Image Processing Reference
In-Depth Information
development to neighboring land). The distinctly defined spatial structure with the
ordered distribution of roads, buildings, etc., clearly distinguishes planned areas
from most unplanned areas.
Unplanned development often commences with land occupation and the hasty
erection of makeshift buildings, which are gradually improved or replaced by more
permanent structures as resources allow and provided there is no expectation of
demolition (Fig. 5.2 ). Over time, settlements are formed, and as the time passes
they may be upgraded through the provision of roads and other infrastructure that
require the demolition of some buildings.
Different types of informal development are found in different contexts. In Peru
and other Latin American countries, well-organized invasion (occupation) of public
land by hundred of families has been known to occur overnight (Hardoy and
Satterthwaitec 1989 ). In contrast, most cities in Tanzania experience extensive
incremental, unplanned development (Sliuzas and Brussel 2000 ; Sliuzas 2001,
2004 ). For example, in Dar es Salaam informal settlements expanded by 5,600 ha
(9% p.a.) while planned residential areas expanded by only 780 ha (2% p.a.)
between 1992 and 1998, respectively (Sliuzas 2004 ).
In well-organized land invasions, care is taken to create an informal plan with a
clear road pattern and plot demarcation as a means of decreasing the risk of demoli-
tion. This strategy has had some success. For urban monitoring purposes using
remote sensing, it is essential to be aware of such contextual information as infor-
mal settlements in Peru may appear much like formally planned developments on
images. Local knowledge therefore remains an important element in the successful
extraction of urban data from remote sensing images.
Area-Based and Object-Based Approaches to Urban
Data Extraction
Traditionally, urban remote sensing applications have focused on classifying areas
of homogeneous land cover (surface material) or land use (function) (see related
discussion in Chapters 4 and 6). However, cities are so complex that large areas
of homogeneous land cover often cannot be readily detected, even when using
high-resolution images. Most urban land uses are associated with surfaces that are
characterized by combinations of various kinds of land cover: buildings, vegetation,
roads, water, bare soil, etc. It is therefore not possible
to relate one specific form of land use to one specific
form of land cover (see Chapter 4 in this volume). The
many-to-many relationship between land cover and
land use (Gorte 1998 ; Weber 2001 ; Ehlers et al. 2002 )
leads to the poor performance of standard automated
pixel-based classifications of urban land use.
The identification and delineation of land uses based on visual interpretation of
remote sensing images by trained human interpreters, who supplement the spatial
there exists a many-
to-many relationship
between land cover
and land use in cities
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