Light use efficiency (LUE) is a critical parameter for estimating carbon exchange in many ecosystem models, especially
those models based on remote sensing algorithms. Estimation and monitoring of LUE and gross primary productivity
(GPP) over wetland is very important for the global carbon cycle research and modelling, since the wetland plays a vital
role in the ecosystem balance. In this paper, carbon flux data observed with an eddy covariance tower over a reedsdominated
wetland in Zhangye, northwest of China, was used to calculate LUE. Through the postprocessing of carbon
flux data and estimation of ecosystem respiration, daily GPP was calculated firstly. Combining with fraction of absorbed
photosynthetically active radiation (FPAR) inversed from HJ-1 satellite, LUE was determined. The maximum value of
LUE was 1.03 g C·MJ-1 occurred in summer. Furthermore, a regional vegetation productivity model based on
meteorological data and remote sensing data was used to estimate the wetland GPP. The results show that the modeled
GPP results were consistent with in situ data.
Accelerated urbanization creates challenges of water shortages, air pollution, and reductions in green space. To address these issues, methods for assessing urban expansion with the goal of achieving reasonable urban growth should be explored. In this study, an improved slope, land use, exclusion, urban, transportation, hillshade (SLEUTH) cellular automata model is developed and applied to the city of Tangshan, China, for urban expansion research. There are three modifications intended to improve SLEUTH: first, the utilization of ant colony optimization to calibrate SLEUTH to simplify the calibration procedures and improve their efficiency; second, the introduction of subregional calibration to replace calibration of the entire study area; and third, the incorporation of social and economic data to adjust the self-modification rule of SLEUTH. The first two modifications improve the calibration accuracy and efficiency compared with the original SLEUTH. The third modification fails to improve SLEUTH, and further experiments are needed. Using the improvements to the SLEUTH model, forecasts of urban growth are performed for every year up to 2020 for the city of Tangshan under two scenarios: an inertia trend scenario and a policy-adjusted scenario.
Soil Moisture and Vegetation Growth are the most important and direct index in drought monitoring, and the
spectral interpretation of vegetation and soil are serious factors in the judgment of drought degree. Based on
the spectral character of water, recently, a new model of Surface Water Capacity Index (SWCI) has been put
forward, and the index is more sensitive to the surface water content, and suit for regional drought
monitoring. The comparative analysis showed: SWCI is more sensitive than NDVI to monitoring surface soil
water content; this is available in real-time soil drought monitoring.
Retrieving land-surface temperature with split-window algorithm was firstly applied to NOAA-AVHRR data.
With the application of MODIS sensor, its data has been used more and more widely. Since MODIS sensor is able
to observe vapor in the air, it can provide the parameters including vapor content and atmospheric transmissivity
for split-window algorithm which can thus be applied more conveniently. The article, adopting the split-window
algorithms of Becker-Li (1990), Sobrino (1991) and Qin Zhihao (2005), retrieves the surface temperature at
daytime and nighttime with MODIS1B data and compares with the surface temperature products of NASA. Finally,
the algorithm of Qin Zhihao is demonstrated to be the one with higher accuracy at daytime and nighttime and the
algorithm for surface temperature at nighttime is simple with acceptable accuracy.
The use of remote sensing technology to estimate regional evapotranspiration has been carried out for many years.
Recently, with the advancements in quantification of remote sensing and the access of MODIS data, more scientists have
been using MODIS data to monitoring regional evapotranspiration (ET) instead of the NOAA/AVHRR data. The surface
energy balance algorithm for land (SEBAL) model combined with NOAA/AVHRR and MODIS data separately is
applied to estimate the 24-hour regional evapotranspiration in a semi-arid agricultural area of northern China. And the
SEBAL regional evapotranspiration model calculated results from MODIS and NOAA/AVHRR data are compared with
the in-situ measured ground surface evaporation. The analysis shows that in estimating regional evapotranspiration of the
satellite based application, MODIS data is more appropriate than NOAA/AVHRR data.
This paper presents the results of an intercomparison study of data fusion methods. Three data fusion techniques, based respectively on the Daubechies wavelet basis method, the IHS transform, and the Principle Component Analysis (PCA), are compared with each other. According to the data set we used in this study, the Daubechies Wavelet Basis method is far more efficient than the PCA and the IHS transform, It thus establishes the advantages for data fusion, formally called multiple resolution analysis. This method is the best among the three for image sharpening and for maintaining the information of the original data. We conclude with the result from this study that the Daubechies Wavelet Basis method has the largest
application potential for merging the spatial and spectral characteristics of multiple resolution remote sensing data with high
efficiency.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.