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纸质出版日期： 2021-04-07 ，
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周红敏，张国东，王昶景，王锦地，程顺，薛华柱，万华伟，张磊.2021.塞罕坝地区高空间分辨率叶面积指数时序估算与变化检测.遥感学报，25（4）： 1000-1012
Zhou H M，Zhang G D，Wang C J，Wang J D，Cheng S，Xue H Z，Wan H W and Zhang L. 2021. Time series high-resolution leaf area index estimation and change monitoring in the Saihanba area. National Remote Sensing Bulletin， 25（4）：1000-1012
叶面积指数LAI（Leaf Area Index）是调节植被冠层生理过程的最重要的生物物理变量之一，高空间分辨率时间序列LAI对于植被生长检测、地表过程模拟与区域和全球变化研究至关重要，但是由于数据缺失和反演方法限制，目前还没有时空连续的高分辨率LAI数据产品。本研究提出了一种生成时间连续的高空间分辨率LAI数据的算法，首先对MODIS LAI产品滤波平滑，生成时间序列LAI的上包络曲线，根据上包络曲线提供的变化信息构建LAI动态模型。然后利用地面实测的LAI数据与Landsat反射率数据构建LAI反演的BP （Back Propagation）神经网络模型。将反演得到的高分辨率LAI数据作为LAI观测数据，利用集合卡尔曼滤波EnKF（Ensemble Kalman Filter）方法实时更新动态模型，生成时间连续的30 m空间分辨率LAI数据集。基于该算法生成了塞罕坝地区2000年—2018年长时间序列LAI数据集，利用Prophet深度学习模型进行模拟和预测，根据预测和原始LAI差异，利用支持向量机SVM（Support Vector Machine）方法检测植被干扰状况。结果表明：EnKF算法能够生成时空连续的高空间分辨率LAI数据，估算结果与地面测量值一致性较高，
R
2
为0.9498，RMSE为0.1577，在区域尺度上与Landsat LAI参考值较为吻合，
R
2
高于0.87，RMSE低于0.61。Prophet与SVM模型检测到研究区2009年，2010年，2013年，2014年， 2015年植被受干扰较为严重，主要由于年降水量偏少和林区作业砍伐造成，检测结果与当地降水量与砍伐数据吻合。本文提出的算法可用于大范围高时空LAI数据反演和植被变化检测，对塞罕坝乃至全国林区规划管理具有重要的参考价值。
A 30 m-spatial-resolution LAI time series estimation method was proposed on the basis of the ensemble Kalman filter (EnKF). Time series LAI of 2000—2018 was produced in the Saihanba area
and vegetation change monitoring was applied. The detected disturbance was consistent with climate condition and field management.
Time series LAI is critical for vegetation growth monitoring
surface process simulation
and global change research. Saihanba is an important ecological environment protection area in China
and long-term monitoring of this area is significant for forest management and development.
In this study
MODIS LAI products and Landsat surface reflectance data were used to generate time series high-resolution LAI datasets from 2004 to 2018 in Saihanba by using EnKF. Vegetation changes were then monitored on the basis of the generated LAI time series with the Prophet model. First
the multistep Savitzky-Golay filtering algorithm was used to smooth the MODIS LAI data
and the upper envelope of time series LAI was generated. A dynamic model was constructed in accordance with the trend of LAI upper envelope to provide a short-range forecast of LAI. Then
the ground measured LAI data and the corresponding Landsat reflectance data were used to train a Back Propagation (BP) neural network. The high-resolution LAI data from the BP model were used to update the dynamic model in real time to generate high-resolution time series LAI data based on the EnKF method. Lastly
the time series LAI data were used as the input of the Prophet deep learning model to obtain the LAI time series prediction values of a certain year. The correlation coefficient and root-mean-square error distribution maps could be obtained from the comparison of the prediction results with the LAI of the current year. A Support Vector Machine (SVM) method was used to classify the disturbed and normal pixels.
The EnKF algorithm can generate continuous high-resolution LAI data
and the estimation results are consistent with the field LAI values with
R
2
of 0.9498 and RMSE of 0.1577. At the regional scale
the estimation LAI maps have high consistency with the Landsat reference LAI maps
the
R
2
is higher than 0.87
and the RMSE is less than 0.61. The Prophet and SVM models detected that the vegetation in Saihanba was severely disturbed in 2009
2010
2013
2014
and 2015
mainly due to the low annual rainfall and deforestation. The detection results are consistent with the local precipitation and logging data.
The algorithm proposed in this paper can be used for time series high-spatial-resolution LAI data inversion on a large scale
and the inversion results can be used for vegetation change detection. This work has important reference significance for the planning and management of Saihanba and even the national forest area.
叶面积指数高时空分辨率集合卡尔曼滤波深度学习变化检测
leaf area indextime series high resolutionthe Ensemble Kalman Filter algorithmdeep learning methodchange detection
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