Seasonal dry deposition and canopy leaching of base cations in a subtropical evergreen mixed forest, China

Abstract

Applying geostatistics for investigations of forest ecosystems using remote sensing imagery

Abstract

Structure of old Pinus sylvestris dominated forest stands along a geographic and human impact gradient in mid-boreal Fennoscandia

Abstract

Temporal trends in chemical parameters of upland forest soils in southern Finland

Abstract

Stratified estimation of forest inventory variables using spatially summarized stratifications

Abstract

Soil organic matter in fire-affected pastures and in an Araucaria forest in South-Brazilian Leptosols

The objective of this work was to evaluate the distribution pattern and composition of soil organic matter (SOM) and its physical pools of Leptosols periodically affected by fire over the last 100 years in South Brazil. Soil samples at 0-5, 5-10, and 10-15 cm depths were collected from the following environments: native pasture without burning in the last year and grazed with 0.5 livestock per hectare per year (1NB); native pasture without burning in the last 23 years and grazed with 2.0 livestock per hectare per year (23NB); and an Araucaria forest (AF). Physical fractionation was performed with the 0-5 and 5-10 cm soil layers. Soil C and N stocks were determined in the three depths and in the physical pools, and organic matter was characterized by infrared spectroscopy and thermogravimetry. The largest C stocks in all depths and physical pools were found under the AF. The 23NB environment showed the lowest soil C and N stocks at the 5-15 cm depth, which was related to the end of burning and to the higher grazing intensity. The SOM of the occluded light fraction showed a greater chemical recalcitrance in 1NB than in 23NB. Annual pasture burning does not affect soil C stocks up to 15 cm of depth.

Inventory of sparse forest populations using adaptive cluster sampling

Abstract

Geometrically accurate time series of archived aerial images and airborne lidar data in a forest environment

Abstract

Generating a raster map presentation of a forest resource by solving a transportation problem

Abstract

Towards Machine Consciousness: Grounding Abstract Models as pi-Processes

We present a two-level model of concurrent communicating systems (CCS) to serve as a basis formachine consciousness. A language implementing threads within logic programming is ¯rstintroduced. This high-level framework allows for the de¯nition of abstract processes that can beexecuted on a virtual machine. We then look for a possible grounding of these processes into thebrain. Towards this end, we map abstract de¯nitions (including logical expressions representingcompiled knowledge) into a variant of the pi-calculus. We illustrate this approach through aseries of examples extending from a purely reactive behavior to patterns of consciousness.

Mating system parameters in a high density population of andirobas in the Amazon forest

The objective of this work was to estimate the mating system parameters of a andiroba (Carapa guianensis) population using microsatellite markers and the mixed and correlated mating models. Twelve open‑pollinated progeny arrays of 15 individuals were sampled in an area with C. guianensis estimated density of 25.7 trees per hectare. Overall, the species has a mixed reproductive system, with a predominance of outcrossing. The multilocus outcrossing rate (t m = 0.862) was significantly lower than the unity, indicating that self‑pollination occurred. The rate of biparental inbreeding was substantial (t m ‑ t s = 0.134) and significantly different from zero. The correlation of selfing within progenies was high (r s = 0.635), indicating variation in the individual outcrossing rate. Consistent with this result, the estimate of the individual outcrossing rate ranged from 0.598 to 0.978. The multilocus correlation of paternity was low (r p(m) = 0.081), but significantly different from zero, suggesting that the progenies contain full‑sibs. The coancestry within progenies (Θ = 0.185) was higher and the variance effective size (Ne(v) = 2.7) was lower than expected for true half‑sib progenies (Θ = 0.125; Ne(v) = 4). These results suggest that, in order to maintain a minimum effective size of 150 individuals for breeding, genetic conservation, and environmental reforestation programs, seeds from at least 56 trees must be collected.

Scheduling forest road maintenance using the analytic hierarchy process and heuristics

Abstract

Environmental data mining and modeling based on machine learning algorithms and geostatistics

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. NIL algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. (C) 2003 Elsevier Ltd. All rights reserved.