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Open AccessArticle

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

1
School of Ecosystem and Forest Sciences, Faculty of Science, The University of Melbourne, 500 Yarra Boulevard, Richmond, Victoria 3121, Australia
2
CSIRO Land and Water Business Unit, GPO Box 1700, Canberra 2601, Australia
3
CSIRO Land and Water Business Unit, 15 College Road, Sandy Bay 7005, Australia
4
School of Ecosystem and Forest Sciences, Faculty of Science, The University of Melbourne, 4 Water Street, Creswick 3363, Australia
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(1), 93; https://doi.org/10.3390/rs11010093
Received: 30 October 2018 / Revised: 11 December 2018 / Accepted: 11 December 2018 / Published: 7 January 2019
(This article belongs to the Special Issue Advances in Remote Sensing of Forest Structure and Applications)
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. View Full-Text
Keywords: Cool Temperate Rainforest; decision-tree; ecological vegetation class; ecotone; mixed forest; plant area volume density; random forest; stand structure. Cool Temperate Rainforest; decision-tree; ecological vegetation class; ecotone; mixed forest; plant area volume density; random forest; stand structure.
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MDPI and ACS Style

Fedrigo, M.; Stewart, S.B.; Roxburgh, S.H.; Kasel, S.; Bennett, L.T.; Vickers, H.; Nitschke, C.R. Predictive Ecosystem Mapping of South-Eastern Australian Temperate Forests Using Lidar-Derived Structural Profiles and Species Distribution Models. Remote Sens. 2019, 11, 93. https://doi.org/10.3390/rs11010093

AMA Style

Fedrigo M, Stewart SB, Roxburgh SH, Kasel S, Bennett LT, Vickers H, Nitschke CR. Predictive Ecosystem Mapping of South-Eastern Australian Temperate Forests Using Lidar-Derived Structural Profiles and Species Distribution Models. Remote Sensing. 2019; 11(1):93. https://doi.org/10.3390/rs11010093

Chicago/Turabian Style

Fedrigo, Melissa; Stewart, Stephen B.; Roxburgh, Stephen H.; Kasel, Sabine; Bennett, Lauren T.; Vickers, Helen; Nitschke, Craig R. 2019. "Predictive Ecosystem Mapping of South-Eastern Australian Temperate Forests Using Lidar-Derived Structural Profiles and Species Distribution Models" Remote Sens. 11, no. 1: 93. https://doi.org/10.3390/rs11010093

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