Key Attributes for Monitoring and Assessment of Australian Forests: A Land Management Perspective

The rapid technological development of active and passive remote sensing has proved of great value for forest monitoring and assessment worldwide. To make full use of this development, Australian land managers need efficient routines and tools tailored for operations in Australian landscapes. The development of these tools should focus on the most important forest attributes from a land management perspective. This paper presents the results of a web-based survey sent to people directly or indirectly involved in land management. The survey results indicate their current needs in terms of key forest attributes necessary for efficient management, decision making, and for fulfilling reporting obligations. Tree height, canopy health and condition, crown density, floristic composition, aboveground biomass, stem density, forest extent, and fire frequency/severity were among the most important attributes identified by the survey respondents. Moreover, many respondents highlighted the importance of continuous monitoring over time in order to detect changes. A literature review was conducted to examine how primary attributes can be combined to form composite attributes for a variety of purposes. A composite attributes, such as canopy health or aboveground biomass, can be estimated based on a combination of primary attributes. A primary attribute can be equally important as a composite product, if it is necessary for its accurate estimation.

[1]  J. Hyyppä,et al.  Estimation of timber volume and stem density based on scanning laser altimetry and expected tree size distribution functions , 2004 .

[2]  R. S. Tripathi,et al.  Tree diversity and population structure in undisturbed and human-impacted stands of tropical wet evergreen forest in Arunachal Pradesh, Eastern Himalayas, India , 2003, Biodiversity & Conservation.

[3]  David B. Lindenmayer,et al.  Re-evaluation of forest biomass carbon stocks and lessons from the world's most carbon-dense forests , 2009, Proceedings of the National Academy of Sciences.

[4]  M. Neteler,et al.  Fusion of airborne LiDAR and satellite multispectral data for the estimation of timber volume in the Southern Alps , 2011 .

[5]  P. Puttonen,et al.  Impact of stand structure on surface fire ignition potential in Picea abies and Pinus sylvestris forests in southern Finland , 2005 .

[6]  Simon J. Grove,et al.  Coarse woody debris, biodiversity and management: a review with particular reference to Tasmanian wet eucalypt forests , 2003 .

[7]  C. Stone,et al.  Crown‐scale evaluation of spectral indices for defoliated and discoloured eucalypts , 2008 .

[8]  Phillip B. Gibbons,et al.  Forest and woodland stand structural complexity: Its definition and measurement , 2005 .

[9]  R. McRoberts,et al.  Remote sensing support for national forest inventories , 2007 .

[10]  S. Tarantola,et al.  Detecting vegetation leaf water content using reflectance in the optical domain , 2001 .

[11]  Barbara Koch,et al.  Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment , 2010 .

[12]  Christine Stone,et al.  Assessing canopy health of native eucalypt forests , 2006 .

[13]  J. Means,et al.  Predicting forest stand characteristics with airborne scanning lidar , 2000 .

[14]  Erik Næsset,et al.  Mapping defoliation during a severe insect attack on Scots pine using airborne laser scanning , 2006 .

[15]  W. Cohen,et al.  Lidar remote sensing of above‐ground biomass in three biomes , 2002 .

[16]  Michael E. Schaepman,et al.  Retrieval of foliar information about plant pigment systems from high resolution spectroscopy , 2009 .

[17]  Eric Hagen,et al.  Bromus tectorum cover mapping and fire risk , 2006 .

[18]  R. Dubayah,et al.  Integrating waveform lidar with hyperspectral imagery for inventory of a northern temperate forest , 2008 .

[19]  C. Margules,et al.  Indicators of Biodiversity for Ecologically Sustainable Forest Management , 2000 .

[20]  L. Monika Moskal,et al.  Strengths and limitations of assessing forest density and spatial configuration with aerial LiDAR , 2011 .

[21]  C. Stone,et al.  Harmonisation of methods for the assessment and reporting of forest health in Australia — a starting point , 2003 .

[22]  Harold E. Burkhart,et al.  Quantifying Stand Density , 2012 .

[23]  Michele Meroni,et al.  Assessment of oak forest condition based on leaf biochemical variables and chlorophyll fluorescence. , 2006, Tree physiology.

[24]  Russell T. Graham,et al.  The effects of thinning and similar stand treatments on fire behavior in Western forests. , 1999 .

[25]  Timo Pukkala,et al.  A fire probability model for forest stands in Catalonia (north-east Spain) , 2006 .

[26]  Jan G. P. W. Clevers,et al.  Modelling the spectral response of the desert tree Prosopis tamarugo to water stress , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[27]  Michael C. Wimberly,et al.  Influences of environment and disturbance on forest patterns in coastal oregon watersheds , 2001 .

[28]  David Freudenberger,et al.  Restore and sequester: estimating biomass in native Australian woodland ecosystems for their carbon-funded restoration , 2011 .

[29]  Erik Næsset,et al.  Using remotely sensed data to construct and assess forest attribute maps and related spatial products , 2010 .

[30]  Christine Stone,et al.  Chlorophyll content in eucalypt vegetation at the leaf and canopy scales as derived from high resolution spectral data. , 2003, Tree physiology.

[31]  Markus Neumann,et al.  The significance of different indices for stand structure and diversity in forests , 2001 .

[32]  E. Næsset Determination of mean tree height of forest stands using airborne laser scanner data , 1997 .

[33]  Claudia M. Castaneda,et al.  Estimating Canopy Water Content of Chaparral Shrubs Using Optical Methods , 1998 .

[34]  Cristopher Brack,et al.  National forest inventories and biodiversity monitoring in Australia , 2007 .

[35]  John C. Z. Woinarski,et al.  Response of vegetation and vertebrate fauna to 23 years of fire exclusion in a tropical Eucalyptus open forest, Northern Territory, Australia , 2004 .

[36]  Susy Svatek Ziegler,et al.  A comparison of structural characteristics between old‐growth and postfire second‐growth hemlock–hardwood forests in Adirondack Park, New York, U. S. A. , 2000 .

[37]  Jerry F. Franklin,et al.  The Structure of Natural Young , Mature , and Old-Growth Douglas-Fir Forests in Oregon and Washington , 2010 .

[38]  Peter Bunting,et al.  Retrieving forest biomass through integration of CASI and LiDAR data , 2008 .

[39]  Grant Wardell-Johnson,et al.  Development of forest structure on cleared rainforest land in eastern Australia under different styles of reforestation , 2003 .

[40]  D. Roberts,et al.  Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales , 2005 .

[41]  Kris Vandekerkhove,et al.  Development of a stand-scale forest biodiversity index based on the state forest inventory , 2000 .

[42]  G. Asner,et al.  A universal airborne LiDAR approach for tropical forest carbon mapping , 2011, Oecologia.

[43]  P. J. Clark,et al.  Distance to Nearest Neighbor as a Measure of Spatial Relationships in Populations , 1954 .

[44]  Russell T. Graham,et al.  The relation between tree burn severity and forest structure in the Rocky Mountains , 2007 .

[45]  James S. Gould,et al.  Quantifying fine fuel dynamics and structure in dry eucalypt forest (Eucalyptus marginata) in Western Australia for fire management , 2011 .

[46]  Mary E. Martin,et al.  Using AVIRIS to assess hemlock abundance and early decline in the Catskills, New York , 2005 .