Integrating plot‐based and remotely sensed data to map vegetation types in a New Zealand warm‐temperate rainforest. Issue 4 (9th December 2022) - Wiser, Susan K.; McCarthy, James K.; Bellingham, Peter J.; Jolly, Ben; Meiforth, Jane J.; ,
Abstract: Questions: (1) What can be learned by extending a national classification into unsampled forest types? (2) Are both remotely sensed and environmental predictors needed to model and map associations? (3) For mapping, are LiDAR‐generated canopy structure parameters or reflectance from spectral imagery more useful? (4) How can we assess uncertainty of a final map? Location: Warawara Forest, Northland, New Zealand. Methods: We sampled 205 vegetation plots and assigned them to an existing national classification using the fuzzy classification framework of noise clustering. Plots too distinct to be assigned were used to define new associations. We produced spatial models of each association using boosted regression trees. Predictors included 11 environmental, 11 canopy reflectance, and 17 canopy structure variables. We created a composite map by assigning each map pixel to the association with the highest occurrence probability. We evaluated uncertainty by examining locations where no class was predicted with probability above 0.2 and by creating a confusion map based on entropy. Results: Forty‐five plots were assigned to six of 79 existing national associations and 147 plots were used to define two new forest associations. Three shrubland types are widespread nationally, whereas two young forest types are northern. Three mature forest types are narrowly distributed nationally, with the new "High‐elevation hardwood forest" largely restricted to Warawara Forest. Three associations were mapped using remotely sensed predictors alone, whereas two also required environmental predictors. Overall, canopy reflectance predictors explained more deviance than canopy structure. Examining locations where no association was predicted well and where multiple associations were predicted equally showed areas mapped as younger forests to have greatest uncertainty. Conclusions: In answering our questions, we present a vegetation classification and map for Warawara Forest that provides a framework to guide the indigenous people's management responses to threats to valued communities and their species. Abstract : We demonstrated a new workflow to extend and map a national classification into a new area. Three associations could be mapped using remotely sensed predictors alone (Sentinel and LiDAR), whereas two also required environmental predictors. Our map will guide the indigenous peoples of the area to manage threats to valued communities and their species.