Automated soil resources mapping based on decision tree and Bayesian predictive modeling.

This article presents two approaches for automated building of knowledge bases of soil resources mapping. These methods used decision tree and Bayesian predictive modeling, respectively to generate knowledge from training data. With these methods, building a knowledge base for automated soil mapping is easier than using the conventional knowledge acquisition approach. The knowledge bases built by these two methods were used by the knowledge classifier for soil type classification of the Longyou area, Zhejiang Province, China using TM bi-temporal imageries and GIS data. To evaluate the performance of the resultant knowledge bases, the classification results were compared to existing soil map based on field survey. The accuracy assessment and analysis of the resultant soil maps suggested that the knowledge bases built by these two methods were of good quality for mapping distribution model of soil classes over the study area.

Assessment of rice fields by GIS/GPS-supported classification of MODIS data.

The new Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite image offers a large choice of opportunities for operational applications. The 1-km Advanced Very High Resolution Radiometer (AVHRR) image is not suitable for retrieval of field level parameter and Landsat data are not frequent enough for monitoring changes in crop parameters during the critical crop growth periods. A methodology to map areas of paddy fields using MODIS, geographic information system (GIS) and global position system (GPS) is introduced in this paper. Training samples are selected and located with the help of GPS to provide maximal accuracy. A concept of assessing areas of potential cultivation of rice is suggested by means of GIS integration. By integration of MODIS with GIS and GPS technologies the actual areas of rice fields in 2002 have been mapped. The classification accuracy was 95.7% percent compared with the statistical data of the Agricultural Bureau of Zhejiang Province.

In situ hyperspectral data analysis for pigment content estimation of rice leaves.

Analyses of the correlation between hyperspectral reflectance and pigment content including chlorophyll-a, chlorophyll-b and carotenoid of leaves in different sites of rice were reported in this paper. The hyperspectral reflectance of late rice during the whole growing season was measured using a Spectroradiometer with spectral range of 350-1050 nm and resolution of 3 nm. The chlorophyll-a, chlorophyll-b and carotenoid contents in rice leaves in rice fields to which different levels of nitrogen were applied were measured. The chlorophyll-a content of upper leaves was well correlated with the spectral variables. However, the correlation between both chlorophyll-b and caroteniod and the spectral variables was far from that of chlorophyll-a. The potential of hyperspectral reflectance measurement for estimating chlorophyll-a of upper leaves was evaluated using univariate correlation and multivariate regression analysis methods with different types of predictors. This study showed that the most suitable estimated model of chlorophyll-a of upper leaves was obtained by using some hyperspectral variables such as SD(r), SD(b) and their integration.

An analysis on spatial variation of urban human thermal comfort in Hangzhou, China.

Urban human thermal comfort (UHTC) is affected for interacting of weather condition and underlying surface framework of urban area. Urban underlying surface temperature value and Normalized Difference Vegetation Index (NDVI) were calculated using image interpreting and supervised classification technique by ERDAS IMAGE software using 1991 and 1999 Landsat TM images data. Reference to the relational standard of assessing human thermal comfort and other meteorology data of Hangzhou City in summer, air temperature and relative humidity variation of different land types of underlying surface were inversed. By choosing discomfort index as an indictor, the spatial distribution characteristic and the spatial variation degree of UHTC were estimated and mapped on a middle scale, that is, in six districts of Hangzhou. The main characteristics of UHTC spatial variation from 1991 to 1999 were revealed using a GIS-based calculation model. The variation mechanism were analyzed and discussed from the viewpoint of city planning, construction and environmental protection.

Nitrogen contents of rice panicle and paddy by hyperspectral remote sensing.

The nitrogen content or crude protein content in rice grains is one of the important indices to evaluate the nutrition and taste quality of rice. Normal determination of their contents by chemical methods is highly expensive and time consuming. The hyperspectral reflectances of the canopy, flag leaf and panicle of 5 rice varieties are measured by a ASD FieldSpec Pro FR in field under 3 nitrogen support levels in maturing process. The nitrogen contents of stems, leaves, flag leaves, panicles and rice paddy and their crude protein contents are determined. The correlation among them is analyzed. The panicles nitrogen contents (%) are very significantly correlate not only to that of stems, leaves and flag leaves and chlorophyll contents (mg g(-1)) of flag leaves at milking and maturing stages, but also to the spectral reflectance rho(lambda), the first derivative spectra D(lambda) and RVI of canopy, flag leaf and panicle itself. The nitrogen contents (%) of rice paddy are very significantly correlative to that of stems and leaves and the spectral reflectance rho(lambda), the first derivative spectra D(lambda) and RVI of canopy at some wave bands at booting, milking and maturing stages. For the squared multiple correlation coefficients (R2) of estimating the nitrogen contents of panicle and paddy by canopies spectra, we find R2 > 0.80 at milking stage, R2 > 0.75 at maturing stage, but for the estimation of panicle by the spectra of flag leaf and panicle itself, we have R2 > 0.65. It indicates that it can be feasible for estimating the contents of nitrogen and crude protein in rice grains by hyperspectral remote sensing. It provide basis for monitoring rice quality by remote sensing.