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Eucalyptus plantations cover extensive areas in tropical regions and require accurate growth monitoring for efficient management. Traditional in-situ measurements, while necessary, are labor-intensive and impractical for large-scale assessments. Very high-resolution satellite stereo imagery is playing an increasingly important role in the estimation of fine Digital Surface Models (DSMs) across landscapes. However, its ability to estimate canopy height models (CHMs) has not been widely investigated. This study investigates the integration of high-resolution satellite stereo imagery from the Pleiades sensor with airborne or satellite Lidar data to estimate canopy height over eucalyptus plantations. Two study sites were selected in Brazil, representing flat and semi-mountainous topographies, Mato Grosso do Sul (MS) and Sao Paulo (SP), respectively. Digital Surface Models generated from Pleiades images (DSMP) were combined with Digital Terrain Models extracted from airborne Lidar data (DTMALS) to create Canopy Height Models (CHMALS). The evaluation of the CHMALS was based on two in situ canopy height measurements (Hmax and Hmean). For the SP site, the CHMALSmax, which is the average height of top 10% pixel values within each plot, correlated well with in situ Hmean, which is the average height of 10 central trees (r = 0.98), showing a bias of 1.4 m, RMSE of 3.1 m, and rRMSE of 18.5%. At the MS site, CHMALSmax demonstrated a bias of 1.9 m, RMSE of 2.3 m, rRMSE of 17.3%, and r correlation of 0.92. Despite a tendency to underestimate heights below 20 m in young tree plantations with open canopy, the results indicate reliable canopy height estimation. The study also investigates the potential of Global Ecosystem Dynamics Investigation (GEDI) elevation data as an alternative to DTMALS in absence of airborne Lidar data. The resulting CHMGedi is promising but slightly less accurate than Lidar-based CHMs. The best GEDI-based CHM (CHMGedimax) showed a bias and rRMSE of 1.3 m and

The extent of the potassium (K) limitation of forest productivity is probably more widespread than previously thought, and K limitation could influence the response of forests to future global changes. To understand the effects of K limitation on forest primary production, we have developed the first ecophysiological model simulating the K cycle and its interactions with the carbon (C) and water cycles. We focused on the limitation of the gross primary productivity (GPP) by K availability in tropical eucalypt plantations in Brazil. We used results from stand-scale fertilisation experiments as well as C flux measurements in two tropical eucalypt plantations to parameterise the model. The model was parameterised for fertilised conditions and then used to test for the effects of contrasting additions of K fertiliser. Simulations showed that K deficiency limits GPP by more than 50 % during a 6-year rotation, a value in agreement with estimations in K-limited eucalypt stands. Simulations showed a decrease of modelled canopy transpiration of around 50 % and a decrease in modelled water-use efficiency WUEGPP of 10 %. Through a sensitivity analysis, we used the model to identify the most critical processes to consider when studying K limitation of GPP. The inputs of K to the stands, such as the atmospheric deposition and weathering fluxes, and the regulation of the cycle of K within the ecosystem were critical for the response of the system to K deficiency. Litter leaching processes were of lower importance, since residence time of K in litter was low. The new forest K-cycle model developed in the present study includes multiple K processes interacting with the carbon and water cycles, and strong feedbacks on GPP were outlined. This is a first step in identifying the source or sink limitation of forest growth by K.

The Global Ecosystem Dynamics Investigation LiDAR (GEDI) is a new full waveform (FW) based LiDAR system that presents a new opportunity for the observation of forest structures globally. The backscattered GEDI signals, as all FW systems, are distorted by topographic conditions within their footprint, leading to uncertainties on the measured forest variables. In this study, we explore how well several approaches based on waveform metrics and ancillary digital elevation model (DEM) data perform on the estimation of stand dominant heights (Hdom) and wood volume (V) across different sites of Eucalyptus plantations with varying terrain slopes. In total, five models were assessed on their ability to estimate Hdom and four models for V. Results showed that the models using the GEDI metrics, such as the height at different energy quantiles with terrain data from the shuttle radar topography mission's (SRTM) digital elevation model (DEM) were still dependent on the topographic slope. For Hdom, an RMSE increase of 14% was observed for data acquired over slopes higher than 20% in comparison to slopes between 10 and 20%. For V, a 74% increase in RMSE was reported between GEDI data acquired over slopes between 0–10% and those acquired over slopes higher than 10%. Next, a model relying on the height at different energy quantiles of the entire waveform (HTn) and the height at different energy quartiles of the bare ground waveform (HGn) was assessed. Two sets of the HGn metrics were generated, the first one was obtained using a simulated waveform representing the echo from a bare ground, while the second one relied on the actual ground return from the waveform by means of Gaussian fitting. Results showed that both the simulated and fitted models provide the most accurate estimates of Hdom and V for all slope ranges. The simulation-based model showed an RMSE that ranged between 1.39 and 1.66 m (between 26.76 and 39.26 m3·ha−1 for V) while the fitting-based method showed an RMSE that

Over the past two decades spaceborne LiDAR systems have gained momentum in the remote sensing community with their ability to accurately estimate canopy heights and aboveground biomass. This article aims at using the most recent global ecosystem dynamics investigation (GEDI) LiDAR system data to estimate the stand-scale dominant heights ( Hdom ), and stand volume ( V ) of Eucalyptus plantations in Brazil. These plantations provide a valuable case study due to the homogenous canopy cover and the availability of precise field measurements. Several linear and nonlinear regression models were used for the estimation of Hdom and V based on several GEDI metrics. Hdom and V estimation results showed that over low-slopped terrain the most accurate estimates of Hdom and V were obtained using the stepwise regression, with an root-mean-square error (RMSE) of 1.33 m ( R 2 of 0.93) and 24.39 m 3 .ha −1 ( R 2 of 0.90) respectively. The principal metric explaining more than 87% and 84% of the variability ( R 2 ) of Hdom and V was the metric representing the height above the ground at which 90% of the waveform energy occurs. Testing the postprocessed GEDI metric values issued from six available different processing algorithms showed that the accuracy on Hdom and V estimates is algorithm dependent, with a 16% observed increase in RMSE on both variables using algorithm a5 vs. a1. Finally, the choice to select the ground return from the last detected mode or the stronger of the last two modes could also affect the Hdom estimation accuracy with 12 cm RMSE decrease using the latter.

Eucalyptus plantations in Brazil are among the most productive forests of the world, reaching mean annual increments of about 50 m3 ha-1 yr-1 over short (6 - 7 yrs) rotations. We continuously monitored water vapor, CO2 and energy flux through the eddy-covariance method over a 9 year-period in southeastern Brazil. Tree growth, Leaf Area Index (LAI), water table depth, and soil water content (SWC) down to 10 m depth were also monitored. Rooting depth and vertical fine root distribution were assessed at various ages. Mean annual evapotranspiration (AET; 1383 mm yr-1) represented 90% of the annual precipitations (P; 1539 mm yr-1). AET reached maximum values (1598 mm yr-1) about 2-3 years after planting (a.p) when LAI peaked and when deep rooting (about 15 m deep 2.5 yrs a.p) provided access to the large amount of water stored in deep soil layers during the first months after clear-cutting and replanting. Most (88%) of the available energy (3852 MJ yr-1) was partitioned to the evaporation process (latent heat fluxes), with very low sensible heat fluxes over the rotation, except after harvesting and replanting when LAI was low, and later in the rotation during dry events. Deep drainage after harvest of the first stand allowed the water table to rise from -18 to -12 m over the first 2 years after replanting. Both measurements of SWC and model simulations showed that deep water storage and subsequent uptake played a major role in supporting the very high wood production and dampening seasonal droughts.

Stemwood productivity in forest ecosystems depends on the amount of light absorbed by the trees (APAR) and on the Light Use Efficiency (LUE), i.e. the amount of stemwood produced per amount of absorbed light. In fertilized Eucalyptus plantations of Brazil, growth is expected to be strongly limited by light absorption in the first years after planting, when trees can benefit from high soil water stocks, recharged after clearcutting the previous stand. Other limiting factors, such as water or nutrient shortage are thought to increase in importance after canopy closure, and changes in allocation patterns are expected, affecting the LUE. Studying changes in APAR and LUE along a complete rotation is paramount for gaining insight into the mechanisms that drive the inter- and intra-genotype variabilities of productivity and stemwood biomass at the time of harvest. Here, we present a 6-year survey of productivity, APAR and LUE of 16 Eucalyptus genotypes of several species used in commercial plantations and planted in 10 randomized replications in the São Paulo Region, Brazil. APAR was estimated using the MAESTRA tridimensional model parameterized at tree scale for each tree in each plot (a total of 16,000 trees) using local measurements of leaf and canopy properties. Stand growth was estimated based on allometric relationships established through successive destructive biomass measurements at the study site. Allometric relationships predicting biomass of tree components, leaf surface, crown dimension and leaf inclination angle distribution throughout the rotation for the 16 productive genotypes are shown. Results at stand scale showed that (1) LUE increased with stand age for all genotypes, from 0.15 at age 1 yr to 1.70 g MJ−1 at age 6 yrs on average; (2) light absorption was a major limiting factor over the first year of growth (R2 between APAR and stand biomass ranging from 0.5 to 0.95), explaining most of the inter- and intra-genotype growth variability; (3) at rotation

Carbon and water budgets of forest plantations are spatially and temporally variable and hardly empirically predictable. We applied G'DAY, a process-based ecophysiological model, to simulate carbon and water budgets and stem biomass production of Eucalyptus plantations in São Paulo State, Brazil. Our main objective was to assess the drivers of spatial variability in plantation production at regional scale. We followed a multi-site calibration approach: the model was first parameterized using a detailed experimental dataset. Then a subset of the parameters were re-calibrated on two independent experimental datasets. An additional genotype-specific calibration of a subset of parameters was performed. Model predictions of key carbon-related variables (e.g., gross primary production, leaf area index and stem biomass) and key water-related variables (e.g., plant available water and evapotranspiration) agreed closely with measurements. Application of the model across ca. 27,500 ha of forests planted with different genotypes of Eucalyptus indicated that the model was able to capture 89% of stem biomass variability measured at different ages. Several factors controlling Eucalyptus production variability in time and space were grouped in three categories: soil, climate, and the planted genotype. Modelling analysis showed that calibrating the model for genotypic differences was critical for stem biomass prediction at regional scale, but that taking into account climate and soil variability significantly improved the results. We conclude that application of process-based models at regional scale can be used for accurate predictions of Eucalyptus production, provided that an accurate calibration of the model for key genotype-specific parameters is conducted.