CV Lattes
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Universidade de São Paulo (USP). Escola Superior de Agricultura Luiz de Queiroz (ESALQ) (Institutional affiliation from the last research proposal) Birthplace: Brazil
Matheus Ferreiras principle research interest focuses on the use of remotely sensed data for providing information related to forested ecosystems and resources. His Ph.D work focused on the use of hyperspectral remote sensing to study biodiversity of tropical forests. This research includes the development of tree species mapping methods using airborne imaging spectroscopy and retrieval of canopy foliar traits with 3D radiative transfer modeling. Outside the remote sensing based work Matheus experience, include analysis of land-use and cover change processes, assessment of forest transition and development of GIS based applications for forest fire risk mapping. (Source: Lattes Curriculum)
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The fragmentation of native vegetation due to anthropogenic actions occurs when deforestation is carried out to make way for other land uses, such as agriculture or urbanization. This process promotes the formation of habitat "islands," where certain species are unable to migrate between habitats due to the impermeability of the landscape imposed by the anthropogenic matrix. This populati…
Conservation and management of the Atlantic Forest (AF), one of the most threatened biomes of Brazil, depends on high quality information about its forest resources. Most of our knowledge about the AF comes from field measurements performed at the plot level (d 1ha), but data encompassing broad spatial extents are needed to better understand the structure and function of this important bi…
Investigations involving floristic composition and biogeochemical cycles of tropical forests are crucial for its conservation and sustainable management. In this context, hyperspectral remote sensing has been used for tree species discrimination and canopy chemistry studies. These studies have just recently become feasible, due to the acquisition of airborne hyperspectral images over trop…
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