Feasibility Study of Land Cover Classification Based on Normalized Difference Vegetation Index for Landslide Risk Assessment

Unfavorable land cover leads to excessive damage from landslides and other natural hazards, whereas the presence of vegetation is expected to mitigate rainfall-induced landslide potential. Hence, unexpected and rapid changes in land cover due to deforestation would be detrimental in landslide-prone areas. Also, vegetation cover is subject to phenological variations and therefore, timely classification of land cover is an essential step in effective evaluation of landslide hazard potential. The work presented here investigates methods that can be used for land cover classification based on the Normalized Difference Vegetation Index (NDVI), derived from up-to-date satellite images, and the feasibility of application in landslide risk prediction. A major benefit of this method would be the eventual ability to employ NDVI as a stand-alone parameter for accurate assessment of the impact of land cover in landslide hazard evaluation. An added benefit would be the timely detection of undesirable practices such as deforestation using satellite imagery. A landslide-prone region in Oregon, USA is used as a model for the application of the classification method. Five selected classification techniques—k-nearest neighbor, Gaussian support vector machine (GSVM), artificial neural network, decision tree and quadratic discriminant analysis support the viability of the NDVI-based land cover classification. Finally, its application in landslide risk evaluation is demonstrated

Enhancement of Rainfall-Triggered Shallow Landslide Hazard Assessment at Regional and Site Scales Using Remote Sensing and Slope Stability Analysis Coupled with Infiltration Modeling

Landslides cause significant damage to property and human lives throughout the world. Rainfall is the most common triggering factor for the occurrence of landslides. This dissertation presents two novel methodologies for assessment of rainfall-triggered shallow landslide hazard. The first method focuses on using remotely sensed soil moisture and soil surface properties in developing a framework for real-time regional scale landslide hazard assessment while the second method is a deterministic approach to landslide hazard assessment of the specific sites identified during first assessment. In the latter approach, landslide inducing transient seepage in soil during rainfall and its effect on slope stability are modeled using numerical analysis. Traditionally, the prediction of rainfall-triggered landslides has been performed using pre-determined rainfall intensity-duration thresholds. However, it is the infiltration of rainwater into soil slopes which leads to an increase of porewater pressure and destruction of matric suction that causes a reduction in soil shear strength and slope instability. Hence, soil moisture, pore pressure and infiltration properties of soil must be direct inputs to reliable landslide hazard assessment methods. In-situ measurement of pore pressure for real-time landslide hazard assessment is an expensive endeavor and thus, the use of more practical remote sensing of soil moisture is constantly sought. In past studies, a statistical framework for regional scale landslide hazard assessment using remotely sensed soil moisture has not been developed. Thus, the first major objective of this study is to develop a framework for using downscaled remotely sensed soil moisture available on a daily basis to monitor locations that are highly susceptible to rainfall- triggered shallow landslides, using a well-structured assessment procedure. Downscaled soil moisture, the relevant geotechnical properties of saturated hydraulic conductivity and soil type, and the conditioning factors of elevation, slope, and distance to roads are used to develop an improved logistic regression model to predict the soil slide hazard of soil slopes using data from two geographically different regions. A soil moisture downscaling model with a proven superior prediction accuracy than the downscaling models that have been used in previous landslide studies is employed in this study. Furthermore, this model provides satisfactory classification accuracy and performs better than the alternative water drainage-based indices that are conventionally used to quantify the effect that elevated soil moisture has upon the soil sliding. Furthermore, the downscaling of soil moisture content is shown to improve the prediction accuracy. Finally, a technique that can determine the threshold probability for identifying locations with a high soil slide hazard is proposed. On the other hand, many deterministic methods based on analytical and numerical methodologies have been developed in the past to model the effects of infiltration and subsequent transient seepage during rainfall on the stability of natural and manmade slopes. However, the effects of continuous interplay between surface and subsurface water flows on slope stability is seldom considered in the above-mentioned numerical and analytical models. Furthermore, the existing seepage models are based on the Richards equation, which is derived using Darcy’s law, under a pseudo-steady state assumption. Thus, the inertial components of flow have not been incorporated typically in modeling the flow of water through the subsurface. Hence, the second objective of this study is to develop a numerical model which has the capability to model surface, subsurface and infiltration water flows based on a unified approach, employing fundamental fluid dynamics, to assess slope stability during rainfall-induced transient seepage conditions. The developed model is based on the Navier-Stokes equations, which possess the capability to model surface, subsurface and infiltration water flows in a unified manner. The extended Mohr-Coulomb criterion is used in evaluating the shear strength reduction due to infiltration. Finally, the effect of soil hydraulic conductivity on slope stability is examined. The interplay between surface and subsurface water flows is observed to have a significant impact on slope stability, especially at low hydraulic conductivity values. The developed numerical model facilitates site-specific calibration with respect to saturated hydraulic conductivity, remotely sensed soil moisture content and rainfall intensity to predict landslide inducing subsurface pore pressure variations in real time

Feasibility Study of Land Cover Classification Based on Normalized Difference Vegetation Index for Landslide Risk Assessment

Unfavorable land cover leads to excessive damage from landslides and other natural hazards, whereas the presence of vegetation is expected to mitigate rainfall-induced landslide potential. Hence, unexpected and rapid changes in land cover due to deforestation would be detrimental in landslide-prone areas. Also, vegetation cover is subject to phenological variations and therefore, timely classification of land cover is an essential step in effective evaluation of landslide hazard potential. The work presented here investigates methods that can be used for land cover classification based on the Normalized Difference Vegetation Index (NDVI), derived from up-to-date satellite images, and the feasibility of application in landslide risk prediction. A major benefit of this method would be the eventual ability to employ NDVI as a stand-alone parameter for accurate assessment of the impact of land cover in landslide hazard evaluation. An added benefit would be the timely detection of undesirable practices such as deforestation using satellite imagery. A landslide-prone region in Oregon, USA is used as a model for the application of the classification method. Five selected classification techniques—k-nearest neighbor, Gaussian support vector machine (GSVM), artificial neural network, decision tree and quadratic discriminant analysis support the viability of the NDVI-based land cover classification. Finally, its application in landslide risk evaluation is demonstrated

Feasibility Study of Land Cover Classification Based on Normalized Difference Vegetation Index for Landslide Risk Assessment

Unfavorable land cover leads to excessive damage from landslides and other natural hazards, whereas the presence of vegetation is expected to mitigate rainfall-induced landslide potential. Hence, unexpected and rapid changes in land cover due to deforestation would be detrimental in landslide-prone areas. Also, vegetation cover is subject to phenological variations and therefore, timely classification of land cover is an essential step in effective evaluation of landslide hazard potential. The work presented here investigates methods that can be used for land cover classification based on the Normalized Difference Vegetation Index (NDVI), derived from up-to-date satellite images, and the feasibility of application in landslide risk prediction. A major benefit of this method would be the eventual ability to employ NDVI as a stand-alone parameter for accurate assessment of the impact of land cover in landslide hazard evaluation. An added benefit would be the timely detection of undesirable practices such as deforestation using satellite imagery. A landslide-prone region in Oregon, USA is used as a model for the application of the classification method. Five selected classification techniques—k-nearest neighbor, Gaussian support vector machine (GSVM), artificial neural network, decision tree and quadratic discriminant analysis support the viability of the NDVI-based land cover classification. Finally, its application in landslide risk evaluation is demonstrated

An Improved Data-Driven Approach for the Prediction of Rainfall-Triggered Soil Slides Using Downscaled Remotely Sensed Soil Moisture

The infiltration of rainwater into soil slopes leads to an increase of porewater pressure and destruction of matric suction, which causes a reduction in soil shear strength and slope instability. Hence, surface moisture and infiltration properties must be direct inputs in reliable landslide hazard assessment methods. Since the in situ measurement of pore pressure is expensive, the use of remotely sensed soil moisture is practically feasible. Downscaling improves the spatial resolution of soil moisture for a better representation of specific local conditions. Downscaled soil moisture, the relevant geotechnical properties of saturated hydraulic conductivity and soil type, and the conditioning factors of elevation, slope, and distance to roads are used to develop an improved logistic regression model to predict the soil slide hazard of soil slopes using data from two geographically different regions. A soil moisture downscaling model with a better accuracy than the downscaling models that have been used in previous landslide studies is employed in this study. This model provides a good classification accuracy and performs better than the alternative water drainage-based indices that are conventionally used to quantify the effect that elevated soil moisture has upon the soil slide hazard. Furthermore, the downscaling of soil moisture content is shown to improve the prediction accuracy. Finally, a technique that can provide the threshold probability for identifying locations with a high soil slide hazard is proposed

Assessing Effects of Urban Vegetation Height on Land Surface Temperature in the City of Tampa, Florida, USA

Urban vegetation can mitigate urban heat island (UHI) due to its ability to regulate temperature by directly or indirectly influencing water vapor transport, shading effect, and wind speed and direction. Mechanisms of effects of vegetation cover on land surface temperature (LST) have been extensively documented. Few studies, however, have examined the role of vegetation height in controlling LST. In this study, we examined the relationship between LST and vegetation height by using Light Detection and Range (LiDAR) data from the city of Tampa, Florida, USA. The results revealed that vegetation height has significant impact on LST. Additionally, we also identified the optimal height and fractional cover at which vegetation can exert the greatest influence on LST. In particular, we found that the maximum cooling effect of vegetation can only be achieved when vegetation cover is above 93.33%, an amount of which is nearly impossible to have in most of the cities. On the other hand, LST decreases at an increasing rate with vegetation height, and is optimized at 20 m. This shows that vegetation height can play an important role in regulating UHI in contributing to effect maximization with least cover possible in a city. Findings derived from this study could provide urban planners with critical insights on precise and efficient urban vegetation management in the purpose of UHI mitigation