WORK  SAMPLE  –  ADVANCED  GEO-­‐INFORMATICS  TECHNIQUES    This work sample is from my research project with the Ministry of Defense, India on Avalanche hazard assessment along a road axis in the state of Jammu & Kashmir, India. The road axis along which the study has been conducted has a total length of 71 km. Due to strategic reasons the specific Latitude as well as Longitude of the area could not be displayed. The main objectives of the study were:

• To identify and map the potential avalanche prone zones of the study area. • To compare the two methodologies adopted for Avalanche Hazard Assessment. • To prepare a Digital Avalanche Atlas.

 Data Base  The identification of avalanche hazard zone is dependent upon three major factors i.e. Weather conditions of the area, Snowpack conditions and the Environmental factors. In the present study, the identification is based on the environmental factors i.e. those attributed to terrain conditions of that place. The variables that contribute to the environmental factors are slope, aspect, land cover, curvature and elevation. For the generation of Avalanche hazard map, the following data was used:

• Multi-spectral Satellite Image • Topographic Maps provided by the Indian Ministry of Defense.

 Multi-spectral satellite data  

I. Selection of imagery - Selection of the right imagery was the first step in processing a Satellite data. The main criteria for image selection was to have a cloud-free data in which different land cover classes were easily detectable and had a good resolution. After browsing with the different satellite images, finally IRS- 1D LISS-III image of 29th September 2006 having a resolution of 23.5m was chosen. Importing of the raw images was done using the ERDAS Imagine 9.1.

II. Pre-processing of satellite data – Pre-processing involved geo-referencing and ortho-rectification of the data. Geo-referencing refers to the process of assigning map coordinates to image data. ‘Image-to-image’ geo-referencing was done using a master image of that region in which 24 Ground Control points were used. The geo-referencing process of the satellite image using ERDAS Imagine 9.1 has been shown in the Figure 1.

 Figure  1.    Geo-­‐referencing  of  IRS  1C-­‐  1D/LISS  III  satellite  data  using  ERDAS  

Imagine  9.1  

III. Classification of satellite imagery – Classification is done to categorize all pixels of an image into land cover classes or themes. In the study, classification was done to de-lineate the various land cover classes as required for the detection of avalanche hazard zones of the study area. Both supervised as well as unsupervised Classification was performed using the DN values of all the four bands of LISS – III imageries. In case of supervised classification, pixel categorization was done using representative sample sites of known cover types, known as training sites, which were then compared numerically to each category in the interpretation key and labeled with the name of the category which closely resembled it (Figure 2(a)). Whereas, in unsupervised classification, spectral grouping of the unknown pixels into 15 classes was done which were then re-classified in five classes (Figure 2(b)). For the present purpose, satellite image classified using supervised technique was used in which five classes viz. Shadow, Vegetation, Barren Land, Snow and Cloud were generated in ERDAS Imagine 9.1.

Figure  2(a)  Un-­‐supervised  Classification  of  IRS  1D  -­‐  LISS  III  satellite  image  of  29th  Sep  2006.  

 

Figure  2(b).  Supervised  Classification  of  IRS  1D  -­‐  LISS  III  satellite  image  of  29th  Sep  2006.  

 Topographic maps (SASE maps)

I. Selection of topographic maps – SASE has a wide collection of topographic sheets available at different scales. Since the study area was large and required a

certain amount of detailing, thus, nine topographic maps on the scale of 1:50 000 were integrated.

II. Pre-processing of topographic Map – For pre-processing of Topographic maps, mosaicing was done. Mosaicing, in general terms, can be referred to as stitching of all the maps together into one single map. In this, different topographic maps were scanned and attached together without overlapping using ERDAS Imagine 9.1. Also, the images were smoothened as well as color balanced.

III. Generation of digital elevation model (DEM) – Digital Elevation model is a representation of topography of the area in a digital form. The DEM gives a realistic view of the area and makes it easy to understand the planar nature of the area. Different terrain features e.g. Hills, valleys, gorges and configuration can be identified with ease. This DEM was generated in ERDAS Imagine 9.1. by digitizing the topographic maps in which the contour interval was 40 m. The DEM generated is shown in Figure 3.

Figure  3.  Digital  Elevation  Model  of  the  study  area  

       IV. Topographic  analysis  –  The   important  parameters   that  need   to  be  evaluated  

for  the  present  study  were  slope,  aspect,  curvature,   land  cover  and  altitude.  Thus,  maps  of  all  these  parameters  were  generated  from  the  Digital  Elevation  Model  (DEM)  of  the  image  using  ERDAS  Imagine  9.1.  These  parameters  were  further   sub-­‐classified   on   the   basis   of   their   contribution   towards  Avalanche  occurrence.    

 

 

Methodologies    

1. Multi –Criteria Analysis  In this methodology, Multi-criteria parameters were ranked in order of their importance with reference to Avalanches. Thus, using ArcGIS 9.3 software carried out numeric estimation of the risk, i.e. the adopted values of each parameter. For assigning weightages to different parameters, the avalanches of the study area were analyzed along with the opinion of the expert’s working in this field. Finally, from the output new avalanche sites were identified. A cartographic model of data and analytical methodology has been illustrated in the following Figure.    

                   

           

 

       

   

Satellite  Images  IRS-­‐LISS  III  Data  

Geo-­‐referencing  &  

ortho-­‐rectification  

Image  Classification  

SASE  map  (Scale1:  50  000)  

Generation  of  Digital  Elevation  

Model  

Generation  of  different  

thematic  layers  

Assigning  weightages  to:  

• Slope  • Aspect  • Elevation  • Land  cover  • Curvature  

Identification,  Delineation  and  Mapping  of  Avalanche  prone  areas  

Weighted overlay method - Once appropriate weightages were assigned to different parameters then the next step was to convert these different parameters, which were presently different rasters in different scale levels, into one single raster output. This was done using weighted overlay method which is a technique applied for calculating a common scale for diverse and dissimilar input to create an integrated output. In the present study, weighted overlay method was used for generating an avalanche hazard map by compiling raster maps of different parameters (slope, aspect, elevation, land cover and curvature). The analysis was carried out using a GIS based software known as ArcGIS 9.3. The summarized form of this method has been shown in Figure 4. Herein, firstly all the input rasters were reclassified from continuous form to discrete form since the Weighted Overlay tool accepts only discrete rasters (integer values) as input. These parameters were then evaluated using scale 1 to 5 of Weighted Overlay Method in ArcGIS 9.3 software. Further, depending upon the contribution each input raster had percentage influence was assigned such that the overall influence was 100 percent.

Figure  4.  Model  of  Weighted  overlay  method    

   Avalanche Hazard Map - The different parameter maps that were generated were then integrated together to obtain the final hazard map. This integration was based on the influence that parameters had on Avalanche occurrence. The percentage influence that different input parameters had on the avalanche triggering is represented in Table 1.  

Table  1.    Percentage  Influence  assigned  to  terrain  parameters  for  Multi-­‐criteria  analysis  Parameters   Percentage  Influence  

Slope   30  Land  Cover   22  Curvature   20  Elevation   18  Aspect   10  

 Finally, these parameters were multiplied with their corresponding criterion weights (in the form of percentage influence) and were added together so as to obtain the final output in the form of Avalanche Hazard map as shown in Figure 5. In this Figure, the red area depicts high-hazard zone, Green area represents Medium hazard zone while Yellow depicts Low Hazard Zone.

Figure  5.    Avalanche  hazard  map  generated  using  Multi-­‐criteria  analysis  

 Based on this avalanche hazard map generated, area under different hazard zones was calculated. The results interpreted are shown in Table 2.  

Table  2.  Areas  of  different  Hazard  Zones  computed  from  multi-­‐criteria  analysis

Zone   Area  (in  sq.  km.)   Area  (in  %age)  

High  Hazard   1413   25.6  

Medium  Hazard  Zone   2505   45.55  

Low  Hazard  Zone   1581   28.75  

 Identification of avalanche sites - After the avalanche hazard map was generated, identification of new avalanche sites was done. In the study area already 188 Avalanche sites were detected by earlier studies done in SASE. Apart from those sites, 26 new avalanche sites in high-hazard zone were identified from the final output map that has been shown in Figure 6.  

  Figure  6.    New  Avalanche  sites  identified  using  Multi-­‐criteria  analysis    

   

2. Fuzzy-logic based Classification  The second method applied for mapping and identification of avalanches was the Fuzzy-logic based classification. The stepwise procedure for the generation of output using this method and a model generated in ERDAS Imagine 9.1 software is shown in Figure 7.  

     

Figure 7. Model of Fuzzy logic based methodology

       

Fuzzification - It was the prime step in the fuzzy logic based method. The main purpose of this step was to convert the input values for different parameters, which were presently in the crisp form, into fuzzy sets that had specific membership function for each variable. This membership function was evaluated by taking opinion of the experts working in this field. The classification of hazard zones was done in accordance to their impact on avalanche triggering. Thus, they were classified as no or low hazard Zones, medium hazard zones and high hazard zones. So as to get a better idea about the process I would select the parameter slope and would explain the steps. Fuzzification of Slope – By taking into consideration the impact different slope angles had on avalanches, they were categorized into fuzzy sets ranging between 0 and 1, wherein 0 represented non-membership, 1 represented complete membership while values in between represented partial membership. This has been shown in the Table 3.  

Table  3.  Fuzzy  sets  for  Slope  

Slope   Med.  DZ   Low  DZ   High  DZ  

0   0   1   0  

12   0.4   1   0  

29   0.51   0   1  

45   1   0   1  

55   1   0   0.8  

90   0   1   0  

 Once the fuzzy sets for the required slope angle were marked out, then using Microsoft Excel 2007 the graph of slope v/s range was plotted in order to evaluate the function of the required ranges that helped in determining the algorithm. This graphical representation of slope parameter is shown in Figure 8.

 Figure  8.  Graphical  representation  showing  fuzzy  sets  for  Slope  

   Application of algorithm - Once the crisp variables were converted into fuzzy sets through fuzzification, then the next and major step was the application of the appropriate algorithm. With the help of the graph plotted in the preceding step, the slope of each line was calculated. Using ERDAS Imagine 9.1, except for Curvature, a model was prepared wherein algorithm for low, medium as well as high hazard zones was prepared. The algorithm used here was based on IF-THEN rule where IF clause (antecedent) contained input fuzzy sets while THEN clause (consequent) contained output fuzzy sets.

In case of Curvature, the output was obtained using ArcGIS 9.3 where the model was prepared to apply the given algorithm. This model obtained for curvature was combined with the remaining other parameters using the weighted overlay method in ArcGIS 9.3. Once output images for different parameters representing different hazard zones were obtained, subsequently the statistical sum of these images was evaluated. Application of Algorithm to slope - The table showing the algorithm used for the parameter slope is given in Table 4.  

Table  4.    Fuzzy  Algorithm  for  Slope  Danger  Zone   Algorithm  

Low  Danger  Zone   • IF  0<=x<=12  AND  x>=90  THEN  y=1  • IF  12<x<29  THEN  y=  -­‐0.058x+1.705  • IF  29<=x<=55  THEN  y=0  • IF  55<x<90  THEN  y=0.028x-­‐1.5  

 Medium  Danger  Zone   • IF  x=0  AND  x>=90  THEN  y=0  

• IF  45<x<=55  THEN  y=1  • IF  0<x<=12  THEN  y=0.033x  

• IF  12<x<=29  THEN  y=0.006x+0.322  • IF  29<x<=45  THEN  y=0.030x-­‐0.378  • IF  55<x<90  THEN  y=  -­‐0.028x=2.52  

 High  Danger  Zone   • IF  0<=x<=12  AND  x>=90  THEN  y=0  

• IF  29<=x<=45  THEN  y=1  • IF  12<x<29  THEN  y=0.058x-­‐0.696  • IF  45<x<90  THEN  y=  -­‐0.022x+1.98  

Defuzzification - It was the final and concluding step of fuzzy logic based classification. The main purpose was to convert the output that was obtained in fuzzy form into crisp value i.e. generally acceptable. There are different methods that can be used for defuzzification; the method used in this study was the Fuzzy centroid algorithm.

Using ArcGIS 9.3 a model was generated (as shown in Figure 9) wherein the output in form of statistical sum for each parameter was multiplied by their respective output membership function center points and summed. The center points were decided based on expert’s opinion as well as field data, the value varied between 0 and 1.

[Slope*0.35 +Aspect*0.12+LandCover*0.28+Elevation*0.25]/4 Once this output was obtained then all the values were summed up and divided by the weighted membership function strengths and the result was taken as final crisp output. This output result obtained was then combined with the Curvature map using weighted overlay method. The fuzzy output raster was given a percent weightage of 80 whereas the curvature was given a percent weightage of 20, which sums up a total of 100.

Figure  9.    Model  of  Defuzzification  generated  using  ArcGIS  9.3.  

 Avalanche hazard map - The first output obtained was an avalanche hazard map in which the area was divided into three major zones i.e. low hazard zone, medium hazard zone and high hazard zone. This has been presented in Figure 10 while the details about the area lying in different hazard zones have been shown in Table 5.

 

Figure  10.      Avalanche  Hazard  map  using  fuzzy  logic  based  classification  

                 

  Identification of avalanche sites - Once the Avalanche hazard map was generated, thereafter, as for Multi-criteria analysis, the new avalanche sites along the axis were identified. In this analysis, a total of 31 new Avalanche sites lying in high hazard zone were detected. These avalanche sites have been shown in Figure 11.

Table  5.  Areas  of  different  Hazard  Zones  computed  from  Fuzzy-­‐logic  based  Classification.  

Zone   Area  (in  sq.  km.)   Area  (in  %  age)  

High  Hazard   1642   29.8  

Medium  Hazard  Zone   3496   63.5  

Low  Hazard  Zone   361   6.5  

Figure  11.  New  Avalanche  Hazard  Sites  identified  using  Fuzzy-­‐logic  based  classification  

   Comparison Of Two Methodologies Multi-criteria analysis is a software-oriented application whereinby assigning weightages to different parameters results are obtained. On the other hand, Fuzzy-logic based classification is a complex and detailed application that involves use of different algorithms to generate the required output. A comparison of these two methodologies was done in order to check the effectiveness of one methodology over the other. The avalanche hazard maps that were generated using both the methodologies were compared with the data obtained from the field observations. It was found that the accuracy in results was more in case of Fuzzy-logic based classification with greater percentage of hazard zone area and thus more avalanche sites were identified. From this it could be inferred that this methodology gave a greater accuracy in determining the avalanche hazard zones as well as avalanche sites in comparison to multi-criteria analysis. However, multi-criteria analysis is a simple, speedy and straight-forward technique. The accuracy of the results obtained in multi-criteria analysis is dependent on the weightage assigned.  Avalanche Atlas In order to assess the impact avalanches would have on the road axis knowledge of avalanches in terms of their behavior pattern, location of strike, nature of strike etc. is important. Keeping this in view, a Digital Avalanche Atlas was prepared which could be of immense use to Military Commanders and Border Roads Organization (BRO) personnel in the deployment of troops.

 

Methodology For the preparation of Avalanche Atlas, a step wise procedure was followed which involved creation of a database using satellite images, topographic map as well as avalanche hazard map. From this data, the required information was extracted using ERDAS Imagine 9.1 in form of Microsoft Excel Spreadsheet. This data was then converted into a digital form using Visual Basic 6.0 with well defined Graphical User Interface {GUI} and user friendly menu driven screens for ease of the operations, using Client Server Technology. The cartographic model of the methodology that is followed in the present study has been shown below.  

     

Programming The data base constituting the above mentioned parameters was then converted into a computer application with the help of Visual Basic 6.0 with well defined Graphical User Interface {GUI} and user friendly menu driven screens for ease of the operations , using Client Server Technology. The main objective of customized Digital Avalanche Atlas Application was to track the complete information about each demarcated Avalanche prone site.  

Final Output Atlas is customized to track the complete information about each demarcated Avalanche prone site present along the road axis. Important parameters of the avalanches embedded in the software that can be accessed by the user are Digital Elevation Model (DEM), Map references, ground cover, slope, aspect, Cross-sectional profile as well as Track Profile

etc. (as shown in Annexure B3). The GIS based digital avalanche atlas also contain features like General information on avalanches, the climate as well as geo-morphology of the study area, control measures etc. The Atlas has different sections from which information can be extracted by a single click. These sections are:

1. Home page

2. General information

3. Avalanche information

• Conventional atlas

• GIS based atlas

Home Page Home page has been planned to start the application titled as “Welcome to Digital Avalanche Atlas”. This page further continues to the next page that displays the mission as well as a brief history about  Snow  and  Avalanche  Study  Establishment  (SASE).  

   

General Information This section gives a general introduction about the Atlas, Avalanche Classification, Avalanche safety and rescue, Avalanche formation & contributory factors, Methods of Control and Glossary. This page also contains brief information about DEM, Slope, Aspect, Map, Images and DTM that can be viewed by moving the cursor to the respective words. Thereafter, by clicking on area axes the required area can be studied by logging into the Atlas.    

   

Avalanche Information This section gives details about two types of Atlases:

• Conventional Atlas • GIS Atlas

The avalanche atlas has been designed to access both Conventional and GIS based data. The conventional information contains information available in the hardcopy of atlas whereas GIS-based atlas gives overall information about the avalanche sites. The overall information such as location, climate, geo-morphology about the study area is available in this section. Apart from this, the following information about each individual avalanche is present in this section:  

Table  6.  Comparison  between  Conventional  and  Digital  Avalanche  Atlas  Database  S.No

. CONVENTIONAL   GIS  

1   Avalanche  No./Name   Avalanche  No./Name  

2   Location/Distance   Location/Distance  

3   Map  Ref/No.   Map  Ref/No  

4   -­‐-­‐   Latitude/Longitude  

6   Photograph  of  each  site.   -­‐-­‐  

7   Elevation  Formation  Zone  (Min.-­‐Max.)   Elevation  Formation  Zone  (Min.-­‐Max.)  

8   Elevation  Run  out  Zone  (Min.-­‐Max.)   Elevation  Run  out  Zone  (Min.-­‐Max.)  

9   -­‐-­‐   Aspect  

10   -­‐-­‐   Ground  Cover  

11   Map(50k)   Map(50k)  

12   Terrain  information  about  Formation  Zone,  Track  

and  Run  out  Zone.  

Terrain  information  about  Formation  Zone,  

Track  and  Run  out  Zone  

13   Length  of  Avalanche  Path   Length  of  Avalanche  Path  

14   Effect  on  highway   Effect  on  highway  

15   -­‐-­‐  

     

 

9  GIS  based  characteristics  for  each  site  such  

as  DEM,  Slope,  Aspect,  Track  Profile,  Cross-­‐

sectional  Profile,  Summer  images,  Winter  

images,  DTM,  Ground  Cover.  

16  

   

   Thus, Digital Avalanche Atlas can be considered as an easily understandable tool in which all the basic information regarding avalanches of that particular study area has been summed up in form of a computer application that is of immense use to Military Commanders and Border Roads Organization (BRO). Significance Of The Study The main purpose of this study was:

• To operationalize a working methodology to develop models for mapping of avalanche-prone areas using remotely sensed data and GIS.

• To aid in processing and properly presenting to the user the relevant information gathered from the fusion of remote sensor data with other data.

• To assess the intensity of individual Avalanche in the study area by taking digital avalanche atlas, i.e. prepared in the study, as a reference.