Snow cover area is a very critical parameter for hydrologic cycle of the Earth. Furthermore, it will be a key factor for the effect of the climate change. An unbelievable situation in mapping snow cover is the existence of clouds. Clouds can easily be found in any image from satellite, because clouds are bright and white in the visible wavelengths. But it is not the case when there is snow or ice in the background. It is similar spectral appearance of snow and clouds. Many cloud decision methods are built on decision trees. The decision trees were designed based on empirical studies and simulations. In this paper a classification trees were used to build the decision tree. And then with a great deal repeating scenes coming from the same area the cloud pixel can be replaced by “its” real surface types, such as snow pixel or vegetation or water. The effect of the cloud can be distinguished in the short wave infrared. The results show that most cloud coverage being removed. A validation was carried out for all subsequent steps. It led to the removal of all remaining cloud cover. The results show that the decision tree method performed satisfied.
Marine oil pollution is one of the most serious pollutants on the damage to the contemporary marine environment, with the characteristics of a wide range of proliferation, which is difficult to control and eliminate. As a result, marine oil pollution has caused huge economic losses. The remote sensing sensors can detect and record the spectral information of sea film and background seawater. Here we chose to use 250-resolution MODIS data in the area of Dalian Xingang, China where ill spill case was happened on April.4th, 2005. Based on the image pre-processing and enhanced image processing, the spectral features of different bands were analyzed. More obvious characteristics of the spectral range of film was obtained. The oil-water contrast was calculated to evaluate the feature of oil at different spectral band. The result indicates that IR band has the maximum value of reflective. So band ratio was used between 400nm and 800nm and the original radiance images were used between 800nm and 2130nm. In order to get the most obvious images of entropy windows of different sizes were tested in order to decide the optimum window. At last, a FCM fuzzy clustering method and image texture analysis was combined for the MODIS images of the oil spill area segmentation. At last, the oil spill zone was estimated, the results were satisfied.
This paper presents a method to estimate the soil moisture using ENVISAT ASAR data in the Three Gorges Reservoir
area in China. Firstly, this study introduces a semi-empirical model for bare surface scattering and a water-cloud model
for the elimination of the impact of the vegetation cover. Secondly, a new combined roughness parameter is introduced to
describe the roughness of soil surface. Thirdly, we analyze the relationship between soil roughness and radar
backscattering coefficient and that between soil moisture content and backscattering coefficient respectively. Then the
soil moisture inversion algorithm is achieved through programming. Finally, an evident logarithmic relationship between
radar backscattering coefficient and soil moisture is presented. In conclusion, the experiments prove that the method is
fast, efficient and widely applicable.
Soil moisture is the important indicator of climate, hydrology, ecology, agriculture and other parameters of the land
surface and atmospheric interface. Soil moisture plays an important role on the water and energy exchange at the land
surface/atmosphere interface. Remote sensing can provide information on large area quickly and easily, so it is
significant to do research on how to monitor soil moisture by remote sensing. This paper presents a method to assess soil
moisture status using Landsat TM data over Three Gorges area in China based on TVDI. The potential of Temperature-
Vegetation Dryness Index (TVDI) from Landsat TM data in assessing soil moisture was investigated in this region. After
retrieving land surface temperature and vegetation index a TVDI model based on the features of Ts-NDVI space is
established. And finally, soil moisture status is estimated according to TVDI. It shows that TVDI has the advantages of
stability and high accuracy to estimating the soil moisture status.
Soil moisture plays a very vital role in slope failure events because it reduces the soil strength and increases the soil
stress. Traditional in situ surveying cannot provide enough large-scale and so long time soil moisture and precipitation
information; in addition it will takes much time and money. The Tropical Rainfall Measuring Mission (TRMM)
instrument can provides precipitation data from 1997 to present. This paper discuss a active/passive microwave remote
sensing approach to estimate soil moisture using PR and TMI data onboard the TRMM satellite. Then we develop a link
between the soil moisture developed from PR and TMI data, precipitation data and major landslide events in the Three
Gorges of the Yangtze River. Case studies in Three Gorges indicated that most reservoir area of the Three Gorges had
slope movement when soil moisture showed high values.
The Tibetan Plateau, called "the third pole" of the Earth by its highest altitude, is a very sensitive area for hydrological
cycle and climatic change. To estimate and map the snow covered areas of the Tibetan Plateau is very important for the
regional climatic change and hydrological cycle. The fractional snow cover of an image pixel is estimated with a linear
mixture approach, where the reflectance of a "mixed" pixel is represented as sum of the reflectance of each pure land
cover type weighted by their respective area proportion in the Instrument Field of View (IFOV). A method based on
linear spectral unmixing using Moderate Resolution Imaging Spectroradiometer (MODIS) data in the Tibetan Plateau
was presented and validated by Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 15m data
in this study. The change of the snow covers in the whole Tibetan Plateau was also analyzed. In the year of 2004 the area
of the snow covers increased much from October to February of the following year. However, then declined to be a
relatively small area until September of 2005. SRTM DEM data was applied to identify the relationship between snow
distribution and terrain altitude.
Snow cover area is a very critical parameter for hydrologic cycle of the Earth. Furthermore, it will be a key factor for the
effect of the climate change. Most research on estimating snow cover area is binary: pixels are verified either "snow" or
"not snow". Most pixels, however, are mixed with snow, vegetation, soil, rock or water. This paper presents a spectral
unmixing to estimate sub pixel snow cover. Firstly, a manmade selection for endmember was set up based on PCA method.
Then an automatic selection of snow endmember and nonsnow endmember based on NDSI and NDVI can be achieved.
The algorithm was tested on several different MODIS scenes in Tibetan Plateau. The efficiency and precision of
classification equals that obtainable from the PCA method but is faster, cheaper. Lastly, Two sub pixel snow cover mapping
means (regression method based on NDSI and spectral unmixing method based on the endmember automatic selection)
was compared and analysised. And it takes the ASTER 15m data as ground true data to calculate the percentage of snow
cover for 500m cells. It shows that the spectral unmixing can map fractional snow cover more precision and the automatic
selection mean is stable and robost.
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