Search

A good landslide inventory map is a prerequisite for landslide hazard and risk analysis. In tropical countries, such as Malaysia, preparation of the landslide inventory is a challenging task because of the rapid growth of vegetation. Thus, it is crucial to use rapid and accurate technique and effective parameters. For this purpose, Dempster Shafer theory (DST) was applied in fusing high resolution LiDAR derived data products and Greenness index derived from orthophoto imagery. Two sites were selected, for the implementation and evaluation of the DST model; site “A” for DST implementation and site “B” for the comparison. For model implementation, vegetation index, slope and height were used as effective parameters for identifying automatic landslide detection. Two type of DST based fusions were evaluated; (greenness and height) and (greenness and slope). Furthermore, validation techniques were used to validate the accuracy are confusion matrix and area under the curve. The overall accuracy of the first and second evaluated fusions were (73.4% and 84.33%), and area under the curve were (0.76 and 0.81) respectively. Additionally, the result was compared with Random Forest (RF) based detection approach. The results showed that DST does not require a priori knowledge. [ABSTRACT FROM AUTHOR]

Forests in Honduras are endangered as a result of the relentless occurrence of wildfires during the dry season, and their frequency and area burned have been gradually increasing, a pattern attributable to the numerous ignition sources. For this reason, there is a substantial need to identify the major drivers of wildfires and map the regions where they are most likely to occur. In this study, we integrated the wildfire occurrences throughout the 2010–2015 period with a series of variables using the random forest algorithm. We included variables related to human activities such as the continuous distances to infrastructure and settlements. Other variables included are satellite observations that reflect the seasonal vegetation change, climatic conditions over the country, and topographical variables. The analysis of the explanatory variables revealed that the dry fuel conditions and low precipitation combined with the proximity to non-paved and paved roads were the major drivers of wildfires in the region. The estimated area with high and very high wildfire susceptibility was 15% of the country, located mainly in the central and eastern regions. The proposed national-scale wildfire susceptibility map can lead to enhanced preventive measures to minimize risk and the impacts caused by wildfires. [ABSTRACT FROM AUTHOR]