=Paper=
{{Paper
|id=Vol-1328/GSR2_Axelsson
|storemode=property
|title=Key Attributes for Monitoring and Assessment of Australian Forests: A Land Management Perspective
|pdfUrl=https://ceur-ws.org/Vol-1328/GSR2_Axelsson.pdf
|volume=Vol-1328
|dblpUrl=https://dblp.org/rec/conf/gsr/AxelssonJHSWWSM12
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==Key Attributes for Monitoring and Assessment of Australian Forests: A Land Management Perspective==
https://ceur-ws.org/Vol-1328/GSR2_Axelsson.pdf
Key attributes for monitoring and assessment of Australian forests: a land
management perspective
Christoffer Axelsson*a, Simon Jonesa, Andrew Haywoodb, Lola Suareza, Phillip Wilkesa, William
Woodgatea, Mariela Soto-Berelova, Andrew Mellorab
a
SMGS, RMIT University, GPO Box 2476, Melbourne, VIC 3001
b
Victorian Department of Sustainability and Environment, PO Box 500, East Melbourne, VIC 3002
* Corresponding author (christoffer.axelsson@rmit.edu.au)
Abstract
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.
Key words: forest attributes, forest monitoring, forest assessment, forest inventory
Author biography: Christoffer Axelsson has a M.Sc. in Surveying from Lund University. He then worked with GIS and spatial
databases in Sweden, in both local government and the private sector, before returning to academia to nurture an interest in remote
sensing, and environmental monitoring and modelling. In 2011 he graduated from University of Twente with a M.Sc. in Geo-
Information Science and Earth Observation for Environmental Modelling and Management. Currently, he is doing a PhD at RMIT
University. Christoffer’s main interests are in environmental analysis and modelling using remote sensing technologies.
Introduction
Australia is the world’s sixth largest country with an area of 769 million ha, and has a total forested area of 149 million ha
(Commonwealth of Australia 2012). These forests constitute an important natural resource by providing timber, supplying fresh
water, sequestering carbon, and playing host to a large variety of life forms, many of which are endemic to the continent (Brack
2007). As a participant in the Montréal Process, Australia has agreed to report on the state of its forests using a set of criteria and
indicators for biodiversity conservation and sustainable management (Montreal Process Implementation Group for Australia
2008). For monitoring and assessment, Australian land managers are in need of operational and cost-efficient remote sensing
tools. Currently, many forest managers rely on field plots, aerial photography surveys, and vegetation indices based on space
borne sensors. There is a growing interest in the development of light detection and ranging (LiDAR) technology, data fusion, and
efficient up-scaling methods. Airborne LiDAR is of particular importance in forest inventories because it detects the three-
dimensional vegetation structure, and enables estimation of structural attributes, such as canopy height, stand basal area, and stem
density, with higher accuracy than earlier technologies. The field of remote sensing is constantly evolving and the trend goes
towards better sensors, higher spatial and spectral resolution, more data sources and more possibilities to combine different
datasets. The technological development of active and passive remote sensing has proved of great value for forest monitoring and
assessment worldwide. These technologies are increasingly ready for operational applications at reasonable cost. However, many
forest managers still lack the necessary routines to make remote sensing tools an integral and cost-efficient part of their
operations. In order to target the development of routines and operational procedures to their specific needs, we need to
investigate which forest attributes are the most important from a land management perspective.
This paper presents the results of a web-based survey sent to professionals involved with forest management predominately in
Australia and a few in New Zealand. The aim of the survey is to identify core attributes for forest characterisation of importance
for both commercial and ecological interests. The survey results provide us with a direct comparison of attribute importance,
which we were not able to find in the existing literature. The literature is predominately influenced by experiences from Europe
and North America. Our results should reflect needs related to the characteristics of Australian forests, and Australian regulations
and reporting policies.
Forest attributes
Forest inventories are often based on field plots where a variety of structural and floristic attributes are measured. Using remote
sensing data, it is possible to model relationships with the plot data and create forest attribute maps over larger areas (McRoberts
et al. 2010). Table 1 contains a list of forest attributes, compiled from the literature (e.g. McElhinny et al. 2005) and our own
experience. The list is not exhaustive but aims to capture some of the most useful attributes at characterising forests for both
ecological and silvicultural purposes.
Table 1 Attributes for forest characterisation, grouped under the stand element they describe.
Forest stand element Attribute
Foliage Foliage projective cover (FPC)
Leaf area
Vertical structure Canopy height
Canopy height profiles (CHP)
Horizontal structure Canopy/crown cover
Stand basal area
Stand volume
Standard deviation of Diameter at Breast Height (DBH)
Stem density
Stem clustering, e.g. Clark-Evans Index (Clark and Evans 1954)
Deadwood Coarse woody debris (number, volume, or basal area of stags)
Litter (biomass or cover)
Floristics/type Dominant type/species
Species diversity/richness
Foliar biochemistry Leaf chlorophyll content
Leaf water content
Most of the attributes in Table 1 are commonly estimated from remote sensing data, but some (course woody debris and litter) are
extremely difficult. Our aim is not to examine how the attributes are estimated, but to evaluate their importance and show how
they potentially can be combined into composite attributes. While many of them carry important information by themselves, an
even greater source of information comes from combining them in different ways. The literature indicates that this small set of
attributes is informative for a wide range of applications. These applications can be called composite attributes since they are
estimated from a combination of primary attributes. Table 2 gives examples of relationships between primary and composite
attributes.
Table 2 Composite attributes and the primary attributes informative for predicting them.
Composite attribute Primary attribute Reference
Aboveground biomass Canopy height (Lefsky et al. 2002; Koch 2010)
and carbon Stand basal area (Jonson and Freudenberger 2011; Asner et al. 2012)
Canopy/crown cover (Lefsky et al. 2002; Lucas et al. 2008)
Course woody debris (Stokland 2001; Keith et al. 2009)
Dominant type/species (Anderson et al. 2008; Koch 2010)
Biodiversity Standard deviation of DBH (Van Den Meersschaut and Vandekerkhove 2000;
Neumann and Starlinger 2001)
Coarse woody debris (Stokland 2001; Grove and Meggs 2003)
Species diversity/richness (Lindenmayer et al. 2000; Van Den Meersschaut and
Vandekerkhove 2000; Clark et al. 2005)
Canopy health Leaf area (Solberg et al. 2006; Stone and Haywood 2006)
Leaf chlorophyll content (Coops et al. 2003; Rossini et al. 2006)
Leaf water content (Pontius et al. 2005; Chávez et al. 2013)
Fire hazard and risk Canopy height profiles (Tanskanen et al. 2005; Jain and Graham 2007)
Stem density and clustering (Graham et al. 1999; Richardson and Moskal 2011)
Litter (Link et al. 2006; Gould et al. 2011)
Dominant type/species (Graham et al. 1999; Gonzalez et al. 2006)
Leaf water content (Ustin et al. 1998; Ceccato et al. 2001)
Forest age and Stand basal area (Ziegler 2000; Kanowski et al. 2003)
successional stages Standard deviation of DBH (Spies and Franklin 1991; Wimberly and Spies 2001)
(including Stem density (Spies and Franklin 1991; Woinarski et al. 2004)
identification of old- Coarse woody debris (Spies and Franklin 1991; Kanowski et al. 2003)
growth forest) Dominant type/species (Franklin and Spies 1991; Woinarski et al. 2004)
Forest extent and Canopy height (Montreal Process Implementation Group for
categorisation Australia 2008)
(Australia) Crown cover (Montreal Process Implementation Group for
Australia 2008)
Dominant type/species (Montreal Process Implementation Group for
Australia 2008)
Timber volumes Canopy height (Næsset 1997)
Stand basal area (Means et al. 2000; Burkhart and Tomé 2012)
Stand volume (Maltamo et al. 2004; Tonolli et al. 2011)
Dominant type/species (Tonolli et al. 2011)
These relationships between primary and composite attributes are not necessarily generic. All ecosystems are different and the list
of significant attributes and their level of influence varies. Which attributes that are used in a specific case will also depend on
data availability and quality, collinearity between datasets, as well as methodology. Some attributes are mutually exclusive. For
example, LiDAR-based estimates of aboveground biomass and carbon generally use either a combination of canopy cover and
height (Koch 2010), or a combination of basal area and height (Asner et al. 2012). In both cases, stratification based on species
composition is important for obtaining reliable estimates.
There are numerous methodologies for combining attributes. For example, Gonzalez et al. (2006) developed a model for forest
fire probability in Catalonia, Spain, using different structural attributes, species composition, and altitude. They found that dense
stands, high variety in DBH, dominance by coniferous species, and low altitude were significant in modelling fire occurrence.
Canopy health and biodiversity are two fairly subjective composite attributes. In field based studies, there are methodologies for
combining attributes using indices, where estimates of different attributes are added together to yield a final score. The Crown
Damage Index (CDI), developed for estimating canopy health in eucalypt plantations, is one example. Estimates of crown
defoliation, dead leaf tissue, and discoloration each contribute equally to the final CDI score (Stone et al. 2003). Van Den
Meersschaut and Vandekerkhove (2000) constructed a similar index for assessing biodiversity in forest stands. A whole range of
structural and floristic attributes contribute to the final score. All the attributes in these two indices might not be detectable using
remote sensing, but a similar approach could be taken to create canopy health and biodiversity indices from attributes that are
predictable from air or space.
Forest attribute survey
We constructed a web-based survey with the objective to learn about land managers’ needs for forest attributes. The
SurveyMonkey web survey application (SurveyMonkey, Palo Alto, CA) was used for constructing the survey form and compiling
the results. It was sent on May 4th, 2012, with the deadline set to May 31st. The survey was sent to 81 people of whom 32
responded. The respondents were directly or indirectly engaged with forest management, at a variety of agencies; state and federal
government, private companies, and universities. Most were active in Australia and a few in New Zealand.
Table 3 Questions asked in the survey form.
# Question Type Rationale
1 What type of agency do you work for? Multiple choices. One answer Learn about the perspective of the
allowed. respondents.
2 What is your primary land management Multiple choices. One answer Learn about the perspective of the
responsibility? allowed. respondents.
3 What data do you currently utilise for forest Multiple choices. One answer Learn about current inventory
assessment and reporting? per category. methods.
4 What are the five most important forest metrics to Open-ended question. Let the respondents brainstorm their
capture using remote sensing from a forest own list of metrics.
management perspective?
5 Rank the importance of forest metrics from a forest Multiple choices. One answer Let respondents rank our list of
management perspective. per metric. metrics.
The survey contained five questions (Table 3) about both forest attributes and the professional background of the respondents. The
respondents were not forced to fill in answers to all parts of the survey form. In questions 3 and 5, respondents could tick some of
the choices and leave others blank. Results for those questions are therefore presented in % of received answers. Question 4 is
open-ended and generated a variety of answers. These were then grouped together with answers of similar meaning. The term
forest metric, in questions 4 and 5, is used interchangeably with forest attribute. For question 5, we compiled a list of important
forest attributes based on the literature and our own knowledge. Question 4 was intentionally placed on a page before question 5
so that the respondents did not see our list of forest metrics before compiling their own.
Results
Of the 32 survey respondents, about half were employed by state agencies and most of these were engaged
with either timber production or biodiversity/conservation (Table 4). The second largest employment type
was research institute.
Table 4 Employment type and primary responsibility of respondents.
Employment Federal State agency Research Private sector Total
Primary type agency institute
responsibility
Timber production 1 5 1 1 8
Biodiversity/ 1 7 1 9
Conservation
Water 1 1
Fire management 2 2
Research 2 3 6 1 12
Total 5 17 7 3 32
Figure 1 shows which data is currently used in forest inventories. Respondents with “research” as primary responsibility are
displayed as a separate group in order to highlight differences between current operational and research methodologies. All of the
listed methodologies are widely used, either routinely or occasionally. The more routinely used methodologies are field
monitoring plots (72% of respondents), followed by spaceborne multi- or hyperspectral imagery (65%), and aerial photography
(62%).
Figure 1 Currently used data sources for assessment and reporting. MS and HS stand for multispectral and hyperspectral.
The respondents list of important attributes (Table 5) reveals some clear trends. Tree height was considered the most important
attribute, followed by condition and health, crown density, and species/type mapping. These are all common forest attributes that
often are obligatory in plot-based inventories (Brack 2007; McRoberts and Tomppo 2007).
Table 5 Important forest attributes listed by the respondents.
Forest attribute 1st 2nd 3rd 4th 5th Total
Tree height 6 2 4 2 14
Forest condition and health 4 3 2 2 11
Density of tree crowns (LAI or FPC) 3 4 1 2 10
Species/type mapping 3 1 2 3 1 10
Change detection 2 2 1 2 2 9
Forest cover extent 5 2 1 8
Fire frequency and severity 1 1 3 1 2 8
Timber volumes 2 1 2 5
Vertical foliage density profile 2 1 1 1 5
Biomass/carbon 1 3 1 5
Basal area 1 2 1 4
Productivity 1 1 1 1 4
Growth stage mapping 1 2 3
Canopy disturbance 1 1 1 3
Fragmentation 2 1 3
Forest diversity, mortality, stocking, crown shape, extent of
understorey vegetation
- - - - - 2
Fire risk, DEM, water stress, nativeness of non-woody
vegetation, drainage mapping, canopy connectivity,
understorey LAI, main substructure type (small
- - - - - 1
tree,shrub,grass), fuel load
Attributes receiving one or two votes have been aggregated; only the total number of votes is shown.
Figure 2 contains results for the ranking of our list of forest attributes. The respondents assigned a level of importance to each
attribute. Interpretation of the results depends on if focus is set on the extremely important, the very important, or the important
level. With focus on the important level, attributes are ordered based on the percentage of votes falling into the categories of
important, very important and extremely important. That results in aboveground biomass at the top, followed by change detection
and canopy health. With a focus on the very important, change detection would be first, followed by canopy height and fire fuel
loads. At the bottom, canopy water content, litter, and nutrient status, are the three least important according to either focus.
Figure 3 compares the results for respondents divided into the three most common primary responsibility categories;
biodiversity/conservation, timber production, and research. It only shows the percentage of votes at the important to extremely
important levels.
Figure 2 Ranking of forest attributes.
Figure 3 Comparison of attribute importance between respondent groups.
Discussion
The results of a survey cannot be fully analysed without knowing the respondents’ perspectives. In this case, they belong to a
variety of different agencies with focus on different aspects of land management (Table 4). This broad range of perspectives well
represents Australian land managers, and the results can be seen as an indicator of their views. The most commonly used
operational methods for data capture (Figure 1) are based on mature technologies, such as spaceborne optical products and aerial
photography, which have been available for decades. The results indicate that airborne multispectral/hyperspectral imagery and
LiDAR are often used in research projects, but still not as widely applied in operational inventories. Their role in operational
programmes is expected to grow as they become more cost-efficient and with the development of better operational routines. The
common use of field monitoring plots is bound to remain as there will always be a need for validation and calibration data no
matter what remote sensing technology is used.
The list of important forest attributes listed by the respondents (Table 5) is similar to the one we compiled (Figure 2). One
attribute that was considered important, but was not on our list, is crown density. To summarise, the results show that the most
important attributes are tree height, canopy health and condition, crown density, floristic composition, aboveground biomass,
change detection, stem density, forest extent, and fire frequency/severity. Change detection is probably more accurately described
as a methodology than a forest attribute. Nevertheless, its high ranking indicates a need for running monitoring programmes over
longer time periods in order to detect changes. Change detection was also advocated by Brack (2007) for the case of plant
biodiversity monitoring. The least important attributes include canopy water content, litter, nutrient status, and coarse woody
debris. One common characteristic of these attributes, that may have influenced their low ranking, is that they are very difficult to
scale up to larger areas using remote sensing. However, if they could be accurately estimated, they would be important as building
blocks to get to composite attributes. The list of attributes contains many composites which require other attributes for estimation.
Leaf nutrient status and water content are indicative of the canopy health status (Barry et al. 2008; Ustin et al. 2009; Chávez et al.
2013), and the amount of litter can be used for estimating fire fuel loads and fire hazard (Link et al. 2006; Gould et al. 2011).
The importance of attributes to different groups of respondents is shown in Figure 3. Some of the attributes, such as aboveground
biomass and canopy height, appeal equally to people involved in timber production, biodiversity/conservation, and research. This
reflects the close link of aboveground biomass to both timber resources and carbon stocks. The timber production group is
relatively more interested in timber volumes, while the biodiversity/conservation group is relatively more concerned with stem
density, floristic composition, fragmentation, and litter. Floristics and fragmentation are typical biodiversity attributes, while stem
density can be indicative of growth stage and forest disturbances (Spies and Franklin 1991; Bhuyan et al. 2003).
Conclusions
The results of the web-based survey indicate a number of important forest attributes. The foremost are tree height, canopy health
and condition, crown density, floristic composition, aboveground biomass, stem density, forest extent, and fire frequency/severity.
In addition, the high ranking of change detecting highlight a need for continuous monitoring over time to detect changes and
disturbances. We have shown how the attributes relate to each other; that primary attributes can inform the estimation of
composite attributes such as biodiversity and canopy health. An attribute that is ranked low by the survey can thus still be
important if it is informative for a highly ranked composite product.
Acknowledgements
This paper is presented as part of the Cooperative Research Council for Spatial Information (CRCSI) Project 2.07 and the authors
would like to thank CRCSI and project partners for their financial support.
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