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Zhou Z.,Zhejiang University | Zhou Z.,Zhejiang University of Science and Technology | Huang J.,Zhejiang University | Wang J.,Zhejiang University | And 7 more authors.
PLoS ONE | Year: 2015

Most areas planted with sugarcane are located in southern China. However, remote sensing of sugarcane has been limited because useable remote sensing data are limited due to the cloudy climate of this region during the growing season and severe spectral mixing with other crops. In this study, we developed a methodology for automatically mapping sugarcane over large areas using time-series middle-resolution remote sensing data. For this purpose, two major techniques were used, the object-oriented method (OOM) and data mining (DM). In addition, time-series Chinese HJ-1 CCD images were obtained during the sugarcane growing period. Image objects were generated using a multi-resolution segmentation algorithm, and DM was implemented using the AdaBoost algorithm, which generated the prediction model. The prediction model was applied to the HJ-1 CCD time-series image objects, and then a map of the sugarcane planting area was produced. The classification accuracy was evaluated using independent field survey sampling points. The confusion matrix analysis showed that the overall classification accuracy reached 93.6% and that the Kappa coefficient was 0.85. Thus, the results showed that this method is feasible, efficient, and applicable for extrapolating the classification of other crops in large areas where the application of high-resolution remote sensing data is impractical due to financial considerations or because qualified images are limited. © 2015 Zhou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Source

Li L.,Guangxi Zhuang Autonomous Region Institute of Meteorological and Disaster Mitigation Research | Li L.,National Satellite Meteorological Center | Kuang Z.,Guangxi Zhuang Autonomous Region Institute of Meteorological and Disaster Mitigation Research | Kuang Z.,National Satellite Meteorological Center | And 4 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2013

Guangxi is one of the annual precipitation-rich regions of the country. But seasonal drought occurs very frequently because of spatio-temporal nonuniform distribution of rainfall. Seasonal drought has a rather large influence on the water resources, industrial production, and human life, especially on the agricultural production of Guangxi. The research there has been mostly aimed at some characteristics of a certain domain (such as agriculture) in the application. And some evaluation processes too depended on subjective experience or a simple formula, and did not comprehensively consider the influence to society's economy and environment of drought disaster, the vulnerability of the disaster bearing body, and the ability to prevent and reduce disaster. In order to strengthen the risk assessment and emergency management capability of the autumn drought disasters of Guangxi, an autumn drought risk assessment indicator system was established, and its indicators were determined according to drought risk, sensitivity of disaster environment, vulnerability of disaster bearing body, ability of disaster prevention and reduction, included rainfall anomaly, little rain days anomaly, topography, hydrographic net, Karst landform, population density, gross domestic product (GDP), arable area, pecuniary loss and real GDP per capita, by use of meteorological data, such as daily rainfall of 88 meteorologic stations, from 1961 to 2010, basic geographic information, consisting of 1:50000 scale county boundary and hydrographic net, digital elevation model (DEM) (about 100m resolution), and Karst landform (about 1000 m resolution), and socio-economic data, containing population density, GDP, real GDP per capita, arable area and pecuniary loss, taking the county as a unit. Then factor weights were obtained by an analytic hierarchy process (AHP), which relied on a judgement matrix and its eigenvalues and eigenvectors, and a consistency test of the matrix, and comprehensive assessment models for agriculture and social economy were established and calculated to get the autumn drought disaster risk index, which ranked distribution by a geographical information system (GIS). The distribution indicated that the higher risk area contains the west of Chongzuo, central and south of Baise, east of Hechi, south of Liuzhou, east and south of Guilin, central and northwest of Laibin, urban of Guigang, and some counties of Nanning. The lower risk area contains the northern mountainous area of Baise, Hechi, Liuzhou and Guilin, most of Fangcheng and Beihai, central and south of Yulin, and most of Pingnan, Guiping, Zhaoping, and Mengshan. Finally the condition of drought disaster was used to validate the distribution. The correlation coefficients were 0.58441 and 0.60393, respectively, of agriculture and social economy, through significance test by 0.05, using the correlate analysis method, by the multivariate analysis tool of ARCGIS. The results showed that the distribution of autumn drought disaster risk is basically consistent with the spatial distribution of drought disaster losses, which in the high risk area are mainly distributed in the middle basin and mountain area in the west, and are low in the mountain areas in the northwest and north, coastal area in south, and parts of the southeast of Guangxi. The results reflected preferably regional differences of drought risk, which were due to the distinction of formation, environment, bearing body and prevention of autumn drought disaster of Guangxi. The drought assessment model and method used combine the advantage of AHP and GIS, and can make drought evaluation procedure and evaluation results more scientific, increasing practicability and maneuverability. Source

Li L.,Guangxi Zhuang Autonomous Region Institute of Meteorological and Disaster Mitigation Research | Kuang Z.,Guangxi Zhuang Autonomous Region Institute of Meteorological and Disaster Mitigation Research | Zhong S.,Guangxi Zhuang Autonomous Region Institute of Meteorological and Disaster Mitigation Research | Mo J.,Guangxi Zhuang Autonomous Region Institute of Meteorological and Disaster Mitigation Research | And 2 more authors.
Lecture Notes in Electrical Engineering | Year: 2012

A, B satellite data were pretreated, on board the Environment and disaster monitoring and forecasting satellite of China. The research carries out a series of steps to discriminate sugarcane, selects the sugarcane plots and samples, analyses the spectral characteristics, classifies the satellite data with decision tree classification and supervised classification, the plantation data of Guangxi base library of remote sensing information and slope data as ancillary. The comparison between information from Remote sensing discrimination and official statistics shows that combination of decision tree classification and supervised classification method can effectively discriminate sugarcane. © Springer-Verlag 2012. Source

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