Abstract:The spatio-temporal analysis of land use changes could provide basic information for managing the protection, conservation and production of forestlands, which promotes a sustainable resource use of temperate ecosystems. In this study we modeled and analyzed the spatial and temporal dynamics of land use of a temperate forests in the region of Pueblo Nuevo, Durango, Mexico. Data from the Landsat images Multispectral Scanner (MSS) 1973, Thematic Mapper (TM) 1990, and Operational Land Imager (OLI) 2014 were used. Supervised classification methods were then applied to generate the land use for these years. To validate the land use classifications on the images, the Kappa coefficient was used. The resulting Kappa coefficients were 91%, 92% and 90% for 1973, 1990 and 2014, respectively. The analysis of the change dynamics was assessed with Markov Chains and Cellular Automata (CA), which are based on probabilistic modeling techniques. The Markov Chains and CA show constant changes in land use. The class most affected by these changes is the pine forest. Changes in the extent of temperate forest of the study area were further projected until 2028, indicating that the area of pine forest could be continuously reduced. The results of this study could provide quantitative information, which represents a base for assessing the sustainability in the management of these temperate forest ecosystems and for taking actions to mitigate their degradation.

Combined use of new geospatial techniques and non-parametric multivariate statistical methods enables monitoring and quantification of the biomass of large areas of forest ecosystems with acceptable reliability. The main objective of the present study was to estimate the aboveground forest biomass (AGB) in the Sierra Madre Occidental (SMO) in the state of Durango, Mexico, using the M5 model tree (M5P) technique and the analysis of medium-resolution satellite-based multi-spectral data, and field data collected from a network of 201 permanent forest growth and soil research sites (SPIFyS). Research plots were installed by systematic sampling throughout the study area in 2011. The digital levels of the images were converted to apparent reflectance (ToA) and surface reflectance (SR). The M5P technique that constructs tree-based piecewise linear models was used. The fitted model with SR and tree abundance by species group as predictive variables (ASG) explained 73% of the observed AGB variance (the root mean squared error (RMSE) = 39.40 Mg¨ha´1). The variables that best discriminated the AGB, in order of decreasing importance, were the normalized difference vegetation index (NDVI), tree abundance of other broadleaves species (OB), Band 4 of Landsat 5 TM (Thematic Mapper) satellite and tree abundance of pines (Pinus). The results demonstrate the potential usefulness of the M5P method for estimating AGB based in the surface reflectance values (SR).

The Sierra Madre Occidental mountain range (Durango, Mexico) is of great ecological interest because of the high degree of environmental heterogeneity in the area. The objective of the present study was to estimate the biomass of mixed and uneven-aged forests in the Sierra Madre Occidental by using Landsat-5 TM spectral data and forest inventory data. We used the ATCOR3 ® atmospheric and topographic correction module to convert remotely sensed imagery digital signals to surface reflectance values. The usual approach of modeling stand variables by using multiple linear regression was compared with a hybrid model developed in two steps: in the first step a regression tree was used to obtain an initial classification of homogeneous biomass groups, and multiple linear regression models were then fitted to each node of the pruned regression tree. Cross-validation of the hybrid model explained 72.96% of the observed stand biomass variation, with a reduction in the RMSE of 25.47% with respect to the estimates yielded by the linear model fitted to the complete database. The most important variables for the binary classification process in the regression tree were the albedo, the corrected readings of the short-wave infrared band of the satellite (2.08-2.35 µm) and the topographic moisture index. We used the model output to construct a map for estimating biomass in the study area, which yielded values of between 51 and 235 Mg ha -1 . The use of regression trees in combination with stepwise regression of corrected satellite imagery proved a reliable method for estimating forest biomass.

Los modelos de índice de sitio representan una herramienta silvícola muy importante para clasificar la productividad de los bosques. El objetivo de este trabajo fue desarrollar ecuaciones dinámicas de índice de sitio derivadas, mediante el método de Diferencias Algebraicas Generalizadas (GADA), para cuatro especies de pino en la Sierra Norte del estado de Oaxaca, México. Para el ajuste de las ecuaciones se utilizaron datos de análisis de tronco de árboles de Pinus oaxacana, P. douglasiana, P. patula y P. pseudostrobus. Los árboles se seleccionaron en rodales mixtos e irregulares, intentando cubrir las diferentes calidades de estación presentes en las áreas sujetas a manejo forestal en los bosques de la Unidad de Manejo Forestal Regional 2001. Los ajustes se realizaron con el método iterativo y una estructura de error autoregresiva de tiempo continuo de segundo orden (CAR2), para corregir la autocorrelación del término del error. Los resultados indicaron que la formulación GADA del modelo de Bertalanffy-Richards puede ser utilizada para describir con precisión el índice de sitio de las cuatro especies estudiadas. La función desarrollada es polimórfica, dinámica, invariable con la edad de referencia y tiene múltiples asíntotas. El modelo genera, para las cuatro especies, estimaciones compatibles de índice de sitio y de altura dominante.

Lack of knowledge of individual tree growth in species-rich, mixed forest ecosystems impedes their sustainable management. In this study, species-specific models for predicting individual diameter at breast height (dbh) and total tree height (h) growth were developed for 30 tree species growing in mixed and uneven-aged forest stands in Durango, Mexico. Growth models were also developed for all pine, all oaks, and all other species of the genus Arbutus (strawberry trees). A database of 55,158 trees with remeasurements of dbh and h of a 5-year growth period was used to develop the models. The data were collected from 217 stem-mapped plots located in the Sierra Madre Occidental (Mexico). Weighted regression was used to remove heteroscedasticity from the species-specific dbh and h growth models using a power function of the tree size independent variables. The final models developed in the present study to predict dbh and total tree height growth included size variables, site factors, and competition variables in their formulation. The developed models fitted the data well and explained between 98 and 99% and of the observed variation of dbh, and between 77 and 98% of the observed variation of total tree height for the studied species and groups of species. The developed models can be used for estimating the individual dbh and h growth for the analyzed species and can be integrated in decision support tools for management planning in these mixed forest ecosystems.