Your browser doesn't support javascript.
loading
Remote sensing detection of plastic-mulched farmland using a temporal approach in machine learning: case study in tomato crops.
de Souza, Marlon F; Lamparelli, Rubens A C; Oliveira, Murilo H S; Nogueira, Guilherme P; Bliska, Antonio; Franco, Telma T.
Afiliação
  • de Souza MF; Center for Plasticulture Engineering (CEP), Center for Energy Planing (NIPE), Universidade Estadual de Campinas (UNICAMP), Campinas, 13083896, Brazil. marlonf@unicamp.br.
  • Lamparelli RAC; Center for Plasticulture Engineering (CEP), Center for Energy Planing (NIPE), Universidade Estadual de Campinas (UNICAMP), Campinas, 13083896, Brazil.
  • Oliveira MHS; Center for Plasticulture Engineering (CEP), Center for Energy Planing (NIPE), Universidade Estadual de Campinas (UNICAMP), Campinas, 13083896, Brazil.
  • Nogueira GP; Center for Plasticulture Engineering (CEP), Center for Energy Planing (NIPE), Universidade Estadual de Campinas (UNICAMP), Campinas, 13083896, Brazil.
  • Bliska A; Center for Plasticulture Engineering (CEP), Center for Energy Planing (NIPE), Universidade Estadual de Campinas (UNICAMP), Campinas, 13083896, Brazil.
  • Franco TT; Center for Plasticulture Engineering (CEP), Center for Energy Planing (NIPE), Universidade Estadual de Campinas (UNICAMP), Campinas, 13083896, Brazil.
Environ Sci Pollut Res Int ; 2024 Oct 05.
Article em En | MEDLINE | ID: mdl-39367946
ABSTRACT
The increasing use of plastics in rural environments has led to concerns about agricultural plastic waste (APW). However, the plasticulture information gap hinders waste management planning and may lead to plastic residue leakage into the environment with consequent microplastic formation. The location and estimated quantity of the APW are crucial for territorial planning and public policies regarding land use and waste management. Agri-plastic remote detection has attracted increased attention but requires a consensus approach, particularly for mapping plastic-mulched farmlands (PMFs) scattered across vast areas. This article tests whether a streamlined time-series approach minimizes PMF confusion with the background using less processing. Based on the literature, we performed a vast assessment of machine learning techniques and investigated the importance of features in mapping tomato PMF. We evaluated pixel-based and object-based classifications in harmonized Sentinel-2 level-2A images, added plastic indices, and compared six classifiers. The best result showed an overall accuracy of 99.7% through pixel-based using the multilayer perceptron (MLP) classifier. The 3-time series with a 30-day composite exhibited increased accuracy, a decrease in background confusion, and was a viable alternative for overcoming the impact of cloud cover on images at certain times of the year in our study area, which leads to a potentially reliable methodology for APW mapping for future studies. To our knowledge, the presented PMF map is the first for Latin America. This represents a first step toward promoting the circularity of all agricultural plastic in the region, minimizing the impacts of degradation on the environment.
Palavras-chave
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Environ Sci Pollut Res Int Assunto da revista: SAUDE AMBIENTAL / TOXICOLOGIA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Brasil País de publicação: Alemanha
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Environ Sci Pollut Res Int Assunto da revista: SAUDE AMBIENTAL / TOXICOLOGIA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Brasil País de publicação: Alemanha