values for diverse applications. By overcoming the challenges of direct thermal measurements, this method provides an easier way to monitor land surface temperatures across different environments.
How to cite: Hordiienko, O. and Langhammer, J.: Modeling Land Surface Temperature Using UAV-Derived RGB and NIR Data Through Machine Learning Techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-698, https://doi.org/10.5194/egusphere-egu25-698, 2025.
The availability of global land cover products has been increasing recently, however, its regional quality differs substantially. In this work, the contemporary global datasets including Google’s Dynamic World (GDW), ESA’s World Cover (ESA WC), Esri Land Cover (ELC) map were compared in Poland to assess their usefulness for the forest cover change studies in Central-European conditions. We grouped land cover categories of the three global datasets into 6 land cover classes (forest, semi-natural vegetation, cropland, built-up area, water and other). To assess the accuracy of the global products we used the EU Land Use/Cover Area Frame Survey (LUCAS) points. The results showed that precision for forest class was higher for ESA WC (0.92) than for the other two products (ELC: 0.85; GDW 0.41). Forest class accuracy was the highest for ELC (0.92) and lower for ESA WC (0.90) and GDW (0.81). Overall, ELC had the highest F1 score for the forest class (0.76), slightly higher than ESA WC (0.71), with GDW showing a significantly lower value of 0.57. Our analysis indicated that for forested areas ELC performed better than two other global products, suggesting its usefulness for forest cover change studies in Central Europe.
Acknowledgements:
This research was funded in whole or in part by the National Science Centre, Poland (UMO-2024/53/N/ST10/02518). For the purpose of Open Access, the author has applied a CC-BY public copyright license to any Author Accepted Manuscript (AAM) version arising from this submission.
How to cite: Shahbandeh Vayghan, M., Kaim, D., and Kozak, J.: Assessing contemporary global land cover products to study land cover change in Poland, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2557, https://doi.org/10.5194/egusphere-egu25-2557, 2025.
The global increase in fine particulate matter, particularly in East Asia, has emphasized the importance of satellite-based monitoring for rapid and extensive observation of atmospheric changes. In South Korea, the Geostationary Environment Monitoring Spectrometer(GEMS) has been deployed to monitor air quality, including aerosol optical depth(AOD). The accuracy of AOD retieval relies significantly on surface reflectance, which is typically estimated using the minimum reflectivity method. However, this approach has limitations as it does not only account for observation geometry conditions(such as SZA, VZA) but also atmospheric conditions such as nitrogen dioxide(NO2) concentrations, which can significantly influence surface reflectance calculations.
This study aimed to assess the impact of NO2 concentrations in atmospheric correction for retrieving surface reflectance. By utilizing VLIDORT RTM(Vector Linearized Radiative Transfer Model for the Solution of Inverse Problems), surface reflectance values adjusted for NO2 were evaluated against those calculated without NO2 consideration. The results demonstrate that accounting for NO2 can lead to enhancining the accuracy of surface reflectance retirevals.
The findings of this research suggest the possibility of improving atmospheric correction by considering NO2 as a factor, in surface reflectance estimation for improved products such as AOD retrieval, ultimately leading to accurate fine particulate matter monitoring. These advancements are expected to contribute to various applied research fields, enhancing the utility of satellite-based environmental monitoring systems like GEMS.
Acknowledgement
This research was supported by Particulate Matter Management Specialized Graduate Program through thet Korea Environmental Industry & Technology Institude(KEITI) funded by the Ministry of Environment(MOE).
How to cite: Kim, S. and Han, K.: Evaluation of the Impact of NO2 in Atmospheric Correction for Surface Reflectance Estimation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5453, https://doi.org/10.5194/egusphere-egu25-5453, 2025.
Accurate surface reflectance retrieval is crucial for satellite-based Earth observation and various environmental applications. The radiative transfer model (RTM) known as the Second Simulation of a Satellite Signal in the Solar Spectrum vector (6SV) is widely utilized for atmospheric correction of optical satellite data, effectively accounting for various aerosol types and concentrations to derive surface reflectance. However, the accuracy of surface reflectance is significantly affected by the types and concentrations of aerosols in the atmosphere. In particular, anthropogenic aerosols generated from industrialization in East Asia, such as fine particulate matter (PM), constitute a significant proportion of continentally derived aerosols. In this complex atmospheric environment, coastal regions experience a mixture of marine-origin and continent-origin aerosols, which complicates the accurate retrieval of surface reflectance.
Although previous studies have applied 6SV to high-resolution satellite data, comparative analyses of various aerosol types and studies reflecting the characteristics of coastal regions in East Asia remain limited. Therefore, this study focuses on the coastal regions of East Asia, comparing surface reflectance retrieved using standard aerosol models (Continental, Maritime, Urban) provided by 6SV with those officially provided by Sentinel-2A. Additionally, comparisons with surface reflectance derived from aerosol data provided by the AErosol RObotic NETwork (AERONET) were conducted to evaluate the accuracy of each method.
This study is expected to contribute to enhancing the applicability and reliability of remote sensing data by incorporating and analyzing the aerosol characteristics of coastal regions in East Asia.
Acknowledgments
This research was supported by Particulate Matter Management Specialized Graduate Program through the Korea Environmental Industry & Technology Institute(KEITI) funded by the Ministry of Environment(MOE).
How to cite: Lee, S. and Han, K.: Comparative analysis of the accuracy of surface reflectance in East Asian coastal areas according to aerosol models based on 6sv rtm, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5754, https://doi.org/10.5194/egusphere-egu25-5754, 2025.
Sea fog detection is a critical aspect of meteorological monitoring due to its significant impact on maritime safety and navigation. However, accurately detecting sea fog poses challenges due to its dynamic nature and the limitations of conventional detection methods. Recent advancements in remote sensing technology and deep learning provide an opportunity to overcome these challenges. This study leverages the capabilities of Korea’s geostationary satellites, GK2A AMI and GK2B GOCI-II, and applies a state-of-the-art deep learning model, Swin Transformer-based UPerNet, to develop an efficient sea fog detection system. To achieve this, satellite images from AMI and GOCI-II were collected, preprocessed, and labeled using manual and automated methods. Composite images, generated from selected spectral bands effective for fog detection, served as inputs to the model. The datasets were augmented and standardized to enhance model performance and generalization. The trained model was evaluated using metrics such as overall accuracy (ACC) and critical success index (CSI), achieving 98.8% ACC and 78.76% CSI, respectively, on the test dataset. The results demonstrate the potential of the proposed approach to improve sea fog detection, with applications extending to operational meteorology and maritime safety. Although limitations such as minor distortions in detection accuracy were observed, these can be addressed in future studies by incorporating more advanced models and additional data sources. This research highlights the synergy between geostationary satellite data and deep learning for environmental monitoring and provides a foundation for further advancements in remote sensing applications.
This research was supported by a grant (2021-MOIS37-002) of "Intelligent Technology Development Program on Disaster Response and Emergency Management" funded by Ministry of Interior and Safety (MOIS, Korea).
How to cite: Kang, J. and Lee, Y.: Sea fog detection from GK2A AMI and GK2B GOCI-II satellite images using swin transformer, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8117, https://doi.org/10.5194/egusphere-egu25-8117, 2025.
Accurately monitoring atmospheric carbon dioxide (CO₂) is vital for understanding global carbon fluxes and shaping climate mitigation policies. This study explores the seasonal variability of biases between satellite-derived OCO-2 XCO₂ observations and ground-based TCCON XCO₂ measurements at the Caltech TCCON station over nine years (2014–2023). The study categorized the data by observation mode (nadir or glint) and month and investigated the distributions of deviations from the mean bias.
Distinct seasonal patterns emerged in the bias variability. Nadir mode observations demonstrated consistent median deviations, ranging from -0.6 to 0.4 ppm, indicating minimal bias variability. In contrast, glint mode observations showed substantial variability, with absolute median deviations surpassing 1 ppm during January, March, and September. Skewness analysis revealed asymmetries in the data distributions and the presence of significant outliers. A strong correlation was observed between monthly Normalized Difference Vegetation Index (NDVI) values and glint mode skewness (R² = 0.76), highlighting its sensitivity to surface reflectance and vegetation dynamics. In comparison, nadir mode skewness demonstrated greater stability with minimal correlation to NDVI.
The study underscores the need to consider environmental factors, such as vegetation coverage and observation mode differences when interpreting OCO-2 data. By identifying the role of seasonal variability in satellite-ground measurement discrepancies, these findings contribute to refining retrieval algorithms and enhancing satellite-based XCO₂ monitoring accuracy. Improved accuracy supports the development of more reliable carbon flux models, which are essential for effective climate policy and mitigation strategies. Future studies should replicate this analysis at other TCCON stations and incorporate additional environmental variables to further elucidate the drivers of seasonal biases in OCO-2 observations.
How to cite: Nacson, T., Broday, D., and Kizel, F.: Seasonal Bias in OCO-2 XCO2 Satellite Observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9725, https://doi.org/10.5194/egusphere-egu25-9725, 2025.
Environmental, Social, and Governance (ESG) principles are critical for improving sustainable mining practices and ensuring mining accountability in mitigating environmental and social impacts. Geometallurgy enhances the efficiency of resource extraction and processing by integrating geological, mineralogical, and metallurgical data throughout exploration and operational phases. Mining operations can contribute to ESG goals, decrease waste, and maximise resource utilisation by identifying temporal changes in mine surface features and their environmental impacts for optimisation. These impacts arise from processes throughout the mine life cycle, requiring continuous monitoring of mine expansion and environmental footprint. Hence, predicting the feasibility of a mining project as early as possible is crucial to minimising the impact of exploration activities and avoiding later failures that could have been anticipated. To this purpose, we develop a strategy to include ESG aspects as early as possible in addition to the now common geometallurgical aspects. This study integrates a range of satellite sources (SPOT 1-5, Landsat 5-9, Sentinel-2, and high-resolution Google Earth imagery) to quantify temporal changes in mine surface features across four mines (Vametco, Mogalakwena, Trident, and Gamsberg) representing diverse commodities (vanadium, platinum, copper, and zinc) and identify environmental impact trends for assessment and planning. We manually mapped key mine features, such as pits, overburden waste dumps, tailing dams, slag dumps, stockpiles, and processing areas. Using deep learning methods, we used mine features as training data to explore temporal multiclass change detection with multisource satellite data. We compare the manually mapped results with the deep learning methods and analyse correlations across mine sections, focusing on lateral expansions of mine surface features rather than vertical or depth expansions. Additionally, we assess how mine operations affect environmental components like vegetation, land use, and carbon emissions. Our results demonstrate the usage of satellite data for cost-effective mine monitoring to improve transparency and support compliance with ESG guidelines.
How to cite: Nwazelibe, V., Yu, W., Gloaguen, R., Kirsch, M., Tolosana-Delgado, R., and Thiele, S.: Satellite-Based Quantification of Temporal Changes in Mine Areas and their Environmental Footprint, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15180, https://doi.org/10.5194/egusphere-egu25-15180, 2025.
The Bidirectional Reflectance Distribution Function (BRDF) quantifies the reflected light from a surface as a function of illumination and observation angles. It is a crucial yet challenging aspect of remote sensing, essential for characterizing surface reflectance and properties. Accurate BRDF measurements are integral to surface property analysis and various remote sensing applications. Conventional methods, such as goniometers, provide precise angle-dependent evaluations. However, their high cost, bulkiness, and limited portability significantly hinder their deployment in diverse real-world scenarios. Alternatively, free-handed BRDF measurement techniques eliminate fixed setups but suffer from human error and subjectivity, leading to inconsistent results.
We propose a novel automated system combining a robotic arm and spectral sensors to address these limitations. The system utilizes a robotic arm to precisely maneuver the sensor on a hemispherical trajectory around the target surface, ensuring consistent angles and distances throughout the measurement process. Specifically, the UR10e robotic arm by Universal Robots, with its 12.5 kg payload, 1300 mm reach, and six flexible joints, was employed for its precision, flexibility, and advanced motion control capabilities.
Programming the robotic arm for BRDF measurements required solving a constrained generalized inverse kinematics problem optimized using fuzzy logic to ensure collision-free movement and clear sensor fields of view. Experimental validation demonstrated exceptional sensor localization accuracy, achieving an angular precision of 0.1° under optimal conditions. This automated system facilitates spectral BRDF measurement and modeling across various surfaces with enhanced accuracy, speed, and operational feasibility.
Once the method is fully validated under controlled laboratory conditions, we intend to extend the application of this system to outdoor, real-life scenarios. The robotic arm will be mounted on a platform to conduct measurements in natural environments. This next step aims to evaluate the system's robustness and effectiveness in capturing BRDF data under varying environmental conditions, ultimately confirming its suitability for real-world applications. Such advancements will significantly enhance the accuracy and practicality of BRDF measurements for diverse industries and research domains.
How to cite: Amir, A. and Kizel, F.: A Novel Approach for Automatically Measuring the Bidirectional Reflectance Distribution Function (BRDF) of Surfaces Using Spectral Sensors and a Robotic Arm., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15542, https://doi.org/10.5194/egusphere-egu25-15542, 2025.
Landslides represent a significant natural hazard on a global scale, resulting in considerable economic losses and indirect social impacts. Italy is one of the European countries most affected by landslides, with more than 500,000 mapped events, of which approximately 100,000 are located in Tuscany. Remote sensing has emerged as a powerful tool for the investigation and monitoring of ground deformation. Earth observation techniques, particularly the Interferometric Synthetic Aperture Radar (InSAR) analysis and optical imagery enable ground deformation measurement with millimetric to centimetric precision and high temporal frequency.
The present study focuses on the municipality of Zeri, in the province of Massa-Carrara (Tuscany), specifically on the hamlets of Patigno and Coloretta affected by quiescent and active landslides. These areas have been selected considering the displacement recorded by the Interferometric Synthetic Aperture Radar (InSAR) data from Sentinel-1 for the period 2019-2023. Data is made available from the European Ground Motion Service (EGMS), enabling precise measurements of ground deformation over time with millimetric accuracy through the time-series analysis. For this reason, Zeri municipality was chosen as a case study for exploring the interplay between optical and radar satellite-derived deformation data and in-situ information on buildings impacted by landslides. A multi-temporal analysis integrating advanced remote sensing techniques employs optical imagery, acquired from high-resolution sensors such as WorldView-2/3, SPOT-7, and other oldest aerial optical datasets, to provide long-term information on surface changes, vegetation displacement, and impact of the landslides on the structures and infrastructure.
The integration of these datasets allows for a comprehensive assessment of the spatial and temporal evolution of ground movements, highlighting areas of active deformation and their direct impact on built structures. By correlating both radar and optical satellite-derived deformation trends with detailed in-situ surveys of buildings, the study aims to identify patterns of structural vulnerability and the progression of damage linked to ongoing ground instability. This dual approach leverages the strengths of optical and SAR data to enhance the understanding of landslide dynamics in this geologically complex area, providing a robust basis for further risk assessment and mitigation planning.
The research is part of the PRIN-PNRR project SMILE: Statistical Machine Learning for Exposure development, funded by the European Union- Next Generation EU, Mission 4 Component 1 (CUP F53D23010780001), which aims to investigate how Machine Learning (ML) can be used to assemble or update exposure layers by combining crowdsourced data gathered by trained citizens, ancillary data (such as national census data), and remote sensing images.
How to cite: Nardini, O., Poggi, F., Del Soldato, M., Bianchini, S., and Scaini, C.: Long term ground deformation analysis of landslide integrating remote sensed and in-situ data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15819, https://doi.org/10.5194/egusphere-egu25-15819, 2025.
Uncrewed Aerial Vehicles (UAVs) have transformed remote sensing, offering unparalleled flexibility and spatial resolution across diverse applications. Many of these applications rely on mapping flights using snapshot imaging sensors, for creating 3D models of the area, or for generating orthomosaics from RGB, multispectral, hyperspectral or thermal cameras. Based on a literature review, comprehensive guidelines for executing mapping flights for the different sensors are here formulated, addressing flight preparation, planning and execution. Key considerations in flight preparation and planning covered include sensor selection, flight altitude and GSD, flight speed, overlap settings, flight pattern, direction and viewing angle; considerations in flight execution include on-site preparations (GCPs, camera settings, sensor calibration and reference targets) as well as on-site conditions (weather conditions, time of the flights) to take into account. In all these steps, high-resolution and high-quality data acquisition needs to be balanced with feasibility constraints such as flight time, data volume and post-flight processing time. The formulated guidelines are based on literature consensus. However, knowledge gaps for mapping flight settings are identified, particularly in flight direction and for thermal imaging in general. These guidelines and identified knowledge gaps are useful to advance the harmonization of UAV mapping practices, promoting reproducibility and enhanced data quality across diverse applications.
How to cite: Maes, W.: Practical guidelines for performing UAV mapping flights , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18572, https://doi.org/10.5194/egusphere-egu25-18572, 2025.
The inland rivers under the geographical pattern of the mountain-basin system in the arid areas of China have special ecosystem types and landscape appearances. The inland river basin, represented by the Tarim River, has given birth to different ecosystem types and landscape appearances of river corridors-vegetation or farmland patches-desert matrix patterns. Through multi-source remote sensing(RS) data, geographic element monitoring data, ecological environment statistical survey data, model simulation and other multi-source data, the data verification and normalization are carried out, and the ecological environment quality(EEQ) characteristics are obtained through model operation and analysis.The results show that due to the constraints and influences of the mountain-basin system, precipitation shortage, strong evaporation, and severe drought lead to the widespread salinization of the basin. In addition, the sparse vegetation and the frequent occurrence of sandstorm disasters have led to drastic changes in the spatiotemporal dynamics of desertification. Under the multiple stresses of climate change and the development of man-made water resources and land resources, a series of changes have taken place in the EEQ. The analysis of panchromatic aerial RS images (1959), color aerial RS images (1992), JERS-1 OPS RS images (1995) and MODIS RS images (2023) shows that desert riparian forests show a discontinuous distribution on the north and south sides of the main stream corridor, and the vegetation tends to be degraded from the source area to the upper, middle and lower reaches of the main stream. Since 2000, the water resources allocation project in the basin has alleviated the vulnerability of water resources to spatiotemporal changes, slowed down the degradation trend of EEQ in the basin, and significantly improved the EEQ in some areas.Based on the systematic analysis of multi-source data such as hydrology, soil, climate, vegetation and landscape pattern changes in the basin, combined with the SSP climate scenario model, it is found that the future temperature and precipitation will show an upward trend under the SSP2-4.5 and SSP5-8.5 scenarios. Through the establishment of the ecological risk index (ERI) model, the quantitative evaluation showed that the ERI values of the Aksu River Basin in the headwaters were 0.08 and 0.06 in 1998 and 2023, respectively, indicating that the EEQ was in a stable and improving state in the past 25 years, and the EEQ continued to improve. It is estimated that by 2040, drought and flood disasters in the basin will be further aggravated, and the evolution of EEQ will be complex and uncertain.
How to cite: Ranghui, W.: Evolution of ecological environment quality in China's inland river basin based on multi-source data and model analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4052, https://doi.org/10.5194/egusphere-egu25-4052, 2025.
The evolution of Inland Water Area (IWA) is strongly influenced by climate change. Against the backdrop of frequent extreme heat events in recent years, the impact of summer heat on IWA warrants further attention. Using the Google Earth Engine platform and integrating multi-source remote sensing data, this study developed a refined identification scheme capable of effectively capturing small inland water bodies. Then, the linear slope and coefficient of variation were used to reveal the spatio-temporal variation characteristics of IWA at county scale in Shaanxi Province during the summer seasons from 2016 to 2022. Subsequently, coupled with the estimated daily maximum temperature data, the spatio-temporal correlation between summer heat and IWA was quantified by applying Pearson correlation coefficient and the global Moran's index. Finally, the driving process of summer heat on IWA was explored by using Geodetector and geographical weighted regression model in conjunction with various natural factors. The results show that: (1) The overall accuracy of the inland water body identification scheme developed in this study is 0.967, with the Kappa coefficient of 0.924. The spatial distribution of inland water bodies in Shaanxi Province during the study period is uneven, with higher fluctuations in areas with fewer water bodies. And there is an overall increasing trend in the spatio-temporal variation of IWA. (2) The spatial and temporal correlations between heat indices represented by TXx, TX5d, TX7d, TX10d, TX15d, and TX90p and IWA are all negatively correlated. The highest correlation was observed between TX10d and IWA, with a Pearson correlation coefficient of -0.812 and a Global Moran’s I of -0.173. (3) The summer heats in Shaanxi Province negatively inhibit IWA, with regression coefficients ranging from -0.110 to -0.483. In addition, the dry areas with fewer IWA in northern Shaanxi and flatter terrains in Guanzhong are susceptible to summer heat inhibition. Moreover, the absolute values of the regression coefficients between TX10d and IWA gradually decreased from the arid, low-precipitation climate of northern Shaanxi to the humid, high-precipitation climate of southern Shaanxi, indicating that the arid climate and insufficient precipitation will magnify the inhibitory effect of summer heat, whereas the humid climate and abundant rainfall will alleviate the negative effect of summer heat. On the other hand, surface runoff showed the positive effect on IWA, with regression coefficients ranging from 0.110 to 0.449. Climate warming induces an increase in surface runoff, which is conducive to the expansion of IWA. This study provides a scientific reference for the rational planning and management of surface water resources under climate warming scenarios.
How to cite: Gao, M., Wen, Y., Li, J., Tan, Z., and Li, Z.: Evolution of Inland Water Bodies in Summer and Their Response to Heatwaves: A Case Study of Shaanxi Province in China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18858, https://doi.org/10.5194/egusphere-egu25-18858, 2025.
Monitoring and managing recreational trails are necessary to promote recreation and conservation of protected natural areas (PNAs). However, the relationship between the usage of recreational trails and its impact on the immediate environment can be complex, where a small increase in number of visitors can cause significant damage, or conversely, intensive usage of recreational trails could have a very minimal impact due to other factors like management practices, trial design, the behaviours of users, soil type and resilience make it a non-linear relationship. This research seeks to assess and investigate the relationship between recreational trail width and several morphometric parameters of the trail and its vicinity (e.g., trail gradient, trail aspect, landform gradient, landform aspect, and topographic wetness index). The main aim of this study is to use geographic information systems (GIS) and unmanned aerial vehicle (UAV) data to produce consistent morphometric information about trail conditions. We present a step-by-step workflow demonstrating how to use orthomosaic and digital elevation models (DEMs) generated from UAV surveys to delimitate trail tread and subsequently provide data on trail width, gradient and aspect in a semi-automatic, objective way. The deliverable of our work is a toolbox for ArcPro, which can be implemented to generate trail and terrain characteristics in any area for which trail tread polygon and DEM are provided. Analysing relationships between trail width and other morphometric parameters will help understand factors affecting trail conditions. This study is supported by Polish National Science Center project OPUS-22 2021/43/B/ST10/00950.
How to cite: Dwamena, T. K., Ewertowski, M., and Tomczyk, A.: Application of UAV surveys and GIS to investigate factors affecting recreational trail width , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10367, https://doi.org/10.5194/egusphere-egu25-10367, 2025.
This research investigates two methodologies for correcting the Bidirectional Reflectance Distribution Function (BRDF) in Sentinel-2 imagery, focusing on transition zone in Israel between arid and sub-humid climatic zones across a year-long temporal span. BRDF correction is critical in remote sensing applications, especially for multi-temporal analysis, as it accounts for variations in surface reflectance due to changing illumination and viewing geometries. The study aims to compare the performance of these methods in normalizing surface reflectance and minimizing angular effects, enhancing the accuracy of time-series analysis for environmental monitoring. In particular, we test the performance of the traditional semi-empirical kernel-driven BRDF model, namely the Ross-Thick-Maignan (RTM) volumetric kernel and the Li-Transit-Reciprocal (LTR) geometric kernel and a recently proposed correction method RTM-LS-UMx, which relies on the kernel-driven model but incorporates the spectral unmixing results within the inversion process. This method was reported to be advantageous for mosaics of airborne images and laboratory data in previous work. Therefore, we aim to test this technique on satellite images influenced by seasonal changes in the sun’s position. We analyzed images of a transition zone in Israel between arid and sub-humid climatic zones to achieve this. The images were acquired in summer, mid-fall, and winter; thus, the sun’s position at this latitude affects the measured reflectance. Pre-processing steps included radiometric calibration, atmospheric correction, and cloud masking to ensure consistency across datasets. Quantitative evaluation used performance metrics, including the Normalized Difference Vegetation Index (NDVI) stability, Root Mean Square Error (RMSE) against a ground-truth dataset, and angular dependency reduction. Results indicated that both methods significantly improved reflectance consistency compared to uncorrected imagery. However, the unmixing-based model RTM-LS-UMx was advantageous concerning all examined metrics. The study further explored the impact of BRDF correction on long-term environmental monitoring applications. Time-series analysis revealed that both methods enhanced the detection of subtle surface changes previously obscured by angular variations.
How to cite: Vidro, Y. and Kizel, F.: Traditional and Unmixing-Based semi-empirical models for BRDF correction in time series data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19616, https://doi.org/10.5194/egusphere-egu25-19616, 2025.
Posters virtual: Tue, 29 Apr, 14:00–15:45 | vPoster spot 4
EGU25-15381 | ECS | Posters virtual | VPS19
Prediction of landuse landcover using CA-Markov model for the valley regions of Manipur, IndiaTue, 29 Apr, 14:00–15:45 (CEST) | vP4.9
Changes in land use and cover (LULC) serve as critical indicators of socioeconomic and environmental shifts induced by both natural and man-made factors. This assessment was carried out in the Imphal valley region to forecast changes in land use and land cover. In order to examine the spatiotemporal distributions of LULC, the LULC Classification was analysed using Landsat images from 2007, 2014, and 2017. The CA-Markov Chain model was used to simulate the future LULC for the year 2030 of Imphal valley region based on these the past LULCs. The model result showed that wetland herbaceous will decline by 3.3% and settlement area will expand by 28.71%. The Imphal city area is where the majority of the expanding settlement area is located. As a vital resource for future planning initiatives, this study suggests planners, environmentalists, and decision-makers to prioritise sustainable practices and make appropriate decisions for the sustainability of the region.
How to cite: Nongthouba, M., Oinam, B., and Sachidananda, K.: Prediction of landuse landcover using CA-Markov model for the valley regions of Manipur, India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15381, https://doi.org/10.5194/egusphere-egu25-15381, 2025.
EGU25-7921 | ECS | Posters virtual | VPS19
Integrating high-resolution satellite and multispectral drone Imagery for monitoring vegetation in the Chaschoc-Sejá lagoon systemTue, 29 Apr, 14:00–15:45 (CEST) | vP4.10
Seasonal ecosystems play a crucial role in environmental regulation and biodiversity by hosting complex ecological dynamics that vary with climatic conditions. The Chaschoc-Sejá wetlands are a key example of such systems in southeastern Mexico. The interaction between the lagoon system and the Usumacinta River is highly dynamic; during the rainy season, the lagoons increase in volume, reaching depths of 8 to 10 meters. However, the lagoons completely dry up during the dry season, leaving vegetation at the surface level.
This project aims to analyze the dynamics of vegetation cover in this environment by comparing high-resolution satellite images (Planet, 3 m) and ortho-mosaics generated with a DJI Mavic 3 Multispectral drone (10 cm). By combining these datasets, we aim to improve our previous vegetation maps and obtain a more accurate and detailed assessment of the Chaschoc-Sejá Lagoon system. Understanding vegetation patterns at a larger scale during specific periods and the variations in plant life within the lagoon and along its shores is a key focus.
Data processing involved classifying vegetation cover and identifying seasonal changes using indices such as NDVI and NDWI. We also generated 3D models to estimate vegetation height. Results show that integrating both techniques significantly improves spatial resolution and temporal accuracy in monitoring these ecosystems. This study provides essential tools for managing seasonal systems and their conservation in the face of climatic and anthropogenic factors. This monitoring will aid in understanding vegetation status, identifying plant species, and contributing to managing and preserving the lagoon system.
How to cite: Nieto, J., Ramírez Serrato, N. L., Romero Herrera, A., Peralta Carreta, C., Herrera Zamarrón, G., Hernández Hernández, M. A., Hernández García, G. D. J., Olea Olea, S., Morales Casique, E., and Cortez Silva, A.: Integrating high-resolution satellite and multispectral drone Imagery for monitoring vegetation in the Chaschoc-Sejá lagoon system, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7921, https://doi.org/10.5194/egusphere-egu25-7921, 2025.
