中国农业气象

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 基于植被长势的香蕉区域估产信息扩散模型

 蔡大鑫,刘少军,陈汇林,田光辉   

  1.  海南省气象科学研究所/海南省南海气象防灾减灾重点实验室,海口 570203
  • 出版日期:2020-09-20 发布日期:2020-09-13
  • 作者简介:蔡大鑫,E-mail:cdxxxhyn@126.com
  • 基金资助:
     国家自然科学基金(41765007;41465005;41675113);海南省基础与应用基础研究计划(自然科学领域)高层次人才项目(2019RC359);海南省气象局科研项目(HNQXMS201502)

 Information Diffusion Model of Banana Yield Estimation Based on Vegetation Growth

 CAI Da-xin,LIU Shao-jun,CHEN Hui-lin,TIAN Guang-hui   

  1.  1. Research Institute of Hainan Meteorological Bureau/Key Laboratory of Meteorological Disaster Preventing and Reducing of South China Sea, Haikou 570203, China
  • Online:2020-09-20 Published:2020-09-13
  • Supported by:
     

摘要:  基于Landsat-8和MODIS数据,首先采用面向对象方法对海南岛香蕉种植区的空间分布进行初次提取,然后采用基于时序植被指数的马氏距离方法进行二次提取,最后对两次提取结果进行空间叠加,采用随机选点实地验证的方法对分类精度进行评价。针对区域估产样本数量少的问题,统计2014?2015年的MODIS数据和2015年的香蕉区域产量数据,以全生育期香蕉长势为输入变量建立信息扩散区域估产模型,利用交叉验证方法评价估产精度,同时评价估产模型对于产量增减变化趋势模拟的准确性。通过组合多个生育阶段构建三种信息扩散估产方案,对比各方案的估产精度。结果表明:(1)采用面向对象和马氏距离的综合分类方法精度较高,总分类精度和Kappa系数分别为82.5%和0.7338,一致性检验的结果较好。(2)基于全生育期香蕉长势的信息扩散模型估产精度较高,平均相对误差为26.0%,决定系数为0.9216,解释能力和稳定性较好;对年际间产量趋势变化的预估准确率达到83.3%。(3)基于全生育期构建的单变量信息扩散估产方案精度最高,相比其它两种多变量建模方案平均相对误差分别降低2.9个百分点和10.4个百分点。可见,信息扩散方法对于小样本数据的处理能力较强,以全生育期为输入变量构建的估产模型精度较高,适应性较好,可为热带经济作物区域估产提供重要参考。

关键词:  , 信息扩散, 估产模型, 遥感, 经济作物, 香蕉

Abstract:  Banana is an important tropical fruit in Hainan. Limited by meteorological disasters and the level of production technology, banana production has weak stability and large inter-annual fluctuations in Hainan. Remote sensing yield estimation is currently one of the most widely used crop yield estimation methods, especially suitable for large-area, uniformly distributed planting types. Therefore, carrying out research on banana yield estimation by remote sensing and accurately grasping the change trend of yield is of great significance to the planning and stable development of the banana planting industry. Based on Landsat-8 and MODIS data, the object-oriented method was first used to extract the spatial distribution of banana growing areas in Hainan Island, and then the Mahalanobis distance method based on the time series vegetation index was used for the second extraction, and finally the results of the two extractions were spatially overlaid. The method of field verification at random selected points was used to evaluate the classification accuracy. Aiming at the problem of the small number of regional yield estimation samples, the MODIS data from 2014 to 2015 and the banana regional yield data in 2015 were collected. The growth of banana throughout the growth period was used as an input variable to establish an information diffusion model for regional yield estimation. NDVI data was calculated by daily MODIS images, and synthesized to monthly data. The monthly composite values of NDVI was summed to obtain the cumulative value of NDVI throughout the growth period. Using the obtained vector files of banana planting areas, the cumulative NDVI values of bananas were extracted in 18 counties during the whole growth period. As the input and output variables of the information diffusion model, the cumulative value of NDVI and yield data were logarithmically normalized. The normal diffusion function was used to diffuse the sample information into the whole field, and an information matrix composed of information increments was established. Then the fuzzy relationship between the cumulative value of NDVI and the yield was obtained from the information matrix, that was the fuzzy relationship matrix. Finally, through the fuzzy approximate reasoning method, the simulated yield was obtained from the input samples. The cross-validation method was used to evaluate the accuracy of production estimation, and at the same time, the accuracy of the production estimation model for the simulation of production change trends was evaluated. Three kinds of information diffusion estimation schemes were constructed by combining multiple growth stages: scheme I was the NDVI cumulative value modeling scheme for the whole growth period; scheme Ⅱ was the scheme of joint input of vegetative growth stage and reproductive growth stage; scheme Ⅲ was the scheme of joint input during the budding stage and fruit development stage. The estimation accuracy of each plan was compared at last. The results showed that: (1) the comprehensive classification method using object-oriented and Mahalanobis distance had a higher accuracy. The total classification accuracy and Kappa coefficient were 82.5% and 0.7338 respectively, and the result of the consistency test was better. (2) The information diffusion model based on banana growth during the whole growth period had high yield estimation accuracy, with an average relative error of 26.0%, a coefficient of determination of 0.9216, and good explanatory power and stability; the accuracy of the estimation of inter-annual yield change trends reached 83.3%. (3) The univariate information diffusion estimation scheme based on the entire growth period had the highest accuracy, and the average relative error was reduced by 2.9 and 10.4 percentage point respectively compared with the other two multivariate modeling schemes. Based on the above results, it could be found that a fuzzy relationship was established with the information diffusion method between banana growth and yield by constructing a fuzzy set. The model was estimated with high accuracy, good explanatory ability and stability, and the accuracy rate for predicting the inter-annual yield change trend was also high, which could meet actual business needs. Through the establishment of a phase-by-growth yield estimation program to compare the impact of each developmental stage on the yield estimation accuracy, it was found that the effect of information diffusion model with whole growth period as the input variable was the best, which could take into account the impact of meteorological disasters and sensitive growth periods on yield. The accuracy of the yield estimation scheme II, which inputted both the vegetative growth period and the reproductive growth period, was higher than that of the program III, which only considered the reproductive growth period. The performance of information diffusion method with advantages in the processing of small sample data and ability to simulate nonlinear relationships was better. Applicability of yield estimation model based on full growth period in banana planting areas was judged satisfactory. The yield estimation model could be applied in the late stage of banana fruit expansion, and could also be used to carry out regular rolling forecasts after budding, to improve timeliness and provided scientific basis for agricultural departments and farmers to rationally arrange production and sales.

Key words:  Information diffusion, Estimated model, Remote sensing, Cash crops, Banana

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