Tableros de fibra y partículas

Information about vegetation and vegetation dynamics is increasingly being recognised as a fundamental dataset that is required to inform public debate with regard to audits of resource condition. Therefore, vegetation information is required to effectively plan and report on resource development based on biophysical, social and economic aspirations (Thackway et al., 2007). There are two primary national level forest and vegetation mapping initiatives currently underway in Australia: the National Forest Inventory (NFI) and the National Vegetation Information System (NVIS). NVIS will be briefly described in this section followed by an outline of current NFI forest assessments and limitations. The proposed CFMF design was previously described in Chapter 1. A summary of the typical requirements and rationale for permanent plot based sampling strategies are outlined in this chapter, and a description of different sampling systems is given. The integration of field data for remote sensing calibration is discussed in terms of data sources, accuracy, and compilation issues. This provides context and rationale for the assessment of the utility of LiDAR to improve forest structure measurement, within integrated sampling schemes.

A consistent and practical definition of forest is fundamental to monitoring and reporting effectively Australia’s forest estate. The current definition of forest that was adopted by Australia’s National Forest Inventory and used in State of the Forests Reports (National Forest Inventory, 1998; 2003), as well as by the Australian Greenhouse Office (AGO) (Richards, 2002) is:

An area, incorporating all living and non-living components, that is dominated by trees having usually a single stem and a mature or potentially mature stand height exceeding two metres and with existing or potential crown cover of overstorey strata about equal to or greater than 20 per cent. This includes Australia’s diverse native forests and plantations, regardless of age. It is also sufficiently broad to encompass areas of trees that are sometimes described as woodlands.

(National Forest Inventory, 1998)

The definition is based on the 1992 National Forest Policy Statement, signed by all State, Territories and the Australian Government, but modified to remove uncertainty relating to

crown cover and height, and to meet operational implementation requirements. The minimum potential crown cover to qualify an area of trees as forest is now 20 %, which puts into effect the National Forest Policy Statement requirements that ‘forest’ is to include what has sometimes been called ‘woodland’. The definition also refers to ‘trees having usually a single stem’ and sets the lower tree height limit at two metres, which allowed the inclusion of the forest-forming mallees. Shrublands are excluded, even if they are taller than two metres, because of the requirement to be of tree formation. This definition is biologically based, rather than focused on particular forest uses (Hnatiuk, et al., 2003). It is similar to the single internationally agreed definition used by the United Nations Food and Agriculture Organisation, which is:

Land with tree crown cover (or equivalent stocking level) of more than 10 per cent and area of more than 0.5 hectares. The trees should be able to reach a minimum height of 5 metres at maturity in situ. May consist either of closed forest formations where trees of various storeys and undergrowth cover a high proportion of the ground; or of open forest formations with a continuous vegetation cover in which tree crown cover exceeds 10 per cent.

(FAO, 1998)

National Vegetation Information System

The National Vegetation Information System is a consistent attribute and database framework for describing, translating and compiling existing mapped information for all vegetation types across the whole landscape, and at regular intervals by the respective Australian State and Territory land and vegetation management agencies (NLWRA, 2001). The NVIS framework describes native vegetation using the concept of a ‘definitive vegetation type’, which details the structure and floristics of vegetation at the association or sub-association level and within mapped vector polygons (NLWRA, 2001). This is in contrast with the NFI requirement to only measure forest at the genus level with a broad structural classification (National Forest Inventory, 2003). The NVIS structure was developed in response to an increasing requirement to provide information for the long-term sustainable use and integrated natural resource management of regional ecosystems. However, as indicators of resource condition have yet to be fully developed, it is necessary to investigate approaches that encompass all vegetation information across landscapes at a level of detail relevant to regional

decision-making. Whilst ‘definitive vegetation types’ represent an integration or aggregation of many attributes (i.e., structure and floristics), they are not readily suited for measuring and monitoring trends, mainly due to the categorical nature of the vegetation type descriptions (Thackway et al., 2007).

Current National Forest Inventory reporting

At the national level Australia’s native forests are classified into three crown cover classes: woodland (20–50 %), open (50–80 %), and closed (80–100 %). Three height categories are used to classify Australia’s native forests: low (2-10 m), medium (11-30 m), and tall (> 30 m). Almost two-thirds of the native forest estate is woodland, with around 70 % being of medium height (Table 1). In New South Wales, Victoria and the Australian Capital Territory the majority of the forests are classified as open forest (Figure 2).

Traditionally, the primary sources of cover information that the States and Territories feed into the NFI compilation process are derived from air photo interpretation, and more recently satellite imagery (Hnatiuk, et al., 2003) (see Figure 3 for a range of sources). Crown cover (CC) is interpreted over generally homogeneous areas from aerial photography. Crown cover is expressed as the percentage of crown area projection per unit land area, with crown area defined as the total area contained within the external boundaries of the tree crown, where crowns are considered opaque (McDonald, et, al., 1998). Foliage projective cover (FPC) is based on the vertical projection of the crown foliage, and is derived from satellite imagery (e.g., Landsat TM). A related measure, foliage-branch cover (FBC) also includes branch elements in the assessment of cover, rather than just foliage. With both foliage measures, the density can vary according to species, crown type, age, location and time of year (National Forest Inventory, 2003; VicDNRE, 2000). Foliage projective cover is considered to provide a better indication of the photosynthetic potential of a plant community because trees generally have irregular canopy shape and low foliage density (Specht and Specht 1999). However, McDonald, et, al., (1998) recommend that crown cover be used as the primary structural attribute because foliage cover can seasonally vary, but crown size will remain constant unless major disturbance occurs.

Table 1: Area of NFI forest types ('000 ha) across the States and Territories in Australia. Source: National Forest Inventory State of the Forests Report, 2003. Forest type Australian Capital

Territory

New South Wales

Northern

Territory Queensland Australia South Tasmania Victoria Australia Western Australia native forest Percent of

Acacia 0 1 251 1 613 6 984 1 939 74 63 4 563 16 488 10

Callitris 0 1 240 386 387 261 1 56 0 2 330 1

Casuarina 0 1 000 14 216 763 1 4 40 2 039 1

Eucalypt 116 22 218 27 911 38 706 7 849 2 476 7 562 20 184 127 024 78

Eucalypt mallee woodland 0 9 0 122 5 180 0 1 171 3 918 10 400 –

Eucalypt mallee open 0 13 0 0 864 0 0 1 051 1 929 –

Eucalypt low woodland 3 114 16 368 1 373 1 207 65 246 2 616 21 992 –

Eucalypt medium woodland 18 2 269 5 532 32 696 554 1 274 598 10 321 53 263 –

Eucalypt tall woodland 0 91 0 1 130 0 289 219 0 1 728 –

Eucalypt low open 4 72 257 0 1 0 273 22 629 –

Eucalypt medium open 63 15 921 5 703 3 326 42 7 2 809 2 048 29 920 –

Eucalypt tall open 28 3 729 0 59 1 841 2 246 170 7 073 –

Eucalypt low closed 0 0 18 0 0 0 0 8 27 –

Eucalypt medium closed 0 0 33 0 0 0 0 30 63 –

Eucalypt tall closed 0 0 0 0 0 0 0 0 0 –

Mangrove 0 3 355 196 19 0 2 173 749 <1

Melaleuca 0 44 1 593 5 301 1 19 96 0 7 056 4

Rainforest 0 486 224 2 885 0 598 16 5 4 214 3

Other 0 415 738 1 059 34 0 135 398 2 780 2

Total native forest 117 26 658 32 836 55 734 10 866 3 169 7 935 25 365 162 680 100

Total forest (2003)1 _{133 26 981 32 843 55 942 11 015 3 364 8 295 25 717 164 290}

Total land area 240 80 160 134 620 172 720 98 400 6 780 22 760 252 550 768 230

Forest as per cent of land area 55 34 24 32 11 50 36 10 21

census

Resolution 10cm 50cm 1m 2m 5m 10m 15m 20m 25m 30m 40m 50m 100m 250m 500m 1km 1% 2% 5% 25% 50% 100%

MODIS - FPC

returns | voxels - FBC

Landsat TM derived FPC (e.g. Qld SLATS) ETM+ pan

SPOT5 - CC or FPC?

2 bands

Aerial Photo Interpretation (API) - polygons with broad CC classes only

Stand

pan (2.5m)

ICESat footprints (50-100m) - varies between CC or FBC

Landscape sample

Hemispherical photos - e.g. a single photo with estimated 50m view - FBC

Plot tree map and crown measurements - CC

Hyperspectral & high resolution imagery - CC - (e.g. Ikonos, Quickbird, CASI - 2m crowns; Hymap 5-10m crowns)

R emot ely s ens ed da ta

Scale Tree component Tree crown

Field transects - e.g. 1m interval, 50m long - FBC, FPC Plot

F

iel

d da

ta

SAR or other imagery - theoretical cover assessment ability, but need further research Crown delineations - CC

LiDAR

Figure 3: Illustration of different cover measurement sensors and range of scale and spatial resolution, both field and remotely sensed (McDonald, et al 1998; McCloy, 2006).

According to the NFI, there is currently no nationally consistent standard for mapping tree height, and it was noted that mapping compiled for national level reporting had nearly 150 different height classes (Wood, et al., 2006). Accurate and consistent height information is not extensively available across the continent due to the time, effort and resources required to collect the data in the field or interpret from stereo aerial photography. Generally, the majority of accurate height information is only available for areas that are, or were, managed as State Forests (National Forest Inventory, 2003). Height information in other forested areas are usually broad estimates from a few field plots, or infrequent high quality study sites that have API or LiDAR for example. These measurements are then extrapolated to similar environmental and/or forest conditions within broad scale datasets.

Limitations with NFI forest height and cover reporting

The National Forest Inventory compiles national scale forest information using data from the States and Territories. A major difficulty with this approach is that each source of data is provided at different assessment scales (e.g., pixel spatial resolution or aerial photographic scale), level of attribute detail, accuracy (spatial and attribute) and dates of collection (Wood et al., 2006). Mismatches between categorical cover and height classes are observed when compiling and translating API derived data into National Forest Inventory classes. The observed mismatches make accurate national aggregation of the height or cover class area difficult, and limit the effective use of the data in modelling and calibration of other data. Examples of mismatches between height and cover classes when using National Forest Inventory, Queensland and Victorian API schemes are given in Chapter 3. A related issue is that, due to large class ranges and subjective interpretation, there is low sensitivity to change when comparing data from different dates, unless it occurs at a class boundary. The low sensitivity with the current classes prevents effective monitoring other than at very broad scales (Scarth et al., 2001).

To integrate different sources of cover information, the National Forest Inventory provides a broad conversion between foliage projective cover and crown cover (Table 2). With

continuous cover information now available from satellite imagery (e.g., Landsat TM derived foliage projective cover; QDRM, 2003), a continuous and objective transfer function is needed to make better use of both historical and current/future information. Whilst there are methods for translating between cover metrics when using field data (e.g., McDonald et al., 1998), these require the subjective assessment of canopy openness, which cannot be done using medium scale remotely sensed data. This issue is highlighted by the AGO, who state that there is no direct relationship between tree crown cover and woody foliage projective cover, but that generally 20 % tree crown cover equates to around 10-15 % woody foliage projective cover (Table 2), with the relationship varying according to geography and vegetation community (AGO, 2003). Therefore a major research gap exists in the development of an objective translation between cover metrics, derived from different remotely sensed data.

Table 2: NFI translation between foliage projective cover (FPC) and crown cover (CC) (National Forest Inventory, 1998).

Cover Class FPC range CC range Forest Cover Type

1 0-10% 0-20% Non Forest

2 10-30% 20-50% Woodland

3 30-70% 50-80% Open Forest

4 70-100% 80-100% Closed forest

There is a need for an integrated sampling framework for forest assessment due to the current ‘snap-shot’ data compilation approach used by National Forest Inventory and National Vegetation Information System. For example, when comparing the two forest area estimates made in the 1998 and 2003 State of the Forests Reports (SOFR), the forest extent in 2003 was an increase over that reported in 1998. However, it was determined that the increase largely represents more comprehensive forest mapping of the continent rather than an actual increase in the area of forest (National Forest Inventory, 2003). Current information on woody cover change indicates that total forest cover in Australia is in fact decreasing, though because of recent State and Federal legislation, the rate of current forest clearance has reduced from the higher rates experienced in the 1970s and early 1980s (AGO, 2005). The confusion arising from incomplete compilation and reporting, and a reliance on a wide range of data sources and processing methodology, highlights the need for an integrated continental sampling and

monitoring strategy. Such a strategy is required if accurate reporting across large areas with detailed information is to be achieved (Wood et al., 2006).

The lack of an appropriate minimum area for defining forest is another limitation of the current National Forest Inventory mapping process. In Australia, the Australian Greenhouse Office uses a minimum woody area of 0.2 ha, and for Kyoto Protocol reporting the minimum area of forest ranges from 0.05-1.0 ha (Furby, 2002; AGO, 2005). The variable application of a minimum forest area by different agencies within a country is a common issue (Lund, 2002). Currently for the National Forest Inventory, sensor spatial resolution or public reporting scale determines the minimum area of forest. For example, the National Forest Inventory aggregates 25 m Landsat TM derived cover pixels to a minimum reporting level of 100 m, which is considered suitable for national reporting (Wood et al., 2006). Conversely, API mapped forest polygons are converted to 100 m raster grid cells, with the resultant issues for accuracy in area and shape when converting from vector to raster formats. API can readily utilise a standard minimum area based on the interpreter’s ability to discern homogeneous regions at the scale of the photo (e.g., 3 ha in Tasmania, with 1:20,000 scale photography; Stone, (1998)). However, there is no nationally consistent minimum area available due to the range of photographic scales in use (Wood et al., 2006). When using satellite imagery to classify forest cover, the pixel size becomes the minimum area defined as woody cover (e.g., a Landsat TM 25 m pixel is 0.0625 ha). If the strict NFI definition of ‘forest’ is applied to each pixel, then the vegetation has to be

≥ 20% crown cover and ≥ 2 m height within the pixel. Without appropriate calibration and validation of the assumed cover and height thresholds, the resulting forest classification can be inaccurate, and generate potentially large variation in national level spatial and temporal estimates of the forest estate (Lund, 2002).

When defining a minimum area, there is the issue of when does a tree or group of trees with ≥ 20 % crown cover per unit area become classifiable as a forest? With moderate scale image spatial resolutions (~25 m+) it is conceivable that a single pixel will, in theory, be larger than all but the very largest tree crowns. In sparser forests, it is possible that a single tree can be found within a single pixel, and be large enough to register greater than the 20 % cover

threshold. This single tree per pixel as ‘forest’ concept, whilst currently meeting a strictly applied definition of NFI forest, is unlikely to be of use to ecologists or foresters (or other users) that may have different perceptions of, or requirements for, what a “forest” should be. Whatever the perceptions, it is likely that users of the forest data consider that a forested area should (or does) contain more than one tree, and it should at least be a self-sustaining area made up of a number of trees (Lund, 2002). Additionally, when using automated crown delineation routines to derive then aggregate individual tree crown objects into ‘forest’ (however defined), a fundamental requirement is the minimum area of the reporting unit. For example, a requirement could be that the forest area is made up of more than one tree. The issue then becomes one of how to empirically and objectively determine the minimum area and density that is required to meet these criteria. Wood et al., (2006) state that the NFI Steering Committee is still examining the best way to define a minimum area for national forest compilation and reporting. This research gap will form a component of the methods and results undertaken in this thesis to address the primary research question.