Remote Sensing is a technology that can offer data and information across an extremely broad range of topics, making it an invaluable tool to researchers, scientists, and many people across the world today. One such invaluable use of remote sensing is in the application of forestry. In the past, scientists used to collect data on the ground from a relatively small section of a forest, and assume that their data would be an accurate representation of the forest as a whole. Today, studying forests hands-on is mostly done as ground truthing for data originally collected by remote sensing. There is a broad range of applications in forestry that remote sensing is incredibly helpful with. This paper will look at the application of remote sensing in estimating forest composition, detecting clear-cutting, the detection of disease, and its use in estimating leaf area index.
The ability to determine forest species composition is an important aspect in forest management. Species composition can give scientists an idea of whether a forest is old-growth or second-growth, and can help them to determine what kind of ecosystem exists within an area and what other types of animal or plant species can be expected to be found within a particular forest. Tree species information is also critical when determining biophysical properties of trees, such as stem volume and biomass. This information on tree species is very important to decision makers, whose focus is often centered on regeneration choices and harvest scheduling (Ørka, Dalponte, Gobakken, Næsset, & Ene, 2013). Tree species also influences habitat quality and biodiversity, so it is very helpful to be able to determine the composition of a forest when studying habitats or trying to maint...
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