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Background: New Zealand does not have a national forest inventory based on ground measurements. The National Exotic Forest Description (NEFD) is based on surveys of forest owners but lacks reliability in describing area and yield for small-scale forests, given that over half of the small-scale forests were not included in the direct NEFD survey. This has led to an insufficient understanding of the wood supply from these forests, which will raise problems as the majority of the small-scale forests are approaching maturity and are anticipated to contribute more than 40% of wood supply in the next decade. Therefore, having accurate estimation of this resources is critical for marketing, harvesting and logistic planning. Furthermore, the current NEFD does not incorporate a spatial representation. The aim of this study was to test the utility of remote sensing dataset for providing information on the area and yield of small-scale forest resources through a case study in the Wairarapa region of New Zealand. Methods: Classification and regression tree analysis was applied to both RapidEye and LiDAR datasets to map the net stocked area of small-scale plantation forests in Wairarapa. Results were compared against forest areas that have been determined from manual digitisation. For each forest, stand variables mean top height, basal area, volume and stand age were estimated using LiDAR-derived metrics. This allowed the development of maps for these stand variables. Results: The automatically mapped plantation area was 4.2% (1?614 ha) less than the reference area (i.e. manually digitised plantation area), together with manually digitised young plantations which were not detected from automated approach, the area differed only by -0.6% (235 ha) from the reference area. The yield tables developed using modelled stand variables were all within a realistic range and were comparable to wood availability forecast (WAF) yield tables, producing on average 27 m3 ha-1 less than the WAF yield. Conclusions: The mapping approach produced comparable results to the area calculated using manual digitisation. However, this approach struggles to detect young plantings due to the resolution of the remote sensing datasets used; hence manual digitisation is required to map the young plantations that were within 3–5 years of planting. This study also confirmed that the remote sensing approach could be used to describe forest yield, although the approach failed to predict the full range of ages of the mapped forest resource. The estimation of plantation age could be improved by including plot data with a wider range of stand ages or investigating different models for age estimation. It is also possible to review time-series satellite imagery to detect establishment periods for the forests.

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