Mapping lichen in a caribou habitat of Northern Quebec, Canada, using an enhancement-classification method and

spectral mixture analysis

J.Théau, D.R. Peddle, C.R. Dubuay

Cristina Garcia

- Studies on caribou herds are important to understand wildlife population dynamics and conservation.- Many factors affect caribou migration: hunting, climate, predation, human activities… In northern Quebec, Canada is regulated by food availability.

-Lichen, specially Cladina sp. (fig.1), is a seasonal food source.

- Lichen is very sensitive and easily damaged.

Fig.1.- Cladina stellaris

-Lichen damage has an important role in caribou herd demography .- Due to this role, evidence of overgrazing and over trampling in lichen is a good indicator of caribou activity and habitat health.

- The study area is not easily accessible and covers a large territory (fig.2), making field studies limited.

Fig.2. Study area and location of Landsat TM image used.

- Aerial surveys cannot be conducted frequently, and provide fragmented formation.

- Satellite remote sensing offers the synoptic view and temporal resolution necessary for mapping and monitoring land cover in caribou habitat.

- Why the use of remote sensing in this study?

- Previous studies on habitat mapping using remote sensing have been conducted.

* Nordberg and Allard (2002) used Landsat TM imagery to monitor lichen degradation above tree line by correlating differences in NDVI between two dates in Sweden.

* Muskox (Ovibos moschatus) habitats were studied by Pearce (1991) using SPOT HRV imagery.

- Classification has been the main method used to map lichen but the problem of misclassification due to pixel heterogeneity can be significant.

- Spectral mixture analysis has not been used to study lichen.

- Objectives of this study: 1) develop a classification method designed for boreal area. 2) identify a possible synergistic classification and SMA approach.

Methodology

- Remote sensing data: * Cloud and snow free Landsat-5 TM acquired on July, 11 1996. * Bands 3, 4 and 5 were used for the false-colour image and for the image analysis. * Image geometrically corrected and geo-referenced to the UTM coordinate system, 25m spatial resolution. Ground control points extracted from 1:50,000 topographic maps.

- Field data: * Six areas were chosen based on accessibility by air, their representation of the land cover classes and their spatial distribution over the image. Each site had a homogeneous groups of pixels and within one pixel accuracy to the geo-referenced image.

* Parameters recorded from air: land cover class, type and % coverage of canopy layer to describe the classes used in classification (table 1).

* Sites were -visited on the ground when identification was ambiguous.

* Of the 37 sites used, 24 were lichen sites and 13 were non-lichen.

* 17 sites (1703 pixels) used as training areas to perform supervised classification. 20 sites (3536 pixels) for independent validation of the classification results.

Class CodeClass Code

Lichen heath LH Lichen dwarf (heath) LDSHLichen woodland LW Lichen dwarf (woodland) LDSWShrub forest SF Burn (no regeneration) BWLRMoss woodland MW Wetland WetRock R Water W

Table 1. Classes used for Landsat TM classification

Enhancement-Classification method (ECM)

- ECM used to perform image classification. Four different steps involved:

1) linear contrast enhancement on TM bands 3, 4 and 5 to maximize visual discrimination between bands.

2) unsupervised k-means classification with 150 clusters produced. The high number of clusters show the variability of spectral information visible on the enhanced image.

3) supervised reclassification reducing the cluster numbers to 50. The selection of these clusters was performed with the use of mode and sieve filters on the classification.

4) cluster agglomeration and labeling based on the land cover classes.

Spectral mixture analysis

- SMA used as it provided information of lichen abundance that was not possible to obtain by classification.

- SMA was performed to end with the problem of spatially heterogeneous or mixed TM pixels.

- It involves retrieving the spatial fraction of individual end members (surface cover components) within each pixel.

- Mixture analysis performed using ENVI software package on TM bands 3 and 4.

Fig 3. Scatter plot of 2D spectral data used for identifying end members.

- Selection of end members process: * identification of individual components of the surface. * lichen, canopy and shadow chosen as are the simplest and the more representative of the study area (fig.3). * obtaining of their spectral properties by the use of image-based values.

- Confirmation of the end members were done on the field.

- For an appropriate end member model, the fractions of each member should be 1 (or 100%). In cases where this rule did not apply, a deviation (+/-10%) was tolerated.

Fig.4. False colour composite of Landsat TM bands 3, 4, and 5 (top). ECM classification map.

ECM results

- Overall ECM accuracy was 74.5%.

-Overall lichen classes well discriminated from non-lichen classes.

-Accuracy of individual lichen classes varied between 30% and 92%.

Fig.5. False colour composite of Landsat TM bands 3, 4 and 5 (top). SMA lichen fraction abundance maps.

SMA results

- Non lichen sites identified as lichen sites due to the presence of small lichen spots covering other land cover classes.

- Good sensitivity of SMA in detecting even small portions of lichen per pixel.

Fig.6.- Scatter plot between % lichen from aerial estimation and % lichen from spectral mixture analysis.

-SMA accurately discriminated lichen sites from non lichen sites in 77% of the sites, 10 out of 13 sites (fig.6).

Conclusion

- ECM was found a efficient method to discriminate lichen from non-lichen classes which was useful to produce a land cover map.

- ECM has a limited capability to discriminate amongst lichen classes.

- For environmental applications, SMA was found to be more relevant as it provides information about lichen abundance.

- SMA good discriminator amongst lichen classes.

- These two methods can be used together to allow a stratification of analysis and refinement of results.

Théau, J., Peddle, D.R. & Duguay, C.R. (2005) Mapping lichen in a caribou habitat of Northern Quebec, Canada, using an enhancement-classification method and spectral mixture analysis. Remote Sensing of the Environment, 94, 232-243.