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.
An uneven growing winter wheat will be slower to reach full ground cover and will be lead to uneven yield and quality
for cropland. The traditional investigation of crop uniformity is mainly depends on manpower. Remote sensing technique
is a potentially useful tool for monitoring the crop uniformity status for it can provide an area global view for entire field
within the crop growth season with scathelessness. The objective of this study was to use remote sensing imagery to
evaluate the crop growth uniformity, as well as the yield and grain quality variation for a winter wheat study area. One
Quickbird image on winter wheat booting stage was collected and processed to monitoring the uniformity of wheat
growth. The results indicated that the spectrum parameters of Quickbird image can reflect the spatial uniformity of
winter wheat growth in the study areas. Meanwhile the spatial uniformity of wheat growth in early stage can reflect the
uniformity of yield and grain quality. The wheat growth information at the booting stage has strong positive correlations
with yield, and strong negative correlation with grain protein. The correlation coefficient between OSAVI (optimized
soil adjusted vegetation index) and wheat yield was 0.536. It was -0.531 for GNDVI (Greeness-normalized difference
vegetation index) and grain protein content. The study also indicated that diverse spectrum parameters had different
sensitivity to the wheat growth spatial variance. So it is feasible to use remote sensing data to investigate the crop growth
and quality spatial uniformity.
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.
The quantitative effect of crop canopy reflected spectrum by leaf area index (LAI) and average leaf angle (ALA) was
studied. Effect of ALA on canopy reflected spectrum can not be ignored with inversion of LAI and monitoring of crop
growth condition by remote sensing technology. It indicated that canopy reflected spectrum has significant difference
between erective and horizontal cultivars by radiative transfer model and measured experiment data. Investigations have
been made on identification of erective and horizontal cultivars by bidirectional canopy reflected spectrum. The
bidirectional reflectance of visible and near infrared bands at 15°, 30°and 45°field of view for the main viewing plane
could be used for identification of plant structural types based on bidirectional data. For erective varieties, the
bidirectional canopy reflectance at near infrared was f45°>f15°> f30°, at visible band was f45°>f15°≈f30°; For middle
varieties, the bidirectional canopy reflectance at near infrared and visible band was f15°>f45°> f30°; For loose varieties,
the bidirectional canopy reflectance at near infrared and visible band was f45°>f30°> f15°. So, it is feasible to identify loose, middle and erective varieties of wheat by bidirectional canopy reflected spectrum. The result indicates that the
application of EVI is affected by LAD, so LAD should be considered when retrieve LAI using enhanced vegetation
index (EVI).
Construction of network clusters and identifying hub nodes from networks has attracted more and more attentions in
spatial network analysis. In this paper, we proposed clustering algorithm and outlying node detection algorithm for
spatial road network analysis. Network clustering algorithm consists of constructing clusters and creating a simplified
structure of the network. When performing clustering on the network, we introduced the definitions of strong cluster and
weak cluster, where each node has more connections within the cluster than with the rest of the graph, for achieving
reliable and reasonable clusters. For users' understanding the structure of the network, we constructed a simplified graph
approximation of the network, whose nodes were representative nodes in clusters of the network, and edges were the
connections between those representative nodes. In outlying node detection algorithm, a node is identified as an outlier,
not because of its distribution different from that of other nodes but for its unexpected statistical information. Whether a
node is an outlier or not is examined with centrality index. The larger the node has centrality indexes, the more
probabilistically it may be identified as an outlier. The experimental results on artificial data sets demonstrated that two
algorithms are very efficient and effective.
Naked cropland elimination is an important part of Beijing Olympic ecological project. In this paper, Multi-temporal
satellite data were used to monitor and position the naked croplands. Three Landsat TM images and two "Beijing-
1"Micro-Satellite images were selected to calculate NDVI series according to crop phenological calendars and
investigated information of agricultural cropping structures in Beijing suburb. Based on the phenological spectral
characteristics of main agricultural land use types, a classification scheme was proposed to extract the naked croplands.
Considering the structural characteristic hierarchical classification and various demands of feature selection in different
periods, decision tree algorithm and a stepwise masking technology were employed to extract typical crops in each
season, and hence the naked croplands were left. Accuracy assessment of the naked croplands in winter and spring were
performed with comparison of the monitoring areas with statistical data. The results show that the area of the naked
croplands in winter and spring was 170368.1ha in Beijing. The areas of the top five districts (Yanqing, Shunyi, Daxing,
Miyun and Tongxian) were 17933.3ha, taking a percent of 69.2% of that of Beijing. The areas of the naked cropland
were 25719.6 ha, 4485.4 ha and 3325 ha in summer, autumn and all the year round respectively. Experimental results
demonstrated that our method would fast and simply monitor agricultural land use.
The Advanced technology in space-borne determination of grain crude protein content (CP) by remote sensing can help
optimize the strategies for buyers in aiding purchasing decisions, and help farmers to maximize the grain output by
adjusting field nitrogen (N) fertilizer inputs. We performed field experiments to study the relationship between grain
quality indicators and foliar nitrogen concentration (FNC). FNC at anthesis stage was significantly correlated with CP,
while spectral vegetation index was significantly correlated to FNC. Based on the relationships among nitrogen
reflectance index (NRI), FNC and CP, a model for CP prediction was developed. NRI was able to evaluate FNC with a
higher coefficient of determination of R2=0.7302. The method developed in this study could contribute towards
developing optimal procedures for evaluating wheat grain quality by ASTER image at anthesis stage. The RMSE was
0.893 % for ASTER image model, and the R2 was 0.7194. It is thus feasible to forecast grain quality by NRI derived
from ASTER image.
The advanced technology in site-specific and spaceborne determination of grain crude protein content (CP) by remote sensing can help optimize the strategies for buyers in aiding purchasing decisions, and help farmers to maximize the grain output by adjusting field nitrogen (N) fertilizer inputs. We performed field experiments to study the relationship between grain quality indicators and foliar nitrogen concentration (FNC). FNC at anthesis stage was significantly correlated with CP, while spectral vegetation index was significantly correlated to FNC. Based on the relationships among nitrogen reflectance index (NRI), FNC and CP, a model for CP prediction was developed. NRI was able to evaluate FNC with a higher coefficient of determination of R2= 0.7302 in Experiment A. The relationship between laboratory measured and remotely sensed FNC had a coefficient of determination of R2=0.7279 in Experiment B. The method developed in this study could contribute towards developing optimal procedures for evaluating wheat grain quality by in situ canopy-reflected spectrum and ASTER image at anthesis stage. CP derived from both in situ spectrum and the ASTER image exhibited high accuracy and the precision in Experiment C. The RMSE were 0.893 % for in situ spectrum model and 1.654 % for ASTER image model, and the R2 were 0.7661 and 0.7194 for both, respectively. It is thus feasible to forecast grain quality by NRI derived from in situ canopy-reflected spectrum and ASTER image. Our results indicated that the inversion of FNC and the evaluation of CP by NRI were surprisingly good.
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.
Advanced site-specific determination of grain protein content by remote sensing can provide opportunities to optimize the strategies for purchasing and pricing grain, and to maximize the grain output by adjusting field inputs. Field experiments were performed to study the relationship between grain quality indicators and foliar nitrogen concentration. Foliar nitrogen concentration at the anthesis stage is suggested to be significantly correlated with grain protein content, while spectral vegetation index is significantly correlated to foliar nitrogen concentration around the anthesis stage. Based on the relationships among nitrogen reflectance index (NRI), foliar nitrogen concentration, and grain protein content, a statistical evaluation model of grain protein content was developed. NRI proved to be able to evaluate foliar nitrogen concentration with a coefficient of determination of R2= 0.7302 in year 2002. The relationship between measured and remote sensing derived foliar nitrogen concentration had a coefficient of determination of R2=0.7279 in year 2003. The results mentioned above indicate that the inversion of foliar nitrogen concentration and the evaluation of grain protein content by NRI are surprisingly good.
In this paper, a portable diagnostic instrument was designed and tested, which can measure the normalized difference vegetation index (NDVI) and structure insensitive pigment index (SIPI) of crop canopy in field. The instrument have a valid survey area of 1 m*1 m when the height between instrument and the ground was fixed to 1.3 meter The crop growth condition can be assessed based on their NDVI and SIPI values, so it will be very important for crop management to get these values. The instrument uses sunlight as its light source. There are six special different photoelectrical detectors within red, blue and near infrared bands, which are used for detecting incidence sunlight and reflex light from the canopy of crop. This optical instrument includes photoelectric detector module, signal process and A/D convert module, the data storing and transmission module and human-machine interface module. The detector is the core of the instrument which measures the spectrums at special bands. The microprocessor calculates the NDVI and SIPI value based on the A/D value. And the value can be displayed on the instrument's LCD, stored in the flash memory of instrument and can also be uploaded to PC through the PC's RS232 serial interface. The prototype was tested in the crop field at different view directions. This paper also provided the method of calibration, the results showed that the average measurement error to SIPI value of instrument was 5.25% and the average measurement error to NDVI value in vegetation-covered region is 6.40%. It reveals the on-site and non-sampling mode of crop growth monitoring by fixed on the agricultural machine traveling in the field.
In this paper, a portable diagnostic instrument for crop quality analysis was designed and tested, which can measure the normalized difference vegetation index (PRI) and structure insensitive pigment index (NRI) of crop canopy in the field. The instrument have a valid survey area of 1m×1m when the height between instrument and the ground was fixed to 1.3 meter. The crop quality can be assessed based on their PRI and NRI values, so it will be very important for crop management to get these values. The instrument uses sunlight as its light source. There are six special different photoelectrical detectors within red, blue and near infrared bands, which are used for detecting incidence sunlight and reflex light from the canopy of crop. This optical instrument includes photoelectric detector module, signal process and A/D convert module, the data storing and transmission module and human-machine interface module. The detector is the core of the instrument which measures the spectrums at special bands. The microprocessor calculates the NDVI and SIPI value based on the A/D value. And the value can be displayed on the instrument's LCD, stored in the flash memory of instrument and can also be uploaded to PC through the PC's RS232 serial interface. The prototype was tested in the crop field at different view directions. It reveals the on-site and non-sampling mode of crop growth monitoring by fixed on the agricultural machine traveling in the field. Such simple instruments can diagnose the plant growth status by the acquired spectral response.
This study was to develop the time-specific and time-critical method to overcome the limitations of traditional field sampling methods for variable rate fertilization. Farmers, agricultural managers and grain processing enterprises are interested in measuring and assessing soil and crop status in order to apply adequate fertilizer quantities to crop growth. This paper focused on studying the relationship between vegetation index (OSAVI) and nitrogen content to determine the amount of nitrogen fertilizer recommended for variable rate management in precision agriculture. The traditional even rate fertilizer management was chosen as the CK. The grain yield, ear numbers, 1000-grain weight and grain protein content were measured among the CK, uniform treatments and variable rate fertilizer treatments. It indicated that variable rate fertilization reduced the variability of wheat yield, ear numbers and dry biomass, but it didn't increased crop yield and grain protein content significantly and did not decrease the variety of 1000-grain weight, compared to traditional rate application. The nitrogen fertilizer use efficiency was improved, for this purpose, the variable rate technology based on vegetation index could be used to prevent under ground water pollution and environmental deterioration.
Winter wheat canopy spectrum is dominated by wheat canopy closures, in this study. Our purpose is to study the quantitative influence of canopy closures on field canopy spectrum by quantitative reduced canopy stem densities. It indicated that canopy reflectance of winter wheat under different canopy stem densities has significant difference in near infrared bands. It has line relationship between spectral reflectance of 100% canopy stem densities and spectral reflectance under canopy stem densities, all the coefficients of determination (R2) for the equations are exceeding 0.8452, and all the results are surprised well. Canopy reflectance difference of winter under different stem densities were also studied, they all have line relationships between canopy reflectance of 100% canopy stem densities and quantitative reduced canopy stem densities, the simulation equations are different for the erective cultivars and loose cultivars. Relationship between canopy closures and canopy stem densities were also studied too, it has positive relationship between canopy closures and canopy stem densities, it reveals a very good agreement between canopy closures and canopy stem densities, with a coefficient of determination (R2) 0.8467, so the canopy stem densities can be simulated by canopy closures.
Field experiments were conducted to examine the influence factors of cultivar, nitrogen application and irrigation on grain protein content, gluten content and grain hardness in three winter wheat cultivars under four levels of nitrogen and irrigation treatments. Firstly, the influence of cultivars and environment factors on grain quality were studied, the effective factors were cultivars, irrigation, fertilization, et al. Secondly, total nitrogen content around winter wheat anthesis stage was proved to be significant correlative with grain protein content, and spectral vegetation index significantly correlated to total nitrogen content around anthesis stage were the potential indicators for grain protein content. Accumulation of total nitrogen content and its transfer to grain is the physical link to produce the final grain protein, and total nitrogen content at anthesis stage was proved to be an indicator of final grain protein content. The selected normalized photochemical reflectance index (NPRI) was proved to be able to predict of grain protein content on the close correlation between the ratio of total carotenoid to chlorophyll a and total nitrogen content. The method contributes towards developing optimal procedures for predicting wheat grain quality through analysis of their canopy reflected spectrum at anthesis stage. Regression equations were established for forecasting grain protein and dry gluten content by total nitrogen content at anthesis stage, so it is feasible for forecasting grain quality by establishing correlation equations between biochemical constitutes and canopy reflected spectrum.
A field trial was conduct to investigate the relationship between spectral feature of winter wheat canopy and LAI as well as leaf nitrogen (N) under different status of leaf water in field situation. The objective of this study is to investigate effect of water status in plants on the accuracy of estimating leaf area index (LAI) and plant nitrogen. The new defined spectral index, IAFC = (R2224-R2054)/ (R2224+R2054), where R is the reflectance at 2224nm or 2054nm, was significantly (α=0.05) or extremely significantly (α=0.01) correlated with LAI at all the six dates for water insufficient plants, but not significantly correlated for water sufficient plants at five of the six dates and the difference of leaf water content between the water insufficient plants and water sufficient plants was only about 2% at some dates. The study provided strong evidence that leaf water has a strong masking effect on the 2000-2300nm spectral feature, which could be strongly associated with LAI and leaf N even when the leaf water content was as high as about 80% if the water was insufficient for plant growth. The results indicated that the masking effect of leaf water on the 2000-2300nm spectral feature was not only dependent on the absolute plant water content but also on the water status and that remotely sensed data in the 2000-2300nm region could be of potential in monitoring plant canopy biophysics and biochemistry in drought condition.
Investigations have been made on agronomy parameters as leaf area index (LAI), chlorophyll content (Chl), total Nitrogen (TN) and specific leaf weight (SLW) to describe growth status of winter wheat. More comprehensive parameters such as chlorophyll index (CI), projective chlorophyll index (CIp), Nitrogen index (NI) and projective Nitrogen index (NIp) have been developed to describe the dynamic growth information for foliage vertical layers by studying their vertical distribution characteristics along canopy and their spectral reflectance. Results are that from the canopy top to the ground surface, TN and Chl have shown an obvious gradient decreasing trend, while LAI and SLW have shown the ovate distribution. Compared with NI, CI and LAI, the absolute values of NIp, CIp and LAIp are more affected by canopy shape. The ratio of NIp to NI in different layers of erective varieties is significantly lower than that of loose varieties. Correlation analysis between canopy spectral reflectance and those developed parameters in different foliage layers at stage of anthesis shows that foliage Chl in upper layer is very sensitive to 400 nm-750 nm and 1300 nm-1750 nm while that in the middle layer is very sensitive to 750 nm -1300 nm. Higher correlation coefficient between spectral reflectance and TN is found in middle-under layer and it decreases upward.
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.