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?104 hm2, including 52.37?104, 56.93?104 and 16.99?104 hm2 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.