Information and Knowledge Management using ArcGIS ModelBuilder

Teresa Santos, Ségio Freire and José António Tenedório
the generalization of the soil polygons using the aggregate polygons tool from ArcGIS. The
parameters were merging polygons that distance 2 m, and considering areas grater or equal to 100
m 2 .
The map of impervious areas includes a wide range of materials, some of which have very different
spectral properties (e.g., pavement, concrete, roof tiles, etc.). The 1 st level class “Impervious Surface”
corresponds to the land surface after masking out the “Vegetation”, “Soil”, “Shadow”, and “Water”
classes. In the 2 nd level of the nomenclature, three classes were distinguished: “Buildings”, “Roads”
and “Others”, based on the pansharp image and the nDSM.
“Buildings” were extracted in three stages, considering different roof materials. For the red tiles, the
parameters used were Manhattan representation, width 7, and 75 pixels of aggregation. For the
darker roof materials and for the brighter ones, Manhattan representation, width 7, and 100 pixels of
aggregation were the selected parameters. All learning’s were followed by remove clutter or add
missing data iterations to reach the final “Buildings” class. The last step included generalizing the
building polygons using the same parameters as for the “Soil” class: merging polygons that distance 2
m, and considering areas grater or equal to 100 m 2 .
The class “Roads” was extracted in three independent processes, using different parameters. For the
larger roads, Bull’s Eye 2, width 25, and 1100 pixels of aggregation were considered. For the narrow
roads, Bull’s Eye 2, width 19, and 500 pixels of aggregation were considered. The remaining asphalt
pavement was extracted with Bull’s Eye 2, width 25, and 500 pixels of aggregation. These three
layers were then merged to produce the “Roads” class. The final layer was obtained by
generalization, using 2 m as merging distance and 100 m 2 as minimum area.
The “Other impervious surfaces” (like sidewalks or railroads), were the remaining areas within the
“Impervious surface” class, after masking out the “Buildings” and “Roads” classes.
Figure 2 shows the final Land Cover Map produced for 2008 (LCM2008), for the city of Lisbon, using
satellite data.
3.3 Accuracy assessment
The thematic accuracy of the LCM2008 was evaluated based on a stratified random sampling. For
each strata (i.e., each level 2 land cover class), 100 random points were analyzed through visual
analysis of the imagery and ancillary data. From the 700 samples, 2 were excluded from the
evaluation because it was not possible to correctly identify the class. The samples were then used to
build the error matrix and to derive thematic accuracy indexes. From this analysis, we conclude that
the LCM2008 has an Overall Accuracy of 89% and a KHAT statistic of 87%, in the most detailed level.
These values indicate great agreement between the reference data and the classified map.
4. Building urban indicators
Studies on impacts of urbanization, responses to natural and man-made disasters, vulnerability
analysis or housing conditions, all require updated land cover information. In this case study, we
propose a set of indicators, strictly assessed from VHR imagery.
From LCM2008, two variables are extracted for building the proposed urban indicators: the area
(Table 2) and the spatial distribution of each land cover class in the city (Figure 2). Using the area of
each land cover, urban environmental indicators strictly based on VHR imagery can be assessed
(Table 3).
5. Conclusions
The LCM2008 provides a detailed and cost-effective digital map of the city of Lisbon. This case study
demonstrates the utility of land cover mapping for building urban indicators for monitoring planning
actions. The mapping methodology presented, ensures that urban planners have updated data on
land cover at a regular basis. This tool can be used for monitoring the incidence of land cover change
within the city, decide on which areas of priority intervention, or assess natural resource sites for
preservation and restoration.
440