With the recent developments of unmanned aerial vehicle (UAV) remote sensing, it is possible to monitor the growth condition of trees with the high temporal and spatial resolutions of data. In this study, the daily high-throughput RGB images of pear trees were captured from a UAV platform. A new index was generated by integrating the spectral and textural information using the improved adaptive feature weighting method (IAFWM). The inter-relationships of the air climatic variables and the soil’s physical properties (temperature, humidity and conductivity) were firstly assessed using principal component analysis (PCA). The climatic variables were selected to independently build a linear regression model with the new index when the cumulative variance explained reached 99.53%. The coefficient of determination (R²) of humidity (R² = 0.120, P = 0.205) using linear regression analysis was the dominating influencing factor for the growth of the pear trees, among the air climatic variables tested. The humidity (%) in 40 cm depth of soil (R² = 0.642, P < 0.001) using a linear regression coefficient was the largest among climatic variables in the soil. The impact of climatic variables on the soil was commonly greater than those in the air, and the R² grew larger with the increasing depth of soil. The effects of the fluctuation of the soil-climatic variables on the pear trees’ growth could be detected using the sliding window method (SWM), and the maximum absolute value of coefficients with the corresponding day of year (DOY) of air temperature, soil temperature, soil humidity, and soil conductivity were confirmed as 221, 227, 228, and 226 (DOY), respectively. Thus, the impact of the fluctuation of climatic variables on the growth of pear trees can last 14, 8, 7, and 9 days, respectively. Therefore, it is highly recommended that the adoption of the integrated new index to explore the long-time impact of climate on pears growth be undertaken.

中文翻译：

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整合光谱和纹理信息以使用来自无人机平台的光学图像监测梨树的生长

随着无人飞行器（UAV）遥感的最新发展，有可能以高的时间和空间分辨率数据监视树木的生长状况。在这项研究中，梨树的每日高通量RGB图像是从无人机平台捕获的。通过使用改进的自适应特征加权方法（IAFWM）整合光谱和纹理信息，生成了一个新索引。首先使用主成分分析（PCA）评估了空气气候变量与土壤物理特性（温度，湿度和电导率）之间的相互关系。当解释的累积方差达到99.53％时，选择气候变量以使用新指数独立构建线性回归模型。测定系数（R ²在测试的空气气候变量中，使用线性回归分析得出的湿度（R ² = 0.120，P = 0.205）是影响梨树生长的主要因素。使用线性回归系数，在40厘米深度土壤中的湿度（％）（R ² = 0.642，P <0.001）在土壤气候变量中最大。气候变量对土壤的影响通常大于空气中的影响，R ²随着土壤深度的增加而变大。可以使用滑动窗法（SWM）检测土壤气候变量的波动对梨树生长的影响，并且系数的最大绝对值与气温，土壤的相应年份（DOY）有关温度，土壤湿度和土壤电导率分别确定为221、227、228和226（DOY）。因此，气候变量波动对梨树生长的影响分别可以持续14、8、7和9天。因此，强烈建议采用综合新指数来探讨气候对梨生长的长期影响。