Search

Background: Relying solely on radiata pine (Pinus radiata D.Don) leaves New Zealand's plantation forest industry vulnerable to fluctuations in market demand and at risk from a potentially devastating pest or disease outbreak. Therefore, the New Zealand government and forestry industry urge to diversify the forest resource and wood supply beyond the reliance on radiata pine. Unfortunately, the lack of accurate information on minor species' area, composition, and location poses challenges to forecasting potential log supply and logistics planning. Methods: The objective of this study is to classify minor species in New Zealand using imagery and phenological features extracted from data collected by the Copernicus Sentinel-2 satellite. The study collected reference data of minor species from large-scale forest owners and applied Random Forest classification using Sentinel-2 imagery to classify nine minor species classes in the Hawke's Bay region of New Zealand. Results: The study achieved an overall classification accuracy of 92.2% for minor species in New Zealand's Hawke's Bay region. Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) and Eucalyptus species had the highest accuracies, exceeding 90% for both producer's and user's accuracies. Acacia, larch, and other mixed species had lower accuracies, likely due to their lower occurrence. The most important input variable for classification was the Digital Elevation Model, indicating the significance of elevation in differentiating plantation species. The Greenness Index (GI) and Red edge bands also proved useful in the classification. The phenological measure Mean-EVI2 was found useful in classifying deciduous species such as larch and poplar. Conclusions: To the best of our knowledge, this study is the first to map the spatial extent and distribution of minor plantation species in New Zealand at the regional level, providing promising results for potentially expanding the study to national-level species mapping. [ABSTRACT FROM AUTHOR]

In New Zealand, approximately 70% of plantation forests are large-scale (over 1000 ha) with accurate resource description. In contrast, the remaining 30% of plantation forests are small-scale (less than 1000 ha). It is forecasted that these small-scale forests will supply nearly 40% of the national wood production in the next decade. However, in-depth description of these forests, especially those under 100 ha, is very limited. This research evaluates the use of remote sensing datasets to map and estimate the net stocked plantation area for small-scale forests. We compared a factorial combination of two classification approaches (Nearest Neighbour (NN), Classification and Regression Tree (CART)) and two remote sensing datasets (RapidEye, RapidEye plus LiDAR) for their ability to accurately classify planted forest area. CART with a combination of RapidEye and LiDAR metrics outperformed the other three combinations producing the highest accuracy for mapping forest plantations (user's accuracy = 90% and producer's accuracy = 88%). This method was further examined by comparing the mapped plantations with manually digitised plantations based on aerial photography. The mapping approach overestimated the plantation area by 3%. It was also found that forest patches exceeding 10 ha achieved higher conformance with the digitised areas. Overall, the mapping approach in this research provided a proof of concept for deriving forest area and mapping boundaries using remote sensing data, and is especially relevant for small-scale forests where limited information is currently available. [ABSTRACT FROM AUTHOR]