Ecosystem services (ES) hotspot/cold spot analysis aids sustainable planning in rapidly urbanizing African cities. Contact online >>
Ecosystem services (ES) hotspot/cold spot analysis aids sustainable planning in rapidly urbanizing African cities.
We mapped ES hotspot/cold spot dynamics in Greater Asmara Area, Eritrea, from 2009 to 2020.
GAA''s ES potential is low but stable, showing some improvements over time.
It is crucial to interpret observed ES cold/hotspot dynamics in GAA with caution.
Our approach is replicable in other resource-scarce, rapidly urbanizing African cities.
In this study, we propose a MAES pilot to support sustainable planning in the rapidly urbanizing Greater Asmara Area (GAA) in Eritrea. This study aims at mapping and analyzing ES hotspots and coldspots dynamics in the GAA to identify recent trends and opportunities for enhancing ES potential. The GAA, the largest urban area in Eritrea and including a UNESCO World Heritage Site, houses 50–60% of the country''s population (Ministry of Public Works and BCEOM 2006; Ghebru et al. 2011). It faces risks from local urbanization, regional rural resource degradation, and global climate change impacts (MoLWE 2012a). This pilot study in the GAA seeks to raise awareness of these challenges and their effects on ecosystems and their services.
We map and assess six illustrative ES using land cover data from 2009 and 2020, obtained via remote sensing. To evaluate changes in ES supply potential, we employ the ES matrix approach by (Burkhard et al. 2010), a tier 1 MAES method suitable for data-scarce regions, as demonstrated in Eritrea and Kenya (Wangai et al. 2019; Adem Esmail et al. 2023). This analysis identifies recent trends and future land use opportunities by considering changes in ES hotspots and cold stops distribution across the GAA.
Like many African countries, Eritrea relies heavily on natural capital, making it vulnerable to environmental challenges, such as droughts, which are exacerbated by climate change (MoLWE 2012a; Wangai et al. 2016; IPBES 2018). The country faces a variety of challenges, including low availability of arable land and water scarcity, which can significantly impact food security and overall human well-being. Studies suggest that the average temperatures in Eritrea could increase by up to 3.39°C by 2080 (Hunt et al. 2019). These environmental stressors underscore the urgent need for effective governance and planning strategies informed by ES knowledge, particularly in urban contexts.
Study area location in Eritrea, in the Horn of Africa [A] and in the Maekel or Central region, one of the six administrative regions in the country [B]. The Greater Asmara Area (GAA) divided into six subzones for analysis based on the sub-regional boundaries in the Zoba Maekel [C]. NB. The subzones in the GAA are not official administrative areas; rather, they are analytical units for the present study only. (Sources: Google Earth, FAO, digitization of the SUDP)
The research design comprises three main steps (Fig. 2). Firstly, we analyze land cover changes between 2009 and 2020 using remote sensing data. Secondly, we map and assess the potential supply of ES and calculate changes between 2009 and 2020. Finally, we produce hotspots and coldspots maps to analyze the changes between 2009 and 2020 and draw conclusions for the further spatial development of the GAA.
Global datasets on land use and cover, such as those released by ESRI (Karra et al. 2021) and (Zhang et al. 2024), provide a comprehensive overview of land cover patterns across the world. However, upon a detailed examination of the land cover data in the GAA, noticeable uncertainties emerge, particularly in the classification and mixture of built-up areas and bare ground. Therefore, we produced our own land cover dataset for the GAA, following four key phases: dataset selection and pre-processing, land cover and use classification, accuracy assessment and land cover changes analysis.
Furthermore, to remove the "salt-and-pepper" noise that persists after classification, post-processing techniques were applied (Wang et al. 2019), such as post-classification smoothing filter with a kernel size of 2.5. The results were subject to manual review for both study periods to ensure the reflection of changes in land cover, such as the disappearance of water bodies.
All data was prepared and clipped in the same reference system (WGS_1984_UTM-Zone 37N). The land cover maps for 2009 and 2020, created in Google Earth Engine, were imported into ArcMap 10.5.1 for further analysis (see Figs. A1 and A2 in the Supplementary Material). The analysis of land cover changes at the regional and sub-regional levels between 2009 and 2020 was conducted in accordance with the methodology outlined by the ESCAP Statistics Division. This involved the generation of a transition matrix and a table of percent land cover changes relative to 2009. Zonal statistics were also calculated at both levels.
This study follows the tiered approach to ES mapping and assessment, as proposed by Grêt-Regamey et al. (2015). The tiers represent different levels of data integration and modeling complexity. Our study is associated with the coarsest level of analysis (level 1, as described by (Burkhard et al. 2010), where the assessment of ES is mainly based on land cover types. Although this coarseness limits its usefulness in detailed land use decisions, necessitating supplementary fieldwork and site-specific assessments, it is adequate for estimating the potential supply of ES on a regional scale and their spatial and temporal distribution (Montoya-Tangarife et al. 2017).
The ES potential for different land cover classes was mainly derived from a study conducted at the national level in Eritrea by Adem Esmail et al. (2023), where minimum, maximum, and average values were obtained based on a targeted literature review. For this study, average values were adopted to avoid extremes that were too high or too low. Additionally, a study by Augstburger et al. (2018), which specifically refers to local agroecosystems, was also taken into consideration to refine some of the values. The final ES matrix is presented in Table 1.
The six ES were mapped for the years 2009 and 2020 based on the land cover data and the ES potential values presented in Table 1. Zonal statistical analysis was conducted with consideration of the sub-regional administrative boundaries within the GAA, thus facilitating a comparison of changes between the years 2009 and 2020. Accordingly, the trend was characterized as follows: "increasing/decreasing" (with a delta greater than ±0.4), "moderately increasing/decreasing" (with a delta between ±0.2 and ±0.4), and "stable" (a delta between −0.2 and 0.2).
Three maps were created for each year: two maps for the provisioning ES (ES1 and ES2), two maps for regulation and maintenance ES (ES3, ES4, and ES5), and two maps for the total ES potential. The input maps for the hotspots analysis, were calculated as an arithmetic mean. No hotspots map has been created for "Recreation" as an individual cultural ES, as it only depends on Water and Forests according to the ES matrix. Finally, the changes in terms of hotspots and coldspots were analyzed, to understand the recent dynamics and provide recommendations for future spatial intervention options, including those for enhancing ES coldspots.
The overall accuracy of the land cover classification was 0.79 in 2009 and 0.75 in 2020. The Kappa value, reflecting the agreement between observed and predicted classifications, dropped from 0.74 in 2009 to 0.67 in 2020. Of note, four classes (i.e. Irrigated agriculture, Rainfed agriculture, Shrubland, and Fallow Land showed very low producer accuracy (0–0.33) and consumer accuracy (0–0.5). For more details of the accuracy assessment results refer to Table A1 in the SM.
Urbanization has intensified in the GAA (Fig. 3), with urban and artificial land cover expanding by notable 1179.3 ha (or 48.5%). Forested areas have also seen a remarkable increase of 225 ha (91%), while bare land has significantly decreased by 3917.4 hectares (a reduction of 49.8%). Grazing land has also experienced a decline, shrinking by 516.6 ha (−11.2%). On the agricultural front, both irrigated and rainfed farming has expanded, with increases of 486.9 ha (a 45.7% increase) and 374.4 ha (a 39.7% increase), respectively. For detailed analysis, please refer to Figs. A3 to A5 in the SM.
Comparison of land cover classifications for 2009 and 2020 and Proportions of classes for 2009 and 2020
Urbanization stands out prominently, notably in Berik and Northern Mereb, i.e. 479 ha and 185 ha, respectively (Fig. 4). In contrast, forested areas have increased steadily in Gala Nefhi, South Mereb, and South Asmara (64 ha, 63 ha, and 46.3 ha, respectively), indicating regeneration. Shifts in agriculture can be observed in the increased irrigated farming in Gala Nefhi and Northern Mereb (+284.9 ha and 189.6 ha, respectively), with declines in rainfed agriculture in Northern Asmara and Southern Mereb (22 ha and 28.2 ha, respectively).
Sub-zonal and total land cover change in the GAA between 2009 and 2020 in absolute terms (ha) and percentage. Increases are shown in blue and decreases in red
Percentage changes reveal nuanced patterns: for instance, Northern Asmara had a slight decrease in water area (−0.6%) but an increase in forest cover (+1.8%). Southern Asmara experienced a decline in irrigated agriculture (−1.8%) but significant forest expansion (+1.9%). Southern Mereb witnessed decrease of rainfed agriculture (−1.1%) and more shrubland (+11.1%), while Northern Mereb observed increases in both irrigated and rainfed agriculture (+4.7%) alongside declines in bare land and grazing land. Gala Nefhi witnessed increased irrigated agriculture (+5.3%) and decreased bare land (−33.6%), while Berik saw a rise in rainfed agriculture (+3.8%) alongside declines in bare land and grazing land.
Looking at the total ES potential in the GAA, the comparison between 2009 and 2020 confirms a low potential with some slight improvements (Fig. 6). There is a notable decrease of the areas with no potential (dark red) particularly in the southern regions (Gala Nefhi and Berik) and an increase of the areas with low potential (yellow) in the northern part of GAA (Northern Mereb and Berik).
Between 2009 and 2020, the ES potential in the GAA remained mostly stable, with notable improvements observed in ES3—Control of erosion rates (+028) and ES5— Maintaining nursery populations and habitats (+0.26)—see Fig. 7. Gala Nefhi stands out with increasing trends (delta greater than 0.4) in the total ES potential, particularly in ES5 and ES3. Northern Mereb shows a notable positive trend in cultivated terrestrial plants (ES1). Other regions, such as Northern and Southern Asmara, Southern Mereb, and Berik, exhibit a stable overall trend (delta between ±0.2). These MAES findings are described in more detail in the SM, including the individual ES potential maps and overall statistics (Figs. A6–A12).
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