Caceres M.D., Martinez-Vilalta J., Coll L., Llorens P., Casals P., Poyatos R., Pausas J.G., Brotons L. (2015) Coupling a water balance model with forest inventory data to predict drought stress: The role of forest structural changes vs. climate changes. Agricultural and Forest Meteorology. 213: 77-90.LinkDoi: 10.1016/j.agrformet.2015.06.012
Mechanistic water balance models can be used to predict soil moisture dynamics and drought stress in individual forest stands. Predicting current and future levels of plant drought stress is important not only at the local scale, but also at larger, landscape to regional, scales, because these are the management scales at which adaptation and mitigation strategies are implemented. To obtain reliable predictions of soil moisture and plant drought stress over large extents, water balance models need to be complemented with detailed information about the spatial variation of vegetation and soil attributes. We designed, calibrated and validated a water balance model that produces annual estimates of drought intensity and duration for all plant cohorts in a forest stand. Taking Catalonia (NE Spain) as a case study, we coupled this model with plot records from two Spanish forest inventories in which species identity, diameter and height of plant cohorts were available. Leaf area index of each plant cohort was estimated from basal area using species-specific relationships. Vertical root distribution for each species in each forest plot was estimated by determining the distribution that maximized transpiration in the model, given average climatic conditions, soil attributes and stand density. We determined recent trends (period 1980-2010) in drought stress for the main tree species in Catalonia; where forest growth and densification occurs in many areas as a result of rural abandonment and decrease of forest management. Regional increases in drought stress were detected for most tree species, although we found high variation in stress changes among individual forest plots. Moreover, predicted trends in tree drought stress were mainly due to changes in leaf area occurred between the two forest inventories rather than to climatic trends. We conclude that forest structure needs to be explicitly considered in assessments of plant drought stress patterns and trends over large geographic areas, and that forest inventories are useful sources of data provided that reasonably good estimates of soil attributes and root distribution are available. Our approach coupled with recent improvements in forest survey technologies may allow obtaining spatially continuous and precise assessments of drought stress. Further efforts are needed to calibrate drought-related demographic processes before water balance and drought stress estimates can be fully used for the accurate prediction of drought impacts. © 2015 Elsevier B.V.
Martin-Alcon S., Coll L., De Caceres M., Guitart L., Cabre M., Just A., Gonzalez-Olabarria J.R. (2015) Combining aerial LiDAR and multispectral imagery to assess postfire regeneration types in a Mediterranean forest. Canadian Journal of Forest Research. 45: 856-866.LinkDoi: 10.1139/cjfr-2014-0430
Wildfires play a major role in driving vegetation changes and can cause important environmental and economic losses in Mediterranean forests, especially where the dominant species lacks efficient postfire regeneration mechanisms. In these areas, postdisturbance vegetation management strategies need to be based on detailed, spatially continuous inventories of the burned area. Here, we present a methodology in which we combine airborne LiDAR and multispectral imagery to assess postfire regeneration types in a spatially continuous way, using a Mediterranean black pine (Pinus nigra Arn ssp. salzmannii) forest that burned in 1998 as a case study. Five postfire regeneration types were obtained by clustering field-plot data using Ward's method. Two of the five regeneration types presented high tree cover (one clearly dominated by hardwoods and the other dominated by pines), a third type presented low to moderate tree cover, being dominated by hardwoods, and the remaining two types matched to areas dominated by soil–herbaceous or shrub layers with very low or no tree cover (i.e., very low to no tree species regeneration). These five types of regeneration were used to conduct a supervised classification of remote sensing data using a nonparametric supervised classification technique. Compared with independent field validation points, the remote sensing based assessment method resulted in a global classification accuracy of 82.7%. Proportions of regeneration types in the study area indicated a general shift from the former pine-dominated forest toward hardwood dominance and showed no serious problems of regeneration failure. Our methodological approach appears to be appropriate for informing postdisturbance vegetation management strategies over large areas. © 2015, (publisher). All Rights Reserved.
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