TY - JOUR

T1 - Landslides Hazard Mapping in Rwanda Using Bivariate Statistical Index Method

AU - Nahayo, L.

AU - Mupenzi, C.

AU - Habiyaremye, G.

AU - Kalisa, E.

AU - Udahogora, M.

AU - Nzabarinda, V.

AU - Li, L.

N1 - Final publication is available from Mary Ann Liebert, Inc., publishers http://dx.doi.org/10.1089/ees.2018.0493

PY - 2019/8/31

Y1 - 2019/8/31

N2 - Landslides hazard mapping (LHM) is essential in delineating hazard prone areas and optimizing low cost mitigation measures. This study applied the Geographic Information System and statistical index method in LHM in Rwanda. Field surveys identified 336 points that were employed to construct a landslides inventorymap. Ten landslides predicting factors were analyzed: normalized difference vegetation index, elevation, slope, aspects, lithology, soil texture, distance to rivers, distance to roads, rainfall, and land use. The factor variables were converted into categorized variables according to the percentile divisions of seed cells. Then, values of each factor’s class weight were calculated and summed to create landslides hazard map. The estimated hazard map was split into five hazard classes (very low, low, moderate, high, and very high). The results indicated that the northern, western, and southern provinces are largely exposed to landslides hazard.The major landslides hazard influencing factors are elevation, slope, rainfall, and poor land management.Overall, this LHM would help policy makers to recognize each area’s hazard extent, key triggering factors, and the required hazard mitigation measures. These measures include planting trees to enhance vegetation cover and reduce the runoff, and construction of buildings on low steep slope areas to reduce people’s hazard exposure; while agroforestry and bench terraces would reduce sediments that take out the exposed soil (erosion) and pollute water quality.

AB - Landslides hazard mapping (LHM) is essential in delineating hazard prone areas and optimizing low cost mitigation measures. This study applied the Geographic Information System and statistical index method in LHM in Rwanda. Field surveys identified 336 points that were employed to construct a landslides inventorymap. Ten landslides predicting factors were analyzed: normalized difference vegetation index, elevation, slope, aspects, lithology, soil texture, distance to rivers, distance to roads, rainfall, and land use. The factor variables were converted into categorized variables according to the percentile divisions of seed cells. Then, values of each factor’s class weight were calculated and summed to create landslides hazard map. The estimated hazard map was split into five hazard classes (very low, low, moderate, high, and very high). The results indicated that the northern, western, and southern provinces are largely exposed to landslides hazard.The major landslides hazard influencing factors are elevation, slope, rainfall, and poor land management.Overall, this LHM would help policy makers to recognize each area’s hazard extent, key triggering factors, and the required hazard mitigation measures. These measures include planting trees to enhance vegetation cover and reduce the runoff, and construction of buildings on low steep slope areas to reduce people’s hazard exposure; while agroforestry and bench terraces would reduce sediments that take out the exposed soil (erosion) and pollute water quality.

KW - geographic information system;

KW - Hazard analysis

KW - Landslides

KW - Rwanda

U2 - 10.1089/ees.2018.0493

DO - 10.1089/ees.2018.0493

M3 - Journal article

VL - 36

SP - 892

EP - 902

JO - Environmental Engineering Science

JF - Environmental Engineering Science

IS - 8

ER -