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The plantation of fast-growing tree species plays a crucial role in supplying forest products to growing economies while relieving pressure on native forests reducing the overexploitation of native ecosystems. Grown in more than 90 countries, Eucalyptus spp. are fast-growing tree species driving economic production comprising pulp, wood, or thermal energy. Climate change is increasing the frequency and severity of extreme drought episodes: the 2019 drought in Mato Grosso, Brazil, was the second driest episode ever recorded. Forest inventory data comprising thousands of individual tree measurements collected in consecutive surveys - years 2019, 2020, 2021 – were modelled with mixed-effects models to identify significant factors influencing tree mortality after drought. Time-series of growth dynamics of the four eucalypt commercial clones were tested. For the landscape assessment, surveys using unmanned aerial vehicle (UAV) supported a high-resolution image mosaicking to derive digital surface models and the vegetation indexes driving segmentation and classification methods using ground plot-level observations as training data. The impact of drought was measured in terms of mortality, growth dynamics and the financial losses computed when valuing the 1400-ha plantation along the 3-year series. The modelling results showed high-stocking plantations suffered more from extreme water-stress conditions. The interaction between stocking and clone was analysed. On average, the 2019 drought triggered an estimate loss of $3500 per hectare. The estimation of live and dead conditions using UAV-based outcomes reached an overall accuracy of 89% and 0.7 in the Cohen's kappa used to map forest health wall-to-wall. Our findings advocate for a reduction of tree planting density and enhanced clone selection to turn eucalypt plantations more resilient to extreme water-stress. Combining UAV-based remote sensing technology, ground truth data and management expertise in eucalypt management

Global high-resolution imagery is a well-assimilated technology in forest mapping. The release of the Norway's International Climate & Forests Initiative (NICFI) Planet tropical basemaps time-series starting in 2015 at a 4.77-m resolution represents a unique opportunity to forecast climate change consequences such as drought episodes. Using multi-temporal ground surveys over 144 plots and publicly available high-resolution Planet dove time-series imagery we evaluate forest mortality patterns driven by imaging spectroscopy methods in Mato Grosso (Brazil) over an area planted with eucalypts severely affected by the 2019 drought. Changes in vegetation indexes before and after the 2019 drought were modelled using the effective logistic regression modelling to explain variation in tree mortality between the surveys, the dependent variable. We aimed to straightforwardly model tree mortality using change vectors in Planet's image mosaics co-registering in time with the observed tree mortality measurements in the field. The results showed differences in Normalized Difference Vegetation Index (NDVI) as the most significant predictor variable under the effective logistic regression modelling performed. The efficacy of 80.98% in concordance pairs correctly classified represented 0.81 of area under the Receiver Operating Curve (ROC). The release of the 2015-2020 Planet imagery in the tropics at 4.77-m resolution represents a valuable dataset to better understand previous natural disturbances and a powerful technology to detect in advance, and monthly after September 2020, eucalypt areas prone to harmful and increasingly frequent water-stress episodes.

Earth Observation data are uniquely positioned to estimate forest aboveground biomass density (AGBD) in accordance with the United Nations Framework Convention on Climate Change (UNFCCC) principles of ‘transparency, accuracy, completeness, consistency and comparability’. However, the use of space-based AGBD maps for national-level reporting to the UNFCCC is nearly non-existent as of 2023, the end of the first global stocktake (GST). We conduct an evidence-based comparison of AGBD estimates from the NASA Global Ecosystem Dynamics Investigation and ESA Climate Change Initiative, describing differences between the products and National Forest Inventories (NFIs), and suggesting how science teams must align efforts to inform the next GST. Between the products, in the tropics, the largest differences in estimated AGBD are primarily in the Congolese lowlands and east/southeast Asia. Where NFI data were acquired (Peru, Mexico, Lao PDR and 30 regions of Spain), both products show strong correlation to NFI-estimated AGBD, with no systematic deviations. The AGBD-richest stratum of these, the Peruvian Amazon, is accurately estimated in both. These results are remarkably promising, and to support the operational use of AGB map products for policy reporting, we describe targeted ways to align products with Intergovernmental Panel on Climate Change (IPCC) guidelines. We recommend moving towards consistent statistical terminology, and aligning on a rigorous framework for uncertainty estimation, supported by the provision of open-science codes for large-area assessments that comprehensively report uncertainty. Further, we suggest the provision of objective and open-source guidance to integrate NFIs with multiple AGBD products, aiming to enhance the precision of national estimates. Finally, we describe and encourage the release of user-friendly product documentation, with tools that produce AGBD estimates directly applicable to the IPCC guideline methodologies. With these steps, spac

Knowledge about forest road networks is essential for sustainable forest management and fire management. The aim of this study was to assess the accuracy of a new hierarchical-hybrid classification tool (HyClass) for mapping paved and unpaved forest roads with LiDAR data. Bare-earth and low-lying vegetation were also identified. For this purpose, a rural landscape (area 70 ha) in northwestern Spain was selected for study, and a road network map was extracted from the cadastral maps as the ground truth data. The HyClass tool is based on a decision tree which integrates segmentation processes at local scale with decision rules. The proposed approach yielded an overall accuracy (OA) of 96.5%, with a confidence interval (CI) of 94.0–97.6%, representing an improvement over pixel-based classification (OA = 87.0%, CI = 83.7–89.8%) using Random Forest (RF). In addition, with the HyClass tool, the classification precision varied significantly after reducing the original point density from 8.7 to 1 point/m2. The proposed method can provide accurate road mapping to support forest management as an alternative to pixel-based RF classification when the LiDAR point density is higher than 1 point/m2

Estimating forest inventory variables is important in monitoring forest resources and mitigating climate change. In this respect, forest managers require flexible, non-destructive methods for estimating volume and biomass. High-resolution and low-cost remote sensing data are increasingly available to measure three-dimensional (3D) canopy structure and to model forest structural attributes. The main objective of this study was to evaluate and compare the individual tree volume estimates derived from high-density point clouds obtained from airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) in Eucalyptus spp. plantations. Object-based image analysis (OBIA) techniques were applied for individual tree crown (ITC) delineation. The ITC algorithm applied correctly detected and delineated 199 trees from ALS-derived data, while 192 trees were correctly identified using DAP-based point clouds acquired from Unmanned Aerial Vehicles (UAV), representing accuracy levels of respectively 62% and 60%. Addressing volume modelling, non-linear regression fit based on individual tree height and individual crown area derived from the ITC provided the following results: Model Efficiency (Mef) = 0.43 and 0.46, Root Mean Square Error (RMSE) = 0.030 m3 and 0.026 m3, rRMSE = 20.31% and 19.97%, and an approximately unbiased results (0.025 m3 and 0.0004 m3) using DAP and ALS-based estimations, respectively. No significant difference was found between the observed value (field data) and volume estimation from ALS and DAP (p-value from t-test statistic = 0.99 and 0.98, respectively). The proposed approaches could also be used to estimate basal area or biomass stocks in Eucalyptus spp. plantations.

This study aimed to develop ALS-based models for estimating stem, crown and aboveground biomass in three types of Mediterranean forest, based on low density ALS data. Two different modelling approaches were used: (i) linear models with different variable selection methods (Stepwise Selection [SS], Clustering/Exhaustive search [CE] and Genetic Algorithm [GA]), and (ii) previously Published Models (PM) applicable to diverse types of forest. Results indicated more accurate estimations of biomass components for pure Pinus pinea L. (rRMSE = 25.90-26.16%) than for the mixed (30.86-36.34%) and Quercus pyrenaica Willd. forests (32.78-34.84%). All the tested approaches were valuable, but SS and GA performed better than CE and PM in most cases.

High spatial resolution imagery provided by unmanned aerial vehicles (UAVs) can yield accurate and efficient estimation of tree dimensions and canopy structural variables at the local scale. We flew a low-cost, lightweight UAV over an experimental Pinus pinea L. plantation (290 trees distributed over 16 ha with different fertirrigation treatments) to determine the tree positions and to estimate individual tree height (h), diameter (d), biomass (wa), as well as changes in these variables between 2015 and 2017. We used Structure from Motion (SfM) and 3D point cloud filtering techniques to generate the canopy height model and object-based image analysis to delineate individual tree crowns (ITC). ITC results were validated using accurate field measurements over a subsample of 50 trees. Comparison between SfM-derived and field-measured h yielded an R2 value of 0.96. Regressions using SfM-derived variables as explanatory variables described 79% and 86–87% of the variability in d and wa, respectively. The height and biomass growth estimates across the entire study area for the period 2015–2017 were 0.45 m ± 0.12 m and 198.7 ± 93.9 kg, respectively. Significant differences (t-test) in height and biomass were observed at the end of the study period. The findings indicate that the proposed method could be used to derive individual-tree variables and to detect spatio-temporal changes, highlighting the potential role of UAV-derived imagery as a forest management tool

This study aimed to develop ALS-based models for estimating stem, crown and aboveground biomass in three types of Mediterranean forest, based on low density ALS data. Two different modelling approaches were used: (i) linear models with different variable selection methods (Stepwise Selection [SS], Clustering/Exhaustive search [CE] and Genetic Algorithm [GA]), and (ii) previously Published Models (PM) applicable to diverse types of forest. Results indicated more accurate estimations of biomass components for pure Pinus pinea L. (rRMSE = 25.90-26.16%) than for the mixed (30.86-36.34%) and Quercus pyrenaica Willd. forests (32.78-34.84%). All the tested approaches were valuable, but SS and GA performed better than CE and PM in most cases

Aim of study: The study aims to analyse the potential use of lowcost unmanned aerial vehicle (UAV) imagery for the estimation of Pinus pinea L. variables at the individual tree level (position, tree height and crown diameter). Area of study: This study was conducted under the PINEA project focused on 16 ha of umbrella pine afforestation (Portugal) subjected to different treatments. Material and methods: The workflow involved: a) image acquisition with consumer-grade cameras on board an UAV; b) orthomosaic and digital surface model (DSM) generation using structure-from-motion (SfM) image reconstruction; and c) automatic individual tree segmentation by using a mixed pixel‑and region‑based algorithm. Main results: The results of individual tree segmentation (position, height and crown diameter) were validated using field measurements from 3 inventory plots in the study area. All the trees of the plots were correctly detected. The RMSE values for the predicted heights and crown widths were 0.45 m and 0.63 m, respectively. Research highlights: The results demonstrate that tree variables can be automatically extracted from high resolution imagery. We highlight the use of UAV as a fast, reliable and cost-effective technique for small scale applications.

Este estudio presenta avances en la metodología de inventario forestal a nivel de masa (area-based approach, ABA) con datos LiDAR aerotransportado de baja densidad y destaca la utilidad de los datos LiDAR disponibles para España a escala nacional para realizar cartografía de las principales variables dasométricas en un bosque Mediterráneo de pino piñonero, caracterizado por una compleja orografía. Para ello, se ajustaron modelos lineales de regresión en cada tipo de bosque, a partir de los datos LiDAR de baja densidad (0,5 primeros retornos m–2), proporcionados por el PNOA (Plan Nacional de Ortofotografía Aérea) y los datos obtenidos en campo. Además, se investigó la influencia de los umbrales de altura usados en la extracción de los estadísticos de la nube de puntos LiDAR (MHT: Minimun Height ThresholdyBHT: Break Height Threshold). Los mejores modelos de regresión explicaron un 61-85%, 67-98%, 74-98% de la variabilidad en altura de masa, área basimétrica y volumen, respectivamente. El error de estimación en las variables de masa fue mayor en bosques cerrados mixtos y puros de caducifolias que en los bosques más homogéneos de coníferas. Los resultados demostraron que los umbrales de altura no fueron especialmente críticos en la estimación de las variables de masa en bosques de coníferas, pero hubo diferencias sustanciales en el caso de volumen, cuando aumentaron los umbrales de altura (HBTy MHT) en las masas de estructura más compleja (bosque mixto y puro de caducifolias). Un análisis métrica a métrica reveló la existencia de diferencias significativas en la mayor parte de las variables explicativas extraídas a partir de diferentes umbrales de altura (HBTy MHT). Los mejores modelos de predicción se aplicaron a los rodales de referencia y se elaboró una cartografía espacialmente explícita que representa las principales variables de masa, facilitando así la toma de decisiones para la gestión forestal sostenible en los ecosistemas de bosque mediterráneo, This study reports progress in forest inventory methods involving the use of low density airborne LiDAR data and an area-based approach (ABA). It also emphasizes the usefulness of the Spanish countrywide LiDAR dataset for mapping forest stand attributes in Mediterranean stone pine forest characterized by complex orography. Lowdensity airborne LiDAR data (0.5 first returns m–2) was used to develop individual regression models for a set of forest stand variables in different types of forest. LiDAR data is now freely available for most of the Spanish territory and is provided by the Spanish National Aerial Photography Program (Plan Nacional de Ortofotografía Aérea, PNOA). The influence of height thresholds (MHT: Minimun Height Threshold and BHT: Break Height Threshold) used in extracting LiDAR metrics was also investigated. The best regression models explained 61-85%, 67-98% and 74-98% of the variability in ground-truth stand height, basal area and volume, respectively. The magnitude of error for predicting structural vegetation parameters was higher in closed deciduous and mixed forest than in the more homogeneous coniferous stands. Analysis of height thresholds (HT) revealed that these parameters were not particularly important for estimating several forest attributes in the coniferous forest; nevertheless, substantial differences in volume modelling were observed when the height thresholds (MHT and BHT) were increased in complex structural vegetation (mixed and deciduous forest). A metric-by-metric analysis revealed that there were significant differences in most of the explanatory variables computed from different height thresholds (HBT and MHT).The best models were applied to the reference stands to yield spatially explicit predictions about the forest resources. Reliable mapping of biometric variables was implemented to facilitate effective and sustainable management strategies and practices in Mediterranean Forest ecosystem