developing geospatial data for an informed africa · developing geospatial data for an informed...

Geomatics Indaba 2016 – Stream 2

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Developing geospatial data for an informed Africa

by Stuart Martin, GeoTerraImage

Abstract

There is a lack of systematic mapping programmes across Africa and this lack of consistent and repeatable

geospatial data across the continent limits our ability to comprehend the impact of past policy/decisions and

how to plan for a sustainable future. Most data is developed in support of project-specific outcomes and will

only cover a given project area. GeoTerraImage (GTI) has been using the open access data provided by the

global data democracy initiatives in combination with innovative mapping processes to develop land use and

land cover data in support of these requirements. This includes basic land cover and mining, agriculture and

human settlement related land uses and provides an up-to-date and repeatable source of information which can

be used to model patterns, trends and relationship between these synergistic and in some cases conflicting land

uses.

Keywords

Africa, land cover, land use, agriculture, forestry, mining, human settlements

Introduction

In order to plan for a sustainable future in Africa, decision makers and officials tasked with policy formulation,

require up-to-date and reliable information on which to base their informed decisions. They also need

information to evaluate the impact of past decisions as this will assist them in understanding the effectiveness of

those decisions and will also provide insight into what needs to be done in the future. In many cases, the lack of

relevant and up-to-date maps (spatial data) may limit this ability to fully understand the past, present and future

impacts of decisions. Having consistent, reliable and repeatable maps can assist in human settlement, natural

resources, resource extraction, water resource and agricultural management and planning.

As Africa is a vast, developing continent, there is a usually a lack of systematic mapping programmes to provide

the types of information required to assist decision making. Most mapping projects are carried out to fulfil a

specific funding-driven mandate and may never be repeated into the future. This limits the value of the

information and in many cases provides pockets of information which does not provide a complete picture of the

area. Most mapping projects are used to quantify either land cover and land use. However, when one starts using

temporal data mapped consistent over time, it is possible to use the spatial patterns, trends and relationships to

infer or qualify why the change happened and be able to use this to influence decision making.

Being able to develop spatial datasets in a consistent, sustainable and repeatable way will assist in populating

spatial data infrastructures with relevant and up-to-date data, which people can rely on. Dataset development

needs to be economically and managerially sustainable and should be driven by cross-cutting initiatives and not

focused on a single application area.

Background

Project based mapping initiatives are typically driven by funding agency requirements and focus on specific

application areas as required by the project. This usually provides detailed and accurate data which may have

limited application outside of the project and would not necessary be repeated on a regular basis.

Mapping exercises can be carried out in a variety of ways, from detailed and costly field surveys through to

satellite imagery based desktop mapping. Each has its benefits and applications and the cost, timeframes and

accuracy requirements all play a significant role in deciding which approach to follow given your requirements.

Certain projects cannot be served through a traditional remote sensing or image interpretation exercise and will

need to rely on detailed ground-based mapping, however, there is a large number of application areas where

remote sensing from medium (10 m to 30 m), high (1 m to 10 m) and very high (< 1 m) resolution imagery can

provide consistent, accurate and repeatable data over large areas. Williams Anders, of the Apollo 8 mission to

the moon stated that “We came all this way to explore the moon, and the most important thing is that we

discovered the earth”. Using this insight, we are able to map and produce relevant information in support of

growth and development, thereby ensuring a sustainable future.

With this in mind, GeoTerraImage (GTI) has been investigating and developing methodologies and processes to

map large parts of southern Africa using open access data, which has been facilitated by the intergovernmental


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Group on Earth Observation (GEO), to which South Africa is a party. This includes two specific sensors,

namely Sentinel-2 (European Space Agency - ESA) and Landsat 8 (United States Geological Survey – USGS).

Open access data implies that the data is freely available and accessible to all users and this ability to download

up-to-date and relevant data changes our ability to respond to information requirements by providing value

added services based on this data. Firstly, the cost to the end-user is reduced as we do not have to acquire

imagery data and secondly, being able to access multi-temporal data at no cost, allowed us to test and evaluate

semi-automated mapping techniques and develop innovative mapping approaches to the benefit of the end-

users. This paper will illustrate the downstream benefits of these data democracy initiatives.

A very significant example of this approach was the development of the 1990 and 2013/14 RSA National Land

Cover datasets using Landsat 5 and Landsat 8 imagery accessed from the USGS GLOVIS database. Being able

to access multi-temporal data, for both 1990 and 2013/14, for 76 Landsat image frames allowed the GTI land

cover mapping team to use semi-automated land cover mapping approached to develop both a 35 class and a 72

class land cover in-line with the Department of Environmental Affairs’ mandate. This data has been made

available to all users in South Africa through an open data license hereby providing a comparable change

detection dataset over 23 years (1990 to 2013/14) and as it has been developed from standardised data sources

using robust and repeatable methodologies, it can be updated in the future. This ability to respond to

requirements in a cost effective, accurate and repeatable way is crucial when mapping large areas in Africa.

Please refer to Fig. 1 for maps illustrating the 1990 and 2013/14 RSA National Land Cover datasets.

Fig. 1: Maps illustrating the 1990 and 2013/14 RSA National Land Cover datasets, provided as part of the

Department of Environmental Affairs open data licence.

The ability to provide up-to-date datasets into the future significantly enhances the value of the data as it can be

used to understand the impact of policy decisions and allows one to quantify the ever-changing demographic,

environmental and socio-economic nature of the country. Refer to Fig. 2 for an infographic illustrating the major

changes that have occurred in South Africa since 1990.

A Chinese-funded initiative has recently published an African Land Cover dataset (part of the Chinese Global

Land Cover initiative) using similar mapping approaches, using Landsat 8 imagery. This illustrates the

application of the approach. This dataset is a traditional land cover dataset focusing on shrubs, trees, grassland,

water and cultivated land.

With the requirement for up-to-date, consistent and repeatable geospatial data for Africa, the Bill and Melinda

Gates Foundation has recently initiated a project to strengthen geospatial data in Africa and are looking at

innovative mapping approaches which can fulfil this mandate. GTI is developing methodologies and mapping

processes to be able to provide modern and sustainable mapping approaches in line with these continent-wide

initiatives and some of the projects will be discussed in this paper.

Approach

Since the launch and successful commissioning of Landsat 8 in March 2013, we have been able to access up-to-

date imagery over the sub-continent through the USGS GLOVIS catalogue. The Landsat Continuity Mission

(LCM) has once again provided a regular, high quality, well-calibrated and global source of high (10 m) and

medium resolution (30 m) multi-spectral and multi-temporal data.

2013/14

1990


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Fig. 2: Infographic illustrating 24 years of land cover change in South Africa (1990 to 2013/14).

In the process of developing a current regional land cover dataset, we needed to consider the development of a

semi-automated/automated mapping procedure to reduce processing time, and ensure data standards and

repeatability in support of change detection requirements. The development of innovative mapping processes

needed to integrate GTI’s wide range of land cover, land use and landscape interpretation skills into an

integrated modelling approach. It was important to minimise manual inputs to ensure a more standardised

output, however, user intervention was required to select the optimal image acquisition dates and prepare the

imagery.

The development of this approach replaced conventional pixel-based classification techniques for traditional

land cover mapping and was able to take seasonal and phenological characteristics of various vegetation and

land cover features into account.

For land use data such as surface mining activity, human settlements and agricultural cultivation, a combination

of techniques including the above-mentioned approached combined with auxiliary data and image interpretation

was used to generate the necessary details and classification required.

Southern African land cover

A basic land cover dataset was generated for ten countries in southern Africa, using a semi-automated mapping

approach. The land cover can be simplified into three broad categories, mainly water, bare surfaces and

vegetation surfaces. Using time series imagery, one is able to evaluate seasonality or permanence of a land cover

class and this assists with the classification.

Modelling vegetation based on seasonality and the phenological responses of the various vegetation types in an

area makes it easier to distinguish between the dominant classes based on these specific characteristics. This is

made easy by using up to 8 Landsat images throughout a year, which represent the yearly cycles in the

vegetation. Within the vegetated surfaces, these can be sub-divided into herbaceous (grassland) and woody (tree,

bush and shrub) classes.

This modelling approach initially provides spectrally dependent foundation land cover classes which are

converted into spectrally independent land cover in line with traditional land cover norms and user expectations.

The land cover classes are as follows:

Bare ground

Cultivated land

Low vegetation/grassland

Sports grounds/golf courses

Tree-dominated vegetation

Tree/bush dominated vegetation

Water

The mapping approach produces a consistent dataset over the ten countries and based on the inputs and resource

requirements, is repeatable into the future. Refer to Fig. 3 for a map of the simplified land cover for the ten


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countries in southern Africa. These include Angola, Botswana, Lesotho, Malawi, Mozambique, Namibia, South

Africa, Swaziland, Zambia and Zimbabwe and portions of the southern Democratic Republic of Congo (DRC).

We have tested the same techniques in South America and as well as Khartoum in Northern Sudan where we are

working with different seasonal patterns and vegetation types and have been able to map land cover to the same

accuracy and consistency. This technique is, therefore, portable across Africa and can be applied to develop a

systematic mapping program across the continent.

Fig. 3: Basic land cover map for ten southern African countries.

Agriculture

As food security is a vital issue in southern Africa due to the growing population and the unpredictable rainfall,

cultivation practices need to be mapped and monitored. The impact of subsistence and small-scale farmers

cannot be underestimated in Africa and developing methodologies to map and quantify the cultivated area for

these farming activities is important.

The mapping of large scale commercial farming including rain-fed and irrigated fields is fairly simple and well

tested in the South African context. GTI has been mapping agricultural field boundaries on behalf of the Crop

Estimates Committee using SPOT 5 since 2006. This data is used to stratify the country in support of fieldwork

and aerial surveys as part of the Producer Independent Crop Estimates Committee (PICES). The agricultural

field boundaries are used to report cropping patterns as part of a crop typing exercise which is conducted on an

annual basis. In southern Africa, the impact of subsistence and small-scale farmers cannot be discounted and

methodologies had to be developed to quantify these cultivated fields. Please refer to Fig. 4 for a map showing

the digitised field boundaries for an area in the Free State overlaid on Landsat imagery.

GTI has used crowd sourcing to develop training datasets, flagging cultivated fields based on their unique

characteristics and the specific cultivation practices and field sizes throughout the sub-continent. These training

sites as used in combination with innovative image classification techniques such as Random Forest Classifiers

and the Support Vector Machine to classify the image and produce a repeatable and consistent dataset of

cultivation practices for a variety of field sizes representing the full extent of agricultural practices in a region.


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Fig. 4: Agricultural field boundaries overlaid on Landsat imagery.

In the case of Zambia, GTI classified 500 000 sample sites manually to provide an adequate training dataset for

the modelling approach and this has produced excellent results. Being able to map both large-scale commercial

and small-scale subsistence agricultural fields forms the basis of understanding food security on the continent

and will become a foundation dataset to quantify the impact of droughts or potentially catastrophic events such

as flooding or tropical storms, etc. This data can be used to evaluate crop suitability and assist in water use

planning in the area.

Refer to Fig. 5 for a map illustrating the results of the modelling approach for an area in Zambia.

Fig. 5: Landsat image of rural area of Zambia (left) and map showing the mapped cultivated fields (orange)

overlaid on the image (right).

Mining

Resource extraction plays a significant role in the economies of African countries and being able to map the

location of these mining activities in the context of human settlements, water sources or agriculture cultivation

practices allows one to fully understand the impact, both positive and negative, of the extraction process.

Using the basic land cover discussed earlier and focusing on the bare ground land cover class, we undertook a

project to classify bare ground into surface mining related activities which results in a dramatic loss of

vegetation. Using a process of visual image interpretation, non-natural bare ground areas were classified as

mining and this has resulted in a spatial dataset of mining activity for eleven countries in southern Africa.


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This comprehensive map of surface mining activity can be integrated with commodity or mineral databases to

classify the mines according to the resource being extracted. The mines identified range from small gravel pits

or quarries through to large scale open cast mines such as Kolwezi in the Democratic Republic of Congo (DRC).

Having the basic land cover for the area was instrumental in being able to complete this mapping exercise as it

automatically identified potential mining sites (bare ground) which were evaluated manually and classified

accordingly. As far as we are aware, this is the first definitive map of surface mining activity for the sub-

continent and we will be enhancing this dataset with appropriate attributes over time. This data can be used to

understand the impact of mining on the natural environment and aspects such as a loss of agricultural or forest

land. Knowing the distribution of the mines can also be instrumental in understanding the impacts on the socio-

economic environment in an area, especially when mines impact the health of the local communicates through

air or water pollution or where mines have a limited lifespan and the local communities need to be considered

when the mine reaches its end of life.

Refer to Fig. 6 for a map of the surface mining activities across Zimbabwe. Note the mining activity along the

dyke in central Zimbabwe.

Fig. 6: Map of surface mining activity (red) across Zimbabwe, overlaid on the basic land cover map.

Human settlements

To improve the lives of people across the continent, we need to understand the location and developed nature of

human settlements and use this information to quantify the socio-economic and demographics of an area. As

many people still live in rural environments, these villages and small towns need to be mapped and where

possible population estimates associated with them to understand the service delivery requirements.

Using a combination of the basic land cover data (bare ground) in combination with spectral texture based

analysis, areas of potential human settlement were identified and further classified using night-time imagery,

indicating night-time illumination of the area. Using the number of road intersections and the road density in an

area is also indicative of the human population and when all of these factors are modelled as a whole, a clear

pattern of human settlements and population densities is established. This modelled data is then verified through

visual image interpretation to ensure that the human settlement patterns are correctly identified across the sub-

continent. Refer to Fig. 7 for a map illustrating the human settlement patterns in northern Namibia.


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This data indicating the extent of human settlements across southern Africa can be used to disaggregate reported

population statistics (reported per district, province, etc.) and provide an indication of the population in an area

and the associated population densities. This information is crucial in understanding service delivery

requirements and mitigating against adverse weather events such as flooding or drought conditions where using

this data in combination with data on subsistence or small-scale farming can assist to quantify food security

requirements.

Being able to understand land uses such a mining, agricultural cultivation practices and human settlements and

analyse them in the context of water sources or other natural vegetation data allows you to understand an area

and assists in developing policy in support of the area. This ability to holistically model an area and understand

the patterns, trends and relationships between synergistic and conflicting land uses are crucial in ensuring

sustainable development and growth.

Fig. 7: Map of northern Namibia illustrating the basic land cover (left) and the human settlements classified

according to population densities (right).

Conclusion

GTI is striving to “unleash the power of imagery, thereby improving your business intelligence”. Using open

access data and innovative semi-automated/automated mapping routines in combination with tried and tested

remote sensing and image interpretation has resulted in the systematic mapping of ten countries in southern

Africa. Being able to provide this consistent and repeatable data into the future forms the backbone of decision-

making and policy formulation across the continent.

This paper illustrates the ability to take advantage of open access data (data democracy initiatives) such as

Landsat 8 and provide real downstream value to the stakeholders in the region. Meaningful and repeatable

information can be rapidly developed for the continent in a cost effective manner. The techniques discussed in

this paper can be applied to ESA’s Sentinel 2 data (10 m and 20 m) and GTI is engaged in projects to integrate

this data into processes and thereby provide enhanced services and products. This allows GTI to remain on the

forefront of innovation and take advantage of developments and initiatives in the fields of imagery and remote

sensing techniques. This pro-active development of relevant datasets in a cost effective manner are crucial for

Africa and has positioned GTI as a leading data and information provider in southern Africa.

The data which is developed is not linked to a specific project or country and provides a sub-continent level map

of relevant land cover and land uses. Whilst the data may have been mapped from medium and high-resolution

imagery sources and has been developed for district, provincial, national and regional applications, it provides

reconnaissance level mapping in support of detailed high-resolution dataset development where very high-

resolution imagery or fieldwork is required.

GTI is presently finalising the above-mentioned datasets and is looking at enhancing the water bodies layer

through the identification of man-made versus natural water bodies. This continual improvement and

enhancement of the data will ensure applicable data for the continent and will support decision making and

governance.

Contact Stuart Martin, GeoTerraImage, Tel 012 807-9480, [email protected]

mailto:[email protected]

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