A system for monitoring crop growth conditions has been developed at the Remote Sensing Centre, Institute of Geodesy and Cartography. It determines crop conditions with the use of an index based on Copernicus data resampled to a 1 km2 spatial resolution. The index, called the Drought Identification Satellite System (DISS), is a function of the Temperature Condition Index (TCI) and meteorological index, characterizing climatic conditions on the territory of Poland, i.e., the hydrothermal coefficient (HTC). The DISS drought index is generated within a successive ten-day period during a vegetation season, starting from the end of March. The median of the HTC informs us about the average atmospheric conditions (in relation to precipitation and air temperature). These images refer to the growing season (from the end of April to September) and are formed with 10-day-step process. The spatial distribution of HTC median values in Poland is related to climatic conditions, which are influenced by Poland’s topography. The daily values of soil moisture are input into the system using Sentinel1 with the polarization VH and VV. The vegetation descriptor is related to VH−VV, and the soil moisture is related to the index σ° VV/VH.

The rapid growth of the global population has led to increased agricultural activity, resulting in significant challenges for waste management and environmental conservation. This study examines the impact of agricultural modernization on carbon emissions, land use, and biodiversity. It highlights the need for sustainable practices, such as watershed development, integrated resource management, and data-driven agriculture, to address these challenges and ensure food security and environmental sustainability. Additionally, this research investigates the potential benefits and risks of
using nanomaterials and organic fertilizers in agriculture.

Given the increasing challenges of sustainable agriculture, Nepal should prioritize policies that integrate digital technologies, sustainable land management practices, and circular economy approaches. This would involve promoting the adoption of Agriculture 5.0, investing in watershed development and management, and encouraging the use of organic fertilizers derived from agricultural waste. Furthermore, Nepal should strengthen its public--private partnerships to address issues like legacy phosphorus pollution and to promote the efficient use of water resources, particularly in the context of climate change and growing population pressures.

Baghdad city, the capital of Iraq, has undergone rapid urban expansion in recent decades due to many reasons, among which one of them is its accelerated economic growth. However, the growth rate of services has not kept up with the population growth, leading to the transformation of agricultural land into land for housing, roads, and other facilities. This rampant land development has caused numerous issues which planners are still grappling with. This study examines the application of remote sensing and Geographic Information System (GIS) techniques to monitor and evaluate the development of Land Use/Land Cover (LULC) in Baghdad city from 1973 to 2007. Moreover, the Land Suitability Analysis method (LSA) is used in this study for land management and forecasting suitable areas for planning and implementing projects in the city. The study area was classified into three zones according to purpose, nature, and restriction. One of these zones represented the best area for planning. The results show that the built-up urban area expanded continuously from 642.248 km² in 1973 to 907.8959 km² in 2003, with the exception of 1983, when it peaked at 1262.289 km². This increase has an impact on green areas and other facilities within the city. The master plan for Baghdad city, created in 1973, is widely regarded as the best for several reasons. It offers a clear vision for the city's future and serves as a comprehensive framework for current urban development. Its significance lies in the effective distribution of land use and functional zoning. The yield map from LSA is an effective tool for evaluating land use planning and management in areas that retain significant natural environmental and historical features. It can assist in developing a comprehensive land use plan for regions or sub-regions by assessing the suitability of each site for various land use options.

Land cover maps disseminate complex earth observation products by categorizing surface features into multipurpose reference maps. Neighborhood-level high resolution (HR) land cover maps give valuable information to cities, states, and governmental agencies (Szantoi et al., 2020). Analysis of HR urban land cover maps quantify fields, such as those bio-geophyical and social--economic, hazard mitigation, and change through time (Griffith & Hay, 2018; O’Neil-Dunne et al., 2014). Geographic Object-Based Image Analysis (GEOBIA) is a methodology of extracting information from remote sensing imagery. The process of GEOBIA applies segmentation algorithms to imagery by grouping similar pixels into objects. The homogenous objects store information about each pixel group, for example, spectral values (RGB, reflectance values), texture (contrast), spatial information (area, height), and contextual properties (length of shared border) (Hossain & Chen, 2019; Kucharczyk et al., 2020). GEOBIA was performed on high-resolution 0.5 feet, 8-bit, RGB-NIR, NAD83(2011) east-Illinois aerial imagery from 2021 for the Justice and Willow Springs neighborhoods of Cook County, Chicago, Illinois. These data were combined with lidar derivatives for a 3D understanding of the landscape. The segmentation was implemented with eCognition Developer 10.4 software. Preprocessing of imagery, lidar derivatives (DEM and nDSM), map schemes similar to National Land Cover Database (NLCD), and statistics analyses were prepared in ArcGIS Pro 3.3. Statistical analysis of accuracy will be completed (2/10/2025) with a comparison of land cover maps and imagery. This project can serve as an example of best practices for the development of high-resolution neighborhood scale land cover maps through GEOBIA. Future work could include the change through time of the Cook County urban environment.

Hyperspectral LiDAR is an emerging active remote-sensing technology that integrates the 3D spatial information acquisition capabilities of traditional LiDAR with the spectral information of hyperspectral imaging. It enables efficient, high-resolution, integrated spatial–spectral information acquisition and holds significant potential in fields like environmental awareness, as well as in forest and urban surveys.

Range resolution, a key metric in remote sensing, determines the ability to distinguish between two objects along a single line of sight. Traditional single-wavelength LiDAR is limited by the pulse width of the laser signal, resulting in limited range resolution. However, hyperspectral LiDAR can enhance the multi-targets’ range resolution through waveform processing and exploiting the correlation between multi-channel echoes without changing the pulse width.

This report first investigates the range-resolution enhancement mechanisms for hyperspectral LiDAR, focusing on waveform-processing techniques. Traditional methods, such as the multispectral waveform decomposition and multichannel interconnection decomposition method by Wuhan University, improve range resolution by comparing waveform decomposition methods or accumulate waveforms in different wavelengths to identify hidden waveform components. As a result, a 2 ns pulse width achieves 20 cm resolution, while a 4 ns pulse width reaches 43 cm resolution. However, these methods struggle with highly overlapped waveforms when target separation is extremely small.

To address this, we propose a novel solution: identifying highly overlapped waveforms before decomposition. When two targets are extremely close, the geometric center positions of overlapped waveforms recorded by different wavelengths exhibit aggregation and asymmetry, which is significantly different from the random distribution of single-target echoes. Then, we propose a new highly overlapped hyperspectral waveforms decomposition method. Our approach can enhance range resolution to 5 cm for a 4 ns pulse width. The report also discusses the potential of these technologies for complex target detection and future trends in hyperspectral LiDAR data processing, providing practical insights for related research.

Heavy rainfall, a rise in inflow, and an increase in water discharge during the 2024 monsoon season resulted in severe flooding across India. The increasing need for an early warning system to provide flood inundation depth and extent with a better lead time demands innovative solutions that combine the power of high-performance computing (HPC) with automated data assimilation. The novelty of this work lies in the parallel, automated, priority-based assimilation and preprocessing of actual and forecasted precipitation and evapotranspiration data for hydrodynamic modeling, leveraging high-performance computing. The hydrodynamic model then automatically utilizes the processed data to simulate flood propagation over discretized triangular mesh elements by solving Saint-Venant equations using a finite volume solver. High-performance computing (HPC) systems with a peak performance of 3.1 PFLOPS are employed to enable automated and parallel processing of vast datasets, significantly reducing the time required for data assimilation and simulation over an area of approximately 1.4 million sq. km. The system automates and optimizes data assimilation, preprocessing, and visualization, emphasizing a seamless workflow to enhance lead times, which are critical for advanced flood prediction. It also addresses manpower constraints, simplifies domain expertise requirements, minimizes data handling errors, and reduces time consumption. Remote sensing plays a pivotal role in this study by providing timely and accurate data, which are necessary for the early detection of potential flooding events. Satellite-based precipitation and evapotranspiration data, alongside other remote sensing technologies, are integrated into the automated data assimilation system. These data sources are crucial for accurately estimating water level changes across vast regions, ensuring that the hydrodynamic model receives up-to-date input for flood propagation simulations. By using remote sensing, we enhance the precision and reliability of the flood predictions, allowing for better forecasting of inundation depth and extent over larger river basins. Furthermore, remote sensing aids in assigning the initial conditions for the model.

This project develops a web-based system to monitor forest restoration using drones and AI, evaluating the effectiveness of restoration efforts at the UHE São Simão reservoir and providing more accurate results. Two field campaigns were conducted across 20 areas, totaling 208 hectares mapped using remote sensing techniques. The monitoring employed a DJI Matrice 210 V2 drone, equipped with a Livox Avia LiDAR sensor and a Sentera 6X multispectral sensor. The multispectral sensor captured images in six spectral bands (R, G, B, red edge, NIR, and RGB) with a spatial resolution of 4.8 cm per pixel, enabling the extraction of indices such as NDVI, SAVI, EVI, and AVI. NDVI proved effective in identifying areas with higher vegetative vigor, while AVI excelled at detecting invasive species. The images were processed and converted into orthomosaics, which were then cropped into smaller sections to facilitate algorithm execution. In total, 8,263 orthophotos, each 550x550 pixels, were generated, with 6,611 used for training and 1,652 for validating the AI models. Semantic segmentation was performed using the Awesome algorithm, and instance segmentation was conducted using Yolact, enabling detailed classification of vegetation, soil, tree species, and wildlife presence. The model achieved 82% accuracy for certain classes and 90% pixel-level accuracy in the final validation. The LiDAR data, with an average density of 200 pts/m², were processed using the R lidR package for manipulating LiDAR data. This enabled tree counting, the generation of DTMs (Digital Terrain Models) and CHMs (Canopy Height Models), and the extraction of metrics such as wood volume and biomass. Validation of the results was carried out using conventional inventories in 100 m² plots, serving as reference points for remote sensing estimates. The data are integrated into an interactive web platform featuring dashboards and interactive maps, which facilitate access to indicators and support real-time decision-making.

Agriculture, construction, search-and-rescue, environmental monitoring, and healthcare are just some of the sectors that utilize drones due to their adaptability and superior capabilities. As a result of the constant evolution of drone technology, their potential implementations are extensive and may provide solutions to new challenges in various industries. However, while the use of drones in conservation is on the rise, there has been a lack of research focusing on tropical forest ecosystems. To address this shortcoming, a lightweight drone was used to monitor wildlife in a tropical primary forest in southern Vietnam. The drone's effectiveness was evaluated during both day and night time. As a result of its compact size, the noise generated by the drone is considerably reduced, making it less intimidating to animals during photography and video recording. The results demonstrated that the drone's combination of thermal and visual sensors substantially improved its ability to distinguish between mammals with similar or dissimilar body sizes. Additionally, population sizes could be determined. Through this presentation, experience and observations regarding the use of drones in tropical forest field research will be shared. The application of drones in identifying the distribution of rarely-seen species or wild mammals in hard-to-reach areas is promising.

Transportation infrastructure strengthens societal and economic activities. However, the ageing of structures, compounded by environmental stressors and increasing usage, amplifies the demand for innovative, dynamic, and scalable monitoring solutions. Traditional methods, such as on-site inspections and static analyses, face limitations in providing consistent, real-time assessments over large-scale networks. To address these challenges, an integrated approach combining Digital Twin (DT) technology, advanced remote sensing, and Building Information Modeling (BIM) is proposed, enabling a transformative paradigm in infrastructure management. Digital Twins act as intelligent digital replicas of physical assets, capable of integrating real-time data from diverse monitoring technologies [1-2]. This study emphasizes the potential of combining Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) data with insights from laser scanner surveys, thermographic analyses, and UAV-based inspections. Laser scanners capture precise geometrical details and deformation patterns, while thermography identifies material degradation and subsurface anomalies. UAVs provide high-resolution imaging and localized monitoring, enhancing the density of data acquisition. These complementary techniques are further augmented by satellite-based information, which provides millimetre displacement measurements across large areas with high temporal and spatial resolution. Central to this research is the concept of data fusion, wherein the integration of multi-source datasets—including satellite imagery, laser scanners, thermal analyses, and UAV surveys—enables a holistic understanding of infrastructure conditions. The fusion of these diverse data streams within BIM and DT frameworks can facilitate dynamic, near-real-time monitoring, predictive maintenance, and optimized decision-making processes. Focusing on critical transportation assets such as bridges and viaducts, this study highlights how these integrated technologies advance the detection of structural anomalies, assess ageing effects, and support lifecycle management. The findings underscore the transformative potential of combining high-resolution satellite constellations, UAV technologies, and BIM-based Digital Twins to create resilient and sustainable infrastructure monitoring systems. This approach offers significant scalability, precision, and efficiency, establishing a new standard for the continuous management and preservation of transportation infrastructure.

Acknowledgements

This research is supported by the Project “PIASTRE” accepted and funded by the Lazio Region, Italy.

References

[1] Gagliardi V., et al. Digital twin implementation by multisensors data for smart evaluation of transport infrastructure. SPIE Optical Metrology. Multimodal Sensing and Artificial Intelligence: Technologies and Applications III, Munich, 2023.

[2] Napolitano A., et al., Integration of Satellite Monitoring data in a Digital Twin of Transport Infrastructure. Proceedings Volume 13197, Earth Resources and Environmental Remote Sensing/GIS Applications XV; 131970Y (2024) https://doi.org/10.1117/12.3034395

The development and widespread deployment of new-generation satellites for Earth Observation (EO) have dramatically enhanced the precision and scope of environmental and infrastructural monitoring. Equipped with advanced technologies such as SAR and multispectral sensors, these systems enable detailed assessments of infrastructure stability, provide high-resolution mapping of coastal erosion, and facilitate the evaluation of natural hazards, including floods and seismic events. The integration of multispectral data, capturing a broad range of frequency bands, introduces novel applications and deeper insights into dynamic environmental processes. Coastal environments and the land–sea interface present particularly significant challenges due to their inherent complexity and ecological sensitivity. These regions are crucial for the protection of valuable habitats and require the implementation of sustainable engineering principles to mitigate the associated risks. Processes such as coastal erosion, the pollution of fragile ecosystems, slope instability, and chemically aggressive conditions resulting from elevated chloride concentrations can severely affect transportation infrastructure in these areas, leading to potential damage or structural failure. Advanced satellite-based monitoring provides a transformative solution to address these challenges, enabling more effective sustainable management and enhancing the resilience of these critical regions. In this context, the computation of indices derived from multispectral data, including the Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), and Normalized Difference Vegetation Index (NDVI), provides quantitative metrics for water body delineation, vegetation water content estimation, and land cover change detection. This study presents preliminary findings on the integration of multi-resolution datasets processed through spectral indices to identify and quantify variations in reflectance properties associated with transport infrastructure and its surrounding environments. These methodologies are designed to inform detailed on-site analyses, utilizing conventional inspection techniques such as total stations and drone-based surveys. An integrated analysis was conducted, utilizing historical time series data from spectral indices derived from the Sentinel-2 multispectral sensor. MT-InSAR was employed to detect and quantify displacements affecting transport infrastructure, while the NDWI and NDMI were applied to monitor temporal variations in coastal regions, facilitating the detection of land cover changes and enabling a comprehensive assessment of both infrastructure and coastal dynamics.