No abstract

International audienceLand use/cover change (LUCC), as an important factor in global change, is a topic that has recently received considerable attention in the prospective modeling domain. There are many approaches and software packages for modeling LUCC, many of them are empirical approaches based on past LUCC such as CLUES , DINAMICA EGO, CA_MARKOV and Land Change Modeler (both available in IDRISI). This study reviews the possibilities and the limits of these four modeling software packages. First, a revision of the methods and tools available for each model was performed, taking into account how the models carry out the different procedures involved in the modeling process: quantity of change estimate, change potential evaluation, spatial allocation of change, reproduction of temporal and spatial patterns, model evaluation and advanced modeling options. Additional considerations, such as flexibility and user friendliness were also taken into account. Then, the four models were applied to a virtual case study to illustrate the previous descriptions with a typical LUCC scenario that consists of four processes of change (conversion of forest to two different types of crops, crop abandonment and urban sprawl) that follow different spatial patterns and are conditioned by different drivers. The outputs were compared to assess the quantity of change estimates, the change potential and the simulated prospective maps. Finally, we discussed some basic criteria to define a " good " model

Community forests and protected areas have each been proposed as strategies to stop deforestation. These management strategies should be regarded as hypotheses to be evaluated for their effectiveness in particular places. We evaluated the community-forestry hypothesis and the protected-area hypothesis in community forests with commercial timber production and strict protected areas in the Maya Forest of Guatemala and Mexico. From land-use and land cover change (LUCC) maps derived from satellite images, we compared deforestation in 19 community forests and 11 protected areas in both countries in varying periods from 1988 to 2005. Deforestation rates were higher in protected areas than in community forests, but the differences were not significant. An analysis of human presence showed similar deforestation rates in inhabited protected areas and recently inhabited community forests, but the differences were not significant. There was also no significant difference in deforestation between uninhabited protected areas, uninhabited community forests, and long-inhabited community forests. A logistic regression analysis indicated that the factors correlated with deforestation varied by country. Distance to human settlements, seasonal wetlands, and degree and length of human residence were significant in Guatemala, and distance to previous deforestation and tropical semideciduous forest were significant in Mexico. Varying contexts and especially colonization histories are highlighted as likely factors that influence different outcomes. Poorly governed protected areas perform no better as a conservation strategy than poorly governed community forests with recent colonists in active colonization fronts. Long-inhabited extractive communities perform as well as uninhabited strict protected areas under low colonization pressure. A review of costs and benefits suggests that community forests may generate more local income with lower costs. Small sample sizes may have limited the statistical power of our comparisons, but descriptive statistics on deforestation rates, logistic regression analyses, LUCC maps, data available on local economic impacts, and long-term ethnographic and action-research constitute a web of evidence supporting our conclusions. Long-inhabited community forest management for timber can be as effective as uninhabited parks at delivering long-term forest protection under certain circumstances and more effective at delivering local benefits.

A partir de fines de 1 999, la SEMARNAP decidió desarrollar la primera fase del inventario forestal nacional de México. A tal efecto, el Instituto de Geografía de la UNAM presentó en febrero de 2000 una propuesta técnica que fue adjudicada por la SEMARNAP. La propuesta del instituto de Geografía respondió al interés de SEMARNAP de elaborar una estrategia que rebasara los alcances de los inventarios forestales tradicionales, e incorporara una perspectiva ecológica complementaria. Se evaluaron 194 198 411 ha (1 941 984 km2, porción continental del territorio nacional). De esta superficie, los "Matorrales" ocupan la mayor proporción con casi un 30%; le siguen en orden descendiente de superficie los "Bosques", "Cultivos", "Pastizales" y "Selvas" con superficies entre 15 y 17% del total. Las otras tres formaciones ocupan en conjunto alrededor del 5% de la superficie total del país (véase gráfica más abajo). Los resultados obtenidos se plasmaron en los siguientes productos: 121 mapas de cubierta vegetal y uso del suelo, a escala 1:250 000, y 1 21 espaciomapas, ambos en formato digital e impresos. Evaluación cuantitativa de la calidad de la cartografía mediante fotografía digital detallada. Datos de superficies forestales y otras cubiertas a varios niveles de agregación. Diccionario de datos, glosario y metadatos. Página en la INTERNET donde se presentan el informe, los anexos y los productos obtenidos. Esta investigación permitirá a muy corto plazo hacer estimaciones confiables sobre las tasas de deforestación, ya que las categorías mapeadas son compatibles con los sistemas de clasificación de mayor uso en el país.

Background: Reliable information about the spatial distribution of aboveground biomass (AGB) in tropical forests is fundamental for climate change mitigation and for maintaining carbon stocks. Recent AGB maps at continental and national scales have shown large uncertainties, particularly in tropical areas with high AGB values. Errors in AGB maps are linked to the quality of plot data used to calibrate remote sensing products, and the ability of radar data to map high AGB forest. Here we suggest an approach to improve the accuracy of AGB maps and test this approach with a case study of the tropical forests of the Yucatan peninsula, where the accuracy of AGB mapping is lower than other forest types in Mexico. To reduce the errors in field data, National Forest Inventory (NFI) plots were corrected to consider small trees. Temporal differences between NFI plots and imagery acquisition were addressed by considering biomass changes over time. To overcome issues related to saturation of radar backscatter, we incorporate radar texture metrics and climate data to improve the accuracy of AGB maps. Finally, we increased the number of sampling plots using biomass estimates derived from LiDAR data to assess if increasing sample size could improve the accuracy of AGB estimates. Results: Correcting NFI plot data for both small trees and temporal differences between field and remotely sensed measurements reduced the relative error of biomass estimates by 12.2%. Using a machine learning algorithm, Random Forest, with corrected field plot data, backscatter and surface texture from the L-band synthetic aperture radar (PALSAR) installed on the on the Advanced Land Observing Satellite-1 (ALOS), and climatic water deficit data improved the accuracy of the maps obtained in this study as compared to previous studies (R 2 = 0.44 vs R 2 = 0.32). However, using sample plots derived from LiDAR data to increase sample size did not improve accuracy of AGB maps (R 2 = 0.26). Conclusions: This study reveals that the suggested approach has the potential to improve AGB maps of tropical dry forests and shows predictors of AGB that should be considered in future studies. Our results highlight the importance of using ecological knowledge to correct errors associated with both the plot-level biomass estimates and the mismatch between field and remotely sensed data.

Antes de ser utilizados para tomar decisiones, los mapas temáticos, las bases de datos cartográficos y las imágenes clasificadas, deben ser evaluados para conocer su confiabilidad. Este artículo presenta una revisión de la literatura especializada sobre el proceso de evaluación de la confiabilidad temática y puede ser utilizado como guía práctica para llevar a cabo este tipo de evaluaciones.

No abstract

No abstract