Developing a forest description from remote sensing: Insights from New Zealand

Abstract

Remote sensing is increasingly being used to create large-scale forest descriptions. In New Zealand, where radiata pine (Pinus radiata) plantations dominate the forestry sector, the current national forest description lacks spatially explicit information and struggles to capture data on small-scale forests. This is important as these forests are expected to contribute significantly to future wood supply and carbon sequestration. This study demonstrates the development of a spatially explicit, remote sensing-based forest description for the Gisborne region, a major forest growing area. We combined deep learning-based forest mapping using high-resolution aerial imagery with regional airborne laser scanning (ALS) data to map all planted forest and estimate key attributes. The deep learning model accurately delineated planted forests, including large estates, small woodlots, and newly established stands as young as 3-years post planting. It achieved an intersection over union of 0.94, precision of 0.96, and recall of 0.98 on a withheld dataset. ALS-derived models for estimating mean top height, total stem volume, and stand age showed good performance (R² = 0.94, 0.82, and 0.94 respectively). The resulting spatially explicit forest description provides wall-to-wall information on forest extent, age, and volume for all sizes of forest. This enables stratification by key variables for wood supply forecasting, harvest planning, and infrastructure investment decisions. We propose satellite-based harvest detection and digital photogrammetry to continuously update the initial forest description. This methodology enables near real-time monitoring of planted forests at all scales and is adaptable to other regions with similar data availability.