Land Cover Mapping from Remotely Sensed and Auxiliary Data for Harmonized Official Statistics

Hugo Costa,Francisco Vala,Mário Caetano,Filipe Marcelino,Diana Almeida

doi:10.3390/ijgi7040157

Abstract

This paper describes a general framework alternative to the traditional surveys that are commonly performed to estimate, for statistical purposes, the areal extent of predefined land cover classes across Europe. The framework has been funded by Eurostat and relies on annual land cover mapping and updating from remotely sensed and national GIS-based data followed by area estimation. Map production follows a series of steps, namely data collection, change detection, supervised image classification, rule-based image classification, and map updating/generalization. Land cover area estimation is based on mapping but compensated for mapping error as estimated through thematic accuracy assessment. This general structure was applied to continental Portugal, successively updating a map of 2010 for the following years until 2015. The estimated land cover change was smaller than expected but the proposed framework was proved as a potential for statistics production at the national and European levels. Contextual and structural methodological challenges and bottlenecks are discussed, especially regarding mapping, accuracy assessment, and area estimation.

Highlights

Up to date land cover and land use (LCLU) statistics are paramount for policy and decision making and, impact largely on economy and society
The estimated overall accuracy of the 2010 map is 87.5%. This result is closely related to COS of 2010 (COS2010), as this official map was the basis for the map production
A general framework for frequent land cover mapping was explored with the ultimate goal of producing harmonized land cover statistics across Europe

Summary

Introduction

Up to date land cover and land use (LCLU) statistics are paramount for policy and decision making and, impact largely on economy and society. The production of LCLU statistics becomes increasingly challenging when cross-border regions, such as political or geographical units formed by autonomous states, are involved, which commonly use their own means and criteria for statistics production. In Europe, for example, national and international authorities have formed a partnership, the European Statistical System (ESS), for the development, production, and dissemination of comparable statistics at the European Union level. The ESS functions as a network in which the European Union authority for statistics, Eurostat, in close cooperation with national statistical authorities, leads the way in the harmonization of statistics

Methods

Results

Discussion

Conclusion