An Alternative Method for the Generation of Consistent Mapping to Monitoring Land Cover Change: A Case Study of Guerrero State in Mexico

René Vázquez-Jiménez, Raúl Romero-Calcerrada, Rocío N. Ramos-Bernal, Patricia Arrogante-Funes and Carlos J. Novillo
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René Vázquez-Jiménez: Cuerpo Académico UAGro CA-93 Riesgos Naturales y Geotecnología, FI, Research Group on Technologies for Landscape Analysis and Diagnosis (TADAT), Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas s/n, CU, 39070 Chilpancingo, Guerrero, Mexico
Raúl Romero-Calcerrada: Research Group on Technologies for Landscape Analysis and Diagnosis (TADAT), Facultad de Ciencias Jurídicas y Sociales, Universidad Rey Juan Carlos, Paseo de los Artilleros s/n., Vicálvaro, 28032 Madrid, Spain
Rocío N. Ramos-Bernal: Cuerpo Académico UAGro CA-93 Riesgos Naturales y Geotecnología, FI, Research Group on Technologies for Landscape Analysis and Diagnosis (TADAT), Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas s/n, CU, 39070 Chilpancingo, Guerrero, Mexico
Patricia Arrogante-Funes: Research Group on Technologies for Landscape Analysis and Diagnosis (TADAT), Departamento de Tecnología Química y Ambiental, ESCET, Universidad Rey Juan Carlos, C/Tulipán s/n, Móstoles, 28933 Madrid, Spain
Carlos J. Novillo: Research Group on Technologies for Landscape Analysis and Diagnosis (TADAT), Departamento de Tecnología Química y Ambiental, ESCET, Universidad Rey Juan Carlos, C/Tulipán s/n, Móstoles, 28933 Madrid, Spain

Land, 2021, vol. 10, issue 7, 1-24

Abstract: Land cover is crucial for ecosystems and human activities. Therefore, monitoring land cover changes has become relevant in recent years. This study proposes an alternative method based on conventional change detection techniques combined with maximum likelihood (MaxLike) supervised classification of satellite images to generate consistent Land Use/Land Cover (LULC) maps. The novelty of this method is that the supervised classification is applied in an earlier stage of change detection exclusively to identified dynamics zones. The LULC categories of the stable zones are acquired from an initial date’s previously elaborated base map. The methodology comprised the use of Landsat images from 2011 and 2016, applying the Sun Canopy Sensor (SCS + C) topographic correction model enhanced through the classification of slopes, using derived topographic corrected images with NDVI, and employing Tasseled Cap (TC) Brightness-Greenness-Wetness indices and Principal Components (PCs). The study incorporated a comparative analysis of the consistency of the LULC mapping, which is generated based on control areas. The results show that the proposed method, although slightly laborious, is viable and fully automatable. The generated LULC map is accurate and robust and achieves a Kappa concordance index of 87.53. Furthermore, the boundary consistency was visually superior to the conventional classified map.

Keywords: land use/land cover (LULC); LULC mapping; image classification; change detection (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:10:y:2021:i:7:p:731-:d:592873

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