3.1. Mapping of land-cover by image classification
At higher elevations the problem aroused that spectral sig-nature of bare soils and non-photosynthetic vegetation are very similar because siliceous geology with podsolic soils is predom-inant. The rusty colour of the podsolic soils is similar to the colour of non-photosynthetic vegetation. With the available train-ing data either classifytrain-ing landslide areas in low elevations as non-photosynthetic vegetation or non-photosynthetic vegetation at higher elevations as bare soil was possible. At higher eleva-tion NDVI of non-photosynthetic vegetaeleva-tion training areas was above 0.2 while bare soil areas were below. Thus, subsequent to the supervised classification all pixels classified as bare soil above 2000 m a.s.l. with a NDVI greater than 0.2 were changed to non-photosynthetic vegetation. The elevation above 2000 m a.s.l. was chosen because it produced best classification results compared to the ground truth data. The problem of the discrimination between non-photosynthetic vegetation and soil might be solved by using multi-temporal QuickBird imagery. In the summer season (July, August) the elevated non-photosynthetic vegetation is green veg-etation.
The achieved overall accuracy of land-cover classification using the independent pixel dataset is 93.3% (3000/3217 Pixels) and the = 0.92. In total, 0.81% of the dataset remained unclassified.
The land-cover classes of bare soil (after correction), grassland, rock, snow, water, forest, and shadow have high classification accuracies (82–100%), while classes of dwarf-shrub, shrub, and non-photosynthetic vegetation have lower classification
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Table 1
Confusion matrix for the supervised land-cover classification (npv = non-photosynthetic vegetation).
Class Bare soil Grassland Dwarf-shrubs Shrubs Rock Snow Water Forest npv Shadow Total Produces accuracy (%) User accuracy (%)
Unclassified 0 0 0 0 0 0 26 0 0 0 26
Total 220 360 214 238 415 236 258 274 153 846 3217
cies (67–78%) (Table 1). Percentage values of each land-cover class within the study site could not be determined due to large shad-owed areas. In comparison to the former available low resolution land-cover dataset (1:25,000 of Swisstopo), it was possible to derive more details (Fig. 2, left). Of special interest for erosion mapping is the detection of bare soil areas that have the highest potential erosion rates. In addition, it was possible to map dwarf-shrub veg-etation that has lower erosion potential than grassland (Konz et al., 2009).
Detailed mapping of land-cover and separation of different vegetation types is crucial for soil erosion assessment because different vegetation types produce different sediment yields (Isselin-Nondedeu and Bedecarrats, 2007). The supervised classifi-cation of the image worked well. However, in an area with distinct elevation gradients, changes in spectral signature of a class due to altering vegetation composition, nutritional status or of soil genesis may occur. For instance, for higher elevated areas the discrimina-tion between non-photosynthetic vegetadiscrimina-tion and bare soil becomes difficult. The main problem is the classification of bare soil with high spectral variability depending on soil genesis (reductive, oxidative conditions), erosion status and parent material due to the limited number of spectral bands of QuickBird imagery. Sufficient ground truth data is needed that may serve for knowledge-based correction algorithms as was applied here.
3.2. Mapping of bare soil and vegetation abundance relative to ground truth measurements
The abundance images derived using LSU for vegetation, bare soil and RMSE are shown inFig. 2(right). The figures show high spatial variability that represents the different cover conditions in the study area. Vegetation abundance is highest close to the valley bottom. The evident bare soil areas correspond to landslides, over-grazed areas, road- and river-banks and areas affected by sheet erosion. The RMSE image shows no recognizable pattern for the grassland areas indicating that the chosen endmembers are valid.
High RMSE values are evident for areas covered with rock, land-slides with rock outcrops and shrubs. These land-cover types were subsequently masked. The mean RMSE value for the masked image is 0.52.
The scatter plot between the ground truth FVC and the NDVI (Fig. 3) has difficulties discerning different levels of ground truth FVC for high abundances (40–100%). The low abundances (0–5%) are overestimated by NDVI. Linear regression yielded an overall coefficient of determination of r2= 0.64.
The disadvantage of mapping FVC by NDVI is its depen-dence on the vitality of the vegetation (De Jong, 1994). Spectral indices enhance the spectral contribution of green vegetation in images while minimizing contributions from bare soil, atmo-sphere and illumination angle. Although spectral indices produce
reliable estimates of green vegetation cover, the accuracy of non-photosynthetic vegetation is less satisfactory (De Jong, 1994).
Moreover, quantification of low FVC using NDVI is weakened by the increasing influence of the variability of background bare soil albedo (Harris and Asner, 2003). The chosen ground truth sites showed minor influence of non-photosynthetic vegetation. Larger errors might be expected for grassland with a higher proportion of non-photosynthetic vegetation. While NDVI utilizes only the visi-ble and near infrared band, the unmixing approach makes use of the entire spectral reflectance (De Asis and Omasa, 2007).
Scatter plots between ground truth FVC and percentage bare soil and abundances determined with LSU and MTMF are given inFig. 4.
Using linear regression between ground truth data and the satellite derived abundances could be described with a coefficient of deter-mination of r2of 0.85 for LSU and 0.71 for MTMF. Low, but still significant (at a 0.01 level) regression was obtained between bare soil abundance and ground truth bare soil fractions with r2= 0.28 for LSU and r2= 0.39 for MTMF. The abundances of vegetation and bare soil do not sum up to 100% because of the high RMSE of the unmixed images caused by the confusion between bare soil and shadow.
Only residues of the regression between FVC and LSU-derived veg-etation abundance have no significant (at a 0.05 level) difference to a normal distribution. The LSU- and MTMF-derived abundances represent a physical percentage of a material on the ground-surface and should lie on a 1:1 line with the values measured on the ground.
The measured ground truth values were generally higher than the corresponding LSU- and MTMF-derived abundances. For the FVC this can be explained by the choice of the grassland endmem-ber that was located on a golf course situated in the study site.
Different runs using other grassland endmember spectra yielded higher grassland abundance, however, the linear regression to the
Fig. 3. Scatter plot of NDVI versus the ground truth fractional vegetation cover.
Fig. 4. Scatter plots of linear spectral unmixing and mixture tuned matched filtering-derived abundances versus the ground truth data. Linear regression lines are shown.
ground truth data and RMSE was worse. The better performance of the golf course endmember might be due to the absence of non-photosynthetic vegetation. In October (time of image capture) the chlorophyll content in natural grassland already decreased com-pared to the irrigated golf course. The unmixing might work also with a generic grassland endmember, which would spare the proce-dure for local image selection. Still a direct application (without the correction for the deviation from the 1:1 line) in soil erosion models would not be feasible due to the underestimation of the physical percentage. Using the regression equation established between the vegetation abundance maps and the ground truth data it is possible to convert the vegetation abundance map to the FVC map.
For the MTMF-derived abundances, we observe even stronger deviations, which might be due to the partial unmixing algorithm, where the abundances were not constrained to sum to unity. If an endmember is not found in the pixel, the pixel is classified as background material (Boardman, 1995). In our case not every end-member is found in every pixel, which caused zeros on the scatter plots, especially for the bare soil abundance (Fig. 4).
The high coefficient of determination yielded for the mapping of vegetation abundances may be due to the very distinct reflectance curves of vegetation if they are compared with reflectance of bare soil, rock and water. The less distinct spectral signature of bare soil is probably the main reason for the worse estimates of bare soil abundances. Another explanation is the higher spectral variability of the soil depending on its genesis and erosion status. Regarding the abundance of bare soil, we believe the obtained results are not precise enough for further application.
In general, QuickBird is not the ‘optimal’ sensor for unmixing approaches due to the small number of spectral bands. For the mapping of bare soil abundances the systems seems to reach its limits. This is mainly due to the lack of short wave infrared channels
that are important for mineral and rock discrimination since min-erals have numerous absorption bands in this wavelength range.
This problem might be solved by using images of the WorldView-2 satellite (launched in October 2009) that provides 8 spectral bands at 1.8 m resolution at nadir. Nonetheless, the results from the linear regression show that the ground measured FVC and the abundances mapped by LSU and MTMF had a significant correlation. Especially the LSU-derived linear regression equation between vegetation abundance and ground truth FVC may be used to quantitatively predict ground FVC. The FVC map allows for a first allocation of potential soil erosion risk areas and improves estimates of soil ero-sion models (Meusburger et al., submitted for publication).
4. Conclusion
The supervised classification identified the typical land-cover types of alpine regions successfully with an overall accuracy of 93.3%. Relative abundance of vegetation cover could be mapped for grassland areas using NDVI, linear spectral unmixing and mixture tuned matched filtering with coefficients of determination between ground truth and derived FVC of r2= 0.64 for NDVI, r2= 0.85 for LSU, and r2= 0.71 for MTMF. The spectral unmixing approaches (LSU, MTMF) failed to map bare soil abundance, which might be due to the low spectral resolution of the QuickBird imagery and the high variability of the spectral signature of bare soil. The small scale heterogeneity and extreme topography make high demands for the remote sensing of an alpine environment. High spatial resolutions are needed that only few satellite systems such as IKONOS and QuickBird offer. The disadvantage of these systems is the small spectral resolution that causes difficulties in separat-ing bare soil areas from non-photosynthetic vegetation and rock.
Sufficient ground truth data is required to handle these
difficul-K. Meusburger et al. / International Journal of Applied Earth Observation and Geoinformation 12 (2010) 201–207 207
ties. The produced abundance maps of vegetation are preferable to only parameterising thematic land-cover classes because it retains spatial and distributional information.
Acknowledgement
This study was funded by the Swiss Federal Office for the Envi-ronment (Contract No.: StoBoBio/810.3129.004/05/0X).
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