Revegetation success is a key element of mine site rehabilitation. A number of criteria related to mine site close-out are associated with revegetation. The monitoring of mine site revegetation efforts have traditionally been undertaken using field-based plot or transect methods. Often the sampling design for this monitoring is limited due to resource constraints, therefore reducing the statistical power of the data and missing information over most of the mine site. The recent advances in Remotely Piloted Aircraft Systems (RPAS) technology for remote sensing enables the collection of appropriate scale data over entire mine sites reducing the need for sampling and eliminating potential bias. This paper describes an object-based technique for extracting woody cover and estimating proportional woody cover from RPAS imagery over the rehabilitated Jabiluka mine site located in the tropical north of Australia. The technique was tested on three data sets that covered three different dates, two different sensors, and two different processing methods. Overall woody cover detection accuracies from each data set were over 95%. Proportional woody cover derived from the technique showed strong linear relationships with manually estimated cover (r² > 0.88). This study shows that the technique is robust and works with a range of RPAS data sets and enables at scale analysis of woody cover change between dates. The technique will be an important component of ongoing monitoring of mine site revegetation in the region.

植被恢复是矿场恢复的关键要素。与植被关闭有关的许多标准与植被恢复有关。传统上，使用基于野外的绘图或样带法来进行矿场植被恢复工作的监控。通常，由于资源限制，用于此监视的采样设计受到限制，因此会降低大多数矿场上数据的统计能力和丢失的信息。用于遥感的遥控飞机系统（RPAS）技术的最新进展使得能够在整个矿场内收集适当的比例尺数据，从而减少了采样需求并消除了潜在的偏差。本文介绍了一种基于对象的技术，该技术可用于从位于澳大利亚北部热带地区经过修复的Jabiluka矿区的RPAS影像中提取木本植物并估算比例木本植物。在覆盖三个不同日期，两个不同传感器和两种不同处理方法的三个数据集上对该技术进行了测试。每个数据集的总体木质覆盖率检测准确性均超过95％。从该技术得出的比例木质覆盖物与人工估算的覆盖物显示出很强的线性关系（r ²  > 0.88）。这项研究表明，该技术是鲁棒的，可以与一系列RPAS数据集配合使用，并且可以大规模分析日期之间的木质覆盖物变化。该技术将成为对该地区矿场植被的持续监测的重要组成部分。