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Lewis, Kennedy
[1]
;
de V. Barros, Fernanda
[1]
;
Cure, Marcio B.
[2]
;
Davies, Christian A.
[3]
;
Furtado, Mariana N.
[4]
;
Hill, Timothy C.
[1]
;
Hirota, Marina
[5]
;
Martins, Demetrius L.
[4]
;
Mazzochini, Guilherme G.
[4]
;
Mitchard, Edward T. A.
[6]
;
Munhoz, Cassia B. R.
[7]
;
Oliveira, Rafael S.
[4]
;
Sampaio, Alexandre B.
[8]
;
Saraiva, Nicholas A.
[9]
;
Schmidt, Isabel B.
[10]
;
Rowland, Lucy
[1]
Número total de Autores: 16
|
| Afiliação do(s) autor(es): | [1] Univ Exeter, Coll Life & Environm Sci, Exeter, Devon - England
[2] Univ Fed Santa Catarina UFSC, Dept Ecol & Zool, Florianopolis, SC - Brazil
[3] Shell Technol Ctr, Shell Int Explorat & Prod Inc, Houston, TX - USA
[4] Univ Estadual Campinas UNICAMP, Inst Biol, Dept Plant Biol, Campinas - Brazil
[5] Univ Fed Santa Catarina UFSC, Dept Phys, Florianopolis, SC - Brazil
[6] Univ Edinburgh, Sch GeoSci, Edinburgh, Midlothian - Scotland
[7] Univ Brasilia, Dept Bot, Brasilia, DF - Brazil
[8] Inst Chico Mendes Conservacao Biodiversidade ICMB, Ctr Nacl Avaliacao Biodiversidade & Pesquisa & Co, Brasilia, DF - Brazil
[9] Fundacao Oswaldo Cruz, Rio De Janeiro - Brazil
[10] Univ Brasilia, Dept Ecol, Brasilia, DF - Brazil
Número total de Afiliações: 10
|
| Tipo de documento: | Artigo Científico |
| Fonte: | SCIENTIFIC REPORTS; v. 12, n. 1 JAN 28 2022. |
| Citações Web of Science: | 0 |
| Resumo | |
Native vegetation across the Brazilian Cerrado is highly heterogeneous and biodiverse and provides important ecosystem services, including carbon and water balance regulation, however, land-use changes have been extensive. Conservation and restoration of native vegetation is essential and could be facilitated by detailed landcover maps. Here, across a large case study region in Goias State, Brazil (1.1 Mha), we produced physiognomy level maps of native vegetation (n = 8) and other landcover types (n = 5). Seven different classification schemes using different combinations of input satellite imagery were used, with a Random Forest classifier and 2-stage approach implemented within Google Earth Engine. Overall classification accuracies ranged from 88.6-92.6% for native and non-native vegetation at the formation level (stage-1), and 70.7-77.9% for native vegetation at the physiognomy level (stage-2), across the seven different classifications schemes. The differences in classification accuracy resulting from varying the input imagery combination and quality control procedures used were small. However, a combination of seasonal Sentinel-1 (C-band synthetic aperture radar) and Sentinel-2 (surface reflectance) imagery resulted in the most accurate classification at a spatial resolution of 20 m. Classification accuracies when using Landsat-8 imagery were marginally lower, but still reasonable. Quality control procedures that account for vegetation burning when selecting vegetation reference data may also improve classification accuracy for some native vegetation types. Detailed landcover maps, produced using freely available satellite imagery and upscalable techniques, will be important tools for understanding vegetation functioning at the landscape scale and for implementing restoration projects. (AU) | |
| Processo FAPESP: | 19/07773-1 - Restaurando ecossistemas neotropicais secos - seria a composição funcional das plantas a chave para o sucesso? |
| Beneficiário: | Rafael Silva Oliveira |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |