Land use/land cover in view of earth observation: data sources, input dimensions, and classifiers—a review of the state of the art

Prem Chandra Pandey*, Nikos Koutsias, George P. Petropoulos, Prashant K. Srivastava, Eyal Ben Dor

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

Abstract

Land use/land cover (LULC) is a fundamental concept of the Earth's system intimately connected to many phases of the human and physical environment. Earth observation (EO) technology provides an informative source of data covering the entire globe in a spatial and spectral resolution appropriate to better and easier classify land cover than traditional or conventional methods. The use of high spatial and spectral resolution imagery from EO sensors has increased remarkably over the past decades, as more and more platforms are placed in orbit and new applications emerge in different disciplines. The aim of the present review work is to provide all-inclusive critical reflection on the state of the art in the use of EO technology in LULC mapping and change detection. The emphasis is placed on providing an overview of the different EO datasets, spatial-spectral-temporal characteristics of satellite data and classification approaches employed in land cover classification. The review concludes providing recommendations and remarks on what should be done in order to overcome hurdle faced using above-mentioned problems in LULC mapping. This also provides information on using classifier algorithms depending upon the data types and dependent on the regional ecosystems. One of the main messages of our review is that in future, there will be a need to assemble techniques specifically used in LULC with their merit and demerits that will enable more comprehensive understanding at regional or global scale and improve understanding between different land cover relationship and variability among them and these remains to be seen.

Original languageEnglish
Pages (from-to)957-988
Number of pages32
JournalGeocarto International
Volume36
Issue number9
DOIs
StatePublished - 2021

Keywords

  • LULC mapping
  • hyperspectral
  • landsat
  • multi-source
  • multi-temporal
  • spatial-spectral dimensions

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