Canopy nitrogen content has strong relationship with net primary productivity, litter nitrogen and nitrogen mineralization rate, so the estimation of nitrogen content can provide valuable understanding of large-scale terrestrial carbon and nitrogen cycle. Hyperspectral remote sensing technology demonstrates the capacity for accurate biochemical component estimation of vegetation. This paper employed Hyperion hyperspectral data acquired over Xishuangbanna tropical area in Yunnan province, China to estimate nitrogen content based on normalized band depth (BNC) method. Hyperion data geometric and radiometric corrections were first made, and then Hyperion reflectance of 56 samples in 35 plots was extracted. Continuum removal was applied to the selected absorption features related to nitrogen. The BNC of 56 samples were calculated. Relationships between BNC values in Hyperion image and in situ field measured nitrogen content were analyzed using stepwise multiple linear regression. Results showed that central wavelengths in the model predicting nitrogen were 650.67nm, 2213.93nm, 2173.53nm and 671.02nm, and coefficient of determination (R2) was 0.505. Bands 650.67nm and 671.02nm coincided with chlorophyll absorption features highly related to nitrogen; 2213nm and 2173nm corresponded to protein and nitrogen absorption features. Correlation analysis showed that the biggest correlation coefficient between nitrogen and BNC was -0.573, which was at 650.67nm.
Land degradation is one of the serious environmental problems that can lead to poverty. North Hebei province is one part
of eco-fragile region in North China, and is a transitional zone from farming to forest and grassland. This paper
evaluated land degradation of North Hebei province from 1987 to 2001. Remote sensing data were time-series MODIS
500m 8-day surface reflectance and 16-day EVI product in 2001 and Landsat TM data in 1987 and 2000. TM and
MODIS data were processed in ERDAS and ModisTool software including projection transformation and subset.
Degradation evaluation indices, including enhanced vegetation index (EVI), land surface water index (LSWI) and
modified soil adjusted vegetation index (MSAVI) of three subregions were calculated. Results showed that the total land
degradation area was 64439km2 in 2001, of which high and severe land degradation level area were 20734km2 and
3948km2, accounting for 32.18% and 6.13%, respectively. Low land degradation level appeared in Yanshan
mountainous area; medium land degradation level mainly appeared in Bashang plateau region, where vegetation types
were cultivated land and grassland, and high land degradation level mainly appeared in Bashang plateau region and the
basin area, where grass land degradation and soil erosion were serious.
The sensitivity of hyperspectral indices to LAI, chlorophyll contents and leaf internal structure at canopy level are
investigated using simulated canopy reflectance dataset, this method can avoid expensive and impractical surface
reflectance measurement, providing a theoretical basis for satellite-borne remote sensing. The model employed is
PROSAIL that couples leaf reflectance model PROSPECT and canopy radiative transfer model SAIL. Hyperspectral
indices used are NDVI, EVI, GI, RI, TVI, SIPI, PRI, TCARI, OSAVI, TCARI/ OSAVI, mNDVI705 and NDWI. Using
PROSAIL model, leaf and canopy reflectance under different chlorophyll contents, leaf internal structures, LAI and
water contents are first simulated and compared. Then using PROSAIL simulated canopy reflectance data, different
hyperspectral indices are calculated, the sensitivity of vegetation indices to LAI and chlorophyll contents is analyzed in
detail. And the sensitivity of vegetation indices to leaf internal structure is also analyzed. Results show that relationships
between hyperspectral indices and LAI are approximately logarithmic while the relationship between hyperspectral
indices and leaf internal structure is linear. EVI and TVI are good indicators to estimate LAI. GI, RI, TCARI, MNDVI705
can be used to estimate chlorophyll content. N has great influence on hyperspectral indices.
The evaluation index system of urban land intensive use potential is built with IKONOS remote sensing data as the main data source. Based on this, quantitative evaluation model of land intensive use potential is constructed through BP neural network method. This model is applied to evaluate the potential of land intensive use in Shijiazhuang. It has been proved that this kind of evaluation system, i.e. remote sensing image as the main data resource and BP model as the evaluation method, to evaluate urban land intensive use potential, is more effective, more practicable, more convenient and the evaluation result is more objective.
To meet the demand of large-scale agricultural monitoring system with remote sensing, extracting crop area planted must
be rapid, precise and reliable. In this paper, winter wheat identification with MODIS data in 2004 is taken as example in
North China. Applying spectral analysis and integrating genetic algorithm with neural network (GA-BP) is proposed,
which gives attention to two optimization algorithm, genetic algorithm and back propagation algorithm. According to the
spectral and biological characteristics of winter wheat, Red, Blue, NIR, ESWIR, LSWI, EVI are selected as characteristic
parameters. Then GA-BP algorithm is used for winter wheat identification. Results show that compared with maximum
likelihood and back propagation neural network classification algorithm, the GA-BP algorithm can not only run with
better efficiency, but also achieve best accuracy of identification. Therefore, it is the operational method for agricultural
condition monitoring with remote sensing and information service system at national level.