Abstract: One of the most important tasks in urban remote
sensing is the detection of impervious surfaces (IS), such as roofs and
roads. However, detection of IS in heterogeneous areas still remains
one of the most challenging tasks. In this study, detection of concrete
roof using an object-based approach was proposed. A new rule-based
classification was developed to detect concrete roof tile. This
proposed rule-based classification was applied to WorldView-2
image and results showed that the proposed rule has good potential to
predict concrete roof material from WorldView-2 images, with 85%
accuracy.
Abstract: Land surface temperature (LST) is an important
parameter to study in urban climate. The understanding of the
influence of biophysical factors could improve the establishment of
modeling urban thermal landscape. It is well established that climate
hold a great influence on the urban landscape. However, it has been
recognize that climate has a low priority in urban planning process,
due to the complex nature of its influence. This study will focus on
the relatively cloud free Landsat Thematic Mapper image of the study
area, acquired on the 2nd March 2006. Correlation analyses were
conducted to identify the relationship of LST to the biophysical
factors; vegetation indices, impervious surface, and albedo to
investigate the variation of LST. We suggest that the results can be
considered by the stackholders during decision-making process to
create a cooler and comfortable environment in the urban landscape
for city dwellers.