Recently, Tibetan plateau (TP) has become a hot area of climate change research. And Land Surface Temperature (LST) is one of key factors in the research. In order to get a long time-series, high spatial resolution and high accuracy LST dataset, we carried out analysis of influence essential factor of LST retrieval from AVHRR oriented Tibetan plateau area. First, choose MODTRAN5.2 to simulate the impact of land surface, atmospheric, geometric factors on bright temperatures of channel 4 and channel 5 for special features of TP using stand atmospheric models. Result showed that emissivity, boundary temperature, water vapor amount and view zenith angle were the principal elements of bright temperature. Second an improved algorithm from Wanz-Dozier split window model was established considering these factors. At last, differences between LST retrieval result considering different factors were given.
The objective of this study was to integrate the advantages of multi-source remote sensing to
monitor dust storms and better discriminate between regions where dust storms occur. Firstly, The
traditional evidence theory algorithm was improved by not only considering the certainty of the
evidence, but also considering the average level of support for the subsets of evidence in the
discrimination framework in the process of evidence combination by reducing the conflict between
synthesized data. Then the algorithm is applied to the FY-2E infrared difference dust index (IDDI)
and the FY-3A dust strength index (DSI) to categorize the study region as either a dust storm area,
non-dust storm area, or possible dust storm area. Finally, the result was validated and analyzed using
the monitored data from ground stations. Both the accuracy and reliability of the dust monitoring
results were considerably improved using our method.
In order to analyze the changes of underlay features in the urbanization process and their impact on characteristics of
spatial distribution of LST in Beijing-Tianjin-Tangshan (Jingjintang) metropolitan region, MODIS LST Product and
SPOT VGT NDVI Product are collected and their statistical features are calculated, then LST, Land cover/use (LULC)
and their relationship are studied in detail. The main conclusions drawn from this research are as following: (1) there is
different LST in different land surface with different land cover type. LST in urban and built-up region is maximum,
LST in water region is minimum. And there is a negative correlation between NDVI and LST. The higher NDVI value
is, the lower LST value is. (2) In Jingjintang region, there is higher NDVI and lower LST in 2006 than 2002, about
38.56% and 18.10% in turn. About in the single city, there are different change values. The change value of LST presents
Beijing > Tangshan > Tianjin, (3) Comparison 2006 to 2002, the surface of Jingjintang region is dominated by class with
both NDVI and LST increase and the percentage is 41.79%. The percentage of the class with NDVI increase and LST
decrease is about 29.23%, 29.9.% and 39.40% in Jingjintang region, Beijing and Tianjin. And that with NDVI decrease
and LST increase is about 20.57%, 14.75% and 12.12% in Jingjintang region, Beijing and Tianjin. Tangshan urban
region is dominated by the class with NDVI decrease and LST increase, up to 35.54%. On the other hand, the percentage
of NDVI increase and LST increase is about 26.36%. The different NDVI and LST change trend shown in different
regions may result in their different urbanization level.
Wetlands are among the most important ecosystems on Earth and the major feature of the landscape in almost all parts of
the world, it provides a wide range of ecological regulation services and is very important to the atmospheric humidity in
a region. In order to recognize the influence of wetland on the atmospheric humidity, this paper takes Beijing-Tianjin-
Tangshan region as the study object and quantitatively analyzes their relationship with the help of MODIS satellite
images, monitoring data observed with the ground weather stations, wetland data, and other land surface data. This
research finds that (1) Globe temporal feature of atmospheric humidity is closed to the seasonal change and alternation.
(2) Globe spatial feature of atmospheric humidity is influence by the landform, land cover type. Because study region
adjoins to sea, it is influenced by the distance away from coastline. (3) Wetland's spatial distribution influences with the
spatial distribution of atmospheric humidity. With the distance away from wetland is bigger and bigger, atmospheric
humidity is less and less. However, its change trend is mild.
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