Search

At present, there is a high demand for carbon (C) sequestration alternatives; thus, understanding tree growth and the efficacy of remote sensing techniques to capture forest plantation ecophysiology is crucial. This study evaluated the effect of contrasting stockings of Gmelina arborea on its initial growth and aboveground Carbon stock, and the efficacy of aerial images obtained using drones to capture the crown cover at different stockings. The results indicated that denser stockings showed greater tree heights and stem diameter increments, contrary to traditional measurements. The C storage capacity of Gmelina arborea was promising, with an aboveground estimated C stock of about 13 Mg ha−1 in 9 months, making it a valuable and promising species for CO2 sequestration under the context of climate change. The use of simple Red-Green-Blue (RGB) cameras and drones to detect and estimate crown areas in young plantations was mainly viable within the commercial range of stockings (500–2000 trees ha−1), and can be used as a powerful tool to better understand tree initial growth. The results showed effective discrimination without weeds independently of the stocking level; however, when weeds were present, the effectiveness decreased. This research provides valuable insights into forest management and improves the understanding of the silviculture behavior of a potential native species for reforestation in the tropics. [ABSTRACT FROM AUTHOR]

Austropuccinia psidii Winter, causal agent of myrtle rust, is one of the main pathogens in Eucalyptus plantations in Brazil. We investigated the effects of the pathogen on wood yield and light use efficiency (LUE) in a Eucalyptus grandis x Eucalyptus urophylla clone in a field trial at the end of a five‐year rotation. The study comprised two treatments that are commonly adopted by managers. One treatment with curative fungicide spray (Triadimenol 250 g l−1) seven months after planting and another with five preventive sprays of the same fungicide, from the 1st to 7th month age of saplings. At 5 years old, the control plots attained a mean annual increment of 35 m³ ha−1 yr−1, the curative and preventive treatments were 39 and 47 m³ ha−1 yr−1 (11% and 34% higher, respectively). LUE was also higher when fungicides were applied, 8% and 26% higher in curative and preventive treatments (1.06 and 1.24 g MJ−1) compared with the control plots (0.98 g MJ−1). To confirm the negative effect of rust at a larger scale, we tracked 102 inventory plots (from 1,234 ha) from 2 to 6 years old with the same clone, half infected and half with rust corrective control. Growth difference decreased with time, from an average of 41% at age 2%–5% at age 6. These results highlight the importance of managing myrtle rust in Eucalyptus plantations by selecting a disease‐tolerant genotype or adopting other strategies for disease control. [ABSTRACT FROM AUTHOR]

Eucalyptus plantations around the world have been largely used by the paper industry. Optimizing the management of resources is a common practice in this highly competitive industry and new forest growth models may help to understand the impact of climate change on the decisions of the optimization processes. Current optimized management plans use empirical equations to predict future forest stands growth, and it is currently impractical to replace these empirical equations with physiological models due to data input requirements. In this paper, we present a different approach, by first carrying out a preliminary assessment with the process-based physiological model 3PG to evaluate the growth of Eucalyptus stands under climate change predictions. The information supplied by 3PG was then injected as a modifier in the projected yield that feeds the management plan optimizer allowing the interpretation of climate change impacts on the management plan. Modelling results show that although a general increase of rain with climate change is predicted, the distribution throughout the year will not favor the tree growth. On the contrary, rain will increase when it is less needed (summer) and decrease when it is most needed (winter), decreasing forest stand productivity between 3 and 5%, depending on the region and soil. Evaluation of the current optimized plan that kept constant the relation between wood price/cellulose ton shows a variation in different strategic management options and an overall increase of costs in owned areas between 2 and 4%, and a decrease of cumulated net present value, initially at 15% with later stabilization at 6–8%. This is a basic comparison to observe climate change effects; nevertheless, it provides insights into how the entire decision-making process may change due to a reduction in biomass production under future climate scenarios. This work demonstrates the use of physiological models to extract information that could be merged with existing and already implemented empirical models. The methodology may also be considered a preliminary alternative to the complete replacement of empirical models by physiological models. Our approach allows some insight into forest responses to different future climate conditions, something which empirical models are not designed for. [ABSTRACT FROM AUTHOR]

Short-rotation plantations are extending worldwide due to the increased demand for pulp and wood. Reliable estimations of recent expansion of short-rotation plantation areas and associated land use changes are a prerequisite to assess their environmental impact on regional carbon and water cycles, and on climate. A binary classification methodology using MODerate resolution Imaging Spectroradiometer (MODIS) 16-day 250 m NDVI time series was developed and applied to classify Eucalyptus plantations across Brazil. The identification of Eucalyptus plantations specific patterns in the time series was based on the calculation of matching functions between the NDVI time series and a ~ 2 years long reference time series. Among the seven tested matching functions, the bounding envelope was the most successful. This method was robust to residual noise on the NDVI time series, and a threshold coefficient for the binary classification was adjusted using an omission-commission criteria. With this method, it was possible to detect any presence of Eucalyptus between 2003 and 2009 at monthly time-steps, including the periods of bare soils between two rotations that are typically 6–7 years long. The dates of first afforestation, of clear-cut at the end of a rotation, and of re-planting at the beginning of a new rotation were retrieved from the NDVI time series with a precision of ~ 66 days. The final almost continuous tri-dimensional map (space and time) was validated with three different datasets, from local to regional data. All three datasets gave similarly high global accuracy statistics, but a global underestimation of Eucalyptus areas compared to large scales census was observed. Discrepancies and way to improve the Eucalyptus area estimates were discussed in this study. The developed methodology could be applied to other short-rotation tree plantations. [ABSTRACT FROM AUTHOR]

Abstract: The use of remote sensing is necessary for monitoring forest carbon stocks at large scales. Optical remote sensing, although not the most suitable technique for the direct estimation of stand biomass, offers the advantage of providing large temporal and spatial datasets. In particular, information on canopy structure is encompassed in stand reflectance time series. This study focused on the example of Eucalyptus forest plantations, which have recently attracted much attention as a result of their high expansion rate in many tropical countries. Stand scale time-series of Normalized Difference Vegetation Index (NDVI) were obtained from MODIS satellite data after a procedure involving un-mixing and interpolation, on about 15,000ha of plantations in southern Brazil. The comparison of the planting date of the current rotation (and therefore the age of the stands) estimated from these time series with real values provided by the company showed that the root mean square error was 35.5days. Age alone explained more than 82% of stand wood volume variability and 87% of stand dominant height variability. Age variables were combined with other variables derived from the NDVI time series and simple bioclimatic data by means of linear (Stepwise) or nonlinear (Random Forest) regressions. The nonlinear regressions gave r-square values of 0.90 for volume and 0.92 for dominant height, and an accuracy of about 25m3/ha for volume (15% of the volume average value) and about 1.6m for dominant height (8% of the height average value). The improvement including NDVI and bioclimatic data comes from the fact that the cumulative NDVI since planting date integrates the interannual variability of leaf area index (LAI), light interception by the foliage and growth due for example to variations of seasonal water stress. The accuracy of biomass and height predictions was strongly improved by using the NDVI integrated over the two first years after planting, which are critical for stand establishment. These results open perspectives for cost-effective monitoring of biomass at large scales in intensively-managed plantation forests. [Copyright &y& Elsevier]