Predictive Ecosystem Mapping of South-Eastern Australian Temperate Forests Using Lidar-Derived Structural Profiles and Species Distribution Models

Melissa Fedrigo,Sabine Kasel,Lauren T Bennett,Stephen H Roxburgh,Stephen B Stewart,Helen Vickers,Craig R Nitschke

doi:10.3390/rs11010093

Melissa Fedrigo, Sabine Kasel + Show 5 more

Open Access

https://doi.org/10.3390/rs11010093

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Journal: Remote sensing	Publication Date: Jan 7, 2019
Citations: 18	License type: CC BY 4.0

Affiliation: University of Melbourne, CSIRO Land and Water

Abstract

Modern approaches to predictive ecosystem mapping (PEM) have not thoroughly explored the use of ‘characteristic’ gradients, which describe vegetation structure (e.g., light detection and ranging (lidar)-derived structural profiles). In this study, we apply a PEM approach by classifying the dominant stand types within the Central Highlands region of south-eastern Australia using both lidar and species distribution models (SDMs). Similarity percentages analysis (SIMPER) was applied to comprehensive floristic surveys to identify five species which best separated stand types. The predicted distributions of these species, modelled using random forests with environmental (i.e., climate, topography) and optical characteristic gradients (Landsat-derived seasonal fractional cover), provided an ecological basis for refining stand type classifications based only on lidar-derived structural profiles. The resulting PEM model represents the first continuous distribution map of stand types across the study region that delineates ecotone stands, which are seral communities comprised of species typical of both rainforest and eucalypt forests. The spatial variability of vegetation structure incorporated into the PEM model suggests that many stand types are not as continuous in cover as represented by current ecological vegetation class distributions that describe the region. Improved PEM models can facilitate sustainable forest management, enhanced forest monitoring, and informed decision making at landscape scales.

Highlights

Understanding the composition and structure of forest ecosystems is critical for ecological understanding, and for developing effective forest management strategies
This study demonstrates a novel approach to predictive ecosystem mapping (PEM) that integrates a range of data types including field-based, optical remotely-sensed, and lidar data to model the continuous distribution of key stand types in the temperate forests of south-eastern Australia
By exploring the use of varying environmental and characteristic predictor variables and identifying those species which best separated stand types, we generated a landscape species model based on the combination of multiple species distribution models (SDMs)

Summary

Introduction

Understanding the composition and structure of forest ecosystems is critical for ecological understanding, and for developing effective forest management strategies. PEM models are defined as methods that identify ecological-landscape relationships from spatial environmental data and field observations (as available) to predict vegetation composition across a landscape [1,2,4,5,6,7]. 2019, 11, 93 species distribution models (SDMs) [2,3,8,9], bioclimatic envelope models [10,11], habitat suitability or decision-support models [12,13,14], and ecological niche models [15,16] Such models originated from ecological niche theory whereby vegetation distribution is predicted using variables that either correlate with or define tolerance ranges of species [1,8,9,10,17,18]. There are three typical approaches to modelling community distributions from species and environmental data: (1) assemble species into communities and predict, (2) predict species individually assemble into communities, and (3) assemble and predict species together [19,20,21,22,23]

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