Liang Jiaxi, Sawut Mamat, Cui Jintao, Hu Xin, Xue Zijing, Zhao Ming, Zhang Xinyu, Rouzi Areziguli, Ye Xiaowen, Xilike Aerqing
College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, 830046, Xinjiang, China.
Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, 830046, Xinjiang, China.
Sci Rep. 2024 Aug 6;14(1):18230. doi: 10.1038/s41598-024-68991-7.
Fruit tree identification that is quick and precise lays the groundwork for scientifically evaluating orchard yields and dynamically monitoring planting areas. This study aims to evaluate the applicability of time series Sentinel-1/2 satellite data for fruit tree classification and to provide a new method for accurately extracting fruit tree species. Therefore, the study area selected is the Tarim Basin, the most important fruit-growing region in northwest China. The main focus is on identifying several major fruit tree species in this region. Time series Sentinel-1/2 satellite images acquired from the Google Earth Engine (GEE) platform are used for the study. A multi-scale segmentation approach is applied, and six categories of features including spectral, phenological, texture, polarization, vegetation index, and red edge index features are constructed. A total of forth-four features are extracted and optimized using the V feature importance index to determine the best time phase. Based on this, an object-oriented (OO) segmentation combined with the Random Forest (RF) method is used to identify fruit tree species. To find the best method for fruit tree identification, the results are compared with three other widely used traditional machine learning algorithms: Support Vector Machine (SVM), Gradient Boosting Decision Tree (GBDT), and Classification and Regression Tree (CART). The results show that: (1) the object-oriented segmentation method helps to improve the accuracy of fruit tree identification features, and September satellite images provide the best time window for fruit tree identification, with spectral, phenological, and texture features contributing the most to fruit tree species identification. (2) The RF model has higher accuracy in identifying fruit tree species than other machine learning models, with an overall accuracy (OA) and a kappa coefficient (KC) of 94.60% and 93.74% respectively, indicating that the combination of object-oriented segmentation and RF algorithm has great value and potential for fruit tree identification and classification. This method can be applied to large-scale fruit tree remote sensing classification and provides an effective technical means for monitoring fruit tree planting areas using medium-to-high-resolution remote sensing images.
快速且精确的果树识别为科学评估果园产量和动态监测种植面积奠定了基础。本研究旨在评估时间序列哨兵 - 1/2卫星数据在果树分类中的适用性，并提供一种准确提取果树种类的新方法。因此，选定的研究区域是中国西北最重要的水果种植区塔里木盆地。主要聚焦于识别该地区的几种主要果树种类。研究使用了从谷歌地球引擎（GEE）平台获取的时间序列哨兵 - 1/2卫星图像。应用了多尺度分割方法，并构建了包括光谱、物候、纹理、极化、植被指数和红边指数特征在内的六类特征。总共提取了四十四种特征，并使用V特征重要性指数进行优化以确定最佳时间阶段。基于此，采用面向对象（OO）分割结合随机森林（RF）方法来识别果树种类。为了找到最佳的果树识别方法，将结果与其他三种广泛使用的传统机器学习算法进行比较：支持向量机（SVM）、梯度提升决策树（GBDT）和分类与回归树（CART）。结果表明：（1）面向对象分割方法有助于提高果树识别特征的准确性，九月的卫星图像为果树识别提供了最佳时间窗口，其中光谱、物候和纹理特征对果树种类识别贡献最大。（2）RF模型在识别果树种类方面比其他机器学习模型具有更高的准确性，总体准确率（OA）和kappa系数（KC）分别为94.60%和93.74%，表明面向对象分割和RF算法的结合在果树识别和分类方面具有很大的价值和潜力。该方法可应用于大规模果树遥感分类，并为利用中高分辨率遥感影像监测果树种植面积提供了有效的技术手段。
