A robust object-based woody cover extraction technique for monitoring mine site revegetation at scale in the monsoonal tropics using multispectral RPAS imagery from different sensors

Revegetation success is a key element of mine site rehabilitation. A number of criteria related to mine site closeout 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 (r(2) > 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.