Sentence examples for predictions of forest from inspiring English sources
Validation of the long-term biomass predictions of forest landscape models (FLMs) has always been a challenging task.
Even by excluding comparisons where local variation was high, model predictions of forest growth compared poorly with those estimated from ground survey (r2=0.4).
In conclusion, stand attributes of the tropical plots used in this study largely conform to the predictions of forest stand development theories that have so far mainly been tested in temperate forests.
These results indicate effects of non-native earthworms on the forest floor should be included in predictions of forest ecosystem C budgets to ensure accurate attribution of emissions to heterotrophic respiration versus combustion.
We developed NuCycling-Succession, a simple nutrient cycling and succession extension for the LANDIS-II landscape model of forest dynamics, to examine the interactions between these forest processes in order to develop more realistic predictions of forest response to management practices and global change.
Quantitative spatial predictions of forest soil properties and land qualities can be produced using field measurements of soil layer and site properties as response variables and the more readily available spatial environmental explanatory variables derived from digital elevation models (DEM), terrain analysis, digital climatic surfaces, and geophysical and multi-spectral remote sensing.
Multitemporal Landsat imagery also improves prediction of forest successional stages.
The application of CPGLM is a novel approach for prediction of forest biomass loss.
Prediction of forest dimensions using data obtained from GIS surfaces and satellite imagery has been less widely researched.
Using this process, the percent root mean square error (RMSE) for prediction of forest carbon was 35%%.
Therefore, accurate prediction of forest successional stage using remote sensing imagery at stand scale requires accounting for site-specific factors influence remotely sensed signals in the future.
