Detailed forest information is increasingly coveted not only for forest administration Purposes but also for preserving and consolidating possible forest ecosystems, Although accurate measurements of forests can be collected by field measurements, they are labor heavy and time exhaustion especially when gaining enough measurements over great and heterogeneous forest areas.
Therefore we need automated and accurate systems which can supplement field measurements. High spatial resolution remotely sensed data can be used for this goal because incubating technologies keep increasing spatial resolution and make it possible to handle great amounts of remotely sensed digital data by strong computers at feasible prices. Although high spatial resolution remotely sensed data holds the potential to be a valuable source of information for forest features, a number of challenges still exist in extracting the required information from this data. Therefore, it is ticklish to develop and improve automated methods to extract forest information.
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