A Bayesian approach to combine Landsat and ALOS PALSAR time series for near real-time deforestation detection

To address the need for timely information on newly deforested areas at medium resolution scale, we introduce a Bayesian approach to combine SAR and optical time series for near real-time deforestation detection. Once a new image of either of the input time series is available, the conditional probability of deforestation is computed using Bayesian updating, and deforestation events are indicated. Future observations are used to update the conditional probability of deforestation and, thus, to confirm or reject an indicated deforestation event. A proof of concept was demonstrated using Landsat NDVI and ALOS PALSAR time series acquired at an evergreen forest plantation in Fiji. We emulated a near real-time scenario and assessed the deforestation detection accuracies using three-monthly reference data covering the entire study site. Spatial and temporal accuracies for the fused Landsat-PALSAR case (overall accuracy = 87.4%; mean time lag of detected deforestation = 1.3 months) were consistently higher than those of the Landsat- and PALSAR-only cases. The improvement maintained even for increasing missing data in the Landsat time series

PROBABILISTIC RISK MAPPING COUPLING BAYESIAN NETWORKS AND GIS, AND BAYESIAN MODEL CALIBRATION OF SUBMARINE LANDSLIDES.

A spatial and causal probabilistic methodology is introduced for risk assessment based on the coupling of a conceptual Bayesian Network (BN) model and GIS to generate risk maps. The proposed integration of these spatial events is referred to as BN+GIS, which features forward and inverse modeling, denoted in this work as spatial prognosis and spatial diagnosis, respectively. This approach is illustrated through two case studies: (1) environmental risk associated to oil and gas site developments implemented in the Barnett Shale Play in Texas, and (2) landslide susceptibility in the Elliott State Forest in the Oregon Coastal Range. This approach will equip stakeholders, such as land owners, operators, regulators, government officials, and other related organizations with a platform that can help them improve the assessment of future potential risk scenarios, and to identify likely consequences that would lead to undesirable states of environmental risks ahead of time. A sensitivity analysis was performed on BN+GIS to study the influence of some of the user-defined parameters on the model’s results, such as sample size, spatial interval of the systematic sampling methodology, and the prescribed diagnosis distribution used for decision making purposes. As an additional effort to portray the potential application of the Bayesian paradigm on risk assessment, a parameter estimation methodology is implemented using bathymetry data and CPT logs. This approach is illustrated through a study case, where information was mined from existent landslides to perform a Bayesian calibration on an infinite slope model. This approach allowed to estimate posterior probability distributions of physical parameters given a prescribed factor of safety, to assess the most likely depth of failure, and to identify the optimum amount of samples required to maximize the reliability of the inferences. This work focusses on providing a substantial contribution to improved policymaking and management through the use of integrated sources of evidence such as real data, model predictions and experts educated beliefs

PROBABILISTIC RISK MAPPING COUPLING BAYESIAN NETWORKS AND GIS, AND BAYESIAN MODEL CALIBRATION OF SUBMARINE LANDSLIDES.

A spatial and causal probabilistic methodology is introduced for risk assessment based on the coupling of a conceptual Bayesian Network (BN) model and GIS to generate risk maps. The proposed integration of these spatial events is referred to as BN+GIS, which features forward and inverse modeling, denoted in this work as spatial prognosis and spatial diagnosis, respectively. This approach is illustrated through two case studies: (1) environmental risk associated to oil and gas site developments implemented in the Barnett Shale Play in Texas, and (2) landslide susceptibility in the Elliott State Forest in the Oregon Coastal Range. This approach will equip stakeholders, such as land owners, operators, regulators, government officials, and other related organizations with a platform that can help them improve the assessment of future potential risk scenarios, and to identify likely consequences that would lead to undesirable states of environmental risks ahead of time. A sensitivity analysis was performed on BN+GIS to study the influence of some of the user-defined parameters on the model’s results, such as sample size, spatial interval of the systematic sampling methodology, and the prescribed diagnosis distribution used for decision making purposes. As an additional effort to portray the potential application of the Bayesian paradigm on risk assessment, a parameter estimation methodology is implemented using bathymetry data and CPT logs. This approach is illustrated through a study case, where information was mined from existent landslides to perform a Bayesian calibration on an infinite slope model. This approach allowed to estimate posterior probability distributions of physical parameters given a prescribed factor of safety, to assess the most likely depth of failure, and to identify the optimum amount of samples required to maximize the reliability of the inferences. This work focusses on providing a substantial contribution to improved policymaking and management through the use of integrated sources of evidence such as real data, model predictions and experts educated beliefs