Systematic and timely documentation of triggered (i.e. event) landslides is fundamental to build extensive datasets worldwide that may help define and/or validate trends in response to climate ...change. More in general, preparation of landslide inventories is a crucial activity since it provides the basic data for any subsequent analysis. In this work we present an event landslide inventory map (E-LIM) that was prepared through a systematic reconnaissance field survey in about 1 month after an extreme rainfall event hit an area of about 5000 km
in the Marche-Umbria regions (central Italy). The inventory reports evidence of 1687 triggered landslides in an area of ~550 km
. All slope failures were classified according to type of movement and involved material, and documented with field pictures, wherever possible. The database of the inventory described in this paper as well as the collection of selected field pictures associated with each feature is publicly available at figshare.
This work presents an easy method for barchan dunes automatic extraction from multispectral satellite data. The proposed method based on unsupervised classifications of commonly used bands for sand ...dunes mapping in literature. First, the collected data were atmospherically and spatially enhanced. Moreover, each selected band (band ratio or redness index or crust index) were filtered using low-pass (3x3) filter and transformed with original image (non-filtered) by using principal component analysis (PCA). Additionally, the classifications were achieved for each selected band by using three different algorithms (K-means, Expectation Maximization (EM), and IsoData) after data transformation. Eventually, the obtained maps were segmented and compared with natural colour image. The results indicate that unsupervised classification of crust index selected band, which achieved by IsoData algorithm, presents high performance for barchan dunes detection.
The automated classification of ambient air pollutants is an important task in air pollution hazards assessment and life quality research. Faced with various classification algorithms, environmental ...scientists should select the most appropriate method according to their requirements and data availability. This study describes several types of Decision Tree algorithms for finding the inter-correlation between dominant air pollution index (API) for PM10 percentile values and four other air pollutants such as Sulphur Dioxide (SO2), Ozone (O3), Nitrogen Dioxide (NO2) and Carbon monoxide (CO), in addition to two other meteorological parameters: ambient temperature and humidity, using 22 months records of active air monitoring station in Penang island (northern Malaysia). Classification analysis for the PM10 API was then performed using non-linear Decision Trees within the R programming environment including: Boosted C5.0, Random Forest, PART, and Naive Bayes tree (NBtree). This is in addition to rpart and tree algorithms, which were used to plot the classification trees. The classification performance of the methods is presented and the best classifier in terms of accuracy and processing time was recommended. In R statistical environment, the process of classification by decision tree methods and the classification rules were easy to obtain, while geographic information systems (GIS) software' was used for mapping the study area. Furthermore, the results are clear and easy to understand for environmental and geospatial scientists and relevant agencies, which will facilitate the mitigation of air pollution related disasters in the affected communities.
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