Operational Large-Area Land-Cover Mapping: An Ethiopia Case Study

Abstract

Knowledge of land cover and land use nationally is a prerequisite of many studies on drivers of land change, impacts on climate, carbon storage and other ecosystem services, and allows for sufficient planning and management. Despite this, many regions globally do not have accurate and consistent coverage at the national scale. This is certainly true for Ethiopia. Large-area land-cover characterization (LALCC), at a national scale is thus an essential first step in many studies of land-cover change, and yet is itself problematic. Such LALCC based on remote-sensing image classification is associated with a spectrum of technical challenges such as data availability, radiometric inconsistencies within/between images, and big data processing. Radiometric inconsistencies could be exacerbated for areas, such as Ethiopia, with a high frequency of cloud cover, diverse ecosystem and climate patterns, and large variations in elevation and topography. Obtaining explanatory variables that are more robust can improve classification accuracy. To create a base map for the future study of large-scale agricultural land transactions, we produced a recent land-cover map of Ethiopia. Of key importance was the creation of a methodology that was accurate and repeatable and, as such, could be used to create earlier, comparable land-cover classifications in the future for the same region. We examined the effects of band normalization and different time-series image compositing methods on classification accuracy. Both top of atmosphere and surface reflectance products from the Landsat 8 Operational Land Imager (OLI) were tested for single-time classification independently, where the latter resulted in 1.1% greater classification overall accuracy. Substitution of the original spectral bands with normalized difference spectral indices resulted in an additional improvement of 1.0% in overall accuracy. Three approaches for multi-temporal image compositing, using Landsat 8 OLI and Moderate Resolution Imaging Spectroradiometer (MODIS) data, were tested including sequential compositing, i.e., per-pixel summary measures based on predefined periods, probability density function compositing, i.e., per-pixel characterization of distribution of spectral values, and per-pixel sinusoidal models. Multi-temporal composites improved classification overall accuracy up to 4.1%, with respect to single-time classification with an advantage of the Landsat OLI-driven composites over MODIS-driven composites. Additionally, night-time light and elevation data were used to improve the classification. The elevation data and its derivatives improved classification accuracy by 1.7%. The night-time light data improve producer’s accuracy of the Urban/Built class with the cost of decreasing its user’s accuracy. Results from this research can aid map producers with decisions related to operational large-area land-cover mapping, especially with selecting input explanatory variables and multi-temporal image compositing, to allow for the creation of accurate and repeatable national-level land-cover products in a timely fashion.

Vegetation Dynamics and Climatological Drivers in Ethiopia at the Turn of the Century

“…Much of the land is water-limited, with approximately 65% of the country occupied by drylands and only 15.1% of the total land considered arable, much of which is located within the drylands [45,46]. Figure 1 shows the elevation and distribution of land cover classified at a 30 m resolution [47]. The dominant classes present throughout the country include bare soil in the northwest, savanna in the eastern and western lowlands, and cropland in the highlands.…”

“…The seasonal average maximum temperature was calculated at a pixel level, though no time lag was incorporated, and seasons correspond to those used for DP. Total rainfall, mean temperature, and mean NDVI were then extracted from pixels classified as vegetated land cover, based on the work of Khatami et al [47], for pixels where DP showed significant change. This process resulted in data from 1925 points for each year in the 17-year study period, which were further subset by pixels with significant positive and negative DP.…”

The use of medicinal plants by pregnant women in Africa: A systematic review

“…The regional states and city administrations are further divided into Zones, Woredas (districts) and Kebeles (the smallest administrative unit) [102]. Agriculture is the backbone of the country's economy, accounting for 45% of the gross domestic product, 84% of the exports, and 80% of total employment [109,[113][114][115]. It has a highly diverse population with different religions, languages and ethnic groups [102,109].…”

“…It has a highly diverse population with different religions, languages and ethnic groups [102,109]. Gross national per capita income remains at $850 United States Dollar in 2019 [116] despite the government pushing to reach lower-middleincome status by 2025 [115].…”

Dryland Food Security in Ethiopia: Current Status, Opportunities, and a Roadmap for the Future

“…However, this approach is still insufficient because it is difficult to continuously monitor long-term food threats, and it is even more difficult to trace the driving mechanism that causes food security problems. Ethiopia lacks specialized arable land data products to support its food security assessment [38]. It was not until the emergence of GFSAD African thematic arable land data in 2017 that this gap was filled [39].…”