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An Artificial Intelligence Approach to Predict Gross Primary Productivity in the Forests of South Korea Using Satellite Remote Sensing Data

机译：采用卫星遥感数据预测韩国林源性初级生产力的人工智能方法

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摘要

Many process-based models for carbon flux predictions have faced a wide range of uncertainty issues. The complex interactions between the atmosphere and the forest ecosystems can lead to uncertainties in the model result. On the other hand, artificial intelligence (AI) techniques, which are novel methods to resolve complex and nonlinear problems, have shown a possibility for forest ecological applications. This study is the first step to present an objective comparison between multiple AI models for the daily forest gross primary productivity (GPP) prediction using satellite remote sensing data. We built the AI models such as support vector machine (SVM), random forest (RF), artificial neural network (ANN), and deep neural network (DNN) using in-situ observations from an eddy covariance (EC) flux tower and satellite remote sensing data such as albedo, aerosol, temperature, and vegetation index. We focused on the Gwangneung site from the Korea Regional Flux Network (KoFlux) in South Korea, 2006–2015. As a result, the DNN model outperformed the other three models through an intensive hyperparameter optimization, with the correlation coefficient (CC) of 0.93 and the mean absolute error (MAE) of 0.68 g m ?2 d ?1 in a 10-fold blind test. We showed that the DNN model also performed well under conditions of cold waves, heavy rain, and an autumnal heatwave. As future work, a comprehensive comparison with the result of process-based models will be necessary using a more extensive EC database from various forest ecosystems.
机译：许多基于过程的碳助焊剂预测模型面临着广泛的不确定性问题。大气和森林生态系统之间的复杂相互作用可能导致模型结果中的不确定性。另一方面，人工智能（AI）技术是解决复杂和非线性问题的新方法，已经显示出森林生态应用的可能性。本研究是使用卫星遥感数据对日常森林初级生产率（GPP）预测的多个AI模型之间的目标比较的第一步。我们使用涡流协方差（EC）通量塔和卫星的原位观测建立了支持向量机（SVM），随机森林（RF），人工神经网络（ANN）和深神经网络（DNN）的AI模型。遥感数据，如Albedo，气溶胶，温度和植被指数。我们专注于2006 - 2015年韩国区域助焊网络（Koflux）的Gwangneung网站。结果，DNN模型通过密集的高参数优化优先于其他三种模型，其相关系数（CC）为0.93，平均绝对误差（MAE）为0.68克（MAE），在10倍盲检验中。我们认为DNN模型在冷波，大雨和秋季热浪的条件下也表现良好。作为未来的工作，将使用来自各种森林生态系统的更广泛的EC数据库，与基于过程的模型的结果进行全面比较。

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