Aerial Photograph and Satellite Image Classification
Classification
of remotely gathered data, either satellite imagery or aerial
photographs, is the foundation for a host of the major spatial analysis
components in the Nang Rong Project. While raw satellite imagery
and aerial photography afford the viewer a useful historical overview
of the region, the generation of LULC classifications allows for the
quantification and tracking of landscape changes through time.
Using a variety of supervised and unsupervised methodologies, the Nang
Rong Project has generated a robust time series of classified
images. Moreover, in order to faithfully and accurately characterize the landscape, both the land use and land cover classes were carefully researched and selected, and, finally, tested for accuracy.
Satellite Imagery Classification
Following the generation of Land
Use and Land Cover classifications for the catalog of images, a number
of analyses are enabled. LULC history is of vital importance in
understanding and explaining the complex history of Nang Rong.
This is achieved in a variety of ways. Calculation of the areas
of various LULC classes on a per year basis paints a picture about the
broad changes in the region. Similarly, the structure and composition
of the landscape is performed through the use of pattern metrics, which
helps in understanding the ways that human development is fundamentally
altering and shaping both the make-up and configuration of LULC in Nang
Rong. Another important analysis arising following LULC
classification is the change detection. In performing change
detections, the "From-To" (the beginning and ending image in a time series) LULC values are tracked at a variety of time
steps: intra-annual, inter-annual, decadal, as well as any other
temporal intervals that are of interest to the researchers. A
related means of tracking the dynamics of Nang Rong is through pixel
history and trajectory work, whereby the classifications are analyzed at
the pixel level in order to see how change is manifested at the micro
level. Cellular automata (CA) modeling, whereby the evolution of
the landscape is predicted from the generation of rules of
growth, is another avenue of research that is facilitated through
the time series of classifications. LULC classifications are also
effectively coupled with the rich social survey datasets in order to
examine the determinants of LULC, as well as to investigate the
correspondence between the survey data and the classifications.
Aerial Photograph Classification
The
classification of aerial photographs is also an invaluable process in
the generation of information. While satellite classifications
have tended to take place at the regional level, air photo
classification for the Nang Rong Project has focused instead on smaller
intensive study areas. This divergence in classification
approaches is mostly an artifact of the highly intensive, manual
interpretation that is required for the classification of
photos. In the processing of satellite imagery, on the
other hand, it is no more difficult to process entire scenes as it is
to process a small area of interest. Another major difference
between air photos and satellite images in the approach to
classification results from the nature of the data. Since air
photos lack spectral information, their classification is based on the
texture, pattern, lightness/darkness, and context of the features on
the photos. As such, none of the higher level image
processing that is utilized in the classification of satellite images
is applied to air photo classification.
Since the air photos have a
richer temporal depth, the analyses that stem from the classifications
help to more accurately characterize the development and LULC history
of Nang Rong. Calculations of LULC areas are made at
decadal intervals, helping to chart the evolution of the region.
Likewise, pattern metrics aid in understanding the fundamental changes
in LULC structure and composition that are occuring on the
landscape.
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