This paper present a detail processing procedure about SPOT5 image applied for vegetation type recognition, and
determines the capacity of high spatial resolution satellite image data to discriminate vegetation type in a complex
ecosystem. A high spatial resolution SPOT5 image, captured in April 2005, and coincident field data covering the Dagou
valley, was used in this analysis. Image geometric rectification and image fusion are then introduced to take prepare for
classification. Subsequently, a maximum likelihood classification algorithm was applied to the SPOT5 image data to
map the vegetation classes. Field validation and accuracy assessment are crucial to ensure the reliability of classification
results. The strategy of field work and the resulting accuracy evaluations were presented, and yielded the high
classification accuracy (overall accuracy=83.86%, Kappa=80.23%). The result showed that the information on
vegetation types can be mapped effectively from high spatial resolution satellite image data.
Wetland is a very important land resource and a natural resource, which has many functions like forest, cropland, and ocean, and has close relationship with human being. Northeast China has largest wetland distribution and richest wetland types in China. However, under economic interests driving, wetland in this area is exploited blindly, which causes wetland's functions and benefits decreasing. With the involvement of RS (Remote Sensing) and computer technology, we can monitor wetlands dynamically, which decreases labor intensity of field investigation. Although MODIS, loaded on Terra of new generation EOS, has a coarser spatial resolution than TM, it has higher spatial, temporal, and spectral resolution than AVHRR, which make it capabile to monitor wetland timely and dynamically. The article takes Songnen Plain as study area, uses multi-temporal MODIS-NDVI data to study wetland distribution, and makes validation of result. The research indicates that using multi-temporal MODIS-NDVI data is capable to get wetland distribution, and monitor wetland change effectively.
In order to remove MODIS bowtie effect, an analytical algorithm is proposed, which is based on solid geometry projection and requires no ephemeris information. The geometry projection model is established from the parameters of MODIS platform and the amount of overlapping pixels is quantified as a function of the instantaneous scanning angle. Lookup table is utilized to guide the deletion of overlapping pixels and improve efficiency, and cubic spline interpolation is applied to subpixelly restore data following their profile. Resampling is followed to generate integral pixel coordinates. The border incontinuity problem that occurs due to the gap between different swaths is solved by introducing of a special blocking method. The validity of out algorithm is verified by comparing with three other Non-ephemeris algorithms, and the result shows that not only the bowtie effect within a single swath is effectively removed, the incontinuity caused by conventional pixel grouping method is mostly well eliminated.