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Rice is the most important food crop in China and its production ranks first in three major grain crops which are wheat, rice and corn. Therefore, it is very important to understand planting area and growth of rice. Because change detection is time-sensitive, remote sensing data is often used as a data source for it. Most of widely-used change detection methods using TM image are at pixel level, or only use image of single time phase. Based on multi-temporal Landsat TM images, this research extracted change area of rice planting in Pingle County. The process explored the methods of calculating changes in intensity and determining the threshold. First, the paper selected images of heading stage and harvest stage as data sources, and except for crops, there are no significant changes of ground objects in a few months. Since the spectral characteristics of late rice in the two periods are different from those of other crops, the changes of the images at two stages in the same year could be used to extract the change area of rice planting. In the process, change vector analysis method, correlation coefficient method and vector similarity method were used to calculate the change intensity. Otsu method, minimum error rate method and the method based on double window with variable step size were used to determine the threshold of the change intensity map. The change area maps of rice planting extracted by nine combinations of the methods were compared. Comprehensive utilization of three methods for extracting changes could get change area of rice planting with higher accuracy. This paper chose minimum error rate method based on histogram curvature or the method based on double window with variable step size to determine the threshold. The intersection of change area based on change vector analysis method and change area based on correlation coefficient method could inhibit the pseudo change of river beach and mountain shadow. Then the rice area was extracted exactly according to the difference between NDVI values and the object-oriented method. In order to refine the change detection of rice planting areas, the change areas of all ground objects and the change area of rice planting in two years were intersected to get the final variation for rice planting area. The results showed that the inhibitory effect of change vector analysis method for river beach change was better than correlation coefficient method, and yet the inhibition effect of correlation coefficient method for mountain shadow was better than change vector analysis method. Besides, vector similarity method was sensitive to mountain shadow and had low sensitivity to paddy field change. Among the three threshold determination methods, minimum error rate method was more accurate than Otsu method and more stable than the method based on double window with variable step size. Finally, the overall accuracy of change detection for late rice's planting area in Pingle County reached 96.8% in confusion matrix for verification. The error of the change area was 2.85% compared with the statistical data. This method could effectively extract the change of rice planting area in Pingle County. [ABSTRACT FROM AUTHOR]

Sugarcane identification on specific parcels and the assessment of soil management practices are important for agro-ecological studies, greenhouse gas modeling, and agrarian policy development. Information on the sugarcane cultivation areas is of global economic and environmental significance. The study area is Guigang City located in the central area of Guangxi Province which is a good representation of the agricultural conditions. Traditional pixel-based analysis of remotely sensed data results in inaccurate identification of some crops due to pixel heterogeneity, mixed pixels, spectral similarity. The growing region of sugarcane in Guangxi Province is discrete, so the remote sensing spectral of sugarcane is vulnerable to be impacted on a variety of crops. There are certain difficulties in the use of multi-spectral remote sensing to extract sugarcane. Current techniques for mapping sugarcane are based mainly on MODIS satellite data and may not make full use of the texture characteristics. The objective of this research is to investigate the potential for the application of the China Environment Satellite HJ-1A/1B and Phenology in monitoring sugarcane cultivation areas in Guangxi province in southern China. In our approach, we explored several characteristics such as the time information, spectral characteristics and texture features, used an object-based image analysis method and decision tree method for mapping the sugarcane area over large areas based on multi-temporal China Environment Satellite HJ-1A/B Data. A CCD camera sensor with 30m spatial resolution on board the China Environment Satellite HJ-1A and B has both visible and near infrared bands and a revisit period of four days, thus the temporal Normalized Difference Vegetation Index (NDVI) can be obtained from HJ-1A and B data. A time series of the China Environment Satellite HJ-1A/B Data and DEM images was acquired in order to represent the wide range of pattern variation along the sugarcane crop cycle. Firstly, the phenology differences and between sugarcane and other crops, such as cassavas, rice, corn, in Guigang City were analyzed. Therefore the best recognition sessions of the sugarcane was proposed to be February, May, early August and December. Then, to derive the image objects, the multi-scale segmentation algorithm was used in Definiens Developer in which the classification rule set was established. The rule set consists of the Process Algorithm and Class Description which contain several membership functions of characteristic indexes. The useful characteristic indexes mapping the sugarcane from other crops are Spectral mean value, NDVI, Digital Evaluate Model (DEM) and Gray Level Co-occurrence Matrix (GLCM) homogeneity. Finally, the desired thematic map with sugarcane ready to harvest was generated. The mapping was then evaluated applying the confusion matrix method and Kappa statistics to the independent testing dataset which was composed of 102 field survey points and 48 point samples on Google Earth. The statistics indicated that the classification achieved an overall accuracy of 91.3% and a Kappa coefficient of 0.83. The results of this study show that the Object-Oriented method is very efficient for the sugarcane classification process to maximize elimination of other interfering factors and suggests that the China Environment Satellite HJ-1A/B has great potential in the development of an operational system for monitoring sugarcane growth in southern China. [ABSTRACT FROM AUTHOR]

Pot experiment showed that mixing earthworm feces into soil induced a significantly lower (P < 0.05) cucumber anthracnose than mixing peat, but had no significant difference (P > 0.05) with the treatment of system acquired resistance. The activities of polyphenol oxidase, peroxidase and phenylalanine ammonia-lyase in cucumber leaves treated with and without pathogen were increased in different degrees by mixing earthworm feces, compared with by mixing peat. All these suggested that mixing with earthworm feces could induce the system resistance of cucumber and suppress the plant disease via starting up the recovery enzyme system in plant.

The transformation of organic wastes into useful resources by vermiculture has been a biological technology for ages, and obtained a new development in 1970s. This paper mainly reviewed the methods of vermiculture, the physical and chemical aspects of vermicompost, and the comprehensive utilization of vermicompost, and emphasized the inhibition of vermicompost to soil-borne disease. Although the mechanisms of inhibition have not been fully understood, the application has great potentiality. The disease inhibition is related with the microflora, especially the large number antagonisms enriched in vermicompost.