Abstract
Studies on land use and land cover changes (LULCC) have been a great concern due to their contribution to the policies formulation and strategic plans in different areas and at different scales. The LULCC when intense and on a global scale can be catastrophic if not detected and monitored affecting the key aspects of the ecosystem’s functions. For decades, technological developments and tools of geographic information systems (GIS), remote sensing (RS) and machine learning (ML) since data acquisition, processing and results in diffusion have been investigated to access landscape conditions and hence, different land use and land cover classification systems have been performed at different levels. Providing coherent guidelines, based on literature review, to examine, evaluate and spread such conditions could be a rich contribution. Therefore, hundreds of relevant studies available in different databases (Science Direct, Scopus, Google Scholar) demonstrating advances achieved in local, regional and global land cover classification products at different spatial, spectral and temporal resolutions over the past decades were selected and investigated. This article aims to show the main tools, data, approaches applied for analysis, assessment, mapping and monitoring of LULCC and to investigate some associated challenges and limitations that may influence the performance of future works, through a progressive perspective. Based on this study, despite the advances archived in recent decades, issues related to multi-source, multi-temporal and multi-level analysis, robustness and quality, scalability need to be further studied as they constitute some of the main challenges for remote sensing.
Highlights
The acquisition of information on the terrestrial surface and its resources has been accelerated since the launch of the first artificial Earth Observation (EO) satellite by the Earth Observation System (EOS) program in 1972
Technological developments and tools of geographic information systems (GIS), remote sensing (RS) and machine learning (ML) since data acquisition, processing and results in diffusion have been investigated to access landscape conditions and different land use and land cover classification systems have been performed at different levels
Classification methods in ML can be Binary, which refers to the classification tasks having two class labels such as “true or yes and false or no”; multiclass— which refers to those classification tasks having more than two class labels; and multi-label—which represent the generalization of multiclass classification, where the classes involved in the problem are hierarchically structured, and each example may simultaneously belong to more than one class in each hierarchical level [82] [83]
Summary
The acquisition of information on the terrestrial surface and its resources has been accelerated since the launch of the first artificial Earth Observation (EO) satellite by the Earth Observation System (EOS) program in 1972. It is partly due to the efforts of World Space Agencies (WSA) that in recent years have provided several images of remote sensors coupled mainly to aircraft and satellites publicly available [1] [2] [3] and to technological advances in the computing field with powerful and efficient processors to manage large volumes of data, as well as efforts to develop robust algorithms for processing such data [2] [4]. The demand for remote sensing data for land use and land cover mapping has been growing due to the impact of land use and land cover changes for the terrestrial ecosystems. Through these spatial data it is possible to understand and assess the effects of landscape changes on the environment. Countries that still adopt traditional approaches to remote sensing data processing using commercial image processing software on workstation PC-based systems with proposals to demonstrate how remote sensing data can be used and presentation of GIS packages (due to technical, educational and institutional constraints), leaving aside deeper studies, such as subsurface modeling based on GIS [9], present limited performance in their studies, as related to Big Data management [6] [10] [11], because no matter how powerful the operating systems are, the entire data analysis process, including pre-processing over large areas involving thousands of images, is cumulative, slow and tedious, and it can still be expensive as it requires a lot of resources
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