Initial Hurricane Katrina Per-Building Level Analysis

Per-building level analysis was performed using visual assessment of the pre- and post- storm vertical aerial imagery listed in Table 7.1. Based on a review of building characteristics, five general visual signatures were identified to describe the post-storm conditions of individual buildings: roof intact, roof partially intact, slab (or bare ground), slab (or bare ground) with trees, and slab (or bare ground) with debris.

Of the 908 buildings contained in the original structure database, 471 buildings were assigned a remote sensing damage category, with the remainder of the dataset not examined because of time constraints. Forty-five of the 471 buildings were not included in the analysis because no ground-based damage state was assigned, leaving 426 buildings in the remote sensing dataset. Table 7.2 shows the number of buildings included in the remote sensing analysis divided by the number of buildings classified in the field reconnaissance for each damage state and community. The percentage of buildings analyzed for each damage state is also given in Table 7.2. Flood depth was not included in the WF damage assessment for this study.

After the remote sensing classification was complete, the ground-based WF damage states classified using the initial WF Damage Scale (Table 3.9) were correlated with the remote

131

sensing damage signatures. Using the criteria for roof damage only, the mapping of damage states presented in Table 7.3 was proposed. By creating a correlation between the WF damage states and the remote sensing appearance, the accuracy in describing storm surge damage using the identified remote sensing damage signatures could be investigated.

Table 7.2 Remote Sensing Per-Building Dataset Detaila

Community WF-0 WF-1 WF-2 WF-3 WF-4 Totals Waveland 6 2 2 10 150 170 6 2 2 10 150 170 Gulfport 7 4 4 5 24 44 7 4 4 5 24 44 Biloxi 14 14 2 10 50 90 58 37 23 27 182 327 Ocean Springs 17 0 0 2 16 35 17 0 0 2 16 35 Gautier 0 0 0 0 0 0 51 11 14 0 10 86 Pascagoula 6 0 1 15 65 87 90 6 2 18 70 186 Totals 50 20 9 42 305 426 230 62 48 66 457 863 Buildings Per WF Category 22% 32% 19% 64% 67% 49% a

Buildings in Remote Sensing Dataset/Buildings in Structure Database

Table 7.3 Proposed Ground- and Remote Sensing-Based Damage State Mapping

WF Damage State Remote Sensing Signature

WF-0 No Damage or

Very Minor Damage Roof intact

WF-1 Minor Damage

WF-2 Moderate Damage

Roof partially intact

WF-3 Severe Damage

WF-4 Destruction

Slab (or bare ground) Slab (or bare ground) with trees Slab (or bare ground) with debris

Using the damage state mapping presented in Table 7.3, the following error matrix was created (Table 7.4). A classification error matrix is a square matrix that indicates accuracies for a classification scheme (Lillesand et al., 2004). The error matrix presents overall accuracy of the classification method as the number of correctly classified instances divided by the total instances; commission error (inclusion error) as the percentage of instances incorrectly included in a category; and omission error (exclusion error) as the percentage of instances incorrectly

132

excluded from a category. The shaded cells on the diagonal of the matrix indicate observations that were correctly classified, assuming the proposed mapping scheme.

Table 7.4 Hurricane Katrina Error Matrix Using Three Category Damage Mapping Given in Table 7.3

Remote Sensing Classification

Ground Survey Classification WF-0 to WF-1 WF-2 to WF-3 WF-4 Totals Commission Error (%) Roof Intact 39 24 14 77 49.4 Partially Intact 26 16 27 69 76.8 Slab or Bare Ground 5 11 264 280 5.7 Totals 70 51 305 426 -- Omission Error % 44.3 68.6 13.4 -- Accuracy 319/426 74.9%

The Kappa coefficient further explains the effectiveness of a classification scheme, and relates the improvement in classification over random chance. The Kappa coefficient is calculated according to Equation 7.1 (Lillesand et al., 2004). The Kappa coefficient for the error matrix in Table 7.4 is 0.48, indicating that the classification is 48% better than a random chance distribution of damage states.

(7.1)

where

N = total number of observations r = number of rows in the matrix

xii = number of observations in row i and column i (main diagonal)

xi+ = number of observations in row i

x+i = number of observations in column i

Comparing WF-4 (Destruction) and Slab or Bare Ground, 5.7% of the buildings classified by remote sensing as Slab or Bare Ground were assigned a damage state less than WF-

133

4 (Destruction) in the field reconnaissance. Of the buildings classified as WF-4 (Destruction) in the field reconnaissance, 13.4% were assigned a remote sensing signature of a partially or completely intact roof. Both the commission and omission errors are quite high for damage states less than WF-4 and for remote sensing signatures with either a partially or completely intact roof. To investigate if better overall results could be achieved for the lower damage states, the error matrix was condensed to investigate the more general damage mapping shown in Table 7.5. The error matrix for the two category damage state mapping is presented in Table 7.6 and the Kappa coefficient was calculated as 0.69.

Table 7.5 Condensed Ground- and Remote Sensing-Based Damage State Mapping

WF Damage State Remote Sensing Signature

WF-0 _{Very Minor Damage} No Damage or

Roof intact or partially intact

WF-1 Minor Damage

WF-2 Moderate Damage

WF-3 Severe Damage

WF-4 Destruction

Slab (or bare ground) Slab (or bare ground) with trees Slab (or bare ground) with debris

Table 7.6 Hurricane Katrina Error Matrix Using Three Category Damage Mapping

Remote Sensing Classification

Ground Survey Classification WF-0 to WF-3 WF-4 Totals Commission Error (%) Roof Intact or Partially Intact 105 41 146 28.1 Slab 16 264 280 5.7 Totals 121 305 426 -- Omission Error % 13.2 13.4 -- Accuracy 369/426 86.6%

By condensing the damage mapping, significantly better results were obtained. While the commission and omission errors were reduced and the value of the Kappa coefficient increased considerably, a two category damage mapping scheme may not provide significantly better results than those obtained from a regional or neighborhood level analysis, and certainly does not

134

satisfy the increased delineation of damage that is desired from a per-building analysis and that should be achieved based on required analysis time.