Land cover classification of Landsat images is one of the most important applications developed from Earth observation satellites. The last four decades were marked by different developments in land cover classification methods of Landsat images. This paper reviews the developments in land cover classification methods for Landsat images from the 1970s to date and highlights key ways to optimize analysis of Landsat images in order to attain the desired results. This review suggests that the development of land cover classification methods grew alongside the launches of a new series of Landsat sensors and advancements in computer science. Most classification methods were initially developed in the 1970s and 1980s; however, many advancements in specific classifiers and algorithms have occurred in the last decade. The first methods of land cover classification to be applied to Landsat images were visual analyses in the early 1970s, followed by unsupervised and supervised pixel-based classification methods using maximum likelihood, K-means and Iterative Self-Organizing Data Analysis Technique (ISODAT) classifiers. After 1980, other methods such as sub-pixel, knowledge-based, contextual-based, object-based image analysis (OBIA) and hybrid approaches became common in land cover classification. Attaining the best classification results with Landsat images demands particular attention to the specifications of each classification method such as selecting the right training samples, choosing the appropriate segmentation scale for OBIA, pre-processing calibration, choosing the right classifier and using suitable Landsat images. All these classification methods applied on Landsat images have strengths and limitations. Most studies have reported the superior performance of OBIA on different landscapes such as agricultural areas, forests, urban settlements and wetlands; however, OBIA has challenges such as selecting the optimal segmentation scale, which can result in over or under segmentation, and the low spatial resolution of Landsat images. Other classification methods have the potential to produce accurate classification results when appropriate procedures are followed. More research is needed on the application of hybrid classifiers as they are considered more complex methods for land cover classification.
a b s t r a c tSeismic shaking and tectonic deformation during strong earthquakes can trigger widespread environmental effects. The severity and extent of a given effect relates to the characteristics of the causative earthquake and the intrinsic properties of the affected media. Documentation of earthquake environmental effects in wellinstrumented, historical earthquakes can enable seismologic triggering thresholds to be estimated across a spectrum of geologic, topographic and hydrologic site conditions, and implemented into seismic hazard assessments, geotechnical engineering designs, palaeoseismic interpretations, and forecasts of the impacts of future earthquakes. The 2010-2011 Canterbury Earthquake Sequence (CES), including the moment magnitude (M w ) 7.1 Darfield earthquake and M w 6.2, 6.0, 5.9, and 5.8 aftershocks, occurred on a suite of previously unidentified, primarily blind, active faults in the eastern South Island of New Zealand. The CES is one of Earth's best recorded historical earthquake sequences. The location of the CES proximal to and beneath a major urban centre enabled rapid and detailed collection of vast amounts of field, geospatial, geotechnical, hydrologic, biologic, and seismologic data, and allowed incremental and cumulative environmental responses to seismic forcing to be documented throughout a protracted earthquake sequence. The CES caused multiple instances of tectonic surface deformation (≥3 events), surface manifestations of liquefaction (≥11 events), lateral spreading (≥6 events), rockfall (≥6 events), cliff collapse (≥3 events), subsidence (≥4 events), and hydrological (10s of events) and biological shifts (≥3 events). The terrestrial area affected by strong shaking (e.g. peak ground acceleration (PGA) ≥0.1-0.3 g), and the maximum distances between earthquake rupture and environmental response (R rup ), both generally increased with increased earthquake M w , but were also influenced by earthquake location and source characteristics. However, the severity of a given environmental response at any given site related predominantly to ground shaking characteristics (PGA, peak ground velocities) and site conditions (water table depth, soil type, geomorphic and topographic setting) rather than earthquake M w . In most cases, the most severe liquefaction, rockfall, cliff collapse, subsidence, flooding, tree damage, and biologic habitat changes were triggered by proximal, moderate magnitude (M w ≤ 6.2) earthquakes on blind faults. CES environmental effects will be incompletely preserved in the geologic record and variably diagnostic of spatial and temporal earthquake clustering. Liquefaction feeder dikes in areas of severe and recurrent liquefaction will provide the best preserved and potentially most diagnostic CES features. Rockfall talus deposits and boulders will be well preserved and potentially diagnostic of the strong intensity of CES shaking, but challenging to decipher in terms of single versus multiple events. Most other phenomena will be transient (e.g., distal groundwater r...
The first International Conference on Urban Tree Diversity hosted in June 2014 by the Swedish University of Agricultural Science in Alnarp, Sweden highlighted the need for a better understanding of the current state of urban tree diversity. Here we present and discuss a selection of urban tree diversity themes with the intention of developing and sharing knowledge in a research area that is gaining momentum. We begin by discussing the specific role of species diversity in ecosystem service provision and ecosystem stability. This is followed by exploring the urban conditions that affect
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