CV Lattes
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Ministério da Ciência, Tecnologia, Inovações e Comunicações (Brasil). Instituto Nacional de Pesquisas Espaciais (INPE) (Institutional affiliation for the last research proposal) Birthplace: Brazil
Matheus Ferreira´s 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)
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…
| 2 / 2 | Completed scholarships in Brazil |
| Associated processes | |
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
| Publications | 4 |
| Citations | 39 |
| Cit./Article | 9.8 |
| Data from Web of Science | |
BRAGA, JOSE R. G.; PERIPATO, VINICIUS; DALAGNOL, RICARDO; FERREIRA, MATHEUS P.; TARABALKA, YULIYA; ARAGAO, LUIZ E. O. C.; DE CAMPOS VELHO, HAROLDO E.; SHIGUEMORI, ELCIO H.; WAGNER, FABIEN H.. Tree Crown Delineation Algorithm Based on a Convolutional Neural Network. REMOTE SENSING, v. 12, n. 8, APR 2 2020. Web of Science Citations: 1. (15/50484-0, 18/15001-6, 15/22987-7, 18/06072-7, 16/17652-9)
FERREIRA, MATHEUS PINHEIRO; WAGNER, FABIEN HUBERT; ARAGAO, LUIZ E. O. C.; SHIMABUKURO, YOSIO EDEMIR; DE SOUZA FILHO, CARLOS ROBERTO. Tree species classification in tropical forests using visible to shortwave infrared WorldView-3 images and texture analysis. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, v. 149, p. 119-131, MAR 2019. Web of Science Citations: 7. (16/24977-1, 15/50484-0)
FERREIRA, MATHEUS PINHEIRO; FERET, JEAN-BAPTISTE; GRAU, ELOI; GASTELLU-ETCHEGORRY, JEAN-PHILIPPE; SHIMABUKURO, YOSIO EDEMIR; DE SOUZA FILHO, CARLOS ROBERTO. Retrieving structural and chemical properties of individual tree crowns in a highly diverse tropical forest with 3D radiative transfer modeling and imaging spectroscopy. REMOTE SENSING OF ENVIRONMENT, v. 211, p. 276-291, JUN 15 2018. Web of Science Citations: 6. (15/50484-0, 16/24977-1)
WAGNER, FABIEN HUBERT; FERREIRA, MATHEUS PINHEIRO; SANCHEZ, ALBER; HIRYE, MAYUMI C. M.; ZORTEA, MACIEL; GLOOR, EMANUEL; PHILLIPS, OLIVER L.; DE SOUZA FILHO, CARLOS ROBERTO; SHIMABUKURO, YOSIO EDEMIR; ARAGAO, LUIZ E. O. C.. Individual tree crown delineation in a highly diverse tropical forest using very high resolution satellite images. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, v. 145, n. B, p. 362-377, NOV 2018. Web of Science Citations: 9. (13/50533-5, 16/03397-7, 13/11589-5, 15/50484-0, 16/24977-1, 16/17652-9)
WAGNER, FABIEN H.; SANCHEZ, ALBER; TARABALKA, YULIYA; LOTTE, RODOLFO G.; FERREIRA, MATHEUS P.; AIDAR, MARCOS P. M.; GLOOR, EMANUEL; PHILLIPS, OLIVER L.; ARAGAO, LUIZ E. O. C.. Using the U-net convolutional network to map forest types and disturbance in the Atlantic rainforest with very high resolution images. REMOTE SENSING IN ECOLOGY AND CONSERVATION, v. 5, n. 4, p. 360-375, DEC 2019. Web of Science Citations: 17. (13/50533-5, 16/03397-7, 12/51872-5, 15/50484-0, 16/24977-1)