基于Google Earth Engine的中老缅交界区橡胶林分布遥感提取

天然橡胶林作为东南亚地区主要的经济林和关系国计民生的一种重要战略物资,对当地社会经济发展和环境保护发挥着重要作用。目前对于中、老、缅交界区的橡胶林空间分布情况、分布面积和演变特征等方面相关信息缺乏相应的研究,严重限制了中国农业上关于橡胶产量消费、贸易以及储备。此外,针对橡胶林的时空大尺度的监测主要是采用长时间序列的中低分辨率影像,但此类影像存在大量的混合像元,严重限制了橡胶提取精度。为解决这些问题,该研究基于Google Earth Engine(GEE)云计算平台,利用2015-2019年Landsat OLI的多时相遥感影像数据,通过分析橡胶的物候特征,构建分类参数和模型,应用专家知识决策树的分类方法,并结合2015-2019年间每年12月份实地调查数据对橡胶的分类结果进行验证。结果表明基于GEE平台利用橡胶物候信息计算参数的方法提取较大范围研究区内的橡胶林的准确性较高。总体精度为90.32%,Kappa系数为0.87,可满足一般生产需求。截至2019年中老缅交界区橡胶林总面积达126.29万hm^2,其中,西双版纳区域橡胶林面积有52.37万hm^2,缅甸区域橡胶林面积有56.93万hm^2,老挝北部5省橡胶林面积有16.99万hm^2。分析发现在替代政策发展过程中,由于老挝、缅甸实际情况不同而出现政策差异性,导致区域政策发展不均衡。该研究结果表明应用云计算技术可以克服时空大尺度橡胶监测运算能力不足的问题,可为中老缅甸交界地区橡胶合理布局与区域可持续发展提供科学依据和决策支持。

Remote sensing image extraction for rubber forest distribution in the border regions of China, Laos and Myanmar based on Google Earth Engine platform

Rubber forest has an increasingly important impact on the environment and social economy. The huge demand has created a demand-supply gap in the process of economic globalization and the rubber forest planting, especially that in the border areas of different countries has been widely concerned in various fields. In this study, the rubber forest distribution was extracted by using the cloud computing technology of Google Earth Engine platform and the integration of multiple Landsat OLI remote sensing images from 2015 to 2019 in the border areas of China, Laos and Myanmar. The rubber phenology characteristics were obtained through rubber time series analysis. The different feature parameters were selected, and the differences of each parameter in foliation and defoliation period were compared to distinguish rubber forest and other land coverage types. Then the classification model of expert knowledge decision tree was constructed based on the calculated segmentation threshold of each parameter, and the algorithm is applied to the whole research area of the border areas of China, Laos and Myanmar. The results showed that time series NDVI in February(defoliation period) and April(foliation period) of rubber in the study area had good performance to distinguish rubber forest and other land coverage types. The overall accuracy of extraction was 90.32% and Kappa coefficient was 0.87. Both the overall accuracy and Kappa coefficient met the accuracy requirements of general production. Compared with the former researches, the method based on Google Earth Engine using rubber phenology calculation parameters to extract rubber forest in a large research area has a high accuracy. The total area of rubber forest extracted was 126.29×10^4 hm^2, including 52.37×10^4, 56.93×10^4 and 16.99×10^4 hm^2 of rubber forest extracted from Xishuangbanna, Myanmar and the five northern provinces of Laos, respectively. It is also found that the areas of rubber forests were different in these regions because the different actual situation of Laos and Myanmar produce differential policies in the process of alternative policy development. The cloud computing technology based on Google Earth Engine platform can overcome the lack of computing power of large-scale rubber monitoring in time and space, and provide scientific basis and decision support for the rational rubber layout and regional sustainable development in the border areas of China, Laos and Myanmar.