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Taken Deyang city in 2014 as studied area, 10 rice samples (500 m x 500 m) were set up as training areas, meanwhile rice verification points were established during same period. Applied two adjacent periods multispectral high spatial resolution image (2014/8/05 8 m) and medium-low spatial resolution image (2014/8/13 16 m) of GF-1 with ENVI5.1 as platform, the changing characteristics were analyzed , and the thresholds of NDVI were determined. Based on digital elevation map (DEM) and slope map, the rice planting area with decision tree classification method was monitored. Using verification points to evaluate classification accuracy, the monitoring area by factors from the result of rice samples were corrected. The results showed that： similar to the plains and hilly regions in Deyang, based on digital elevation map (DEM) and two periods images information of GF-1 satellite images, the classification user accuracy could be up to 92. 3 % , and producer accuracy to 96. 5 %. As 78 % multiplication coefficient amended monitoring area, more accurate rice area was obtained. It was concluded that GF-1 satellite image as a new kind of high spatial resolution data, applied to monitor planting rice in Deyang, achieved higher classification accuracy, which could be used as a reliable and free of charge image source in the other regions. [ABSTRACT FROM AUTHOR]

【Objective】This paper aimed to provide timely and accurate agricultural spatial information for agricultural production management, Luliang county in Yunnan province was selected to carry out surveillance research facilities planting area based on the Landsat-8 OLI and Sentinel-2 A MSI images. 【Method】Firstly, the remote sensing features of solar greenhouse and other typical landforms in the region were analyzed, secondly, visual interpretation and four kinds of supervised classification methods such as minimum distance method, Mahalanobis distance method, maximum likelihood method and support vector machine were used to extract facility planting spatial informations of facility planting, then sub-level Google earth historical images were used to create verification sample area for the basemap. Planting area information of sampling facilities by visual interpretation was extracted, which was used to verify the monitoring accuracy of the Landsat-8 and Sentinel-2 A images and choose the optimal monitoring method. 【Result】According to the above methods, the visual interpretation area accuracy based on OLI images was 97. 54 %, and the accuracy of the four kinds of supervised classification methods was 77. 31 %, 91. 35 %, 91. 89 %and 83. 15 %, respectively; The precision of using Sentinel-2 A image based on visual interpretation and four supervised classification methods was 98. 82 %, 80. 80 %, 87. 01 %, 93. 89 % and 85. 41 %, respectively. 【Conclusion】( i) Two kinds of images of greenhouse monitoring were suitable for the maximum likelihood method, and the error of estimation accuracy and visual interpretation accuracy were 5. 65 %and 4. 93 %.( ii) Remote sensing monitoring showed that the solar greenhouse in Luliang county mainly concentrated in the lacustrine plains area in 2016. The area based on Landsat-8 image surveillance was 5381. 62 hm2, and the area based on Sentinel-2 A image surveillance was 5347. 8 4 hm2. [ABSTRACT FROM AUTHOR]

【Objective】The present paper was conducted to study the accuracy of using Landsat8 OLI remote sensing image to monitor the rice planting area in hilly areas of Sichuan province.【Method】Four kinds of rice cultivars from shallow hill and deep hill rice were selected respectively according to the valley height. And the area of rice planting was obtained by ground sampling survey and Google Earth image interpretation in the same growing season as validation data. This result was used to evaluate the monitoring accuracy of rice planting area in the hilly region of Sichuan Province based on OLI fusion images. After orthophoto correction of OLI image,15 m panchromatic band image and30 m multispectral images were used to fusion to get 15 m resolution fusion image. And supervised the classification with maximum likelihood method to get the monitoring result.【Result】Compared with the quadrat verification data,the results of rice planting area monitoring based on OLI images with the same sample area showed that the average accuracy of the shallow hill area was 93. 7 %,the error range was 1 % to 8. 7 %. And the depth area was 92. 5 %,the error range was 1. 5 % to 15. 8 %.【Conclusion】According to the error range of the monitoring results in the shallow hilly area and the deep hilly area,with the increase of the valley height,the terrain tended to be complicated,the plot tended to be narrow and broken,and the uncertainty of the OLI image monitoring result increases. And in the same time,the accuracy of OLI image monitoring has a downward trend. This study provides a reference for improving the accuracy of OLI image monitoring of rice planting area in hilly areas of Sichuan province. [ABSTRACT FROM AUTHOR]

Lodging of cereal crops due to strong wind and rain is a common agricultural disaster in many regions of the world, leading to crop growth retardation and yield reduction. Area of crop lodging is very critical for agricultural hazard assessment and agricultural insurance claim. A survey method of lodging using high resolution remote sensing data acquired from unmanned aerial vehicle or satellite is fast and efficient. Worldview-2 multi-spectral image has a 1.8 m spatial resolution in 8 bands (from 400 to 1040 nm), which is a type of useful remote sensing data of agricultural information in many aspects, but there are few researches about lodging survey. Therefore, a maize lodging survey using Worldview-2 image was discussed in this paper, which was aimed to find an optimum method for estimating the lodging area of maize. In 2012, a Worldview-2 image with the swath of 5 km × 5 km was acquired on September 14th after a lodging event occurred on September 12th in Wanzhuang Agricultural Park of Chinese Academy of Agricultural Sciences, and the precise area of lodging at 3 fields was measured using the photogrammetry of unmanned aerial vehicle. In this paper, the features of spectrum and texture were analyzed for finding out the optimum features and bands in classification. Firstly, typical pixels of normal maize (285 pixels) and lodging maize (284 pixels) were sampled. Spectrum was acquired from multi-spectral image after ortho-rectification and atmospheric correction. Mean texture feature was acquired from reflectance data by applying co-occurrence based filter. The spectral curves showed that there was a significant difference between normal maize and lodging maize. The reflectance of maize increased after lodging, and the values of the 3 bands including red edge, infrared 1 and infrared 2 were greater than 0.1. The change of canopy structure might be the main reason of this phenomenon. Comparing texture feature of lodging maize to normal maize, the values of the 8 bands between the 2 kinds of maize were different, and especially the band of green, red edge, infrared 1 and infrared 2. According to the feature analysis, red edge, infrared 1 and infrared 2 were chosen as the optimum bands for classification in this study. Then, 8 classification methods were carried out by using different features, bands and algorithms of classification. The type of features included spectrum and texture, the bands used for classifying were respectively 3 optimum bands and all 8 bands, and the algorithms of classification were respectively based on Mahalanobis distance and the maximum likelihood. Finally, an optimum method was chosen according to the error. The estimation results of 8 methods showed that, the errors of those methods that used 3 optimum bands were lower than those using 8 bands, and when using the 3 optimal bands, the errors of those methods based on the algorithm of the maximum likelihood were lower than those based on the algorithm of Mahalanobis distance. According to the performance of 8 methods, the method that used reflectance of red edge, infrared 1 and infrared 2 based on the maximum likelihood was the best one, for which the minimum error was 2.2%, the maximum error was 8.9%, and the average of error was 4.7%. According to this study, using Worldview-2 multi-spectral image can estimate the area of lodging maize accurately based on remote sensing data of unmanned aerial vehicle. [ABSTRACT FROM AUTHOR]