Chapter 2: Literature Review
2.11 Accuracy Assessment Techniques
The results of any classification process need to be verified as to their accuracy in correctly defining features into categories of interest. Although this can be a difficult process, it is generally done using certain statistical tests and models. Remote sensing literature gives several examples of these methodologies (Janssen, 2000;
Lillesand and Kiefer, 2000; Biginget al., 1999) while Khorram, et al., (1999) devote a whole publication to addressing this topic, specifically on land-cover change detection accuracy assessment.
When change detection procedures are involved, the issue of accuracy assessment becomes even more complex, but not addressing them can lead to failure in achieving the goals of change detection (Biginget al., 1999).
Lowell (2001) describes an area-based accuracy assessment technique using 500- pixel by 500-pixel areal sample units. A subjective assessment of the amount of change was then made using image enhancement techniques on each sample unit in order to obtain an independent assessment of change. Confidence intervals were then calculated on the basis of these independent estimates of change.
A review of the literature revealed a wide range of classification accuracies were achieved, and appeared to be independent of the classification procedure (Le.
supervised or unsupervised). Varjo and Folving (1997) quoted accuracies of between 87.6% and 93.1 %; Zukowskyj et al. (2001) achieved accuracies of 71.3%
and Rowlinson et al. (1999) reported accuracies of 52.5%. Singh (1989) gives a comprehensive listing of accuracies achieved using a wide variety of different change detection techniques, which ranged from 51.4% to 74.4%. All of these
studies utilised Landsat TM imagery. Heyman et al. (2003) quote a range of achieved accuracies from 42% to 75% from several different studies.
2.12 Review Summary
Based on this review of the literature it is evident that much work has been done in monitoring general land cover/land use change (Hostert et al., 2003; Chen, 2002;
Yang and LO,2002; Petit et al., 2001; Pontius et al., 2001; Smith and Fuller, 2001;
Luque, 2000; Castelli et al., 1999; Chen et f/" 1999; Mas, 1999; Morisette et al., 1999' Yuan et al., 1999; Zhan et al., 1998; Hallum, 1993), as well as a good deal ofI
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research focussing specifically on forest land cover/land use change monitoring (Chen et al., 2005; Nackaerts et al., 2005; Heyman et al., 2003; Boyd et al., 2002;
Kayitakire et al., 2002; Jacobs and Mthembu, 2001; Nilsonet al., 2001; Sader et al., 2001; Puhr and Donoghue, 2000; Cohen and Fiorella, 1999; Harne et al., 1998;
Jeanjean and Achard, 1997; Varjo, 1997; Varjo and Folving, 1997; Hypannen,1996;
Coppin, 1991). In terms of the application of remote sensing to forest management, literature shows that there has been a wide range of applications in this field, including considerable use of change detection techniques for various forest management purposes. In particular, the detection of clear-felled stands is a well- researched application (Cohen and Fiorella, 1999; Hame et al., 1998; Varjo, 1997;
Coppin, 1991).
However, apart from one paper which examined plantation forest inventory data in the United Kingdom (Puhr and Donoghue, 2000) all other work was based on studies done in the Northern Hemisphere boreal and mixed forests (covering North America, Scandinavia and Russia) or tropical rain forest or savannah. Katsch and Vogt (1999) describe remote sensing applications for mapping, mensuration and disease/stress assessment in Southern African plantation forestry, while Katsch and Van Laar (2002) discuss the estimation of growing stock of eucalypt plantation forests. No other published work was found that specifically addressed the monitoring of plantation forest operations. Harne et al. (1998) and Coppin (1991) describe the monitoring of clear-felling, thinning, soil preparation and regeneration operations, but again these are in Northern Hemisphere boreal and mixed forests.
Two works (Gray et al., 2004; Shaw, 2004) that discussed the use of remote sensing for monitoring of weed growth in crop lands were located, but both were based on
agricultural applications, rather than forestry ones. It would appear that limited work has been done in this specific field of interest.
Thus, there is a distinct gap in the literature regarding remote sensing applications in plantation forestry in general, and certainly a dearth of literature covering the monitoring of plantation forestry operations in Southern Africa. This study seeks to address these gaps by investigating whether the application of proven remote sensing techniques can be used to monitor specific forestry operations, namely clear-felling, replanting and weed control, in South African plantation conditions.
Another aspect that this literature review highlighted is that while there have been several studies done on identifying individual tree crowns (e.g. Jacobs and Mthembu, 2001; Wulder et al., 2000), this review did not find any studies that had used tree rows as a means of separating crop and weed in a manner similar to that applied in the high resolution portion of this study. This is another unique contribution that this study attempts to add to the body of remote sensing knowledge.
As the focus of this study was to test the application of proven remote sensing techniques for monitoring plantation forestry operations, it was necessary to select the most appropriate techniques. Image differencing, change vector analysis (CVA) and composite analysis appear to be the preferred choices in forest change detection applications (Cohen and Fiorella, 1999), although Coppin et al. (2004) found that Univariate Image Differencing was the most widely applied change detection algorithm. However, post-classification techniques have also proved to be suitable and popular for land cover change detection (Lunetta, 1999). Variations on the CVA technique, such as the Tasselled Cap Transformation and Autochange Analysis, have also proved successful (Cohen and Fiorella, 1999; Harneet al., 1998;
Collins and Woodcock, 1994). Other methodologies such as vegetation indices (NDVI, TNDVI), Principal Component Analysis (PCA) and non-parametric Kernel methods have also been successfully applied (Varjo, 1997), as have various clustering or segmentation techniques (Katsch, 2003; Pekkarinen, 2002; Viovy, 1997). Based on these findings from the literature, as well as practical considerations such as the availability of software, post-classification change detection was selected as the most appropriate technique for the change detection
process. In addition, a segmentation process using textural analysis techniques was selected to improve the high resolution imagery classification results.
While there is plenty of literature on thresholding (Rosin and loannidis, 2003;
Bruzzone and Fernandez-Prieto, 2000; Lillesand and Kiefer, 2000; Eastman et al., 1995; Singh, 1989 to list a few), no specific thresholding techniques that might be applicable to plantation forest monitoring have been reported. It was therefore decided to test the feasibility of utilising the canopy characteristics to derive suitable thresholds to separate crop from weed.
An important process that requires attention in any of the techniques applied is that of accuracy assessment, due to the fact that none of these techniques is absolute in its application. The basis of classification and change detection is that these processes are an estimation of reality, based on indirect measurement of certain criteria. Therefore, some form of accuracy assessment is required in order to establish levels of confidence in the output results of any change detection procedure. Accuracy needs to be assessed at two points, the first being an evaluation of the classification accuracy, and the second being an assessment of the change detection procedure (Biging et al., 1999, Khorram,et al., 1999).
Having established a framework in terms of published research on the subject matter of this study, it was then necessary to select a suitable study area in which to test the hypotheses. An area of appropriate forestry activity was identified in the KwaZulu-Natal Midlands, the details of which are discussed in the next chapter.