For the characteristics of soil moisture in arid areas existence space heterogeneity, soil moisture remote sensing shall be based on what scales can meets the requirements do not break again operability being a big problem. The paper analyzes scale features of water heat energy parameters, introduces the scale curve. Then we analyze the relationship of spatial scale between spatial heterogeneity of surface parameters and remote sensing observational ability; believe that the intersection curve is helpful to determine the remote sensing observation scale of regional soil moisture. Therefore, study the spatial scale characteristics of surface parameters; in particular test the critical point of key parameters which is possible existence is very important. From case study, we quantitatively test the spatial scale from four aspects: Polygon scale analysis of land cover, pixel scale analysis of land cover, spatial semi-variance analysis of land attributes, multi-Scale consistency index; the suitable spatial scale is less than 1km,250m,1km and 240-480m. Integrated its results, the optimum soil moisture remote sensing research scale is between 300-1000m in arid area.
With the leaping of geological data, the demand to application of geological data is increasing and
complex. How better to extract the excrescent information of geochemical anomalies and models of
extracting altered information have been the important problem of geologists concerned. We analyzed
the spectral characteristics of typical altered minerals, summarized the spectral response characteristics
of typical altered minerals on ETM+. We contrasted three kinds of models of extracting altered
information, they were enhancing model which was basis on spectrum characters, the threshold model
which was basis on PCA and the SAM model which was basis on spectrum vectors, and we firstly built
the MPS model on the base of analysis the method of eliminating familiar interference information.
The research discovered the result of MPS model tallied with the location of the known gold spots, and
avoided the obviously false abnormality information. And we made use of this model to extracting the
altered information in whole study area.
Soil moisture is an indispensable parameter of water, heat and carbon cycle processes in the earth surface system, and
plays a key role in the formation of run-off in arid areas. The retrieval of regional-scale soil moisture is significant in the
monitoring of crop growth and drought in arid regions, and in the modeling of global climatic dynamic surface processes.
The use of multi-source remote sensing data in the soil moisture retrieval can improve the accuracy of reversion and
generally over perform the use of a single remote sensing data due to that the data acquired by different remote sensors
can provide complementary information about the soil moisture. The co-reversion of multi-source remotely sensed data
is a cutting-edge technique for soil moisture retrieval. This study tries to optimize and adjust the existing reversion
models available for different land cover types. A co-reversion scheme model will be designed and used to retrieve soil
moisture of different vegetation types of the arid area by using MODIS and AMSR-E remote sensing data. The
downscaling strategy and field verification will be used to analyze the accuracy, uncertainty and sensitivity of the
reversion models. The popularity and regionality of the model will be also examined to explore the possibility of the
model used for large-scale and dynamic monitoring of soil moisture.
Ecological capital of an ecosystem is the total value of the direct biological products in the system and the value of ecological service. The assessment of ecological capital is a new research area emerged from the challenge in the interdisciplinary research of ecology and social development. It is fundamental to establish a green national economy accounting system. Scientific evaluation of ecological capital is helpful for considering ecological cost in making the decision for economic development, and it is demanded for sustainable development. In this study, a quantitative assessment model of ecological property has been developed based on the analysis of per unit yield in the conventional ecology together with the utilization of remote sensing data from the Landsat TM, CBERS, MODIS, and NOAA database, land use and land cover data, and field measurements. The study area covers Changji Autonomous District, Xinjiang, China on the northern slope of Tianshan Mountain that is located in a typical arid area. Dynamic monitoring of ecological capital was performed using remote sensing techniques. Spatial distribution and temporal variation of ecological properties were characterized. The effects of land cover and land use as well as climate change on those variation and distribution were analyzed. The results show a significant increase in the ecological capital during 1990-2003. The spatial distribution of ecological properties is characterized by a negative gradient from higher altitudes to lower altitudes (plains) and from oases to deserts, which is consistent with the zonal distribution of vegetation in arid areas. Due to global warming, the climate in Xinjiang has been changed into a warmer and wetter environment during the last 50 years, which improves the plant growing conditions in the alpine regions, piedmont hilly regions, and the oases. On the other hand, the natural environment in the arid and semiarid regions in northwest China becomes more severe, and the stress to the natural ecosystems becomes more and more serious. Human activities affect the quality and the area of ecosystems and change the service functions of ecosystems. Consequently the fluctuation of ecological capital occurs.
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