Rodent-infested bald spots are crucial indicators of rodent infestation in grasslands. Leveraging Unmanned Aerial Vehicle (UAV) remote sensing technology for discerning detrimental bald spots among plateau pikas has significant implications for assessing associated ecological hazards. Based on UAV-visible light imagery, we classified and recognized the characteristics of plateau pika habitats with five supervised classification algorithms, i.e., minimum distance classification (MinD), maximum likelihood classification (ML), support vector machine classification (SVM), Mahalanobis distance classification (MD), and neural network classification (NN) . The accuracy of the five methods was evaluated using a confusion matrix. Results showed that NN and SVM exhibited superior performance than other methods in identifying and classifying features indicative of plateau pika habitats. The mapping accuracy of NN for grassland and bald spots was 98.1% and 98.5%, respectively, with corresponding user accuracy was 98.8% and 97.7%. The overall model accuracy was 98.3%, with a Kappa coefficient of 0.97, reflecting minimal misclassification and omission errors. Through practical verification, NN exhibited good stability. In conclusion, the neural network method was suitable for identifying rodent-damaged bald spots within alpine meadows.
鼠害型秃斑是反映草地鼠害的重要表征。利用无人机遥感技术识别高原鼠兔危害型秃斑对于评价其危害情况具有重要意义。本研究基于无人机可见光影像,使用最小距离(MinD)、最大似然(ML)、支持向量机(SVM)、马氏距离(MD)和神经网络(NN)5种监督分类算法对高原鼠兔危害地特征进行分类识别,并采用混淆矩阵对5种分类方法精度进行评价。结果表明: 相较于其他3种方法,NN和SVM对高原鼠兔危害地特征进行识别分类的效果更好。其中,NN对草地与秃斑2种目标地物的制图精度分别为98.1%和98.5%,用户精度分别为98.8%和97.7%,模型总体精度为98.3%,Kappa系数为0.97,像元错分、漏分现象较低。经实践验证,NN表现出较好的稳定性。综上,神经网络方法是高寒草甸鼠害型秃斑识别的优选方法。.
Keywords: alpine meadow; neural network; rodent-infested land; supervised classification; unmanned aerial vehicle.