Qiangzi Li
Chinese Academy of Sciences
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Featured researches published by Qiangzi Li.
International Journal of Digital Earth | 2014
Bingfang Wu; Jihua Meng; Qiangzi Li; Nana Yan; Xin Du; Miao Zhang
Monitoring the production of main agricultural crops is important to predict and prepare for disruptions in food supply and fluctuations in global crop market prices. Chinas global crop-monitoring system (CropWatch) uses remote sensing data combined with selected field data to determine key crop production indicators: crop acreage, yield and production, crop condition, cropping intensity, crop-planting proportion, total food availability, and the status and severity of droughts. Results are combined to analyze the balance between supply and demand for various food crops and if needed provide early warning about possible food shortages. CropWatch data processing is highly automated and the resulting products provide new kinds of inputs for food security assessments. This paper presents a comprehensive overview of CropWatch as a remote sensing-based system, describing its structure, components, and monitoring approaches. The paper also presents examples of monitoring results and discusses the strengths and limitations of the CropWatch approach, as well as a comparison with other global crop-monitoring systems.
International Journal of Applied Earth Observation and Geoinformation | 2012
Wu Bf; Qiangzi Li
Abstract This study presents a crop planting and type proportion (CPTP) method for crop acreage estimation of complex and diverse agricultural landscapes. CPTP has three major components: (1) Crop planting proportion (CPP), estimated with wide-swath satellite remote sensing data to completely cover the monitoring area by segmenting cropped and non-cropped areas through unsupervised classification. (2) Crop type proportion (CTP), estimated by transect sampling and a special GPS-Video-GIS instrument (GVG) and a visual interpretation of crop type proportion in collected pictures for different strata. (3) Multiplication of CPP and CTP with arable land area at the strata level, summed to the province and national level. Validation has been done with in situ data for different agricultural landscapes over China. Both CPP estimation with remote sensing data and CTP estimation through ground survey have a high accuracy with average relative error (RE) and root mean square error (RMSE) equal to 1.42% and 1.67% for CPP and to 2.63% and 2.25% for CTP. The RE for crop acreage estimation equals to 4.09%. The CPTP method thus has a high accuracy, yields timely information at low costs, and is robust and provides objective results. The study concludes that the CPTP method can be used for large area crop acreage estimation of complex agriculture landscapes.
International Journal of Remote Sensing | 2012
Kun Jia; Qiangzi Li; Yichen Tian; Wu Bf; Feifei Zhang; Jihua Meng
Crop classification is a key issue for agricultural monitoring using remote-sensing techniques. Synthetic aperture radar (SAR) data are attractive for crop classification because of their all-weather, all-day imaging capability. The objective of this study is to investigate the capability of SAR data for crop classification in the North China Plain. Multi-temporal Envisat advanced synthetic aperture radar (ASAR) and TerraSAR data were acquired. A support vector machine (SVM) classifier was selected for the classification using different combinations of these SAR data and texture features. The results indicated that multi-configuration SAR data achieved satisfactory classification accuracy (best overall accuracy of 91.83%) in the North China Plain. ASAR performed slightly better than TerraSAR data acquired in the same time span for crop classification, while the combination of two frequencies of SAR data (C- and X-band) was better than the multi-temporal C-band data. Two temporal ASAR data acquired in late jointing and flowering periods achieved sufficient classification accuracy, and adding data to the early jointing period had little effect on improving classification accuracy. In addition, texture features of SAR data were also useful for improving classification accuracy. SAR data have considerable potential for agricultural monitoring and can become a suitable complementary data source to optical data.
Journal of Applied Remote Sensing | 2013
Kun Jia; Bingfang Wu; Qiangzi Li
Abstract The HJ satellite constellation is designed for environment and disaster monitoring by the Chinese government. This paper investigates the performance of multitemporal multispectral charge-coupled device (CCD) data on board HJ-1-A and HJ-1-B for crop classification in the North China Plain. Support vector machine classifier is selected for the classification using different combinations of multitemporal HJ multispectral data. The results indicate that multitemporal HJ CCD data could effectively identify wheat fields with an overall classification accuracy of 91.7%. Considering only single temporal data, 88.2% is the best classification accuracy achieved using the data acquired at the flowering time of wheat. The performance of the combination of two temporal data acquired at the jointing and flowering times of wheat is almost as well as using all three temporal data, indicating that two appropriate temporal data are enough for wheat classification, and much more data have little effect on improving the classification accuracy. Moreover, two temporal data acquired over a larger time interval achieves better results than that over a smaller interval. However, the field borders and smaller cotton fields cannot be identified effectively by HJ multispectral data, and misclassification phenomenon exists because of the relatively coarse spatial resolution.
Journal of remote sensing | 2011
Kun Jia; Bingfang Wu; Yichen Tian; Yuan Zeng; Qiangzi Li
In this article, a vegetation classification hypothesis based on plant biochemical composition is presented. The basic idea of this hypothesis is that the vegetation species/crops have their own biochemical composition characteristics, which are separable from each other for those co-existing species at a specific region. Therefore, vegetation species can be classified based on the biochemical composition characteristics, which can be retrieved from hyperspectral remote-sensing data. In order to test this hypothesis, an experiment was conducted in north-western China. Field data on the biochemical compositions and spectral responses of different plants and an Earth-observing 1 (EO-1) Hyperion image were simultaneously collected. After analysing the relationship between biochemical composition and spectral data collected from Hyperion, the vegetation biochemical compositions were estimated using sample biochemical data and bands of Hyperion data. The vegetation classification was completed using the biochemical content classifier (BCC) and maximum-likelihood classifier (MLC) with all Hyperion bands (MLC_A) and selected bands (MLC_S), which were used for estimating considered biochemical contents (cellulose and carotenoid). The overall classification accuracy of the BCC (95.2%) was as good as MLC_S (95.2%) and better than MLC_A (91.1%), as was the kappa value (BCC 92.849%, MLC_S 92.845%, MLC_A 86.637%), suggesting that the BCC was a feasible classification method. The biochemical-based classification method has higher vegetation classification accuracy and execution speed, reduces data dimension and redundancy and needs only a few spectral bands to retrieve biochemical contents instead of using all of the spectral bands. It is an effective method to classify vegetation based on plant biochemical composition characteristics.
Journal of remote sensing | 2014
Qiangzi Li; Xin Cao; Kun Jia; Miao Zhang; Qinghan Dong
Crop type identification is the basis of crop acreage estimation and plays a key role in crop production prediction and food security analysis. However, the accuracy of crop type identification using remote-sensing data needs to be improved to support operational agriculture-monitoring tasks. In this paper, a new method integrating high-spatial resolution multispectral data with features extracted from coarse-resolution time-series vegetation index data is proposed to improve crop type identification accuracy in Hungary. Four crop growth features, including peak value, date of peak occurrence, average rate of green-up, and average rate for the senescence period were extracted from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) profiles and spatially enhanced to 30 m resolution using resolution merge tools based on a multiplicative method to match the spatial resolution of Landsat Thematic Mapper (TM) data. A maximum likelihood classifier (MLC) was used to classify the TM and merged images. Independent validation results indicated that the average overall classification accuracy was improved from 92.38% using TM to 94.67% using the merged images. Based on the classification results using the proposed method, acreages of two major summer crops were estimated and compared to statistical data provided by the United States Department of Agriculture (USDA). The proposed method was able to achieve highly satisfactory crop type identification results.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | 2015
Kun Jia; Shunlin Liang; Xiangqin Wei; Qiangzi Li; Xin Du; Bo Jiang; Yunjun Yao; Xiang Zhao; Yuwei Li
Forest cover information is essential for natural resource management and for climate change studies. In this paper, the fractional forest cover (FFC) in Northeast China was estimated using neural networks (NNs) based on the Global Inventory Modeling and Mapping Studies (GIMMS3g) Normalized Difference Vegetation Index (NDVI) data with 8-km resolution from 1982 to 2011. Furthermore, the relationship between FFC and two key climatic parameters (temperature and precipitation) was also analyzed. The validation results indicated a satisfactory performance (R2 = 0.81, RMSE = 11.7%) of the FFC estimation method using NNs and time-series GIMMS3g NDVI data. The temporal and spatial characteristics of FFC changes were analyzed. The forest cover had a slightly decreasing trend during the study period for the entire Northeast China region. However, there were two distinct periods with opposite trends in the FFC change. The FFC had first increased from 1982 to 1998 (0.391% year-1), and then decreased from 1998 to 2011 (-0.667% year-1). The correlation analysis between the FFC and the climatic variations suggested that temperature and precipitation were not the decisive factors on controlling FFC changes in most of the Northeast China regions, and active forest disturbance might be the more important factor for FFC change in Northeast China.
IEEE Transactions on Geoscience and Remote Sensing | 2011
Kun Jia; Bingfang Wu; Yichen Tian; Qiangzi Li; Xin Du
Opium is a narcotic obtained from opium poppy and is the raw material of heroin for the illegal drug trade. Monitoring the illegal concentrated cultivation of opium poppy in major regions is critical for the understanding by governments and international communities of the scale of illegal drug trade. This paper investigates whether opium poppy can be discriminated from its coexisting plants using analytical-spectral-device field spectrometer data in the visible to short-wave infrared spectral range. Canopy spectral measurements were conducted during three different growth periods of opium poppy. A synthetic method with three analysis levels was applied to discriminate opium poppy from other species and to select optimal bands for opium poppy discrimination. First, the Mann-Whitney U-test method was used to test the spectral reflectance difference between opium poppy and coexisting crops at each wavelength. Then, the Jeffries-Matusita distance and band correlation analysis were conducted to select the optimal wavebands for discriminating opium poppy using the significant wavebands from the test results. Finally, classification and regression tree analysis was employed to validate the classification accuracy based on the selected optimal wavebands. The results indicated that the spectral reflectance of opium poppy was significantly different from that of coexisting crops in many surveyed wavebands, and opium poppy could be discriminated using a field survey spectrum at canopy level. The best time for discriminating opium poppy from coexisting crops was around flowering time. This paper provided the prerequisite for monitoring opium poppy using satellite remote sensing data in some regions of concern.
Journal of Applied Remote Sensing | 2014
Qiangzi Li; Huanxue Zhang; Xin Du; Ning Wen; Qingshan Tao
Abstract Rice acreage estimation is a key aspect of assessing rice production. A method of estimating rice acreage at the county level is explored, using data from the HJ-1A/B Chinese Environmental Satellite for Hunan Province, which has complex rice cropping patterns. The method combines supervised and unsupervised classification using a mixed-pixel decomposition model. The rice acreage estimation results were validated by interpretation of RapidEye images for early-season rice and ground survey data for medium-season and late-season rice. The results showed a good correlation between the estimates derived from RapidEye and from HJ CCD data for pure rice pixels ( R 2 = 0.99 , 0.99, and 0.97 for early-, medium-, and late-season rice). The discrepancy was < 10 % at the plot level ( 6.5 × 6.5 km ) for early-season rice, while it was 12.20 and 12.36% at the plot level ( 1 × 1 km ) for medium-season and late-season rice. These results suggested that the method proposed in this study is capable of rice acreage estimation at the county level. The method can also be used in mountainous regions and areas of fragmented planting.
International Journal of Drug Policy | 2011
Yichen Tian; Bingfang Wu; Lei Zhang; Qiangzi Li; Kun Jia; Meiping Wen
BACKGROUND Myanmar has long been a focus of the international community as a major opium poppy cultivation region. METHOD This study used remote sensing technology and ground verification to monitor opium poppy cultivation for three opium poppy growth seasons in North Myanmar. RESULTS The study found that opium poppy cultivation has remained high. In 2005-6, 2006-7 and 2007-8 growing seasons the total areas monitored were 52,482 km(2), 178,274 km(2) and 236,342 km(2) and the total cultivated area of opium poppy was 8959 ha, 18,606 ha and 22,300, respectively. This was significantly less than cultivation levels reported during the 1990s. The major cultivation regions were located in Shan State, producing 88% of total poppy cultivation in North Myanmar in 2007-8. The opium poppy was mainly cultivated in the interlocking regions controlled by the local armed forces in Shan State. The field survey noted that most households in this area were poor and poppy cultivation was a main source of income. There were also differences between our figures on poppy cultivation and those reported by United Nations Office on Drugs and Crime. CONCLUSION Our study shows that although the opium poppy cultivation in North Myanmar has reduced over recent years, it remains a major producer of opium and to which the international community needs to pay attention, especially in those areas controlled by local armed forces.