A Modified KNN Method for Mapping the Leaf Area Index in Arid and Semi-Arid Areas of China
A Modified KNN Method for Mapping the Leaf Area Index in Arid and Semi-Arid Areas of China
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DOI：
10.3390/rs12111884
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发表时间：
2020-06-01
期刊：
影响因子：
5
通讯作者：
Sun, Hua
中科院分区：
文献类型：
--
作者：
Jiang, Fugen;Smith, Andrew R.;Sun, Hua
叶面积指数（LAI）作为重要的植被冠层参数，在森林生长模拟和植被健康评价中起着至关重要的作用。LAI的估算有助于了解植被生长和全球生态过程。基于遥感影像的k近邻（kNN）和随机森林（RF）等机器学习方法已被广泛用于LAI制图。然而，在干旱半干旱区，由于地理位置偏远、面积大、野外数据采集成本高、植被冠层空间变异性大，利用这些方法绘制LAI的精度受到限制。本文提出了一种改进的kNN方法，利用中国赣州和康保地区的Sentinel-2和Landsat 8遥感影像和野外数据，对中国干旱和半干旱区的LAI进行制图。将传统的kNN估计与射频分类相结合，开发了改进的kNN。使用三组输入预测因子将结果与单独使用kNN和RF回归的结果进行比较：(i)光谱反射波段（输入1）；（ii）植被指数（输入2）；（iii）光谱反射率波段与植被指数的组合（输入3）。我们的分析表明，在赣州，Sentinel-2图像的红边带与LAI具有较高的相关性。与使用其他光谱变量相比，使用红边带衍生的植被指数可以提高LAI制图的精度。在三组输入预测器中，输入3的预测精度最高。在此基础上，传统kNN、RF和改进kNN的RMSE值分别为0.526、0.523和0.372，改进kNN与单独kNN和RF相比，LAI预测精度分别提高了29.3%和28.9%。输入1和输入2也取得了类似的改进。在康宝实验中，与kNN和RF相比，改进的kNN预测精度提高了31.4%。因此，本研究表明，改进的kNN具有提高干旱半干旱区LAI制图精度的潜力。
As an important vegetation canopy parameter, the leaf area index (LAI) plays a critical role in forest growth modeling and vegetation health assessment. Estimating LAI is helpful for understanding vegetation growth and global ecological processes. Machine learning methods such as k-nearest neighbors (kNN) and random forest (RF) with remote sensing images have been widely used for mapping LAI. However, the accuracy of mapping LAI in arid and semi-arid areas using these methods is limited due to remote and large areas, the high cost of collecting field data, and the great spatial variability of the vegetation canopy. Here, a novel and modified kNN method was presented for mapping LAI in arid and semi-arid areas of China using Sentinel-2 and Landsat 8 images with field data collected in Ganzhou and Kangbao of China. The modified kNN was developed by integrating the traditional kNN estimation and RF classification. The results were compared with those from kNN and RF regression alone using three sets of input predictors: (i) spectral reflectance bands (input 1); (ii) vegetation indices (input 2); and (iii) a combination of spectral reflectance bands and vegetation indices (input 3). Our analysis showed that in Ganzhou, the red-edge bands of the Sentinel-2 image had a high correlation with LAI. Using the red-edge band-derived vegetation indices increased the accuracy of mapping LAI compared with using other spectral variables. Among the three sets of input predictors, input 3 resulted in the highest prediction accuracy. Based on the combination, the values of RMSE obtained by the traditional kNN, RF, and modified kNN were 0.526, 0.523, and 0.372, respectively, and the modified kNN significantly improved the accuracy of LAI prediction by 29.3% and 28.9% compared with the kNN and RF alone, respectively. A similar improvement was achieved for input 1 and input 2. In Kangbao, the improvement of the prediction accuracy obtained by the modified kNN was 31.4% compared with both the kNN and RF. Therefore, this study implied that the modified kNN provided the potential to improve the accuracy of mapping LAI in arid and semi-arid regions using the images.