LiDAR systems are powerful tools for sustainable forest management and ecological research, offering the capability to extract detailed information about canopy structure, tree height, biomass, carbon storage, and biodiversity. Unmanned Aerial Systems (ULSs) mounted on drones provide high spatial resolution, density, and flexibility for capturing detailed forest metrics. Their ability to fly at low altitudes enables the collection of fine-scale details. Conversely, Airborne Laser Scanning (ALS) systems, mounted on aircraft, are ideal for large-scale assessments, despite providing a lower point-cloud density compared to ULSs. This study evaluates individual tree segmentation algorithms in a coniferous forest ecosystem using two data sources: (1) high-density ULS LiDAR data collected with the Zenmuse L1 sensor on a DJI Matrice 300RTK drone, and (2) lower-density ALS LiDAR data from the third coverage of Spain’s PNOA-LiDAR project (Plan Nacional de Ortofotografía Aérea). The general methodology for processing LiDAR data involves preliminary steps to generate Digital Elevation Models (DEMs), Digital Surface Models (DSMs), and Canopy Height Models (CHMs). Subsequently, segmentation techniques are applied to assist tree-level forest analysis. Segmentation is critical for understanding forest structure; however, selecting the most suitable segmentation technique remains an active area of research. To address this issue, a comparative assessment of four commonly used segmentation algorithms (Watershed, Dalponte2016, Silva2016 and Li2012) was conducted using CHMs and normalized 3D point clouds derived from both high- and low-density LiDAR data. Ground-truth reference data were generated by the manual segmentation of individual trees in a representative plot. The results revealed that the Li2012 algorithm demonstrated the best performance in properly segmenting trees in both types of datasets.

Offshore wind turbines are problematic for radar detection due to their large Radar Cross Section (RCS) and micro-Doppler signature, and the multiple scattering path caused by the sea surface. These problems hinder the growth of wind farms and thus our ability to combat climate change. In this paper, we build a model based on Physical Optics (PO) and an improved four-path model, which takes the sea surface randomness into account, using the K-distribution. These methods are chosen as a compromise between model realism and computational efficiency. The proposed model is then applied to an offshore wind turbine observed by an X-band radar: its monostatic RCS and its micro-Doppler signature are computed and analyzed. The obtained results are compared to similar ones obtained at lower frequencies, and show good agreement with both measurements and simulation results at lower frequencies. Emphasis is placed on the impact of the sea surface on the signal scattered by the wind turbine, and on the impact of the high frequency on the model.

The detection of objects by radars is essential for civil applications such as coastal surveillance (detection of ships or oil slicks) and air traffic control (detection of aircraft), and also for military applications (detection of drones). To recover necessary information on the target (position, size, materials, shape), it is mandatory to accurately model the path of the wave emitted by the radar to the object. Therefore, many phenomena must be considered, such as refraction, relief, and ground composition. This is also useful for optimizing an antenna's position or mitigating the effect of man-made structures such as wind turbines or solar panels. In this context, the work presented here focuses on several hybrid models for calculating the radar cross section (RCS) of metallic targets in a maritime environment. The latter is based on a hybridization between two methods. First, the parabolic wave equation (PWE) solved in the wavelet domain is considered to propagate from the source to the target. Its main advantage is its ability to consider, over long distances, the relief (waves, islands), the effects of refraction within the propagation channel, and the ground composition. Second, we hybridize the PWE with different integral equation-based methods to account for the target and compute its RCS. To achieve this, the methods used here are physical optics and physical diffraction theory, which, being asymptotic, have the advantage of being fast, as well as the method of moments, which, being an integral equation method, takes more time but gives more accurate RCS values. The computation time is nevertheless accelerated by using a wavelet basis, which also improves the conditioning of the matrix. Numerical experiments in the VHF band are carried out to validate and compare the different hybrid models.

Green spaces in urban areas are essential for maintaining healthy and sustainable living environments, providing a multitude of environmental, social, and economic benefits. Urban green spaces contribute to air purification, temperature regulation, biodiversity preservation, and improved mental and physical well-being while mitigating urban challenges like the "heat island" effect.
This study focuses on assessing urban green spaces within the construction boundaries of the city of Sofia, Bulgaria, using machine learning (ML), satellite imagery, and geographic information systems (GISs).
This research utilizes satellite data from Sentinel-2 imagery, GIS tools, particularly QGIS, data preprocessing, and semi-automatic classification using the Spectral Angle Mapper (SAM) algorithm. The results were cross-referenced with data from CORINE Land Cover (CLC), a standardized European land classification system.
This study demonstrates how integrating multiple data sources and machine learning (ML) technologies improves the accuracy and efficiency of green space analysis. Semi-automatic classification methods trained with user-defined samples successfully distinguished land cover types, allowing for detailed mapping of vegetation, urban areas, and water bodies. This approach provides valuable insights for sustainable urban planning and natural resource management.
By applying these methods, we estimated the distribution and characteristics of green areas in Sofia, Bulgaria, highlighting the potential for GIS and remote sensing technologies to support evidence-based decision-making. The findings underscore the importance of integrating modern tools and data systems in urban development plans to address environmental and social challenges effectively.

This study demonstrates how integrating multiple data sources and machine learning (ML) technologies improves the accuracy and efficiency of green space analysis. Semi-automatic classification methods trained with user-defined samples successfully distinguished land cover types, allowing for detailed mapping of vegetation, urban areas, and water bodies. This approach provides valuable insights for sustainable urban planning and natural resource management.

The city of Maricá, located on the eastern coast of the state of Rio de Janeiro, has experienced significant population growth in recent decades, driven by economic and infrastructure factors. This study aimed to predict urban expansion for the decades of 2030 and 2040 using dynamic modeling with cellular automata and land use and cover generated from Sentinel orbital images. The objective is to assist in formulating planning and management strategies that balance growth and sustainability. The methodology involved image classification using the GEE platform and adjustments in static variables representative of change (terrain, transportation system, hydrography, and environmental protection units). After classification and validation through fuzzy analysis, future scenarios were generated for the years 2030 and 2040. The results indicate that the built-up area is expected to increase by over 40% by 2030 compared to 2019. The projection for 2040 suggests a continuation of this urban expansion, driven by factors such as oil exploration, infrastructure investments, and innovative social programs that attract new residents. It is concluded that the use of cellular automata and Sentinel images allows for a coherent simulation of urbanization trends and can guide sustainable urban planning actions, providing support for public management to minimize environmental impacts and promote balanced development.

Crop height is a crucial indicator in understanding crop growth, providing insights into developmental stages that are essential for precision agriculture. Sentinel-1’s capability to acquire images regardless of atmospheric conditions makes it ideal for monitoring these dynamics, though modelling wheat height across its growth cycle remains challenging due to structural changes in the plant. This research aimed to effectively capture and predict the height variability in wheat phenology, using clustering as a method to segment data by growth stage for stage-specific analysis. This study was conducted across three wheat fields in Umbria, Italy, from January 30^th to June 10^th 2024, where in-field measurements of plant height and phenology were carried out while acquiring twelve Sentinel-1 images. For image processing, two distinct speckle filters (Lee 7x7 and Refined Lee) were applied and several radar-derived variables were extracted, including VH, VV, CR, Entropy, Anisotropy, Alpha, and the Radar Vegetation Index (RVI). Visual analysis of the variables in relation to plant height suggested clusters within the dataset, which were confirmed through fuzzy C-means clustering. This approach successfully separated data into two phenological groups, allowing us to implement multiple linear regression models tailored to each cluster. The results highlight strong model performance in the early growth stages (from tillering to stem elongation) for both filters (R² 0.76 and RMSE 6.88 for the Lee 7x7 filter; R² 0.79 and RMSE 6.35 for the Refined Lee), while in later stages (from booting to maturity) model performance declined, with Lee 7x7 achieving a higher result (R² 0.51, RMSE 9.33) than the Refined Lee (R² 0.33, RMSE 10.89). These findings provide promising insights into height prediction in the early growth stages of wheat that are crucial for optimizing management strategies and ensuring high yields.

This study aims to develop and apply two spectroscopic techniques to characterize leaf color and identify riparian woody species that play a fundamental role in the stability of riverine ecosystems.

First, a handheld spectrophotometer was used to measure the chromatic coordinates in the green color (G) of leaves from five riparian woody species—Salix salviifolia, Populus nigra, Fraxinus angustifolia, Alnus glutinosa, and Prunus avium—located downstream of the "El Pardo" reservoir in Madrid, Spain (near a military base). Measurements were recorded in the CIELAB color space and subsequently converted to RGB for color differentiation analysis, totaling 1.000 chromatic readings. A generalized linear model (GLM) logistic regression was applied to compare green color (G) variations among the species.

Second, UV-vis spectroscopy was employed to quantify total chlorophylls (Chl), the primary pigments responsible for leaf coloration. A total of 605 plant samples were collected, comprising 11 samples per species. UV-vis spectra were measured from 400 to 700 nm using a quartz cuvette with a 1 cm optical path.

Alnus glutinosa showed the highest green reflectance at 98.8%, followed by Salix salviifolia at 92.3%. Regarding chlorophyll a concentration, Alnus glutinosa exhibited the highest levels (10.16 ± 1.69 mg/L), followed by Populus nigra and Fraxinus angustifolia with concentrations of 9.10 ± 0.71 mg/L and 9.18 ± 1.71 mg/L, respectively. Salix salviifolia and Prunus avium had lower chlorophyll a concentrations. To further assess green color (G) data, two chlorophylls' spectra were analyzed using PNOA orthophotos 2006, 2009, 2011, 2014, 2017, 2020, and 2023.

This study enhances our understanding of the relationship between green reflectance and chlorophyll content, providing valuable insights for ecological monitoring and riparian restoration efforts.

The Earth's environment is significantly impacted by solar radiation storms, caused by high-energy solar energetic particles (SEPs) that are emitted during solar flares or coronal mass ejections (CMEs). Specifically, these storms disrupt satellite operations, interfere with HF communications, and increase radiation exposure for high-altitude flights. To mitigate these effects, Korea Space Weather Center (KSWC) monitors and forecasts solar radiation storms using satellite data and predictive models. In this paper, we introduce KSWC's space weather forecasts and the analysis methodology for satellite data from GOES, SDO, the LASCO coronagraph, and STEREO. We then present the model structure for predicting solar radiation storms, which consists of (1) a machine learning model that is trained on solar flare and CME characteristics obtained from satellite data and (2) a physics-informed model based on SEP generation mechanisms, mediated by CMEs propagating toward Earth. Notably, the machine learning model predicts the maximum intensity of solar radiation storms based on the observed solar activity, while the physics-based model enhances the interpretability of the machine learning model's predictions. The applicability of these models in preventing the technological and biological impacts of solar radiation storms on Earth is also discussed.

Forest growing-stock (GSV) measurements at the national level are laborious and costly; however, integrating satellite data and machine learning (ML) methods provides an appreciable approach with great prospects. Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) were used to predict GSV using Estonian NFI data, Sentinel-2 imagery, and ALS point-cloud data. Four data scenarios were tested: vegetation indices and LiDAR (CO1), vegetation indices and individual band reflectance (CO2), LiDAR and individual band reflectance (CO3), and a combination of vegetation indices, individual band reflectance, and LiDAR (CO4). Comparatively, across Estonia’s geographical regions, RF consistently outperforms other performance models. In the northwest (NW), RF achieved the best performance with the CO3 combination, with an R2 of 0.63 and an RMSE of 125.39 m3/plot. In the southwest (SW), it yielded an R2 of 0.73 and an RMSE of 128.86 m3/plot with the CO4 variable combination. The RF performance in the northeast (NE) resulted in an R2 of 0.64 and an RMSE of 133.77 m3/plot under the CO4 combination. Finally, in the southeast (SE) region, the best performance was achieved with the CO4 combination, yielding an R2 of 0.70 and an RMSE of 120.56 m³/plot. These results underscore RF’s precision in predicting GSV across diverse environments, though refining variable selection and improving tree species data could further enhance accuracy.

Urbanization is one of the most important challenges contributing to important trend of replacing agricultural land with high-value land uses, such as housing, as well as industrial and commercial activities, as a result of significant population growth. To face these challenges and improve urban sustainability, integrating an embedded concept of spatial planning, taking into account urban and peri-urban agriculture, will contribute to mitigating food security and the negative impact of climate change, while improving social and economic development.

This project aims to analyze land use/cover changes in the Casablanca metropolitan area and its surrounding cities undergoing rapid urban growth. To achieve this, time series of remote sensing data were analyzed in order to investigate the spatio-temporal changes in LU/LC and to evaluate the dynamics and spatial pattern of the city’s expansion over the past three decades, at the expense of agricultural land. The study will also examine the relationship between urbanization and agricultural land use change over time. The results of this study show that Casablanca and its outskirts experience significant urban expansion and a decline in arable lands, with rates of 45% and 42%, respectively. The analysis of SDG indicator 11.3.1 has also shown that land consumption in the provinces of Mediouna, Mohammedia, and Nouaceur has exceeded population growth, due to rapid, uncontrolled urbanization at the expense of agricultural land, which highlights the need to develop a new conceptual framework for regenerating land systems based on the implementation of urban and peri-urban agriculture in vacant sites within the urban and peri-urban areas. This will offer valuable insights for policymakers to investigate the measures that can ensure sustainable land use planning strategies that effectively integrate agriculture into urban development.