C03

Research Abstracts on Spatial Information ScienceCSIS DAYS 2016

Mapping urban land use in Lusaka city, Zambia: an expert-based approachMatamyo Simwanda
Graduate School of Life and Environment Sciences, University of Tsukuba
Email: <matamyo@gmail.com>
Based on the assumption that built-up pixels
representing a particular ULU category in 2010
would still represent the same category in thepreceding years (2000 and 1990) if it existed, thesame ULU polygons were used to extract andreclassify the built-up pixels from Landsat Imagery
into their respective ULU categories for all the threetime points (1990, 2000 and 2010).
(3) Results: Figure 1 shows the ULU maps for 1990,
2000 and 2010 while Figure 2 shows the total ULU
area and the percentage of each ULU category in thethree time points. The classification accuracies ofULU maps achieved in this study ranged from 91.2%
to 92.8%, above the recommended minimum
standard of 85%.
(4) Conclusion: An approach capable of overcoming thechallenges and limitations encountered when using
freely available remote sensing datasets and available
classification methods has been proposed. Overall,the proposed approach shows good potential for ULU
classification at local and regional scales. The study
has also revealed some interesting results relevant toland use policy makers and urban development
planners.

Category percentage (%)(1) Purpose: The purpose of this study was to map theurban land use (ULU) of Lusaka city over time(1990-2000-2010). Dividing the built-up area into
different ULU classes (residential, industrial,commercial, public etc.) still remains a challenge due
to spectral confusion in complex urban environments.
The study was aimed at developing an approachcapable of overcoming the challenges and limitations
encountered when using freely available remotesensing datasets (i.e. Landsat and Google Earthimagery) and available classification methods.
(2) Data and methods: The proposed approach in thisstudy involved, first, accurately extracting the built-up
area from three Landsat imageries (1990, 2000, and2010) using a combination of pixel-based andobject-basedclassification
techniquesplus
post-classification image control (PCIC). PCIC wasconducted to identify and correct misclassified pixels
through the integration of spatial ancillary data, visualinterpretation and object-based multi-resolution image
segmentation technique. PCIC ensured that thebuilt-up area was accurately extracted which is acrucial first step in the proposed approach for mapping
ULU. The built-up area was then divided into six ULU
classes based on the study area: (i) Unplanned HighDensity Residential (UHDR), (ii) Unplanned Low
Density Residential (ULDR), (iii) Planned HighDensity Residential (PHDR), (iv) Planned Low
Density Residential (PLDR), (v) Commercial andIndustrial (CMI) and (vi) Public Institutions and Areas(PIA). This was then followed by an expert–based
on-screen digitization of polygons representing theidentified six ULU categories through visualinterpretation using high resolution Google Earthimagery, Object-based multi-resolution image
segments, grids and close reference to detailed
georeferenced cadastral and land use data. A coverageof ULU polygons using Google Earth Imagery for thelatest date (2010) of this study with all the ULU
categories defined was produced.

35.02000

158 8201049 230.084 4199020002010Total Urban Area (Km2)

25.0
20.0
15.0
10.0
5.0
UHDR ULDR

PHDR

PLDR

CMI

PIA

Urban land use categories

Fig. 2: ULU percentage in 1990, 2000 and 2010

Fig. 1: Urban land use maps for 1990, 2000 and 2010

- 31 -1990