The use of CBERS (China-Brazil Earth Resources Satellite) to trace the dynamics of total suspended matter at an urbanized coastal area

Abstract

The distribution of organic and inorganic particles in the water column, or the total suspended matter (TSM), responds to local and remote oceanographic and meteorological processes, potentially impacting biogeochemical cycles. In shallow coastal areas, where particles have distinct origins and compositions and vary in different time scales, the use of remote sensing tools for monitoring and tracing this material is highly encouraged due to the high temporal and spatial data resolution. The objective of this work was to understand the variability of in situ TSM at Santos Bay (Southeastern Brazil) and its response to oceanographic and meteorological conditions. We also aimed to verify the applicability of the satellite data from CBERS-2 sensor in order to map the dynamics of TSM in this region. Our results have shown that the distribution of TSM in Santos Bay varied consistently with winds, currents and tidal cycles, with significant relationships emphasizing the role of south-western winds and spring tides. Neap tides and eastern winds, along with rainfall, play an important role in the input of organic matter into the bay. In conclusion, our analyses showed that the main patterns observed in situ regarding the responses of TSM to the ocean-meteorological processes could be reproduced in the CBERS-2 satellite data, after simple and standard methods of images processing. TSM data retrieval from CBERS-2 or other satellite sensors were shown to be feasible, becoming an essential tool for synoptic observations of the composition and quality of water, especially at urbanized and impacted coastal areas. A distribuição de partículas orgânicas e inorgânicas na coluna d'água, ou o material em suspensão total (MST), responde a processos oceanográficos e meteorológicos locais e remotos, que potencialmente impactam processos biogeoquímicos. Em áreas costeiras, onde partículas possuem diferentes origens e composições e variam em diferentes escalas de tempo, o uso de ferramentas de sensoriamento remoto para monitoramento e mapeamento desse material é altamente indicado devido à alta resolução temporal e espacial dos dados. O objetivo desse trabalho foi compreender a variabilidade do MST in situ na Baía de Santos (sudeste brasileiro) e sua resposta a condições oceanográficas e meteorológicas. Buscou-se também verificar a aplicabilidade dos dados de satélite do sensor CBERS-2 a fim de mapear a dinâmica do MST na região. Nossos resultados mostraram que a distribuição do MST na Baía de Santos variou consistentemente com os ventos, correntes e ciclos de maré, com correlações significantes que enfatizam o papel de ventos de sudoeste e da maré de sizígia. Marés de quadratura e ventos de leste, somados à precipitação, possuem papel importante na entrada de matéria orgânica na baía. Como conclusão, nossas análises mostraram que os principais padrões observados in situ em relação às respostas do MST aos processos meteorológicos e oceanográficos poderiam ser reproduzidas nos dados do satélite CBERS-2, após métodos padrões de processamen...

Assessment of the land use classification surrounding a dam in the semi arid of Pernamuco/Brazil from OLI, MUX images and WorldCover data

“…However, medium resolution data such as the Landsat Multi-Spectral Scanner (MSS), TM and Operational Land Imager (OLI) datasets have been used worldwide for land cover change detection analysis (Roy and Inamdar, 2019). In addition, the medium resolution CBERS-4 data cube brought an improvement in the scope of environmental monitoring studies, such as deforestation mapping, greenhouse gas emission assessment and forest re detection according to Simões et al (2020) and for forest monitoring according to Liu et al (2019) and urbanization (Giannini et al 2017). Machine learning satellite image classi cation algorithms that include Support Vector Machine (SVM), Extreme Learning Machine (ELM) and Extreme Kernel Learning Machine (KELM) have been widely used for image classi cation (Lamine et al 2018).…”