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Proposal of an integrated and operational framework for monitoring the quality of areas in restoration process in the Atlantic Rain Forest

Grant number: 19/09713-6
Support type:Scholarships in Brazil - Post-Doctorate
Effective date (Start): January 01, 2020
Effective date (End): December 31, 2021
Field of knowledge:Physical Sciences and Mathematics - Geosciences
Principal Investigator:Milton Cezar Ribeiro
Grantee:Juliana Silveira dos Santos
Home Institution: Instituto de Biociências (IB). Universidade Estadual Paulista (UNESP). Campus de Rio Claro. Rio Claro , SP, Brazil


Projects aimed at the natural habitats restoration have been implemented and in the next years, the tendency is for an increase of natural vegetation areas in Brazil. In addition to increasing areas of natural vegetation, restoration should promote ecological functions to prevent the establishment of empty forests. In addition, to the immediate challenges to identify priority areas for restoration, in the future it will be necessary to obtain information on the quality of these areas. Monitoring of multiple plots of vegetation in situ is an expensive and time-consuming process and, therefore, auxiliary information from remote sensors can be an efficient alternative to facilitate the acquisition of this information. In this context, our objective in this project is to generate an integrated and operational framework to monitor the restored areas' quality on a large scale. Restored areas in the Atlantic Rain Forest will be used as a model to compose this framework, due to pre-existing in situ information. Spectral traits will be identified to characterize vegetation quality and the relationship between the quality of these areas and the level of landscape hemeroby. With this project we will define for the first time a framework based in situ and orbital data on a large scale for vegetation quality monitoring. We will generate functional information of the vegetation from free orbital technologies, reliable and that may be an option to technologies that due to its specific knowledge and analyses (e.g. LIDAR) and can compromise a large scale monitoring.