| Plant diseases and insect pests is one of the important factors influencing the agricultural product safety and yield. Excessive application of pesticide and fungicide to control the plant diseases and insect pests led to the deterioration of the ecological and food problems. In addition to the crop varieties and the global warming, one of the main factors for the recent increase in frequency and severity of plant diseases and insect pests is still lacking of adequate surveys to detect incipient damage and plan appropriate control operations. The remote sensing technology has great advantage of plant diseases and insect pests forecasting due to the characteristic of real time, dynamic and non-touch. On the basis of field survey and spectrum measurement, understanding to remote sensing mechanism and plant diseases and insect pests feature spectrum theory thoroughly, this paper focused on the feasibility of retrieval of environment elements, the relationship between the feature spectrum in the field and the remote sensing digital image, and using the plant diseases and insect pests feature spectrum to extract the damage information from the satellite remote sensing digital image. The main contents are as follows:The canopy reflectance of different plant diseases and insect pests was measured by using ASD hand-held spectroradiometers. The reflectance data was transformed by the method of first differential coefficient, logarithm and normalization, then stepwise discriminate analysis and hierarchical clustering were used to identify the different plant diseases and insect pests. The results suggested that bands selected from stepwise discrimination analysis mainly lied along the blue, green, red and near-infrared bands. In addition to the blue, green, red and near-infrared bands, the spectral bands along the blue-green edge, green-red edge and red curves were selected by the hierarchical clustering. Plant diseases and insect pests could be identified more accurate by using selected bands than the original data, the highest recognition accuracy of up to 90.6%, and the bands lying along the edges had important information for discrimination of plant diseases and pests. The spectral data, dealt with the transformation of logarithm and differential coefficient, could achieve better accuracy than others.Using ASD Hand-held Spectroradiometers and low altitude remote sensing system measured the canopy reflectance of wheat damaged by wheat powdery mildew and, at the same time, scored the disease index (DI). The correlation between reflectance and DI and normalized difference vegetable index (NDVI) and DI was calculated. The results showed that the correlation between reflectance and DI was significant during the stage of milk-filling. The results of in-field measurement showed that the correlation between near infrared reflectance and DI was higher than that of green band with the correlation coefficient of -0.79 and -0.54, respectively. The reflectance correlation between red, green and blue bands decreased in the order with the correlation coefficient of -0.79, -0.75 and -0.62, respectively. The reflectance of low altitude remote sensing was well correlated with NDVI moreover. The correlation coefficients were 0.70, 0.68 and 0.54 for blue, red and green band, respectively. The land surface temperature (LST) was retrieved from NOAA image by the method of split window algorithm, and it was found that there were correlation coefficients maximal to 0.93 (P<0.01) between LSTs from remote sensing and those from ground observation, but the LST generally higher than observations. Analysis of the time sequence NOAA-NDVI image suggested that winter wheat NDVI had the obvious regulation. On the basis of in field investigation and hyperspectral reflectance data established the correlation equation: NDVI = -3×10-3x + 0.623, R =0.918 (P<0.01). The aphid damage level from NOAA-NDVI image was retrieved based on above equation, at the same time; the other vegetation elements and winter wheat distribution were retrieved by the NOAA-NDVI image.The LST was retrieved from MODIS 1 B image by the method of split window algorithm and other vegetation elements also retrieved, moreover, the retrieval results were compared to NOAA. The retrieval LSTs sequence results showed the LST ranged from 260k to 320k. The correlation analysis suggested its retrieval result from MODIS was more accurate than that from NOAA, and more close to the ground observation. The error was 0.41°C and 0.90°C, respectively. Analysis of the NOAA-NDVI and MODIS-NDVI histogram showed that their shapes were the same, and the range of their values was wider than that of NOAA, so MODIS-NDVI had more sensitivity to vegetation.Based on the occurrence mechanism and feature spectrum of wheat aphid and wheat powdery mildew, the methods of NDVI and Masking, Principal component transformation and Hue adjust (MPH) technique were applied in extraction of the damage information from TM multi-spectral data. The healthy and damaged field samples were located in terms of the GPS information, the extraction information results showed that the NDVI value almost the same before injury, but the damaged sample NDVI value obviously decrease after injury. The statistics of DN value from TM image suggested that the healthy sample winter wheat had a peak at TM4 and decrease at TM5, but the damaged sample was contrary to healthy. From the PCA eigenvector matrix, the healthy and damaged samples in PC 3 of masked imageries before injury were almost the same, but after injury obviously increased. The results indicated that the PC 3 of masked imagery could reveal wheat aphid and wheat powdery mildew feature information apparently.This paper on the basis of understanding to wheat aphid and powdery mildew occurrence mechanism and their feature spectrum thoroughly and in field investigation, using of multi-source remote sensing image retrieved the LST, vegetation and other elements and extraction the damage information. It is prospective for using remote sensing technology archive large scale and dynamic plant diseases and insect pests occurrence information in real time and quickly, at the same time, it is also important to establish the forecasting system and increase the ability of plant diseases and insect pests control. |
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