Metadata, Covariance matrix of PCA from Regulation of dynamics and densities of whitefly <i>Bemisia tabaci</i> 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12处位于中国南部昆明市周边的农业景观，其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″。该12处农业景观的遴选工作于2018年与2019年的番茄种植季内每月开展，采用谷歌地球专业版（Google Earth Profession）与实地核查（ground-truthing）的方式完成。每处景观的地表覆盖类型依据植被类型、人为干扰特征与土地类型特征，划分为10大类。为实现景观数据的降维处理，本研究采用主成分分析（Principal Components Analysis，PCA）。基于该10类地表覆盖类型开展PCA分析，选取单处景观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景观的分类类型。通过变量间的相关性提取主成分轴，且未对所得因子进行旋转变换。