Nearest Neighbor Techniques in Forest Inventory and Mapping

Nearest neighbour methods, notably k-nearest neighbours (kNN), have become integral to forest inventory and mapping by combining field plot measurements with spatially exhaustive data sources. By identifying the most similar observations in a multidimensional feature space—comprising spectral reflectance, topographic attributes and climate variables—these non-parametric approaches infer stand attributes such as biomass, growing stock volume or species composition at unsampled locations. Advances in distance metrics, including incorporation of machine-learning-derived metrics, have enhanced the robustness of neighbour selection in heterogeneous landscapes. The flexibility of kNN models allows for adaptive k-value selection and integration with cloud-based platforms, enabling scalable workflows for national or global mapping. Applications range from biomass estimation in boreal and temperate forests to mapping percentage vegetation cover in arid regions. The capacity to fuse remote sensing imagery (optical, radar or LiDAR) with in-situ data underpins spatially continuous forest property maps that inform carbon accounting, biodiversity monitoring and sustainable management. Ongoing research focuses on optimising k-value determination, refining distance definitions through ensemble metrics and quantifying uncertainty in the imputed estimates. Together, these developments are delivering more accurate, transparent and reproducible forest inventories that support global forest assessments and policy frameworks.

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Nearest Neighbor Techniques in Forest Inventory and Mapping publication trend

The graph below shows the total number of articles in nearest neighbor techniques in forest inventory and mapping across all publications each year (not limited to Nature Index journals).

Technical terms

k-nearest neighbours (kNN): A non-parametric method that estimates unknown sample attributes by averaging values of the k most similar observations in feature space.

Distance metric: A mathematical rule defining similarity between samples, which may incorporate Euclidean, Mahalanobis or ensemble-learnt measures to improve neighbour identification.

Imputation: The process of inferring missing or unsampled attribute values at new locations using statistical or machine-learning models trained on known observations.

Remote sensing data: Satellite or airborne imagery and derived indices (e.g. spectral reflectance, LiDAR returns) that provide spatially continuous environmental information.

References

1. Optimizing kNN for Mapping Vegetation Cover of Arid and Semi-Arid Areas Using Landsat Images. Remote Sensing (2018).
2. Evaluating k-Nearest Neighbor (kNN) Imputation Models for Species-Level Aboveground Forest Biomass Mapping in Northeast China. Remote Sensing (2019).
3. Prediction of Dominant Forest Tree Species Using QuickBird and Environmental Data. Forests (2017).