Conventional land use change detections with remote sensing use annual
remote sensing images because of the limitations of optical sensors that cannot
collect data in bad weather and cloudy conditions. This limits its applications in
rapidly developing areas which are cloudy, such as the Pearl River Delta in China.
These areas also need to detect land use changes in short intervals, such as on a
monthly basis, in order to monitor illegal land use changes and prevent
irreversible land use changes that may damage the environment. The objective of
the thesis is to examine short-interval land use change detection, especially the
change from agriculture to built-up areas, using RADARSAT-1 images which can
go through clouds.
This thesis firstly examines the classification of RADARSAT-1 images with
pixel-based and object-based classification methods respectively. Based on the
classification results, post-classification change detection method are conducted in
order to obtain the detailed information of land use changes for the analysis of
short-interval land use change.
Land use change detection accuracies can be improved as the number of the
RADARSAT-1 images used in land use change detection increased. More
images, which represent longer monitoring period, can obtain better results of
land use change detection. For short-interval land use changes detection, four
time periods is the maximum otherwise the period of monitoring will be too long.
Agricultural activities such as planting and harvesting have significant effects
on the monitoring of land use changes. In planting and harvesting months, the
accuracies of the land use change detection are lower than other months because
its land cover is often confused with other land uses, such as water and bare soils.
The process of construction can be considered as a three-stage process and a
combination of two land uses. However, construction sites are often confused
with vegetation and bare soil in RADARSAT-1 images because the values of
backscatter coefficients of construction sites and the two land uses are very similar.
The land cover changes during the planting and harvest seasons are often
confused with the process of construction. It is found that construction sites can
be identified with their two stages of low values of backscatter coefficients, which
is not found in the pattern curves of backscatter coefficients of other land uses.
By the comparison of the accuracies of identifying construction sites using two,
three and four RADARSAT-1 images, it is found that using three time periods can
get better accuracies which is different from the result of general land use change
detection.
This thesis does not try to evaluate land use change detection methods or find
the best method for monitoring land use changes. Instead, it focused on the
analysis of confusions caused by the time periods of land use change detection
and seasonal variation of vegetations. The main contributions of this study are
as follows: 1) it explores the use of multi-temporal RADARSAT-1 images into the
land use change detection to overcome the problems of cloudy conditions, making
short-interval land use change detection possible for areas which are often
covered by clouds; 2) pixel-based maximum likelihood method and the
object-based classification method were compared for their accuracies in land use
classification of RADARSAT-1 images; 3) it examines the optimal time periods
for land use change detection; and 4) it examines the appropriate number of
images that are needed for monitoring land use changes in different seasons in
order to obtain the best accuracies. / published_or_final_version / Urban Planning and Design / Doctoral / Doctor of Philosophy
| Identifer | oai:union.ndltd.org:HKU/oai:hub.hku.hk:10722/174358 |
| Date | January 2010 |
| Creators | Chen, Xiaoyue, 陈晓越 |
| Contributors | Yeh, AGO |
| Publisher | The University of Hong Kong (Pokfulam, Hong Kong) |
| Source Sets | Hong Kong University Theses |
| Language | English |
| Detected Language | English |
| Type | PG_Thesis |
| Source | http://hub.hku.hk/bib/B47280384 |
| Rights | The author retains all proprietary rights, (such as patent rights) and the right to use in future works., Creative Commons: Attribution 3.0 Hong Kong License |
| Relation | HKU Theses Online (HKUTO) |
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