Dataset resolutions and segmentation parameter selection

Classification accuracy in an OBIA environment is primarily determined by the accuracy of the segmentation scale (Neubert et al., 2006; Weidner, 2006). This can prove to be quite problematic, as there is currently no universally accepted metric for the assessment of segmentation accuracy. In order for a classification to be accurate, the segmented objects must resemble real-world features (Whiteside et al., 2011). Several metrics have been proposed that provide an estimate of agreement between segments and real-world objects (for example (Lucieer and Stein, 2002)), however, there is no metric that assesses the robustness of a segmentation in relation to thematic classes. Spatial agreement between scene and ground objects is most certainly an integral consideration in the assessment and determination of an appropriate segmentation scale, however, there also needs to be an agreement between the established class characteristics and the characteristics exhibited by individual scene objects. The thematic accuracy of an image segmentation can be defined as the degree to which image segments represent the thematic attributes of the classes to which they will ultimately be assigned. Although the optimal segmentation scales for an image in terms of thematic accuracy and geometric accuracy often co-occur, this is not always the case. It is important that both forms of accuracy are optimised within a segmentation in order for it to provide the best possible representation of real-world objects. It is important to remember, however, that measures of segmentation accuracy and spatial agreement are not measures of classification accuracy (Clinton et al., 2008). This is of particular concern in highly heterogeneous environments, such as native grasslands, that may benefit from the generalisation associated with image segmentation. In many cases however, the establishment of hard class boundaries

can be difficult due to strong community intergradation. Therefore, thematic agreement between segments and reference objects is an important consideration in the assessment of segmentation quality and accuracy in this context.

High spectral resolution allows for more detailed spectral signatures to be acquired for each class. Similar classes are easier to distinguish from each other in high spectral resolution datasets with many narrow spectral bands, given the potentially subtle differences in spectral signatures between grassland classes. The WorldView-2 sensor has eight spectral bands, four of which cover the portion of the spectrum between the red and infrared (700 -1,100 nm). Landsat ETM+ has six spectral bands, three of which cover the red and infrared portion of the spectrum. The inclusion of an extra band in the area of 700-1100 nm allows for potentially greater differentiation between classes in the WorldView-2 imagery as opposed to the Landsat ETM+ imagery given an appropriate scale of analysis. However, Landsat ETM+ has two spectral bands in the near-infrared to shortwave infrared portion of the spectrum, which potentially provides valuable information for the identification of vegetation.

Grasslands have been proven to be exceptionally responsive to seasonal changes, and are typically found in areas that exhibit strong seasonal variations in key environmental factors (Tieszen et al., 1997). Therefore, accounting for seasonal variation is an important factor in the identification and differentiation of grassland communities. Tiezen et al. (Foody and Dash, 2010) as well as Foody and Dash (Tieszen et al., 1997) have highlighted the key role that seasonal difference between communities with similar physiological properties and habitat distributions can play in correct identification. The inclusion of temporal data derived from Landsat imagery was tested for this study, but the results were unsatisfactory given the spatial scale of the imagery. Future work could benefit greatly from the inclusion of temporal variables.

5 Conclusions

This chapter outlines an updated approach for mapping lowland native grasslands in the Tasmanian Midlands region using remote sensing methods. This approach provides significant improvements in classification accuracy for all vegetation communities over the TASVEG dataset. Additionally, the methods outlined in this chapter are capable of being regularly repeated, which is an important consideration given the Australian Governments’ mandate for increased community mapping and monitoring.

Two satellite datasets with differing resolutions were trialled as potential classification inputs, Landsat ETM+ and WorldView-2. Additionally, a 25 m DEM was acquired, and various topographic variables were derived for inclusion in the classification models. Object-based texture measures were calculated based on key spectral bands for both datasets. Training and validation data were derived from a pre-existing data source collected by the TLC. A random subset of training points was generated, ensuring a minimum point spacing of 30 m (the coarsest sensor spatial resolution employed in the study) in order to avoid oversampling of individual pixels. Training and validation data were randomly split from the original reference

point dataset using a ratio of 66% training to 33% validation a total of 50 times in order to employ a k-folds cross-validation approach to model training and validation. This approach was selected in order to reduce sampling bias, and ensure that all possible data points were used to train and validate the resulting models. Classification was undertaken using a random forest classifier, with each subset of training points used to train two separate models; one derived from the Landsat ETM+ data, and one from the WorldView-2. Classification was then performed on segmented versions of both datasets, with the reciprocal reference points not used to train the model used for validation. Class accuracies were averaged across the 50 classification results for each dataset, and classification frequency counts for each class tabulated.

Overall, both models showed good results for all classes, with class specific accuracies ranging between 54-87% for the Landsat ETM+ classifications, and 56-87% for the WorldVIew-2 classifications. The performance of the grassland complex class was significantly lower than for other classes, averaging 54% for Landsat ETM+ and 56% for WorldView-2. Classification and training accuracies for all classes across both models showed a high degree of consistency relative to each other, and standard deviations for all classes were low. This indicates that there is no bias introduced into the classification and assessment process as a result of training and validation point selection. Additionally, the TASVEG dataset was validated against all 50 validation subsets, and accuracies compared to the classification results. ANOVA indicated that for all classes, resulting accuracies were significantly higher in both sets of classification result than for TASVEG. The analysis also indicated significant improvements in Themeda grassland, dry woodland and overall accuracies based on the WorldView-2 dataset over the Landsat ETM+.

In conclusion, this study meets the demands of a remotely sensed classification approach that can cover larger areas. This approach can be used to map the spatial extent of grassland communities at an increased temporal resolution given the availability of cloud-free satellite imagery However, due to the use of coarse resolution training data, the approach may not be able to identify fine-scale changes in community distribution, and is best suited to the generation of similarly low spatial resolution results. Despite this however, achieved classification accuracies across both sets of results indicate that multispectral satellite datasets are capable of providing accurate extent predictions for lowland native grassland communities in the Midlands region.