In all cases, the revealed landscape groups generally corresponded to the set of landscape categories we used in our initial classification: forests, mountains, river valleys, waterfalls, lakes, and seacoasts.
However, there was no difference for the MDG of variables after taking the dietary components into the classification forests (p>0.99).
We did not include class variables such as forest/non-forest classification or forest site or type classification, although these are important variables in forest inventory.
Forest types were defined as hardwood or softwood, following the National Land Cover Data NLC D) classification (deciduous forest class: hardwoods; evergreen forest class: softwoods).
It looks at the development of forests, the factors determining their distribution, the classification of forest types, the value and benefits of forests, and the products of forests and their associated trade.
Image classification techniques, forest landscape ecology, and multi-criteria analysis were used to evaluate the evolution of forest fragmentation to develop the landscape metric indexes, and to select potential forest fragments for seed harvesting for the years 1985 and 2013.
Botanical classification places forest trees into two main groups, Gymnospermae and Angiospermae.
Therefore, an accurate and up to date classification of forest cover is essential for conservation planning, including efforts that prioritize areas for conservation activities.
Results of this study show that the PolSAR features extracted from π/4 CP mode, as well as combining the PolSAR features extracted from CP or PQ modes provide a better overall accuracy in classification of forest.
