Narrow-linear and small-area forest disturbance detection and mapping from high spatial resolution...

Narrow-linear and small-area forest disturbance detection and mapping from high spatial resolution

imagery

Yuhong He,a Steven E. Franklin,b Xulin Guo,c Gordon B. Stenhouse d a University of Toronto Mississauga, Department of Geography, 3359 Mississauga Road

North, Mississauga, ON L5L 1C6, CA [email protected]

b Trent University, 1600 West Bank Drive, Peterborough, Ontario K9J 7B8 [email protected]

c University of Saskatchewan, Department of Geography and Planning, 117 Science Place, Saskatoon, SK S7N 5C8, Canada

[email protected] d Alberta Sustainable Resource Development, Fish and Wildlife Division, Box 6330,

Hinton, AB T7V 1X6, Canada [email protected]

Abstract: Widespread disturbance has brought a large amount of narrow-linear and small-area disturbance features (e.g., trails, seismic lines, forest roads, well sites, and cut blocks) to forest areas throughout the past decade. This issue has prompted research into finding the appropriate data and methods for mapping these narrow-linear and small-area disturbance features in order to examine their impacts on wildlife habitat. In this paper, we first described the characteristics of small forest disturbances and presented the nature of problem. We then presented a framework for detecting and extracting narrow-linear and small-area forest disturbance features. Using a SPOT 5 high spatial detail image and existing GIS databases, we applied the framework to map narrow-linear and small-area forest disturbance features in a Bear Management area (BMA) in the eastern slopes of the Rocky Mountains in Alberta, Canada. The results indicated that the proposed framework produced accurate disturbance maps for cut blocks, and forest roads & trails. The high errors of omission in the cut lines map were attributed to inconsistent geometric and radiometric patterns in the ‘rarely-used’ or ‘old’ cut lines. The study confirmed the feasibility of rapidly updating incomplete GIS data with linear and small-area disturbance features extracted from high spatial detail SPOT imagery. Future work will be directed towards improvement of the framework and the extraction strategy to remove a large amount of spurious features and to increase accuracy for cut lines mapping.

Keywords: small forest disturbance, feature detection, feature extraction and labeling, validation

1. INTRODUCTION Developments associated with oil and gas infrastructure, open-pit coal mining, forest management, and recreational facilities have expanded rapidly in the natural forest areas around the world [1,2]. As a consequence of increasing developments in the forest areas, the narrow-linear and small-area disturbances (e.g., ATV trails, seismic lines, pipelines/power lines, well sites, forest roads and cut blocks) increased dramatically in order to explore, access and haul resources [2]. Depending on the severity of the small-scale disturbance process and sensitivity of the ecosystems affected, native plants,

Journal of Applied Remote Sensing, Vol. 3, 033570 (11 December 2009)

© 2009 Society of Photo-Optical Instrumentation Engineers [DOI: 10.1117/1.3283905]Received 5 Jun 2009; accepted 7 Dec 2009; published 11 Dec 2009 [CCC: 19313195/2009/$25.00]Journal of Applied Remote Sensing, Vol. 3, 033570 (2009)

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wildlife, and many natural ecosystem processes are compromised or fundamentally altered [3]. For many species, migration between suitable habitat patches becomes more difficult, leading to smaller population sizes or even possible local extinctions [4].

Reliable and up-to-date disturbance maps that show all scales of forest disturbance are essential to fully understand habitat quality and ensure that adequate consideration is given to the effects of the disturbance on native species. These maps are also required for a wide variety of forest planning, management, and modeling activities. Large-area forest disturbances, such as forest fires, larger cut blocks, and roads, have been mapped by coarse spatial resolution imagery (e.g. AVHRR or MODIS) at a global scale and are delineated by medium spatial resolution (20 - 30 m) satellite images (Landsat, SPOT 4, or ASTER) at a regional scale [5-11]. However, the narrow-linear and small-area disturbance features (e.g., ATV trails, seismic cut lines, and well sites) are too small to be reliably detected and mapped with coarse and medium spatial resolution imagery.

High spatial resolution data are more appropriate for investigating narrow-linear and small-area forest disturbances that require a greater level of spatial detail. In recent years, the increasing availability of high spatial-resolution imagery from several satellite platforms with resolutions better than 10 meters provides greater opportunities for this application. The optimal satellite sensors include SPOT 5, Ikonos, Quickbird and Orbview-3. A substantive body of literature has explored the feasibility of the high resolution images for identifying subtle features, both in linear (e.g. road) and polygonal (e.g. building) shapes [12-15].

A wide variety of techniques and strategies have been developed to process high resolution remote sensing images for linear and polygonal feature extraction [16-18]. In general, those studies covered a broad range of feature detection and mapping methods. The examples of feature extraction methods include edge and line filtering [14], gradient direction profile analysis (GDPA) [19], dynamic programming and least squares B-spline LSB-snakes [15], Self-Organizing Road Map (SORM) [20], multi-resolution modeling [21], knowledge-based algorithms [12,22-24], and back-propagation neural networks [25].

Within the rich body of literature regarding feature extraction, little research has studied the use of high resolution images and advanced image processing methods for mapping small forest disturbance features [26,27]. A successful study [27] dealing with the problem of retrieving small linear features (i.e. transborder trails) from forested areas demonstrated that it is possible to extract narrow-linear forest features based on high spatial resolution images. However, the authors also indicate that it is necessary to test the existing image processing methods and to explore new techniques in order to extract information from high spatial resolution imagery accurately and efficiently. Indeed, forest disturbance features usually have inconsistent geometric and radiometric patterns across space; it is thus a challenge to infer from existing studies which image processing methods have the advantage in mapping narrow-linear and small-area forest disturbances over other methods.

Accordingly, we first developed a framework that is able to map narrow-linear and small-area forest disturbance features. We then examined the proposed framework using existing GIS layers and satellite data from the foothills region of Alberta, Canada. The foothills region is of interest because the natural forest in this region has been inherently modified by considerable human activities (i.e. mining, seismic oil and gas exploration and production, forest harvesting activities, and recreational activities) [2]. The full ecological costs of these impacts have not been fully evaluated. Using appropriate high spatial resolution remote sensing data and image processing methods to map these disturbance features would not only be helpful for wildlife habitat research,

Journal of Applied Remote Sensing, Vol. 3, 033570 (2009)


but also to large mapping projects, which need current and accurate land cover information from every area of Canadian forestry [28].

Small forest disturbance is a worldwide issue and this study provides comprehensive information which can be applied to human-disturbed forest ecosystems to aid forest management activities. The paper is organized as follows. The background information pertinent to small forest features is discussed first; then the nature of the problems related to image data and feature extraction methods are outlined. Thirdly, a framework for detection and mapping of narrow-linear and small-area features are presented and the potential remote sensing data and methods are gathered and examined. Fourth, the experiment results for the framework are discussed and finally the conclusion and potential directions for future research priorities are provided.

2. BACKGROUND INFORMATION FOR MAPPING FOREST DISTURBANCE FEATURES

2.1 Study area The study is conducted in a Bear Management Area (BMA) of Foothills Model Forest, located on the eastern slopes of the Rocky Mountains within the foothills of Alberta, Canada (Fig. 1). This area represents prime grizzly bear habitat but is also an area of significant human use and change due to forestry practices (e.g., harvesting, plantations), and oil and gas exploration and development. The dominant disturbance features include ATV trails, seismic cut lines, cut blocks, and forest roads (Fig. 2). This boreal forest environment contains pure and mixed forest stands of white spruce (Picea glauca), lodgepole pine (Pinus contorta), trembling aspen (Populus tremuloides), and considerable areas of herbaceous meadows, woody shrubs, and treed and non-treed wetlands.

Fig. 1. Location of the study area in Alberta and imagery footprints.



Fig. 2. Pictures of some disturbance features (A. ATV trail, B. pipeline, C.

Seismic cut line, D. Cut block, E. Forest road) taken in the foothills region of Alberta.

2.2 Characteristics of small forest disturbances In order to appropriately detect and extract small-scale forest disturbance features from remote sensing data, we grouped small forest disturbance features into two types: narrow-linear disturbances (forest roads, trails, and seismic cut lines) and small-area disturbances (well sites and cut blocks) (Fig. 3). The list of two types of small forest disturbance features was not exhaustive, but covered the major disturbance features prevalent in the forest areas.

Fig. 3. The image is a subset of a SPOT 5 scene (10-m resolution) in a false

color composite (RGB: NIR, red, and green bands) acquired from the foothills region of Canada in July 21, 2005. The red color in this image indicates high

vegetation cover. The yellow texts and narrow signs in the image indicate some of the narrow-linear and small-area disturbance features.

The characteristics of selected disturbance features can be described from three

aspects - spectral characteristics, geometric characteristics, and topological properties (Table 1). To differentiate the linear features from each other in the remote sensing imagery, the unique characteristic for each feature should be considered. For example, a forest road is relatively wide and has different spectral characteristics (gravel covered) from other linear features; a trail is narrower in comparison with forest roads and sinuous in comparison with other straight linear features; a power/pipe line has a smooth



surface which is covered by grass; and a seismic cut line has a heterogeneous surface and is very straight. As for small-area disturbance features (Table 1), a cut block is characterized by an unnatural polygonal shape, lack of canopy (in the case of fresh clear cuts), or even-aged re-growth species of similar height and structure (in regenerating areas) [28]. Different from the cut blocks, a well site has regular polygon shapes with fixed size, a uniform surface, and is built using materials such as concrete. Consequently, the well sites should have a spectral and geometric pattern different from those of cut blocks (Fig. 3).

Table 1. Characteristics of narrow-linear and small-area disturbance features.

Disturbance features

Spectral characteristics Geometric characteristics Topological property Surface Top Length Curvature Width

Forest road Firm and smooth surface, gravel covered Open Long

Straight with local curvature

Constant ~20-30m Road

networks Trail

Heterogeneous surface, gravel and grass interlaced

Not always open

Short Sinuous Constant ~10-20m

Seismic cut line

Heterogeneous surface, gravel and grass interlaced

open Long Very straight

Constant ~5-10 m

No

Cut block

Heterogeneous surface including mounding, exposed soil, and plough furrows

Open Unnatural polygonal shape ATV road networks

Well sites Smooth surface made from Concrete Open Regular polygonal shape Forest road

networks

2.3 The nature of the problem The increased narrow-linear and small-area disturbance rapidly modified landscape metrics, and is thus affecting habitat use and habitat selection for wildlife species [2]. There might have been some existing GIS disturbance data derived by government, industries and academic researchers for different forest areas, however, the existing GIS database has certain deficiencies, such as inaccuracy, being incomplete, or out of date.

The requirement at hand is therefore to map the disturbed elements. We have stated in section 2.1 that the small forest disturbance features should be able to be identified from high resolution remote sensing imagery theoretically because of their specific characteristics. However, the extraction of disturbance features from remote sensing imagery could be more challenging than previously thought when two key questions are taken into account: 1) Are there data available to map and verify small forest disturbances, and 2) Are there standard methods for mapping and verifying small forest disturbances? Regarding the available remote sensing data, spatial resolution is just one of many variables that influence the extraction of disturbance features using remotely sensed imagery. Atmospheric and illumination conditions, geographical and temporal coverage, and the cost of the image should all be considered when assessing the suitability of an image for a particular investigation. On the subject of mapping methods, there is a large variety available for linear or small-area feature extraction from high spatial resolution imagery, but most of them are specific to roads, buildings, or other small features [29-33]. Therefore, there is a need to review and test the available remote



sensing data and image processing methods, and to decide what is suitable for forest disturbance feature extraction from the high spatial resolution satellite data.

3 A FRAMEWORK FOR SMALL FOREST FEATURE EXTRACTION After reviewing related literature a conceptual framework (Fig. 4) was created for mapping small forest disturbance features. The process framework includes three stages: selecting the input data, processing the data, and producing the output maps. A comprehensive comparison of the data and methods involved in these stages is provided below.

Fig. 4. The process framework for the detection and mapping of narrow-linear

and small-area forest disturbance features.

3.1 Possible and selected input data The input data include images, GIS data, and field data. Table 2 listed the available optical imagery that could be considered in small-scale feature extraction (table 2). It should be noted, however, that some other image sources (such as radar imagery) may be used effectively in small forest disturbance mapping. For example, some authors have reported the use of Radar data for extracting small features, while others utilized LiDAR data [34-36]. We did not consider radar and LiDAR data in this paper since their images are quite different than those acquired from optical instruments, as well most of the available extraction techniques are not suitable to be directly applied to Radar and LiDAR data.

Considering the size of selected disturbance features, remote sensing images with resolutions better than 10 m are of interest. The swath width of the image is also a concern when studying a relatively large geographical area. For instance, an image with a footprint greater than 20×20 km2 is required in order to reduce the work load when mapping area is at landscape level or above. Consequently, a SPOT 5 10-m multispectral panchromatic image with level 1A preprocessing was acquired on 21 July 2005 for the study area (Fig. 1). The SPOT multispectral scene covering approximately 60 km by 60 km includes three VNIR bands and one SWIR band. The satellite image was geometrically and radiometrically corrected using PCI Geomatica 10. An accuracy of 0.3 RMS or better (representing approximately 6 meters or less error on the earth’s surface) was ensured in the geometric correction process based on 30 ground control points. Topography distortions were corrected using a Digital Elevation Model (DEM). Atmospheric and radiometric corrections were completed using the ATCOR-2 algorithm



in PCI Geomatica 10. ATCOR-2 [49] uses a sensor-specific atmospheric database of look-up tables containing the results of pre-calculated radiative transfer calculations to remove the effects of the atmosphere from the spectral values of the data, as well as correcting the influences of solar illumination and sensor viewing geometry.

The reference data that has been used were comprised of major forest roads and trails, cut blocks, and cut lines. The forest roads and trails were considered one feature in the reference data; therefore, we also combined forest roads and trails as one feature in this analysis. The time period for the reference data was from 1995 to 2005.

Table 2 Technical specifications of high resolution satellite sensors

Satellite sensor

Spectral bands (nm)

Spatial resolution (m)

Swath width (km) (at nadir)

Average revisiting time

Price per km2 (USD)

IKONOS

445-516 506-595 632-698 757-853 Pan: 450-900

3.2 3.2 3.2 3.2 0.81

11 2-3 days ~ 18

QuickBird

450-520 520-600 630-690 760-900 Pan: 450-900

2.44 2.44 2.44 2.44 0.61

16.5 1-3.5 days ~ 24

OrbView-3

450-520 520-600 625-695 760-900 Pan: 450-900

4 4 4 4 1

8 < 3 days ~ 20

SPOT 5

480-710 500-590 610-680 1580-1750 Pan: 480-710

10 10 10 20 2.5,5

60 5 days ~ 1.2

3.2 Processing The processing stage involved four steps (Fig. 4): detection, extraction, labeling, and validation. Feature detection is the first essential step to enhance features of interest. Extraction is to remove each detected image patch by a description vector, which will then be used in a supervised classification scheme for the labeling step. Labeling is to mark the attribute of a class to the extracted object.

3.2.1 Potential and proposed feature detection methods Hundreds of feature detection techniques are present in the literature, but we focused on the techniques for detecting two major feature characteristics from high spatial resolution imagery, that is, spectral and geometric. From a spectral perspective, the primary challenge in detecting features from satellite images is maximization of the



feature-to-background ration. The vegetation index approach is the most useful solution to this challenge. For example, one commonly used vegetation index (Normalized Difference Vegetation Index - NDVI) tends to express positive values (say 0.3 to 0.8) when an area contains a dense vegetation canopy, while it will generate rather small positive NDVI values (say 0.1 to 0.2) when an area is covered by a large amounts of soils or concrete. Therefore, using NDVI, it is possible to separate unvegetated forest roads, trails, well sites, and cut blocks from vegetated area. In recent years, the performances for other vegetation indices regarding feature detection have been approved. For instance, Wilson and Sader [37] found Normalized Difference Moisture Index (NDMI) is sensitive to cut block areas (both cleared and partial). The alternative popular approach for fast discrimination of spectral properties between the proposed features and background information is principal component analysis (PCA), which assumes that the set of pixels in the data base is a random vector and PCA is performed in order to obtain a set of eigenvectors which are used as a base for decomposition [38].

Regarding the geometric properties, narrow-linear and small-area features can be identified through the edge detection approach, by locating points of abrupt changes. A large variety of algorithms are available in the literature for edge finding, and the essential edge detector can be viewed as two groups: differentiation and low-pass filtering [39]. One of the greatest problems related with differentiation methods is their low capacity to reject high frequency noise. In the case of a noise free image, the edges can be measured accurately by the differentiation detector, but in forest areas, noise in the image cannot be avoided, making it difficult to estimate the true edge. To solve this problem, some researchers developed edge detectors (e.g. Canny edge detector) using a combination of linear low-pass filtering followed by differentiation [40].

In order to determine the suitable detection methods to extract different disturbance features, a small portion of a SPOT scene was used to examine the performance of each detection method mentioned above. We found each method has its own advantage over others. For example, NDMI had the advantage over other methods on identifying the differences between cut blocks and showing the forest trials within cut blocks. NDVI map had the strongest contrast between major disturbance objects and background information. The first principle component clearly showed the cut lines while other methods could not identify them. The Canny edge detection could result in different degrees of edge details.

In conclusion, no single method can be considered useful for all disturbance features, nor are all methods equally useful for a particular type of disturbance. Consequently, we proposed a hybrid detector which combined vegetation indices, PCA, and the Canny edge detector together to identify the forest disturbance features. Ideally, the major disturbance features could be identified by applying Canny edge detecting to the NDVI map. The forest trails could be detected by combining Canny edge detector with NDMI. The seismic cut lines should be identifiable by applying decision rules to the resultant map after performing Canny edge detector and PCA.

3.2.2 Potential and proposed feature extraction and labeling methods After the feature detection step, feature extraction algorithms can be applied to every possible position of an analyzing window within the image. Feature extraction strategies are often categorized according to the degree of automation required, i.e., automated or semi-automated. An automated extraction approach is intended to extract features from a scene without the need of sustained interactive operations from a human [41]. Many authors have studied the automated extraction of features such as roads, railroads, runways, and river boundaries [12,15,23,33,42,43]. One of the possible problems with automatically extracting small forest disturbances is that both relevant (e.g. forest road,



trials, cut lines, and cut blocks) and irrelevant features (e.g. river boundaries) will be extracted automatically. Different from the automated approach, the semi-automated method uses interactive user-provided information typically in the form of length, widths, and topological information, therefore allowing manual editing of the extraction results [44].

Regardless the advantage and disadvantage of automated and semi-automated approaches, the performance time and energy savings should also be taken into account when performing the extraction techniques. Many authors state that their feature extraction methods provide significant benefits in terms of saving the operator time and effort, however, the tools in the markets commonly require a substantial waiting time while training, especially when using high resolution images for a relative large area. Currently, two commercial tools (e.g. FeatureObjeX™ (http://www.pci.on.ca/products/FeatureObjeX.html) and Feature Analyst™ (http://www.vls-inc.com)) claimed to be able to minimize this problem by using the fast makeup tools. In order to find the suitable tool for the present study, we used both tools to extract the disturbance features for the test image. The features were extracted based on the extraction algorithms – FeatureObjeX™ uses a naïve Bayes algorithm (http://www.geospaceinc.com/featureobjex.htm) and Feature Analyst™ uses Genetic ensemble feature selection [45] – from imagery.

In general, the performing time of FeatureObjecX™ is less than that of Feature Analyst™ for the portion of the test image. However, FeatureObjeX™ could not extract minor objects cut lines, while Feature Analyst™ partially extracted cut lines. In addition, FeatureObjeX™ extracted all disturbance features together and failed to separate the cut blocks from the forest roads. This might be due to the fact that FeatureObjeX™ focuses more on spectral patterns of the objects. The Feature Analysis™ could extract multiple disturbance features in a single output layer by considering both spectral and geometric information using the multi-class extraction function. The boundary of cut blocks and the holes in cut blocks were also more precisely mapped using Feature Analysis™.

From this context, we proposed to use Feature Analyst™ in this study. Specifically, the feature extraction was achieved by interactively selecting a small number of representative features (i.e., training features) from multiple layers composed of image bands and resultant maps from the detection step. Then a model that describes the general characteristics of the feature of interest (such as color, texture, and other characteristics) was established by the tool automatically. In order to incorporate spatial information into the extraction process, a contextual classifier that can be adjusted based on the feature was used. The features were then extracted based on the extraction algorithms from imagery. For each produced map, post refinement was conducted by selecting the examples of "correct," "incorrect," and "missed” areas, which were then used by Feature Analyst™ to produce a more refined output.

3.2.3 Validation approaches The accuracy statistics (errors of commission, and errors of omission) were adopted

to evaluate the accuracy of both linear and polygonal feature extraction work in this analysis. It is assumed that only features within a buffer of a certain width (usually the average width of the features) around the feature, for example two SPOT-5 multispectral pixels on both sides of forest road, were correct. The errors of commission and errors of omission are described as:

• Errors of omission are the percentage of the reference feature data which is not explained by the extracted feature data. The optimum value for omission is 0.

referenceofareaLengthreferencematchedofareaLengthOmissionofErrors

//1−= (1)



• Errors of Commission represent the percentage of incorrectly extracted feature data. The optimum value for commission is 0.

extractionofareaLengthextractionmatchedofareaLengthCommissionofErros

//1−= (2)

As for the polygonal features (i.e. cut blocks), landscape metrics were also used to evaluate the degree of overlap between extracted and reference polygonal data. Our focus was on analyzing the class level metrics, which are integrated over all the patches for a given feature. Four class-level metrics including class area, number of patches, mean patch size, and area-weighted mean patch fractal dimensions were suggested for calculation in this analysis. The four metrics are recommended because 1) size of a patch is an important characteristic of the landscape while analyzing community structure [46]; 2) the smaller the fragmented blocks, the more the density of a population decreases, and the risk of species extinction grows [47]. Consequently, if mean patch size is analyzed in conjunction with other metrics such as the number of patches, it can be used to measure fragmentation; 3) mean patch fractal dimension has been used as a measure of the degree of human disturbance on the landscape. The premise is that boundaries of natural land cover types often have more complex shapes than those cover types that are a result of human activity (e.g., agricultural fields) [48].

4. RESULTS

4.1 Feature detection Results The disturbance features that were detected in the image were 1) forest roads and trails, 2) cut blocks, and 3) seismic cut lines. First, feature detection techniques (NDVI, NDMI, and PCA) were applied to SPOT 5 multispectral scene and then Canny edge detectors were applied to the image to enhance disturbance features.

A A1

B1B

C1C

Fig. 4. The feature detection results - NDVI (A), NDMI (B) and PCA (C). The images in the right column showed a small subset of the resultant maps in the left column. The large features are easily detected in NDVI and NDMI maps.

Both large and small edges can be identified by PCA.



The efficiency of the proposed spectral-based feature detection methods was shown

in fig.s 4 and 5. The NDVI and NDMI maps showed a strong contrast between cut blocks & forest roads and their surroundings, while the PCA resulting map enhanced cut lines (Fig. 4). The canny edge detector successfully extracted edges in the different layers and combined layers. Fig. 5 A1 showed a portion of edges detected from an NDVI image. The boundaries of the cut blocks and forest roads were detected clearly. However, some light grey lines in Fig. 5 A1 indicate potential problematic areas, i.e. areas with small vegetation patches within the cut blocks or with river bands. Fig. 5 B1 and C1 showed the portions of edges detected from an NDMI image and PCA image, respectively. More edges were detected from these two layers than that from NDVI. However, these detected edge layers were very noisy and the contrast of feature edges was relatively ambiguous in comparison with that from NDVI. Fig. 5 D and D1 showed the entire detected edges and a portion of the detected edge, which was detected from SPOT image layers and enhanced layers (i.e. NDVI, NDMI, PCA layers). It is apparent that this last detected layer has more prominent feature edges, thus facilitating the subsequent extraction. In summary, the selected detection process should be a necessary step to enhancing raw images and thus reducing the spurious and missing features in the final map.

A A1

C1C

D1D

B1B

Fig. 5. The Canny edge detector results calculated from NDVI layer (A),

NDMI layer (B), PCA layer (C), and from raw image bands and all maps (D). The images in the right column showed a small subset of the resultant maps in the left column. The large feature edges were identified in NDVI map (A1).

The large feature and partially small feature edges were detected in NDVI and PCA maps (B and C). Both large and small edges can be identified when

considering all layers together.



4.2 Feature extraction and labeling Results The feature extraction and labeling methods were then applied to the detection maps using the Feature AnalystTM tool. For different features, the parameters used in the Feature AnalystTM tool were different, basically defined by feature characteristics discussed in the table 1. For example, the “bull’s-eye” was used for extracting forest roads and trials since it has been demonstrated to work well for extracting linear features (http://www.vls-inc.com). Fig. 6 showed sample output - a portion of raw image (A), extracted cut blocks (B), and existing reference data (C). The color of cut blocks ranging from light blue to bright red in Fig. 6 A indicated the complexity of this feature. The extracted cut blocks in Fig. 6 B illustrated how this framework could efficiently bear differences within one class. However, some confusion can be seen where a river is cutting through the bottom of image from west to east.

The linear features maps (cut lines and forest roads & trails) for the selected area are displayed in Fig. 7 B and C. The according reference maps are shown in Fig. 7 B1 and C1. As can be seen, the cut lines in the south-west of the selected area have been extracted particularly well. However, the cut lines in the north-west corner were not extracted since they entered the natural forest and were covered by vegetation. The major forest roads and trails in the image were extracted well, although some confusion can be seen where the forest roads and trails were within cut blocks.

Fig. 6. The cut block extraction results (A. standard false color composite:

RGB-321, B. extracted cut blocks, and C. reference data.)

A

B

C



Fig. 7. The linear feature extraction results (A. standard false color composite: RGB-321, and A1. The Canny edge detector resultant map, B and B1.

Extracted cut lines and existing reference data, C and C1: extracted road and trails and existing reference data.

4.3 Validation Results To evaluate the results, we calculated omission and commission (for both linear and polygonal features) and landscape metrics (only for polygonal features). The buffer width in this study was determined to be one SPOT-5 multispectral pixel (i.e. 10 meter). The extracted features, which were inside the buffer of the given reference features, were considered successful matches. Only the area with relatively complete reference data was used for the evaluation, as the reference data is not complete over the study area. The area with complete reference data was determined by visual interpretation.

Table 3. The evaluation results for cut blocks.

Landscape Metrics & Accuracy Assessment

Selected area for comparison (Reference-Extracted)/Reference (%) Reference Extracted

Omission 6 % N/A Commission 12 % Number of Patches 30 36 -20.0% Mean Patch Size (m2) 21.5 19.1 11.2% Area Weighted Mean Patch Fractal Dimension 1.6 1.4 12.5%

Class Area (m2) 644 689 -7.0%



The quality of the final cut block map, in terms of errors and landscape metrics, can be found in table 3. There were only 6% errors of omission in the selected area, indicating that most reference features lie within the buffer around the extract area. The errors of commission of 12% indicate that some extracted cut blocks do not lie within the buffer around the reference data. The main reason that higher commission errors occur is because the river bands in the bottom of the scene introduced noise to the extracted GIS data. Landscape metrics results showed that, for the selected area, the number of cut blocks in the extracted map was 30, but 36 in the existing map. This result further explained why there were higher errors of commission for this feature. There were about 11-12% differences in mean patch size and area weighted mean patch fractal dimension between the extracted map and reference map. The extracted map had a lower class area in comparison with the reference map and the difference between them was 7%.

Fig. 8. The evaluation results for cut lines and forest roads & trails.

The resulting errors of omission were 48% and 12% for cut lines, and roads & trails,

respectively (Fig. 8). The high errors of omission for cut lines indicated that large amount of reference cut lines was not explained by the extracted cut lines. This result was attributed to the ‘rarely-used’ or ‘old’ cut lines, which were covered by replanted vegetation and had totally different reflectance features from the recent cut lines. The errors of commission for cut lines and roads & trails were relatively low (5% for cut lines and 16% for roads & trails). The lower errors of commission in the cut lines map indicated that there were less noisy objects in the final maps. The higher errors of commission for roads & trails map were due to the presence of the short lines, which are the edges of other features, such as cut blocks. Some of the edges were removed in the post-processing step when they are short and not connected with forest roads. However, it is difficult to determine a threshold value that would filter all spurious objects in the forest roads and tails map, without eliminating the valuable features.

4.4 Output Fig. 9 showed the overlaid final disturbance maps (A), and a subset of the image (B), and a subset of the final product (C). In correspondence with least-disturbed environment at the bottom left corner, the final products showed less disturbance features in the area. The distribution of cutblocks in the final map - less in the southern area and more in the northern area - also indicated the accuracy of the final products.



Fig. 9. the final product - overlaid all extract disturbance maps

5. CONCLUSIONS AND DISCUSSION Mapping small-scale forest disturbance is a key challenge for researchers. This will hopefully be overcome as high-resolution imagery and new processing techniques becomes more readily available. In this paper, we presented spectral, geometric, and topological characteristics of small forest disturbance features; outlined the nature of problem related to image data and methods; developed the framework for detection and mapping of narrow-linear and small-area features; and tested the framework using a SPOT multispectral image. The results show that it is possible to map narrow-linear and small-area disturbance features with satisfied accuracy, although the extracted cut line map has relatively high error. The values of the proposed framework are: 1) use of multiple image enhancement algorithms, such as vegetation indices, PCA, and Canny edge detector; 2) incorporation of diverse feature attributes into extraction procedures, such as spectral and geometric information; and 3) introducing the landscape metrics as the evaluation parameters for polygonal features.

Understanding the nature of problem, reviewing the literature, and examining the proposed framework provides ample evidence to support the conclusion that high spatial resolution satellite imagery can be effectively used to map small forest disturbance features. At the same time we should also keep in mind that many other factors might influence the reliability and success of such mapping processes. Future work will be directed towards improvement of the framework and the extraction strategy, according to the challenges discussed below.

5.1 Challenges regarding input data selection Image resolution is related directly to the final performance of the feature extraction. Even higher-spatial resolution imagery (i.e. spatial resolution < 5 m) might hold the potential to increase accuracy of cutline extraction. There is recent focus on exploring the capabilities of very-high spatial resolution imagery for feature extraction, and the results show its usefulness. However, higher-resolution imagery generally has smaller footprints and might not be suitable for large-area practical applications. In addition, the higher the spatial resolution, the more noise remains in the image and interferes with



feature extraction techniques. Consequently, higher-resolution imagery with increased complexity (e.g. more noise information) presents significant research challenges.

Satellite data from a combination of sensors have been proved to be able to identify linear features effectively, however, current limitations in the availability, cost, and acquisition strategies for high resolution data from SPOT, Quickbird, and other sensors must be resolved to enable routine monitoring activities.

5.2 Challenges associated with feature extraction methods Complex environment adds complexity to automate or semi-automate the extraction process. Spectrally, difficulties present when extracting features with inconsistent spectra, distinguishing objects with similar spectra, or delineating disconnected line or polygon due to covering over top of features. Consequently, the main challenge for small forest disturbance feature extraction that we learnt from this analysis is the removal of a large amount of spurious features that noisy objects generated during the processing steps. The spurious features were mainly caused by the patches with similar geometric and spectral patterns to the actual features that were needed to extract. Those patches were very common through the entire scene due to the complexity of the background environment in the area. Some of spurious objects may be removed in the post-processing step. However, it was difficult to determine a threshold value that would filter all spurious objects in the extraction map, without eliminating the valuable features. In addition, determining an appropriate threshold is time-consuming.

Geometrically, problems occur when mapping disturbance features with different horizontal profiles due to various widths or trying to differentiate sinuous and linear with curvatures. Although the map of forest roads and trails had relatively low errors of omission and errors of commission, many short lines showed in fig. 6 indicate there is a need to improve the proposed framework. The post-processing analysis was performed in order to remove the noisy objects, but some of the noisy objects were not successfully removed when they were too long.

In comparison with other features, the higher errors of omission for the cut lines map showed the limitations of the extraction tool. Parts of cut lines were not extracted because there was a difficulty to define one cut line model which could fit three types of cut lines in the image. A typical type of cut line is ‘frequently-used’ or ‘recent’ cut lines, which were covered by gravel and had a very bright tone or high reflectance in the image. The second type is ‘rarely-used’ or ‘old’ cut lines, which were covered by replanted vegetation and had totally different reflectance features from the first type of cut lines. The second type of cut line is still observable in the image according to their different reference pattern. A large amount of cut lines are between these two types - where the degree of contrast between them and their surroundings are various. A possible solution for the first two types is to define a model for each type of cut lines, but defining a model to fit the third type remains a challenge.

5.3 Challenges associated with validation When implementing a map accuracy assessment, a major drawback lies in insufficient, outdated, or non-representative reference data. The errors of omission and errors of commission are only suitable for the area with complete and accurate reference data. Manual digitizing is a possible solution to this issue, but adds more complexity to the problem of automating the extraction process. In addition, it is impossible to digitize all the features of interest for the entire scene.



Acknowledgments This study was supported by the Natural Sciences and Engineering Research Council of Canada, the Alberta Innovation and Science Fund, and the many partners of the Foothills Model Forest Grizzly Bear Research Program. We greatly acknowledge the valuable comments and suggestions from Dr. Greg Greg J. McDermid, whose comments helped improve the manuscript.

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