Abstract
The remote sensing based mapping of land cover at extensive scales, e.g., of whole continents, is still a challenging task because of the need for sophisticated pipelines that combine every step from data acquisition to land cover classification. Utilizing the Google Earth Engine (GEE), which provides a catalog of multi-source data and a cloud-based environment, this research generates a land cover map of the whole African continent at 10 m resolution. This land cover map could provide a large-scale base layer for a more detailed local climate zone mapping of urban areas, which lie in the focus of interest of many studies. In this regard, we provide a free download link for our land cover maps of African cities at the end of this paper. It is shown that our product has achieved an overall accuracy of 81% for five classes, which is superior to the existing 10 m land cover product FROM-GLC10 in detecting urban class in city areas and identifying the boundaries between trees and low plants in rural areas. The best data input configurations are carefully selected based on a comparison of results from different input sources, which include Sentinel-2, Landsat-8, Global Human Settlement Layer (GHSL), Night Time Light (NTL) Data, Shuttle Radar Topography Mission (SRTM), and MODIS Land Surface Temperature (LST). We provide a further investigation of the importance of individual features derived from a Random Forest (RF) classifier. In order to study the influence of sampling strategies on the land cover mapping performance, we have designed a transferability analysis experiment, which has not been adequately addressed in the current literature. In this experiment, we test whether trained models from several cities contain valuable information to classify a different city. It was found that samples of the urban class have better reusability than those of other natural land cover classes, i.e., trees, low plants, bare soil or sand, and water. After experimental evaluation of different land cover classes across different cities, we conclude that continental land cover mapping results can be considerably improved when training samples of natural land cover classes are collected and combined from areas covering each Köppen climate zone.
Highlights
Land cover mapping is considered to be one of the most important tasks in remote sensing, as it provides crucial geoinformation for environmental research
We propose a framework for land cover mapping in Africa from multi-source data with Google Earth Engine (GEE), which makes three main contributions: (1) The proposed framework is able to provide reliable land cover maps of the whole African continent at a resolution of 10 m, using GEE’s multi-source remote sensing data
Given that GEE is an efficient platform for large-scale and multi-source data processing and analysis, we have proposed a framework for African land cover mapping at 10 m from multi-source data with GEE, and demonstrated its effectiveness for large-scale land cover mapping
Summary
Land cover mapping is considered to be one of the most important tasks in remote sensing, as it provides crucial geoinformation for environmental research. Existing large-scale land cover mapping products are (1) MODIS Global Land Cover [1]; (2) ESA GlobCover [2]; (3) UMD classification [3]; (4) CCI-LC [4]; (5) GLC-SHARE [5]; (6) GLCNMO [6]; and (7) Copernicus Global Land Cover [7]. These land cover products are limited to coarse spatial resolution in the range of 100 m and 1 km, and this provides insufficient spatial detail. Existing high resolution global land cover products are (1) FROM-GLC10 [8]; (2) FROM-GLC30 [9]; and (3) GlobeLand30 [10], but they are only available for certain years, and not updated regularly
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