Water quality is crucial for human health and the sustainable development of the ecological environment. Traditional water quality monitoring methods rely on discrete in-situ measurements, limiting our understanding of water quality variations at large temporal and spatial scales. While remote sensing technology offers efficient water quality observation, it is mostly confined to monitoring optical active substances, making it challenging to assess water quality changes caused by chemical indicators. This study proposes a concept that changes in water quality status caused by chemical indicators within a certain range are responsive in terms of water-leaving reflection. To validate this hypothesis, water quality index (WQI) was initially calculated using data from water quality monitoring stations, resulting in water quality status data (ranging from excellent to severe pollution). Following this, an information extraction and classification inversion approach was proposed to establish a connection between ZY1-02D hyperspectral imagery and water quality status, leading to the development of a robust water quality status identification model. Validation results showed an average model accuracy of up to 82%, confirming the hypothesis of this study. Subsequently, this model was used to assess the water quality status of 180 large lakes and reservoirs (hereafter referred to as lakes) within China from 2019 to 2023 for the first time. The results indicated that 76.1% of the lakes exhibited excellent to good water quality conditions, with a spatial distribution pattern showing a "better in the west, worse in the east" trend. Over the 4-year period, 33.33% of the lakes showed improvement, while 50% remained stable, with the western and eastern regions primarily exhibiting stability and improvement, respectively. The long-term changes in water quality status are influenced by various interacting factors, with different patterns of influence existing in different time periods and regions. In the early years, natural factors (average elevation) played a dominant role. However, over time, the impact of meteorological factors (precipitation and wind speed) and anthropogenic factors (gross domestic product) gradually increased. These influences can be attributed to significant climate changes and effective management measures over the past two decades. The findings support rapid assessment of environmental conditions and sustainable resource management, highlighting the potential of remote sensing technology in water quality monitoring.

How to cite: Tang, H., Xiao, C., Shang, K., and Wu, T.: A new method for remote sensing assessment of water quality status based on ZY1-02D hyperspectral imagery—A case study of large lakes and reservoirs in China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15052, https://doi.org/10.5194/egusphere-egu24-15052, 2024.

Airborne hyperspectral remote sensing data provide a wide range of rapid, non-destructive and near laboratory quality reflectance spectra for mineral mapping and lithological discrimination, thereby ushering an innovative era of remote sensing. In this study, NEO HySpex cameras, which comprise 504 spectral channels in the spectral ranges of 0.4–1.0 μm and 1.0–2.5 μm, were mounted on a delta wing XT-912 aircraft. The designed flexibility and modular nature of the HySpex aircraft hyperspectral imaging system made it relatively easy to test, transport, install, and remove the system multiple times before the acquisition flights. According to the design fight plan, including the route distance, length, height, and flight speed, we acquired high spectral and spatial resolutions airborne hyperspectral images of Yudai porphyry Cu (Au, Mo) mineralization in Kalatag District, Eastern Tianshan terrane, Northwest China.

Using hyperspectral images on our own HySpex airborne flight, we extracted and identified alteration mineral assemblages of the Yudai porphyry Cu (Au, Mo) mineralization (Kalatag District, northwest China). The main objectives of this study were to (1) acquire HySpex airborne hyperspectral images of the Yudai Porphyry Cu (Au, Mo) mineralization, (2) determine a workflow for processing HySpex images, and (3) identify alteration minerals using a random forest (RF) algorithm and a comprehensive field survey.

By comparing the features of the HySpex hyperspectral data and standard spectra data from the United States Geological Survey database, endmember pixels of spectral signatures for most alteration mineral assemblages (goethite, hematite, jarosite, kaolinite, calcite, epidote, and chlorite) were extracted. After a HySpex data processing workflow, the distribution of alteration mineral assemblages (iron oxide/hydroxide, clay, and propylitic alterations) was mapped using the random forest (RF) algorithm. The experiments demonstrated that the workflow for processing data and RF algorithm is feasible and active, and show a good performance in classification accuracy. The overall classification accuracy and Kappa classification of alteration mineral identification were 73.08 and 65.73%, respectively. The main alteration mineral assemblages were primarily distributed around pits and grooves, consistent with field-measured data. Our results confirm that HySpex airborne hyperspectral data have potential application in basic geology survey and mineral exploration, which provide a viable alternative for mineral mapping and identifying lithological units at a high spatial resolution for large areas and inaccessible terrains.

How to cite: Shanshan, W.: Identifying and Mapping Alteration Minerals Using HySpex Airborne Hyperspectral Data in the Yudai Porphyry Cu (Au, Mo) Mineralization, Kalatag District, NW China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5528, https://doi.org/10.5194/egusphere-egu24-5528, 2024.

Geological mapping in dynamic coastal areas is crucial, but traditional methods are laborious and expensive. Employing uncrewed aerial vehicle (UAV) - based mapping for high-resolution imagery, combined with deep learning for texture classification and segmentation, offers a promising improvement.

In the “AI-aided Assessment of Mass Movement Potentials Along the Steep Coast of Mecklenburg Western Pomerania” project, we explore the use of deep learning for geological mapping. We conduct repetitive UAV surveys across five distinct coastal areas, documenting various cliff types under different lighting and seasonal conditions. The imagery yields texture patterns for categories such as vegetation, chalk, glacial till, sand, water and cobble.

We apply two strategies: classification and semantic segmentation. Classification predicts one label per texture patch, while semantic segmentation labels each pixel. Classification requires distinct files with pre-labeled textures, whereas segmentation needs a training dataset with label masks, assigning class values to each texture pixel.

We employ Convolutional Neural Networks (CNN) for classification tasks, designing custom nets with convolutional blocks and attention layers, and testing existing architectures like ResNet50. We evaluate classification performance using accuracy measures and run sensitivity analysis to identify the smallest effective patch size for texture recognition. The effective patch size determines the final mapped class resolution. Classification is less detailed than segmentation but potentially more generalizable.

For semantic segmentation, we employ UNet architectures with encoder-decoder structures and attention gates for improved image context interpretation. We evaluate segmentation using the intersect over union index (IoU). Due to the need for extensive, accurate training data, we employ data augmentation to create artificial datasets blending real-world textures inspired by the Prague texture dataset.

Classification results show about 95% accuracy across target classes using RGB image input. Notably, the pre-trained ResNet50 exhibits moderate performance in texture recognition and is outperformed by simpler net designs trained from scratch. However, it shows adequate performance when pre-trained weights are neglected. For overall classification improvement, we anticipate that adding a Near Infrared band (NIR) will enhance classification, particularly for vegetation and glacial till, which are currently prone to misclassification.

Semantic segmentation yields IoUs of around 0.94 on artificial datasets. However, when applied to real-world imagery, the models show a noisy performance, yielding significant misclassifications. Thus, better generalization requires further fine-tuning and possibly integrating real-world data along with artificial datasets. Also, further experiments with data augmentation by extending the dataset and introducing different complexity levels could provide better generalization to real-world data.

In summary, combining UAV mapping with AI techniques holds significant potential for improving geological mapping efficiency and accuracy in dynamic coastal environments, providing reliable parametrization for data-driven models that require up-to-date geological information in high resolution.

How to cite: Schüßler, N., Torizin, J., Fuchs, M., Kuhn, D., Balzer, D., Gunkel, C., Prüfer, S., Hahne, K., and Schütze, K.: Rapid geological mapping based on UAV imagery and deep learning texture classification and segmentation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8334, https://doi.org/10.5194/egusphere-egu24-8334, 2024.

As fine-resolution remotely sensed data rapidly evolves, individual trees are increasingly becoming a prevailing unit of analysis in many scientific disciplines such as forestry, ecology, and urban planning. Fusion of airborne LiDAR and aerial photography is a promising means for improving the accuracy of individual tree mapping. However, local misalignments between these two datasets are frequently ignored. Solving this problem using traditional pixel-based image registration methods requires extensive computation and is extremely challenging on large scales. In our earlier research, we proposed an approach that involved determining the optimal offset vector for a local area and using it to rectify the spatial positions of all individual trees in that area. Although the approach is effective in addressing mismatch issues, it still exhibits large errors for some trees and is susceptible to changes in scale. Here, we propose an enhanced algorithm by constructing a data structure called a k-dimensional tree (also known as K-D Tree) to efficiently search for each tree’s unique offset vector and assigning the closest determined offset vector to candidate trees that lack corresponding counterparts in the reference data. The enhanced algorithm significantly improves the matching accuracy of individual trees, elevating it from 0.861 ± 0.152 to 0.911 ± 0.126 (p < 0.01, t-test). Moreover, it substantially reduces the computational time by approximately 70% and successfully overcomes limitations associated with scale changes. The example data, source code, and instructions for the enhanced algorithm are publicly available on GitHub^*.

^*https://github.com/XUYIRS/Individual_trees_matching

How to cite: Xu, Y., Wang, T., and Skidmore, A.: An enhanced algorithm for co-registering individual trees extracted from airborne LiDAR and aerial photographs, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6725, https://doi.org/10.5194/egusphere-egu24-6725, 2024.

Semi-arid terrestrial ecosystems exhibit sparse vegetation, characterised by herbaceous non-woody plants (e.g., forbs or grass) and woody plants (e.g., trees or shrubs). These ecosystems encounter challenges from global climate change, including shifts in rainfall, temperature variations, and elevated atmospheric carbon dioxide (CO2) levels. Effective monitoring is essential for informed decision-making and sustainable natural resource management in the context of rapid environmental changes.

Fractional Vegetation Cover (FVC) is a key biophysical parameter for monitoring ecosystems, indicating their balance and resilience. The assessment of FVC is important for evaluating vegetation biomass and carbon stocks, pivotal components of ecosystem health. The precise mapping of FVC across various scales involves three key cover types: photosynthetic vegetation (PV), representing ground covered by green leaves facilitating photosynthesis; non-photosynthetic vegetation (NPV), encompassing branches, woody stems, standing litter, and dry leaves with reduced or no chlorophyll content; and bare earth (BE), representing the uncovered ground surface without vegetation. FVC offers a quantitative measure of the relative contribution of each cover type to the total ground surface, aiding in characterising vegetation composition.

Efficient and accurate remote sensing techniques are essential to complement conventional field-based methods for performing FVC measurements.  Drone remote sensing technologies provide opportunities to capture fine-scale spatial variability in vegetation, enabling the derivation of ecological (e.g., FVC), biophysical (e.g., aboveground biomass), and biochemical variables (e.g., leaf chlorophyll content). Local calibration and validation of drone products enhance upscaling to coarser spatial scales defined by satellite observations, improving the understanding of vegetation dynamics at the national scale for subsequent change detection analyses.

The research project applies deep learning methods in remote sensing to enhance understanding of ecosystem composition, structure, and function features, with a specific focus on diverse terrestrial ecosystems in Australia. Leveraging drone technologies and advanced deep learning algorithms, the project develops automated workflows for systematic ecosystem health assessments, thereby making a significant contribution to the validation of satellite observations. The research framework emphasises the potential of Deep Learning methods in generating FVC products from RGB and multispectral imagery obtained through drone data. The conclusion highlights the benefits of integrating LiDAR data with Deep Learning approaches for analysing denser vegetation structure scenarios, offering a holistic approach for a comprehensive understanding of ecosystem health and dynamics. This approach provides valuable insights for environmental monitoring and management.

How to cite: Sotomayor, L., Kattenborn, T., Poux, F., Turner, D., and Lucieer, A.: Investigating Deep Learning Techniques to Estimate Fractional Vegetation Cover in the Australian Semi-arid Ecosystems combining Drone-based RGB imagery, multispectral Imagery and LiDAR data., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5766, https://doi.org/10.5194/egusphere-egu24-5766, 2024.

Fractional Vegetation Cover (FVC) is an important vegetation structure factor for agriculture, forestry, and ecology. Due to its simplicity and reasonable precision, the vegetation index-based (VI-based) mixture model is commonly used to estimate vegetation cover from remotely sensed data. Improving the accuracy and computational efficiency of FVC estimations requires rapidly and precisely calculating the model's two most important parameters, namely the pure vegetation index of fully-vegetated and bare soil pixels. However, no pure normalized difference vegetation index (NDVI) values mapping has yet been produced. When there is a lack of pure pixels in many ecosystems, traditional empirical statistical approaches for obtaining pure vegetation index values are unreliable and challenging. In this study, the pure NDVI values mapping over China is achieved by combining the traditional empirical statistical method and the multi-angle remotely sensed inversion method (MultiVI), which can be adapted to various application scenarios for vegetation cover estimation when utilized with vegetation indices with different spatial and temporal resolutions. When the pure NDVI values extracted from a total of 19 GF-2 images in various parts of China were compared to those obtained in this study, the findings showed a good degree of accuracy. Furthermore, in semi-arid areas where fully-vegetated pixels are lacking and vegetation evergreen areas where bare soil pixels are lacking, this study can compensate for the fact that empirical statistical methods are unable to obtain accurate pure NDVI values and provide reasonable endmember NDVI values for vegetation cover estimation using the VI-based mixture model.

How to cite: Zhao, T., Song, W., and Mu, X.: Mapping pure vegetation index values based on multisource remote sensing data over China for estimation of fractional vegetation cover, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14124, https://doi.org/10.5194/egusphere-egu24-14124, 2024.

As the volume of satellite observation data experiences exponential growth, our ability to process this data and extract meaningful insights is struggling to keep pace. This challenge is particularly pronounced when dealing with dynamic and variable phenomena across diverse spatiotemporal scales. Achieving accurate representation of these nuances necessitates data generation at high spatial and temporal resolutions, resulting in significant redundancy in computation and storage.

This issue is notably evident in the case of products that monitor plant phenology over time, which are crucial for assessing the impacts of climate change and monitoring agriculture. Computational complexities often limit these products to coarse resolutions (500m-1km) or short time frames, distorting our understanding of phenology across scales. In contrast, various approaches in hydrology and land surface modeling have utilized tiled grids and meshes to capture spatial heterogeneity and reduce dimensionality for complex modeling. This is accomplished through decomposing or aggregating modeling surfaces into response units representative of system drivers and have been shown to enable improved computational capabilities while still maintaining accurate approximations. We believe that similar modeling techniques can be leveraged to enable phenological modeling at higher resolutions. 

Building on these advancements, we develop a variable resolution scheme to represent land surface heterogeneity for modeling Land Surface Phenology (LSP) and decompose Landsat and Sentinel-2 Enhanced Vegetation Index (EVI) into adaptive areal units. Through this method we operationalize the Bayesian Land Surface Phenology (BLSP) model, a hierarchical Bayesian algorithm capable of constructing LSP data for the complete Landsat archive. While BLSP produces highly valuable results, it faces computational challenges for large-scale applications as its current time series approach necessitates each pixel to be computed individually. Our innovative approach reduces the dimensionality of modeling LSP by an order of magnitude to improve computational efficiency and enable the production of a 30 m BLSP product. These improvements are key to provide a region wide long-term phenometrics product at 30m resolution necessary to support studies into the long-term changes at a fine scale.

How to cite: Smith, O., Gao, X., and Gray, J.: Overcoming Big Data Challenges in Satellite Observation: A Variable Resolution Scheme for Modeling Land Surface Phenology, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12119, https://doi.org/10.5194/egusphere-egu24-12119, 2024.

Shorelines, as interfaces between land and water, are subject to continuous transformation due to various natural phenomena and human-induced activities. Natural processes, such as erosion and sedimentation play an important role in shaping coastal areas, while human activities, like urban expansion, also exert a significant stress on coastal areas. An illustrative instance of human-induced changes is exemplified by the Mamaia beach enlargement project, which was initiated along the coast of Romania at the Black Sea by the end of 2020 and executed throughout 2021. The analysis of this coastal transformation started in 2020, preceding the actual implementation of beach enlargement, and extended until late 2023. This timeframe was selected to capture the entirety of the dynamic changes that can be observed in the study region. Utilizing the advanced multi-temporal CoastSat toolkit, the analysis involved a detailed examination of 130 high-resolution images acquired by Copernicus Sentinel-2 satellites. Implemented within a Jupyter notebook environment using Python, CoastSat showcased its efficacy in extracting shorelines from the multi-temporal dataset, enabling a thorough understanding of the coastal dynamics observed in the Mamia beach enlargement project. The analysis reveals an expansion of over 200 m on the southern part of Mamaia beach. This transformation underscores the significant impact of human activities, emphasizing the need for sustainable coastal management practices in the face of evolving environmental challenges.

How to cite: Toma, A., Sandric, I., Mihai, B., and Scrieciu, A.: Automatic analysis of shoreline dynamics on Sentinel-2 datasets using CoastSat software toolkit, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22091, https://doi.org/10.5194/egusphere-egu24-22091, 2024.

Biodiversity supervising through remote sensing assumes crucial importance in monitoring ecosystem integrity and resilience. This study features the integration of optical and LiDAR data coming from Unmanned Aerial Vehicles (UAV) for Digital Elevation Models (DEM) retrieval of the Lesina (Puglia, Italy) Coastal Dune Systems (CDS), aiming to support ecosystem monitoring for the habitat type 2250* "Coastal dunes with Juniperus spp”. This work aims to provide a Free and Open-Source Software (FOSS) workflow able to extract and calculate Digital Surface Model (DSM), Digital Terrain Model (DTM), and Digital Difference Model (DDM) through LiDAR and optical data in a very dense vegetation environment. By using the contribution of RStudio, Cloud Compare, and Quantum GIS software, it was possible to develop a useful methodology for DDM extraction, to compute Juniperus spp. architecture (areas and volumes), which can reflect habitat reduction and fragmentation when compared at different timescales. According to this, a point cloud integration from the two datasets (optical and LiDAR) was provided. Consequently, the generation of an orthophoto and a DSM occurred, needed for the extraction of a vegetation mask using spectral indices (e.g., Excess Green) and for the choice of a pixel threshold, both able to isolate as much as possible the contribution of the vegetation along the DSM. Using scripts in RStudio it was possible to simplify and speed up the processing procedure, inserting additional useful codes for a further isolation of the vegetation matrix from the terrain. Consequently, areas belonging to the presence of vegetation took “NoData” values. So, to fill these areas with significant elevation values of their surroundings, a linear interpolation technique was used by using Inverse Distance Weight (IDW) interpolator, obtaining a “raw” DTM populated by fewer signal noise due to wind disturbance and shading. Following this, the subsequent processing involves the elimination of persistent noise from the point cloud extracted from the “raw” DTM. By using the segmentation tool in Cloud Compare software, not-inherent cloud’s points were removed, allowing to eliminate altimetric errors in the elevation model. Following this operation, a final DTM was extracted from the point cloud representing more accurately the altimetry of the terrain in the study area. Finally, to obtain the height of the canopies, through the expression “DSM-DTM=DDM” used in the QGIS Raster Calculator, the DDM was obtained. The canopies have been considered as 2.5D geometries, resulting in heights representing only the contribution of the above ground biomass. Finally, vegetation areas and volumes were designed from the DDM computation, where canopies’ total volume calculation occurred by adding all the results obtained for each pixel of interest. Hence, this methodology allowed us to monitor biomass parameters (areas and volumes), by a FOSS methodology, in a CDS context with very dense Juniperus spp. vegetation.

How to cite: Ragazzo, A. V., Mei, A., Tomaselli, V., Carruggio, F., Berton, A., Fontinovo, G., Rana, F. M., and Adamo, M.: Digital Terrain Model retrieval within a Coastal Dune Systems by integrating Unmanned Aerial Vehicles’ optical and LiDAR sensors by using a FOSS workflow, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22442, https://doi.org/10.5194/egusphere-egu24-22442, 2024.