联合U-Net和分水岭算法的高郁闭度杉木纯林树冠信息提取

Extraction of tree crown parameters of high-density pure Cunninghamia lanceolata plantations by combining the U-Net model and watershed algorithm

作为我国重要的用材树种,杉木广泛分布于我国南方地区,其株数和树冠信息对于森林资源的精准监测有重要作用,为此准确掌握杉木林分株数及单木树冠信息尤为重要。对于高郁闭度林分,株数和单木树冠信息正确提取的关键是能够准确分割相互遮挡和粘连的树冠。本研究以福建将乐国有林场为研究区,将无人机影像作为数据源,提出一种基于深度学习方法和分水岭算法的树冠信息提取方法:首先采用深度学习神经网络模型U-Net对杉木树冠覆盖区域进行分割,然后利用传统图像分割算法标记控制分水岭算法进行单木分割得到单木树冠;在保持相同训练集、验证集和测试集的情况下,首先对比U-Net模型与传统机器学习方法[随机森林模型(RF)和支持向量机模型(SVM)]在分割树冠覆盖区域上的表现,接着对比了U-Net模型结合标记控制分水岭算法和只使用标记控制分水岭算法进行单木分割的精度。结果表明:U-Net模型在分割精度、精确率、交互比、精确率与召回率的调和均值4个指标上均高于RF和SVM,与RF相比,4项指标分别提升4.6%、14.9%、7.6%、0.05,与SVM相比,4项指标分别提升3.3%、8.5%、8.1%、0.05。在提取单木株数方面,U-Net模型和标记控制分水岭算法相结合的方法较标记控制分水岭算法总体精度提升3.7%,平均绝对误差(MAE)下降3.1%。在提取单木树冠面积和冠幅方面,R2分别提升了0.11和0.09,均方根误差分别降低8.49 m2和4.27 m,MAE分别下降了2.93 m2和1.72 m。将深度学习U-Net模型与分水岭算法相结合能够在一定程度上克服高郁闭度杉木纯林单木株数及树冠信息难以提取的问题,是一种高效率、低成本的单木树冠提取方法,具有可行性和有效性,可为森林资源监测智能化的发展提供基础方法。

As one of the important timber species in China,Cunninghamia lanceolata is widely distributed in southern China.The information of tree individuals and crown plays an important role in accurately monitoring forest resources.Therefore,it is particularly significant to accurately grasp such information of individual C.lanceolata tree.For high-canopy closed forest stands,the key to correctly extract such information is whether the crowns of mutual occlusion and adhesion can be accurately segmented.Taking the Fujian Jiangle State-owned Forest Farm as the research area and using the UAV image as the data source,we developed a method to extract crown information of individual tree based on deep learning method and watershed algorithm.Firstly,the deep learning neural network model U-Net was used to segment the coverage area of the canopy of C.lanceolata,and then the traditional image segmentation algorithm was used to segment the individual tree to obtain the number and crown information of individual tree.Under the condition of maintaining the same training set,validation set and test set,the extraction results of the canopy coverage area by the U-Net model and traditional machine learning methods[random forest(RF)and support vector machine(SVM)]were compared.Then,two individual tree segmentation results were compared,one using the marker-controlled watershed algorithm,and the other using the combination of the U-Net model and marker-controlled watershed algorithm.The results showed that the segmentation accuracy(SA),precision,IoU(intersection over union)and F1-score(harmonic mean of precision and recall)of the U-Net model were higher than those of RF and SVM.Compared with RF,the value of those four indicators increased by 4.6%,14.9%,7.6%and 0.05,respectively.Compared with SVM,the four indicators increased by 3.3%,8.5%,8.1%and 0.05,respectively.In terms of extracting the number of trees,the overall accuracy(OA)of the U-Net model combined with the marker-controlled watershed algorithm was 3.7%higher than that of the marker-controlled watershed algorithm,with the mean absolute error(MAE)being decreased by 3.1%.In terms of extracting crown area and crown width of individual tree,R2 increased by 0.11 and 0.09,mean squared error decreased by 8.49 m2 and 4.27 m,and MAE decreased by 2.93 m2 and 1.72 m,respectively.The combination of deep learning U-Net model and watershed algorithm could overcome the challenges in accurately extracting the number of trees and the crown information of individual tree of high-density pure C.lanceolata plantations.It was an efficient and low-cost method of extracting tree crown parameters,which could provide a basis for developing intelligent forest resource monitoring.