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

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.

Evaluation and comparison of eight machine learning models in land use/land cover mapping using Landsat 8 OLI: a case study of the northern region of Iran

“…Change of the land cover may affect the climate through manipulation of the composition of pollutant emissions like carbon dioxide [7,8]. Today, LULC statistics are the prerequisites for policy and decision making strategies which have an effect on societies and their economies [9].…”

Current Challenges in Operational Very High Resolution Land-Cover Mapping

“…In a dual perspective, operational effectiveness should be able to deal with both objectives with few changes in the methodology. Many public policies, in particular related to urban areas (imperviousness), require both evolution metrics and accurate location of the underlying phenomena (Costa et al, 2018).…”

Using Random Forest Classification and Nationally Available Geospatial Data to Screen for Wetlands over Large Geographic Regions

“…Random forest has become a widely used method for its ability to handle high dimensional data, incorporate both continuous and categorical data, and produce descriptive variable importance measures (e.g., [21,22]). Researchers have used random forest to integrate multispectral imagery, topography, and other ancillary geospatial data for wetland identification (e.g., [12,[23][24][25][26]). Furthermore, studies show that random forest can produce higher classification accuracies than traditional techniques for land cover classification (e.g., [17,21,[27][28][29]).…”