Although not specially referring to wildlife forestry, Carey (2006) succinctly encapsulated the holistic approach of wildlife forestry as - *"Management … of forest development and landscape dynamics is more likely to be successful in maintaining ecosystem and landscape function than just providing select structural elements in stands and select structural stages in landscapes, as*

*Bottomland hardwood forests* - A preponderance of closed canopy, second-growth forests with relatively homogeneous structure prompted recommendations for increased area of open canopied forests with greater structural diversity and more understory vegetation development (Wilson et al., 2007). Basal area should be reduced to 14-16 m2 ha-1 with ≥25% of the basal area approaching biological maturity (i.e., in older age classes). Forest canopy cover should be 60%-70% with at least 5 stems ha-1 being canopy dominant trees - emergent canopies preferred. Understory vegetation should be robust yet patchily distributed with 25%-40% cover. Density of snags ≥25 cm diameter at breast height (dbh) should be >15 snags ha-1 or have a basal area >0.9 m2 ha-1. Volume of coarse woody debris should exceed 14 m3 ha-1 and advanced reproduction should be present on 30%-40% of the stand (Table 3). *Northwestern U.S. coniferous forests* - Bird species richness and abundance are positively related to proportion of broadleaf forest, from 1% to 25% canopy cover, within coniferous dominated landscapes. Thus, it is import to maintain or restore broadleaf vegetation in these coniferous dominated stands (Betts et al., 2010). Desired stand conditions reflect a structure similar to that of old-growth forest conditions (Cissel et al., 2006). Multiple tree species, representing multiple age cohorts, should occupy stands and this mix should include at least 1 shade tolerant species. Large diameter (>125 cm dbh) conifers should be present at densities of 5-15 stems ha-1. At least half of the 20-30 snags ha-1 should be >60 cm dbh and a

Wildlife forestry; variable retention with canopy gaps

Mixture of species; hard and soft mast; shade tolerant and intolerant

Heterogeneous retention and removal of understory competitors

Heterogeneous retention and thinning; underrepresented species preferably retained

Treatment Increasing forest diversity, structure, and complexity →

species retention Multi-species retention

third of coarse woody debris should be of large (>60 cm) diameter (Table 3).

*Southeastern U.S. pines* **-** Historically open-canopied pine forests were found throughout the southeastern United States. These open forests were maintained by frequent fires. Prescribed burning, complimented with thinning of pine canopies and herbicide treatments

harvest Clearcut Legacy retention;

Herbicide removal of understory and midstory

**3.3 Regional examples of desired stand conditions**

Regeneration

Restoration

Carey, 2003b).

Stand modification (TSI)

planting Monoculture

Thinning Systematic single

*is often suggested for conservation."*

be cautious of excessive application of silviculture intended to promote heterogeneity as these excesses may promote what could be viewed as 'homogeneity' of this habitat structure at a more dispersed scale. As different intensity of silvicultural treatment result in different stand complexity, silvicultural decisions made at each stage of stand development affect forest structure (Table 2).

*Lower Basal Area* – Thinning canopies has a positive or neutral effect on diversity and abundance of most taxa, albeit intensity and biophysical setting influence wildlife response (Verschuyl et al., 2011). In general, lower basal areas are associated with increased density of understory vegetation which adds to structural diversity, provides cover for escape, thermoregulation, and nesting or bedding. Moreover patches of dense understory enhance forage opportunities for species that browse grasses, herbs, or seedlings, species that glean insects from leaves, and frugivores.

*Large, old, decadent trees -* These features tend to be especially characteristic of old-growthlike forest conditions. As such, these characteristics are difficult to achieve given historical forest management where short rotation lengthen and harvest of economically mature trees limit availability of these features. Large cavity trees are used as den sites by bears, as well as other meso-mammalian (e.g., martens, fishers, raccoons, porcupines, weasels) and larger avian species (e.g., woodpeckers, owls, mergansers, and ducks). Similarly, dominant emergent trees provide perches and nesting platforms for large raptors (e.g., eagles, hawks, and kites). Although avian excavators (e.g., woodpeckers) are the primary cavity producers in North America, producing 77% of nesting cavities, this is not the case globally (≤26%) where most cavity formation results from damage and decay (Cockle et al., 2011). Thus, cavity-using communities are highly dependent upon maintaining decadent trees within forest stands. As such, the presence of large, decadent trees may be essential for occupancy of stands by many of these large fauna.

*Abundant standing snags and downed woody debris* – Diversity and abundance of birds and biomass of invertebrates are positively associated with the volume of downed coarse woody debris and snags (Riffell et al., 2011). In unmanaged oak and oak-beech forests, 25% of dead wood was standing, whereas 75% was downed (Vanderkerkhove et al., 2009). Snags (and other decadent trees, such as those with heart rot), are characteristically exploited by primary-cavity excavators (e.g., woodpeckers) as well as those species that forage on saprophytic insects. Excavated cavities are subsequently used by myriad secondary-cavity using species. Lack of snags or other decadent trees may restrict this entire suite of cavity using species (Cockle et al. 2011).

*Providing sufficient and suitable regeneration* – As successional changes in forest species composition are often beyond human life-spans, the need for sufficient and suitable regeneration of canopy trees is often neglected. However, within the concept of wildlife forestry, regeneration of canopy species is ongoing (generally not a single event – as in evenaged forest management). Thus, ensuring sufficient regeneration of marketable trees, and thereby enhancing the long-term merchantability of forest stands, is imperative if desired habitat conditions for priority wildlife species are to be sustained through repeated, prescribed, silvicultural treatments. Notably, where maintaining shade-intolerant species within future generations is deemed desirable for anticipated management options, silviculturally induced gaps (patch cuts or group selections) should be of sufficient area to allow regeneration and development of these shade-intolerant species.

be cautious of excessive application of silviculture intended to promote heterogeneity as these excesses may promote what could be viewed as 'homogeneity' of this habitat structure at a more dispersed scale. As different intensity of silvicultural treatment result in different stand complexity, silvicultural decisions made at each stage of stand development affect

*Lower Basal Area* – Thinning canopies has a positive or neutral effect on diversity and abundance of most taxa, albeit intensity and biophysical setting influence wildlife response (Verschuyl et al., 2011). In general, lower basal areas are associated with increased density of understory vegetation which adds to structural diversity, provides cover for escape, thermoregulation, and nesting or bedding. Moreover patches of dense understory enhance forage opportunities for species that browse grasses, herbs, or seedlings, species that glean

*Large, old, decadent trees -* These features tend to be especially characteristic of old-growthlike forest conditions. As such, these characteristics are difficult to achieve given historical forest management where short rotation lengthen and harvest of economically mature trees limit availability of these features. Large cavity trees are used as den sites by bears, as well as other meso-mammalian (e.g., martens, fishers, raccoons, porcupines, weasels) and larger avian species (e.g., woodpeckers, owls, mergansers, and ducks). Similarly, dominant emergent trees provide perches and nesting platforms for large raptors (e.g., eagles, hawks, and kites). Although avian excavators (e.g., woodpeckers) are the primary cavity producers in North America, producing 77% of nesting cavities, this is not the case globally (≤26%) where most cavity formation results from damage and decay (Cockle et al., 2011). Thus, cavity-using communities are highly dependent upon maintaining decadent trees within forest stands. As such, the presence of large, decadent trees may be essential for occupancy

*Abundant standing snags and downed woody debris* – Diversity and abundance of birds and biomass of invertebrates are positively associated with the volume of downed coarse woody debris and snags (Riffell et al., 2011). In unmanaged oak and oak-beech forests, 25% of dead wood was standing, whereas 75% was downed (Vanderkerkhove et al., 2009). Snags (and other decadent trees, such as those with heart rot), are characteristically exploited by primary-cavity excavators (e.g., woodpeckers) as well as those species that forage on saprophytic insects. Excavated cavities are subsequently used by myriad secondary-cavity using species. Lack of snags or other decadent trees may restrict this entire suite of cavity

*Providing sufficient and suitable regeneration* – As successional changes in forest species composition are often beyond human life-spans, the need for sufficient and suitable regeneration of canopy trees is often neglected. However, within the concept of wildlife forestry, regeneration of canopy species is ongoing (generally not a single event – as in evenaged forest management). Thus, ensuring sufficient regeneration of marketable trees, and thereby enhancing the long-term merchantability of forest stands, is imperative if desired habitat conditions for priority wildlife species are to be sustained through repeated, prescribed, silvicultural treatments. Notably, where maintaining shade-intolerant species within future generations is deemed desirable for anticipated management options, silviculturally induced gaps (patch cuts or group selections) should be of sufficient area to

allow regeneration and development of these shade-intolerant species.

forest structure (Table 2).

insects from leaves, and frugivores.

of stands by many of these large fauna.

using species (Cockle et al. 2011).

Table 2. Effects of wildlife forestry and other silviculture on stand structure (expanded from Carey, 2003b).

Although not specially referring to wildlife forestry, Carey (2006) succinctly encapsulated the holistic approach of wildlife forestry as - *"Management … of forest development and landscape dynamics is more likely to be successful in maintaining ecosystem and landscape function than just providing select structural elements in stands and select structural stages in landscapes, as is often suggested for conservation."*

#### **3.3 Regional examples of desired stand conditions**

*Bottomland hardwood forests* - A preponderance of closed canopy, second-growth forests with relatively homogeneous structure prompted recommendations for increased area of open canopied forests with greater structural diversity and more understory vegetation development (Wilson et al., 2007). Basal area should be reduced to 14-16 m2 ha-1 with ≥25% of the basal area approaching biological maturity (i.e., in older age classes). Forest canopy cover should be 60%-70% with at least 5 stems ha-1 being canopy dominant trees - emergent canopies preferred. Understory vegetation should be robust yet patchily distributed with 25%-40% cover. Density of snags ≥25 cm diameter at breast height (dbh) should be >15 snags ha-1 or have a basal area >0.9 m2 ha-1. Volume of coarse woody debris should exceed 14 m3 ha-1 and advanced reproduction should be present on 30%-40% of the stand (Table 3).

*Northwestern U.S. coniferous forests* - Bird species richness and abundance are positively related to proportion of broadleaf forest, from 1% to 25% canopy cover, within coniferous dominated landscapes. Thus, it is import to maintain or restore broadleaf vegetation in these coniferous dominated stands (Betts et al., 2010). Desired stand conditions reflect a structure similar to that of old-growth forest conditions (Cissel et al., 2006). Multiple tree species, representing multiple age cohorts, should occupy stands and this mix should include at least 1 shade tolerant species. Large diameter (>125 cm dbh) conifers should be present at densities of 5-15 stems ha-1. At least half of the 20-30 snags ha-1 should be >60 cm dbh and a third of coarse woody debris should be of large (>60 cm) diameter (Table 3).

*Southeastern U.S. pines* **-** Historically open-canopied pine forests were found throughout the southeastern United States. These open forests were maintained by frequent fires. Prescribed burning, complimented with thinning of pine canopies and herbicide treatments

Wildlife Forestry 179

more extensive crown structure or shallow, poorly developed roots are more likely to be toppled by wind (Beese, 2001). Retained, sound, dominant trees are less subject to windthrow than decadent or subdominant trees. The dispersion, orientation, and shape of retention also influences susceptibility to windthrow, with tear-drop shaped, aggregated clumps, oriented with prevailing winds better able to withstand wind damage (Franklin et al., 1997). Even so, small (1.5 ha) patch cuts may be least vulnerable to wind (Beese, 2001).

> Pacific northwest conifers (Cissel et al. 2006)

several tree species of varying size and age; large emergent trees

5 – 15 ha-1, >125 cm dbh conifers; 60 – 85 ha-1, 38-76 cm dbh; 250 – 500 ha-1 <38 cm dbh

> multi-layered canopies

open space among the lower branches of canopy

20 – 30 ha-1 >25 cm dbh; 50% > 60 cm dbh; cavities in standing trees

275 linear m >25 cm diameter; 33% > 60 cm dbh

≥2 species (≥1 shade-tolerant); ≥2 age cohorts; >150 years old

Young conifers in northern pacific rainforest Altman and Hagar, 2007)

large conifer trees

<500 ha-1; thin to promote growth

variation in overstory achieved via silviculture, low % canopy cover to encourage understory

retain and protect old shrubs and shrub patches

variation in understory associated with variable canopy

large

retain, protect, and recruit large diameter CWD

encourage regeneration and retention of a diversity of species

retain and promote deciduous trees and shrubs

Longleaf pine (Bragg, 2004; East Gulf Coastal Plain Joint Venture, unpublished)

>4 m2 ha-1, ≥35 cm dbh

cm dbh

40 – 60% (≤5% hardwood) Patchy

> ≤15% (≤5% hardwood) Open

≥7 (12-25) ha-1, red heart; 10 – 50% cull in retained trees

(includes snags)

10% of area with advanced reproduction

fire return ≤3 year

Table 3. Described and quantified desired site (stand) conditions that are deemed to provide enhanced habitat for priority wildlife species in different forest types and seral stages.

Basal area 14 – 16 m2 ha-1 11 – 16 m2 ha-1 9 – 27 m2 ha-1

Stand feature

Size class

Snags & stressed trees

Coarse woody

Reproduction

Other

Canopy cover 60 – 70%

Midstory 25 – 40%

Ground cover 25 – 40% >80%

>15 ha-1, ≥25 cm dbh; or ≥5 ha-1, ≥ 51 cm dbh; or >0.9 m2 ha-1, >25 cm dbh

debris ≥14 m3 ha-1 21 – 54 m3 ha-1

30 – 40% of area with advanced reproduction

>10 visible cavities ha-1; 1 den tree (4 ha)-1

Bottomland hardwood (Wilson et al., 2007)

≥25% of BA approaching biological maturity

Density >5 dominant ha-1 12 – 30 pines ha-1, >76

of encroaching hardwoods can restore these historic conditions. Open canopy conditions with an abundant and floristically diverse understory increase species richness of priority open pine associated amphibians (Guyer, and Bailey, 1993, Steen et al., 2010). Similarly, abundance of endangered gopher tortoises (*Gopherus polyphemus*) and red-cockaded woodpecker (*Picoides borealis*) respond positively to prescribed fire within open pine stands. Desired stand conditions include open (40-60%) canopies that are dominated by large (≥35 cm dbh) pines. These large pines should account for ≥ 4.5 m2 ha-1 of a total basal area of 11 – 16 m2 ha-1. Mid-story should be sparse (<15%) with very limited hardwoods (<5%). Ground cover and understory should be lush (>80% cover) and comprised predominately of grasses and forbs. At least 7 snags ha-1 should be present. Relatively frequent fire (≤3 year interval) is likely required to maintain these desired conditions (Table 3).

#### **4. Economic considerations**

Wildlife forestry's primary intent is to provide suitable habitat conditions for priority wildlife. Even so, silvicultural practices that require long-term commitments or repeated expenditures may not be undertaken and thus, may not provide desired results. Indeed, silvicultural prescriptions intended to enhance wildlife habitat must be cognizant of their expense and their long-term effect on future merchantability of the stand. With that caveat, wildlife forestry silviculture may have both positive and negative economic effects.

A positive impact of most wildlife forestry practices is a reduction in wildfire hazards. Indeed, managing tree density and species composition with prophylactic silvicultural treatments that include thinning, surface fuel treatments, and prescribed fire reduce the risk of wildfire (Graham et al., 1999).

Negative economic factors include wounding of potentially merchantable trees that remain within stands after silvicultural treatments, as these wounds may result in future economic loss due to mortality or reduction in quality of timber (Hennon and DeMars, 1997). Thus, harvest prescriptions should account for this possibility and provide recommendations to reduce unintended damage to residual trees. On the other hand, from a wildlife use perspective, basal wounds to large retained trees have the potential to increase the number of decant large trees within a stand.

Another negative economic impact of wildlife silviculture may be successional changes in species composition. As noted for ensuring adequate tree regeneration within stands, managers should be aware of unintended shifts in species composition as a consequence of prescribed wildlife silviculture, usually due to an increased presence of shade-tolerant species. Therefore, if shade-intolerant species are intended to be a part of future forest canopies, silvicultural treatments must ensure their regeneration and development (Twedt and Somershoe, in press).

All forests are subject to windthrow (toppling of live standing trees due to wind), although its severity is influenced by wind speed, climate, topography, hydrology, and soils (Ruel, 1995): Toppled trees are present in mature unharvested forests as well as along the edges of clearcuts. Even so, windthrow may increase after partial harvest silvicultural treatments. More retention within a stand, however, tends to lessen the severity of windthrow (Franklin et al., 1997). Windthrow can also be mitigated through judicious silviculture, as some species are more susceptible to windthrow, possibly due to differences in form. Trees with

of encroaching hardwoods can restore these historic conditions. Open canopy conditions with an abundant and floristically diverse understory increase species richness of priority open pine associated amphibians (Guyer, and Bailey, 1993, Steen et al., 2010). Similarly, abundance of endangered gopher tortoises (*Gopherus polyphemus*) and red-cockaded woodpecker (*Picoides borealis*) respond positively to prescribed fire within open pine stands. Desired stand conditions include open (40-60%) canopies that are dominated by large (≥35 cm dbh) pines. These large pines should account for ≥ 4.5 m2 ha-1 of a total basal area of 11 – 16 m2 ha-1. Mid-story should be sparse (<15%) with very limited hardwoods (<5%). Ground cover and understory should be lush (>80% cover) and comprised predominately of grasses and forbs. At least 7 snags ha-1 should be present. Relatively frequent fire (≤3 year

Wildlife forestry's primary intent is to provide suitable habitat conditions for priority wildlife. Even so, silvicultural practices that require long-term commitments or repeated expenditures may not be undertaken and thus, may not provide desired results. Indeed, silvicultural prescriptions intended to enhance wildlife habitat must be cognizant of their expense and their long-term effect on future merchantability of the stand. With that caveat,

A positive impact of most wildlife forestry practices is a reduction in wildfire hazards. Indeed, managing tree density and species composition with prophylactic silvicultural treatments that include thinning, surface fuel treatments, and prescribed fire reduce the risk

Negative economic factors include wounding of potentially merchantable trees that remain within stands after silvicultural treatments, as these wounds may result in future economic loss due to mortality or reduction in quality of timber (Hennon and DeMars, 1997). Thus, harvest prescriptions should account for this possibility and provide recommendations to reduce unintended damage to residual trees. On the other hand, from a wildlife use perspective, basal wounds to large retained trees have the potential to increase the number

Another negative economic impact of wildlife silviculture may be successional changes in species composition. As noted for ensuring adequate tree regeneration within stands, managers should be aware of unintended shifts in species composition as a consequence of prescribed wildlife silviculture, usually due to an increased presence of shade-tolerant species. Therefore, if shade-intolerant species are intended to be a part of future forest canopies, silvicultural treatments must ensure their regeneration and development (Twedt

All forests are subject to windthrow (toppling of live standing trees due to wind), although its severity is influenced by wind speed, climate, topography, hydrology, and soils (Ruel, 1995): Toppled trees are present in mature unharvested forests as well as along the edges of clearcuts. Even so, windthrow may increase after partial harvest silvicultural treatments. More retention within a stand, however, tends to lessen the severity of windthrow (Franklin et al., 1997). Windthrow can also be mitigated through judicious silviculture, as some species are more susceptible to windthrow, possibly due to differences in form. Trees with

wildlife forestry silviculture may have both positive and negative economic effects.

interval) is likely required to maintain these desired conditions (Table 3).

**4. Economic considerations**

of wildfire (Graham et al., 1999).

of decant large trees within a stand.

and Somershoe, in press).

more extensive crown structure or shallow, poorly developed roots are more likely to be toppled by wind (Beese, 2001). Retained, sound, dominant trees are less subject to windthrow than decadent or subdominant trees. The dispersion, orientation, and shape of retention also influences susceptibility to windthrow, with tear-drop shaped, aggregated clumps, oriented with prevailing winds better able to withstand wind damage (Franklin et al., 1997). Even so, small (1.5 ha) patch cuts may be least vulnerable to wind (Beese, 2001).

Table 3. Described and quantified desired site (stand) conditions that are deemed to provide enhanced habitat for priority wildlife species in different forest types and seral stages.

Wildlife Forestry 181

vision regarding what each perceives as the desired habitat condition within forests. In the landscape context, these conditions need not be the same for all forests, nor will they be identical for all priority species. Research and monitoring will be required to evaluate species responses to different ranges of habitat conditions perceived as desirable. Judgments may be needed when priority species exhibit conflicting responses to what are perceived as

At present we have little empirical data regarding the range of economic returns from wildlife silviculture relative to traditional production-oriented silviculture. Appropriate measures of economic returns, including an evaluation of the long-term sustainably of different management options, are needed. In addition, we have assessed the faunal responses of relatively few species to wildlife forestry silviculture. Future efforts must assess comparable responses of less-studied priority species and species groups as well as evaluate

To determine if prescribed management actions elicit intended responses from priority species, monitoring is required. Monitoring should target specific management actions and evaluate spatial and temporal responses. Silvicultural prescriptions should consider incorporation of the principles of adaptive management during their development, implementation, and evaluation (Fig. 1). Feedback from such efforts can provide an assessment of the adequacy and sustainability of wildlife forestry silviculture. Science based knowledge of the results of these management practices is crucial to their long-term success, as "*Conservation is paved with good intentions which prove to be futile, or even dangerous, because they are devoid of critical understanding either of the land, or of economic land use"*

I am grateful to the members of the Lower Mississippi Valley Joint Venture Forest Resource Conservation Working Group for thoughtful discussions and arguments regarding the merits of wildlife forestry. Similarly, members of the West Gulf Coastal Plain and Ouachita Mountains Landbird working group provided insight and understanding regarding application of wildlife forestry principles within southern pine systems. Finally, I am grateful to the many pioneers and advocates of alternative silviculture from which most of

Altman, B. & Hagar, J. (2007). *Rainforest birds- A land manager's guide to breeding bird habitat in*

Askins, R. (2001). Sustaining biological diversity in early succession communities: the

Aubry, K.; Halpern, C. & Maguire, D. (2004). Ecological effects of variable-retention harvests

*young conifer forests in the Pacific Northwest,* U.S. Geological Survey Scientific

challenge of managing unpopular habitats. *Wildlife Society Bulletin*, Vol.29, No.2,

in the northwestern United States: the DEMO study. *Forest Snow and Landscape*

desired conditions.

(Leopold, 1953).

**8. References**

pp. 407-412

**7. Acknowledgement**

appropriate demographic responses of all species.

our understanding of wildlife forestry was derived.

*Research*, Vol.78, No.1-2, pp. 119-134

Investigations Report 2006-5304, Reston, Virginia

There is little basis for economic comparisons of wildlife forestry and other, traditional, silvicultural systems. Wildlife forestry may provide sustained revenues from exploitive use of wildlife, as well as from periodic harvest of forest products. But even without income from wildlife, little economic loss was reported from variable retention silviculture compared to even-aged, shelterwood cuts in *Nothofagus pumilio* forests (Pastur et al., 2009). Similarly, simulation models spanning 300 years that compared economic returns and ecological benefits within Pacific northwest forests being managed for biodiversity with forests being managed for timber production, and incorporating narrow riparian reserves (i.e., set asides), found significantly greater ecological benefits associated with biodiversity management (Carey et al.,1999). Concurrently, the economic cost of achieving those benefits was relatively modest, with 18% loss in net present value (NPV), only 4% loss if riparian protection was similar to that afforded within the biodiversity management model, and fully a 13% gain in NPV when newly enacted state laws for riparian protection were enforced (Carey, 2003a). More notably, under management for biodiversity, tree quality improved, decadal revenues increased by 150%, forest-based employment quadrupled, and manufacturing diversified and became more reliant on high quality products and value added manufacturing (Carey, 2003a, Carey et al., 1999, Lippke et al., 1996).

#### **5. Restoration**

If successfully implemented, wildlife forestry is self-sustaining, such that reforestation (e.g., artificial regeneration or stand regeneration) is rare. Even so, appropriate practices are increasingly being sought in conjunction with forest restoration that better enable attainment of desired forest conditions. For example, decades of bottomland afforestation, wherein agricultural lands are being restored to forested wetlands, has provided valuable insight regarding appropriate species and planting densities (Wilson et al., 2007). Management practices similar to those employed for wildlife forestry have also been used to restore longleaf pine forests on sites converted to forests dominated by other species. Similarly, within production-oriented plantation forests management alternatives are sought that will enhance biodiversity and improve wildlife habitat, yet minimally compromise product output.

Recommendations for restoration and mid-rotation management have included planting or maintaining species mixtures to ensure stand diversity and improve forest product quality (Twedt and Best, 2004, Lockhart et al. 2008). Mixed species planting of native species are favored over monocultures of exotic species. Some legacy trees (live or snags) and understory vegetation should be retained at harvest through a second rotation. Sitepreparation should reflect natural disturbances and conserve coarse woody debris. Thinning earlier in the rotation and increasing rotation length also increase structural diversity (Hartley, 2002; Kerr, 1999). Finally, for some forest types, maintaining herbaceous or early succession habitat for a longer period provides benefits to priority bird species (Altman and Hagar, 2007).

#### **6. Evaluating results of management actions**

Quantitatively defined landscape conditions and desired stand conditions have been identified for only a few ecoregional landscapes and only a few forest types within these landscapes. Wildlife biologists, foresters, and conservation planners must develop a shared

There is little basis for economic comparisons of wildlife forestry and other, traditional, silvicultural systems. Wildlife forestry may provide sustained revenues from exploitive use of wildlife, as well as from periodic harvest of forest products. But even without income from wildlife, little economic loss was reported from variable retention silviculture compared to even-aged, shelterwood cuts in *Nothofagus pumilio* forests (Pastur et al., 2009). Similarly, simulation models spanning 300 years that compared economic returns and ecological benefits within Pacific northwest forests being managed for biodiversity with forests being managed for timber production, and incorporating narrow riparian reserves (i.e., set asides), found significantly greater ecological benefits associated with biodiversity management (Carey et al.,1999). Concurrently, the economic cost of achieving those benefits was relatively modest, with 18% loss in net present value (NPV), only 4% loss if riparian protection was similar to that afforded within the biodiversity management model, and fully a 13% gain in NPV when newly enacted state laws for riparian protection were enforced (Carey, 2003a). More notably, under management for biodiversity, tree quality improved, decadal revenues increased by 150%, forest-based employment quadrupled, and manufacturing diversified and became more reliant on high quality products and value

added manufacturing (Carey, 2003a, Carey et al., 1999, Lippke et al., 1996).

If successfully implemented, wildlife forestry is self-sustaining, such that reforestation (e.g., artificial regeneration or stand regeneration) is rare. Even so, appropriate practices are increasingly being sought in conjunction with forest restoration that better enable attainment of desired forest conditions. For example, decades of bottomland afforestation, wherein agricultural lands are being restored to forested wetlands, has provided valuable insight regarding appropriate species and planting densities (Wilson et al., 2007). Management practices similar to those employed for wildlife forestry have also been used to restore longleaf pine forests on sites converted to forests dominated by other species. Similarly, within production-oriented plantation forests management alternatives are sought that will enhance biodiversity and improve wildlife habitat, yet minimally

Recommendations for restoration and mid-rotation management have included planting or maintaining species mixtures to ensure stand diversity and improve forest product quality (Twedt and Best, 2004, Lockhart et al. 2008). Mixed species planting of native species are favored over monocultures of exotic species. Some legacy trees (live or snags) and understory vegetation should be retained at harvest through a second rotation. Sitepreparation should reflect natural disturbances and conserve coarse woody debris. Thinning earlier in the rotation and increasing rotation length also increase structural diversity (Hartley, 2002; Kerr, 1999). Finally, for some forest types, maintaining herbaceous or early succession habitat for a longer period provides benefits to priority bird species (Altman and

Quantitatively defined landscape conditions and desired stand conditions have been identified for only a few ecoregional landscapes and only a few forest types within these landscapes. Wildlife biologists, foresters, and conservation planners must develop a shared

**5. Restoration**

compromise product output.

**6. Evaluating results of management actions**

Hagar, 2007).

vision regarding what each perceives as the desired habitat condition within forests. In the landscape context, these conditions need not be the same for all forests, nor will they be identical for all priority species. Research and monitoring will be required to evaluate species responses to different ranges of habitat conditions perceived as desirable. Judgments may be needed when priority species exhibit conflicting responses to what are perceived as desired conditions.

At present we have little empirical data regarding the range of economic returns from wildlife silviculture relative to traditional production-oriented silviculture. Appropriate measures of economic returns, including an evaluation of the long-term sustainably of different management options, are needed. In addition, we have assessed the faunal responses of relatively few species to wildlife forestry silviculture. Future efforts must assess comparable responses of less-studied priority species and species groups as well as evaluate appropriate demographic responses of all species.

To determine if prescribed management actions elicit intended responses from priority species, monitoring is required. Monitoring should target specific management actions and evaluate spatial and temporal responses. Silvicultural prescriptions should consider incorporation of the principles of adaptive management during their development, implementation, and evaluation (Fig. 1). Feedback from such efforts can provide an assessment of the adequacy and sustainability of wildlife forestry silviculture. Science based knowledge of the results of these management practices is crucial to their long-term success, as "*Conservation is paved with good intentions which prove to be futile, or even dangerous, because they are devoid of critical understanding either of the land, or of economic land use"*  (Leopold, 1953).

#### **7. Acknowledgement**

I am grateful to the members of the Lower Mississippi Valley Joint Venture Forest Resource Conservation Working Group for thoughtful discussions and arguments regarding the merits of wildlife forestry. Similarly, members of the West Gulf Coastal Plain and Ouachita Mountains Landbird working group provided insight and understanding regarding application of wildlife forestry principles within southern pine systems. Finally, I am grateful to the many pioneers and advocates of alternative silviculture from which most of our understanding of wildlife forestry was derived.

#### **8. References**

Wildlife Forestry 183

Carey, A. (2003a). Restoration of landscape function: reserve or active management?

Carey, A. (2003b). Managing for wildlife: A key component for social acceptance of

Carey, A. (2006). Active and passive forest management for multiple values. *Northwestern*

Carey, A., Lippke, B. & Sessions, J. (1999). Intentional systems management: managing

Cissel, J.; Anderson, P.; Olson, D.; Puettmann, K.; Berryman, S.; Chan, S. & Thompson, C.

Cissel, J.; Swanson, F. & Weisberg, P. 1999. Landscape management using historical

Cockle, K.; Martin, K. & Wesołowski, T. (2011). Woodpeckers, decay, and the future of

Costello, C. & Sage Jr., R. (1994). Predicting black bear habitat selection from food

Egan, A. & Luloff, A. (2000). The exurbanization of America's forests: Research in rural

Ford, W.; Russell, K.; Moorman, C. (Eds.). (2002). *Proceedings: the role of fire for nongame*

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**11**

*México*

**Fire Ignition Trends in Durango, México**

*Universidad Juárez del Estado de Durango, Col. Valle del Sur, Durango*

The spatial analysis of wildfire occurrence is a key factor in understanding forest fire incidences in forest ecosystems. Most of the applications are used in Wildfire Threat Rating Systems, and many of them have been completed or are in progress around the world (Lin, 2000; San-Miguel-Ayanz *et al*., 2003). A major issue today is how to undertake the analysis more accurately and efficiently for the purposes of planning and to design suitable

In Mexico, most hazard systems use remote sensing techniques that measure weather and climate assessments, topography and fire interaction. (Flasse and Ceccato, 1996; Giglio *et al.,* 2003; Sepulveda *et al.,* 2001). In Durango State for instance, Drury and Veblen (2008) applied the spatial pattern of forest fire occurrence and showed that the rate of forest fires is correlated to years when extreme variations in weather occur. Renteria (2004) developed models for forest fire risk, which are now applied as support tools for decision-making in fuel management. Furthermore, Avila *et al.* (2010a) analysed the spatial patterns of forest fire occurrence, showing that the ignition locations did not exhibit a random distribution. In a subsequent spatial analysis using geographical weighted regression, Avila *et al.* (2010b) explicitly identified that human activities are the main factors determining the occurrence of

All of these studies were concerned with the analysis of spatial patterns of forest fires, but the majority did not examine fire ignition history. Although electronic data is not available, a Geographic Information System (GIS) layer can be generated from local fire reports or associated records, including attribute data such as the number, date, size, cause, and other relevant information relating to the fires. Creating density points of fire locations helps to identify high fire frequency areas and to find any noticeable trends. It is known from experience that forest fires in Durango have tended to occur in patches, but no study has taken quantitative measurements. This information should give managers and researchers

Recently interest in several statistical techniques that focus on local measurements of spatial dependence have been growing. These techniques make it possible to have a discussion about tests for the detection of clusters without any preconceptions about their locations or spatial trends**.** The Getis-Ord G-statistic is used for the detection of clusters and is especially useful in cases where global traditional statistics, such as kernel estimation, k-function

strategies and actions for forest fire management (Gollberg *et al*., 2001).

forest fires, followed by vegetation and lack of precipitation.

an adequate perspective as to where efforts should be focused.

**1. Introduction**

Marín Pompa-García and Eusebio Montiel-Antuna

*Faculty of Forest Sciences, UJED*

### **Fire Ignition Trends in Durango, México**

Marín Pompa-García and Eusebio Montiel-Antuna

*Universidad Juárez del Estado de Durango, Col. Valle del Sur, Durango Faculty of Forest Sciences, UJED México*

#### **1. Introduction**

190 Global Perspectives on Sustainable Forest Management

Whitman, A. & Hagan, J. (2003). *Legacy Retention: A tool for retaining biodiversity in managed*

Wilson, R.; Ribbeck, K.; King, S. & Twedt, D., (Eds.). (2007). *Restoration, Management, and*

Lower Mississippi Valley Joint Venture, Vicksburg, Mississippi, USA Zenner, E. (2000). Do residual trees increase structural complexity in Pacific Northwest

coniferous forests? *Ecological Applications*, Vol.10, No.3, pp. 800–810

Brunswick, Maine, USA

*forests*. Mosaic Science Notes #2003-1, Manomet Center for Conservation Sciences,

*Monitoring of Forest Resources in the Mississippi Alluvial Valley: Recommendations for Enhancing Wildlife Habitat*. LMVJV Forest Resource Conservation Working Group.

> The spatial analysis of wildfire occurrence is a key factor in understanding forest fire incidences in forest ecosystems. Most of the applications are used in Wildfire Threat Rating Systems, and many of them have been completed or are in progress around the world (Lin, 2000; San-Miguel-Ayanz *et al*., 2003). A major issue today is how to undertake the analysis more accurately and efficiently for the purposes of planning and to design suitable strategies and actions for forest fire management (Gollberg *et al*., 2001).

> In Mexico, most hazard systems use remote sensing techniques that measure weather and climate assessments, topography and fire interaction. (Flasse and Ceccato, 1996; Giglio *et al.,* 2003; Sepulveda *et al.,* 2001). In Durango State for instance, Drury and Veblen (2008) applied the spatial pattern of forest fire occurrence and showed that the rate of forest fires is correlated to years when extreme variations in weather occur. Renteria (2004) developed models for forest fire risk, which are now applied as support tools for decision-making in fuel management. Furthermore, Avila *et al.* (2010a) analysed the spatial patterns of forest fire occurrence, showing that the ignition locations did not exhibit a random distribution. In a subsequent spatial analysis using geographical weighted regression, Avila *et al.* (2010b) explicitly identified that human activities are the main factors determining the occurrence of forest fires, followed by vegetation and lack of precipitation.

> All of these studies were concerned with the analysis of spatial patterns of forest fires, but the majority did not examine fire ignition history. Although electronic data is not available, a Geographic Information System (GIS) layer can be generated from local fire reports or associated records, including attribute data such as the number, date, size, cause, and other relevant information relating to the fires. Creating density points of fire locations helps to identify high fire frequency areas and to find any noticeable trends. It is known from experience that forest fires in Durango have tended to occur in patches, but no study has taken quantitative measurements. This information should give managers and researchers an adequate perspective as to where efforts should be focused.

> Recently interest in several statistical techniques that focus on local measurements of spatial dependence have been growing. These techniques make it possible to have a discussion about tests for the detection of clusters without any preconceptions about their locations or spatial trends**.** The Getis-Ord G-statistic is used for the detection of clusters and is especially useful in cases where global traditional statistics, such as kernel estimation, k-function

Fire Ignition Trends in Durango, México 193

Fig. 1. Location of the Durango State in Mexico, indicating ecozones and fire ignitions from

( ) 1 1

*w dxx ij i j i j i j N N*

G (1)

*N N*

1 1

*x xi j i j*

Where *xixj* is the measured attribute for features *i* and *j,* respectively; and *wij*(*d*) is a symmetric one/zero spatial weight matrix for detecting the vicinity between *i* and *j* at the

In order to indicate how the observed G-statistic is significantly different from the expected value (and hence significantly different from a random distribution), the following formula

2001 to 2010.

The G-statistic is defined as:

distance given by d.

was applied:

analysis, Moran's I index and the semi-variogram, did not display any global spatial pattern. However, in these cases there may still be significant points of clustering (Ord and Getis, 1995). Therefore, wildfire clustering metrics would be useful for providing knowledge to build more sophisticated modelling methods, including fire risk systems, optimizing fuel treatments, and prevention planning.

In this study, the objective was to assess wildfire ignition history in order to develop clustering maps that would be easy for managers to understand in setting up a strategy for fighting forest fires. It was hypothesized that the locations of ignition would have a clustered distribution. However, this study did not attempt to model ignition probability or historical fire regimes.

### **2. Methods**

#### **2.1 Study area**

The state of Durango is located in the northwest of Mexico (22°16′–26°53′N, 102°29′– 107°16′W) and covers a surface of 123,181 km² with a high diversity of ecosystems (Rodríguez *et al.,* 2010). It is divided into four ecozones, which are large ecological units containing landscapes of similar climate, topography, and vegetation (Figure 1). The coniferous and oak forest ecozone, situated on the plateau of the Sierra Madre Occidental, contains several species of pine and oak for commercial activities. The deciduous tropical forest is located in one of the numerous large canyons that cut through the irregular terrain and is mainly composed of tropical and subtropical species. The xeric shrub-lands have species with the ability to grow in dry and saline flat areas. Additionally, the grasslands are big areas with several species of grass, sometimes mixed with scrublands, in rolling terrains, which are mainly used for cattle grazing. Most of the ecosystems are owned communally, but a sprinkling of private owners exists.

#### **2.2 Data sources**

In order to measure the clustering of forest fires, the study area was further subdivided into 1,343 physiographical units (also known as terrestrial systems), digitized on a 1:250,000 scale (SEMARNAT and PGC, 2001). These spatial units provide information regarding the location, slope, land use and main vegetation (Pompa *et al.,* 2011).

A forest fire database, that tracked the dating information with the location of ignition within the area of study, was obtained from a daily report collected in the field by the National Commission for Forestry, for the period 2001 to 2010. This database layer was overlaid with the terrestrial systems data to provide a complete dataset for each system, including the physical properties of the system and the frequency of forest fires.

#### **2.3 Statistical analysis**

Considering that the study area was subdivided into 1,343 polygons whose Cartesian coordinates are known and where each feature has a respective number of fire ignitions, the G-statistic introduced by Getis and Ord (1992) can detect whether features with high values or features with low values are more likely to cluster.

analysis, Moran's I index and the semi-variogram, did not display any global spatial pattern. However, in these cases there may still be significant points of clustering (Ord and Getis, 1995). Therefore, wildfire clustering metrics would be useful for providing knowledge to build more sophisticated modelling methods, including fire risk systems, optimizing fuel

In this study, the objective was to assess wildfire ignition history in order to develop clustering maps that would be easy for managers to understand in setting up a strategy for fighting forest fires. It was hypothesized that the locations of ignition would have a clustered distribution. However, this study did not attempt to model ignition probability or

The state of Durango is located in the northwest of Mexico (22°16′–26°53′N, 102°29′– 107°16′W) and covers a surface of 123,181 km² with a high diversity of ecosystems (Rodríguez *et al.,* 2010). It is divided into four ecozones, which are large ecological units containing landscapes of similar climate, topography, and vegetation (Figure 1). The coniferous and oak forest ecozone, situated on the plateau of the Sierra Madre Occidental, contains several species of pine and oak for commercial activities. The deciduous tropical forest is located in one of the numerous large canyons that cut through the irregular terrain and is mainly composed of tropical and subtropical species. The xeric shrub-lands have species with the ability to grow in dry and saline flat areas. Additionally, the grasslands are big areas with several species of grass, sometimes mixed with scrublands, in rolling terrains, which are mainly used for cattle grazing. Most of the ecosystems are owned communally,

In order to measure the clustering of forest fires, the study area was further subdivided into 1,343 physiographical units (also known as terrestrial systems), digitized on a 1:250,000 scale (SEMARNAT and PGC, 2001). These spatial units provide information regarding the

A forest fire database, that tracked the dating information with the location of ignition within the area of study, was obtained from a daily report collected in the field by the National Commission for Forestry, for the period 2001 to 2010. This database layer was overlaid with the terrestrial systems data to provide a complete dataset for each system,

Considering that the study area was subdivided into 1,343 polygons whose Cartesian coordinates are known and where each feature has a respective number of fire ignitions, the G-statistic introduced by Getis and Ord (1992) can detect whether features with high values

including the physical properties of the system and the frequency of forest fires.

location, slope, land use and main vegetation (Pompa *et al.,* 2011).

or features with low values are more likely to cluster.

treatments, and prevention planning.

but a sprinkling of private owners exists.

historical fire regimes.

**2. Methods 2.1 Study area**

**2.2 Data sources**

**2.3 Statistical analysis**

Fig. 1. Location of the Durango State in Mexico, indicating ecozones and fire ignitions from 2001 to 2010.

The G-statistic is defined as:

$$\mathbf{G} = \frac{\sum\_{i=1}^{N} \sum\_{j=1}^{N} w\_{ij}(d) x\_i x\_j}{\sum\_{i=1}^{N} \sum\_{j=1}^{N} x\_i x\_j} i \neq j \tag{1}$$

Where *xixj* is the measured attribute for features *i* and *j,* respectively; and *wij*(*d*) is a symmetric one/zero spatial weight matrix for detecting the vicinity between *i* and *j* at the distance given by d.

In order to indicate how the observed G-statistic is significantly different from the expected value (and hence significantly different from a random distribution), the following formula was applied:

Fire Ignition Trends in Durango, México 195

ecozones, and even within each, individual ecozone. This type of analysis requires more detailed data pertaining to the factors responsible for the ignition and spread of fires

Fig. 2. Estimated spatial intensity of the occurrence of forest fires in Durango for the whole

Fig. 3. Level of confidence of the clustering of forest fires in Durango for the whole period

(Flannigan *et al*., 2005).

period 2001-2010.

2001-2010.

$$\text{Z}-\text{score} = \frac{\text{G(d)} - \text{G}\_{\text{E}}(\text{d)}}{\text{StdDev}} \tag{2}$$

The test procedure assumes no global autocorrelation, but when global autocorrelation exists it has a significant impact on the expected value of G. This makes any proposed inferences on the presence of local clusters misleading (Haining, 2003). To remedy this problem, the study area was divided into small polygons.

The G-statistic was computed by physiographical units, using the hot spot analysis available in ESRI ArcMap™ version 9.3. G is calculated by looking at each feature within the context of neighbouring features. If a feature's value is high, and the values for all of its neighbouring features are also high, it is part of a hot spot. The local sum of a feature and its neighbours are compared proportionally to the sum of all features. When the local sum is much different than the expected local sum, and that difference is too large to be the result of random chance, a statistically significant Z score is the result (ESRI, 2008).

Due to the fixed spatial scale of the dataset and its reflection of good spatial autocorrelation, the level of clustering among fires was assessed using the fixed distance band option and calculated using the Euclidian distance method. Therefore, the Z-scores were reliable.

### **3. Results**

In order to test whether or not the observations are spatially dependent, Figure 2 shows the clustering found for forest fires in terms of probabilities. The red grid cells indicate a cluster with high attribute values or clustering (hot spots) and are where fires are most likely to occur; while green tones imply low clustering (cold spots) and represent locations where the fires are less likely to occur. Finally, yellow tones denote areas where there is no concentration of either high or low values surrounding the target feature. This occurs when the surrounding values are all near the mean or when the target feature is surrounded by a mix of high and low values.

As a statistical test on the validity of the clustering, *p*-values test was carried out. Figure 3 shows the accuracy of the G-statistics. The best results are represented in red, and the worst are in green. A more conservative significance level was used (α=0.0001) in order to compensate for the effect of the large sample size (N=1,343), which may detect local pockets. Figure 2 reflects the clustering degree of occurrence in the ignition plots and is evidently varied among ecozones, which appeared to be supported by the spatial patterns.

The G-statistic estimates of spatial clustering allowed us to generate maps of annual clustering for forest fires from 2001 to 2010 (Figs. 4 - 13). For comparative purposes, the estimated values in all of these figures were subjected to the same scaling.

The fires exhibited most clustering in the ecozones of the coniferous and oak forests, as a result of fire progressively aggregating in this zone over the considered time period. This suggests that the increased incidences are following a rather stable pattern over time. These areas neighbour the deciduous tropical forest, which have rugged topography and are difficult to access. The ecozone that had less fire activity was the xeric shrub-lands, characterized by sandy areas and an arid climate. Although this study did not attempt to assess intra-ecozone variability and causes of such interaction, the results in Figure 2 lead us to believe that some features (physical, anthropogenic factors, etc) vary considerably among

G(d) G (d) <sup>E</sup> Z score

The test procedure assumes no global autocorrelation, but when global autocorrelation exists it has a significant impact on the expected value of G. This makes any proposed inferences on the presence of local clusters misleading (Haining, 2003). To remedy this

The G-statistic was computed by physiographical units, using the hot spot analysis available in ESRI ArcMap™ version 9.3. G is calculated by looking at each feature within the context of neighbouring features. If a feature's value is high, and the values for all of its neighbouring features are also high, it is part of a hot spot. The local sum of a feature and its neighbours are compared proportionally to the sum of all features. When the local sum is much different than the expected local sum, and that difference is too large to be the result

Due to the fixed spatial scale of the dataset and its reflection of good spatial autocorrelation, the level of clustering among fires was assessed using the fixed distance band option and calculated using the Euclidian distance method. Therefore, the Z-scores were reliable.

In order to test whether or not the observations are spatially dependent, Figure 2 shows the clustering found for forest fires in terms of probabilities. The red grid cells indicate a cluster with high attribute values or clustering (hot spots) and are where fires are most likely to occur; while green tones imply low clustering (cold spots) and represent locations where the fires are less likely to occur. Finally, yellow tones denote areas where there is no concentration of either high or low values surrounding the target feature. This occurs when the surrounding values are all near the mean or when the target feature is surrounded by a

As a statistical test on the validity of the clustering, *p*-values test was carried out. Figure 3 shows the accuracy of the G-statistics. The best results are represented in red, and the worst are in green. A more conservative significance level was used (α=0.0001) in order to compensate for the effect of the large sample size (N=1,343), which may detect local pockets. Figure 2 reflects the clustering degree of occurrence in the ignition plots and is evidently

The G-statistic estimates of spatial clustering allowed us to generate maps of annual clustering for forest fires from 2001 to 2010 (Figs. 4 - 13). For comparative purposes, the

The fires exhibited most clustering in the ecozones of the coniferous and oak forests, as a result of fire progressively aggregating in this zone over the considered time period. This suggests that the increased incidences are following a rather stable pattern over time. These areas neighbour the deciduous tropical forest, which have rugged topography and are difficult to access. The ecozone that had less fire activity was the xeric shrub-lands, characterized by sandy areas and an arid climate. Although this study did not attempt to assess intra-ecozone variability and causes of such interaction, the results in Figure 2 lead us to believe that some features (physical, anthropogenic factors, etc) vary considerably among

varied among ecozones, which appeared to be supported by the spatial patterns.

estimated values in all of these figures were subjected to the same scaling.

of random chance, a statistically significant Z score is the result (ESRI, 2008).

problem, the study area was divided into small polygons.

**3. Results**

mix of high and low values.

Std Dev

(2)

ecozones, and even within each, individual ecozone. This type of analysis requires more detailed data pertaining to the factors responsible for the ignition and spread of fires (Flannigan *et al*., 2005).

Fig. 2. Estimated spatial intensity of the occurrence of forest fires in Durango for the whole period 2001-2010.

Fig. 3. Level of confidence of the clustering of forest fires in Durango for the whole period 2001-2010.

Fire Ignition Trends in Durango, México 197

Fig. 6. Estimated spatial intensity of the occurrence of forest fires in Durango for the 2003

Fig. 7. Estimated spatial intensity of the occurrence of forest fires in Durango for the 2004

year.

Fig. 4. Estimated spatial intensity of the occurrence of forest fires in Durango for the 2001 year.

Fig. 5. Estimated spatial intensity of the occurrence of forest fires in Durango for the 2002 year.

Fig. 4. Estimated spatial intensity of the occurrence of forest fires in Durango for the 2001

Fig. 5. Estimated spatial intensity of the occurrence of forest fires in Durango for the 2002

year.

Fig. 6. Estimated spatial intensity of the occurrence of forest fires in Durango for the 2003 year.

Fig. 7. Estimated spatial intensity of the occurrence of forest fires in Durango for the 2004 year.

Fire Ignition Trends in Durango, México 199

Fig. 10. Estimated spatial intensity of the occurrence of forest fires in Durango for the 2007

Fig. 11. Estimated spatial intensity of the occurrence of forest fires in Durango for the 2008

year.

Fig. 8. Estimated spatial intensity of the occurrence of forest fires in Durango for the 2005 year.

Fig. 9. Estimated spatial intensity of the occurrence of forest fires in Durango for the 2006 year.

Fig. 8. Estimated spatial intensity of the occurrence of forest fires in Durango for the 2005

Fig. 9. Estimated spatial intensity of the occurrence of forest fires in Durango for the 2006

year.

Fig. 10. Estimated spatial intensity of the occurrence of forest fires in Durango for the 2007 year.

Fig. 11. Estimated spatial intensity of the occurrence of forest fires in Durango for the 2008 year.

Fire Ignition Trends in Durango, México 201

Many studies of forest fires have been conducted in Mexico over the past few years, but no effort has been made to quantitatively compare the spatial characteristics of clustering. This spatial analysis allows us to answer the hypothesis that had been posed earlier. Several clusters of fires had been formed in Durango between 2001 and 2010, as was confirmed by our results. The G-statistic was satisfactory as a quantitative tool for such estimations.

In spite of the difficulties in interpreting their causes, the clustering metrics that were used show the relative differences in clustering among terrestrial units. This approach is consistent with the results of Parisien *et al*. (2006) which showed that several clusters of large fires were formed in Canada from 1980 to 1999. They hold that the clustering of fires is a function of cumulative year intervals, which supports an analysis made a few years before by Vasquez and Moreno (2001). They showed that fires aggregate spatially and over time, producing larger interconnected burned patches. Wang and Anderson (2010) have

However, clustering depends on a dataset that has been complied over a long period of time, and therefore, it is likely that it would have shown different patterns in clustering. Many studies have reported from simulations that the clustering varies with vegetation cover (Keane *et al*., 2002; Miller, *et al.,* 2008). According to Cumming (2001), and Duncan and Schmalzer (2004), the extent and configuration of flammable vegetation and non-flammable landscape features clearly influence patterns of fire ignition. They also speculate that these patterns are likely to respond differently to changes in spatial scale. Indeed, Parisien *et al.* (2006) show that splitting the clusters, and therefore reducing study units, will avoid some bias in the estimates and surely dilute the effect of clustering. In this approach, the choice of terrestrial systems as study units was not entirely arbitrary, as it was based on units for

Flannigan *et al*. (2005) showed that smaller study units nested in ecozones performed more poorly than the terrestrial units used here. To this end, these units provided a useful and objective means to spatially stratify the ecozones in order to study fire. Wang and Anderson (2010) highlighted the importance and challenges of analysing different spatial data sets, assessing and identifying the spatial scales that are the most relevant for the study of spatial fire patterns. Podur *et al*. (2003) explicitly identified the spatial scales at which fires exhibit

In addition, our results reaffirm the importance of recording historical ignitions. They are thus consistent with those documented by Van Wagner (1988), Rollins *et al*. (2002) and Podur *et al*. (2003) who noted that historical fire records have been useful in understanding the spatial distribution of fires and in helping managers to conceptualize where ignitions could occur, due the assessment of fire clustering can provide useful new information in predicting potential fire dangers across the ecosystems. They will also enable managers to understand the level of the fire threat. However, creating an ignition density from fire locations only indicates where fires have started, not where or under which conditions the fires will spread or the impact which will occur. Thus, fire occurrence data alone are of

Historical recording was attractive in terms of expediency, simplicity, low costs, and transparency. Although it is not possible to ensure that this database contains all fire

demonstrated that the interaction is dynamic; it changes from year to year.

which fire activity is known to vary throughout Durango.

clustered, random, or inhibited distribution.

limited value to risk assessment (Stratton, 2006).

**4. Discussion**

Fig. 12. Estimated spatial intensity of the occurrence of forest fires in Durango for the 2009 year.

Fig. 13. Estimated spatial intensity of the occurrence of forest fires in Durango for the 2010 year.

#### **4. Discussion**

200 Global Perspectives on Sustainable Forest Management

Fig. 12. Estimated spatial intensity of the occurrence of forest fires in Durango for the 2009

Fig. 13. Estimated spatial intensity of the occurrence of forest fires in Durango for the 2010

year.

year.

Many studies of forest fires have been conducted in Mexico over the past few years, but no effort has been made to quantitatively compare the spatial characteristics of clustering. This spatial analysis allows us to answer the hypothesis that had been posed earlier. Several clusters of fires had been formed in Durango between 2001 and 2010, as was confirmed by our results. The G-statistic was satisfactory as a quantitative tool for such estimations.

In spite of the difficulties in interpreting their causes, the clustering metrics that were used show the relative differences in clustering among terrestrial units. This approach is consistent with the results of Parisien *et al*. (2006) which showed that several clusters of large fires were formed in Canada from 1980 to 1999. They hold that the clustering of fires is a function of cumulative year intervals, which supports an analysis made a few years before by Vasquez and Moreno (2001). They showed that fires aggregate spatially and over time, producing larger interconnected burned patches. Wang and Anderson (2010) have demonstrated that the interaction is dynamic; it changes from year to year.

However, clustering depends on a dataset that has been complied over a long period of time, and therefore, it is likely that it would have shown different patterns in clustering. Many studies have reported from simulations that the clustering varies with vegetation cover (Keane *et al*., 2002; Miller, *et al.,* 2008). According to Cumming (2001), and Duncan and Schmalzer (2004), the extent and configuration of flammable vegetation and non-flammable landscape features clearly influence patterns of fire ignition. They also speculate that these patterns are likely to respond differently to changes in spatial scale. Indeed, Parisien *et al.* (2006) show that splitting the clusters, and therefore reducing study units, will avoid some bias in the estimates and surely dilute the effect of clustering. In this approach, the choice of terrestrial systems as study units was not entirely arbitrary, as it was based on units for which fire activity is known to vary throughout Durango.

Flannigan *et al*. (2005) showed that smaller study units nested in ecozones performed more poorly than the terrestrial units used here. To this end, these units provided a useful and objective means to spatially stratify the ecozones in order to study fire. Wang and Anderson (2010) highlighted the importance and challenges of analysing different spatial data sets, assessing and identifying the spatial scales that are the most relevant for the study of spatial fire patterns. Podur *et al*. (2003) explicitly identified the spatial scales at which fires exhibit clustered, random, or inhibited distribution.

In addition, our results reaffirm the importance of recording historical ignitions. They are thus consistent with those documented by Van Wagner (1988), Rollins *et al*. (2002) and Podur *et al*. (2003) who noted that historical fire records have been useful in understanding the spatial distribution of fires and in helping managers to conceptualize where ignitions could occur, due the assessment of fire clustering can provide useful new information in predicting potential fire dangers across the ecosystems. They will also enable managers to understand the level of the fire threat. However, creating an ignition density from fire locations only indicates where fires have started, not where or under which conditions the fires will spread or the impact which will occur. Thus, fire occurrence data alone are of limited value to risk assessment (Stratton, 2006).

Historical recording was attractive in terms of expediency, simplicity, low costs, and transparency. Although it is not possible to ensure that this database contains all fire

Fire Ignition Trends in Durango, México 203

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ignitions, it does represent the vast majority of them. Therefore, the lack of more available temporal explicit data has made it difficult to undertake a detailed study of spatial fire patterns over a considerable period of time (e.g. decades).

As noted earlier, Avila *et al.* (2010a) have noted the clustering of forest fires; however, they used the Moran I index. The G-statistic focuses on the clustering around each terrestrial system, since it does not take into account the rate of forest fires in the area of study itself. This approach helps to monitor local behaviour due to G being more sensitive to high clusters than low ones. On the other hand, Moran's index is mainly affected by the scale of the clusters (Zhang, 2007). The question of the interaction between local and global coefficients is an important one and much more remains to be done in this context (Ord and Getis, 1995).

#### **5. Conclusion**

In this study, the history of fire ignitions over an ecologically heterogeneous landscape during the period of a decade was compared. The fires of all ecozones from 2001 to 2010 exhibited a clustered spatial distribution remarkable in oak forest ecosystems. However, it was beyond the scope of the present study to thoroughly analyse the underlying mechanisms responsible for the spatially clustered fires. Regardless of the factors that may have contributed to any increase in wildfire frequency, the assessment of fire clustering can provide useful new information to researchers in predicting potential fire dangers and behaviours across the landscape. Also, it is important to take into consideration the importance of the updated geographic data from preceding data fields. Results such as these will hopefully encourage more detailed analyses that investigate the relative roles of weather, fuels, landscape features, and others factors on the progression of fire ignition. The same methods could be applied to smaller and ecologically homogeneous landscape units, such as stand management units, over a finer timescale (e.g. months or even days). Finally, it should also be possible to use these methods to study clustering fires at other levels where ignition data are available.

#### **6. Acknowledgement**

This work was conducted within the framework of Academic Mobility supported by the UJED (University Juarez of Durango State). We are grateful to CONAFOR (Mexican National Forestry Commission) in Durango for sharing their data and thanks to COCYTED (Science & Technology Council in Durango State) and SRNYMA (Ministry of Environment and Natural Resources in Durango State). Also, we would like to extend our thanks to David Mair and Peter Englefield for providing constructive comments on the manuscript.

#### **7. References**

ignitions, it does represent the vast majority of them. Therefore, the lack of more available temporal explicit data has made it difficult to undertake a detailed study of spatial fire

As noted earlier, Avila *et al.* (2010a) have noted the clustering of forest fires; however, they used the Moran I index. The G-statistic focuses on the clustering around each terrestrial system, since it does not take into account the rate of forest fires in the area of study itself. This approach helps to monitor local behaviour due to G being more sensitive to high clusters than low ones. On the other hand, Moran's index is mainly affected by the scale of the clusters (Zhang, 2007). The question of the interaction between local and global coefficients is an important one and much more remains to be done in this context (Ord and

In this study, the history of fire ignitions over an ecologically heterogeneous landscape during the period of a decade was compared. The fires of all ecozones from 2001 to 2010 exhibited a clustered spatial distribution remarkable in oak forest ecosystems. However, it was beyond the scope of the present study to thoroughly analyse the underlying mechanisms responsible for the spatially clustered fires. Regardless of the factors that may have contributed to any increase in wildfire frequency, the assessment of fire clustering can provide useful new information to researchers in predicting potential fire dangers and behaviours across the landscape. Also, it is important to take into consideration the importance of the updated geographic data from preceding data fields. Results such as these will hopefully encourage more detailed analyses that investigate the relative roles of weather, fuels, landscape features, and others factors on the progression of fire ignition. The same methods could be applied to smaller and ecologically homogeneous landscape units, such as stand management units, over a finer timescale (e.g. months or even days). Finally, it should also be possible to use these methods to study clustering fires at other levels where

This work was conducted within the framework of Academic Mobility supported by the UJED (University Juarez of Durango State). We are grateful to CONAFOR (Mexican National Forestry Commission) in Durango for sharing their data and thanks to COCYTED (Science & Technology Council in Durango State) and SRNYMA (Ministry of Environment and Natural Resources in Durango State). Also, we would like to extend our thanks to David Mair and Peter Englefield for providing constructive comments on the manuscript.

Avila F D, Pompa G M, and Vargas P E (2010a). Spatial analysis of fire occurrence in the

Avila F D, Pompa G M, Antonio N X, Rodríguez T D, Vargas P E and Santillán P J (2010b).

Durango State. *Revista Chapingo. Serie Ciencias Forestales* 16(2): 253–260, ISSN 0186-

Driving factors for forest fire occurrence in Durango State of Mexico: A geospatial

patterns over a considerable period of time (e.g. decades).

Getis, 1995).

**5. Conclusion**

ignition data are available.

**6. Acknowledgement**

**7. References**

3231,State of Mexico, Mexico.

perspective. *Chinese Geographical Science* 20:6, 491, ISSN: 1002-0063 (print version) ISSN: 1993-064X (electronic version), Mainland, China.

**12**

*1,3China 2Canada*

*East China Normal University*

*University of Saskatchewan*

*2Department of Geography and Planning*

*3College of Forestry, Northwest A&F University*

**Remote Sensing of Forestry Studies**

Kai Wang1, Wei-Ning Xiang1, Xulin Guo2 and Jianjun Liu3

*1Shanghai Key Laboratory of Urbanization and Ecological Restoration (KLUER)*

Forestry can be simply explained as the science and technology linked with tree resources or forests. According to the Global Forest Resources Assessment 2010 (FRA 2010), "the world's total forest area was just over 4 billion hectares, corresponding to 31 percent of the total land area or average of 0.6 ha per capita". Forest as one of the most important resources on this planet plays a pivotal role in the progress of human civilizations. Forestry studies have always been the hot topic since the naissance of this discipline. With the advent of satellite remote sensing, the forestry studies have made the unprecedented development. This introductory review is intended to introduce the basics of remote sensing in forestry studies, summarize the recent development, and elucidate several

Aerial photos (i.e. airborne remote sensing) or satellite imagery (i.e. spaceborne remote sensing) are widely used in forest studies. If the earliest platforms such as homing pigeons, kites, and hot air balloons, which were quite uncertain and unstable platforms with relatively low altitude, are taken into account, the history will be even longer (Colwell, 1964). Up to now, hundreds of Earth Observation Satellites are in orbit, and delivering assorted remotely sensed data ranging from optical data to radar data, from multispectral imagery to panchromatic imagery, and from local scale to global scale. Remote sensing has long been identified as an effective and efficient tool in forestry studies, such as forest inventory, forest health and nutrition, forest sustainability, forest growth, and forest ecology

Remote sensing is the "noncontact recording of information from the ultraviolet, visible, infrared, and microwave regions of the electromagnetic spectrum by means of instruments such as cameras, scanners, lasers, linear arrays, and/or area arrays located on platform such as aircraft or spacecraft, and the analysis of acquired information by means of visual and digital image processing" (Jensen, 2007). Franklin (2010) emphasized

**1. Introduction**

typical applications in this area.

**1.1 Remote sensing**

(Kohl et al., 2006).

Kai Wang1, Wei-Ning Xiang1, Xulin Guo2 and Jianjun Liu3

*1Shanghai Key Laboratory of Urbanization and Ecological Restoration (KLUER) East China Normal University 2Department of Geography and Planning University of Saskatchewan 3College of Forestry, Northwest A&F University 1,3China 2Canada*

#### **1. Introduction**

204 Global Perspectives on Sustainable Forest Management

Pompa GM, Antonio NX, Carrasco MJ, Mendoza BM (2011). Spatial patterns of soil

Parisien M-A, Peters VS, Wang Y, Little J M, Bosch E M and Stocks B J (2006). Spatial

Renteria A J B (2004). Development of models for fuel control in the management of forest

Rodríguez FJ, Pompa GM, Hernández DC, Juárez RA. 2010. Patrón de distribución espacial

Rollins M G, Morgan P and Swetnam T (2002). Landscape-scale controls over 20th century

San-Miguel-yanz J, Carlson J D, Alexander M, Tolhurst K, Morgan G, Sneeuwjagt Rand

SEMARNAT (Ministry of Environment and Natural Resources) and PGC (Post Graduate

Sepúlveda B J R, Meza S W, Zúñiga C G, Solís G y M and Olguín E (2001). SIG para

Stratton R D (2006). Guidance on spatial wildland fire analysis: models, tools, and

Van Wagner C E (1988) The historical pattern of annual burned area in Canada. *Forestry*

Vasquez A and Moreno J M (2001) Spatial distribution of forest fires in Sierra de Gredos

Wang Yonghe and Anderson Kerry R (2010). An evaluation of spatial and temporal patterns

Zhang Song-lin (2007). Comparison between general Moran's Index and Getis-Ord general

Agriculture, Forest Service, Rocky Mountain Research Station. 15 p

Experimental Costa de Ensenada. Ensenada. México. 37 p.

15(3): 361–374, ISSN: 1049-8001, ISSN: 1448-5516, Australia.

ecosystems in Durango. Mexico: University of Nuevo Leon, 125.

Investigación Agropecuaria 14 (1), 53-65. ISSN: 0188789-0, México.

ISSN 1991-637X, Nairobi.

ISSN: 1572-9761 (electronic version).

Singapore)

scale). National Memory.

version), Mattawa, Canada.

1448-5516, Australia.

1127(00)00436-9, ISSN: 0378-1127, Australia.

2007-04, ISSN: 0529-6579, Guangdong, China.

degradation in Mexico. African Journal of Agricultural Research 6(5), 1109-1113,

patterns of forest fires in Canada, 1980–1999. *International Journal of Wildland Fire*

de la pérdida, degradación y recuperación vegetal en Durango, México. Avances en

fire occurrence in two large Rocky Mountain (USA) wilderness areas. *Landscape Ecology* 17, 539–557. doi:10.1023/A:1021584519109, ISSN: 0921-2973 (print version),

Dudfield M (2003). Current methods to assess fire danger potential. In 'Wildland fire danger estimation and mapping – the role of remote sensing data'. Series in *Remote Sensing* Volume 4. (Ed. E Chuvieco) pp. 21–61. (World Scientific Publishing:

College), 2001. Evaluation of soil degradation caused by man in Mexico (1:250,000

determinar riesgo de incendios forestales en el noroeste de México. *Publ. Téc. Núm. 1.* Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias. Campo

techniques. *Gen. Tech. Rep. RMRS-GTR-183*. Fort Collins, CO: U.S. Department of

*Chronicle* 64, 182–185, ISSN: 0015-7546 (print version), ISSN: 1499-9315 (electronic

(Central Spain). *Forest Ecology and Management* 147, 55–65. doi:10.1016/S0378-

of lightning- and human-caused forest fires in Alberta, Canada, 1980–2007. *International Journal of Wildland Fire* 19(8). p. 1059–1072, ISSN: 1049-8001, ISSN:

G of spatial autocorrelation*. Acta Scientiarum Naturalium Universitatis Sunyatseni*;

Forestry can be simply explained as the science and technology linked with tree resources or forests. According to the Global Forest Resources Assessment 2010 (FRA 2010), "the world's total forest area was just over 4 billion hectares, corresponding to 31 percent of the total land area or average of 0.6 ha per capita". Forest as one of the most important resources on this planet plays a pivotal role in the progress of human civilizations. Forestry studies have always been the hot topic since the naissance of this discipline. With the advent of satellite remote sensing, the forestry studies have made the unprecedented development. This introductory review is intended to introduce the basics of remote sensing in forestry studies, summarize the recent development, and elucidate several typical applications in this area.

#### **1.1 Remote sensing**

Aerial photos (i.e. airborne remote sensing) or satellite imagery (i.e. spaceborne remote sensing) are widely used in forest studies. If the earliest platforms such as homing pigeons, kites, and hot air balloons, which were quite uncertain and unstable platforms with relatively low altitude, are taken into account, the history will be even longer (Colwell, 1964). Up to now, hundreds of Earth Observation Satellites are in orbit, and delivering assorted remotely sensed data ranging from optical data to radar data, from multispectral imagery to panchromatic imagery, and from local scale to global scale. Remote sensing has long been identified as an effective and efficient tool in forestry studies, such as forest inventory, forest health and nutrition, forest sustainability, forest growth, and forest ecology (Kohl et al., 2006).

Remote sensing is the "noncontact recording of information from the ultraviolet, visible, infrared, and microwave regions of the electromagnetic spectrum by means of instruments such as cameras, scanners, lasers, linear arrays, and/or area arrays located on platform such as aircraft or spacecraft, and the analysis of acquired information by means of visual and digital image processing" (Jensen, 2007). Franklin (2010) emphasized

Tables 1 and 2 list commonly used sensors on Earth observation satellite that are still operational. These sensors provide diverse remotely sensed data with a unique configuration of image resolutions, such as spatial resolution, spectral resolution and temporal resolution (see Figure 2). Jensen (2007) defined spatial resolution as "a measure of the smallest angular or linear separation between two objects that can be resolved by the remote sensing system". In other words, the spatial resolution stands for how detailed information the remotely sensed data could provide. Temporal resolution can be defined as "how often the sensor records imagery of a particular area" (Jensen, 2007). For example, the well-known Landsat TM has 16-day temporal resolution. Another key property of a remote sensing system is spectral resolution, which is defined as "the number and dimension (size) of specific wavelength intervals (referred to as bands or channels) in the electromagnetic spectrum to which a remote sensing instrument is sensitive". Likewise, take the Landsat TM as an example. The Landsat TM imagery has 7 bands (6 optical bands plus 1 thermal band). It is noteworthy that the spectral resolution is mainly applied to describe optical imagery, and it cannot be used for Radar or Lidar remotely sensed data. In fact, the aforementioned data collection can be described as selecting the unique configuration of image resolutions (or properties), which can be used to meet certain

Fig. 1. Electromagnetic spectrum (Courtesy of the Antonine Education, UK)

education.co.uk/physics\_gcse/Unit\_1/Topic\_5/topic\_5\_what\_are\_the\_uses\_and\_ha.htm).

research needs.

(http://www.antonine-

that "remote sensing is both technology (sensors, platforms, transmission and storage devices, and so on) and methodology (radiometry, geometry, image analysis, data fusion, and so on)". There are numerous outstanding textbooks providing comprehensive introduction to the remote sensing science (e.g. Lillesand et al., 2007; Campbell, 2007; Jensen, 2007) and the digital image processing (e.g. Jensen, 2004; Richards and Jia, 2006; Lee and Pottiter, 2009).

#### **1.2 Remote sensing process**

Scientists generally summarized the remote sensing process into four phases: statement of the problem, data collection, data-to-information conversion, and information presentation (Jensen, 2007). Several notes derived from Jensen (2007) are provided as follows:

#### **2. Forestry information needs and remote sensing**

In practice, researchers choose one or several types of remotely sensed data according to their information needs. The information needs are converted to specific properties of remotely sensed data, such as spatial resolution, spectral resolution, temporal resolution, etc.

that "remote sensing is both technology (sensors, platforms, transmission and storage devices, and so on) and methodology (radiometry, geometry, image analysis, data fusion, and so on)". There are numerous outstanding textbooks providing comprehensive introduction to the remote sensing science (e.g. Lillesand et al., 2007; Campbell, 2007; Jensen, 2007) and the digital image processing (e.g. Jensen, 2004; Richards and Jia, 2006;

Scientists generally summarized the remote sensing process into four phases: statement of the problem, data collection, data-to-information conversion, and information presentation

In practice, researchers choose one or several types of remotely sensed data according to their information needs. The information needs are converted to specific properties of remotely sensed data, such as spatial resolution, spectral resolution, temporal

(Jensen, 2007). Several notes derived from Jensen (2007) are provided as follows:

question and forming the research hypothesis.

Lee and Pottiter, 2009).

studies.

methods.

accuracy).

resolution, etc.

or database topology design).

**2. Forestry information needs and remote sensing**

**1.2 Remote sensing process**

Tables 1 and 2 list commonly used sensors on Earth observation satellite that are still operational. These sensors provide diverse remotely sensed data with a unique configuration of image resolutions, such as spatial resolution, spectral resolution and temporal resolution (see Figure 2). Jensen (2007) defined spatial resolution as "a measure of the smallest angular or linear separation between two objects that can be resolved by the remote sensing system". In other words, the spatial resolution stands for how detailed information the remotely sensed data could provide. Temporal resolution can be defined as "how often the sensor records imagery of a particular area" (Jensen, 2007). For example, the well-known Landsat TM has 16-day temporal resolution. Another key property of a remote sensing system is spectral resolution, which is defined as "the number and dimension (size) of specific wavelength intervals (referred to as bands or channels) in the electromagnetic spectrum to which a remote sensing instrument is sensitive". Likewise, take the Landsat TM as an example. The Landsat TM imagery has 7 bands (6 optical bands plus 1 thermal band). It is noteworthy that the spectral resolution is mainly applied to describe optical imagery, and it cannot be used for Radar or Lidar remotely sensed data. In fact, the aforementioned data collection can be described as selecting the unique configuration of image resolutions (or properties), which can be used to meet certain research needs.

Fig. 1. Electromagnetic spectrum (Courtesy of the Antonine Education, UK) (http://www.antonineeducation.co.uk/physics\_gcse/Unit\_1/Topic\_5/topic\_5\_what\_are\_the\_uses\_and\_ha.htm).

Optical Remote Sensing

SPOT 4 HRVIR

SPOT 5 HRG

CBERS-2B CCD

Digital Globe Constellation WorldView 2 WV110 DigitalGlobe Corporate

Radar Remote Sensing

Coarse spatial resolution optical sensors NOAA-18 (AVHRR) 1100 2900 Variable / 5 2005-Present 1 Terra/Aqua (MODIS) 250-1000 2330 Variable / 36 1999-Present 1-16 Moderate spatial resolution optical sensors Landsat (TM) 30<PHONE_NUMBER> / 6 1984-Present 16 IRS-P6 (LISS III) 23.5<PHONE_NUMBER> / 4 2003-Present 24 SPOT 4 (HRVIR) 20 60 500-1750 / 4 1998-Present 1-3 SPOT 5 (HRG) 10 (MS); 20 (SWIR) 60 500-1730 / 5 2002-Present 1-3 CBERS-2 20 (Pan and MS) 120 450-890 / 4 2003-Present 3 DMC (SLIM-6) 22/32 600 520-990 / 3 2002-Present 1 Fine spatial resolution optical sensors WorldView 2 (WV110) 0.46 (Pan); 1.85 (MS) 16.4 400-1040 / 8 2009-Present 1.1-3.7 QuickBird 2 (BGIS 2000) 0.65 (Pan); 2.62 (MS) 18.0 430-918 / 4 2001-Present 2.5-5.6 GeoEye-1 (GIS MS) 0.41 (Pan); 1.65 (MS) 15.2 450-920 / 4 2008-Present 2.1-8.3

**(m)a Swath (km) Spectral range**

GeoEye GeoEye-1 GIS MS GeoEye Inc.

RADARSAT Constellation RADARSAT 2 SAR CSA/MDA TanDEM-X TanDEM-X TSX-SAR DLR/Astrium TerraSAR TerraSAR-X TSX-SAR DLR/Astrium

Table 1. The Current Commonly-used Optical and Radar Sensors

**Spatial resolution**

a MS = multispectral, SWIR = shortwave infrared, Pan = panchromatic Table 2. The Detail of the Current Commonly-used Optical Sensors

NOAA 18 AVHRR NOAA

VEGETATION

VEGETATION

Beijing-1 SLIM-6 DMC International Imaging

QuickBird 2 BGIS 2000 DigitalGlobe Corporate

Ltd

**(nm) / Bands**

LISS III LISS IV AWiFS

HRC IRMSS WFI

Spot Image

Spot Image

Organization)

ISRO (India Space Research

CAST (China)/INPE (Brazil)

**Temporal coverage**

**Revisit (day)**

**Satellite Program Satellite Platform Sensor Data Operator**

EOS (Earth Observing System) TERRA/AQUA MODIS NASA/USGS Landsat LANDSAT 5 TM NASA/USGS

> IRS P6 (ResourceSat-1)

POES (Polar Orbiting Environmental Satellites)

de la Terre)

Satellites)

Constellation)

SPOT (Satellite Pour l'Observation

IRS (Indian Remote Sensing

DMC (Disaster Monitoring

CBERS (China-Brazil Earth Resources Satellite)

**Medium resolution sensors**

Wulder et al. (2009) endeavored to clearly demonstrate the relationship of information needs and the selection of appropriate data and processing methods in remote sensing for studies of vegetation condition. The issues need to be taken into account, including "the scale at which the target must be measured (e.g. landscape-level or tree level information); the attributes of interest (change, condition, spatial extent); cost; timeliness; and, repeatability" (Wulder et al., 2009).

Fig. 2. Given a limited bandwidth, trade-offs have to be made between spectral, temporal, and spatial properties of the imagery acquired. For users who require high spatial resolution data, it is possible that multitemporal data can substitute for limited multispectral properties. (*With permission from* Key, T., T.A. Warner, J.B McGraw, and M.A. Fajvan. 2001*. Remote Sensing of Environment 75: 100-112*).

208 Global Perspectives on Sustainable Forest Management

Wulder et al. (2009) endeavored to clearly demonstrate the relationship of information needs and the selection of appropriate data and processing methods in remote sensing for studies of vegetation condition. The issues need to be taken into account, including "the scale at which the target must be measured (e.g. landscape-level or tree level information); the attributes of interest (change, condition, spatial extent); cost; timeliness; and, repeatability"

Fig. 2. Given a limited bandwidth, trade-offs have to be made between spectral, temporal, and spatial properties of the imagery acquired. For users who require high spatial resolution

properties. (*With permission from* Key, T., T.A. Warner, J.B McGraw, and M.A. Fajvan. 2001*.*

data, it is possible that multitemporal data can substitute for limited multispectral

*Remote Sensing of Environment 75: 100-112*).

(Wulder et al., 2009).

Table 1. The Current Commonly-used Optical and Radar Sensors

a MS = multispectral, SWIR = shortwave infrared, Pan = panchromatic

Table 2. The Detail of the Current Commonly-used Optical Sensors

management, forest and watershed management, forest and recreation behavior, behavior and management of forest fires, timber harvesting, wood products, and economics and the management of forests for wood and amenity values); and C. forests and society (e.g. urban forest, and social forstry: the community-based management of natural resources). As a matter of fact, remote sensing has more or less served all the three categories. Several examples in remote sensing of forestry studies are provided as follows. The selected examples were included in the papers that were either highly cited or newly published

Turner et al. (2003) stated that the recent advances in remote sensing, such as the availability of remotely sensed data with high spatial and spectral resolutions, make it possible to detect key environmental parameters, which can be applied to determine the distribution and abundance of species across landscapes via ecological models. This approach, in general referred to as indirect remote sensing of biodiversity, plays a major role in this research area. For example, Defries et al. (2000) applied the 1km Advanced Very High Resolution Radiometer (AVHRR) to estimate and map percentage tree cover and associated proportions of trees with different leaf longevity (evergreen and deciduous) and leaf type

Kokaly and Clark (1999) developed an approach to estimate the concentrations of nitrogen, lignin, and cellulose in dried and ground leaves using ban-depth analysis of absorption features (centered at 1.73 μm, 2.10 μm, and 2.30 μm) and stepwise multiple linear regression. As mentioned above, hyperspectral remote sensing was used to estimate the leaf pigment of sugar maple (*Acer saccharum*) in the Algoma Region, Canada, and promising results were

Jin et al. (2011) developed an algorithm based on a semi-empirical Priestley-Taylor approach to estimate continental-scale evapotranspiration (ET) using MODIS satellite observations. The seasonal variation in ET has been indicated as a key factor to the soil moisture and net ecosystem CO2 exchange through water loss from an ecosystem. Lefsky et al. (2002) reviewed Lidar remote sensing for ecosystem studies. Lidar is capable of accurately measuring vertical information besides the horizontal dimension, such as the threedimensional distribution of plant canopies and subcanopy topography (Lefsky et al., 2002). More specifically, lidar can provide accurate estimates vegetation height, cover, canopy

Due to the difference between dead, diseased, and healthy trees in visible and near-infrared reflectance values, Everitt et al. (1999) used color-infrared digital imagery and successfully detected oak wilt disease in live oak (*Quercus fusiformis*). The outbreak of mountain pine beetle (MPB) has resulted in a huge monetary loss in the western of the North America. It is

structure, leaf area index (LAI), aboveground biomass, etc (Lefsky et al., 2002).

**4.4 Forest trees: Disease and insect interactions**

Science Citation Index (SCI) papers.

(broadleaf and needleleaf).

**4.2 Forest ecophysiology**

obtained (Zarco-Tejada et al., 2001).

**4.3 Forest ecosystem ecology**

**4.1 Species composition (biodiversity)**

#### **3. Underdeveloped remote sensing technologies in forestry studies**

Optical sensors have been commonly used in forestry studies. However, the use of hyperspectral sensors, Radar and Lidar is still relatively underdeveloped. It is worth paying more attention to the application of hyperspectral sensors, Radar and Lidar in forestry studies.

#### **3.1 Hyperspectral sensors**

Optical sensors mentioned above, which are divided from the dimension of spatial resolution, are categorized into multispectral sensors. By contrast, there is a group of sensors called hyperspectral sensors, which accordingly generate hyperspectral data. Wang et al. (2010) stated that "hyperspectral data have the ability to collect ample spectral information across a continuous spectrum generally with 100 or more contiguous spectral bands". Shippert (2004) listed the existing hyperspectral sensors acquiring imagery from space, including the Hyperion sensor on NASA's EO-1 (National Aeronautics and Space Administration's Earth Observing-1), the CHRIS (Compact High Resolution Imaging Spectrometer) sensor on the European Space Agency's PROBA (PRoject for On-Board Autonomy) satellite, and the FTHSI (Fourier Transform Hyperspectral Imager) sensor on the U.S. Air Force Research Lab's MightySat II satellite.

#### **3.2 Radar and lidar**

Besides optical sensors, Radar and Lidar play more and more important roles in remote sensing of forest studies. Radar, the acronym of radio detection and ranging, is based on the transmission of long-wavelength microwaves (e.g., 3–25 cm) through the atmosphere and then recording the amount of energy backscattered from the terrain (Jensen, 2007). Wang et al. (2009) briefly introduced the Phased Array type L-band Synthetic Aperture Radar (PALSAR) on board Advanced Land Observing Satellite (ALOS), and RADARSAT-2 operated by the Canadian Space Agency (CSA) and MacDonald Dettwiler and Associates Ltd (MDA). Both could provide fully polarized SAR data to support PolSAR (Polarimetric SAR) technology (i.e., PolSAR decomposition), which has achieved promising results in many environmental researches (e.g., Lee et al., 2001; McNairn et al., 2009; Shimoni et al., 2009). Light detection and ranging (Lidar), also called Laser altimetry, is an active remote sensing technology that utilizes a laser to illuminate a target object and a photodiode to register the backscatter radiation (Lim et al., 2003; Hyyppa et al., 2009). It has been widely accepted that Lidar is capable of accurate (or even precise) vertical information (Wang et al., 2010). Therefore, it is believed that Lidar will bring forestry studies into an unprecedented age.

#### **4. Case studies**