[Extraction of temperate vegetation phenology thresholds in North America based on flux tower observation data].

Flux tower method can effectively monitor the vegetation seasonal and phenological variation processes. At present, the differences in the detection and quantitative evaluation of various phenology extraction methods were not well validated and quantified. Based on the gross primary productivity (GPP) and net ecosystem productivity (NEP) data of temperate forests from 9 forest FLUXNET sites in North America, and by using the start dates (SOS) and end dates (EOS) of the temperate forest growth seasons extracted by different phenology threshold extraction methods, in combining with the forest ecosystem carbon source/sink functions, this paper analyzed the effects of different threshold standards on the extraction results of the vegetations phenology. The results showed that the effects of different threshold standards on the stability of the extracted results of deciduous broadleaved forest (DBF) phenology were smaller than those on the stability of the extracted results of evergreen needleleaved forest (ENF) phenology. Among the extracted absolute and relative thresholds of the forests GPP, the extracted threshold of the DBF daily GPP= 2 g C.m-2.d-1 had the best agreement with the DBF daily GPP = 20% maximum GPP (GPPmax) , the phenological metrics with a threshold of daily GPP = 4 g C.m-2.d-1 was close to that between daily GPP = 20% GPPmax and daily GPP = 50% GPPmax, and the start date of ecosystem carbon sink function was close to the SOS metrics between daily GPP = 4 g C.m-2.d-1 and daily GPP= 20% GPPmax. For ENF, the phenological metrics with a threshold of daily GPP = 2 g C.m-2.d-1 and daily GPP = 4 g C.m-2.d-1 had the best agreement with the daily GPP = 20% GPPmax and daily GPP = 50% GPPmax, respectively, and the start date of the ecosystem carbon sink function was close to the SOS metrics between daily GPP = 2 g C.m-2.d-1 and daily GPP= 10% GPPmax.

[Analysis on vegetations spectral characteristics along the altitudinal gradients in south-facing slope of Dangxiong Valley].

The present study focused on variation of vegetation types and canopy spectra along the altitudinal gradients in south-facing slope of Dangxiong valley in Tibet. Spectral extraction methods including red edge analysis and vegetation indices were used for vegetation spectral characteristics analysis. Through the hierarchical clustering analysis based on the vegetation spectral features, the feasibility of remote sensing classification of vegetation types along the elevation gradients in the experimental area was evaluated. The experimental results showed that: there were significant differences in spectral features including water index (WI), red edge POSITION (REP), and normalized difference vegetation index (NDVI) in different plots along elevation gradients in the study area, and there were strong correlations between WI and leaf water content, REP and dry biomass, NDVI and vegetation coverage. The hierarchical clustering analysis result of 12 vegetation samples along the altitudinal gradients is consistent with the ground survey, which shows that the selected vegetation spectral features can characterize the vertical distribution of vegetation types in the experimental area. The vegetation spectral analysis in this study can provide the priori knowledge support of spectral characteristics for the vegetation vertical distribution information extraction in the Tibet Plateau.

[Winter wheat growth spatial variation study based on temporal airborne high-spectrum images].

Precision agriculture technology is defined as an information-and technology-based agriculture management system to identify, analyze and manage crop spatial and temporal variation within fields for optimum profitability, sustainability and protection of the environment. In the present study, push-broom hyperspectral image sensor (PHI) image was used to investigate the spatial variance of winter wheat growth. The variable-rate fertilization contrast experiment was carried out on the National Experimental Station for Precision Agriculture of China during 2001-2002. Three airborne PHI images were acquired during the wheat growth season of 2002. Then contrast analysis about the wheat growth spatial variation was applied to the variable-rate fertilization area and uniformity fertilization area. The results showed that the spectral reflectance standard deviation increased significantly in red edge and short infrared wave band for all images. The wheat milky stage spectral reflectance has the maximum standard deviation in short infrared wave band, then the wheat jointing stage and wheat filling stage. Then six spectrum parameters that sensitive to wheat growth variation were defined and analyzed. The results indicate that parameters spatial variation coefficient for variable-rate experiment area was higher than that of contrast area in jointing stage. However, it decreased after the variable-rate fertilization application. The parameters spatial variation coefficient for variable-rate area was lower than that of contrast area in filling and milking stages. In addition, the yield spatial variation coefficient for variable-rate area was lower than that of contrast area. However, the yield mean value for variable-rate area was lower than that of contrast area. The study showed that the crop growth spatial variance information can be acquired through airborne remote sensing images timely and exactly. Remote sensing technology has provided powerful analytical tools for precision agriculture variable-rate management.

[Remote sensing based monitoring of vegetation dynamics and ecological restoration in Beijing mountainous area].

By using the Landsat images in 1979, 1988, 1999, 2005, and 2009, and the linear unmixed model at pixel scale, this paper analyzed the spatiotemporal variation of vegetation coverage in Beijing mountainous area. After detecting the areas of vegetation degradation or restoration, the impacts of elevation, slope, and soil type on vegetation restoration were studied. From 1979 to 1988, the vegetation coverage in the study area had no obvious change, but in the following 12 years, the vegetation coverage was seriously destroyed due to the fast development of social economy. Fortunately, many protective measures were taken since 2000, which improved the vegetation coverage to 72% in 2009, with an increment of 13% compared to the vegetation coverage in 1999. A significant correlation was observed between the variations of vegetation coverage and territorial features. The areas with poor soil or large slope were more easily suffered from degradation than other places, and the flat regions with low elevation were more affected by human activities.

[Real-time analysis of soil N and P with near infrared diffuse reflectance spectroscopy].

Improving the efficiency of fertilization is an effective method of enhancing income for farmers, but it depends on measuring the soil nutrients accurately and rapidly. Near infrared reflectance spectroscopy (NIRS) is a fast method to detect the soil nutrients. In order to evaluate the feasibility of using NIRS to determine the soil N and P contents, the soil samples were collected from different LULC (land use and land cover) types in Daxing district, Beijing, and their biochemical parameters were determined by traditional chemical method. Then, the near infrared reflectance spectra of the samples were acquired, and NIRS models were built using partial least square regression (PLS) and Fourier transform technology for the total N and total P. The determination coefficients of cross validation for the total N and total P were 0.862 6 and 0.668 5 respectively. Ten samples were used to test the performance of the models. The coefficients of correlation between the chemically determined value and the NIRS predicted one were 0.969 8(N) and 0.830 7(P) respectively. The root mean standard error of prediction for N and P were 0.009 5(%) and 0.008 6(%), respectively. The ratios of RMSEP to SD(RPD) were 3.78(N) and 1.69(P). The results showed that the NIRS could be used to evaluate the soil N accurately and the soil P roughly.

[Accuracy of winter wheat identification based on multi-temporal CBERS-02 images].

Chinese-Brazil Earth Resources Satellite No. 2 (CBERS-02) has good spatial resolution and abundant spectral information, and a strong ability in detecting vegetation. Based on five CBERS-02 images in winter wheat growth season, the spectral distance between winter wheat and other ground targets was calculated, and then, winter wheat was classified from each individual image or their combinations by using supervised classification. The train and validation samples were derived from high resolution Aerial Images and SPOT5 images. The accuracies and analyses were evaluated for CBERS-02 images at early growth stages, and the results were compared to those of TM images acquired in the same phenological calendars. The results showed that temporal information was the main factor affecting the classification accuracy in winter wheat, but the characteristics of different sensors could affect the classification accuracy. The multi-temporal images could improve the classification accuracy, compared with the results derived from signal stage, with the producer accuracy of optimum periods combination improved 20.0% and user accuracy improved 7.83%. Compared with TM sensor, the classification accuracy from CBERS-02 was a little lower.

[Inversion of winter wheat foliage vertical distribution based on canopy reflected spectrum by partial least squares regression method].

With the widespread application of remote sensing (RS) in agriculture, monitoring and prediction of crop nutrition condition attracts attention of many scientists. Foliar nitrogen content (N) is one of the most important nutrients for plant growth, and vertical leaf N gradient is an important indicator of crop nutrition situation. Investigations have been made on N vertical distribution to describe the growth status of winter wheat. Results indicate that from the canopy top to the ground surface, N shows an obvious gradient decreasing trend. The objective of this study was to discuss the inversion method of N vertical distribution with canopy reflected spectrum by the partial least squares regression (PLS) method. PLS was selected for the inversion of upper, middle and lower layers of N. To improve the accuracy of prediction, the N in the upper layer as well as in the middle and bottom layers should be taken into consideration when crop nutrition condition is appraised by RS data. The established models by the observed data in year 2001-2002 were validated by the data in year 2003-2004. The inversion precision and error were acceptable. It provided a theoretic basis for widely and non-damaged variable rate nitrogen application of winter wheat by canopy reflected spectrum.

[Crop geometry identification based on inversion of semiempirical BRDF models].

Investigations have been made on identification of erective and horizontal varieties by bidirectional canopy reflected spectrum and semi-empirical bidirectional reflectance distribution function (BRDF) models. The qualitative effect of leaf area index (LAI) and average leaf angle (ALA) on crop canopy reflected spectrum was studied. The structure parameter sensitive index (SPEI) based on the weight for the volumetric kernel (fvol), the weight for the geometric kernel (fgeo), and the weight for constant corresponding to isotropic reflectance (fiso), was defined in the present study for crop geometry identification. However, the weights associated with the kernels of semi-empirical BRDF model do not have a direct relationship with measurable biophysical parameters. Therefore, efforts have focused on trying to find the relation between these semi-empirical BRDF kernel weights and various vegetation structures. SPEI was proved to be more sensitive to identify crop geometry structures than structural scattering index (SSI) and normalized difference f-index (NDFI), SPEI could be used to distinguish erective and horizontal geometry varieties. So, it is feasible to identify horizontal and erective varieties of wheat by bidirectional canopy reflected spectrum.