Detailed and updated information on forest extent, forest types, or species composition is essential for monitoring carbon stocks, mapping forest losses and growth, biodiversity conservation, and supporting national policies on natural resources management and climate mitigation, among others. Within the framework of establishing a National Forest Inventory, the availability of a revised baseline forest types map is very important for the subsequent operations, particularly when existent information is based on measurements that occurred decades ago. Advanced satellite optical data and modern machine learning algorithms are promoting efforts for large-scale, cost-effective, and high-resolution mapping of forests and their attributes at frequent intervals. The aim of this study was to propose a remote sensing-based classification approach for generating spatially explicit forest type information, aligned with the Greek National Forest Inventory's hierarchical classification needs. Seasonal optical imagery and topographic data were utilized for mapping forest types over a large administrative region in Northern Greece. Three machine learning (ML) classification algorithms were also employed LightGBM, Random Forest (RF), and Support Vector Machines (SVM) and evaluated for optimal performance in accuracy and computation efficiency. Methodological steps included fine-tuning of the classifier's parameters and discrimination of the most important features before assessing their performance. LightGBM demonstrated the highest performance, balancing accuracy (OA = 81%) and computational efficiency, although marginal differences can be observed among the classifiers for certain classes. Class-specific results indicated high classification accuracy for dense forests, while misclassification was primarily observed in spectrally similar classes. Despite the complex classification scheme required for the NFI, the proposed methodology highlights the importance of incorporating seasonal imagery and variables such as high-resolution canopy height models for improving systematic mapping of forest types in heterogeneous Mediterranean ecosystems.

Forest biomass is a fundamental component of the global carbon cycle, essential for ecosystem protection and climate change mitigation. A variety of remote sensing data types, including optical, Synthetic Aperture Radar (SAR), have been explored for AGB and carbon quantification. Among these, multispectral and SAR image analyses are the most widely applied methods for large-scale biomass estimation. However, a significant challenge for both approaches is signal saturation, particularly in forested regions with complex topography. While various remote sensing techniques have been applied to estimate AGB, only one study has explored ICEYE's capabilities in AGB estimation. The present study aims to investigate the potential of Sentinel-1 and ICEYE SAR data in reliably estimating above-ground biomass (AGB), which represents the total dry weight of the different tree components. A series of textures and indices were calculated on both datasets and subsequently included as features in the predictive models. Regarding the AGB modelling, a regression analysis was performed to develop the AGB models using the Random Forest (RF) algorithm. Consequently, two models were created (one for each sensor), and their predictive performance was assessed through the k-fold cross-validation method. The results demonstrated that Sentinel-1 can reliably estimate AGB (i.e . R-2= 0.76, RMSE= 1616.66 kg, rRMSE= 23.97%, MA. = 1408.65 kg), while ICEYE data provided weaker performance (i.e. R-2= 0.59, RMSE= 2157.55 kg, rRMSE= 33.82%, MAE= 2502.10 kg). Overall, this study highlights the capability of SAR data to reliably estimate AGB in forests with rugged terrain when combined with Vertical-Horizontal textural characteristics and the RF algorithm.

The estimation of individual roundwood volume, has received limited attention in scientific literature despite its significance in forest sustainable management and wood products trading. This study aimed to develop an automated volume estimation approach, using a low-cost handheld Mobile LiDAR Scanner (MLS) and circle/cylinder fitting models. In addition, this study investigated the effect of seasonality on the MLS estimations by collecting samples in two different seasons. Specifically, field measurements were collected during Summer 2023 and Winter 2024, including the scanning of roundwood with the iPad-LiDAR sensor and the manual measurement of their biometrical characteristics. Two well-established stem modelling algorithms, namely RANdom SAmpling Consensus (RANSAC) and Iterative Reweighted Total Least Squares (IRTLS), were combined with Nelder Mead (NM) optimization for circle and cylinder fitting. These combinations resulted in five models that were investigated in their capacity to extract the length and diameter from the iPad-derived point clouds. Both reference and estimated biometrical characteristics were integrated with the Smalian equation to derive the predicted and reference volumes, respectively. The results demonstrated that the RANCAC circle fit combined with NM optimization achieved the best overall predictive performance for volume estimation yielding rRMSE = 15.04 % and R2 = 0.91. Additionally, the performance of this algorithm was marginally better during the summer, although no statistically significant difference was found between the two models. This study showed that low-cost handheld MLS can be exploited for reliable roundwood volume estimation and further investigation towards MLS automated approaches is needed, to achieve fully traceable forest inventory systems.

This study examines the long-term dynamics of the Paphos forest in Cyprus using Landsat satellite data for Vegetation Indices (VIs), MODIS data for evapotranspiration, and CHIRPS data for precipitation from 1991 to 2022. Sen's slope method was applied to analyse the trends in the data, revealing statistically significant positive trends in the vegetation indices despite the nearly constant precipitation, indicating increased forest vegetation over the past 30 years. Scatterplots were created mainly to examine correlations within the VIs and precipitation data but with low R-squared values ranging between 0.15-0.44. The study outcomes highlight a complex relationship with evapotranspiration and a weak correlation between precipitation and vegetation indices. These findings could be essential in understanding how forests work, especially in a semi-arid environment like Cyprus.

A growing concern in the Mediterranean region is that recent landcover and land use change is increasing wildfire risk, or the exposure and impacts of wildfire to valued resources. However, the magnitude of these effects is not well understood given the widely diverse landscapes of communities, natural vegetation, and agricultural land. In this study, we use wildfire simulation modeling to assess how landcover and land use changes in three areas of southern Greece have affected exposure of- and fire risk to- communities and economically important permanent agriculture such as Olea europaea (European olive) orchards. We mapped agricultural and wildland fuel change from 2000 to 2020 and simulated fuel scenarios in agricultural land to assess the impacts of agricultural land practices, such as understory clearing, on fire risk. We show that wildfire exposure and risk to communities and permanent agriculture has increased in some areas due to natural fuel densification and decreased in other areas, mainly due to agricultural land expansion. These results highlight that wildfire exposure and risk are driven by local conditions including density and location of communities, spatial arrangement of natural fuels and agricultural land, and agricultural land use practices. Human settlement burn probability and modeled permanent crop loss increased with greater unmaintained agricultural area in all study areas, emphasizing the importance of agricultural land maintenance practices, such as understory clearing, to reduce fuel continuity and fire intensity. Ultimately, quantitative fire risk analyses such as this study provide a useful framework to identify areas of concern where either fuel mitigation or landowner incentives to maintain agricultural land in a less-burnable condition could be applied.

Accurate mapping of forest habitats, especially in NATURA sites, is essential information for forest monitoring and sustainable management but also for habitat characterisation and ecosystem functioning. Remote sensing data and spatial modelling allow accurate mapping of the presence and distribution of tree species and habitats and are valuable tools for the long-term assessment of habitat status required by the European Commission. In order to serve the above, the present study aims to propose a methodology to accurately map the spatial distribution of forest habitats in three NATURA2000 sites of Cyprus by employing Sentinel-1 and Sentinel-2 data as well as topographic features using the Google Earth Engine (GEE). A pivotal aspect of the methodology identified was that the best band combination of the Random Forest (RF) classifier achieves the highest performance for mapping the dominant habitats in the three case studies. Specifically, in the Akamas region, eight habitat types have been mapped, in Paphos nine and six in Troodos. These habitat types are included in three of the nine habitat groups based on the EU’s Habitat Directive: the sclerophyllous scrub, rocky habitats and caves and forests. The results show that using the RF algorithm achieves the highest performance, especially using Dataset 6, which is based on S2 bands, spectral indices and topographical features, and Dataset 13, which includes S2, S1, spectral indices and topographical features. These datasets achieve an overall accuracy (OA) of approximately 91–94%. In contrast, Dataset 7, which includes only S1 bands and Dataset 9, which combines S1 bands and spectral indices, achieve the lowest performance with an OA of approximately 25–43%.

The MedRIN (Mediterranean Regional Information Network) established in 2018, is a network composed of investigators in the United States and Europe, with the charter to further Earth Observation (EO) scientific collaboration in the Mediterranean region of the globe [1]. The MedRIN is structured within the framework of the Global Observations of Forests Cover and Land-use Dynamics (GOFC-GOLD), which is a coordinated international program led by the National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA), working to provide ongoing space-based and in-situ observations of the land surface to support sustainable management of terrestrial resources at different scales [2]. The GOFC-GOLD program acts as an international forum to exchange information, coordinate satellite observations, and provide a framework for and advocacy to establish long-term monitoring systems. It was established as a part of a Committee on Earth Observation Satellites (CEOS) pilot project in 1997, with a focus on global observations of forest cover [3]. The GOFC-GOLD supports the structuring of ad-hoc collaborative scientific communities, such as MedRIN, to serve as a liaison between land-cover/land-use change remote sensing researchers, developers, and stakeholders.

Wildland fuel distribution and characteristics are critical components for the development of a national integrated wildfire management strategy. This study presents a methodological framework for the mapping of fuels in Mediterranean ecosystems in the different levels of a new fuel hierarchical classification scheme, using a spectral–spatial approach based on Sentinel-2 timeseries and auxiliary thematic maps. Furthermore, in the context of this research, a novel approach is proposed for separating Mediterranean shrubland vegetation into three broad height categories, using Sentinel-2 images, landscape variables, and climatic data. Two areas in Greece, namely Attica and Euboea, with major wildfire events over the past 3 years were selected as the study areas. The mapping methodology was designed to consist of three complementary mapping processes, each for the identification of specific types of fuels (i.e., urban, agricultural, and vegetation). The results are validated in a two-step approach for different levels of the classification scheme. The results for the first level display an overall accuracy of 88% and kappa of 0.84, while for the second level, overall accuracy was 71.64% and kappa was 0.68. Our research demonstrates the capacity to map fuel types with promising accuracy at different depths, highlighting a viable method that can be potentially exploited for the large-scale fuel mapping of Mediterranean biomes at a national level.

Tree canopy cover is an important forest inventory parameter and a critical component for the in-depth mapping of forest fuels. This research examines the potential of employing single-date Sentinel-2 multispectral imagery, combined with contextual spatial information, to classify areas based on their tree cover density using Random Forest classifiers. Three spatial information extraction methods are investigated for their capacity to acutely detect canopy cover: two based on Gray-Level Co-Occurrence Matrix (GLCM) features and one based on segment statistics. The research was carried out in three different biomes in Greece, in a total study area of 23,644 km2. Three tree cover classes were considered, namely, non-forest (cover < 15%), open forest (cover = 15%–70%), and closed forest (cover ≥ 70%), based on the requirements set for fuel mapping in Europe. Results indicate that the best approach identified delivers F1-scores ranging 70%–75% for all study areas, significantly improving results over the other alternatives. Overall, the synergistic use of spectral and spatial features derived from Sentinel-2 images highlights a promising approach for the generation of tree cover density information layers in Mediterranean regions, enabling the creation of additional information in support of the detailed mapping of forest fuels.

Adaptive forest management strategies require accurate detection of forest disturbance, in various spatial scales. Synthetic Aperture Radar (SAR) data can provide information about the forest attributes, penetrating the canopy at different levels under any weather and lighting conditions. ICEYE consists of the largest constellation of SAR satellites, enabling very high spatial and temporal resolution. In this study, ICEYE data were investigated in the detection of storm damage, in a fir forest with complex topography which was recently hit by the Daniel storm, resulting in severe damage to the forest structure (FS) and alluvium depositions (AD). To identify the best potential for storm damage detection using ICEYE data, an unsupervised change detection approach was employed combining wavelet transform and adaptive thresholds at spatial scales of 0.5 m (R05), 1 m (R1), 2 m (R2) and 3 m (R3). Additionally, two morphological filters were applied in best-performing resolutions to assess the impact of post-processing on the detection accuracy. Finally, FS and AD damage were investigated separately in order to provide detailed information about the detection capabilities of ICEYE. The results showcased that R1 (UA = 32.35, PA = 18.34 and K = 0.31) provided the best detection performance, followed by R05 (UA = 20.62, PA = 20.12 and K = 0.19). Furthermore, the employment of morphological filters slightly increased UA and Kappa metrics in both R1(UA = 33.40 and K = 0.32) and R05 (UA = 25.60 and K = 0.24), suggesting that post-processing is necessary to mitigate false detections. Regarding the investigation of AD and FS damage, it was revealed that post-processed ICEYE data in both R05 and R1 are capable of identifying AD with satisfying accuracy (UA = 47.41, PA = 22.36, K = 0.47), while FS damage detection is more challenging (UA = 10.15%-14.40%, PA = 14.55%-15.11%, and Kappa = 0.10-0.17). Overall, this study demonstrated that ICEYE can be used to detect storm-affected areas in mountainous forest ecosystems, especially in cases where detection with other methods is not feasible.

Above-ground biomass and carbon stock are fundamental components of the global carbon cycle, essential for climate change mitigation. Remote sensing data can provide timely and accurate estimates of various forest attributes, especially over large and remote forested areas. The objective of this research was to investigate the potential of multispectral LiDAR data for estimating the stem biomass (SB) and total biomass (TB) in a multi-layered fir forest using an Edge-tree corrected Area Based Approach (EABA). Subsequently, a Random Forest (RF) regression analysis was performed to develop SB and TB predictive models using LiDAR-derived height metrics. Two RF models were produced and evaluated in terms of their predictive performance. Overall, our work demonstrates the capability of multispectral LiDAR data to provide reliable SB and TB estimates in a complex structured forest, contributing significantly to sustainable forest management.

Implementing adaptation and mitigation strategies in forest management constitutes a primary tool for climate change mitigation. To the best of our knowledge, very little research so far has examined light detection and ranging (LiDAR) technology as a decision tool for operational cut-tree marking. This study focused on investigating the potential of airborne LiDAR data in enhancing operational tree marking in a dense, multi-layered forest over complex terrain for actively supporting long-term sustainable forest management. A detailed tree registry and density maps were produced and evaluated for their accuracy using field data. The derived information was subsequently employed to estimate additional tree parameters (e.g., biomass and tree-sequestrated carbon). An integrated methodology was finally proposed using the developed products for supporting the time- and effort-efficient operational cut-tree marking. The results showcased the low detection ability (R2 = 0.15–0.20) of the trees with low DBH (i.e., regeneration and understory trees), while the dominant trees were accurately detected (R2 = 0.61). The stem biomass was accurately estimated, presenting an R2 of 0.67. Overall, despite some products’ low accuracy, their full and efficient exploitability within the aforementioned proposed methodology has been endeavored with the aim of actively contributing to long-term sustainable forest management.

This study focused on burned area mapping and burn severity estimation for the Arakapas fire event in Cyprus. For the purpose of the study Sentinel-2 images, before and after the fire event were used for the development of the dNBR and the RdNBR spectral indices, which are the most common spectral indices for assessing the burn severity and burned area estimation. For the validation of the fire severity map, field data were collected for the CBI and GeoCBI index calculation. The fire severity maps were compared with the field measurements of the CBI and GeoCBI. Based on Pearson Correlation the RdNBR map has a very high correlation with GeoCBI (PC=83%) and high correlation with CBI (PC=71%) in contrast with the dNBR spectral index which has a moderate correlation (PC=59% and PC=54%) with CBI and GeoCBI respectively. Based on these results the RdNBR spectral index is better for the burned severity mapping.

The Surface Urban Heat Island (SUHI) effect refers to the difference in Land Surface Temperature (LST) between an urban area and its surrounding non-urban area. LST can provide detailed information on the variations in different types of land cover. This study, therefore, analyzes the behavior of LST and SUHIs in fourteen cities in the El Bajío Industrial Corridor, Mexico, using Landsat satellite images from 2020, with QGIS software. It utilizes thermal profiles to identify the land uses that intensify LST, which are essentially those that are anthropologically altered. The results show that the increases in LST and SUHI are more pronounced in cities with greater urban conglomeration, as well as those where there are few green areas and a sizeable industrial or mixed area, with few or no bodies of water. In addition, the increase in temperature in the SUHI is due to certain crops such as vegetables, red fruits, and basic grains such as corn, wheat, and sorghum that use fallow as part of agricultural practices, located around urban areas, which minimizes natural areas with arboreal vegetation.

The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. This paper proposes an automated methodology for mapping burn scars using pairs of Sentinel-2 imagery, exploiting the state-of-the-art eXtreme Gradient Boosting (XGB) machine learning framework. A large database of 64 reference wildfire perimeters in Greece from 2016 to 2019 is used to train the classifier. An empirical methodology for appropriately sampling the training patterns from this database is formulated, which guarantees the effectiveness of the approach and its computational efficiency. A difference (pre-fire minus post-fire) spectral index is used for this purpose, upon which we appropriately identify the clear and fuzzy value ranges. To reduce the data volume, a super-pixel segmentation of the images is also employed, implemented via the QuickShift algorithm. The cross-validation results showcase the effectiveness of the proposed algorithm, with the average commission and omission errors being 9% and 2%, respectively, and the average Matthews correlation coefficient (MCC) equal to 0.93.

The estimation of individual biomass components within tree crowns, such as dead branches (DB), needles (NB), and branch biomass (BB), has received limited attention in the scientific literature despite their significant contribution to forest biomass. This study aimed to assess the potential of multispectral LiDAR data for estimating these biomass components in a multi-layered Abies borissi-regis forest. Destructive (i.e., 13) and non-destructive (i.e., 156) field measurements were collected from Abies borisii-regis trees to develop allometric equations for each crown biomass component and enrich the reference data with the non-destructively sampled trees. A set of machine learning regression algorithms, including random forest (RF), support vector regression (SVR) and Gaussian process (GP), were tested for individual-tree-level DB, NB and BB estimation using LiDAR-derived height and intensity metrics for different spectral channels (i.e., green, NIR and merged) as predictors. The results demonstrated that the RF algorithm achieved the best overall predictive performance for DB (RMSE% = 17.45% and R-2 = 0.89), NB (RMSE% = 17.31% and R-2 = 0.93) and BB (RMSE% = 24.09% and R-2 = 0.85) using the green LiDAR channel. This study showed that the tested algorithms, particularly when utilizing the green channel, accurately estimated the crown biomass components of conifer trees, specifically fir. Overall, LiDAR data can provide accurate estimates of crown biomass in coniferous forests, and further exploration of this method's applicability in diverse forest structures and biomes is warranted.

Earth Observation satellite systems are considered the main source of information used for delivering up-to-date land cover/use maps. Medium to high spatial resolution images, such as the ones provided by Sentinel-2 sensors, can improve significantly mapping and monitoring of vegetation communities and are utilized in a wide range of applications such as the management of natural resources and forest inventories. The aim of this work was to employ Sentinel-2 images for accurately classifying vegetation cover in selected areas of Greece that present diverse vegetation characteristics. Cloudfree Sentinel 2 (L2A) images were acquired for each area during 2021 for the months of February, June, and September, in order to capture the reflectance changes due to seasonal variations. Two machine-learning techniques, namely Random Forest (RF) and Support Vector Machines (SVM), were applied and assessed for their performance in mapping vegetation cover and species in the study areas. The training patterns, used as input in both classifiers, were acquired through photo-interpretation of stratified random points, distributed across forested areas. Consequently, validation of the classification results was performed, in order to estimate accuracy metrics for each model per site. More specifically, the kappa coefficient, overall (OA), user's and producers' accuracy were calculated. The accuracy results demonstrated higher scores for RF (OA over 90% for all areas) than SVM (OA ranging from 81 to 89%, respectively). Overall, our study demonstrates the capability of seasonal Sentinel-2 data to accurately discriminate vegetation communities over diverse biomes, when combined with advanced classification methods.