Metadata, Covariance matrix of PCA from Regulation of dynamics and densities of whitefly Bemisia tabaci by agricultural landscapes in south China

The 12 agriculture landscapes located in the surroundings of Kunming, south China (24°42'45''N-25°22'43''N, 102°22'18''E-103°10'90''E). it was selected by use of Google Earth Profession and field inspections (ground-truthing) once a month during the tomato growing seasons in 2018 and 2019. The cover types in each landscape were divided into 10 types according to vegetation type, human factor interference and land type characteristics. A Principal Components Analysis (PCA) was performed to reduce the dimensions of the landscape data. These ten land cover types were divided for the PCA analysis, the land cover type with the largest area in one landscape and the absolute value of first principal component greater than 0.9 was selected as the landscape type. Principal component axes were extracted using correlations among variables, and the resulting factors were not rotated.本数据集涵盖中国南部昆明周边的12处农业景观样区，地理坐标范围为北纬24°42′45″至25°22′43″、东经102°22′18″至103°10′90″。样区选取工作依托谷歌地球专业版（Google Earth Pro）结合野外实地核查（ground-truthing）完成，于2018年及2019年番茄生长期内每月开展一次核查。依据植被类型、人为干扰强度与土地利用特征，将各景观样区的地表覆盖类型划分为10类。为实现景观数据降维，采用主成分分析（Principal Components Analysis，PCA）对其进行处理。基于该10类地表覆盖类型开展主成分分析，选取单样区内面积最大且第一主成分绝对值大于0.9的地表覆盖类型作为该样区的景观类型。主成分轴通过变量间的相关系数提取得到，且未对提取得到的因子进行旋转操作。