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Integrating Remote Sensing, Spatial Analysis And Certainty Factor Model For Waste Dumping Risk Assessment Yi-Shiang Shiu a , Meng-Lung Lin b , Yi-Chieh Chen c , Shien-Ta Fan c , Chao-Hsiung Huang c , Tzu-How Chu a a Department of Geography, National Taiwan University, Taiwan b Department of Tourism, Aletheia University, Taiwan c Spatial Information Research Center, College of Science, National Taiwan University, Taiwan IEEE IGARSS 2011 VANCOUVER, CANADA
Outline 1. Introduction 2. Materials and Methods 3. Results and Discussion 4. Conclusions
Waste dumping is one of the main pollution causing land deterioration and resource depletion.
With the growing awareness of environmental issues, environmental protection authorities in many countries have also implemented various waste controlling measures to deter waste dumping (Nemerow and Agardy, 2008; Tam and Le, 2009)
Inspectors’ routine patrols are necessary to increase the deterrence.
However, these tasks are also manpower and time-consuming.
1. Introduction Construction waste dumping Mixed construction and industrial waste dumping Industrial and household waste dumping
Certainty factor (CF) can be used to evaluate the reliability of the rules induced from the decision support system (Sinha and Zhao, 2008).
Rules evaluation with CF could be helpful for the safety assessment of industrial equipment (Kumar et al, 2009), level crossing surveillance (Tao and Lin, 2008) and thermal power plant (Zhang et al, 2009).
Geographic information system (GIS)-based risk assessment with CF model can be beneficial for landslide susceptibility mapping (Binaghi et al, 1998; Lan et al, 2004; Aboye, 2009)
The 8th factor is the suspected waste dumping area mapped with FORMOSAT-2 imagery and hybrid classification.
General Specification of FORMOSAT-2 Launch Year 2004 PAN 0.45~0.90μm MS 0.45~0.52μm （ Blue ） 0.52~0.60μm （ Green ） 0.63~0.69μm （ Red ） 0.76~0.90μm （ Near Infrared ） Remote Sensing Ground Resolution PAN （ Black/white ） Image 2 meters MS (color) Image 8 meters Image Swatch 24 kilometers
Suspected waste dumping mapping is not an easy task because waste dumping usually consists of various materials which show high spectral heterogeneity in satellite imagery.
To overcome high spectral heterogeneity and overlap, hybrid of supervised and unsupervised classification could be helpful (Turner and Congalton, 1998).
Unsupervised clustering is useful to stratify input images and cluster the manually collected training data into spectrally homogeneous subclasses for the use in the subsequent supervised classification (Bauer et al, 1994; Stuckens et al, 2000; Tømmervik et al, 2003; Lo and Choi, 2004).
Hybrid approaches could be helpful to overcome difficulties in delineating appropriate training samples for complex study areas such as mountainous (Kuemmerle et al, 2006) or waste dumping areas.
The CF model was used to combine all 8 factors and generate waste dumping risk map.
The CF at each pixel is defined as the change in certainty that a proposition is true (i.e. an area is waste dumping prone) from without the evidence to given the evidence at each pixel for each factor (Binaghi et al, 1998):
‘ No evidence’ means prior probability of having waste dumping cases in the study area
‘ With evidence’ means the conditional probability of having waste dumping cases given a certain class of a causative factor.