基于随机森林回归的草场植被盖度反演模型研究——以新疆阿勒泰地区布尔津县为例

作为草地资源大国,我国正面临严峻的草场退化形势。掌握草场植被盖度的历史演变趋势,是草场退化驱动力识别及风险评估的基础。目前已有研究多以参数回归方法估算植被盖度,但并未充分考虑其苛刻的使用条件。利用Landsat系列卫星遥感影像及地面植被盖度监测资料建立非参数回归——随机森林回归模型,并与传统线性回归方法进行比较,在此基础上应用随机森林回归模型估算近10年来布尔津县草场植被盖度的变化趋势,并对结果的不确定性进行分析。结果显示:传统的线性回归方法很难满足其基本的统计学假设条件,而随机森林模型不但无需进行假设条件检验,而且预测的准确性也优于以往普遍应用的线性模型。基于Landsat ETM+标准数据得到的反演结果较之TM和OLI数据普遍偏小,地表反射率数据虽然可以大幅降低传感器不同对反演结果所造成的影响,但结果仍存在约±10%的不确定性。涉及的草场类型众多,为了提高反演精度,后续研究需要分别计算其植被指数,并尽量减低传感器差异带来的不确定性。

Grassland vegetation cover inversion model based on random forest regression: A case study in Burqin County, Altay, Xinjiang Uygur Autonomous Region

As a large country with extensive grassland resources, China is facing severe grassland degradation. Studying trends in grassland vegetation cover change provides a basis for identifying the driving forces of grassland degradation and associated risk assessment. In previous studies, parametric regression models have typically been applied to estimate vegetation cover. However, the harsh assumptions of parametric regression have always been neglected. In the current study, vegetation cover monitoring data and vegetation indices (NDVI, SAVI, MSAVI, EVI), extracted from Landsat remote sensing images, were used to build random forest regressions, which are non-parametric models. These models were subsequently compared with traditional linear regression models. To build and test these models, 205 samples were collected from 2010 to 2015 (data from 2012 were not included) in alpine meadow, mountain meadow, lower-flat meadow, temperate meadow steppe, desert steppe, steppe desert, and desert in Burqin County, Xinjiang Uygur Autonomous Region. Among these samples, 150 samples were used to build models, and the remainder was used as testing data. Two sets of Landsat remote sensing images, Level 1 Standard Product and Surface Reflectance Product, were applied separately, and both included TM data for 2011-2012 and OLI data for 2013-2015. In total, two random forest models and 23 linear models were built. The results indicated that the predictive ability of the random forest models was clearly stronger than that of most of the linear models. Moreover, it was not necessary to test the assumptions for the random forest models, whereas none of the linear models'' assumptions in this study could be satisfied perfectly. In the case study, random forest regression was applied to estimate the trend in grassland vegetation cover change in the last decade in the study area based on 663 sampling points. Among these, data for 2005-2009 were based on Landsat ETM+, data for 2010-2011 were based on Landsat TM, and data for 2013-2015 were based on Landsat OLI. It was clear that sensor differences would have a certain influence on the inversion results. Therefore, we also simultaneously built a random forest model for MODIS-EVI data, as this would not be affected by sensor differences, and the results calculated using MODIS data were considered to be a standard. For Level 1 standard data, the results based on Landsat ETM+ were significantly smaller than the results based on MODIS data. For surface reflectance data, the influence of different sensors on the results could be markedly reduced. Finally, to quantify the uncertainty of vegetation cover change trend based on surface reflectance data, we used a random forest model to verify vegetation cover extracted from different sensors during the same period, and found that the uncertainty was between -10% and 11%. In conclusion, random forest regression is assumed to be a better model to inverse vegetation cover than linear models. For its application in time series studies, Landsat surface reflectance production could significantly reduce the influence of sensor difference, although the uncertainty was still approximately ±10%. In the current study, we assessed many grassland types, and to improve the accuracy of prediction, vegetation indexes should be calculated separately in further studies. In addition, efforts should be made to reduce the uncertainty associated with the data from different types of sensor.