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Forest edge quantification by line intersect sampling in aerial photographs
Forest Ecology and Management 230 (2006) 32–42 www.elsevier.com/locate/foreco
Forest edge quantification by line intersect sampling in aerial photographs Per-Anders Esseen a,*, K. Ulrika Jansson a, Mats Nilsson b b
a Department of Ecology and Environmental Science, Umea˚ University, SE 901 87 Umea˚, Sweden Department of Forest Resource Management and Geomatics, Swedish University of Agricultural Sciences, SE 901 83 Umea˚, Sweden
Received 31 January 2005; received in revised form 27 March 2006; accepted 3 April 2006
Abstract There is a need for accurate and efficient methods for quantification and characterisation of forest edges at the landscape level in order to understand and mitigate the effects of forest fragmentation on biodiversity. We present and evaluate a method for collecting detailed data on forest edges in aerial photographs by using line intersect sampling (LIS). A digital photogrammetric system was used to collect data from scanned colour infrared photographs in a managed boreal forest landscape. We focused on high-contrast edges between forest (height 10 m) and adjoining open habitat or young, regenerating forest (height 5 m). We evaluated the air photo interpretation with respect to accuracy in estimated edge length, edge detection, edge type classification and structural variables recorded in 20 m radius plots, using detailed field data as reference. The estimated length of forest edge in the air photo interpretation (52 8.8 m ha1; mean standard error) was close to that in the field survey (58 9.3 m ha1). The accuracy in edge type classification (type of open habitat) was high (88% correctly classified). Both tree height and canopy cover showed strong relationships with the field data in the forest, but tree height was underestimated by 2.3 m. Data collection was eight times faster and five times more cost-effective in aerial photographs than in field sampling. The study shows that line intersect sampling in aerial photographs has large potential application as a general tool for collecting detailed information on the quantity and characteristics of high-contrast edges in managed forest ecosystems. # 2006 Elsevier B.V. All rights reserved. Keywords: Canopy cover; Edge influence; Forest fragmentation; Line intersect sampling; Photogrammetry; Tree height
1. Introduction Forest edges are important structures in both natural and managed forested ecosystems and have numerous implications for ecosystem structure, function and dynamics (Cadenasso et al., 2003; Harper et al., 2005). Edges provide critical habitat and resources for many organisms and the amount, variety and structural characteristics of edges thus strongly influence biodiversity. It is now widely recognized that increased amount of edge habitat and the resulting edge influence on biodiversity is one of the most important consequences of forest fragmentation (Murcia, 1995; Matlack and Litvaitis, 1999; Gascon et al., 2000; Lindenmayer and Franklin, 2002). Forest ecotones are dynamic and changes in their location or structural attributes over time can be used to indicate environmental changes (Fortin et al., 2000). Detailed knowledge of the
* Corresponding author. Tel.: +46 90 786 5523; fax: +46 90 786 6705. E-mail address: [email protected] (P.-A. Esseen). 0378-1127/$ – see front matter # 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.foreco.2006.04.012
quantity, quality and dynamics of edges is required to understand the functional roles of edges and how they interact with biodiversity. An important step in this process is to have efficient tools for edge detection, classification and characterisation. A forest edge may be defined as the interface between forested and non-forested ecosystems, or between two forests of contrasting composition or structure (Harper et al., 2005). In this paper, we use the term forest edge for a sharp forest boundary following Fagan et al. (2003). Forest edges are best characterized as complex threedimensional entities, which can be described by many attributes reflecting their origin, maintenance, structure, function and dynamics (Cadenasso et al., 2003; Strayer et al., 2003). The magnitude and distance of edge influence are controlled by many factors, including both local and regional factors. Patch contrast, i.e. the difference in composition, structure, function or microclimate between adjoining habitats, plays a key role in determining edge influence (Harper et al., 2005). It has been shown that vegetation structure may strongly affect the extent of edge influence in tropical forest fragments (Didham and
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Lawton, 1999). To assess the ecological consequences of forest edges at landscape level and to consider edge effects in management plans we need to have knowledge of: (1) the quantity of forest edges across the landscape, (2) the type, origin and maintenance of edges, (3) the vegetation structure of the habitats on either side of the edge and (4) information of local factors such as edge orientation, edge shape, width and size of the open habitat and other local or regional context factors (Saunders et al., 1991; Harper et al., 2005). Several issues are involved in the detection and quantification of edges (Johnston et al., 1992; Csillag et al., 2001; Fortin and Edwards, 2001; Fagan et al., 2003). The detection of edges is strongly scale dependent and therefore tightly connected to the resolution (grain) of the spatial data (Gosz, 1993; Fortin et al., 2000). Both the scale of measurement and the type of data must be appropriate for the questions addressed. A wide range of methods have been developed for edge detection, classification and quantification and are reviewed by Johnston et al. (1992), Fortin et al. (2000), Jacquez et al. (2000) and Fagan et al. (2003). Transects have been frequently used to collect detailed field data on vegetation structure across forest ecotones (Johnston et al., 1992; Esseen and Renhorn, 1998; Harper et al., 2004). Data from transects can give information on ecotone location, width and patch contrast, but are timeconsuming to collect. Remote sensing data, particularly satellite imagery, have been widely used to obtain information about forest ecotones at different spatial scales (Metzger and Muller, 1996; Fortin et al., 2000; Zheng and Chen, 2000). Satellite images cover large geographical areas and produce a grid of data (lattice) covering all types of ecotones in the landscape. A major advantage is that lattice data can be subjected to analysis by different statistical edge detection methods suitable to detect either sharp or gradual ecotonal transitions (Fortin, 1994; Fortin and Drapeau, 1995; Fortin et al., 2000). However, the resolution in satellite images, such as SPOT HRVIR and Landsat TM, is too low to permit collection of detailed spatial and attribute data of forest edges. Sensors, such as IKONOS, have higher resolution but are not as well suited as aerial photographs for collecting detailed forest edge data. Aerial photographs have high spatial resolution and have for a long time provided data for vegetation and resource mapping (Lillesand and Kiefer, 2000). They have been widely used for detection of vegetation boundaries but the focus has been on the patches rather than on the ecotones (Johnston et al., 1992). Only a few studies (Hildebrandt, 1973; Schuerholz, 1974; Corona et al., 2004) have used different line sampling designs in aerial photographs for obtaining information about length of forest edges. The potential to extract detailed vegetation and forest data from aerial photographs have improved much during the last decades due to the technological development (Holmgren et al., 1997; Franklin, 2001; Hall, 2003). The position of forest edges can be determined with high accuracy by air photo interpretation (Næsset, 1998). The possibility to measure tree heights, estimate crown cover and other variables makes aerial photography a very interesting technique for collecting data on forest edges.
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In this paper, we present and evaluate a method for quantification and characterisation of sharp forest edges in aerial photographs by using line intersect sampling (LIS). We studied edges between forests and adjoining open habitats or young regenerating forest stands. Data on forest edges were extracted by manual interpretation of colour infrared aerial photographs using a digital photogrammetric system. For comparison and validation, we made a detailed field survey in the study area using the same sampling design as in the air photo interpretation. We assessed the accuracy of the air photo interpretation with respect to estimated edge length, edge detection, edge classification, and forest structural variables (tree height and canopy cover). We also assessed the costeffectiveness of the method and discuss its advantages, limitations and potential applications. 2. Material and methods 2.1. Study area The study area was a typical managed boreal forest landscape in Fennoscandia with a size of 4 km 4 km (1600 ha). It is located in Va¨sterbotten county, north-eastern Sweden (midpoint 64830 N, 20890 E), 25 km north of Umea˚, in the middle boreal zone. The terrain is slightly undulating with elevations ranging between 90 and 180 m above sea level. The study area is covered by 85% forested land, 9% agricultural land, 5% open wetland and 1% water (Swedish land cover data). Forestry is the dominant land use followed by agriculture. The fragmented landscape is a mosaic of agricultural land, clearcuts, young forests of various successional stage and mature stands. Clearcuts and young forests (height < 5 m) constitute 28% and forests with tree height > 10 m constitute 46% of the area. Old forests of semi-natural character constitute a minor proportion. Conifers dominate with Pinus sylvestris constituting 45% by volume, followed by Picea abies (40%), Betula spp. (14%) and other deciduous trees (1%). 2.2. Line intersect sampling Line intersect sampling is a well-known and efficient technique for sampling different kinds of objects (de Vries, 1979; Kaiser, 1983; Shiver and Borders, 1996). The method has been widely used for estimation of the amount of coarse woody debris (Warren and Olsen, 1964; Van Wagner, 1982) but has also been used for sampling objects such as roads (Mate´rn, 1964), streams, hedges, tree crowns and canopy gaps (Battles et al., 1996). LIS is a simple and statistically robust method that can be used to estimate the total length of linear objects, the area of two-dimensional objects, the volume of threedimensional objects and variables that are measured on these objects. The precision of the estimates is primarily dependent on total sample size, i.e. the total length of the sample lines and the number of lines. LIS can be described as a strip transect of infinitesimal width in which the sample line constitute a vertical plane (Van Wagner, 1982). The data collected are the number of intersections of objects encountered as one follows the sample
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P.-A. Esseen et al. / Forest Ecology and Management 230 (2006) 32–42 Table 1 Criteria used for detection of sharp forest edges in this study Variable 2
Patch area (m ) Patch widtha (m) Tree height (m) Canopy cover (%) Cover of trees >5 m high (%) a b c
Fig. 1. Layout of sample lines in the study area (4 km 4 km). One 1 km sample line was placed in each 1 km 1 km quadrat.
line. The practical aspects of LIS have been discussed by, e.g. Van Wagner (1982) and Ringvall and Sta˚hl (1999). LIS provides unbiased estimates of the total length of objects under the assumption that the sample lines are randomly oriented in the area of interest (Kaiser, 1983), or that the objects to be sampled are randomly oriented with no preferred orientation (de Vries, 1979). We established a system of 16 sample lines (each 1 km long) in a 4 4 grid in the study area (Fig. 1). One line was placed in the middle of each 1 km 1 km quadrat. For practical reasons, we used a sampling layout with eight sample lines oriented in south–north direction and eight in west–east direction to minimise effects due to systematic orientation of forest edges. 2.3. Edge detection criteria A fundamental question in quantification of forest edges is the criteria used to identify the vegetation gradients that qualify as an edge and to separate these from other gradients. Edge detection criteria should be formulated in relation to the objective of the specific study, the resolution of the available spatial data, and the method of data collection. We used the ‘a priori’ approach and recorded only edges that fulfilled a set of predefined ‘sharp edge’ criteria for two reasons. First, sharp forest edges were the primary concern because of their documented effect on biodiversity (Murcia, 1995; Harper et al., 2005). Second, objects sampled with LIS should have a welldefined ‘reference line’ that is easy to recognize. We developed a ‘sharp edge definition’ based on ecotone width, patch area, patch width, tree height and canopy cover (Table 1). The open habitat–forest ecotone was not allowed to be wider than 10 m to qualify as sharp edge. Consequently, gradual transitions, e.g. from an open wetland to forested wetland or mesic forest, were not sampled. The definition also includes criteria for the ‘open habitat’ and the ‘forest’. The most important criterion was the contrast in tree height: ‘open habitat’ was lower than 5 m, while ‘forests’ were taller than 10 m. We allowed a maximum cover of 15% for emergent remnant trees (height > 5 m) left on
Open habitat
Forest habitat
1000 15 5 b 0–100 15
1000 15 10c 30 –
Measured from the intersection point across the patch. Arithmetical mean based on number of stems in the dominant layer. Mean weighted by basal area.
clearcuts and other habitats. The edge detection criteria were defined to exclude edges in connection with small canopy gaps in the forest (<1000 m2; Table 1), or small forest patches (<1000 m2) in open habitat, as well as along linear elements with <15 m wide canopy opening, such as small roads, streams and lines or strips of trees. LIS can also be used to record such edges but this was beyond the scope of the study. 2.4. Air photo interpretation Data on forest edges were extracted from aerial photographs using a digital photogrammetric system. The system consisted of a Dell Precision workstation, a high-end graphic card (3Dlabs Wildcat 7110), two monitors; one equipped with a stereoscopic viewing panel (Stereographics Monitor Zscreen), and polarized eyewear. The software was ArcGIS Version 8.2 and Stereo Analyst for ArcGIS Version 1.3 (Curry, 2003). This system provides tools for accurate recording, interpretation and analysis of 3D geographical data. All data were recorded in the National Reference Grid of Sweden (RT90 2.5 g V). We used three colour infrared photographs with 60% overlap to achieve stereo coverage of a 5 km 5 km area. The photographs were taken on 5 June 2002 with a Wild RC30 metric camera from a flight height of 4600 m, resulting in an approximate image scale of 1:30,000. The photographs had a ground resolution of 0.5 m and they were scanned with 14 mm resolution, corresponding to 0.4 m on the ground. The absolute orientation of the two stereo models resulted in a root mean square error (RMSE) of approximately 1 m for both models. The 16 sample lines were adjusted to local topography through interpolation from a digital elevation model (DEM). The DEM had a 50 m 50 m horizontal resolution and was produced by the Swedish Land Survey. The lines were then superimposed on the stereo model so that they followed the terrain. The air photo interpretation was done with a viewing scale of 1:2000. We followed the sample lines and recorded all forest edges that fulfilled the ‘sharp edge’ criteria (Table 1) at the intersection point (Fig. 2). The exact position of the edge was defined as the canopy drip line at a height of 10 m above ground. A circular plot with 20 m radius (1256 m2) was created around each digitised point (Fig. 3) using a buffer in ArcMap and adjusted to local topography. The 20 m plots were divided into two sectors, forest and open habitat, separated by the edge (canopy drip line). We recorded data separately for each sector. The open habitat was classified into one of the following types:
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Fig. 2. Colour infrared aerial photograph of the centre 2 km 2 km part of the study area. Broken lines denote sample lines and points denote forest edge intersections recorded in air photo interpretation.
clearcut (tree height 1.3 m), young forest (tree height 1.3– 5 m), agricultural land, open wetland, water, road and other habitat. Tree height was measured to the nearest m by placing the pointer on the ground and at the top of the canopy and recording the z-coordinates. Total canopy cover was visually estimated to the nearest 5%. We used templates with different degree of cover to increase the accuracy of the estimates. The proportion coniferous trees of total canopy cover was estimated, and the cover of coniferous and deciduous trees was calculated. In the open habitat, estimates of tree height, canopy cover and tree species composition were done separately for trees in the dominant cohort (mean height 5 m) m) and for remnant emergent trees (height > 5 m).
2.5. Field survey The field survey was done independently of the air photo interpretation to render an objective comparison of the two methods. The field sampling was done 16 months after the aerial photographs were taken. We developed a sampling protocol based upon methods used in the National Inventory of Landscapes in Sweden (Esseen et al., 2004) and the Swiss National Forest Inventory (Bra¨ndli, 2001). We used a GPS receiver (Garmin 12 XL) for locating the start and end of the sixteen sample lines. The positional accuracy of the GPS was within 5–10 m. We walked along each 1 km line using a compass for orientation and the GPS for continuously adjusting
Fig. 3. Part of a 1 km sample line with 20 m radius plots used for data collection: (A) sharp clearcut edge; (B) edge that did not fulfil sharp edge criteria; (C) sharp edge against young forest that fulfilled criteria in air photo interpretation (tree height 3.5 m) but not in the field survey (tree height 5.8 m); (D) sharp edge against agricultural land.
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our position along the line. All edges that fulfilled the ‘sharp edge’ criteria (Table 1) were recorded at the intersection point. The positions of edges were obtained from the GPS. As in the air photo interpretation, we established a 20 m radius plot at the intersection point and divided it into two sectors. The plot boundary was determined with an ultra sonic measuring instrument (Vertex III, Haglo¨f Sweden AB). For stands lower than 5 m, tree height was calculated as the arithmetical mean, based on number of stems in the dominant layer. In stands higher than 10 m, a basal area weighted mean height was calculated. We selected one to two representative trees in each plot sector and used a Suunto hypsometer for height determination. Total canopy cover of trees was estimated visually to the nearest percentage. The estimates were done by two persons to increase the precision of the data. Tree species composition was estimated visually by species based on the proportional cover of each species. In the open habitat, estimates of tree height, canopy cover and tree species composition were done separately for the dominant layer and for remnant taller trees.
where yˆ is the photo interpreted variable and y is the field measured variable (ground truth). The root mean square error (RMSE) and relative root mean square error (RMSE%) were calculated as:
2.6. Data analysis
3.1. Edge length
We evaluated the air photo interpretation using the field data as reference with respect to number of detected edges, estimated edge length, edge type (type of open habitat), tree height, canopy cover and tree species composition. All line intersect observations of edges were classified as ‘matching’ or ‘non-matching’. A ‘matching’ observation is an edge detected at the same geographical position in both the air photo interpretation and in the field survey. A maximum positional deviation of 15 m between the intersection points was allowed for a matching observation. We estimated the length of forest edge in the study area (m ha1) and length by edge type, according to the line intersect sampling formula (Mate´rn, 1964; de Vries, 1986):
Most of the sampled forest edges were sharp, high-contrast edges. The mean ecotone width was 1.0 m (range 0–6 m) based on field measurements. We recorded a total of 57 edges in the aerial photograph from 2002 that fulfilled the sharp edge detection criteria (Table 1). This corresponds to an estimated edge length of 56.0 m ha1 in the study area. During the field survey in 2003, we recorded a total of 66 edges, with a resulting estimate of 64.8 m ha1. These two estimates are not directly comparable as they refer to different years. Logging operations in the winter of 2002/2003 resulted in several new edges while others were lost. To compare the two methods for the year 2002, we scrutinized the data set and removed four edge observations from the air photo data and seven from the field data, which were connected to logging operations performed after the aerial photograph was taken. Consequently, this reduced data set will slightly underestimate the length of forest edge in the study area. The number of line intersect observations in this data set was slightly lower (10%) in the air photo interpretation than in the field survey, 53 and 59 edges, respectively (Table 2). The number of detected edges per 1 km sample line was 3.3 0.6 (aerial photo) and 3.7 0.6 (field; mean S.E., n = 16). The range was one to nine edges in both methods. The estimated length of edge in the air photo interpretation was close to that of the field survey, 52.0 8.8 and 57.9 9.3 m ha1 (mean S.E.), respectively (Table 2). The distribution of edge types was similar in the two methods. Clearcuts and young forests formed over one-half of the total edge length and about one-quarter constituted edges adjoining agricultural land (Table 2). Consequently, created edges are a dominant and ecologically important component in this boreal forest landscape. Natural edges, such as against wetland and water, were sparse and constituted 7% of total edge length.
pm 10; 000 Yˆ ¼ 2L
(1)
where m is the total number of line intersections and L is the total length (m) of sample lines. In the present case, with 16 km sample lines, Eq. (1) simplifies to Yˆ ¼ 0:982 m. We compared results from the air photo interpretation with that from the field survey by calculating the mean and standard error of the estimated edge length, considering the 16 sample lines as independent samples. The accuracy of edge type classification in the air photo interpretation was analysed for matching edges. We evaluated the accuracy of tree height and canopy cover measurements in the air photo interpretation separately for the open habitat and the forest sectors of the 20 m plots. The possible systematic error (bias) was estimated as the mean difference between photo interpreted and field measured variables (height and cover): Bias ¼
n 1X ðˆy yi Þ n i¼1 i
(2)
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n 1X ðˆy yi Þ2 RMSE ¼ n i¼1 i
RMSE% ¼ 100
RMSE y¯
(3)
(4)
where y¯ is the mean (height or cover) of the field data. We did not analyse the accuracy of the height and cover estimates for emergent remnant trees in the open habitat. The few and scattered remnant trees made these estimates very prone to positional errors. 3. Results
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Table 2 Number of observations and estimated length of forest edge in the study area based on line intersect sampling in aerial photographs and in field survey Type of edge
Air photo interpretation
Field survey 1
Observations
Length (m ha )
Clearcut Young forest Agricultural land Wetland Road Other
24 8 12 4 3 2
23.6 7.9 11.8 3.9 2.9 2.0
Total
53
52.0
Observations
Length (m ha1)
45 15 23 8 6 4
20 13 15 4 3 4
19.6 12.8 14.7 3.9 2.9 3.9
34 22 25 7 5 7
100
59
57.9
100
Proportion (%)
Proportion (%)
Data refers to 2002.
3.2. Edge detection
3.3. Edge classification accuracy
Although both the air photo interpretation and the field survey are prone to sampling and measuring errors, we consider the field data as the ‘truth’ with respect to edge detection criteria. Fifty of the forest edge observations were classified as matching, constituting 94 and 85% of all edges recorded in the air photo interpretation and the field survey, respectively (Table 3). This shows that the two methods have a high degree of consistency with respect to detection of forest edges in the same geographical positions. The positional difference of matching observations along the sample lines was 3.4 0.5 m (mean S.E.; absolute values) based on a comparison of GPScoordinates with positions from the photogrammetric system. The difference was 3 m in 68% and 5 m in 80% of the edges—the maximum difference was 13 m (two edges). There were several reasons for lack of agreement in the detection of individual edges between the two methods. Three of the non-matching edges in the air photo interpretation did not fulfil the edge detection criteria in the field survey. In contrast, three edges recorded in the field were not recorded the air photo interpretation as they did not fulfil the detection criteria. Mismatches due to detection criteria were in most cases due to the difficulty in determining tree height in the air photo interpretation. Six edges recorded in the field survey were classified as non-matching due to geographical position. Most of these mismatches were apparently due to GPS errors. It was easier to follow the sample lines during the air photo interpretation than in the field.
The accuracy in classification of type of forest edge in the air photo interpretation was validated with the field data for the 50 matching edges. The overall classification accuracy was high— 88% of the edges were correctly classified (Table 4). The number of observations was too small to permit a detailed accuracy assessment by edge type. However, three edges against young forest and two edges against agricultural land were misclassified as clearcuts. One edge against young forest was misclassified as agricultural land. Classification errors were often due to the difficulty in detecting and measuring the height of small trees. 3.4. Tree height The range in tree height in the forest was smaller in the air photo interpretation than in the field measurements, 10–21 and 10–26 m, respectively. Height measurements made with the photogrammetric system showed a strong, significant relationship (r2 = 0.52, P < 0.0001, n = 50) with the field observations (Fig. 4A). The slope of the regression line was close to 1 and the intercept was 2.3 m. However, the wide scatter of points indicates that the precision of the air photo measurements was moderate. The RMSE was relatively high (3.4 m) corresponding to 21% of mean tree height in the field (RMSE%). Tree height was systematically underestimated in the air photo data compared to the field data, with a mean systematic error of 2.3 m (14%). This was mainly due to the difficulty to locate
Table 3 Agreement between the air photo interpretation and the field survey for line intersect observations of forest edges Air photo interpretation No. of edges Matching observations Non-matching observations Incorrectly detected: did not fulfil sharp edge criteria in field survey Not detected: did not fulfil sharp edge criteria in air photo interpretation Positional mismatch Total observations
Field survey %
No. of edges
%
50
94
50
85
3 – 0
6 – 0
– 3 6
– 5 10
53
100
59
100
Observations were classified as matching (detected at the same position) or non-matching. The data refers to 2002.
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Table 4 Error matrix for classification of edge type in air photo interpretation Air photo interpretation
a
Clearcut Young forestb Agricultural land Wetland Road Other %Correct
Field survey Clearcut
Young forest
Agricultural land
Wetland
Road
Other
%Correct
19 – – – – – 100
3 5 1 – – – 56
2 – 11 – – – 85
– – – 4 – – 100
– – – – 3 – 100
– – – – – 2 100
79 100 92 100 100 100 88
Data from 50 matching edge observations. a Tree height <1.3 m. b Tree height 1.3–5.0 m.
the treetops in conifers, with their sharp tips (cf. Sheng et al., 2003). Tree height was underestimated by more than 1 m in 66% of the cases (the maximum difference was 9 m) and overestimated by more than 1 m in only 4% of the cases. There was a good agreement (1 m difference) between the two methods in 30% of the measurements. The height measurements in open habitat showed a weaker relationship than in the forest (Fig. 4B; data from plots with trees, n = 48). The RMSE was 1.5 m (RMSE%, 95%) and the systematic error was 0.8 m (49%). The high relative errors indicate that the precision was low for tree heights lower than 5 m. Small trees were difficult to measure in the photographs. 3.5. Canopy cover Canopy cover estimated from the aerial photographs showed significant relationships with the field data in both the forest (r2 = 0.52, P < 0.0001) and the open habitat (r2 = 0.62, P < 0.0001; Fig. 5). The wide scatter of points resulted in large RMSE both in the forest and in the open habitat, 11.5% (RMSE%, 19%) and 11.9% (RMSE%, 78%), respectively. The canopy cover estimates were particularly error prone in open
habitat with low cover and small trees (Fig. 5B). The systematic error was positive in the forest, 5.8% (RMSE%, 10%) and negative in the open habitat, 1.6% (RMSE%, 10%). Cover of both coniferous and deciduous trees in the forest was strongly related to the field data, with r2-values of 0.42 and 0.52, respectively. Estimates from the aerial photographs were higher than the field measurements for both groups (Table 5). In the open habitat, the relationships were strong for both total cover and for deciduous tress, with r2-values of 0.40 and 0.56, respectively. In contrast, the air photo interpretation did not succeed in estimating cover of coniferous trees (r2 = 0.00). This was due to the difficulty of identifying small conifers in the aerial photographs. The deciduous trees were often higher than the conifers in the open habitat. 3.6. Time and cost for data collection We recorded the time for data collection during the air photo interpretation and the field survey, not counting time for method development. It took one person 1.5 days to collect all forest edge data for the whole study area (16 km2) with the digital photogrammetric system. The field survey took five days to
Fig. 4. Relationship between tree heights measured in the aerial photograph and in the field: (A) forest habitat and (B) open habitat. Data from sectors of 20 m radius plots for matching edges. The solid line denotes the regression line and the broken line a 1:1 relationship.
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Fig. 5. Relationship between canopy cover measured in the aerial photograph and in the field: (A) forest habitat and (B) open habitat. Data from sectors of 20 m radius plots for matching edges. The solid line denotes the regression line and the broken line a 1:1 relationship.
Table 5 Comparison of total canopy cover (%) and by subgroups (mean S.E.) in air photo interpretation and field survey Canopy cover
Total Coniferous trees Deciduous trees Picea abies Pinus sylvestris Betula spp. Populus tremula Other deciduous trees
Open habitat
Forest habitat
Air photo interpretation
Field survey
Air photo interpretation
Field survey
13.7 2.7 4.0 0.9 9.7 2.5 – – – – –
15.3 2.4 6.3 1.1 9.0 1.6 2.0 0.5 4.2 1.0 7.8 1.6 0.2 0.2 1.0 0.4
65.2 1.6 45.7 3.3 19.5 3.6 – – – – –
59.4 2.0 43.4 2.3 16.0 2.3 23.6 2.6 19.8 2.6 14.7 2.1 0.6 0.3 0.8 0.4
Estimates for open habitat and adjoining forest habitat were made in sectors of 20 m radius plots. N = 50 (matching observations). Table 6 Comparison of estimated time for collecting data on forest edges by air photo interpretation and field survey in this study
interpretation, but it will still be less expensive than the field survey.
Time
Field survey
4. Discussion
5 – 5
2 23 58
4.1. Forest edge quantification
10
83
Preparation/GIS-operations (h) Transportationa (h) Data collection (h) Total time (h)
Air photo interpretation
Data were collected from 16 line transects in a 4 km 4 km area. Time is in man-hours. a To study area and between transects.
complete for a team of two field surveyors. It should be noted, however, that the system of sample lines was not optimised for field surveys. Overall, data collection was eight times faster in the air photo interpretation than in the field survey (Table 6). The cost per man-hour is 70% higher for the air photo interpretation than for the field survey. This is primarily due to higher costs for equipment, software, technical support and higher salary. Therefore, the total cost for collecting forest edge data was five times lower for the air photo interpretation, excluding image costs. Including costs for taking new photographs will reduce the cost-efficiency of the air photo
Detailed quantitative studies of forest edges at the landscape level are still scare despite documented effects on biodiversity (Murcia, 1995; Harper et al., 2005). Our analysis shows that critical data on forest edges can be extracted from infrared colour aerial photographs with almost the same accuracy as in time-consuming field surveys. This suggests that line intersect sampling in combination with plot measurements in aerial photographs has large potential as an efficient tool for estimating both the quantity and characteristics of sharp forest edges in man-modified landscapes. The method has several advantages. First, the high spatial resolution in aerial photographs makes it possible to collect detailed information for both the edge itself and the adjoining habitats. Second, by using a digital photogrammetric system it is possible to determine the position of edges with high precision, to perform measurements in three dimensions and to standardize the
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viewing scale used for edge detection across the entire study area. Third, important boundary attributes, such as tree height, canopy cover and tree species composition of adjacent habitats, can be collected with the method. Patch contrast can be described by the difference in these variables (Strayer et al., 2003; Harper et al., 2005). One limitation is that the method cannot be used for all kinds of forest ecotones. It is best suited for detection of sharp edges, i.e. edges that form a more or less well-defined ‘reference line’ at the detection scale used during data collection. Sharp edges are primarily characterized by high-contrast in tree height and a narrow ecotone, less than ca. 10 m wide. We do not consider this as a severe limitation—sharp edges between stands of different age are very frequent in most fragmented forest ecosystems. Analysis of satellite data is better suited to cover the whole range of forest ecotones that occur in nature (Fortin et al., 2000; Jacquez et al., 2000). However, such edge detection methods are more complex than the present approach. No single edge detection technique can probably address all types of user needs (Fortin et al., 2000). One advantage of our method is that different edge detection criteria may be applied (e.g. Table 1) and that the variables used for construction of these criteria can be measured in the aerial photographs. Applying other criteria will of course affect the accuracy and precision of the edge length estimates. 4.2. Practical aspects of line intersect sampling of edges Line intersect sampling of forest edges is simple to apply both in aerial photographs and in the field. However, some aspects of LIS are important to consider as they affect the accuracy of the estimates (Van Wagner, 1982; Ringvall and Sta˚hl, 1999). The sampling layout must be such that it addresses the problem with non-random orientation of edges. Both Hildebrandt (1973) and Schuerholz (1974) recommended layouts with radial lines for sampling edges in aerial photographs. A practical layout for field surveys is an equilateral triangle (Van Wagner, 1982). Corona et al. (2004) found no difference in the accuracy of estimated edge length between systematic and random line intersect sampling designs. Our sampling layout with lines in two directions (Fig. 1) addresses the problem with non-random orientation. This layout, with 1 km sample line per 1 km 1 km quadrat, was practical to apply in the air photo interpretation. For future application we recommend this layout, or preferably, randomly oriented sample lines. Bias may also be caused by differences among surveyors. Ringvall and Sta˚hl (1999) found that systematic differences between surveyors were not a serious problem in line intersect sampling of coarse woody debris. However, because forest edges are more difficult to define and to locate we expect the differences to be larger. The high proportion of matching edges (Table 3) shows that LIS in aerial photographs is very successful in locating the same edges as in the field survey. The extent of matching edges depends on the positional accuracy of both methods and their ability to the judge whether an edge fulfils edge detection
criteria or not. In practice, it was much easier to follow the sample lines during air photo interpretation than in the field. On the other hand, the edge detection criteria were easier to apply in the field. 4.3. Variable estimates The accuracy of the air photo estimates of tree height and canopy cover is influenced by several potential sources of error. These include positional errors, including both differences in the location of the edge (intersection point) and in the division of the 20 m plot into open habitat and forest sectors, photo interpreter bias and errors in the field data used as reference. It was difficult to separate different error sources. However, positional errors were probably not the most important error source. Eighty percent of matching edges had a positional deviation of less than 5 m. The photogrammetric system had a high geographical accuracy (<2 m) while the GPS showed slightly lower accuracy (<10 m). Measurements of tree height in the digital photogrammetric system showed a strong relationship with field data in the forest but had a poor relation in the open habitat. It is known that tree height can be measured in aerial photographs with nearly the same precision as obtained in field surveys (Sta˚hl, 1992). However, there is a tendency that heights are underestimated in aerial photos. In our study, tree height was underestimated by 2.3 m (14%) in the forest habitat. Lundquist (2005) used the same digital photogrammetric system with black and white photographs to measure mean height of trees at stand level. He compared the results with an analogue instrument (Wild B8) using field data from 10 m radius plots for validation. The RMSE% was only slightly higher in the digital system (12%) than in the analogue instrument (9%). However, the systematic error was markedly higher in the digital system, 1.5 m (9%) and 0.2 m (1%), respectively (Lundquist, 2005). Thus, we strongly recommend using field data from reference plots for calibration and adjustment of height measurements in the digital photogrammetric system. It was more difficult to estimate canopy cover than to measure tree height. Cover estimates in both aerial photographs and in the field are subjected to high errors. The viewing geometry in a stereo model makes it more difficult to view the ground in a forested area in the peripheral part of the stereo model than in the central part. This might lead to biased crown cover estimates. Calibration of interpreters should be applied prior to the air photo interpretation to reduce surveyor bias during operational use of the method. The precision of the height and canopy cover estimates was higher in forest than in open habitat. We conclude that structural variables, such as tree height, canopy cover and tree species composition, can be measured with relatively high precision in the digital photogrammetric system. 4.4. Air photo interpretation compared to field survey One of the main issues in the choice of methods for inventory of forest resources is the effectiveness in terms of
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time and cost. Corona et al. (2004) found that line intersect sampling in aerial photographs was several times more effective than polygon delineation to estimate edge length. The most efficient sampling designs took 4–14% of the time compared to complete delineation of polygons. We found that line intersect sampling in aerial photographs was eight times faster, and five times more cost-effective, than field sampling. This suggests that considerable cost savings can be achieved by using the method. Interpretation of aerial photographs has the potential to provide critical information of many additional attributes of forest edges, including several of the boundary attributes of Strayer et al. (2003), as well as data on local or regional context variables (Harper et al., 2005). For example, aerial photographs can be used to collect data on edge orientation, edge sinuosity, edge profile, presence of linear objects near the edge, etc. Important context variables include the size, shape, direction, local topography and vegetation structure of the open habitat, which affect the depth and strength of edge influence. Field surveys are better suited to collect detailed data on edge origin, edge age, disturbance and forest attributes (e.g. tree species, stem density, diameter, vertical and horizontal structure). 5. Conclusion There is a need for simple, accurate and efficient methods to collect data on forest edges to understand their role in forest ecosystems and for management purposes. Our study shows that line intersect sampling in infrared colour aerial photographs, in combination with plots, is a robust and efficient method for estimating both the quantity and characteristics of sharp forest edges in managed forest ecosystems. The main advantages of the method are the accuracy in edge detection, variable estimates and overall speed. The method has several potential applications. It is particularly suitable for large-scale comparisons of the amount of forest edge and key edge variables in fragmented landscapes. The method can also be used in retrospective studies of long-term changes in the amount and type of edges using older photographs. Aerial photographs are also suitable to collect data on many local and regional context variables that are important in determining edge influence on biodiversity. The method should be tested in other types of forest ecosystems to give a more general picture of its usefulness. Future studies should also address differences among surveyors and the measurement of additional structural and context variables. We conclude that the method represents a promising tool for application in research on forest edges and their effects on biodiversity as well as in monitoring and forest management. Acknowledgements This study was supported by a grant from the Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (Formas). We thank Per Lo¨fgren for help with the fieldwork, Bjo¨rn Nilsson, Karin Pramborg and Anna Allard for technical advice during air photo interpretation and Mats
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Ho¨gstro¨m for help with ArcGIS and data handling. Thanks are also due to So¨ren Holm and Go¨ran Sta˚hl for statistical advice. At last, we thank two anonymous reviewers for constructive comments. References Battles, J.J., Dushoff, J.G., Fahey, T.J., 1996. Line intersect sampling of forest canopy gaps. For. Sci. 42, 131–138. Bra¨ndli, U.-B., 2001. Nature protection function. In: Brassel, P., Lischke, H. (Eds.), Swiss National Forest Inventory: Methods and Models of the Second Assessment. Swiss Federal Research Institute WSL, Birmensdorf, pp. 265– 282. Cadenasso, M.L., Pickett, S.T.A., Weathers, K.C., Jones, C.G., 2003. A framework for a theory of ecological boundaries. Bioscience 53, 750–758. Corona, P., Chirici, G., Travaglini, D., 2004. Forest ecotone survey by line intersect sampling. Can. J. For. Res. 34, 1776–1783. Csillag, F., Boots, B., Fortin, M.-J., Lowell, K., Potvin, F., 2001. Multiscale characterization of boundaries and landscape ecological patterns. Geomatica 55, 291–307. Curry, K., 2003. Using Stereo Analyst for ArcGIS. Leica Geosystems GIS & Mapping, LLC, Atlanta, GA. de Vries, P.G., 1979. Line intersect sampling—statistical theory, applications, and suggestions for extended use in ecological inventory. In: Cormack, R.M., Patil, G.P., Robson, D.S. (Eds.), Sampling Biological Populations. International Co-operative Publishing House, Fairland, MD, pp. 1–70. de Vries, P.G., 1986. Sampling Theory for Forest Inventory. Springer-Verlag, Berlin, Germany. Didham, R.K., Lawton, J.H., 1999. Edge structure determines the magnitude of changes in microclimate and vegetation structure in tropical forest fragments. Biotropica 31, 17–30. Esseen, P.-A., Renhorn, K.-E., 1998. Edge effects on an epiphytic lichen in fragmented forests. Cons. Biol. 12, 1307–1317. Esseen, P.-A., Glimska¨r, A., Sta˚hl, G., Sundquist, S., 2004. Fa¨ltinstruktion fo¨r Nationell Inventering av Landskapet i Sverige, NILS. Department of Forest Resource Management and Geomatics, Swedish University of Agricultural Sciences, Umea˚, Sweden. Fagan, W.F., Fortin, M.-J., Soykan, C., 2003. Integrating edge detection and dynamic modelling in quantitative analyses of ecological boundaries. Bioscience 53, 730–738. Fortin, M.-J., 1994. Edge detection algorithms for two-dimensional ecological data. Ecology 75, 956–965. Fortin, M.-J., Drapeau, P., 1995. Delineation of ecological boundaries: comparison of approaches and significance tests. Oikos 72, 323–332. Fortin, M.-J., Edwards, G., 2001. Delineation and analysis of vegetation boundaries. In: Hunsaker, C.T., Goodchild, M.F., Friedl, M.A., Case, T.J. (Eds.), Spatial Uncertainty in Ecology. Implications for Remote Sensing and GIS Applications. Springer, New York, pp. 158–174. Fortin, M.-J., Olson, R.J., Ferson, S., Iverson, L., Hunsaker, C., Edwards, G., Levine, D., Butera, K., Klemas, V., 2000. Issues related to the detection of boundaries. Landsc. Ecol. 15, 453–466. Franklin, S.E., 2001. Remote Sensing for Sustainable Forest Management. Lewis, Boca Raton, FL. Gascon, C., Williamson, G.B., da Fonseca, G.A.B., 2000. Receding forest edges and vanishing reserves. Science 288, 1356–1358. Gosz, J.R., 1993. Ecotone hierarchies. Ecol. Appl. 3, 369–376. Hall, R.J., 2003. The roles of aerial photographs in forestry remote sensing image analysis. In: Wulder, M.A., Franklin, S.E. (Eds.), Remote Sensing of Forest Environments. Concepts and Case Studies. Kluwer Academic Publishers, Norwell, MA, pp. 47–75. Harper, K.A., Lesieur, D., Bergeron, Y., Drapeau, P., 2004. Forest structure and composition at young fire and cut edges in black spruce boreal forest. Can. J. For. Res. 34, 289–302. Harper, K.A., Macdonald, S.E., Burton, P.J., Chen, J., Brosofske, K.D., Saunders, S.C., Euskirchen, E.S., Roberts, D., Jaiteh, M.S., Esseen, P.-A., 2005. Edge influence on forest structure and composition in fragmented landscapes. Cons. Biol. 19, 768–782.
42
P.-A. Esseen et al. / Forest Ecology and Management 230 (2006) 32–42
Hildebrandt, G., 1973. Die Verwendung von Luftbild-Linienstichproben zur Ermittlung der La¨nge Linienfo¨rmiger Gela¨ndeobjekte. In: Proceedings of the Symposium on IUFRO Subject Group 5.6.05, Freiburg, Germany, pp. 267–284. Holmgren, P., Thuresson, T., Holm, S., 1997. Estimating forest characteristics in scanned aerial photographs with respect to requirements for economic and forest management planning. Scand. J. For. Res. 12, 189–199. Jacquez, G.M., Maruca, S., Fortin, M.-J., 2000. From fields to objects: a review of geographic boundary analysis. J. Geogr. Syst. 2, 221–241. Johnston, C.A., Pastor, J., Pinay, G., 1992. Quantitative methods for studying landscape boundaries. In: Hansen, A.J., di Castri, F. (Eds.), Landscape Boundaries, Consequences for Biotic Diversity and Ecological Flows. Springer-Verlag, New York, pp. 107–125. Kaiser, L., 1983. Unbiased estimation in line-intercept sampling. Biometrics 39, 965–976. Lillesand, T.M., Kiefer, R.W., 2000. Remote Sensing and Image Interpretation, 4th ed. Wiley, New York. Lindenmayer, D.B., Franklin, J.F., 2002. Conserving Forest Biodiversity. A Comprehensive Multiscaled Approach. Island Press, WA. Lundquist, F., 2005. Digitalt stereoinstrument som hja¨lpmedel vid skogsuppskattning—en ja¨mfo¨relse med analoga instrument. Department of Forest Resource Management and Geomatics, Swedish University of Agricultural Sciences, Umea˚, Sweden, Working Report. Mate´rn, B., 1964. A method for estimating the total length of roads by means of a line survey. Stud. For. Suec. 185, 68–70. Matlack, G.R., Litvaitis, J.A., 1999. Forest edges. In: Hunter, M.L. (Ed.), Maintaining Biodiversity in Forest Ecosystems. Cambridge University Press, Cambridge, UK, pp. 210–233. Metzger, J.P., Muller, E., 1996. Characterizing the complexity of landscape boundaries by remote sensing. Landsc. Ecol. 11, 65–77.
Murcia, C., 1995. Edge effects in fragmented forests: implications for conservation. Trends Ecol. Evol. 10, 58–62. Næsset, E., 1998. Positional accuracy of boundaries between clearcuts and mature stands delineated by means of aerial photointerpretation. Can. J. For. Res. 28, 368–374. Ringvall, A., Sta˚hl, G., 1999. Field aspects of line intersect sampling for assessing coarse woody debris. For. Ecol. Manage. 119, 163–170. Saunders, D.A., Hobbs, R.J., Margules, C.R., 1991. Biological consequences of ecosystem fragmentation: a review. Cons. Biol. 5, 8–32. Schuerholz, G., 1974. Quantitative evaluation of edge from aerial photographs. J. Wildl. Manage. 38, 913–920. Sheng, Y., Gong, P., Biging, G.S., 2003. Model-based conifer canopy surface reconstruction from photographic imagery: overcoming the occlusion, foreshortening, and edge effects. Photogr. Eng. Rem. Sens. 69, 249–258. Shiver, B.D., Borders, B.E., 1996. Sampling Techniques for Forest Resource Inventory. John Wiley and Sons, New York. Sta˚hl, G. 1992. En studie av kvalitet i skogliga avdelningsdata som insamlats med subjektiva inventeringsmetoder. Department of Biometry and Forest Management Planning, Swedish University of Agricultural Sciences, Umea˚, Sweden, Report 24. Strayer, D.L., Power, M.E., Fagan, W.F., Pickett, S.T.A., 2003. A classification of ecological boundaries. Bioscience 53, 723–729. Van Wagner, C.E., 1982. Practical aspects of the line intersect method. Report PI-X-12. Petawawa National Forestry Institute, Canadian Forest Service, Chalk River, Ont., Canada. Warren, W.G., Olsen, P.F., 1964. A line intersect technique for assessing logging waste. For. Sci. 14, 20–26. Zheng, D., Chen, J., 2000. Edge effects in fragmented landscapes: a generic model for delineating area of edge influences (D-AEI). Ecol. Model. 132, 75–190.
Forest edge quantification by line intersect sampling in aerial photographs Per-Anders Esseen a,*, K. Ulrika Jansson a, Mats Nilsson b b
a Department of Ecology and Environmental Science, Umea˚ University, SE 901 87 Umea˚, Sweden Department of Forest Resource Management and Geomatics, Swedish University of Agricultural Sciences, SE 901 83 Umea˚, Sweden
Received 31 January 2005; received in revised form 27 March 2006; accepted 3 April 2006
Abstract There is a need for accurate and efficient methods for quantification and characterisation of forest edges at the landscape level in order to understand and mitigate the effects of forest fragmentation on biodiversity. We present and evaluate a method for collecting detailed data on forest edges in aerial photographs by using line intersect sampling (LIS). A digital photogrammetric system was used to collect data from scanned colour infrared photographs in a managed boreal forest landscape. We focused on high-contrast edges between forest (height 10 m) and adjoining open habitat or young, regenerating forest (height 5 m). We evaluated the air photo interpretation with respect to accuracy in estimated edge length, edge detection, edge type classification and structural variables recorded in 20 m radius plots, using detailed field data as reference. The estimated length of forest edge in the air photo interpretation (52 8.8 m ha1; mean standard error) was close to that in the field survey (58 9.3 m ha1). The accuracy in edge type classification (type of open habitat) was high (88% correctly classified). Both tree height and canopy cover showed strong relationships with the field data in the forest, but tree height was underestimated by 2.3 m. Data collection was eight times faster and five times more cost-effective in aerial photographs than in field sampling. The study shows that line intersect sampling in aerial photographs has large potential application as a general tool for collecting detailed information on the quantity and characteristics of high-contrast edges in managed forest ecosystems. # 2006 Elsevier B.V. All rights reserved. Keywords: Canopy cover; Edge influence; Forest fragmentation; Line intersect sampling; Photogrammetry; Tree height
1. Introduction Forest edges are important structures in both natural and managed forested ecosystems and have numerous implications for ecosystem structure, function and dynamics (Cadenasso et al., 2003; Harper et al., 2005). Edges provide critical habitat and resources for many organisms and the amount, variety and structural characteristics of edges thus strongly influence biodiversity. It is now widely recognized that increased amount of edge habitat and the resulting edge influence on biodiversity is one of the most important consequences of forest fragmentation (Murcia, 1995; Matlack and Litvaitis, 1999; Gascon et al., 2000; Lindenmayer and Franklin, 2002). Forest ecotones are dynamic and changes in their location or structural attributes over time can be used to indicate environmental changes (Fortin et al., 2000). Detailed knowledge of the
* Corresponding author. Tel.: +46 90 786 5523; fax: +46 90 786 6705. E-mail address: [email protected] (P.-A. Esseen). 0378-1127/$ – see front matter # 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.foreco.2006.04.012
quantity, quality and dynamics of edges is required to understand the functional roles of edges and how they interact with biodiversity. An important step in this process is to have efficient tools for edge detection, classification and characterisation. A forest edge may be defined as the interface between forested and non-forested ecosystems, or between two forests of contrasting composition or structure (Harper et al., 2005). In this paper, we use the term forest edge for a sharp forest boundary following Fagan et al. (2003). Forest edges are best characterized as complex threedimensional entities, which can be described by many attributes reflecting their origin, maintenance, structure, function and dynamics (Cadenasso et al., 2003; Strayer et al., 2003). The magnitude and distance of edge influence are controlled by many factors, including both local and regional factors. Patch contrast, i.e. the difference in composition, structure, function or microclimate between adjoining habitats, plays a key role in determining edge influence (Harper et al., 2005). It has been shown that vegetation structure may strongly affect the extent of edge influence in tropical forest fragments (Didham and
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Lawton, 1999). To assess the ecological consequences of forest edges at landscape level and to consider edge effects in management plans we need to have knowledge of: (1) the quantity of forest edges across the landscape, (2) the type, origin and maintenance of edges, (3) the vegetation structure of the habitats on either side of the edge and (4) information of local factors such as edge orientation, edge shape, width and size of the open habitat and other local or regional context factors (Saunders et al., 1991; Harper et al., 2005). Several issues are involved in the detection and quantification of edges (Johnston et al., 1992; Csillag et al., 2001; Fortin and Edwards, 2001; Fagan et al., 2003). The detection of edges is strongly scale dependent and therefore tightly connected to the resolution (grain) of the spatial data (Gosz, 1993; Fortin et al., 2000). Both the scale of measurement and the type of data must be appropriate for the questions addressed. A wide range of methods have been developed for edge detection, classification and quantification and are reviewed by Johnston et al. (1992), Fortin et al. (2000), Jacquez et al. (2000) and Fagan et al. (2003). Transects have been frequently used to collect detailed field data on vegetation structure across forest ecotones (Johnston et al., 1992; Esseen and Renhorn, 1998; Harper et al., 2004). Data from transects can give information on ecotone location, width and patch contrast, but are timeconsuming to collect. Remote sensing data, particularly satellite imagery, have been widely used to obtain information about forest ecotones at different spatial scales (Metzger and Muller, 1996; Fortin et al., 2000; Zheng and Chen, 2000). Satellite images cover large geographical areas and produce a grid of data (lattice) covering all types of ecotones in the landscape. A major advantage is that lattice data can be subjected to analysis by different statistical edge detection methods suitable to detect either sharp or gradual ecotonal transitions (Fortin, 1994; Fortin and Drapeau, 1995; Fortin et al., 2000). However, the resolution in satellite images, such as SPOT HRVIR and Landsat TM, is too low to permit collection of detailed spatial and attribute data of forest edges. Sensors, such as IKONOS, have higher resolution but are not as well suited as aerial photographs for collecting detailed forest edge data. Aerial photographs have high spatial resolution and have for a long time provided data for vegetation and resource mapping (Lillesand and Kiefer, 2000). They have been widely used for detection of vegetation boundaries but the focus has been on the patches rather than on the ecotones (Johnston et al., 1992). Only a few studies (Hildebrandt, 1973; Schuerholz, 1974; Corona et al., 2004) have used different line sampling designs in aerial photographs for obtaining information about length of forest edges. The potential to extract detailed vegetation and forest data from aerial photographs have improved much during the last decades due to the technological development (Holmgren et al., 1997; Franklin, 2001; Hall, 2003). The position of forest edges can be determined with high accuracy by air photo interpretation (Næsset, 1998). The possibility to measure tree heights, estimate crown cover and other variables makes aerial photography a very interesting technique for collecting data on forest edges.
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In this paper, we present and evaluate a method for quantification and characterisation of sharp forest edges in aerial photographs by using line intersect sampling (LIS). We studied edges between forests and adjoining open habitats or young regenerating forest stands. Data on forest edges were extracted by manual interpretation of colour infrared aerial photographs using a digital photogrammetric system. For comparison and validation, we made a detailed field survey in the study area using the same sampling design as in the air photo interpretation. We assessed the accuracy of the air photo interpretation with respect to estimated edge length, edge detection, edge classification, and forest structural variables (tree height and canopy cover). We also assessed the costeffectiveness of the method and discuss its advantages, limitations and potential applications. 2. Material and methods 2.1. Study area The study area was a typical managed boreal forest landscape in Fennoscandia with a size of 4 km 4 km (1600 ha). It is located in Va¨sterbotten county, north-eastern Sweden (midpoint 64830 N, 20890 E), 25 km north of Umea˚, in the middle boreal zone. The terrain is slightly undulating with elevations ranging between 90 and 180 m above sea level. The study area is covered by 85% forested land, 9% agricultural land, 5% open wetland and 1% water (Swedish land cover data). Forestry is the dominant land use followed by agriculture. The fragmented landscape is a mosaic of agricultural land, clearcuts, young forests of various successional stage and mature stands. Clearcuts and young forests (height < 5 m) constitute 28% and forests with tree height > 10 m constitute 46% of the area. Old forests of semi-natural character constitute a minor proportion. Conifers dominate with Pinus sylvestris constituting 45% by volume, followed by Picea abies (40%), Betula spp. (14%) and other deciduous trees (1%). 2.2. Line intersect sampling Line intersect sampling is a well-known and efficient technique for sampling different kinds of objects (de Vries, 1979; Kaiser, 1983; Shiver and Borders, 1996). The method has been widely used for estimation of the amount of coarse woody debris (Warren and Olsen, 1964; Van Wagner, 1982) but has also been used for sampling objects such as roads (Mate´rn, 1964), streams, hedges, tree crowns and canopy gaps (Battles et al., 1996). LIS is a simple and statistically robust method that can be used to estimate the total length of linear objects, the area of two-dimensional objects, the volume of threedimensional objects and variables that are measured on these objects. The precision of the estimates is primarily dependent on total sample size, i.e. the total length of the sample lines and the number of lines. LIS can be described as a strip transect of infinitesimal width in which the sample line constitute a vertical plane (Van Wagner, 1982). The data collected are the number of intersections of objects encountered as one follows the sample
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P.-A. Esseen et al. / Forest Ecology and Management 230 (2006) 32–42 Table 1 Criteria used for detection of sharp forest edges in this study Variable 2
Patch area (m ) Patch widtha (m) Tree height (m) Canopy cover (%) Cover of trees >5 m high (%) a b c
Fig. 1. Layout of sample lines in the study area (4 km 4 km). One 1 km sample line was placed in each 1 km 1 km quadrat.
line. The practical aspects of LIS have been discussed by, e.g. Van Wagner (1982) and Ringvall and Sta˚hl (1999). LIS provides unbiased estimates of the total length of objects under the assumption that the sample lines are randomly oriented in the area of interest (Kaiser, 1983), or that the objects to be sampled are randomly oriented with no preferred orientation (de Vries, 1979). We established a system of 16 sample lines (each 1 km long) in a 4 4 grid in the study area (Fig. 1). One line was placed in the middle of each 1 km 1 km quadrat. For practical reasons, we used a sampling layout with eight sample lines oriented in south–north direction and eight in west–east direction to minimise effects due to systematic orientation of forest edges. 2.3. Edge detection criteria A fundamental question in quantification of forest edges is the criteria used to identify the vegetation gradients that qualify as an edge and to separate these from other gradients. Edge detection criteria should be formulated in relation to the objective of the specific study, the resolution of the available spatial data, and the method of data collection. We used the ‘a priori’ approach and recorded only edges that fulfilled a set of predefined ‘sharp edge’ criteria for two reasons. First, sharp forest edges were the primary concern because of their documented effect on biodiversity (Murcia, 1995; Harper et al., 2005). Second, objects sampled with LIS should have a welldefined ‘reference line’ that is easy to recognize. We developed a ‘sharp edge definition’ based on ecotone width, patch area, patch width, tree height and canopy cover (Table 1). The open habitat–forest ecotone was not allowed to be wider than 10 m to qualify as sharp edge. Consequently, gradual transitions, e.g. from an open wetland to forested wetland or mesic forest, were not sampled. The definition also includes criteria for the ‘open habitat’ and the ‘forest’. The most important criterion was the contrast in tree height: ‘open habitat’ was lower than 5 m, while ‘forests’ were taller than 10 m. We allowed a maximum cover of 15% for emergent remnant trees (height > 5 m) left on
Open habitat
Forest habitat
1000 15 5 b 0–100 15
1000 15 10c 30 –
Measured from the intersection point across the patch. Arithmetical mean based on number of stems in the dominant layer. Mean weighted by basal area.
clearcuts and other habitats. The edge detection criteria were defined to exclude edges in connection with small canopy gaps in the forest (<1000 m2; Table 1), or small forest patches (<1000 m2) in open habitat, as well as along linear elements with <15 m wide canopy opening, such as small roads, streams and lines or strips of trees. LIS can also be used to record such edges but this was beyond the scope of the study. 2.4. Air photo interpretation Data on forest edges were extracted from aerial photographs using a digital photogrammetric system. The system consisted of a Dell Precision workstation, a high-end graphic card (3Dlabs Wildcat 7110), two monitors; one equipped with a stereoscopic viewing panel (Stereographics Monitor Zscreen), and polarized eyewear. The software was ArcGIS Version 8.2 and Stereo Analyst for ArcGIS Version 1.3 (Curry, 2003). This system provides tools for accurate recording, interpretation and analysis of 3D geographical data. All data were recorded in the National Reference Grid of Sweden (RT90 2.5 g V). We used three colour infrared photographs with 60% overlap to achieve stereo coverage of a 5 km 5 km area. The photographs were taken on 5 June 2002 with a Wild RC30 metric camera from a flight height of 4600 m, resulting in an approximate image scale of 1:30,000. The photographs had a ground resolution of 0.5 m and they were scanned with 14 mm resolution, corresponding to 0.4 m on the ground. The absolute orientation of the two stereo models resulted in a root mean square error (RMSE) of approximately 1 m for both models. The 16 sample lines were adjusted to local topography through interpolation from a digital elevation model (DEM). The DEM had a 50 m 50 m horizontal resolution and was produced by the Swedish Land Survey. The lines were then superimposed on the stereo model so that they followed the terrain. The air photo interpretation was done with a viewing scale of 1:2000. We followed the sample lines and recorded all forest edges that fulfilled the ‘sharp edge’ criteria (Table 1) at the intersection point (Fig. 2). The exact position of the edge was defined as the canopy drip line at a height of 10 m above ground. A circular plot with 20 m radius (1256 m2) was created around each digitised point (Fig. 3) using a buffer in ArcMap and adjusted to local topography. The 20 m plots were divided into two sectors, forest and open habitat, separated by the edge (canopy drip line). We recorded data separately for each sector. The open habitat was classified into one of the following types:
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Fig. 2. Colour infrared aerial photograph of the centre 2 km 2 km part of the study area. Broken lines denote sample lines and points denote forest edge intersections recorded in air photo interpretation.
clearcut (tree height 1.3 m), young forest (tree height 1.3– 5 m), agricultural land, open wetland, water, road and other habitat. Tree height was measured to the nearest m by placing the pointer on the ground and at the top of the canopy and recording the z-coordinates. Total canopy cover was visually estimated to the nearest 5%. We used templates with different degree of cover to increase the accuracy of the estimates. The proportion coniferous trees of total canopy cover was estimated, and the cover of coniferous and deciduous trees was calculated. In the open habitat, estimates of tree height, canopy cover and tree species composition were done separately for trees in the dominant cohort (mean height 5 m) m) and for remnant emergent trees (height > 5 m).
2.5. Field survey The field survey was done independently of the air photo interpretation to render an objective comparison of the two methods. The field sampling was done 16 months after the aerial photographs were taken. We developed a sampling protocol based upon methods used in the National Inventory of Landscapes in Sweden (Esseen et al., 2004) and the Swiss National Forest Inventory (Bra¨ndli, 2001). We used a GPS receiver (Garmin 12 XL) for locating the start and end of the sixteen sample lines. The positional accuracy of the GPS was within 5–10 m. We walked along each 1 km line using a compass for orientation and the GPS for continuously adjusting
Fig. 3. Part of a 1 km sample line with 20 m radius plots used for data collection: (A) sharp clearcut edge; (B) edge that did not fulfil sharp edge criteria; (C) sharp edge against young forest that fulfilled criteria in air photo interpretation (tree height 3.5 m) but not in the field survey (tree height 5.8 m); (D) sharp edge against agricultural land.
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our position along the line. All edges that fulfilled the ‘sharp edge’ criteria (Table 1) were recorded at the intersection point. The positions of edges were obtained from the GPS. As in the air photo interpretation, we established a 20 m radius plot at the intersection point and divided it into two sectors. The plot boundary was determined with an ultra sonic measuring instrument (Vertex III, Haglo¨f Sweden AB). For stands lower than 5 m, tree height was calculated as the arithmetical mean, based on number of stems in the dominant layer. In stands higher than 10 m, a basal area weighted mean height was calculated. We selected one to two representative trees in each plot sector and used a Suunto hypsometer for height determination. Total canopy cover of trees was estimated visually to the nearest percentage. The estimates were done by two persons to increase the precision of the data. Tree species composition was estimated visually by species based on the proportional cover of each species. In the open habitat, estimates of tree height, canopy cover and tree species composition were done separately for the dominant layer and for remnant taller trees.
where yˆ is the photo interpreted variable and y is the field measured variable (ground truth). The root mean square error (RMSE) and relative root mean square error (RMSE%) were calculated as:
2.6. Data analysis
3.1. Edge length
We evaluated the air photo interpretation using the field data as reference with respect to number of detected edges, estimated edge length, edge type (type of open habitat), tree height, canopy cover and tree species composition. All line intersect observations of edges were classified as ‘matching’ or ‘non-matching’. A ‘matching’ observation is an edge detected at the same geographical position in both the air photo interpretation and in the field survey. A maximum positional deviation of 15 m between the intersection points was allowed for a matching observation. We estimated the length of forest edge in the study area (m ha1) and length by edge type, according to the line intersect sampling formula (Mate´rn, 1964; de Vries, 1986):
Most of the sampled forest edges were sharp, high-contrast edges. The mean ecotone width was 1.0 m (range 0–6 m) based on field measurements. We recorded a total of 57 edges in the aerial photograph from 2002 that fulfilled the sharp edge detection criteria (Table 1). This corresponds to an estimated edge length of 56.0 m ha1 in the study area. During the field survey in 2003, we recorded a total of 66 edges, with a resulting estimate of 64.8 m ha1. These two estimates are not directly comparable as they refer to different years. Logging operations in the winter of 2002/2003 resulted in several new edges while others were lost. To compare the two methods for the year 2002, we scrutinized the data set and removed four edge observations from the air photo data and seven from the field data, which were connected to logging operations performed after the aerial photograph was taken. Consequently, this reduced data set will slightly underestimate the length of forest edge in the study area. The number of line intersect observations in this data set was slightly lower (10%) in the air photo interpretation than in the field survey, 53 and 59 edges, respectively (Table 2). The number of detected edges per 1 km sample line was 3.3 0.6 (aerial photo) and 3.7 0.6 (field; mean S.E., n = 16). The range was one to nine edges in both methods. The estimated length of edge in the air photo interpretation was close to that of the field survey, 52.0 8.8 and 57.9 9.3 m ha1 (mean S.E.), respectively (Table 2). The distribution of edge types was similar in the two methods. Clearcuts and young forests formed over one-half of the total edge length and about one-quarter constituted edges adjoining agricultural land (Table 2). Consequently, created edges are a dominant and ecologically important component in this boreal forest landscape. Natural edges, such as against wetland and water, were sparse and constituted 7% of total edge length.
pm 10; 000 Yˆ ¼ 2L
(1)
where m is the total number of line intersections and L is the total length (m) of sample lines. In the present case, with 16 km sample lines, Eq. (1) simplifies to Yˆ ¼ 0:982 m. We compared results from the air photo interpretation with that from the field survey by calculating the mean and standard error of the estimated edge length, considering the 16 sample lines as independent samples. The accuracy of edge type classification in the air photo interpretation was analysed for matching edges. We evaluated the accuracy of tree height and canopy cover measurements in the air photo interpretation separately for the open habitat and the forest sectors of the 20 m plots. The possible systematic error (bias) was estimated as the mean difference between photo interpreted and field measured variables (height and cover): Bias ¼
n 1X ðˆy yi Þ n i¼1 i
(2)
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n 1X ðˆy yi Þ2 RMSE ¼ n i¼1 i
RMSE% ¼ 100
RMSE y¯
(3)
(4)
where y¯ is the mean (height or cover) of the field data. We did not analyse the accuracy of the height and cover estimates for emergent remnant trees in the open habitat. The few and scattered remnant trees made these estimates very prone to positional errors. 3. Results
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Table 2 Number of observations and estimated length of forest edge in the study area based on line intersect sampling in aerial photographs and in field survey Type of edge
Air photo interpretation
Field survey 1
Observations
Length (m ha )
Clearcut Young forest Agricultural land Wetland Road Other
24 8 12 4 3 2
23.6 7.9 11.8 3.9 2.9 2.0
Total
53
52.0
Observations
Length (m ha1)
45 15 23 8 6 4
20 13 15 4 3 4
19.6 12.8 14.7 3.9 2.9 3.9
34 22 25 7 5 7
100
59
57.9
100
Proportion (%)
Proportion (%)
Data refers to 2002.
3.2. Edge detection
3.3. Edge classification accuracy
Although both the air photo interpretation and the field survey are prone to sampling and measuring errors, we consider the field data as the ‘truth’ with respect to edge detection criteria. Fifty of the forest edge observations were classified as matching, constituting 94 and 85% of all edges recorded in the air photo interpretation and the field survey, respectively (Table 3). This shows that the two methods have a high degree of consistency with respect to detection of forest edges in the same geographical positions. The positional difference of matching observations along the sample lines was 3.4 0.5 m (mean S.E.; absolute values) based on a comparison of GPScoordinates with positions from the photogrammetric system. The difference was 3 m in 68% and 5 m in 80% of the edges—the maximum difference was 13 m (two edges). There were several reasons for lack of agreement in the detection of individual edges between the two methods. Three of the non-matching edges in the air photo interpretation did not fulfil the edge detection criteria in the field survey. In contrast, three edges recorded in the field were not recorded the air photo interpretation as they did not fulfil the detection criteria. Mismatches due to detection criteria were in most cases due to the difficulty in determining tree height in the air photo interpretation. Six edges recorded in the field survey were classified as non-matching due to geographical position. Most of these mismatches were apparently due to GPS errors. It was easier to follow the sample lines during the air photo interpretation than in the field.
The accuracy in classification of type of forest edge in the air photo interpretation was validated with the field data for the 50 matching edges. The overall classification accuracy was high— 88% of the edges were correctly classified (Table 4). The number of observations was too small to permit a detailed accuracy assessment by edge type. However, three edges against young forest and two edges against agricultural land were misclassified as clearcuts. One edge against young forest was misclassified as agricultural land. Classification errors were often due to the difficulty in detecting and measuring the height of small trees. 3.4. Tree height The range in tree height in the forest was smaller in the air photo interpretation than in the field measurements, 10–21 and 10–26 m, respectively. Height measurements made with the photogrammetric system showed a strong, significant relationship (r2 = 0.52, P < 0.0001, n = 50) with the field observations (Fig. 4A). The slope of the regression line was close to 1 and the intercept was 2.3 m. However, the wide scatter of points indicates that the precision of the air photo measurements was moderate. The RMSE was relatively high (3.4 m) corresponding to 21% of mean tree height in the field (RMSE%). Tree height was systematically underestimated in the air photo data compared to the field data, with a mean systematic error of 2.3 m (14%). This was mainly due to the difficulty to locate
Table 3 Agreement between the air photo interpretation and the field survey for line intersect observations of forest edges Air photo interpretation No. of edges Matching observations Non-matching observations Incorrectly detected: did not fulfil sharp edge criteria in field survey Not detected: did not fulfil sharp edge criteria in air photo interpretation Positional mismatch Total observations
Field survey %
No. of edges
%
50
94
50
85
3 – 0
6 – 0
– 3 6
– 5 10
53
100
59
100
Observations were classified as matching (detected at the same position) or non-matching. The data refers to 2002.
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Table 4 Error matrix for classification of edge type in air photo interpretation Air photo interpretation
a
Clearcut Young forestb Agricultural land Wetland Road Other %Correct
Field survey Clearcut
Young forest
Agricultural land
Wetland
Road
Other
%Correct
19 – – – – – 100
3 5 1 – – – 56
2 – 11 – – – 85
– – – 4 – – 100
– – – – 3 – 100
– – – – – 2 100
79 100 92 100 100 100 88
Data from 50 matching edge observations. a Tree height <1.3 m. b Tree height 1.3–5.0 m.
the treetops in conifers, with their sharp tips (cf. Sheng et al., 2003). Tree height was underestimated by more than 1 m in 66% of the cases (the maximum difference was 9 m) and overestimated by more than 1 m in only 4% of the cases. There was a good agreement (1 m difference) between the two methods in 30% of the measurements. The height measurements in open habitat showed a weaker relationship than in the forest (Fig. 4B; data from plots with trees, n = 48). The RMSE was 1.5 m (RMSE%, 95%) and the systematic error was 0.8 m (49%). The high relative errors indicate that the precision was low for tree heights lower than 5 m. Small trees were difficult to measure in the photographs. 3.5. Canopy cover Canopy cover estimated from the aerial photographs showed significant relationships with the field data in both the forest (r2 = 0.52, P < 0.0001) and the open habitat (r2 = 0.62, P < 0.0001; Fig. 5). The wide scatter of points resulted in large RMSE both in the forest and in the open habitat, 11.5% (RMSE%, 19%) and 11.9% (RMSE%, 78%), respectively. The canopy cover estimates were particularly error prone in open
habitat with low cover and small trees (Fig. 5B). The systematic error was positive in the forest, 5.8% (RMSE%, 10%) and negative in the open habitat, 1.6% (RMSE%, 10%). Cover of both coniferous and deciduous trees in the forest was strongly related to the field data, with r2-values of 0.42 and 0.52, respectively. Estimates from the aerial photographs were higher than the field measurements for both groups (Table 5). In the open habitat, the relationships were strong for both total cover and for deciduous tress, with r2-values of 0.40 and 0.56, respectively. In contrast, the air photo interpretation did not succeed in estimating cover of coniferous trees (r2 = 0.00). This was due to the difficulty of identifying small conifers in the aerial photographs. The deciduous trees were often higher than the conifers in the open habitat. 3.6. Time and cost for data collection We recorded the time for data collection during the air photo interpretation and the field survey, not counting time for method development. It took one person 1.5 days to collect all forest edge data for the whole study area (16 km2) with the digital photogrammetric system. The field survey took five days to
Fig. 4. Relationship between tree heights measured in the aerial photograph and in the field: (A) forest habitat and (B) open habitat. Data from sectors of 20 m radius plots for matching edges. The solid line denotes the regression line and the broken line a 1:1 relationship.
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Fig. 5. Relationship between canopy cover measured in the aerial photograph and in the field: (A) forest habitat and (B) open habitat. Data from sectors of 20 m radius plots for matching edges. The solid line denotes the regression line and the broken line a 1:1 relationship.
Table 5 Comparison of total canopy cover (%) and by subgroups (mean S.E.) in air photo interpretation and field survey Canopy cover
Total Coniferous trees Deciduous trees Picea abies Pinus sylvestris Betula spp. Populus tremula Other deciduous trees
Open habitat
Forest habitat
Air photo interpretation
Field survey
Air photo interpretation
Field survey
13.7 2.7 4.0 0.9 9.7 2.5 – – – – –
15.3 2.4 6.3 1.1 9.0 1.6 2.0 0.5 4.2 1.0 7.8 1.6 0.2 0.2 1.0 0.4
65.2 1.6 45.7 3.3 19.5 3.6 – – – – –
59.4 2.0 43.4 2.3 16.0 2.3 23.6 2.6 19.8 2.6 14.7 2.1 0.6 0.3 0.8 0.4
Estimates for open habitat and adjoining forest habitat were made in sectors of 20 m radius plots. N = 50 (matching observations). Table 6 Comparison of estimated time for collecting data on forest edges by air photo interpretation and field survey in this study
interpretation, but it will still be less expensive than the field survey.
Time
Field survey
4. Discussion
5 – 5
2 23 58
4.1. Forest edge quantification
10
83
Preparation/GIS-operations (h) Transportationa (h) Data collection (h) Total time (h)
Air photo interpretation
Data were collected from 16 line transects in a 4 km 4 km area. Time is in man-hours. a To study area and between transects.
complete for a team of two field surveyors. It should be noted, however, that the system of sample lines was not optimised for field surveys. Overall, data collection was eight times faster in the air photo interpretation than in the field survey (Table 6). The cost per man-hour is 70% higher for the air photo interpretation than for the field survey. This is primarily due to higher costs for equipment, software, technical support and higher salary. Therefore, the total cost for collecting forest edge data was five times lower for the air photo interpretation, excluding image costs. Including costs for taking new photographs will reduce the cost-efficiency of the air photo
Detailed quantitative studies of forest edges at the landscape level are still scare despite documented effects on biodiversity (Murcia, 1995; Harper et al., 2005). Our analysis shows that critical data on forest edges can be extracted from infrared colour aerial photographs with almost the same accuracy as in time-consuming field surveys. This suggests that line intersect sampling in combination with plot measurements in aerial photographs has large potential as an efficient tool for estimating both the quantity and characteristics of sharp forest edges in man-modified landscapes. The method has several advantages. First, the high spatial resolution in aerial photographs makes it possible to collect detailed information for both the edge itself and the adjoining habitats. Second, by using a digital photogrammetric system it is possible to determine the position of edges with high precision, to perform measurements in three dimensions and to standardize the
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viewing scale used for edge detection across the entire study area. Third, important boundary attributes, such as tree height, canopy cover and tree species composition of adjacent habitats, can be collected with the method. Patch contrast can be described by the difference in these variables (Strayer et al., 2003; Harper et al., 2005). One limitation is that the method cannot be used for all kinds of forest ecotones. It is best suited for detection of sharp edges, i.e. edges that form a more or less well-defined ‘reference line’ at the detection scale used during data collection. Sharp edges are primarily characterized by high-contrast in tree height and a narrow ecotone, less than ca. 10 m wide. We do not consider this as a severe limitation—sharp edges between stands of different age are very frequent in most fragmented forest ecosystems. Analysis of satellite data is better suited to cover the whole range of forest ecotones that occur in nature (Fortin et al., 2000; Jacquez et al., 2000). However, such edge detection methods are more complex than the present approach. No single edge detection technique can probably address all types of user needs (Fortin et al., 2000). One advantage of our method is that different edge detection criteria may be applied (e.g. Table 1) and that the variables used for construction of these criteria can be measured in the aerial photographs. Applying other criteria will of course affect the accuracy and precision of the edge length estimates. 4.2. Practical aspects of line intersect sampling of edges Line intersect sampling of forest edges is simple to apply both in aerial photographs and in the field. However, some aspects of LIS are important to consider as they affect the accuracy of the estimates (Van Wagner, 1982; Ringvall and Sta˚hl, 1999). The sampling layout must be such that it addresses the problem with non-random orientation of edges. Both Hildebrandt (1973) and Schuerholz (1974) recommended layouts with radial lines for sampling edges in aerial photographs. A practical layout for field surveys is an equilateral triangle (Van Wagner, 1982). Corona et al. (2004) found no difference in the accuracy of estimated edge length between systematic and random line intersect sampling designs. Our sampling layout with lines in two directions (Fig. 1) addresses the problem with non-random orientation. This layout, with 1 km sample line per 1 km 1 km quadrat, was practical to apply in the air photo interpretation. For future application we recommend this layout, or preferably, randomly oriented sample lines. Bias may also be caused by differences among surveyors. Ringvall and Sta˚hl (1999) found that systematic differences between surveyors were not a serious problem in line intersect sampling of coarse woody debris. However, because forest edges are more difficult to define and to locate we expect the differences to be larger. The high proportion of matching edges (Table 3) shows that LIS in aerial photographs is very successful in locating the same edges as in the field survey. The extent of matching edges depends on the positional accuracy of both methods and their ability to the judge whether an edge fulfils edge detection
criteria or not. In practice, it was much easier to follow the sample lines during air photo interpretation than in the field. On the other hand, the edge detection criteria were easier to apply in the field. 4.3. Variable estimates The accuracy of the air photo estimates of tree height and canopy cover is influenced by several potential sources of error. These include positional errors, including both differences in the location of the edge (intersection point) and in the division of the 20 m plot into open habitat and forest sectors, photo interpreter bias and errors in the field data used as reference. It was difficult to separate different error sources. However, positional errors were probably not the most important error source. Eighty percent of matching edges had a positional deviation of less than 5 m. The photogrammetric system had a high geographical accuracy (<2 m) while the GPS showed slightly lower accuracy (<10 m). Measurements of tree height in the digital photogrammetric system showed a strong relationship with field data in the forest but had a poor relation in the open habitat. It is known that tree height can be measured in aerial photographs with nearly the same precision as obtained in field surveys (Sta˚hl, 1992). However, there is a tendency that heights are underestimated in aerial photos. In our study, tree height was underestimated by 2.3 m (14%) in the forest habitat. Lundquist (2005) used the same digital photogrammetric system with black and white photographs to measure mean height of trees at stand level. He compared the results with an analogue instrument (Wild B8) using field data from 10 m radius plots for validation. The RMSE% was only slightly higher in the digital system (12%) than in the analogue instrument (9%). However, the systematic error was markedly higher in the digital system, 1.5 m (9%) and 0.2 m (1%), respectively (Lundquist, 2005). Thus, we strongly recommend using field data from reference plots for calibration and adjustment of height measurements in the digital photogrammetric system. It was more difficult to estimate canopy cover than to measure tree height. Cover estimates in both aerial photographs and in the field are subjected to high errors. The viewing geometry in a stereo model makes it more difficult to view the ground in a forested area in the peripheral part of the stereo model than in the central part. This might lead to biased crown cover estimates. Calibration of interpreters should be applied prior to the air photo interpretation to reduce surveyor bias during operational use of the method. The precision of the height and canopy cover estimates was higher in forest than in open habitat. We conclude that structural variables, such as tree height, canopy cover and tree species composition, can be measured with relatively high precision in the digital photogrammetric system. 4.4. Air photo interpretation compared to field survey One of the main issues in the choice of methods for inventory of forest resources is the effectiveness in terms of
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time and cost. Corona et al. (2004) found that line intersect sampling in aerial photographs was several times more effective than polygon delineation to estimate edge length. The most efficient sampling designs took 4–14% of the time compared to complete delineation of polygons. We found that line intersect sampling in aerial photographs was eight times faster, and five times more cost-effective, than field sampling. This suggests that considerable cost savings can be achieved by using the method. Interpretation of aerial photographs has the potential to provide critical information of many additional attributes of forest edges, including several of the boundary attributes of Strayer et al. (2003), as well as data on local or regional context variables (Harper et al., 2005). For example, aerial photographs can be used to collect data on edge orientation, edge sinuosity, edge profile, presence of linear objects near the edge, etc. Important context variables include the size, shape, direction, local topography and vegetation structure of the open habitat, which affect the depth and strength of edge influence. Field surveys are better suited to collect detailed data on edge origin, edge age, disturbance and forest attributes (e.g. tree species, stem density, diameter, vertical and horizontal structure). 5. Conclusion There is a need for simple, accurate and efficient methods to collect data on forest edges to understand their role in forest ecosystems and for management purposes. Our study shows that line intersect sampling in infrared colour aerial photographs, in combination with plots, is a robust and efficient method for estimating both the quantity and characteristics of sharp forest edges in managed forest ecosystems. The main advantages of the method are the accuracy in edge detection, variable estimates and overall speed. The method has several potential applications. It is particularly suitable for large-scale comparisons of the amount of forest edge and key edge variables in fragmented landscapes. The method can also be used in retrospective studies of long-term changes in the amount and type of edges using older photographs. Aerial photographs are also suitable to collect data on many local and regional context variables that are important in determining edge influence on biodiversity. The method should be tested in other types of forest ecosystems to give a more general picture of its usefulness. Future studies should also address differences among surveyors and the measurement of additional structural and context variables. We conclude that the method represents a promising tool for application in research on forest edges and their effects on biodiversity as well as in monitoring and forest management. Acknowledgements This study was supported by a grant from the Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (Formas). We thank Per Lo¨fgren for help with the fieldwork, Bjo¨rn Nilsson, Karin Pramborg and Anna Allard for technical advice during air photo interpretation and Mats
41
Ho¨gstro¨m for help with ArcGIS and data handling. Thanks are also due to So¨ren Holm and Go¨ran Sta˚hl for statistical advice. At last, we thank two anonymous reviewers for constructive comments. References Battles, J.J., Dushoff, J.G., Fahey, T.J., 1996. Line intersect sampling of forest canopy gaps. For. Sci. 42, 131–138. Bra¨ndli, U.-B., 2001. Nature protection function. In: Brassel, P., Lischke, H. (Eds.), Swiss National Forest Inventory: Methods and Models of the Second Assessment. Swiss Federal Research Institute WSL, Birmensdorf, pp. 265– 282. Cadenasso, M.L., Pickett, S.T.A., Weathers, K.C., Jones, C.G., 2003. A framework for a theory of ecological boundaries. Bioscience 53, 750–758. Corona, P., Chirici, G., Travaglini, D., 2004. Forest ecotone survey by line intersect sampling. Can. J. For. Res. 34, 1776–1783. Csillag, F., Boots, B., Fortin, M.-J., Lowell, K., Potvin, F., 2001. Multiscale characterization of boundaries and landscape ecological patterns. Geomatica 55, 291–307. Curry, K., 2003. Using Stereo Analyst for ArcGIS. Leica Geosystems GIS & Mapping, LLC, Atlanta, GA. de Vries, P.G., 1979. Line intersect sampling—statistical theory, applications, and suggestions for extended use in ecological inventory. In: Cormack, R.M., Patil, G.P., Robson, D.S. (Eds.), Sampling Biological Populations. International Co-operative Publishing House, Fairland, MD, pp. 1–70. de Vries, P.G., 1986. Sampling Theory for Forest Inventory. Springer-Verlag, Berlin, Germany. Didham, R.K., Lawton, J.H., 1999. Edge structure determines the magnitude of changes in microclimate and vegetation structure in tropical forest fragments. Biotropica 31, 17–30. Esseen, P.-A., Renhorn, K.-E., 1998. Edge effects on an epiphytic lichen in fragmented forests. Cons. Biol. 12, 1307–1317. Esseen, P.-A., Glimska¨r, A., Sta˚hl, G., Sundquist, S., 2004. Fa¨ltinstruktion fo¨r Nationell Inventering av Landskapet i Sverige, NILS. Department of Forest Resource Management and Geomatics, Swedish University of Agricultural Sciences, Umea˚, Sweden. Fagan, W.F., Fortin, M.-J., Soykan, C., 2003. Integrating edge detection and dynamic modelling in quantitative analyses of ecological boundaries. Bioscience 53, 730–738. Fortin, M.-J., 1994. Edge detection algorithms for two-dimensional ecological data. Ecology 75, 956–965. Fortin, M.-J., Drapeau, P., 1995. Delineation of ecological boundaries: comparison of approaches and significance tests. Oikos 72, 323–332. Fortin, M.-J., Edwards, G., 2001. Delineation and analysis of vegetation boundaries. In: Hunsaker, C.T., Goodchild, M.F., Friedl, M.A., Case, T.J. (Eds.), Spatial Uncertainty in Ecology. Implications for Remote Sensing and GIS Applications. Springer, New York, pp. 158–174. Fortin, M.-J., Olson, R.J., Ferson, S., Iverson, L., Hunsaker, C., Edwards, G., Levine, D., Butera, K., Klemas, V., 2000. Issues related to the detection of boundaries. Landsc. Ecol. 15, 453–466. Franklin, S.E., 2001. Remote Sensing for Sustainable Forest Management. Lewis, Boca Raton, FL. Gascon, C., Williamson, G.B., da Fonseca, G.A.B., 2000. Receding forest edges and vanishing reserves. Science 288, 1356–1358. Gosz, J.R., 1993. Ecotone hierarchies. Ecol. Appl. 3, 369–376. Hall, R.J., 2003. The roles of aerial photographs in forestry remote sensing image analysis. In: Wulder, M.A., Franklin, S.E. (Eds.), Remote Sensing of Forest Environments. Concepts and Case Studies. Kluwer Academic Publishers, Norwell, MA, pp. 47–75. Harper, K.A., Lesieur, D., Bergeron, Y., Drapeau, P., 2004. Forest structure and composition at young fire and cut edges in black spruce boreal forest. Can. J. For. Res. 34, 289–302. Harper, K.A., Macdonald, S.E., Burton, P.J., Chen, J., Brosofske, K.D., Saunders, S.C., Euskirchen, E.S., Roberts, D., Jaiteh, M.S., Esseen, P.-A., 2005. Edge influence on forest structure and composition in fragmented landscapes. Cons. Biol. 19, 768–782.
42
P.-A. Esseen et al. / Forest Ecology and Management 230 (2006) 32–42
Hildebrandt, G., 1973. Die Verwendung von Luftbild-Linienstichproben zur Ermittlung der La¨nge Linienfo¨rmiger Gela¨ndeobjekte. In: Proceedings of the Symposium on IUFRO Subject Group 5.6.05, Freiburg, Germany, pp. 267–284. Holmgren, P., Thuresson, T., Holm, S., 1997. Estimating forest characteristics in scanned aerial photographs with respect to requirements for economic and forest management planning. Scand. J. For. Res. 12, 189–199. Jacquez, G.M., Maruca, S., Fortin, M.-J., 2000. From fields to objects: a review of geographic boundary analysis. J. Geogr. Syst. 2, 221–241. Johnston, C.A., Pastor, J., Pinay, G., 1992. Quantitative methods for studying landscape boundaries. In: Hansen, A.J., di Castri, F. (Eds.), Landscape Boundaries, Consequences for Biotic Diversity and Ecological Flows. Springer-Verlag, New York, pp. 107–125. Kaiser, L., 1983. Unbiased estimation in line-intercept sampling. Biometrics 39, 965–976. Lillesand, T.M., Kiefer, R.W., 2000. Remote Sensing and Image Interpretation, 4th ed. Wiley, New York. Lindenmayer, D.B., Franklin, J.F., 2002. Conserving Forest Biodiversity. A Comprehensive Multiscaled Approach. Island Press, WA. Lundquist, F., 2005. Digitalt stereoinstrument som hja¨lpmedel vid skogsuppskattning—en ja¨mfo¨relse med analoga instrument. Department of Forest Resource Management and Geomatics, Swedish University of Agricultural Sciences, Umea˚, Sweden, Working Report. Mate´rn, B., 1964. A method for estimating the total length of roads by means of a line survey. Stud. For. Suec. 185, 68–70. Matlack, G.R., Litvaitis, J.A., 1999. Forest edges. In: Hunter, M.L. (Ed.), Maintaining Biodiversity in Forest Ecosystems. Cambridge University Press, Cambridge, UK, pp. 210–233. Metzger, J.P., Muller, E., 1996. Characterizing the complexity of landscape boundaries by remote sensing. Landsc. Ecol. 11, 65–77.
Murcia, C., 1995. Edge effects in fragmented forests: implications for conservation. Trends Ecol. Evol. 10, 58–62. Næsset, E., 1998. Positional accuracy of boundaries between clearcuts and mature stands delineated by means of aerial photointerpretation. Can. J. For. Res. 28, 368–374. Ringvall, A., Sta˚hl, G., 1999. Field aspects of line intersect sampling for assessing coarse woody debris. For. Ecol. Manage. 119, 163–170. Saunders, D.A., Hobbs, R.J., Margules, C.R., 1991. Biological consequences of ecosystem fragmentation: a review. Cons. Biol. 5, 8–32. Schuerholz, G., 1974. Quantitative evaluation of edge from aerial photographs. J. Wildl. Manage. 38, 913–920. Sheng, Y., Gong, P., Biging, G.S., 2003. Model-based conifer canopy surface reconstruction from photographic imagery: overcoming the occlusion, foreshortening, and edge effects. Photogr. Eng. Rem. Sens. 69, 249–258. Shiver, B.D., Borders, B.E., 1996. Sampling Techniques for Forest Resource Inventory. John Wiley and Sons, New York. Sta˚hl, G. 1992. En studie av kvalitet i skogliga avdelningsdata som insamlats med subjektiva inventeringsmetoder. Department of Biometry and Forest Management Planning, Swedish University of Agricultural Sciences, Umea˚, Sweden, Report 24. Strayer, D.L., Power, M.E., Fagan, W.F., Pickett, S.T.A., 2003. A classification of ecological boundaries. Bioscience 53, 723–729. Van Wagner, C.E., 1982. Practical aspects of the line intersect method. Report PI-X-12. Petawawa National Forestry Institute, Canadian Forest Service, Chalk River, Ont., Canada. Warren, W.G., Olsen, P.F., 1964. A line intersect technique for assessing logging waste. For. Sci. 14, 20–26. Zheng, D., Chen, J., 2000. Edge effects in fragmented landscapes: a generic model for delineating area of edge influences (D-AEI). Ecol. Model. 132, 75–190.