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The estimation of forest residual biomass is of interest to assess the availability of green energy resources. This study relates the Pinus halepensis Miller forest residual biomass (FRB), estimate...

The account of total biomass can assist with the evaluation of climate regulation policies from local to global scales. This study estimates total biomass (TB), including tree and shrub biomass fractions, in Pinus halepensis Miller forest stands located in the Aragon Region (Spain) using Airborne Laser Scanning (ALS) data and fieldwork. A comparison of five selection methods and five regression models was performed to relate the TB, estimated in 83 field plots through allometric equations, to several independent variables extracted from ALS point cloud. A height threshold was used to include returns above 0.2 m when calculating ALS variables. The sample was divided into training and test sets composed of 62 and 21 plots, respectively. The model with the lower root mean square error (15.14 tons/ha) after validation was the multiple linear regression model including three ALS variables: the 25th percentile of the return heights, the variance, and the percentage of first returns above the mean. This study confirms the usefulness of low-density ALS data to accurately estimate total biomass, and thus better assess the availability of biomass and carbon content in a Mediterranean Aleppo pine forest.

El aumento de la mortalidad y la pérdida de vitalidad de los bosques son procesos que afectan a diversas especies de árboles y biomas. Estos fenómenos de decaimiento forestal se han asociado a la interacción de múltiples factores relacionados con el cambio global, como el aumento de las sequías o la mayor incidencia de factores bióticos que generan estrés. Sin embargo, el papel que juegan estos organismos en el decaimiento del bosque en relación a la sequía no está del todo comprendido. En este trabajo caracterizamos retrospectivamente los efectos que ejercen dichos organismos sobre el crecimiento radial de árboles estresados por sequía utilizando dendrocronología. El efecto combinado del muérdago y las sequías reducen el crecimiento del pino silvestre y aumentan la defoliación, siendo estos efectos más evidentes en el ápice del árbol. La procesionaria induce pérdidas de crecimiento en pino laricio, pero en general se observa una recuperación dos años después. No encontramos un papel determinante de los hongos patógenos de raíz en el decaimiento del abeto. Finalmente, pinos carrasco muertos que presentaban ataques de insectos perforadores mostraron patrones de crecimiento similares a pinos vivos. Este estudio nos permite caracterizar algunos de los complejos y múltiples efectos de diferentes factores bióticos de estrés sobre el crecimiento y la vulnerabilidad de los bosques susceptibles a los efectos negativos de las sequías. Increasing mortality and decline of forests are affecting several tree species and biomes. These events of forest decline have been related to the interaction of several global change elements, like the increase in drought stress or a higher incidence of biotic stress factors. However, the roles played by biotic stressors have been scarcely explored as related to drought stress. In this paper we retrospectively characterize the effects of these organisms on radial growth of trees already stressed by drought using dendrochronology. The combined effect of mistletoe and droughts reduce tree growth in Scots pine and increase defoliation, being these effects more noticeable in the apex. The pine processionary moth induces growth losses in the affected stands of black pine, but they usually recover two years after the outbreak. We did not find a determinate role of root pathogens on silver fir dieback. Finally, dead Aleppo pine trees infested by bark beetles showed similar growth patterns than living pine trees. This study allows characterizing some of the complex and multiple effects of different biotic stressors on growth and vulnerability of forests subjected to the negative effects exerted by droughts. Este estudio ha sido financiado por los proyectos CGL-2007-6066-C04-02/BOS, CGL2008-04847-C02-01, CGL2011-26654 (MINECO), 387/2011 (Organismo Autónomo de Parques Naciona-les) y PROCLIM (La Caixa-Gobierno de Aragón).

Outbreak insects are among the major biotic disturbances affecting Mediterranean forests by reducing their growth and vitality through severe defoliations. Despite their relevance for the management of these drought-prone areas, we lack information on the relationships between growth, canopy cover and insect defoliations at ample spatial and temporal scales. Here, we combine remote sensing (vegetation indexes) and dendrochronological information (basal area increment, BAI) to assess the effects of pine processionary moth (Thaumetopoea pityocampa, PPM) defoliations on growth and cover changes of several pine species, mainly focusing on Pinus nigra. We compared both datasets with a long-term field survey of PPM stand defoliations carried out in eastern Spain during the period 1970-2012. Lastly, we fitted linear-mixed models of BAI using as predictors climatic variables and a multi-scalar drought index to distinguish drought-related growth reductions from those attributed to PPM outbreaks. PPM severe defoliations mainly affected edge or low-density stands. Several vegetation indices (Moisture Stress Index, Normalized Burn Ratio and Normalized Difference Infrared Index) reflected the cover decrease corresponding to severe PPM defoliations. We found that trees affected by various severe PPM defoliations took at least 2. years to recover BAI values similar to those observed before the outbreak. The combined use of remote sensing and dendrochronology allows monitoring the impact of PPM defoliations from stands to trees, and these are valuable approaches to forecast outbreaks and their effects on Mediterranean forest. We consider both powerful tools to further assess the interacting effects of climate warming and PPM dynamics on drought-prone forests. © 2014 Elsevier B.V., This study was supported by projects PROCLIM and CGL2011-26654 financed by La Caixa-Aragón Government and the Spanish Commission of Science and Technology-FEDER, respectively. JJC acknowledges support by ARAID. We sincerely thank J. D. Galván, A.Q. Alla and the personal from the Forest Health Laboratory (Mora de Rubielos, Gobierno de Aragón) for the help in the field works. The authors acknowledge the Landsat scenes provided by the European Space Agency (ESA) in the context of the project C1P.15257.

Se analiza el impacto y la evolucion de la estructura del paisaje en un incendio del Prepirineo aragones utilizando 6 imagenes Landsat 5 TM, identificando relaciones entre valores de heterogeneidad espacial y comunidades vegetales. Se extraen los NDVI (Normalized Difference Vegetation Index), se calcula el indice de Shannon y se cartografia para cada ano. Se definen tres componentes representativos de los valores de diversidad (preincendio, a corto plazo y a medio plazo). Se concluye que a nivel de comunidad vegetal el incendio ha supuesto un incremento de la diversidad interna en algunas comunidades. No obstante, a medio plazo y a escala global el fuego provoca la homogeneizacion del paisaje, tal y como describen otros autores utilizando fuentes de informacion similar.

Remote sensing has been shown to be an efficient tool in the study of forest-fire processes. However, a lack of information on the amount of biomass burnt reduces the accuracy of fire severity and emission models. In this study, we use imagery from the Landsat Thematic Mapper to map crown biomass and burn severity for a large Mediterranean area. Considering the specific characteristics of the Mediterranean environment, two methods to extract useful remote sensing data were employed; both sought to analyze relationships between crown biomass and spectral information. As a result, a crown biomass map of Pinus spp. was created for the entire study area, applying nonlinear regression using the variable MID57 (TM5 + TM7) (R2 = 0.651). Considering only P. halepensis pixels that were burnt in the selected fire scar, the relationships between crown biomass and burn severity were found to be high and significant, yielding an R2 value of 0.516. Finally, a logistic regression model was constructed to map the presence or otherwise of high burn severity levels using crown biomass as the independent variable, yielding in the confusion matrix an overall percentage of data points correctly classified of 77% and a Kappa statistic in the validation sample of 0.554.

Tree biomass estimate is essential for carbon accounting, bioenergy feasibility studies, and forest sustainable management. This fact, added to the availability of airborne laser scanning (ALS) information, provided by the Spanish National Plan for Aerial Orthophotography (PNOA), and the existence of little research focusing on the use of ALS technology in Mediterranean Aleppo pine (Pinus halepensis Mill.) forest, determined the main objective of this research. Thus, this study aims to test the suitability of the low point density (0.5 points/m2), discrete, multiple-return, PNOA-ALS data, to estimate and map the total biomass (TB) and its carbon content in Pinus halepensis Mill. forest stands, located in Aragon (north-eastern Spain). TB was calculated in 45 field plots, using allometric equations, and related through a multivariate linear regression analysis with a collection of independent variables extracted from the ALS data. The predictive model was validated using a leave-one-out cross-validation (LOOCV) technique. Then, a regular grid with cell size 25 x 25 m corresponding to the sample plot size was generated by means of GIS, in order to compute TB at stand level and convert biomass to carbon by using the 0.5 conversion factor. The maximum height, kurtosis and the percentage of returns above 1 meter, were the ALS metrics included in the fitted model, which presented a R2 value of 0.89. The implementation of the model in a GIS showed an average of 68633 kg/ha of TB and 34247.95 kg/ha of carbon fixed. The results indicate that despite the low point density of the ALS data, the final model is accurate enough to be used in forestry applications.

Aim of study: Human settlements and activities have completely modified landscape structure in the Mediterranean region. Vegetation patterns show the interactions between human activities and natural processes on the territory, and allow understanding historical ecological processes and socioeconomic factors. The arrangement of land uses in the rural landscape can be perceived as a proxy for human activities that often lead to the use, and escape, of fire, the most important disturbance in our forest landscapes. In this context, we tried to predict human-caused fire occurrence in a 5-year period by quantifying landscape patterns.Area of study: This study analyses the Spanish territory included in the Iberian Peninsula and Balearic Islands (497,166 km2).Material and Methods: We evaluated spatial pattern applying a set of commonly used landscape ecology metrics to landscape windows of 10x10 sq km (4751 units in the UTM grid) overlaid on the Forest Map of Spain, MFE200.Main results: The best logistic regression model obtained included Shannon’s Diversity Index, Mean Patch Edge and Mean Shape Index as explicative variables and the global percentage of correct predictions was 66.3 %.Research highlights: Our results suggested that the highest probability of fire occurrence at that time was associated with areas with a greater diversity of land uses and with more compact patches with fewer edges.Keywords: human-caused wildfires; landscape ecology; logistic regression.

[1] Alterations in the hydrological cycle following wildfire due to the loss of ground cover vegetation and changes in soil properties have been documented in many studies. Nevertheless, the rapid process of vegetation recovery reduces such negative effects. Vegetation cover before fire, fire severity, and geophysical properties are important factors that control spatial discontinuities involved in the vegetation-covering process. The objective of this study was to estimate the probability of high erosion in order to map erosion-sensitive areas after fire. The analysis was carried out in different plant communities burnt by summer wildfires in the pre-Pyrenean area (Spain). Three-year Landsat Thematic Mapper (TM) images have been used for mapping wildfire areas and severity levels. Conversion to spectral reflectance has been applied for radiometric correction by normalizing topographic and atmospheric effects. Likewise, other physical variables have also been incorporated into the geographic information system (GIS): vegetation types, parent material, illumination, slope, aspect, and precipitation. The dependent variable has been characterized by means of fieldwork and a photointerpretation process based on high-resolution digital aerial orthophotographs taken 11-12 years after the fire. Different logistic regression models have been used for mapping the probability of erosion. Results indicate that prefire normalized difference vegetation index values and aspect are the most important variables for estimating erosion-sensitive areas after fire (Nagelkerke r2 = 0.66; Kappa values = 0.65). Finally, the use of nonparametric models with environmental digital information based on GIS can facilitate the management of burnt areas.