Earth Observation for Sustainable Development Urban Development Project This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 685761. ESA Ref: AO/1-8346/15/I-NB Doc. No.: City Operations Report Issue/Rev.: 3.0 Date: 24.04.2018 EO4SD-Urban Project: Kigoma City Report Lead: Partners: Financed by:
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Earth Observation for Sustainable Development
Urban Development Project
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 685761.
ESA Ref: AO/1-8346/15/I-NB
Doc. No.: City Operations Report
Issue/Rev.: 3.0
Date: 24.04.2018
EO4SD-Urban Project: Kigoma City Report
Lead: Partners: Financed by:
Earth Observation for Sustainable Doc. No.: City-Operations Report
Development – Urban Project Issue/Rev-No.: 3.0
EO4SD-Urban Kigoma City Operations Report Page I
Consortium Partners
No. Name Short Name Country
1 GAF AG GAF Germany
2 Système d'Information à Référence Spatiale SAS SIRS France
3 GISAT S.R.O. GISAT Czech Republic
4 Egis SA EGIS France
5 Deutsche Luft- und Raumfahrt e. V DLR Germany
6 Netherlands Geomatics & Earth Observation B.V. NEO The Netherlands
7 JOANNEUM Research Forschungsgesellschaft mbH JR Austria
8 GISBOX SRL GISBOX Romania
Disclaimer:
The contents of this document are the copyright of GAF AG and Partners. It is released by GAF AG on
the condition that it will not be copied in whole, in section or otherwise reproduced (whether by
photographic, reprographic or any other method) and that the contents thereof shall not be divulged to
any other person other than of the addressed (save to the other authorised officers of their organisation
having a need to know such contents, for the purpose of which disclosure is made by GAF AG) without
prior consent of GAF AG.
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Summary
This document contains information related to the provision of geo-spatial products from the European
Space Agency (ESA) supported project “Earth Observation for Sustainable Development” Urban Applications (EO4SD-Urban) to the World Bank Urban Planning Study for Tanzania Programme for
the City of Kigoma.
Affiliation/Function Name Date
Prepared GAF AG J. Freitas-Santos, A.
Broszeit
19/04/2018
Reviewed GAF AG D. Angelova 20/04/2018
Approved GAF AG, Project Coordinator T. Haeusler 24/04/2018
The document is accepted under the assumption that all verification activities were carried out correctly
and any discrepancies are documented properly.
Distribution
Affiliation Name Copies
ESA Z. Bartalis electronic copy
World Bank Chyi-Yun Huang electronic copy
Document Status Sheet
Issue Date Details
1.0 14/09/2017 First Document Issue
2.0 15/11/2017 Second Document Issue
3.0 24/04/2018 Third Document Issue
Document Change Record
# Date Request Location Details
1 15/11/2017 Ch. 2.6, 2.7,
2.8, 3.5, 3.6,
4.4, 4.5, 4.6
and Annexe 2
Three products were added to the Service
Operations Report: 1. Urban Green Areas 2.
Planned and Unplanned Settlement Areas 3.
Population Distribution and Density
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2 15/11/2017 Annexe 1 Description of processing methods included
for: Transport Network, Urban Green Areas,
Planned and Unplanned Settlement Areas,
Population Distribution and Density.
3 24/04/2018 Ch. 2.1, 3.3.1,
4.2, 4.3 and
4.4
After a User’s request, the spatial datasets for
the LULC, Transport network and Green
Areas products were adjusted. These updates
implied changes in the following Sections of
the City Operations Report.
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Executive Summary
The European Space Agency (ESA) has been working closely together with the International Finance
Institutes (IFIs) and their client countries to demonstrate the benefits of Earth Observation (EO) in the
IFI development programmes. Earth Observation for Sustainable Development (EO4SD) is a new ESA
initiative, which aims to achieve an increase in the uptake of satellite based information in the regional
and global IFI programmes. The overall aim of the EO4SD Urban project is to integrate the application
of satellite data for urban development programmes being implemented by the IFIs or Multi-Lateral
Development Banks (MDBs) with the developing countries. The overall goal will be achieved via
implementation of the following main objectives:
To provide a service portfolio of Baseline and Derived urban-related geo-spatial products
To provide the geo-spatial products and services on a geographical regional basis
To ensure that the products and services are user-driven
This Report describes the generation and the provision of EO-based information products to the World
Table 14: Statistics of changes of agricultural areas between 2006 and 2016. ................................. 29
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List of Abbreviations
CDS City Development Strategy
CS Client States
DEM Digital Elevation Model
DLR German Space Agency
EEA European Environmental Agency
EGIS Consulting Company for Environmental Impact Assessment and Urban Planning, France
EO Earth Observation
ESA European Space Agency
EU European Union
GAF GAF AG, Geospatial Service Provider, Germany
GIS Geographic Information System
GISAT Geospatial Service Provider, Czech Republic
GISBOX Romanian company with activities of Photogrammetry and GIS
GUF Global Urban Footprint
HR High Resolution
HRL High Resolution Layer
IFI International Financing Institute
INSPIRE Infrastructure for Spatial Information in the European Community
ISO/TC 211 Standardization of Digital Geographic Information
JR JOANNEUM Research, Austria
LC / LU Land Cover / Land Use
LULCC Land Use and Land Cover Change
MMU Minimum Mapping Unit
NDVI Normalized Difference Vegetation Index
NEO Geospatial Service Provider, The Netherlands
QA Quality Assurance
QC Quality Control
QM Quality Management
SP Service Provider
VHR Very High Resolution
WB World Bank
WBG World Bank Group
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1 General Background of EO4SD-Urban
Since 2008 the European Space Agency (ESA) has worked closely together with the International
Finance Institutes (IFIs) and their client countries to harness the benefits of Earth Observation (EO) in
their operations and resources management. Earth Observation for Sustainable Development (EO4SD)
is a new ESA initiative, which aims to achieve an increase in the uptake of satellite based information
in the regional and global IFI programmes. The EO4SD-Urban project initiated in May 2016 (with a
duration of 3 years) has the overall aim to integrate the application of satellite data for urban
development programmes being implemented by the IFIs with the developing countries. The overall
goal will be achieved via implementation of the following main objectives:
To provide the services on a regional basis (i.e. large geographical areas); in the context of the
current proposal with a focus on S. Asia, SE Asia and Africa, for at least 35-40 cities.
To ensure that the products and services are user-driven; i.e. priority products and services to
be agreed on with the MDBs in relation to their regional programs and furthermore to implement
the project with a strong stakeholder engagement especially in context with the validation of the
products/services on their utility.
To provide a service portfolio of Baseline and Derived urban-related geo-spatial products that
have clear technical specifications, and are produced on an operational manner that are
stringently quality controlled and validated by the user community.
To provide a technology transfer component in the project via capacity building exercises in the
different regions in close co-operation with the MDB programmes.
This Report supports the fulfilment of the third objective which requires the provision of geo-spatial
Baseline and Derived geo-spatial products to various stakeholders in the IFIs and counterpart City
authorities. The Report provides a Service Description, and then in Chapter 3 systematically reviews the
main production steps involved and importantly highlights whenever there are Quality control (QC)
mechanisms involved with the related QC forms in the Annexe of this Report. The description of the
processes is kept intentionally at a top leave and avoiding technical details as the Report is considered
mainly for non-technical IFI staff and experts and City authorities. Finally Chapter 4 presents the
standard analytical work undertaken with the products which can be inputs into further urban
development assessments, modelling and reports.
2 Service Description
The following Section summarises the service as it has been realised for the city of Kigoma, Tanzania
within the EO4SD-Urban Project and as it had been delivered to the Task Team Leader (TTL) of the
World Bank programme Urban Planning Study for Tanzania in August 2017.
2.1 Stakeholders and Requirements
The EO4SD-Urban products described in this Report were provided to the World Bank (WB) Technical
Assistance project “Urban Planning Study-Impact & Effectiveness of Urban Planning on City
Spatial Development” for Tanzania. The Urban Planning Study is linked to the larger WB project
‘Tanzania Strategic Cities Project’ (TSCP). The TSCP supports the seven secondary cities: Tanga,
Arusha, Mwanza, Kigoma, Dodoma, Mbeya and Mtwara, in addition to the Capital Development
Authority (CDA) in Dodoma, the national capital. These cities are of strategic importance as they are
within the top ten most populous cities in Tanzania with high population growth rates. In the context of
the current EO4SD Urban project, three of the overall seven cities - Arusha, Kigoma, Dodoma - could
be provided with the geo-spatial datasets. The higher objective of the Urban Planning Study, as stated
in its Draft Concept Note (World Bank, 2016a) is, “to enhance the urban development and inform policies and development strategies of cities in Tanzania through gaining further insights on urban
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planning system and development processes and the effectiveness of master and detailed urban
plans.” It aims to increase the appreciation of practitioners and local authorities on the importance of
urban plans for guiding urban growth and mitigating the potential problems and higher costs of
retrofitting unplanned development. In this context, the Bank is collaborating with the Consortium to
investigate the spatial development characteristics of Tanzanian cities with and without urban plans, and
attempt to assess the impact and effectiveness of such urban plans. This specific Version of the Report
focusses on providing the new results based on the improvements made of the LULC, Transport
infrastructure and Green areas products.
2.2 Service Area Specification
The Areas of Interest (AoI) for mapping the Urban and the Peri-Urban Areas for Kigoma was depicted
in a power point slide, and sent to the Users for verification. The boundaries depicted were based on the
municipality and administration boundaries of the cities. These boundaries were obtained from the
GADM database of Global Administrative Areas (http://www.gadm.org/).
As the city administrative boundary data availability is different for each country/city, the AoIs for the
Urban and Peri-Urban Area were in some cases adjusted to areas which could provide the best examples
of the geo-spatial products that Users may require. The adjustments are based on population distribution
data from LandScan (http://web.ornl.gov/sci/landscan/) and on visual interpretation of built-up areas as
evidenced on Google Earth. LandScan is the finest resolution global population distribution data (~1km
spatial resolution) available and represents an ambient population.
Figure 1: Illustration of Core and Peri-Urban Areas of Mapping for Kigoma.
The Core region has an area of 84 km2 and the Peri-Urban has an area of 209 km2, for a total service or
mapping area of 293 km2.
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2.3 Product List and Product Specifications
During the discussions related to the AoIs the potential geo-spatial products that could be provided for
the Cities were also reviewed with the WB Team and users. It was noted that the Baseline Land
Use/Land Cover (LU/LC) products (for the Core and Peri-Urban areas) were a standard product that
would be provided for all Cities as it is required for the derived products. In the case of Kigoma, the full
list of products for both the Core and Peri-Urban areas are as follows:
Urban Land Use/ Land Cover
Urban Extent
Urban Green Areas
Extent and Type of Informal Settlements
Population Distribution and Density
Transport Infrastructure - Road Network
For each of these products two time slots were used to provide historic and recent information; thus for
Kigoma it was 2006 and 2016. Due to EO data availability the time epochs varied with a plus/minus
year or two around these 2 time slots. The current Report will focus on the provision of the Baseline
LU/LC products and the Transport Infrastructure for Kigoma.
2.4 Land Use/Land Cover Nomenclature
A pre-cursor to starting production was the establishment with the stakeholders on the relevant Land
Use/Land Cover (LU/LC) nomenclature as well as class definitions. The approach taken was to use a
standard remote sensing based LU/LC nomenclature and then adapt it to the user’s LU requirements. Thus the remote-sensing based LU/LC classes in the urban context can be grouped into 5 Level 1 classes,
which are Artificial Areas, Natural/ Semi Natural, Agricultural, Wetland, Water Bodies. These classes
can then be sub-divided into several different more detailed classes such that the dis-aggregation can be
down to Level 2-4. This hierarchical classification system is often used in operational Urban mapping
programmes and is the basis for example of the European Commission’s Urban Atlas programme which provides pan-European comparable LU/LC data for with regular updates. A depiction of the way the
levels and classes are structured is presented as follows:
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Information on overall Product in terms of: Point of contact for product generation, date of
creation
Identification of Product: Resource title, Abstract (a short description of product) and Locator
Classification of Spatial Data
Keywords (that define the product)
Geographic information: Area Coverage of the Product
Temporal Reference: Temporal extent; date of publication; date of last revision; date of
creation
Quality and Validity: Lineage, spatial resolution
Conformity: degree of conformance to specifications
Data access constraints or Limitations
Responsible party: contact details and role of contact group/person
These elements (not exhaustive) constitute the core information that has to be provided to meet the
minimum requirements for Metadata compliancy. Each element and its sub-categories or elements have
specific definitions; for example in the element “Quality” there is a component called “Lineage” which has a specific definition as follows: “a statement on process history and/or overall quality of the spatial data set. Where appropriate it may include a statement whether the data set has been validated or quality
assured, whether it is the official version (if multiple versions exist), and whether it has legal validity.
The value domain of this element is free text,” (INSPIRE Metadata Technical Guidelines, 2013). The
detailed information on the Metadata elements and their definitions can be found in the “INSPIRE Metadata Implementing Rules: Technical Guidelines,” (2013). Each of the EO4SD-Urban products will
be accompanied by such a descriptive metadata file. It should be noted that the internal use of metadata
in these institutions might not be established at an operational level, but the file format (*.xml) and the
web accessibility of data viewers enable for the full utility of the metadata.
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4 Analysis of Mapping Results
This Chapter will present and assess all results which have been produced within the framework of the
current project, in the context of presentation of the Urban Extent product, the LU/LC products and the
Transport Infrastructure product. Furthermore the Sections that follow will provide the results of some
standard analytics undertaken with these products including the following:
Urban Extent – Developments from 2000, 2005, 2010 to 2016
Land Cover Land Use - Status and Trends between 2006 and 2016
Transport Infrastructure - Status and Change between 2006 and 2016
Urban Green Areas – Status and Change between 2006 and 2016
Planned and Unplanned Settlement Areas – Status and Change between 2006 and 2016
Population Distribution and Density – Status and Change between 2006 and 2016
It is envisaged that these analytics provide information on general trends and developments in the Core
and Peri-Urban areas which can then be further interpreted and used by urban planners and the City
Authorities for city planning.
It should be noted that all digital data sets for these products are provided in concurrence with this City
Report with all the related metadata and Quality Control documentation
4.1 Urban Extent – Developments 2000, 2005, 2010 and 2015
The Urban Extent product in the EO4SD-Urban project is provided by the German Aerospace Centre
(DLR) and is provided for 4 points in time; the 2015 Global Urban Footprint (GUF) Plus product has
been produced jointly exploiting multi-temporal 30m Landsat-8 and ESA Sentinel-1 data with 10m
resolution acquired in 2014-2015. And for the years 2000, 2005 and 2010, the Urban Extent products
generated – given the unavailability of freely and easily accessible multi-temporal radar data at high
resolution – were based only on multi-temporal 30m Landsat-5 and Landsat-7 imagery, and scaled up
to 10m resolution.
As the Urban Extent 2015 product was based on the ESA Sentinel-1 dataset which is a Synthetic
Aperture Radar (SAR) in C band it should be noted that some structures which are flat in nature such as
airport runways were not classified; this is due to the fact that radar relies on backscatter which is more
prominent from vertical features. The GUF+ 2015 products will be validated and available as public
domain data from October 2017 onwards on the Urban Thematic Exploitation Platform (TEP) supported
by the DLR.
In the current project the Urban Extent product for Kigoma was first used to assess historical
developments from 2000-2015. Further analysis by overlaying administrative boundaries can be
performed to assess urbanisation extent patterns based on administrative units.
Results:
The first result provided using the different Urban Extent products from 2000 to 2015 is illustrated in
Figure 6 which shows the urban development in the Core and Peri-Urban areas as well as surrounding
regions of Kigoma. The Urban Extent developments after 2000 can be examined by Urban Planners to
identify different patterns of growth such as “Edge Growth” or “Leapfrog Growth” depending on the
location of the developments.
From the depiction of urban extent developments between 2000 and 2015 in Figure 8 and Figure 9, one
can note that urban extent development occurred in Kigoma in small blocks around existing residential
areas. It stretches towards north, west and south-west in the Core Urban and mainly towards north-east
in the Peri-Urban.
The urban development till 2005 stretches towards east in very dense patches in most of the areas. For
the next years, 2005 until 2010 the residential areas stretched in small fragmented patches scattered
through the entire Core Urban, especially around the existing residential areas. Additionally, in the
eastern, north-eastern and in the very south parts of Peri-Urban the extension of residential areas between
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the years 2005 and 2015 were predominant. The last time interval, 2010 until 2015, presents a very
dense extension concentrated in the northern part of the Core Urban.
Figure 8: Urban Extent developments in the epochs 2000 to 2005, 2005 to 2010 and 2010 to 2015 in Kigoma
and surrounding region.
Figure 9: Urban Extent developments in the epochs 2000 to 2005, 2005 to 2010 and 2010 to 2015 in Kigoma
within the Core Urban Area.
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4.2 Land Cover/ Land Use 2006 and 2016
This Section will present the results of the LU/LC mapping for 2006 and 2016 as well the statistical
information on the changes between these two epochs. The LU/LC overview map for 2016 is depicted
in Figure 10 and a cartographic version with map layout design is provided as geo-pdf files in addition
to the geo-spatial product.
Figure 10: Detailed Land Cover Land Use 2016 in Kigoma.
For the epoch 2006 the most dominant LU/LC classes occurring in the Overall area were Agriculture
(41.68% of total area), Forests (15.62%) and Residential (14.06% of total area). By 2016 these classes
remained as the most dominant in the Overall area with a slight increase of 2.37% in the Residential
class (accounting for 16.44 % of the total area by 2016), and minor increase in Forest area with 3.54%.
The Agricultural class decreased by 5.85%. Further information on the class disaggregation and area
coverage is presented in Figure 11 and Figure 12 for the epochs 2006 and 2016 respectively. Detailed
information on the area, percentage distribution and changes can be further observed in Table 12.
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Figure 11: Detailed Land Cover Land Use 2006 structure: Presented as Overall, Core Urban and in Peri-
Urban Zone in percentages (left) and square kilometres (right)
Similarly to the Overall area, Agriculture was the most dominant class in 2006 in both the Core and the
Peri-Urban areas with 34.33% and 44.65%, respectively. The main difference in these two areas is the
coverage and density of the Residential class. For instance, 31.56% (26.60 km2) from the Core Urban
area in 2006 was represented by the Residential class, whereas the class accounted only for 7% (14.61
km2) from the Peri-Urban area during the same year. Additionally, residential areas with high (50 – 80%
of soil sealing) and medium densities (30 – 50% of soil sealing) were predominant in the Core Urban.
Considering the Peri-Urban, residential areas are comprised of the low (10 – 30% of soil sealing) and
very low (less than 10% of soil sealing) density classes and these are scattered over larger areas.
By 2016 the land use distribution patterns within the Core Urban, Overall and Peri-Urban areas are
similar to those in 2006, except that the agricultural class decreased in all three areas. Furthermore, the
Residential class represented the largest area coverage in the Core Urban area by 2016 with 39.82%
(33.56 km2) of the area.
Figure 12: Detailed Land Cover Land Use 2016 structure: Presented as Overall, Core Urban and in Peri-
Urban Zone in percentages (left) and square kilometres (right).
The next Section will highlight the LU/LC change information between the two epochs in more detail.
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Description of LULC Changes:
In addition to the overall LU/LC classification for the two epochs it is interesting to assess the different
trends between classes over the 10 year time period. The quantitative figures for each class (combined
Core and Peri-Urban) are first provided in Table 12 to get an overview.
Table 12: Detailed information on area and percentage of total area for each class for 2006 and 2016 as
The Transport Network was created for both points in time (2006 and 2016) using three levels for the
road type classification. The Arterial roads and Collector roads were integrated in the LULC map by
applying a buffer of 12 m and 8 m for the Arterial and Collector roads, respectively. Local roads are
only part of the vector data set, which are provided to the user.
Figure 17 depicts the Transport Network for both points in time. The left figure presents the Transport
Network in 2006 and the right Figure the Transport Network in 2016. The railway is shown as well. The
main changes of the Transport Network occurred in terms of densification of Local roads in 2016 in the
southern part and northern parts of the Core Urban area. Primary roads and Secondary roads remained
the same through the ten years period.
Figure 17: Transport Network of Kigoma in 2006 (left) and 2016 (right).
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4.4 Urban Green Areas
The location and extent of green areas are determined within the product of urban land use/ land cover
at Level I. Urban green areas refer to land within and on the edges of a city that is partly or completely
covered with grass, trees, shrubs, or other vegetation. The product delivered provides accurate
information (1 m resolution) on the spatial location and extent of green areas located within the Urban
Extent (Level I class: 1000) derived from the baseline LULC information product. Detecting and
monitoring urban green coverage needs very high resolution optical satellite images, which explains the
product generation over the Core Urban Area of Kigoma only.
The overview map in Figure 18 shows loss, gain and stable urban green areas in Kigoma. The map is
delivered as separate product of high resolution for printing at paper size DIN A0, which is 84.1 cm x
118.9 cm. Figure 18 gives an overview of the mapping result and the structure of the map, which is
designed for a print out at DIN A0 paper size (84.1 x 118.9 cm).
Figure 18: Map overview of Urban Green Areas in Kigoma. Green area loss, gain and stable green areas
can be identified. The map is delivered as separate product of high resolution for printing at
paper size DIN A0, which is 84.1 cm x 118.9 cm.
The spatial distribution of the change types as depicted in Figure 19 show that changes are distributed
across the entire Core Urban area of Kigoma, whereas most of the non-green areas can be found in the
denser built up parts of the city. Overall 18.51% of the entire area was covered by vegetation in 2006
and 2016. The percentage of gain of green area (17.14%) is a little bit higher than the percentage of loss
of green area (13.62%) resulting in a general increase of urban green areas in Kigoma. About 50.72%
of the artificial areas (Level I class) were not covered by vegetation in 2006 and 2016.
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Figure 19: Percentage of urban green areas within the core area of Kigoma. The pie chart illustrates the
status and change of urban green areas in-between 2006 and 2016.
The changes within the urban green areas can be illustrated as overall area coverage. Figure 20
represents the green area coverage in square kilometres in 2006 and 2016, compared to the amount of
the artificial areas class. Both classes demonstrated a relatively equal growth within the 10 year period.
Urban areas showed an increase of 4.3 km² (from 23.02 km² to 27.32 km²) and green areas an increase
of 3.91 km² (from 10.90 km² to 14.81 km²).
Figure 20: Bar charts for both points in time presenting the total area of urban greenery versus non-green
areas.
4.5 Planned and Unplanned Settlement Areas
The location and extent of planned and unplanned settlement areas are determined within the classes
“1100 Residential” and “1211 Commercial Areas” of the urban land use/ land cover product. Polygons
with the LU class “Commercial Areas” were only included for classification in this product, when they
also contained residential buildings.
In an effort to use spatial patterns to depict the class of “planned settlements” (see Figure 21) there was
a focus on residential areas where the houses are oriented in the same direction, similar in size and shape
and have a well-defined road network, often in a grid pattern which provides almost direct access to
every house. For “unplanned settlements” (see Figure 22) houses are not oriented in the same direction
50.72%
18.51 %
13.62 %
17.14 %
non green stable green green loss green gain
0
5
10
15
20
25
30
2006 2016
area
in k
m²
urban (km2) green (km2)
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and streets, if existing are curved, small and appear to have been constructed in an ad hoc manner (no
distinct patterns). Also not every building can be reached by a road.
Figure 21: Planned settlement areas in Kigoma in 2016.
Figure 22: Unplanned settlement areas in Kigoma in 2016.
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To further support the classification effort, the Kigoma-Ujiji Master Plan 2017-2037, Draft Report
(2016) page 49 was used to orient in locating of planned and unplanned settlement areas. There was also
an information exchange with the World Bank (WB) team on potential locations of planned/unplanned
settlements in Kigoma; feedback on locations of these 2 settlements was obtained from MaryGrace
Weber of the WB Team after her trip to Kigoma and in consultation with the local Authorities (received
on 18th May 2017); this information was also included. The input is illustrated in Figure 23 and
highlights areas as planned and unplanned to support the interpretation process.
Figure 23: Planned and unplanned settlement areas received via email from MaryGrace Weber.
Due to the complexity of identifying these classes, the detection and monitoring planned and unplanned
settlements needs VHR optical satellite images; thus the product was generated only over the Core
Urban Area of Kigoma.
The overview map in Figure 24 shows the spatial distribution of planned and unplanned areas between
the two years 2006 and 2016 in Kigoma. The map is delivered as separate product of high resolution for
printing at paper size DIN A0, which is 84.1 cm x 118.9 cm. gives an overview of the mapping result
and the structure of the map, which is designed for a print out at DIN A0 paper size (84.1 x 118.9 cm).
Figure 24: Map overview of changes in planned and unplanned settlement areas in Kigoma during the
years 2006 and 2016
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From the Figure it can be seen that planned settlements are mainly located in the inner city, to the north,
south-west and south-east. Only the surrounding residential areas have unplanned settlements.
An analysis of the changes is depicted in Figure 25. Overall about 40% of the residential area is and
remained planned settlement (40.46%) in 2006 and 2016. Only 27.78% of the whole residential area is
unplanned settlement. However, during the 10 year period, more unplanned settlements (18.79%)
developed than planned settlements (10.05%).
Figure 25: Percentage of planned and unplanned areas within the core area of Kigoma. The pie chart
illustrates the status and change of planned and unplanned areas in-between 2006 and 2016.
The changes in the overall area coverage for both the planned and unplanned settlement areas are
illustrated in Figure 26; the bar chart represents the planned and unplanned settlement areas in square
kilometres in 2006 and 2016. Both classes increased overall in area within the 10 years: in 2006 planned
settlements covered an area of 13.11km2, and this area extended in 2016 to 15.77km2; unplanned
settlements covered 13.70km2 in 2006, and this settlement type increased in area in the following 10
years more than the planned settlement type to 18.09 km2.
Figure 26: Bar charts for both points in time presenting the total area of planned and unplanned settlement
areas.
27.78%
40.46%
18.79%
2.91%0.89%
10.05%
No Change in Unplanned Settlement Area No Change in Planned Settlement Area
Expansion of Unplanned Settlement Area Expansion of Planned Settlement Area
Decrease of Unplanned Settlement Area Unplanned to Planned Settlement Area
0
2
4
6
8
10
12
14
16
18
20
2006 2016
Are
a [
km
2]
Unplanned Settlement Area Planned Settlement Area
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4.6 Population Distribution and Density
The Population Distribution and Density Change product contains spatial explicit information about
population distribution within the Core Urban Districts of Kigoma. The product is derived for all units
which belong to the residential class (LULC class 11), using five different data sources: 1) Land
Use/Land Cover Baseline Product; 2) WorldPop data (2015) with a spatial resolution of 100m; 3)
Official Population census data from 2002 and 2012; 4) Imperviousness/Soil Sealing layer (see Annex
1 for a full description of this product) and 5) Administrative boundaries. An important note is that the
product has been always developed for the period 2005 – 2015, due to data availability (the global
WorldPop dataset was only available for the year 2015), and to meet the user requirements, regarding
time interval of 10 years between the historic and current state of the product.
The overview map in Figure 27 shows eight different change classes which were identified, based on a
frequency distribution analysis of the changes that occur: Unchanged Population Distribution; Up to –100% decrease; Up to 200% increase; 201% - 400% increase; 401% - 600% increase; 601% - 800%
increase; 801% - 1000% increase; More than 1000% increase. The Unchanged Population Distribution
class is defined as the residential units which experienced stable population distribution between 2005
and 2015, with values within the overall annual population growth rate (-4.09% to 4.09%). The map is
delivered as separate product of high resolution for printing at paper size DIN A0, which is 84.1 cm x
118.9 cm. It has to be noted that the GIS data set allows map printouts at any other scale which however
would require different map sheet layouts and cartographic designs.
Figure 27: Overview Map of Population Distribution Change in Kigoma (2005 – 2015).
The spatial distribution of the change types as depicted in Figure 27 can be grouped in 3 main categories:
1) unchanged areas; 2) areas which experienced population decrease and 3) areas which increased in
terms of population distribution at a different degree. The overview map shows that most of the
residential units in Kigoma experienced increase of up to 200% (depicted in light yellow colour). The
decrease in population (depicted in green colour on the map) is mainly observed on the south of the city
in the Bangwe district, which is closely linked by the Decrease of Unplanned Settlement Areas and the
conversion of Unplanned to Planned Settlement Areas, as observed in Figure 24.
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On the other hand, the highest degree of increase (More than 1000%, shown in dark brown colour) is
observed mainly on the north east (in Buhanda and Businde districts), and also in the very north part of
Bangwe district. Based on the analysis of the mapping results, several conclusions can be drawn which
justify the trends of extreme population growth in the above mentioned 3 districts, such as: 1) residential
expansion in Buhanda and Businde districts (see Figure 13); 2) urban densification in Bangwa (see
Figure 13); 3) conversion of lots of agricultural plots into residential areas, especially in Buhanda and
Businde districts (see Figure 15); 4) existence of unplanned settlement in Buhanda and Businde districts;
5) expansion of Unplanned Settlements, especially in Buhanda district (see Figure 24); 6) attractiveness
of the areas in the very north part of Bangwa district, which are located closer to the Central Business
District of Kigoma and the port.
An analysis of the change types depicted in Figure 28, shows that 55.97% of the area was subject to
population increase of up to 200%, followed by areas which experienced decrease in population
distribution (12.07% from the total area). The third most common class was represented by areas which
experienced population growth between 201% and 400% (11.31% from the total area). Only 2.28%
remained unchanged (within the range of the annual growth rate of -4.09% to 4.09%)
Figure 28: Population Distribution Change within the Core Urban Districts of Kigoma between 2005 and
2015.
Figure 29 provides further detail on the population distribution changes between 2005 and 2015, in
relation to build up areas which are based on the amount of soil sealing degree in each Core Urban
District.
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Figure 29: Changes in Population Distribution, in relation to build up areas or soil sealing degree in
Kigoma between 2005 and 2015.
For instance, it is noted that in Kibirizi (the second biggest district in terms of population) more than
9000 people moved to build up areas with a sealing degree of 30 – 50%. Similarly, a high proportion of
the residents in Gungu, Mwanga Kaskazini and Mwanga Kusini moved to areas with sealing degree of
30 – 50%. The population growth in most of the Core Urban Districts was mainly distributed among the
Very Low to Medium Density classes. A common trend is observed in the Very High Density class (80
– 100%) as well, which decreased in most of the districts. This phenomena can be driven by different
factors, such as: rent price in central districts, lack of land availability, and conversion of residential
units to commercial, industrial, central business districts in the city centre, etc. However, the information
presented in Figure 29 cannot explicitly define those driving forces. Therefore, further research is needed
to identify and justify the drivers behind such phenomena, thus helping urban planners and local officials
in the decision – making process.
4.7 Concluding Points
This Chapter 4 presented only a summary and overview of what is possible in term of analytics with the
geo-spatial datasets provided for Kigoma in the current project. This Report is a living document and
will be complemented with further analysis during the project. Important would be to further analyse
the EO4SD Urban datasets with the City Master Plan for Kigoma in order to enhance the latter for
planning purposes.
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5 References
Kigoma-Ujiji Draft Master Plan 2017-2037 (2016). Ministry of Lands, Housing and Human
Development, Tanzania.
Czaplewski, R. L. (2003). Chapter 5: Accuracy assessment of maps of forest condition: statistical design
and methodological considerations, pp. 115–140. In Michael A. Wulder, & Steven E. Franklin (Eds.),
Remote sensing of forest environments: concepts and case studies. Boston: Kluwer Academic Publishers
(515 pp.).
European Union (2011). Mapping Guide for a European Urban Atlas, Version 11.0
Goodchild, M., Chih-Chang, L. and Leung, Y. (1994): Visualizing fuzzy maps, pp. 158-67. In
Heamshaw, H.H. and Unwin, D.J. (Eds.), Visualization in geographical information systems.
Chichester: Wiley.
Olofsson, P., Foody, G. M., Stehman, S. V., & Woodcock, C. E. (2013). Making better use of accuracy
data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified
estimation. Remote Sensing of Environment, 129, 122–131. doi:10.1016/j.rse.2012.10.031
Selkowitz, D. J., & Stehman, S. V. (2011). Thematic accuracy of the National Land Cover Database
(NLCD) 2001 land cover for Alaska. Remote Sensing of Environment, 115(6), 1401–1407.
doi:10.1016/j.rse.2011.01.020.
Internet
Road classification, European Commission, 2017, https://ec.europa.eu/transport/road_safety/specialist-
/knowledge/road/designing_for_road_function/road_classification_en, last accessed 2017.08.17
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Annex 1 – Processing Methods for EO4SD-Urban Products
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Summary of Processing Methods
Urban and Peri-Urban Land Use/Land Cover and Change
The input includes Very High Spatial Resolution (VHR) imagery from different sensors acquired at
different time. The data is pre-processed to ensure a high level of geometric and radiometric quality
The complexity when dealing with VHR images comes from the internal variability of the information
for a single land-use. For instance, an urban area is represented by a high number of heterogeneous pixel
values hampering the use of automated pixel-based classification techniques.
For these VHR images, it is possible to identify textures (or pattern) inside an entity such as an
agricultural parcel or an urban lot. In other words, whereas pixel-based techniques focus on the local
information of each single pixel (including intensity / DN value), texture analysis provides global
information in a group of neighbouring pixels (including distribution of a group intensity / DN values
but also spatial arrangement of these values). Texture and spectral information are combined with a
segmentation algorithm in an Object Based Image Analysis (OBIA) approach to reach a high degree of
automation for most of the Peri-Urban rural classes. However, within urban land, land use information
is often difficult to obtain from the imagery alone and ancillary/in situ data needs to be used. The
heterogeneity and format of these data mean that another information extraction method based on
Computer Aided Photo-Interpretation techniques (CAPI) need to be used to fully characterise the LULC
classes in urban areas. Therefore, a mix of automated (OBIA) and CAPI are used to optimise the
cost/quality ratio for the production of the LULC/LUCC product. The output format is typically in vector
form which makes it easier for integration in a GIS and for subsequent analysis.
Level 4 of the nomenclature can be obtained based on additional information. These can be generated
by more detailed CAPI (e.g. identification of waste sites) or by an automated approach based on
derived/additional products. An example is illustration by categorising the density of the urban fabric
which is related to population density and can then subsequently used for disaggregating population
data.
Information on urban fabric density can be obtained through several manners with increasing level of
complexity. The Imperviousness Degree (IMD) or Soil Sealing (SL) layer (see separate product) can be
produced relatively easily based on the urban extent derived from the LULC product and a linear model
between imperviousness areas and vegetation vigour that can be obtained from Sentinel 2 or equivalent
NDVI time series. This additional layer can be used to identify continuous and discontinuous urban
fabric classes. Five urban fabric classes can be extracted based on a fully automated procedure:
Continuous urban fabric (IMD > 80%)
Discontinuous dense urban fabric: (IMD 50-80 %)
Discontinuous medium density urban fabric (IMD: 30-50 %)
Discontinuous low density urban fabric (IMD 10-30 %)
Discontinuous very low density urban fabric (IMD < 10 %)
Manual enhancement is the final post-processing step of the production framework. It will aim to
validate the detected classes and adjust classes’ polygon geometry if necessary to ensure that the correct MMU is applied. Finally, a thorough completeness and logical consistency check is applied to ensure
the topological integrity and coherence of the product.
Change detection: Four important aspects have to be considered to monitor land use/land cover change
effectively with remote sensing images: (1) detecting that changes have occurred, (2) identifying the
nature of the change, (3) characterising the areal extent of the change and (4) assessing the spatial pattern
of the change.
The change detection layer can be derived based on an image-to-image approach provided the same
sensor is used. An original and efficient image processing chain is promoted to compare two dates’ images and provide multi-labelled changes. The approach mainly relies on texture analysis, which has
the benefits to deal easily with heterogeneous data and VHR images. The applied change mapping
approach is based on spectral information of both dates’ images and more accurate than a map-to-map
comparison.
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Summary of Processing Methods
Urban Extent and Change
Reliably outlining urban areas is of high importance since an accurate characterization of the urban
extent is fundamental for accurately estimating, among others, the population distribution, the use of
resources (e.g., soil, energy, water, materials), infrastructure and transport needs, socioeconomic
development, human health and food security. Moreover, monitoring the change in the extent of urban
areas over time is of great support for properly modelling the spatial-temporal patterns of urbanisation
The product is a binary mask outlining in the area of interest the urban areas (intended as built-up
structures) with respect to all other land-cover classes merged together into a single information class.
The urban class and the non-urban class are associated with value “255” and “0”, respectively. Regrouping of relevant LULC thematic classes can be used to depict urban extent precisely. Instead, if
a detailed LULC product is not available for the selected study region, then the information will be
derived by the following approach.
The product is generated at 30 m spatial resolution by properly exploiting Landsat-4/5/7/8 multi-
temporal imagery acquired over the peri-urban and urban area within a given time interval of interest in
which no relevant changes are expected to occur (typically a time period of 1-2 years allows to obtain
very accurate results). For all the considered scenes, cloud masking and, optionally, atmospheric
correction are performed. Next, a series of features specifically suitable for delineating urban areas are
derived for each image. These include both spectral indexes (e.g., the normalized different vegetation
index (NDVI), the atmospherically resistant vegetation index (ARVI), the normalized difference water
index (NDWI), etc.) and texture features (e.g., occurrence textures, co-occurrence texture, local
coefficient of variation, etc.). The core idea is then to compute per each pixel key temporal statistics for
all the extracted features, like temporal maximum, minimum, mean, variance, median, etc. This allows
compressing all the information contained in the different multi-temporal acquisitions, but at the same
time to easily and effectively characterize the underlying dynamics. It is worth noting that for different
pixels in the study area, different number of scenes might be available. However, in the hypothesis of a
sufficient minimum number of acquisitions for computing consistent statistics, this does not represent
an issue. Moreover, in this framework it is also possible to obtain spatially consistent datasets to be
employed for the desired analyses even when investigating large areas. Training data for the urban and
non-urban class are then extracted by employing a strategy based on the analysis of the DLR Global
Urban Footprint (GUF) layer (which varies depending whether the target period of interest refers to a
time interval before or after that which the GUF refers to). Afterwards, a Support Vector Machines
(SVM) classifier is employed where a Radial Basis Function (RBF) kernel is used.
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Summary of Processing Methods
Transport Network
The transportation network is mainly manually digitised and mapped on the basis of very high resolution
(VHR) optical satellite imagery. Requested features are obtained by integration of auxiliary data such
as OpenStreetMap (OSM) or local datasets as a starting point for the product generation. The revision
and update of the auxiliary data is realised by using up-to-date VHR data. The workflow can be specified
as follows:
Quality check of available ancillary data such as local data sets.
Processing of optical satellite data – dependent on satellite data product level (geometric,
atmospheric and radiometric corrections, enhancements – colour optimization, mosaicking,
tiling).
Identification, collection and integration of available ancillary data (e.g. Open Street Map)
Identification and adjustment of spatial inconsistencies. The OSM data is used as spatial
reference. Upon User request other data sets can be used.
Update of the network by visual photo-interpretation according road hierarchy (see description
below).
Update of attributes by photointerpretation.
Generalization, application of MMU (minimum allowable dangling length)
Quality control and accuracy assessment
o Statistical sampling of check points
o Independent evaluation of products (second interpreter, third party assessment)
Change detection
The road hierarchy used in the classification is based on the international road classification
standards. One definition is specified by the European Commission. Roads are divided into three
groups - arterial or through traffic flow routes (in our case Arterial Roads), distributor road (in our
case Collector Roads), and access roads (in our case Local Roads). The three road types are defined
as follows: Arterial Roads - roads with a flow function allow efficient throughput of (long distance)
motorized traffic. All motorways and express roads as well as some urban ring roads have a flow
function. The number of access and exit points is limited. Collector Roads - roads with an area
distributor function allow entering and leaving residential areas, recreational areas, industrial zones,
and rural settlements with scattered destinations. Local Roads - roads with an access function allow
actual access to properties alongside a road or street. Arterial roads and collector roads were the
main focus of the classification. These types of roads were identified for the entire Area of Interest.
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Summary of Processing Methods
Urban Green Areas
The location and extent of green areas are determined within the product of urban land use/ land cover
at Level I. Urban green areas refer to land within and on the edges of a city that is partly or completely
covered with grass, trees, shrubs, or other vegetation. This includes public parks, private gardens,
cemeteries, forested areas as well as trees, river alignments, hedges etc. The product delivered within
EO4SD-Urban project thus provides accurate information (1 m resolution) on the spatial location and
extent of the green areas located within the Urban Extent (Level I class: 1000) derived from the baseline
LULC information product.
Detecting and monitoring urban green coverage needs very high resolution optical satellite images,
which explains the product generation over the Core Urban Area of AOI only. The same images have
been logically used for generating the LULC information product. Consequently, the usual preliminary
quality check and pre-processing tasks were already implemented.
Urban Green Areas have been detected using automated non-supervised classification method. More
precisely, each single multispectral VHR scene has been classified by specifying the most appropriate
algorithm and class number. Then, pixel units from the classes considered as representing green areas
have been combined into 1 single class. From this operation results the required binary raster product.
At this stage, it only remains necessary to apply some post-processing steps:
Morphological filter is applied to fill small gaps within the green areas (caused by shadow)
Resampling of the data to the provided spatial resolution of 1m
Removing small pixel groups under the minimum mapping unit.
Integrating the information provided by the LULC product (e.g. class Urban Parks, Cemeteries).
Validation of Mapping results
Furthermore, using archive very high resolution images, current and historic extent of urban green areas
are compared to identify their temporal evolution – extent growth or reduction. Quality control and
accuracy assessment tasks are performed by means of visual interpretation considering also the LULC
dataset.
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Summary of Processing Methods
Planned and Unplanned Settlements
The location and extent of planned and unplanned settlement areas are determined within the classes
“1100 Residential” and “1211 Commercial Areas” of the urban land use/ land cover product. Polygons
with the LU class “Commercial Areas” were only included for classification in this product, when they also contained residential buildings.
Two distinct between the two types following rules were applied:
In planned settlement areas (see Figure 1 left side) houses are oriented in the same direction, are
similar in size and shape and show a good road network reaching every house.
In unplanned settlements (see Figure 1 right side) houses are not oriented in the same direction
and streets, if existing are curved and small. Not every building can be reached by a road.
Figure 1: Planned (left) and unplanned (right) settlement areas in Kigoma in 2016.
To further improve the results, the City Urban Master plans were used to help in the distinction in
planned and unplanned settlement areas. Detecting and monitoring planned and unplanned settlements
need very high resolution (VHR) optical satellite images; therefore this product was only generated over
the Core Urban Area of Kigoma only. As the same images VHR images were used for generating the
LULC information product, the usual preliminary quality check and pre-processing tasks were already
implemented. Planned and unplanned areas have been detected by visual interpretation of the actual and
historic VHR image by following the rules described above and with the help of the City Master Plans
of the three cities.
Quality control and accuracy assessment tasks are also performed by means of visual interpretation
considering also the LULC dataset.
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Summary of Processing Methods
Population Distribution and Density
This product aims at providing information on the spatial distribution of residents in a specified Area of
Interest (AOI). Additionally, it depicts: a) location of people; b) estimated population in a specific spatial
unit; and c) changes in population distribution throughout the years.
Data sources and types
To derive the Population Distribution and Density product, five main ancillary data types are used: 1)
Land Use/Land Cover Baseline Product; 2) WorldPop data; 3) Population census data; 4)
Imperviousness/Soil Sealing layer produced by DLR and 5) Administrative boundaries.
1) LULC product is used to extract the residential units within the Core Urban Districts of the city.
2) WorldPop data is a globally available dataset with spatial resolution of 100m, which provides
disaggregated population counts at a specific spatial unit (on a pixel level) for the year 2015.
Therefore, this dataset is used to interpolate the population counts for each residential unit in
the current state of the product, whereas the historic state is modelled, based on soil sealing
information and official population census data.
3) Population census data is further applied to improve the quality of the current state of the product
by adjusting population residuals, and to extrapolate the population size in the historic state of
the product.
4) Imperviousness/Soil Sealing layer has been produced by the German Space Agency (DLR). The
product provides for each pixel identified as urban the corresponding estimated Percentage
Impervious Surface (PIS). When used with the population data this PIS layer supports the
disaggregation of the population counts from district level to pixel level, using the mean sealing
degree for each urban fabric class.
5) Administrative boundaries (district level) are downloaded from the Global Administrative
Areas Website. Furthermore, they are clipped with the specified AOI and used to inform the
total number of inhabitants in each district, and to estimate the overall population density on a
district level.
Methodology
This section describes the general structure of the methodology which is used to compute Population
Distribution and Density for the specific study period. It covers the following two parts: 1) Interpolation
and projection of the population data; and 2) Spatial disaggregation and adjustment of the population
residuals.
1. Interpolation and projection of the population data
The first step from this methodological approach is to interpolate and to project the total number of
inhabitants per district for the specific reference year, based on official population census data. To
support this analysis, population census datasets from 2002 and 2012 are acquired from the Integrated
Public Use Microdata Series (IPUMS) and the United Republic of Tanzania, Ministry of East African
Cooperation, in order to estimate the population size in the Core Urban Districts of Kigoma for 2005
and 2015, respectively(IPUMS; MEAC, 2013) . First, the overall annual population growth rate is
calculated based on the total population size, using the following formula as in Kindu et al. (2015): 𝑃 = 𝑃 𝑒 (1)
where: 𝑃0 is the total population in 2002, 𝑃 is the total population in 2012,
represents the number of years between the two periods,
is the average annual growth rate.
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Next, the average annual population growth rate per district is derived, based on the available census
data (2002 – 2012). Thus, the population size for each district is projected and estimated for 2015, using
equation (1). Finally, the population size in each district in 2005 is interpolated, based on the average
annual population growth rate per district or the overall annual growth rate (when census data for the
initial reference year (2002) was not available).
The population census datasets are used as a supportive parameter in the computation of the current state
of the product, in order to re-distribute the population residuals, created from the interpolation of the
WoldPop data. In contrast, it is the main source for population disaggregation in the historic state, since
no historic WorldPop data is available.
2. Spatial disaggregation and adjustment of the population residuals
In order to improve the quality of the current state of the product (based on WorldPop data), and to
interpolate the historic, further disaggregation analysis is conducted, following the general approach of
Batista e Silva et al. (2013): 𝐾 = 𝑃𝑆∑ 𝑈𝑐∗𝑆𝑐 𝑐 (2)
where: 𝐾 represents the number of inhabitants per unit (%) of sealed surface in each district,
Ps accounts for the total population in the specific district, based on the official census data,
Uc is the number of urban pixels (in raster data), or the sum of class area within the district (in
a vector data),
Sc is the mean soil sealing degree of each urban fabric class c.
Next, the estimated number of residents for each urban fabric class c (𝑃𝑐 is derived, as in Batista e
Silva et al. (2013): 𝑃𝑐 = 𝑘 ∗ 𝑆𝑐 (3)
Finally, a dasymetric population technique is applied to disaggregate the number of residents per spatial
unit and to adjust the population residuals. This approach takes into consideration the residential
polygons from the LULC product, denoted as ‘target’ zone, and the number of inhabitants per district, based on the official census data as ‘source’ zone, using the following formula (Batista e Silva, et al. 2013, p.17): 𝑃 ’ = 𝑃 ∗ 𝐴𝑖∗𝑊𝑖∑𝑖 𝐴𝑖∗𝑊𝑖 (4)
where: 𝑃𝑖′ refers to the estimated population in the target zone i, 𝑃 is the known population in the ‘source’ zone s
Ai is the area of the ‘target’ zone polygons
Wi is the weighting parameter related to the population density of the ‘target’ zone. n corresponds to the number of transitional polygons within each source polygon.
Output product
The output product is delivered in a vector format, presenting number of inhabitants and population
density (inhabitants/sqkm) for each residential spatial unit from the LULC product.
Validation
The final product, which is developed by a disaggregated procedure such as the one herein described is
never less accurate than the original source data (JRC Technical Report, 2013). Further detail on the
quality and accuracy of the product is directly linked to the JRC Technical Report (2013), as follows:
‘By disaggregating numerical data from one coarse geometry to a finer geometry, we always gain detail
and approximate ground truth without the risk of deteriorating the source information. The degree to
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which the disaggregation approximates reality, however, varies greatly, and it depends chiefly on: 1) the
quality of the ancillary data and 2) the appropriateness of the disaggregation algorithm and its
parameters.’
Reference
Batista e Silva, F., Gallego, J. & Lavalle, C. (2013). A high-resolution grid map for Europe. Journal of
Maps, 9(1), 16-28.
Batista e Silva, F. (DG JRC), Poelman, H. (DG Regional Policy), Martens, V. (DG Regional Policy),
Lavalle, C. (DG JRC). (2013). " Population Estimation for the Urban Atlas Polygons" Joint
Research Center (JRC) Technical Reports.
Kindu, M., Schneider, T., Teketay, D., & Knoke, T. (2015). Drivers of land use/land cover changes in
Munessa-Shashemene landscape of the south-central highlands of Ethiopia. Environmental
monitoring and assessment, 187(7), 452.
Minnesota Population Center. Integrated Public Use Microdata Series, International (IPUMS –
International): Version 6.5. Tanzania – Population and Housing Census 2002. Minneapolis:
University of Minnesota, 2017. http://doi.org/10.18128/D020.V6.5.
United Republic of Tanzania, Ministry of East African Cooperation (MEAC). (2013). 2012
POPULATION AND HOUSING CENSUS TANZANIA. Population Distribution by
Product 1 (28) Urban Land Use/ Land Cover and Change
Abstract
Land Use/Land Cover (LU/LC) information product contains spatial explicit information on different land use and land cover occurring in both the Core and Peri-Urban areas of the City of Kigoma. The Core area has detailed LU/LC nomenclature that is either at Level 3 or 4 whereas the Peri-Urban area LU/LC nomenclature is at an aggregated Level 1 or 2. The input data for the Core area was the Very High Resolution (VHR) data of Pleiades (2016), Quickbird (2005) and the input data for the Peri-Urban area was Sentinel-2 (2016), Landsat 5 (2005). The LU/LC product is the Baseline Product from which various derived products (such as Green Areas and Informal Settlements) are produced.
Service / Product Specifications
Area Coverage
Country: Tanzania A) Wall-to-wall:
City: Kigoma Selected Sites: 1
Area km² Core Urban: 84 B) Sampling based: n/a
Peri-Urban: 209
Time Period - Update Frequency
A) Baseline Year(s): B) Update Frequency
2006 and 2016 2 points in time
Comments:
Geographic Reference System
WGS 84 UTM zone 35S
Mapping Classes and Definitions
Residential (Built-Up Areas)
Built-up areas and their associated land, such as gardens, parks, planted areas and non-surfaced public
areas and the infrastructure, if these areas are not suitable to be mapped separately with regard to the
minimum mapping unit size.
Very High: Average degree of soil sealing: 80 -100% Residential buildings, roads and other artificially
surfaced areas.
High: Average degree of soil sealing: > 50 - 80% Residential buildings, roads and other artificially surfaced
areas.
Medium: Average degree of soil sealing: > 30 - 50% Residential buildings, roads and other artificially
surfaced areas. The vegetated areas are predominant, but the land is not dedicated to forestry or
agriculture.
Low: Average degree of soil sealing: 10 - 30% Residential buildings, roads and other artificially surfaced
areas. The vegetated areas are predominant, but the land is not dedicated to forestry or agriculture.
Very Low: Average degree of soil sealing: <10 % Residential buildings, roads and other artificially surfaced
areas. The vegetated areas are predominant, but the land is not dedicated to forestry or agriculture.
Example: exclusive residential areas with large gardens.
Commercial* Warehouses, CBD, shopping malls, markets, other commercial facilities
Industrial* Factories, … and associated land
University* University and associated land
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
Schools* Schools and associated land including sport fields.
Government* Governmental Buildings.
Military* Military and associated land.
Hospitals* Hospitals and associated land.
Public Buildings*
“Big” public buildings like churches, bibliotheca, etc.
Arterial Line Highways, connecting the city with other cities.
Collector Line Bigger Connecting roads within the city.
Railway Railway facilities including stations, cargo stations and associated land.
Airport Administrative area of airports, mostly fenced. Included are all airport installations: runways, buildings and associated land.
Port Port and associated area.
Mining Area and Dump Sites
Open pit extraction sites (sand, quarries) including water surface, if < Minimum Mapping Unit (MMU), open-cast mines, inland salinas, oil and gas fields.
Construction sites
Spaces under construction or development, soil or bedrock excavations for construction purposes or other earthworks visible in the image.
Land without current use
Areas in the vicinity of artificial surfaces still waiting to be used or re-used. The area is obviously in a transitional position, “waiting to be used”.Waste land, removed former industry areas, (“brown fields”) gaps in between new construction areas or leftover land in the urban context (“green fields”). No actual agricultural or recreational use. No construction is visible, without maintenance, but no undisturbed fully natural or semi-natural vegetation (secondary ruderal vegetation).
Urban Parks
Public green areas for predominantly recreational use such as gardens, zoos, parks, castle parks. Suburban natural areas that have become and are managed as urban parks. Forests or green areas extending from the surroundings into urban areas are mapped as green urban areas when at least two sides are bordered by urban areas and structures, and traces of recreational use are visible.
Recreation facilities
All sports and leisure facilities including associated land, whether public or commercially managed: Golf courses, Sports fields (also outside the settlement area), Camp grounds, Riding grounds, Racecourses, Amusement parks, Swimming resorts etc., Glider or sports airports.
Cemeteries Cemeteries and associated area.
Agricultural Area
Cultivated areas non-irrigated or permanently irrigated including rice fields: arable land (annual crops), permanent crops, complex or mixed cultivation, orchards; pasture and meadow under agricultural use, grazed or mechanically harvested.
Forest and Shrub lands
High woody vegetation in natural forests; transitional woodland; low vegetation cover with bushes and shrubs.
Natural Areas Natural area where there is little vegetation and does not serve as construction site.
Bare Soil Natural Area with no vegetation.
Wetlands Areas flooded or liable to flooding during a large part of the year by fresh, brackish or standing water with specific vegetation coverage made of low shrub, semi-ligneous or herbaceous species; shallow water areas covered with reed.
Water Visible water areas like lakes, rivers, ponds (natural, artificial).
Comment: * These classes have to be map only in 2015. For the mapping of the historic land cover these classes were merged to the class Commercial, Public, Military and Private Units.
Cloud and Cloud Shadow Detection and Removal
Information for the entire AOI is required and therefore clouded areas have to be replace by other Earth Observation (EO) data.
Spatial Resolution
n.a. (Product provided as Shapefile)
Minimum Mapping Unit (MMU)
Minimum Mapping Unit is 0.25 ha for the urban area, 0.5 ha for the peri-urban area.
Data Type & Format
Shapefile *.shp and GeoPDF
Bit Depth
n.a.
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
tanzania_health_facility_registry6322wgs84.shp: Due to missing Metadata the Accuracy and Completeness of the data set is unknown. KIG_Roads.shp: Due to missing Metadata the Accuracy and Completeness of the data set is unknown.
Kig_Rivers_v2.shp: Due to missing Metadata the Accuracy and Completeness of the data set is unknown. OSM_Data_Kigoma_Tanzania: Were used in addition to the Road layers received from the city of Kigoma. They were visually checked and corrected
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
Land Use/Land Cover (LU/LC) information product contains spatial explicit information on different land use and land cover occurring in both the Core and Peri-Urban areas of the City of Kigoma. The Core area has detailed LU/LC nomenclature that is either at Level 3 or 4 whereas the Peri-Urban area LU/LC nomenclature is at an aggregated Level 1 or 2. The input data for the Core area was the Very High Resolution (VHR) data of Pleiades (2016), Quickbird (2005) and the input data for the Peri-Urban area was Sentinel-2 (2016), Landsat 5 (2005). The LU/LC product is the Baseline Product from which various derived products (such as Green Areas and Informal Settlements) are produced.
N/A - V Q Visually and quantitatively/qualitatively checked
- N/A Not applicable
Readability (1) Check readability of all required input data. Can the data be stored again?
Header / Metadata (2) Check Image Header Information and/or Metadata for completeness / distinctive features.
Data Format (3) For digital data, please give file format (e.g. *.tiff, *.shp).
Data Type (4) Please specify the type of the data (e.g. raster, vector or analogue).
Bit Depth (5) Please give pixel depth and sign of raster data (e.g. 8 bit unsigned integer).
Dynamic Range (6) Check dynamic range of all image bands. Visual check of dynamic range should be accompanied by histograms and statistics.
Dropped Lines & Artefacts (7) Check Image for dropped lines and other artefacts. If such occurs, please give description of extent and influence in the Comments section.
Methodology of in situ or reference/auxiliary data sampling scheme should be outlined. In case of sample plots, also state how the plot positions have been determined (e.g. from GPS measurements, topographic maps, terrestrial triangulation, EO data, etc.) and how the sampling grid was established.
Positional Accuracy (11)
Positional accuracy of collected in situ data should be given. For reference/auxiliary data it MUST be given. If unknown, the data’s use must be explained. For DEM or other data with 3D information please specify both vertical and horizontal Positional Accuracy. For analogue data (e.g. maps) try to give approximate accuracy related to mapping scale.
Completeness (12) Data should be checked for spatial/temporal/content gaps.
Model / Algorithm (13)
Give the name of the software and its version. Specify the software module/algorithm used for: a) geometric correction, e.g., Polynomial and its degree, Rational Functions, Thin Plate Spline, etc. b) classification, e.g., ISODATA, Maximum Likelihood, Neural Networks, etc.
Always specify completely, i.e. at minimum the Projection (+Zone, if applicable), Spheroid / Ellipsoid, Map Datum. Give additional information if necessary to unambiguously define the reference system.
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
Give the number, distribution and RMS of used Ground Control Points (as average per scene) as obtained from the Geometric Correction, in meters [m]. Optionally, also RMSx and RMSy may be given. Distribution of GCPs should be attached as a snapshot, or described in the Comments section.
Tie Points (TPs) (18)
In case of mosaicking, give the number of used Tie Points and their total RMS (as average per scene) as obtained from the Geometric Correction, in meters [m]. Optionally, also RMSx and RMSy may be given. Distribution of TPs should be attached as a snapshot, or described in the Comments section.
Check Points (CPs) (19)
The real measure of positional accuracy and the only measure which should be examined as to its Acceptable Range. Give the independent Check Points’ total RMS (as average per scene) as obtained from the Geometric Correction, in meters [m]. Optionally, also RMSx and RMSy may be given. Distribution of CP's should be attached as a snapshot, or described in the Comments section.
Resampling Method (20) Specify, if / which resampling algorithm has been used (e.g. NN=Nearest Neighbour, BIL=Bilinear Interpolation, CC=Cubic Convolution).
Sampling unit (21) Specify sampling unit of Accuracy Assessment as POINT, FRAME, POLYGON and how it is treated (e.g. pixel center, polygon centre, etc.).
Sampling Design (22) SYST=Systematic, RAND=Random, STRAT=Stratified, SBCLASS=Stratified by class, SBAREA=Stratified by area
Sample exclusion criteria (23)
Describe which criteria you apply for sampling point selection resp. exclusion of certain points. For example, if the point is too close to a class boundary (less than 1 pixel), it is excluded. If the selected sample point is not representative of the class , it is excluded. For these reasons, it is recommended to oversample by 10% to compensate for sample point exclusion.
Thematic Accuracy (24)
Provide a detailed description of the Accuracy Assessment results in the form of error matrices showing commission and omission errors, user’s , producer’s and overall accuracies and other measures of Thematic Accuracy, as the confidence level (usually fixed at 95%) and the respective confidence interval, at least for the overall accuracy. If classification is done in a phased approach, e.g. if Forest Area and subsequently Forest Type are mapped, independent reports have to be produced.
Completeness of Coverage (25) State whether the coverage is limited to a subset, or portion of the final product.
Completeness of Classification (26)
State whether classification was constrained to a subset, or portion of the final product.
Completeness of Verification (27)
State whether verification applied to lineage, positional, or thematic accuracy is constrained to a subset, or portion of the final product.
Definition (INSPIRE 2015): Lineage is “a statement on process history and/or overall quality of the spatial data set. Where appropriate it may include a statement whether the data set has been validated or quality assured, whether it is the official version (if multiple versions exist), and whether it has legal validity. The value domain of this element is free text.”
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
Source(30) Definition (INSPIRE, 2015): “This is the description of the organisation responsible for the establishment, management, maintenance or distribution of the resource. This description shall include: name of the organisation and contact email address.”
Earth Observation for Sustainable Doc. No.: City-Operations Report
Development – Urban Project Issue/Rev-No.: 3.0
Annex 2 to EO4SD-Urban Kigoma City Operations Report Page 3
This page is intentionally left blank!
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
Urban Green Areas information product contains spatial explicit information on green areas within the urban extent. Green areas include linear green features such as river alignments, hedges and trees, as well as public parks, private gardens, forested areas, etc. Urban Green Areas & Change dataset is based on Very High Resolution (VHR) satellite imagery by means of automated classification processing techniques.
Service / Product Specifications
Area Coverage
Country: Tanzania A) Wall-to-wall: n/a
City: Kigoma Selected Sites: Area of Interest defined with end-users
Area km² Core Urban: 84 B) Sampling based: n/a
Time Period - Update Frequency
A) Baseline Year(s): B) Update Frequency
2006 and 2016 2 points in time
Comments: None
Geographic Reference System
WGS84 UTM Zone 35S
Mapping Classes and Definitions
Single date
0 Non-urban green area
1 Urban green area
Change product
0 Non-urban green area
1 Permanent urban green area
2 Loss of urban green area
3 New urban green area
Cloud and Cloud Shadow Detection and Removal
n/a
Spatial Resolution
1 m
Minimum Mapping Unit (MMU)
1 m²
Data Type & Format
Raster data in GEOTIFF
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
Urban Green Areas information product contains spatial explicit information on green areas within the urban extent. Green areas include linear green features such as river alignments, hedges and trees, as well as public parks, private gardens, forested areas, etc. Urban Green Areas & Change dataset is based on Very High Resolution (VHR) satellite imagery by means of automated classification processing techniques.
N/A - V Q Visually and quantitatively/qualitatively checked
- N/A Not applicable
Readability (1) Check readability of all required input data. Can the data be stored again?
Header / Metadata (2) Check Image Header Information and/or Metadata for completeness / distinctive features.
Data Format (3) For digital data, please give file format (e.g. *.tiff, *.shp).
Data Type (4) Please specify the type of the data (e.g. raster, vector or analogue).
Bit Depth (5) Please give pixel depth and sign of raster data (e.g. 8 bit unsigned integer).
Dynamic Range (6) Check dynamic range of all image bands. Visual check of dynamic range should be accompanied by histograms and statistics.
Dropped Lines & Artefacts (7) Check Image for dropped lines and other artefacts. If such occurs, please give description of extent and influence in the Comments section.
Methodology of in situ or reference/auxiliary data sampling scheme should be outlined. In case of sample plots, also state how the plot positions have been determined (e.g. from GPS measurements, topographic maps, terrestrial triangulation, EO data, etc.) and how the sampling grid was established.
Positional Accuracy (11)
Positional accuracy of collected in situ data should be given. For reference/auxiliary data it MUST be given. If unknown, the data’s use must be explained. For DEM or other data with 3D information please specify both vertical and horizontal Positional Accuracy. For analogue data (e.g. maps) try to give approximate accuracy related to mapping scale.
Completeness (12) Data should be checked for spatial/temporal/content gaps.
Model / Algorithm (13)
Give the name of the software and its version. Specify the software module/algorithm used for: a) geometric correction, e.g., Polynomial and its degree, Rational Functions, Thin Plate Spline, etc. b) classification, e.g., ISODATA, Maximum Likelihood, Neural Networks, etc.
Always specify completely, i.e. at minimum the Projection (+Zone, if applicable), Spheroid / Ellipsoid, Map Datum. Give additional information if necessary to unambiguously define the reference system.
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
Give the number, distribution and RMS of used Ground Control Points (as average per scene) as obtained from the Geometric Correction, in meters [m]. Optionally, also RMSx and RMSy may be given. Distribution of GCPs should be attached as a snapshot, or described in the Comments section.
Tie Points (TPs) (18)
In case of mosaicking, give the number of used Tie Points and their total RMS (as average per scene) as obtained from the Geometric Correction, in meters [m]. Optionally, also RMSx and RMSy may be given. Distribution of TPs should be attached as a snapshot, or described in the Comments section.
Check Points (CPs) (19)
The real measure of positional accuracy and the only measure which should be examined as to its Acceptable Range. Give the independent Check Points’ total RMS (as average per scene) as obtained from the Geometric Correction, in meters [m]. Optionally, also RMSx and RMSy may be given. Distribution of CP's should be attached as a snapshot, or described in the Comments section.
Resampling Method (20) Specify, if / which resampling algorithm has been used (e.g. NN=Nearest Neighbour, BIL=Bilinear Interpolation, CC=Cubic Convolution).
Sampling unit (21) Specify sampling unit of Accuracy Assessment as POINT, FRAME, POLYGON and how it is treated (e.g. pixel center, polygon centre, etc.).
Sampling Design (22) SYST=Systematic, RAND=Random, STRAT=Stratified, SBCLASS=Stratified by class, SBAREA=Stratified by area
Sample exclusion criteria (23)
Describe which criteria you apply for sampling point selection resp. exclusion of certain points. For example, if the point is too close to a class boundary (less than 1 pixel), it is excluded. If the selected sample point is not representative of the class , it is excluded. For these reasons, it is recommended to oversample by 10% to compensate for sample point exclusion.
Thematic Accuracy (24)
Provide a detailed description of the Accuracy Assessment results in the form of error matrices showing commission and omission errors, user’s , producer’s and overall accuracies and other measures of Thematic Accuracy, as the confidence level (usually fixed at 95%) and the respective confidence interval, at least for the overall accuracy. If classification is done in a phased approach, e.g. if Forest Area and subsequently Forest Type are mapped, independent reports have to be produced.
Completeness of Coverage (25) State whether the coverage is limited to a subset, or portion of the final product.
Completeness of Classification (26)
State whether classification was constrained to a subset, or portion of the final product.
Completeness of Verification (27)
State whether verification applied to lineage, positional, or thematic accuracy is constrained to a subset, or portion of the final product.
Lineage (29) Definition (INSPIRE 2015): Lineage is “a statement on process history and/or overall quality of the spatial data set. Where appropriate it may include a statement whether the data set has been validated or quality assured, whether it is the official version (if multiple versions exist), and whether it has legal validity. The value domain of this element is free text.”
Source(30) Definition (INSPIRE, 2015): “This is the description of the organisation responsible for the establishment, management, maintenance or distribution of the resource. This description shall include: name of the organisation and contact email address.”
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
Product 1 (28) Planned and Unplanned Settlement Areas 2006 and 2016
Abstract
The product Planned and Unplanned Settlements contains spatial explicit information on the two settlement types within the core urban area. The distinction into the two settlement types is restricted to the residential area (LULC class 11), and to commercial areas (LULC class 1211) which also have a residential component. The Planned and Unplanned Settlements, and Change dataset is developed, based on Very High Resolution (VHR) satellite imagery by means of visual interpretation.
Service / Product Specifications
Area Coverage
Country: Tanzania A) Wall-to-wall: n/a
City: Kigoma Selected Sites: Area of Interest defined with end-users
Area km² Core Urban: 85 B) Sampling based: n/a
Time Period - Update Frequency
A) Baseline Year(s): B) Update Frequency
2006 and 2016 2 points in time
Comments: None
Geographic Reference System
WGS84 UTM Zone 35S
Mapping Classes and Definitions
Single date
Planned settlements
Unplanned settlements
Change product
No change in planned settlement area
No change in unplanned settlement area
Expansion of planned settlement area
Expansion of unplanned settlement area
Decrease of planned settlement area
Decrease of unplanned settlement area
Unplanned to planned settlement area
Cloud and Cloud Shadow Detection and Removal
n/a
Spatial Resolution
n.a. (Product provided as Shapefile)
Minimum Mapping Unit (MMU)
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
Product 1 (28) Planned and Unplanned Settlement Areas 2006 and 2016
Abstract
The product Planned and Unplanned Settlements contains spatial explicit information on the two settlement types within the core urban area. The distinction into the two settlement types is restricted to the residential area (LULC class 11), and to commercial areas (LULC class 1211) which also have a residential component. The Planned and Unplanned Settlements, and Change dataset is developed, based on Very High Resolution (VHR) satellite imagery by means of visual interpretation.
N/A - V Q Visually and quantitatively/qualitatively checked
- N/A Not applicable
Readability (1) Check readability of all required input data. Can the data be stored again?
Header / Metadata (2) Check Image Header Information and/or Metadata for completeness / distinctive features.
Data Format (3) For digital data, please give file format (e.g. *.tiff, *.shp).
Data Type (4) Please specify the type of the data (e.g. raster, vector or analogue).
Bit Depth (5) Please give pixel depth and sign of raster data (e.g. 8 bit unsigned integer).
Dynamic Range (6) Check dynamic range of all image bands. Visual check of dynamic range should be accompanied by histograms and statistics.
Dropped Lines & Artefacts (7) Check Image for dropped lines and other artefacts. If such occurs, please give description of extent and influence in the Comments section.
Methodology of in situ or reference/auxiliary data sampling scheme should be outlined. In case of sample plots, also state how the plot positions have been determined (e.g. from GPS measurements, topographic maps, terrestrial triangulation, EO data, etc.) and how the sampling grid was established.
Positional Accuracy (11)
Positional accuracy of collected in situ data should be given. For reference/auxiliary data it MUST be given. If unknown, the data’s use must be explained. For DEM or other data with 3D information please specify both vertical and horizontal Positional Accuracy. For analogue data (e.g. maps) try to give approximate accuracy related to mapping scale.
Completeness (12) Data should be checked for spatial/temporal/content gaps.
Model / Algorithm (13)
Give the name of the software and its version. Specify the software module/algorithm used for: a) geometric correction, e.g., Polynomial and its degree, Rational Functions, Thin Plate Spline, etc. b) classification, e.g., ISODATA, Maximum Likelihood, Neural Networks, etc.
Always specify completely, i.e. at minimum the Projection (+Zone, if applicable), Spheroid / Ellipsoid, Map Datum. Give additional information if necessary to unambiguously define the reference system.
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
Give the number, distribution and RMS of used Ground Control Points (as average per scene) as obtained from the Geometric Correction, in meters [m]. Optionally, also RMSx and RMSy may be given. Distribution of GCPs should be attached as a snapshot, or described in the Comments section.
Tie Points (TPs) (18)
In case of mosaicking, give the number of used Tie Points and their total RMS (as average per scene) as obtained from the Geometric Correction, in meters [m]. Optionally, also RMSx and RMSy may be given. Distribution of TPs should be attached as a snapshot, or described in the Comments section.
Check Points (CPs) (19)
The real measure of positional accuracy and the only measure which should be examined as to its Acceptable Range. Give the independent Check Points’ total RMS (as average per scene) as obtained from the Geometric Correction, in meters [m]. Optionally, also RMSx and RMSy may be given. Distribution of CP's should be attached as a snapshot, or described in the Comments section.
Resampling Method (20) Specify, if / which resampling algorithm has been used (e.g. NN=Nearest Neighbour, BIL=Bilinear Interpolation, CC=Cubic Convolution).
Sampling unit (21) Specify sampling unit of Accuracy Assessment as POINT, FRAME, POLYGON and how it is treated (e.g. pixel center, polygon centre, etc.).
Sampling Design (22) SYST=Systematic, RAND=Random, STRAT=Stratified, SBCLASS=Stratified by class, SBAREA=Stratified by area
Sample exclusion criteria (23)
Describe which criteria you apply for sampling point selection resp. exclusion of certain points. For example, if the point is too close to a class boundary (less than 1 pixel), it is excluded. If the selected sample point is not representative of the class , it is excluded. For these reasons, it is recommended to oversample by 10% to compensate for sample point exclusion.
Thematic Accuracy (24)
Provide a detailed description of the Accuracy Assessment results in the form of error matrices showing commission and omission errors, user’s , producer’s and overall accuracies and other measures of Thematic Accuracy, as the confidence level (usually fixed at 95%) and the respective confidence interval, at least for the overall accuracy. If classification is done in a phased approach, e.g. if Forest Area and subsequently Forest Type are mapped, independent reports have to be produced.
Completeness of Coverage (25) State whether the coverage is limited to a subset, or portion of the final product.
Completeness of Classification (26)
State whether classification was constrained to a subset, or portion of the final product.
Completeness of Verification (27)
State whether verification applied to lineage, positional, or thematic accuracy is constrained to a subset, or portion of the final product.
Lineage (29) Definition (INSPIRE 2015): Lineage is “a statement on process history and/or overall quality of the spatial data set. Where appropriate it may include a statement whether the data set has been validated or quality assured, whether it is the official version (if multiple versions exist), and whether it has legal validity. The value domain of this element is free text.”
Source(30) Definition (INSPIRE, 2015): “This is the description of the organisation responsible for the establishment, management, maintenance or distribution of the resource. This description shall include: name of the organisation and contact email address.”
Earth Observation for Sustainable Doc. No.: City-Operations Report
Development – Urban Project Issue/Rev-No.: 3.0
Annex 2 to EO4SD-Urban Kigoma City Operations Report Page 4
This page is intentionally left blank!
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
Product 1 (28) Population Distribution and Density Change 2005 and 2015
Abstract
The Population Distribution and Density Change product contains spatial explicit information about population distribution changes at a certain degree, within the Core Urban Districts of Kigoma. The following eight change classes are identified, based on a frequency distribution analysis of the changes that occur: Unchanged Population Distribution; Up to –100% decrease; Up to 200% increase; 201% - 400% increase; 401% - 600% increase; 601% - 800% increase; 801% - 1000% increase; More than 1000% increase. The Population Distribution and Density Change product is estimated for all objects which belong to the residential class (LULC class 11), using 5 different data sources: LULC Product, WorldPop data (2015) with a spatial resolution of 100m, official population census data from 2002 and 2012, Soil Sealing layer and Administrative Boundaries.
Service / Product Specifications
Area Coverage
Country: Tanzania A) Wall-to-wall: n/a
City: Kigoma Selected Sites: Area of Interest defined with end-users
Area km² Core Urban: 85 km2 B) Sampling based: n/a
Time Period - Update Frequency
A) Baseline Year(s): B) Update Frequency
2005 and 2015 2 points in time
Comments: None
Geographic Reference System
WGS84 UTM Zone 35S
Mapping Classes and Definitions
Single date
1 Population Density between 0 – 150 inhabitants/ km2
2 Population Density between 151 – 300 inhabitants/ km2
3 Population Density between 301 – 600 inhabitants/ km2
4 Population Density between 601 – 1500 inhabitants/ km2
5 Population Density between 1501 – 3000 inhabitants/ km2
6 Population Density between 3001 – 5000 inhabitants/ km2
7 Population Density between 5001 – 9000 inhabitants/ km2
8 Population Density between 9001 – 18000 inhabitants/ km2
9 Population Density between 18001 - 41000 inhabitants/ km2
Change product
1 Unchanged Population Distribution
2 Up to -100% decrease
3 Up to 200% increase
4 201% - 400% increase
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
Lineage(29): The Urban Land Use/Land Cover Baseline Product and the derived Soil Sealing layer are used to interpolate the population distribution and density for each residential unit, within the Core Urban Districts of Kigoma.
Lineage(29): The WorldPop Dataset is used to interpolate the current population data for each residential object in the Core Urban Districts of Kigoma.
Source (30): Downloaded from WorldPop: http://www.worldpop.org.uk/
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
2017-09-01 n.a. 2012-08-26 Tanzania National coverage
Document
pdf n.a. n.a. n.a. n.a. n.a.
Lineage(29): The 2012 Population and Housing Census report is used to project the number of inhabitants per district, and to adjust the population residuals in the current Population Distribution and Density product.
Source (30): Downloaded from United Republic of Tanzania, Ministry of East African Cooperation: http://meac.go.tz/
Dataset 5 (14)
Report Date of Receipt from Client
Metadata (2)
Reference Mapping Date / Date of Creation
Geographic Area/- City / Region / Country
Area Coverage
Data Type (4)
Data Format (3)
Projection / Spheroid (16)
Positional Accuracy (11)
No. of Classes
Thematic Accuracy
Availability of Class Definitions
2002 POPULATION AND HOUSING CENSUS
2017-09-01 Yes
(The World Bank)
2002-08-22 Tanzania National coverage
Document
pdf n.a. n.a. n.a. n.a. n.a.
Lineage(29): The 2002 Population and Housing Census report is used to interpolate the population data in the historic Population Distribution and Density product.
Source (30): Downloaded from the IPUMS – International Website: https://international.ipums.org/international/
Dataset 6 (14)
Administrative
Boundaries
Date of Receipt from Client
Metadata (2)
Reference Mapping Date / Date of Creation
Geographic Area/- City / Region / Country
Area Coverage
Data Type (4)
Data Format (3)
Projection / Spheroid (16)
Positional Accuracy (11)
No. of Classes
Thematic Accuracy
Availability of Class Definitions
TZA_adm3 2017-09-01 Yes
2015 Tanzania
National coverage
Vector *.shp WGS 84 unknown n.a. unknown Yes
Lineage(29): This shapefile provides with information, regarding the administrative boundaries at a district level in Tanzania.
Source (30): Downloaded from Global Administrative Areas: http://www.gadm.org/country
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
The estimated population per district corresponds to the 2012 population and housing census data.
In a disaggregation procedure, such as the one herein described, the outcome of the process is never less accurate than the original source data. By disaggregating
numerical data from one coarse geometry to a finer geometry, we always gain detail and approximate ground truth without the risk of deteriorating the source
information. The degree to which the disaggregation approximates reality, however, varies greatly, and it depends chiefly on: 1) the quality of the ancillary data
and 2) the appropriateness of the disaggregation algorithm and its parameters.
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
Product 1 (28) Population Distribution and Density Change 2005 and 2015
Abstract
The Population Distribution and Density Change product contains spatial explicit information about population distribution changes at a certain degree, within the Core Urban Districts of Kigoma. The following eight change classes are identified, based on a frequency distribution analysis of the changes that occur: Unchanged Population Distribution; Up to –100% decrease; Up to 200% increase; 201% - 400% increase; 401% - 600% increase; 601% - 800% increase; 801% - 1000% increase; More than 1000% increase. The Population Distribution and Density Change product is estimated for all objects which belong to the residential class (LULC class 11), using 5 different data sources: LULC Product, WorldPop data (2015) with a spatial resolution of 100m, official population census data from 2002 and 2012, Soil Sealing layer and Administrative Boundaries.
N/A - V Q Visually and quantitatively/qualitatively checked
- N/A Not applicable
Readability (1) Check readability of all required input data. Can the data be stored again?
Header / Metadata (2) Check Image Header Information and/or Metadata for completeness / distinctive features.
Data Format (3) For digital data, please give file format (e.g. *.tiff, *.shp).
Data Type (4) Please specify the type of the data (e.g. raster, vector or analogue).
Bit Depth (5) Please give pixel depth and sign of raster data (e.g. 8 bit unsigned integer).
Dynamic Range (6) Check dynamic range of all image bands. Visual check of dynamic range should be accompanied by histograms and statistics.
Dropped Lines & Artefacts (7) Check Image for dropped lines and other artefacts. If such occurs, please give description of extent and influence in the Comments section.
Methodology of in situ or reference/auxiliary data sampling scheme should be outlined. In case of sample plots, also state how the plot positions have been determined (e.g. from GPS measurements, topographic maps, terrestrial triangulation, EO data, etc.) and how the sampling grid was established.
Positional Accuracy (11)
Positional accuracy of collected in situ data should be given. For reference/auxiliary data it MUST be given. If unknown, the data’s use must be explained. For DEM or other data with 3D information please specify both vertical and horizontal Positional Accuracy. For analogue data (e.g. maps) try to give approximate accuracy related to mapping scale.
Completeness (12) Data should be checked for spatial/temporal/content gaps.
Model / Algorithm (13)
Give the name of the software and its version. Specify the software module/algorithm used for: a) geometric correction, e.g., Polynomial and its degree, Rational Functions, Thin Plate Spline, etc. b) classification, e.g., ISODATA, Maximum Likelihood, Neural Networks, etc.
Always specify completely, i.e. at minimum the Projection (+Zone, if applicable), Spheroid / Ellipsoid, Map Datum. Give additional information if necessary to unambiguously define the reference system.
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
Give the number, distribution and RMS of used Ground Control Points (as average per scene) as obtained from the Geometric Correction, in meters [m]. Optionally, also RMSx and RMSy may be given. Distribution of GCPs should be attached as a snapshot, or described in the Comments section.
Tie Points (TPs) (18)
In case of mosaicking, give the number of used Tie Points and their total RMS (as average per scene) as obtained from the Geometric Correction, in meters [m]. Optionally, also RMSx and RMSy may be given. Distribution of TPs should be attached as a snapshot, or described in the Comments section.
Check Points (CPs) (19)
The real measure of positional accuracy and the only measure which should be examined as to its Acceptable Range. Give the independent Check Points’ total RMS (as average per scene) as obtained from the Geometric Correction, in meters [m]. Optionally, also RMSx and RMSy may be given. Distribution of CP's should be attached as a snapshot, or described in the Comments section.
Resampling Method (20) Specify, if / which resampling algorithm has been used (e.g. NN=Nearest Neighbour, BIL=Bilinear Interpolation, CC=Cubic Convolution).
Sampling unit (21) Specify sampling unit of Accuracy Assessment as POINT, FRAME, POLYGON and how it is treated (e.g. pixel center, polygon centre, etc.).
Sampling Design (22) SYST=Systematic, RAND=Random, STRAT=Stratified, SBCLASS=Stratified by class, SBAREA=Stratified by area
Sample exclusion criteria (23)
Describe which criteria you apply for sampling point selection resp. exclusion of certain points. For example, if the point is too close to a class boundary (less than 1 pixel), it is excluded. If the selected sample point is not representative of the class , it is excluded. For these reasons, it is recommended to oversample by 10% to compensate for sample point exclusion.
Thematic Accuracy (24)
Provide a detailed description of the Accuracy Assessment results in the form of error matrices showing commission and omission errors, user’s , producer’s and overall accuracies and other measures of Thematic Accuracy, as the confidence level (usually fixed at 95%) and the respective confidence interval, at least for the overall accuracy. If classification is done in a phased approach, e.g. if Forest Area and subsequently Forest Type are mapped, independent reports have to be produced.
Completeness of Coverage (25) State whether the coverage is limited to a subset, or portion of the final product.
Completeness of Classification (26)
State whether classification was constrained to a subset, or portion of the final product.
Completeness of Verification (27)
State whether verification applied to lineage, positional, or thematic accuracy is constrained to a subset, or portion of the final product.
Lineage (29) Definition (INSPIRE 2015): Lineage is “a statement on process history and/or overall quality of the spatial data set. Where appropriate it may include a statement whether the data set has been validated or quality assured, whether it is the official version (if multiple versions exist), and whether it has legal validity. The value domain of this element is free text.”
Source(30) Definition (INSPIRE, 2015): “This is the description of the organisation responsible for the establishment, management, maintenance or distribution of the resource. This description shall include: name of the organisation and contact email address.”