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Estimating forest biomass by remote sensing radar data in Brazil

Santos dos J. R., Gama F. F., Bispo P. C.

Drewno : prace naukowe, doniesienia, komunikaty

2014

vol. 57, nr 192

119--132

Remote sensing-radar was used to analyze forest mapping and biomass estimates on Brazilian territory . Two examples of SAR attributes for the modeling of the aboveground biomass of forest stands are presented: (1) full-polarimetric attributes of PALSAR/ALOS (Phased Array type L-band Synthetic Aperture Radar /Advanced Land Observing Satellite) for modeling in the Amazonian tropical forest, considering the influence of the geomorphometric aspects on this radar response, and (2) polarimetric and interferometric airborne data (XHH and full-polarimetric of P-band ) for modeling Eucalyptus sp. stands. In both cases, an analysis of forest structure variability through polarimetric signatures was conducted. A multivariate regression technique was used to integrate the variables from polarimetric and /or interferometric radar attributes and field inventory. Considering the terrain aspects where the tropical forest was located, the most significant variables for the biomass modeling were the Volumetric Scattering of Freeman-Durden target decomposition, Anisotropy, Relief Elevation, Slope, and the first and third helicity components of the Touzi model . For the Eucalyptus biomass model, the Interferometry Heigt and Canopy Scattering Index variables were significant. The statistical analysis based on field survey measures to validate each model, indicated a margin of error below 20% for the biomass estimations , showing the importance of SAR attributes for models of natural and planted forest stock density .

Effects of environmental variation and spatial distance on the beta diversity of woody plant functional groups in a tropical forest

Zhang Z., Zang R., Wang L., Wang G., Zhang J.

Polish Journal of Ecology

2012

Vol. 60, nr 3

525-533

Tropical forests are among the most complex ecosystems on Earth. The high alpha-diversity of tropical forests has been amply documented, however, beta diversity, equally important for us to understand the mechanism of biodiversity maintenance, has seldom been studied. The main current hypotheses about the origin of beta diversity are as follows: (1) species composition is uniform over large areas; (2) species composition fluctuates in a random, auto-correlated way emphasizing spatially limited dispersal; (3) species composition is related to environmental conditions. Testing these hypotheses is important to understand ecosystem function and to manage ecosystems effectively. In this study, we quantified the relative influence of environmental variation and spatial distance on the beta diversity of woody plant functional groups in a tropical forest of Hainan Island, China. Floristic and environmental data were collected from 135 grid-sample plots. We classified woody plant functional groups based on frequency, growth forms and successional status, respectively. To see whether environmental variation and spatial distance influence beta diversity, the simple and partial Mantel tests, in conjunction with linear and spline regression models were performed. The results showed that environmental heterogeneity and spatial distance were the primary determinants of pair-wise species composition differences between plots in the study area. Common, tree and climax species were more sensitive than the others to changes in environmental heterogeneity and spatial distance. Except for rare, pioneer and shrub species, the others had apparently negative relationship between spatial distance and similarity. The distance decay of similarity was determined by joint influences of spatial distance and spatially-structured environmental variables. Spatial distance had the clearest effect on beta diversity at distances <15 km and with the increase in spatial distance, beta diversity was gradually controlled by environmental heterogeneity. This study provides further support for neutral theory and environmental control hypothesis as opposed to uniformly distributed hypothesis.