Análise do Uso e Cobertura da Terra Utilizando Imagens Sentinel-2A e Inteligência Artificial

Wolfer, Matheus Frigo; Marchesan, Juliana; Alba, Elisiane; Schuh, Mateus Sabadi; Honnef, Dionatas Henrique; Oliveira, Helena Silva; Pereira, Rudiney Soares

doi:10.11137/2020_4_395_403

Cited by 1 publication

(1 citation statement)

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“…The machine learning algorithm SVM was used for supervised classification of the study area based on Landsat 7/ETM+ and Landsat 8/OLI annual images, as presented in section 2.3.1, due to promising results expressed by this technique for identifying physiognomies (Alba 2020;Wolfer et al 2020). Thus, this technique uses a supervised classification in the present study, aiming at controlling the sampling and ensuring adequate methods for comparing the results.…”

Section: Training Of the Machine Learning Algorithmmentioning

confidence: 99%

Comparison Between Three Methods to Monitor Reservoir Extension in the Brazilian Semi-Arid Region

Carvalho

Bezerra

Alba

et al. 2023

Anu. Inst. Geociênc.

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The State of Pernambuco covers an extensive semi-arid area where the Caatinga biome dominates. This region is characterized by long periods of drought, highlighting the need for water resource optimization. This paper aimed to compare three methods to assess reservoir changes: MapBiomas' products, the Normalized Difference Water Index (NDWI), and a support vector machine (SVM) algorithm. Initially, we obtained the monthly precipitation from 1987 to 2019 and calculated the yearly accumulation. Mapbiomas, Landsat 7 ETM, and Landsat 8 OLI data from 2012-2018 were accessed and processed using the Google Earth Engine platform. We obtained the annual image with the median pixel criterion to determine the NDWI and quantify the annual reservoir area. For the supervised classification with SVM, samples from different land-use types of the study area were used to train the algorithm. From 2012 to 2018, a reservoir reduction of 63.42% was observed with MapBiomas images, 69.49% with NDWI images, and 67.69% using the SVM algorithm. The results obtained using NDWI were the most similar to those from the artificial intelligence classification, indicating that NDWI can be used to monitor the reservoir conditions.

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Section: Training Of the Machine Learning Algorithmmentioning

confidence: 99%