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The global monitoring of forest structure worldwide is increasingly being supported by refined and enhanced satellite mission datasets. Forest canopy height is a global metric to characterise and monitor dynamics in forest ecosystems worldwide. Satellite mapping missions as NASA's Global Ecosystem Dynamics Investigation (GEDI) are creating opportunities to refine global forest canopy height models adding forest structural information to time-series satellite imagery. A recent global canopy height model presented by Lang et al., (2022) using GEDI and 10-m Sentinel-2 and the map from Potapov et al., (2020) using GEDI and Landsat are both tested in this study using multi-temporal tree-level data collected over eucalypt plantations in Brazil. Our results at plot-level showed Lang et al., (2022)’s estimates of canopy height came short compared to 2020 maximum and mean tree height records in the plots, 7.6 and 3.6 m, respectively, but adding CHM standard deviation improves the agreement of ground records for maximum tree height. Higher errors were computed for the plots in 2019 using the Potapov's 30-m CHM: 14.2 and 9.5 m, respectively. Averaged stand values were more similar between the three sources tested. We report improvement from the 30-m CHM to the 10-m, but still height saturation problems were observed when accounting for height differences in tall eucalypt trees. As more global products for forest height and biomass are becoming available to users, more validation exercises as presented in this study are needed to assess the suitability of CHM products to forestry needs, and facilitate the uptake and actionability of the next generation of global height and biomass products. We provide recommendations and insights on the use of GEDI laser data for global mapping and on the potential of commercial forestry areas to benchmark the accuracy of satellite mapping missions focusing on tree height estimation in the tropics.

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

The use of airborne laser scanning (ALS) has enhanced forest inventory during the last decades due to the increasing capability of lasers to describe the three-dimensional structure of forests. This research focuses on the integration of ALS-based forest inventory into forest planning when the aim is to create dynamic treatment units (DTU). In this approach, the management units are not fixed and predefined. They are temporary and formed by aggregating fine grained inventory units. Management objectives and forest dynamics are the drivers of that aggregation process. The research was conducted in two pine forests in Castilla y León (Spain) in which ground and ALS data were collected to estimate forest attributes. This PhD thesis reviews four manuscripts concerning the implementation of DTU, the implications of using alternative forest inventory units (FIU) in two types of problem formulations and the impact of plot positioning errors on the whole planning process. The forest management plans included spatial and non-spatial objective variables. Heuristic-based optimization was effective at solving the spatial combinatorial problems. The findings of this work highlight the good performance of irregular types of FIU derived with segmentation techniques. The use of spatial optimization improved the spatial layout of forest plans at a minor cost compared to non-spatial formulations. The use of spatial goals and spatially explicit harvest cost functions enhanced the aggregation of FIUs. This PhD shows how the combination of ALS-based methods, widely used in forestry practice, and spatial optimization contribute to the development of modern forest planning methods., El uso de sensores laser aerotransportados (ALS) ha mejorado el inventario forestal en las últimas décadas debido a la capacidad del láser de describir la estructura del bosque en tres dimensiones. Esta investigación se centra en la integración del inventario forestal basado en ALS dentro de la planificación para crear rodales dinámicos (DTUs). En este enfoque, las unidades de gestión no son fijas ni predefinidas. Son temporales y se forman mediante la agregación de unidades de pequeña escala. Los objetivos de la planificación y los modelos de dinámica forestal son los vectores de esa agregación. La investigación se realizó en dos bosques en Castilla y León (España) en donde se tomaron observaciones de campo y datos ALS para estimar las características del bosque. Esta tesis incluye cuatro manuscritos sobre la implementación de los rodales dinámicos, las consecuencias de utilizar distintas unidades de inventario (FIU) y el impacto de los errores de posicionamiento. Los modelos de planficación incluyeron objetivos espaciales y no espaciales. Los métodos heurísticos de optimización fueron eficaces a la hora de solventar el problema de combinatoria. Los resultados destacan el buen rendimiento de las formas irregular de FIU generadas con segmentación. La optimización espacial mejoró la configuración espacial de las soluciones y con un coste bajo comparado con las soluciones no espaciales. El uso de objetivos espaciales y funciones de coste mejoró a su vez la agregación. Esta tesis muestra como la combinación de datos ALS y la optimización espacial contribuyen al desarrollo de métodos contemporáneos de planificación.