Carlos Antonio López-Sánchez

A Density Management Diagram Including Stand Stability and Crown Fire Risk for Pseudotsuga Menziesii (Mirb.) Franco in Spain

Abstract A static stand density management diagram was constructed for Douglas fir (Pseudotsuga menziesii [Mirb.] Franco) plantations in Spain on the basis of 3 equations that were fitted simultaneously by the full information maximum likelihood procedure to data derived from 172 plots measured across the Cantabrian and pre-Pyrenean ranges. The first equation relates quadratic mean diameter to the number of stems per hectare and dominant height. The other 2 equations relate stand volume and stand aboveground biomass to quadratic mean diameter, number of stems per hectare, and dominant height. An estimation of the average slenderness coefficient for the 250 largest trees per hectare and the canopy bulk density were included. The stand density management diagram outlined here enables rapid, straightforward comparisons among different thinning schedules for forest plantations in mountain regions, in which timber production, risk of crown fire, and the risk of damage from wind or snow are considered.

Relasphone—Mobile and Participative In Situ Forest Biomass Measurements Supporting Satellite Image Mapping

Due to the high cost of traditional forest plot measurements, the availability of up-to-date in situ forest inventory data has been a bottleneck for remote sensing image analysis in support of the important global forest biomass mapping. Capitalizing on the proliferation of smartphones, citizen science is a promising approach to increase spatial and temporal coverages of in situ forest observations in a cost-effective way. Digital cameras can be used as a relascope device to measure basal area, a forest density variable that is closely related to biomass. In this paper, we present the Relasphone mobile application with extensive accuracy assessment in two mixed forest sites from different biomes. Basal area measurements in Finland (boreal zone) were in good agreement with reference forest inventory plot data on pine ( R 2 = 0 . 75 , R M S E = 5 . 33 m 2 /ha), spruce ( R 2 = 0 . 75 , R M S E = 6 . 73 m 2 /ha) and birch ( R 2 = 0 . 71 , R M S E = 4 . 98 m 2 /ha), with total relative R M S E ( % ) = 29 . 66 % . In Durango, Mexico (temperate zone), Relasphone stem volume measurements were best for pine ( R 2 = 0 . 88 , R M S E = 32 . 46 m 3 /ha) and total stem volume ( R 2 = 0 . 87 , R M S E = 35 . 21 m 3 /ha). Relasphone data were then successfully utilized as the only reference data in combination with optical satellite images to produce biomass maps. The Relasphone concept has been validated for future use by citizens in other locations.

The Relationship between Species Diversity and Genetic Structure in the Rare Picea chihuahuana Tree Species Community, Mexico

Species diversity and genetic diversity, the most basic elements of biodiversity, have long been treated as separate topics, although populations evolve within a community context. Recent studies on community genetics and ecology have suggested that genetic diversity is not completely independent of species diversity. The Mexican Picea chihuahuana Martínez is an endemic species listed as “Endangered” on the Red List. Forty populations of Chihuahua spruce have been identified. This species is often associated with tree species of eight genera in gallery forests. This rare Picea chihuahuana tree community covers an area no more than 300 ha and has been subject of several studies involving different topics such as ecology, genetic structure and climate change. The overall aim of these studies was to obtain a dataset for developing management tools to help decision makers implement preservation and conservation strategies. However, this unique forest tree community may also represent an excellent subject for helping us to understand the interplay between ecological and evolutionary processes in determining community structure and dynamics. The AFLP technique and species composition data were used together to test the hypothesis that species diversity is related to the adaptive genetic structure of some dominant tree species (Picea chihuahuana, Pinus strobiformis, Pseudotsuga menziesii and Populus tremuloides) of the Picea chihuahuana tree community at fourteen locations. The Hill numbers were used as a diversity measure. The results revealed a significant correlation between tree species diversity and genetic structure in Populus tremuloides. Because the relationship between the two levels of diversity was found to be positive for the putative adaptive AFLP detected, genetic and species structures of the tree community were possibly simultaneously adapted to a combination of ecological or environmental factors. The present findings indicate that interactions between genetic variants and species diversity may be crucial in shaping tree communities.

A Comparison of Machine Learning Techniques Applied to Landsat-5 TM Spectral Data for Biomass Estimation

Abstract. Machine learning combines inductive and automated techniques for recognizing patterns. These techniques can be used with remote sensing datasets to map aboveground biomass (AGB) with an acceptable degree of accuracy for evaluation and management of forest ecosystems. Unfortunately, statistically rigorous comparisons of machine learning algorithms are scarce. The aim of this study was to compare the performance of the 3 most common nonparametric machine learning techniques reported in the literature, vis., Support Vector Machine (SVM), k-nearest neighbor (kNN) and Random Forest (RF), with that of the parametric multiple linear regression (MLR) for estimating AGB from Landsat-5 Thematic Mapper (TM) spectral reflectance data, texture features derived from the Normalized Difference Vegetation Index (NDVI), and topographical features derived from a digital elevation model (DEM). The results obtained for 99 permanent sites (for calibration/validation of the models) established during the winter of 2011 by systematic sampling in the state of Durango (Mexico), showed that SVM performed best once the parameterization had been optimized. Otherwise, SVM could be outperformed by RF. However, the kNN yielded the best overall results in relation to the goodness-of-fit measures. The findings confirm that nonparametric machine learning algorithms are powerful tools for estimating AGB with datasets derived from sensors with medium spatial resolution. Résumé. L’apprentissage automatique combine des techniques inductives et automatisées pour la reconnaissance des formes. Ces techniques peuvent être utilisées avec des ensembles de données de télédétection pour cartographier la biomasse aérienne « aboveground biomass » (AGB) avec un degré de précision acceptable pour l’évaluation et la gestion des écosystèmes forestiers. Malheureusement, des comparaisons statistiquement rigoureuses des algorithmes d’apprentissage automatique sont rares. Le but de cette étude était de comparer les performances des 3 méthodes d’apprentissage automatique non paramétriques les plus fréquemment rapportées dans la littérature, vis., les machines à vecteurs de support « Support Vector Machine » (SVM), les k plus proches voisins « k-nearest neighbor » (kNN) et les forêts aléatoires « Random Forest » (RF), avec celle de la régression linéaire multiple paramétrique (MLR) pour l’estimation de l’AGB provenant des données de réflectance spectrale de Landsat-5 Thematic Mapper (TM), des caractéristiques de texture dérivées de l’indice de végétation par différence normalisée « Normalized Difference Vegetation Index » (NDVI) et des caractéristiques topographiques dérivées d’un modèle numérique de terrain « digital elevation model » (DEM).Les résultats obtenus pour 99 sites permanents (pour la calibration/validation des modèles) établis au cours de l’hiver 2011 par l’échantillonnage systématique dans l’État de Durango (Mexique), ont montré que les SVM montrent leurs meilleures performances une fois que le paramétrage a été optimisé. Par ailleurs, les SVM pourraient être surpassées par les RF. Cependant, les kNN ont donné les meilleurs résultats globaux par rapport aux mesures d’ajustement. Les résultats confirment que les algorithmes d’apprentissage automatique non paramétriques sont des outils puissants pour l’estimation de l’AGB avec des ensembles de données provenant de capteurs avec une résolution spatiale moyenne.

Modelling dominant height growth in plantations of Pseudotsuga menziesii (Mirb.) Franco in Spain

A model for predicting dominant height growth and site index of Pseudotsuga menziesii (Mirb.) Franco in Spain was constructed. Data from stem analysis of 117 site trees were used. Four dynamic equations using the algebraic difference approach (ADA) and its generalisation (GADA), which have provided good results in previous studies, were evaluated. The model parameters were estimated with the base‑age‑invariant method of dummy variables, which considers both global (common to all sites) and local parameters (specific to each site). A GADA equation based on the Bertalanffy–Richards base model yielded the best results. The model provides polymorphic curves with multiple asymptotes. A base age of 20 years is proposed to reference site index.

The Role of Climatic Variables for Estimating Probability of Abundance of Tree Species

ABSTRACT We analyzed the influence of climatic variables on the abundance of native tree species in 1,490 sampling plots systematically distributed in the Sierra Madre Occidental (state of Durango, Northwestern Mexico, 26°50′ and 22°17′N and 107°09′ and 102°30′W). We used the Weibull distribution and the finite Gaussian mixture model to study the climatic limits of 15 tree species in relation to seven variables thought to affect species abundance. We found that although they may occur in the same geographical region, some species display a wider range of ecological tolerance than others. Of the 15 species under study, only two (Quercus magnoliifolia and Q. arizonica) can be considered generalists in relation to some climatic variables, while the other 13 species behaved as specialists, implying a narrower range of distribution. The analytical techniques used enabled us to demarcate the zones in which the probability of abundance of each species is highest in relation to the climate variables considered. The findings could be used to help define climate for the 15 studied tree species of economic and ecological interest.

Genetic Diversity and Conservation of Mexican Forest Trees

Over the last 200 years, humans have impacted the genetic diversity of forest trees. Because of widespread deforestation and over-exploitation, about 9000 tree species are listed worldwide as threatened with extinction, including more than half of the ~600 known conifer taxa. A comprehensive review of the floristic-taxonomic literature compiled a list of 4331 recorded tree species in Mexico. The highest diversity of pine and oak worldwide is located in the Mexican temperate forests. Because species and genetic diversity are often positively associated, a very high trans-specific genetic diversity in Mexican tree species is thus expected. Contrasting with its high species and genus richness, studies of genetic diversity in Mexican forest trees are rather scarce, and often biased to particular families, like the Pinaceae. Moreover, even within those particular families the available surveys have a penchant for specific genus. The markers used in most of these studies include the traditional and “universal” isozymes and chloroplast microsatellites and, to a lesser extent, the anonymous SSRs, AFLPs, and RAPDs. More studies on more varied taxa and using more advanced technologies and markers seem thus necessary. Because of the poor comparability of the genetic diversity estimates among the studied Mexican tree species, it is extremely difficult to discern general trends across species or regions. We thus recommend that genetic diversity should be measured across species with an identical type of genetic marker, by surveying similar numbers of loci, individuals and populations, and using identical indices of genetic diversity, relevant to conservation of trees.

A taper function for Pseudotsuga menziesii plantations in Spain

Five stem taper models belonging to three different taper function categories were fitted to data corresponding to 282 Pseudotsuga menziesii trees. The trees were selected in the area surrounding 61 research plots installed in Galicia, Asturias and the Basque Country, northern Spain. The models were simultaneously fitted to observed values of diameter outside bark and inside bark. A third-order continuous-time autoregressive error structure was used to account for autocorrelation. Selection of the best model was based on both numerical (goodness-of-fit statistics) and graphical analysis (plots of residuals against position along the stem and against tree size). The three-segmented taper model finally selected has the advantage of being compatible with both a merchantable and a total stem volume equation.

Diagramas para el manejo de la densidad en bosques mixtos e irregulares de Durango, México

Los diagramas para el manejo de la densidad (DMD) son herramientas graficas utilizadas en el diseno de los regimenes silvicolas de bosques regulares e irregulares; representando las relaciones entre la densidad, el diametro cuadratico, el incremento y la altura dominante del rodal mediante relaciones alometricas y ecologicas como: regla del autoaclareo, efecto de la densidad sobre el crecimiento y calidad de sitio. Los DMD describen eficientemente la estructura del rodal, siendo utiles en la determinacion de las caracteristicas de la produccion final. Se construyeron DMD, para coniferas y latifoliadas, en dos condiciones de productividad en bosques mixtos e irregulares del noroeste de Durango, Mexico. Adicionalmente, se construyeron dos DMD que incluyen todas las especies presentes en el rodal para cada nivel de productividad y un DMD elaborado como la suma de los dos niveles de productividad de todas las especies. Los datos empleados provienen de 15.360 sitios temporales de muestreo, de los cuales se seleccionaron 333 que representan los valores maximos de densidad (para el grupo de especies de coniferas) para representar la isolinea superior de los DMD. El DMD de las especies de coniferas en su nivel de productividad alto fue empleado como un caso de estudio para ilustrar como estos diagramas pueden ser usados en evaluar las condiciones actuales del rodal, pronosticar su desarrollo y comparar diferentes alternativas de manejo.

Predicting Pinus monophylla forest cover in the Baja California desert by remote sensing

The Californian single-leaf pinyon (Pinus monophylla var. californiarum), a subspecies of the single-leaf pinyon (the world’s only one-needled pine), inhabits semi-arid zones of the Mojave Desert (southern Nevada and southeastern California, US) and also of northern Baja California (Mexico). This tree is distributed as a relict subspecies, at elevations of between 1,010 and 1,631 m in the geographically isolated arid Sierra La Asamblea, an area characterized by mean annual precipitation levels of between 184 and 288 mm. The aim of this research was (i) to estimate the distribution of P. monophylla var. californiarum in Sierra La Asamblea by using Sentinel-2 images, and (ii) to test and describe the relationship between the distribution of P. monophylla and five topographic and 18 climate variables. We hypothesized that (i) Sentinel-2 images can be used to predict the P. monophylla distribution in the study site due to the finer resolution (×3) and greater number of bands (×2) relative to Landsat-8 data, which is publically available free of charge and has been demonstrated to be useful for estimating forest cover, and (ii) the topographical variables aspect, ruggedness and slope are particularly important because they represent important microhabitat factors that can determine the sites where conifers can become established and persist. An atmospherically corrected a 12-bit Sentinel-2A MSI image with 10 spectral bands in the visible, near infrared, and short-wave infrared light region was used in combination with the normalized differential vegetation index (NDVI). Supervised classification of this image was carried out using a backpropagation-type artificial neural network algorithm. Stepwise multiple linear binominal logistical regression and Random Forest classification including cross validation were used to model the associations between presence/absence of P. monophylla and the five topographical and 18 climate variables. Using supervised classification of Sentinel-2 satellite images, we estimated that P. monophylla covers 6,653 ± 319 ha in the isolated Sierra La Asamblea. The NDVI was one of the variables that contributed most to the prediction and clearly separated the forest cover (NDVI > 0.35) from the other vegetation cover (NDVI < 0.20). Ruggedness was the most influential environmental predictor variable, indicating that the probability of occurrence of P. monophylla was greater than 50% when the degree of ruggedness terrain ruggedness index was greater than 17.5 m. The probability of occurrence of the species decreased when the mean temperature in the warmest month increased from 23.5 to 25.2 °C. Ruggedness is known to create microclimates and provides shade that minimizes evapotranspiration from pines in desert environments. Identification of the P. monophylla stands in Sierra La Asamblea as the most southern populations represents an opportunity for research on climatic tolerance and community responses to climate variability and change.