Mapping Coastal Wetlands Using Satellite Imagery and Machine Learning in a Highly Urbanized Landscape

Juan Munizaga, Mariano García, Fernando Ureta, Vanessa Novoa, Octavio Rojas and Carolina Rojas
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Juan Munizaga: Departamento de Planificación Territorial y Sistemas Urbanos, Facultad de Ciencias Ambientales y Centro EULA-Chile, Universidad de Concepción, Víctor Lamas 1290, Concepción 4070386, Chile
Mariano García: Universidad de Alcalá, Departamento de Geología, Geografía y Medio Ambiente, Environmental Remote Sensing Research Group, C/Colegios, 2, Alcalá de Henares, 28801 Madrid, Spain
Fernando Ureta: Departamento de Ingeniería Metalúrgica, Facultad de Ingeniería, Universidad de Concepción, Víctor Lamas 1290, Concepción 4070386, Chile
Vanessa Novoa: Departamento de Planificación Territorial y Sistemas Urbanos, Facultad de Ciencias Ambientales y Centro EULA-Chile, Universidad de Concepción, Víctor Lamas 1290, Concepción 4070386, Chile
Octavio Rojas: Departamento de Planificación Territorial y Sistemas Urbanos, Facultad de Ciencias Ambientales y Centro EULA-Chile, Universidad de Concepción, Víctor Lamas 1290, Concepción 4070386, Chile
Carolina Rojas: Instituto de Estudios Urbanos y Territoriales, Pontificia Universidad Católica de Chile, Centro de Desarrollo Urbano Sustentable CEDEUS, Instituto Milenio en Socio-Ecología Costera SECOS, El Comendador 1916, Providencia, Santiago 7820245, Chile

Sustainability, 2022, vol. 14, issue 9, 1-19

Abstract: Coastal wetlands areas are heterogeneous, highly dynamic areas with complex interactions between terrestrial and marine ecosystems, making them essential for the biosphere and the development of human activities. Remote sensing offers a robust and cost-efficient mean to monitor coastal landscapes. In this paper, we evaluate the potential of using high resolution satellite imagery to classify land cover in a coastal area in Concepción, Chile, using a machine learning (ML) approach. Two machine learning algorithms, Support Vector Machine (SVM) and Random Forest (RF), were evaluated using four different scenarios: (I) using original spectral bands; (II) incorporating spectral indices; (III) adding texture metrics derived from the grey-level covariance co-occurrence matrix (GLCM); and (IV) including topographic variables derived from a digital terrain model. Both methods stand out for their excellent results, reaching an average overall accuracy of 88% for support vector machine and 90% for random forest. However, it is statistically shown that random forest performs better on this type of landscape. Furthermore, incorporating Digital Terrain Model (DTM)-derived metrics and texture measures was critical for the substantial improvement of SVM and RF. Although DTM did not increase the accuracy in SVM, this study makes a methodological contribution to the monitoring and mapping of water bodies’ landscapes in coastal cities with weak governance and data scarcity in coastal management.

Keywords: coastal wetlands; remote sensing; coastal cities; RapidEye; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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