KEYWORDS: Image fusion, Information fusion, Earth observing sensors, High resolution satellite images, Distortion, Near infrared, Remote sensing, Image segmentation, 3D modeling, Sun
Shadow exists obviously in high resolution remote sensing images. Automatic extracting shadow is quite important for
removing shadow as noise or for mining shadow information. A new method of IKONOS shadow extraction in urban
region was presented in this paper based on the principal component (PC) fusion information distort. First, the NIR (near
infrared) band with more shadow information was selected for shadow extraction, and the information distort of PC
fusion was assessed; it was found that shadow was sensitive to difference index. Second, a relative difference index was
structured to enhance shadow information, as a result the values of relative difference index in shadow region were
higher and the ones in non-shadow region were lower. Third, possible shadow was distinguished from non-shadow by
threshold. Finally standard deviation was used to differentiate shadow from water for possible shadow, and the shadow
was extracted. The results show that this shadow extraction method was simple with high accuracy, not only the shadow
of high building but also that of low trees were all detected.
Height is one of important parameters for evaluating winter wheat growth. It can be not only used to indicate growth
status of winter wheat, but also play a very important role in wheat growth environmental simulating models. Remote
sensing images can reflect vegetation information and variation trend on different spatial scales, and using remote
sensing has become a very important means of retrieving crop growth indices such as H(height), F(vegetation coverage
fraction), LAI(leaf area index) and so on. In the paper, firstly LAI was estimated with a gradient-expansion algorithm by
combining remote sensing images of Landsat5 TM with field data of winter wheat measured in Shunyi&Tongzhou
District, Beijing in 2008, and then applied the dimidiate pixel model with NDVI (Normalized Difference Vegetation
Index) from landsat5 TM to calculate F(vegetation coverage fraction), lastly taking the ratio of LAI and F as the factor
built the model to estimate winter wheat growth height. The result displayed that the determinant coefficient R2 arrived at
0.48 between the field measured and the fit value by the wheat height estimating model, which showed it was feasible to
apply the model with multispectral remote sensing images to estimate the wheat height.
KEYWORDS: 3D modeling, Statistical modeling, Solar radiation models, Data modeling, Computer simulations, Vegetation, Scattering, Near infrared, Reflectivity, Performance modeling
In this paper we present an analytical model for the computation of radiation transfer of discontinuous vegetation
canopies. Some initial results of gap probability and bidirectional gap probability of discontinuous vegetation canopies,
which are important parameters determining the radiative environment of the canopies, are given and compared with a 3-
D computer simulation model. In the model, negative exponential attenuation of light within individual plant canopies is
assumed. Then the computation of gap probability is resolved by determining the entry points and exiting points of the
ray with the individual plants via their equations in space. For the bidirectional gap probability, which determines the
single-scattering contribution of the canopy, a gap statistical analysis based model was adopted to correct the dependence
of gap probabilities for both solar and viewing directions. The model incorporates the structural characteristics, such as
plant sizes, leaf size, row spacing, foliage density, planting density, leaf inclination distribution. Available experimental
data are inadequate for a complete validation of the model. So it was evaluated with a three dimensional computer
simulation model for 3D vegetative scenes, which shows good agreement between these two models' results. This model
should be useful to the quantification of light interception and the modeling of bidirectional reflectance distributions of
discontinuous canopies.
Advanced technology in airborne detection of crop growth can help optimize the strategies of fertilization, and help
maximize the grain output by adjusting field inputs. In this study, Push-broom Hyperspectral Image sensor (PHI) was
used to investigate the influence of soil nitrogen supplied and variable-rate fertilization to the growth of winter wheat.
The objective was to determine to what extent the reflectance obtained in the 80 visible and near-infrared (NIR)
wavebands (from 410nm to 832nm) might be related to differences of variance of soil nitrogen and variable-rate
fertilization. Management plots were arranged at Beijing Precision Farming Experimental Station. Three flights were
made during the wheat growing season. Several field experiments, including the crop sampling, soil sampling and
variable-rate fertilization were carried out in the field. Data were analyzed for each flight and each band separately.
Some spectrum indices were derived from PHI images and statistical correlation analysis were carried out among the
spectrum indices and soil nitrogen, variable-rate fertilization amount. In addition, the spectrum indices difference
between elongation stage and grain filling stage are calculated and the correlation analysis was also carried out. The
analysis results indicated that the reflectance of winter wheat is significantly influenced at certain wavelength by the soil
nitrogen and the variable-rate fertilization. The soil nitrogen effect was detectable in all the three flights. Differences in
response due to soil nitrogen variance were most evident at spectrum indices, such as dλ red, INFLEX, Green/Red, NIRness,
DVI and RDVI. Furthermore, analysis results also indicated that the variable fertilization can reduce the growth
difference of winter wheat caused by spatial distribution difference of soil nitrogen.
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