The surface environment and the thermal infrared information of remote sensing have been widely used to study urban
climate. In this paper, the Landsat Thematic Mapper (TM) data acquired in 2008 were applied to study the relationship
between urban surface temperature and surface characteristics within the Beijing 5th ring road area of China. The thermal
band data of TM combined with classification-based surface emissivity were utilized to estimate land surface
temperature (LST). Meanwhile, surface characteristics parameters, such as the Normalized Difference Vegetation Index
(NDVI), the Modified Normalized Difference Water Index (MNDWI), the Normalized Difference Building Index (NDBI)
and the Normalized Difference Bareness Index (NDBaI) were calculated according to related arithmetic respectively.
The quantitative relationship between LST and NDVI, MNDWI, NDBI and NDBaI were investigated according to urban
main land use/cover types (water body, vegetation and built-up surfaces). The results showed there were negative
correlations between LST and NDVI, MNDWI for vegetation and built-up land use/cover types, positive correlations
between LST and NDBI, NDBaI for vegetation and built-up land use/cover types. In general, in the area 5th ring road of
Beijing the distribution of NDVI, MNDWI and NDBI directly defined the distribution of LST. For built-up land
use/cover type, the NDVI was small, However, NDBI and LST were high. While in the area with more water and
vegetation, the NDVI and MNDWI were high and LST was small. There were obvious correlation between LST and
urban surface characteristics.
Land surface albedo is one of most important parameters in weather and climate numeric models. The albedo differences
between urban and rural land surfaces and the albedo variations due to urbanization have not been well studied. In this
study, temporal comparisons of albedoes in the urban, rural and hill regions of the Beijing area in China were analyzed
by converting broad albedoes from narrow band reflectances using NASA pathfinder released reflectance and NDVI data.
Results showed that with increased urbanization the original albedoes exhibited a decreasing trend and the urban areas
had lower albedoes than the rural areas. In the hill area with dense vegetation, there were the lowest albedoes. Monthly
measurements of albedo variation in the urban and rural regions showed that the albedoes have obvious seasonal
unimodal trends. In the summer the albedoes are the highest while in the winter, the albedoes are lowest. For the hill area,
results also showed that the albedoes have obvious seasonal characteristics. The maximal value occurs during May and
July. The results can be used to adjust numerical model parameters to improve urban land surface simulation.
The Normalized Difference Vegetation Index (NDVI) and the thermal infrared information of remote sensing have been widely used to study urban climate. In this study, the time series of 8 km-spatial-resolution NDVI images obtained from the Pathfinder NOAA-AVHRR Land (PAL) dataset in the period of 1982 to 2000 was used to assess the trends of NDVI in Beijing both urban and rural areas. The relationship between the NDVI variation and urban heat island (UHI) intensity was investigated. And the results showed that the NDVI in Beijing urban region decreased with time and the NDVI in Beijing rural region had no obvious change. The annual UHI intensity increased with the increasing of NDVI difference between urban and rural regions. Meanwhile, the data of Landsat Enhanced Thematic Mapper Plus (ETM +) combined with classification-based surface emissivity in Beijing area were utilized to estimate land surface temperature (LST). The relationship between LST and NDVI derived from ETM+ showed an obviously negative correlation. In general, in Beijing area the distribution of NDVI directly defined the distribution of LST. In the urban region the NDVI was small and LST was high, while in the rural region the NDVI was large and LST was small. The derived LST from ETM+ was also compared with measured air temperature at weather stations. There was an obvious correlation between surface temperature and air temperature. This indicates that the surface temperature derived from Landsat can describe the distribution of UHI.
The objective of this paper was to determine hyperspectral narrow wavebands that are best suited for estimating rice biophysical characteristics. The paper studied the variational process of leaf area index (LAI), leaf chlorophyll density (CH.D) and hyperspectral data during the period of rice growing season. Correlation between hyperspectral data and LAI, CH.D of rice was analyzed. Spectral derivatives technique was used to suppress the effects of low frequency spectral noises on background. Stepwise regression method was used to create multivariate linear equations for predicting LAI and CH.D of rice with the data of reflectance and the first-order derivatives of reflectance as forecast factors. Results show that: 1) The first order derivatives of reflectance spectrum can enhance the correlation and improve the precision of predicting LAI and CH.D; 2) The first order derivatives of reflectance and CH.D more markedly correlate than LAI at some wavelength, CH.D is more available to express crop canopy spectrum information than leaf area index.
As the growth cycling of crops is usually shorter than the one of forest. The change of status of crop growth was pronounced and quickly with development stages. Therefore it is useful to know the change of the features of crop growth during growth cycling in hyperspectral remote sensing. The reflected spectra of canopies of five crops, early rice, later rice, summer maize, cotton and soybean were measured with SE590 spectrometer from June to September 1997, in Beijing. The features of first-order derivatives of canopy reflectances for each of the five crops are changed regularly with the phenological evolution. The possibilities of identification of crop types with the features of red edge are discussed.
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