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We need to learn to initiate burns in complex forests, and this will require new techniques.
Simple regression modeling also produced poor model fits; the Random Forest models produced somewhat better biomass predictions than either the universal or regression models, and had low predictive power as measured by R2 but root mean squared errors were comparable to those from other studies in complex forests.
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In this study, we analyse some of the transformations that can occur in complex forest-shrubland socio-ecological systems undergoing biophysical and socioeconomic change.
Remote sensing hyperspectral sensors are important and powerful instruments for addressing classification problems in complex forest scenarios, as they allow one a detailed characterization of the spectral behavior of the considered information classes.
Pierce et al. (2009) found that a variant of k-NN, the gradient nearest neighbour, generally performed better than linear regression models or classification trees in temperate marine forests but that regression performed better in the more complex forests of adjacent temperate steppe and Mediterranean forests.
A slightly lower activity of Cs, 4800 ± 61 Bq kg−1 dm in caps and 2180 ± 190 Bq kg−1 dm in stipes, was found in B. badius collected in 1995 1996 from the complex forests of the Wdzydze Landscape Park which is very close to Bory Tucholskie (Malinowska et al. 2006; Falandysz et al. 2003a, b).
In comparison, 3D data derived from the multispectral data via structure-from-motion produced lower correlations with field-measured variables, especially in dense and structurally complex forests.
The proposed algorithm is thus shown a cost effective tool for forest monitoring using LiDAR data that is able to detect canopy parameters in complex broadleaves forests with high vegetation density and overlapping crowns and with consequent significant reduction of the field surveys, limiting them over only the calibration site.
Generally, event-based sampling in complex structured forests (conditions that favor comparatively long autocorrelations in throughfall) requires the largest extents.
The main advantage of such an approach is that it allows us to decipher the spatial abilities and memory capacities that are required to solve the real challenges faced daily by animals in rich and complex forest environments (Boesch 1993; Gould 2004).
As an example, this paper illustrates configurational changes on a complex forest-savannah dynamics in a tropical biodiversity hotspot, New Caledonia.
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