Sentence examples for forests estimation from inspiring English sources

In an era where destructive samples of belowground tree components is incomplete across the world's forests, estimation strategies that merge the attributes of both allometric equations and C climate relationships may refine forest C stocks estimations especially given emerging science that supports the relationships between climate, forest biomass, and C pools [1, 3, 8, 27].

In an era where destructive samples of belowground tree components is incomplete across the world's forests, estimation strategies that merge the attributes of both allometric equations and C climate relationships may refine forest C stocks estimations especially given emerging science that supports the relationships between climate, forest biomass, and C pools [ 1, 3, 8, 27].

In the case of USA forests, estimations yield about 11% of all forest located within 85 m of a road (Riitters and Wickham 2003).

Although there has been ample discussion of the technical nature of ALS-assisted forest estimation, few studies move beyond the initial analysis and results with an eye to future management and monitoring.

More recently, studies on forest carbon estimation combining forest inventory data with remote sensing data have gradually proliferated (Xue et al. [2009]; Pang and Li [2012]; Liu et al. [2012]; Tu and Peng [2012]; Huang and Chen [2013]; Huang et al. [2013]; Wang et al. [2014]).

Asner et al. (2011) developed a simple universal forest carbon estimation method for tropical forests that reduces the amount of required field measurements.

For forest area estimation, ALS-assisted estimates showed an RE of 3.7 4.6, while InSAR, RapidEye, and global Landsat and PALSAR maps resulted in RE values of 1.0 1.3, 2.0 2.1, 1.4 1.8, and 1.7, respectively.

Data saturation related to optical sensors is reported as a major limitation or source of uncertainty in forest biomass estimation in complex stand structures and closed canopy forests [10, 22, 24, 63].

Spatially explicit urban forest carbon estimation provides a baseline map for understanding the variation in forest vertical structure, informing sustainable forest management and urban planning.

The k-Nearest Neighbor (kNN) method of forest attribute estimation and mapping has become an integral part of national forest inventory methods in Finland in the last decade.

TSI based PolInSAR inversion modeling showed the most accurate result for forest height estimation.