Evaluation of Earth Observation Solutions for Namibia’s SDG Monitoring System
- Indicator 6.6.1: Change in the extent of water-related ecosystems over time.
- Indicator 9.1.1: Proportion of the rural population who live within 2 km of an all-season road.
- Indicator 15.1.1: Forest area as a proportion of total land area.
- Indicator 15.3.1: Proportion of land that is degraded over total land area.
2. Material and Methods
2.1. Change of Surface Water (Indicator 6.6.1)
- Define the time frame for analysis based on the user’s selection of a different trend type:
- For yearly trends, each year of the selected time frame is considered.
- For monthly trends, the time frame contains only the selected year.
- Loop over the defined time range (monthly or yearly).
- Retrieve the water layers from the selected dataset.
- Clip the dataset to the AOI.
- Calculate the area of permanent and seasonal inland water for yearly trend, or overall water area for monthly trends.
- Store the water area for the selected year or month.
- If a yearly trend was requested:
- Calculate the 5-year average change β based on the calculated areas between 2001 and 2005.
- Calculate the 5-year average change γ based on the calculated areas for every available year (1984 to 2015).
- Calculate the percentage change 100 × (β-γ)/β
- Plot the permanent and seasonal water area and area change over time
- Otherwise plot the water area over the month of the selected year.
2.2. Access to Roads (Indicator 9.1.1)
- The NSA’s definition of administrative areas: For instance, the region of Omaheke, that was studied in detail, has only two towns declared as urban area , namely, Otjinene and Gobabis, meaning the rest of the population living outside these areas are considered to be rural.
- The GHSL urban/rural classification model (GHS-SMOD)  where
- Urban Centre >1500 inhabitants/km2.
- Urban Cluster >300 inhabitants/km2.
- Rural <300 inhabitants/km2.
- Segmentation using the Simple Non-Iterative Clustering (SNIC) algorithm .
- Retrieve the population distribution from the global datasets.
- Calculate the rural population using a segmentation algorithm or urban boundaries masks.
- Retrieve the road network.
- Calculate the road network’s buffer zone (± 2 km around the road lines) as a mask with 1 on the buffer zone, 0 elsewhere.
- Optionally calculate the effect of terrain.
- Retrieve the DEM.
- Calculate the projected distance from the DEM slope information. The projected distance is equal to .
- Multiply the projected distance by the road network’s buffer zone layer.
- Update the buffer zone mask by removing all the pixels with a value lower than 2, and set the remaining pixels value to 1.
- Optionally calculate the effect of water:
- Retrieve the permanent water distribution from the GSW dataset.
- Remove any intersection between the water distribution and the road network from the buffer mask.
- Calculate the intersection between the rural population and the buffer mask.
- Sum the pixel values of the intersection to retrieve the total rural population within 2 km of a road.
- Calculate the RAI by setting the population into perspective to the overall population.
2.3. Forest Coverage (Indicator 15.1.1)
2.4. Land Degradation (Indicator 15.3.1)
3.1. Change of Surface Water (Indicator 6.6.1)
3.2. Access to Roads (Indicator 9.9.1)
- between 2010 and 2018, despite the variation in the results, the ratio of rural population with respect to the overall population was decreasing for all sources if the exact same approach is used, indicating a general trend of migration into the urban areas;
- the results from WorldPop and Facebook’s HRPD appear overestimated when compared to the NSA census and NIDS data. GPW with a NSA rural definition for 2010 and the GHSL with SNIC segmentation for 2015 seem to have more accurate results compared to the NSA data if the rural/urban is considered.
- The volatility caused by the urban/rural definition is relatively low with respect to the RAI for Omaheke but high for the rural/urban ratio of the population count.
- The estimates are highly dependent on the population grid, ranging from 37.6% (GPW, 2015) to over 75% for Worldpop. It appears that the low-resolution population grids (GPW and GHSL) are more inaccurate. This could be the result from the uniform distribution of the population within each grid cell. For a 2 km RAI, it can be assumed that the grid cell must be significantly lower than 1000 m. Unfortunately, for the RAI comparison, no DU frame update could be provided as reference after 2011. In general, the estimates for GHSL and HRPD with the NSA urban definitions of 2015 are respectively around 60%, which would be comparable to the 2011 NSA figures.
- For Omaheke, no effect of terrain and water is visible. Tests with regions with more water bodies such as Zambezi, and mountainous regions such as Kunene show evenly no difference, indicating that with the chosen approach to include terrain and water no changes in the results exist. Hence, in Namibia access to roads appears marginally affected by natural obstacles but mostly determined by road infrastructure. However, further investigations are needed to assess the weight of terrain and water for certain remote settlements in Namibia.
3.3. Forest Coverage (Indicator 15.1.1)
3.4. Land Degradation (Indicator 15.3.1)
- First, using open data such as those originating from the Copernicus Program of ESA or Landsat from NASA in combination with innovative web applications (e.g., Sentinel-Hub) with integrated analysis functions (e.g., NDWI) reduces the limitations in storage and processing hardware fundamentally. In addition, these resources allow for the flexibility to use the information for specific investigations and post processing methods with the benefit of hosting updated information in a predictable manner. On the contrary, expertise is needed to process the data for specific analysis.
- Second, the utilization of platforms such as GEE allows us to source, in addition to raw satellite data, a variety and growing number of pre-processed datasets (e.g., GSW) and user-specific data layer combinations for a customized analysis (e.g., the different methods for Indicator 9.1.1). Moreover, stored pre-processed data layers rely on global expert knowledge and peer reviewed methodologies, which represents a remarkable support for non-experts. However, the user has no influence on the maintenance and updates of the datasets stored in GEE (e.g., GSW) and their applicability in the given context (e.g., Hansen et al. Forest Cover dataset for Indicator 15.1.1).
- Third, the utilization of fixed standalone tools or plugins such as Trends.Earth ensures a high level of comparability of data and reduces the risks of wrong processing. On the other hand, it limits the user’s potential to decide on the methodology, apply customized approaches, and use updated or supplementary data layers. Still, these tools provide the biggest benefit for non-technicians and decision makers, enabling them to compare a state over time and space.
5.1. Change of Surface Water (Indicator 6.6.1)
5.2. Access to Roads (Indicator 9.1.1)
5.3. Forest Coverage (Indicator 15.1.1) and Land Degradation (Indicator 15.3.1)
Conflicts of Interest
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|Google Earth Engine (GEE) App||Global Surface Water (GSW) ||Water detection using multi-temporal orthorectified Landsat 5, 7, and 8 archives .|
Yearly History Layer: “Permanent Water” 
Monthly History Layer: “Water detected” 
|GAUL , USDOS , NSA shapefile||GAUL by the FAO and USDOS LSIB by the U.S Government are administrative boundaries on a global level and are not validated by authoritative national sources. Coastlines are not completely validated by satellite imagery .|
NSA shapefile with official national boundaries
|Sentinel Hub||Sentinel 2 Imagery & Bands, NDWI||S2 provides high-resolution imagery in the visible and infrared part of the spectrum. |
L1C (orthorectified Top-Of-Atmosphere reflectance) and L2A (orthorectified Bottom-Of-Atmosphere reflectance).
Level-1C: NDWI = according to Gao 
NDWI values of water bodies are larger than 0.5 
|GEE App||WorldPop ||Gridded Population Density based on a semi-automated dissymmetric modeling approach that incorporates detailed census and ancillary data in a flexible, “Random Forest” estimation technique .|
Information used: “Whole continent” datasets 
|GPW v4 ||Minimally modeled gridded population data collection that incorporates population estimates. Extrapolated from the raw census estimates and a set of estimates that have been nationally adjusted to data from the United Nation’s World Population Prospects . |
Information used: “Population-count”: Estimated number of persons per 30 arc-second grid cell .
|GHSL ||The GHS framework uses heterogeneous data including global archives of fine-scale satellite imagery, census data, and volunteered geographic information .|
Information used: GHS population grids (GHS-POP) ,
GHS urban/rural classification model (GHS -SMOD) 
|HRPD ||A mixture of machine learning techniques, high-resolution satellite imagery, and population data (census) from SEDAC/CIESIN |
Information used: High Resolution Population Density Map Namibia – Population Layer 
|GSW ||See information under 6.6.1|
|SRTM DEM v4 ||The SRTM data is available as 3 arc second (approx. 90m resolution) DEMs. A 1 arc second data product was also produced, but is not available for all countries .|
Information used: “Elevation” 
|GAUL, USDOS, NSA shapefile||See information under 6.6.1|
|Road Network ||Roads for Namibia from NSDI|
Information used: Trunk, main and district roads 
|QGIS||DU Frame||DU collected during 2011 PHC at NSA. Urban/ Rural definition based on administration units. |
Information used: DU Omaheke. Urban areas: Otjinene and Gobabis 
|GFW||Hansen et al. ||“Tree cover” is defined as all vegetation taller than 5 meters in height. “Tree cover” is the biophysical presence of trees in the form of natural forests or plantations .|
Information used: Tree cover with >15% canopy density 
|Trends.Earth||ESA CCI LC ||37 land cover classes based on United Nations Land Cover Classification System (UN-LCCS) |
- Tree Cover, broadleaved, evergreen and deciduous;
needle-leaved, evergreen and deciduous; mixed leaf type, closed to open (>15%)
- Mosaic tree and shrub (>50%)/herbaceous cover
|FAO database||FRA [54,56]||Biophysical and Land Use criteria combined: |
Forest cover is defined as land spanning more than 0.5 hectares with trees higher than 5 meters and a canopy cover of more than 10 percent, or trees able to reach these thresholds in-situ. It does not include land that is predominantly under agricultural or urban land use .
Information used: Forest Coverage published through the FAO 
|Trends.Earth||NDVI time series|
ESA CCI LC
Soil Organic Carbon (SOC)
|Uses 3 factors that determine Land Degradation: Land Cover, Land Productivity, Carbon Stock . |
Direct computation of SDG 15.3.1 through 3 sub- indicators
-NDVI time series (trajectory, performance, state) for Land Productivity
- ESA CCI LC for Land Cover
- Soil Organic Carbon 
|Bush Information System||Landsat 8 satellite images, aerial photographs, and field data||7 land classes used for Otjozondjupa Region Namibia .|
Degradation land based on class: Bushland
|Region||Permanent Surface Water Coverage|
|Year||World Pop 100 m Res||GPW 1000 m Res||GHSL 250 m Res||HRPD 30 m Res||PHC/NIDS NSA|
12,128 (Dwelling Units)
|2018||-||-||-||85,437||75,734 (projected, medium variant) |
|Year||Rural Definition||World Pop 100 m Res||GPW 1000 m Res||GHSL 250 m Res||HRPD 30 m Res||PHC/NIDS NSA|
|GHSL Rural mask||-||-||-||-||-|
|GHSL Rural mask||55,438|
|46,343 (73.5%)||58,390 (72.8%)||-||-|
|44,477 (70.5%)||44,145 (55.1%)||-||-|
|SNIC Segmentation||-||-||-||54,276 (63.5%)||-|
|Year||Rural Mask||Terrain/Water Considered||World Pop (no UN adjustment) 100 m Res||GPW 1000 m Res||GHSL 250 m Res||HRPD 30 m Res||DU NSA (Ind. Unit)|
|2010||NSA||No/No||35,009 (73.9%)||15,793 (37.6%)||-||-||-|
|GHSL Rural mask||No/No||-||-||-||-||-|
|NSA||Yes/Yes||35,009 (73.9%)||15,793 (37.6%)||-||-||-|
|2011||NSA||No/No||35,265 (73.9%)||-||-||-||DU = 4,816 (59.2%)|
|SNIC Segmentation||No/No||40,462 (76.0%)||-||-||-||-|
|2015||NSA||No/No||36,310 (74.0%)||15,680 (37.6%)||34,512 (63.5%)||-||-|
|SNIC Segmentation||No/No||-||-||-||32,341 (59.6%)||-|
|Region||Population Overall||Rural Population||RAI|
|Zambezi||112,939||65961 (58.4%)||50,760 (76.9%)|
|Kunene||109,999||68,350 (62.1%)||43,387 (63.5%)|
|Erongo||203,437||27,602 (13.6%)||19,930 (72.2%)|
|Otjozondjupa||173,172||70,413 (40.7%)||49,364 (70.1%)|
|Oshana||213,232||120,939 (56.7%)||80,180 (66.3%)|
|Oshikoto||222,515||186,744 (83.9%)||82,211 (44.0%)|
|Omusati||289,450||274,491 (94.8%)||137,318 (50.0%)|
|Ohangwena||295,692||270,159 (91.4%)||144,945 (53.6%)|
|Kavango East||172,897||68,514 (39.6%)||58,179 (84.9%)|
|Kavango West||106,237||74,848 (70.5%)||53,861 (72.0%)|
|Omaheke||85,437||54,276 (63.5%)||32,341 (59.6%)|
|Hardap||98,125||31,835 (32.4%)||21,027 (66.1%)|
|Khomas||446,461||23,041 (5.2%)||13,974 (60.7%)|
|!Karas||93,814||26,965 (28.7%)||17,671 (65.5%)|
|National||2,623,407||1,364,138 (52.0%)||805,148 (59.0%)|
|Method||Source||Description of Forest/Tree Coverage||Tree Coverage|
(Own Calculation for Namibia)
|- Tree cover, broadleaved, evergreen and deciduous, |
closed to open (>15%)
- Tree cover, needle leaved, evergreen and deciduous,
closed to open (>15%)
- Tree cover, mixed leaf type, closed to open (>15%)
- Mosaic tree and shrub (>50%)/herbaceous cover
over total land area (excluding water bodies)
|B||FAO (FRA) ||Forest cover is defined as land spanning more than 0.5 hectares with trees higher than 5 meters and a canopy cover of more than 10 percent, or trees able to reach these thresholds in-situ. It does not include land that is predominantly under agricultural or urban land use.||2000: 9.8%|
|C||GFW||For the purpose of this study, “tree cover” was defined as all vegetation taller than 5 meters in height. “Tree cover” is the biophysical presence of trees and may take the form of natural forests or plantations existing over a range of canopy densities.||2000: 0.30% 2010: 0.32%|
|Justification||Do you need to use EO? Is there a better alternative source?|
|Suitability||Can EO provide the required data products?|
|Spatial resolution||What is the appropriate size of pixel?|
|Temporal frequency||What is the required frequency of these EO data acquisitions?|
|Record length||How far back in time does your data record need to go?|
|Reliability||Do you need guaranteed continuation of data supply into the future?|
|Accuracy||What degree of accuracy is needed in the information product?|
|Maturity||Do you want to use only information products that are well documented and are commonly used?|
|Complexity||What data management, processing and analysis capacity is available?|
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Mariathasan, V.; Bezuidenhoudt, E.; Olympio, K.R. Evaluation of Earth Observation Solutions for Namibia’s SDG Monitoring System. Remote Sens. 2019, 11, 1612. https://doi.org/10.3390/rs11131612
Mariathasan V, Bezuidenhoudt E, Olympio KR. Evaluation of Earth Observation Solutions for Namibia’s SDG Monitoring System. Remote Sensing. 2019; 11(13):1612. https://doi.org/10.3390/rs11131612Chicago/Turabian Style
Mariathasan, Vincent, Enrico Bezuidenhoudt, and K. Raymond Olympio. 2019. "Evaluation of Earth Observation Solutions for Namibia’s SDG Monitoring System" Remote Sensing 11, no. 13: 1612. https://doi.org/10.3390/rs11131612