Comparing probabilistic and statistical methods in landslide susceptibility modeling in Rwanda/Centre-Eastern Africa

Application of suitable methods to generate landslide susceptibility maps (LSM) can play a key role in risk management. Rwanda, located in centre-eastern Africa experiences frequent and intense landslides which cause substantial impacts. The main aim of the current study was to effectively generate susceptibility maps through exploring and comparing different statistical and probabilistic models. These included weights of evidence (WoE), logistic regression (LR), frequency ratio (FR) and statistical index (SI). Experiments were conducted in Rwanda as a study area. Past landslide locations have been identified through extensive field surveys and historical records. Totally, 692 landslide points were collected and prepared to produce the inventory map. This was applied to calibrate and validate the models. Fourteen maps of conditioning factors were produced for landslide susceptibility modeling, namely: elevation, slope degree, topographic wetness index (TWI), curvature, aspect, distance from rivers and streams, distance to main roads, lithology, soil texture, soil depth, topographic factor (LS), land use/land cover (LULC), precipitation and normalized difference vegetation index (NDVI). Thus, the produced susceptibility maps were validated using the receiver operating characteristic curves (ROC/AUC). The findings from this study disclosed that prediction rates were 92.7%, 86.9%, 81.2% and 79.5% respectively for WoE, FR, LR and SI models. The WoE achieved the highest AUC value (92.7%) while the SI produced a lowest AUC value (79.5%). Additionally, 20.42% of Rwanda (5048.07 km2) was modeled as highly susceptible to landslides with the western part the highly susceptible comparing to other parts of the country. Conclusively, the comparison of produced maps revealed that all applied models are promising approaches for landslide susceptibility studying in Rwanda. The results of the present study may be useful for landslide risk mitigation in the study area and in other areas with similar terrain and geomorphological conditions. More studies should be performed to include other important conditioning factors that exacerbate increases in susceptibility especially anthropogenic factors. © 201

Geospatial assessment of vegetation status in Sagbama oilfield environment in the Niger Delta region, Nigeria

Forest ecosystems, species habitats and vegetation resources in Sagbama oilfield axis of the Niger Delta ecological zone are disproportionately stressed out by increased oil and gas industrial activities and are rapidly degrading. This study aims to achieve a conservation-driven assessment of vegetation dynamics under such human-induced disturbances, as a strategy for informing the natural resources sector policy formulation. A 26-years change detection starting from 1987 to 2013 was performed on Landsats 4TM, 7ETM and 8 OLI/TIRS datasets at 30 m resolution. The Normalized Difference Vegetation Index (NDVI) and the Maximum Likelihood Classifier (MLC) supervised methods of geospatial techniques in Remote Sensing and Geographic Information System (GIS) were applied on these datasets. Results indicates severe decline of healthy forests and vegetation resources as revealed by 0.23 deviation in NDVI of 0.55 (41.98%) in 1987 to 0.32 (24.43%) in 2002. Nonetheless, in 2013, a 15.76% vegetation gain was registered given an NDVI value of 0.44 (33.59%), yet, falls below the initial NDVI threshold of 0.55. Thereby, implying that rates of forest and vegetation recovery are much slower compared to rates of degradation. However, this study provides baseline statistics and other helpful information for the effective management of vegetation and natural resources in the coming years