Data/hora: 04/06/2024 02:43:26

Biblioteca(s): | Embrapa Meio Ambiente.
Data corrente: | 02/01/2019
Data da última atualização: | 17/01/2023
Autoria: | SANCHES, I. D.; FEITOSA, R. Q.; ACHANCCARAY, P.; MONTIBELLER, B.; LUIZ, A. J. B.; SOARES, M. D.; PRUDENTE, V. H. R.; VIEIRA, D. C.; MAURANO, L. E. P.
Afiliação: | IEDA DEL'ARCO SANCHES, INPE; RAUL QUEIROZ FEITOSA, PUC Rio; PEDRO ACHANCCARAY, PUC Rio; BRUNO MONTIBELLER, INPE; ALFREDO JOSE BARRETO LUIZ, CNPMA; MARINALVA DIAS SOARES, PUC Rio; VICTOR HUGO ROHDEN PRUDENTE, INPE; D C VIEIRA, INPE; LUIS EDUARDO PINHEIRO MAURANO, INPE.
Título: | Lem benchmark database for tropical agricultural remote sensing application.
Ano de publicação: | 2018
Fonte/Imprenta: | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. 42, n. 1, p. 387-392, 2018. Edition of the proceedings ISPRS TC I Mid-term Symposium ?Innovative Sensing ? From Sensors to Methods and Applications?, 10-12 October 2018, held a Karlsruhe, Germany.
Páginas: | 387-392.
Idioma: | Inglês
Conteúdo: | Abstract: The monitoring of agricultural activities at a regular basis is crucial to assure that the food production meets the world population demands, which is increasing yearly. Such information can be derived from remote sensing data. In spite of topic?s relevance, not enough efforts have been invested to exploit modern pattern recognition and machine learning methods for agricultural land-cover mapping from multi-temporal, multi-sensor earth observation data. Furthermore, only a small proportion of the works published on this topic relates to tropical/subtropical regions, where crop dynamics is more complicated and difficult to model than in temperate regions. A major hindrance has been the lack of accurate public databases for the comparison of different classification methods. In this context, the aim of the present paper is to share a multi-temporal and multi-sensor benchmark database that can be used by the remote sensing community for agricultural land-cover mapping. Information about crops in situ was collected in Luís Eduardo Magalhães (LEM) municipality, which is an important Brazilian agricultural area, to create field reference data including information about first and second crop harvests. Moreover, a series of remote sensing images was acquired and pre-processed, from both active and passive orbital sensors (Sentinel-1, Sentinel-2/MSI, Landsat-8/OLI), correspondent to the LEM area, along the development of the main annual crops. In this paper, we describe the LEM database (crop field boundaries, land use reference data and pre-processed images) and present the results of an experiment conducted using the Sentinel-1 and Sentinel-2 data.
Palavras-Chave: | Agricultura tropical; Agricultural mapping/monitoring; C-band SAR data; Double gropping systems; Free available database; Mapeamento; Multispectral instrument.
Thesagro: | Agricultura; Base de dados; Sensoriamento remoto.
Categoria do assunto: | --
URL: | http://ainfo.cnptia.embrapa.br/digital/bitstream/item/189604/1/2018AA07.pdf
Registro original: | Embrapa Meio Ambiente (CNPMA)
Exemplares: | CNPMA - (16214-1) - UPC - AA - DD -  -  -  - DIS