Abstract

Digital elevation model (DEM) data were used with climate data to estimate productivity in 19 Eucalyptus plantations in Minas Gerais state, Brazil. Typically, plantation and individual stand growth and productivity estimates, such as Site Index (SI) and Mean Annual Increment (MAI), are based on field measures of height, tree diameter and age. Using a Random Forest modelling approach, SI and MAI were related to: (i) DEM-based geomorphometric variables and (ii) WorldClim historical macro-climatic measures. Three operational SI classes (high, medium and low productivity) in 180 stands were mapped with an overall accuracy of 91.6%. Medium and high productivity sites were the most accurately classified. Low productivity sites had 76.5% producer’s accuracy and 92.9% user’s accuracy, and were the most extensive in the study area. Such sites are considered of high importance from a plantation management perspective since additional forestry operations are likely required to address low productivity and growth.

摘要

数字高程模型 (DEM) 数据与气候数据一起用于估算 19棵桉树的生产力巴西米纳斯吉拉斯州的种植园。通常，人工林和个体林分生长和生产力估算，例如场地指数 (SI) 和平均年增量 (MAI)，是基于对高度、树木直径和年龄的实地测量。使用随机森林建模方法，SI 和 MAI 与：（i）基于 DEM 的地貌变量和（ii）WorldClim 历史宏观气候测量值相关。绘制了 180 个展位中的三个运营 SI 等级（高、中和低生产率），总体准确率为 91.6%。中和高生产率站点是最准确分类的。低产站点生产者准确率为76.5%，用户准确率为92.9%，是研究范围内最广泛的站点。