Abstract
Recent applications of Landsat 8 Operational Land Imager (L8/OLI) and Sentinel-2 MultiSpectral Instrument (S2/MSI) data for acquiring information about land use and land cover (LULC) provide a new perspective in remote sensing data analysis. Jointly, these sources permit researchers to improve operational classification and change detection, guiding better reasoning about landscape and intrinsic processes, as deforestation and agricultural expansion. However, the results of their applications have not yet been synthesized in order to provide coherent guidance on the effect of their applications in different classification processes, as well as to identify promising approaches and issues which affect classification performance. In this systematic review, we present trends, potentialities, challenges, actual gaps, and future possibilities for the use of L8/OLI and S2/MSI for LULC mapping and change detection. In particular, we highlight the possibility of using medium-resolution (Landsat-like, 10–30 m) time series and multispectral optical data provided by the harmonization between these sensors and data cube architectures for analysis-ready data that are permeated by publicizations, open data policies, and open science principles. We also reinforce the potential for exploring more spectral bands combinations, especially by using the three Red-edge and the two Near Infrared and Shortwave Infrared bands of S2/MSI, to calculate vegetation indices more sensitive to phenological variations that were less frequently applied for a long time, but have turned on since the S2/MSI mission. Summarizing peer-reviewed papers can guide the scientific community to the use of L8/OLI and S2/MSI data, which enable detailed knowledge on LULC mapping and change detection in different landscapes, especially in agricultural and natural vegetation scenarios.
Highlights
Land Use and Land Cover (LULC) are classical concepts and crucial information to understand the relationship between humans and the environment [1]
Land Cover refers to the physical characteristics of the Earth’s surface, such as vegetation, water, and soil, while Land Use refers to the purposes for which humans exploit the Land Cover, such as changes made by anthropogenic activities [2]
The temporal analysis of the research terms occurrence in the network reveals the consolidation of target covers (’agriculture’, ’grassland’, ’deforestation’) and the connection increase among different features, techniques, and methods for LULC classification
Summary
Land Use and Land Cover (LULC) are classical concepts and crucial information to understand the relationship between humans and the environment [1]. The importance of accurate LULC data has been expanded to promote the implementation of policies related to the management of natural resources and environmental problems, such as food security, climate change, deforestation, and agriculture dynamics [4,5]. This reinforces the need for detailed mappings to enable sustainable development [6,7,8]. Pandey et al [48] analyzed the different EO platforms and datasets, spatial-spectral-temporal characteristics of satellite data, and approaches applied in LULC classification and change detection, providing recommendations to generate accurate maps. The knowledge of using L8/OLI and S2/MSI, showing trends, gaps, and future possibilities in the face of modern concepts, methods, and technologies, has not yet been summarized
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