A Large-Scale Deep-Learning Approach for Multi-Temporal Aqua and Salt-Culture Mapping

Diniz, Cesar; Cortinhas, Luiz; Pinheiro, Maria Luize; Sadeck, Luís; Fernandes Filho, Alexandre; Baumann, Luis R. F.; Adami, Marcos; Souza-Filho, Pedro Walfir M.

doi:10.3390/rs13081415

Submit to this Journal Review for this Journal Edit a Special Issue

Article Menu

...

Open AccessArticle

A Large-Scale Deep-Learning Approach for Multi-Temporal Aqua and Salt-Culture Mapping

^1,2,*

^1,3,

^1,3,

^1,4,

Alexandre Fernandes Filho

^1,5,

Luis R. F. Baumann

⁶

Marcos Adami

^2,7

and

Pedro Walfir M. Souza-Filho

^2,8

Solved—Solutions in Geoinformation, Belém 66075-750, Brazil

Geoscience Institute, UFPA—Federal University of Pará, Belém 66075-110, Brazil

Technology Institute, UFPA—Federal University of Pará, Belém 66075-110, Brazil

⁴

Philosophy and Human Sciences Institute, UFPA—Federal University of Pará, Belém 66075-110, Brazil

⁵

Institute of Agricultural Sciences, UFRA—Federal Rural University of the Amazon, Belém 66077-813, Brazil

⁶

Institute of Mathematics and Statistics, UFG—Federal University of Goias, Goiania 74690-900, Brazil

⁷

INPE—National Institute for Space Research, Amazon Spatial Coordination, São Paulo 12227-010, Brazil

⁸

ITV—Instituto Tecnológico Vale, Belém 66055-090, Brazil

Author to whom correspondence should be addressed.

Academic Editor: Konstantinos Topouzelis

Remote Sens. 2021, 13(8), 1415; https://doi.org/10.3390/rs13081415

Received: 25 February 2021 / Revised: 31 March 2021 / Accepted: 31 March 2021 / Published: 7 April 2021

(This article belongs to the Special Issue State-of-the-Art Remote Sensing in South America)

View Full-Text Download PDF

Browse Figures

Citation Export

Abstract

Aquaculture and salt-culture are relevant economic activities in the Brazilian Coastal Zone (BCZ). However, automatic discrimination of such activities from other water-related covers/uses is not an easy task. In this sense, convolutional neural networks (CNN) have the advantage of predicting a given pixel’s class label by providing as input a local region (named patches or chips) around that pixel. Both the convolutional nature and the semantic segmentation capability provide the U-Net classifier with the ability to access the “context domain” instead of solely isolated pixel values. Backed by the context domain, the results obtained show that the BCZ aquaculture/saline ponds occupied ~356 km² in 1985 and ~544 km² in 2019, reflecting an area expansion of ~51%, a rise of 1.5× in 34 years. From 1997 to 2015, the aqua-salt-culture area grew by a factor of ~1.7, jumping from 349 km² to 583 km², a 67% increase. In 2019, the Northeast sector concentrated 93% of the coastal aquaculture/salt-culture surface, while the Southeast and South sectors contained 6% and 1%, respectively. Interestingly, despite presenting extensive coastal zones and suitable conditions for developing different aqua-salt-culture products, the North coast shows no relevant aqua or salt-culture infrastructure sign. View Full-Text

Keywords: aquaculture; salt-culture; U-Net; Tensor-Flow; Google Earth Engine; Landsat

▼ Show Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Share and Cite

MDPI and ACS Style

Diniz, C.; Cortinhas, L.; Pinheiro, M.L.; Sadeck, L.; Fernandes Filho, A.; Baumann, L.R.F.; Adami, M.; Souza-Filho, P.W.M. A Large-Scale Deep-Learning Approach for Multi-Temporal Aqua and Salt-Culture Mapping. Remote Sens. 2021, 13, 1415. https://doi.org/10.3390/rs13081415

AMA Style

Diniz C, Cortinhas L, Pinheiro ML, Sadeck L, Fernandes Filho A, Baumann LRF, Adami M, Souza-Filho PWM. A Large-Scale Deep-Learning Approach for Multi-Temporal Aqua and Salt-Culture Mapping. Remote Sensing. 2021; 13(8):1415. https://doi.org/10.3390/rs13081415

Chicago/Turabian Style

Diniz, Cesar, Luiz Cortinhas, Maria Luize Pinheiro, Luís Sadeck, Alexandre Fernandes Filho, Luis R. F. Baumann, Marcos Adami, and Pedro Walfir M. Souza-Filho. 2021. "A Large-Scale Deep-Learning Approach for Multi-Temporal Aqua and Salt-Culture Mapping" Remote Sensing 13, no. 8: 1415. https://doi.org/10.3390/rs13081415

Find Other Styles

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Abstract views Pdf views Html views

Article Menu

A Large-Scale Deep-Learning Approach for Multi-Temporal Aqua and Salt-Culture Mapping

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Article Access Map by Country/Region

Further Information

Guidelines

MDPI Initiatives

Follow MDPI