No abstract

Knowledge of the spatial and temporal changes caused by episodic disturbances and seasonal variability is essential for understanding the dynamics of mangrove forests at the landscape scale, and for building a baseline that allows detection of the effects of future environmental change. In combination with LiDAR data, we calculated four vegetation indices from 150 Landsat TM images from 1985 to 2011 in order to detect seasonal changes and distinguish them from disturbances due to hurricanes and chilling events in a mangrove‐dominated coastal landscape. We found that normalized difference moisture index (NDMI) performed best in identifying both seasonal and event‐driven episodic changes. Mangrove responses to chilling and hurricane events exhibited distinct spatial patterns. Severe damage from intense chilling events was concentrated in the interior dwarf and transition mangrove forests with tree heights less than 4 m, while severe damage from intense hurricanes was limited to the mangrove forest near the coast, where tree heights were more than 4 m. It took 4–7 months for damage from intense chilling events and hurricanes to reach their full extent, and took 2–6 yr for the mangrove forest to recover from these disturbances. There was no significant trend in the vegetation changes represented by NDMI over the 27‐yr period, but seasonal signals from both dwarf and fringe mangrove forests were discernible. Only severe damage from hurricanes and intense chilling events could be detected in Landsat images, while damage from weak chilling events could not be separated from the background seasonal change.

Sinkholes are the major cause of concern in Florida for their direct role on aquifer vulnerability and potential loss of lives and property. Mapping sinkhole susceptibility is critical to mitigating these consequences by adopting strategic changes to land use practices. We compared the analytical hierarchy process (AHP) based and logistic regression (LR) based approaches to map the areas prone to sinkhole activity in Marion County, Florida by using long-term sinkhole incident report dataset. For this study, the LR based model was more accurate with an area under the receiver operating characteristic (ROC) curve of 0.8 compared to 0.73 with the AHP based model. Both models performed better when an independent future sinkhole dataset was used for validation. The LR based approach showed a low presence of sinkholes in the very low susceptibility class and low absence of sinkholes in the very high susceptibility class. However, the AHP based model detected sinkhole presence by allocating more area to the high and very high susceptibility classes. For instance, areas susceptible to very high and high sinkhole incidents covered almost 43.4% of the total area under the AHP based approach, whereas the LR based approach allocated 20.7% of the total area to high and very high susceptibility classes. Of the predisposing factors studied, the LR method revealed that closeness to topographic depression was the most important factor for sinkhole susceptibility. Both models classified Ocala city, a populous city of the study area, as being very vulnerable to sinkhole hazard. Using a common test case scenario, this study discusses the applicability and potential limitations of these sinkhole susceptibility mapping approaches in central Florida.

No abstract

Ramaroshan of Sudurpaschim Province is rich in biodiversity, and ecologically distinct but scientifically unexplored rural and remote area of Nepal. There is a holy perennial Kailash River as a lentic and aesthetically well-known Ramarosan Lake Cluster (Batula Lake) as a lotic environment located in the region. This study has analyzed and then characterized the water quality status of both the lentic and lotic environments. The physicochemical parameters such as temperature, pH, electrical conductivity (EC), total dissolved solids (TDS), and major ions were analyzed for the hydrochemical characterization. The results revealed that the dominancy orders of cations and anions of the study area were found to be Ca2+ > Na+ >Mg2+ > K+ > NH4+ and HCO3- > Cl- > SO42- > PO43- > NO3-, respectively. The concentrations of most of the parameters were relatively higher in the Kailash River than the Batula Lake except for the SO42-, PO43- and NH4+. The water facies displayed the Ca-HCO3 type indicating the calcium carbonate type of lithology in the area. Additionally, the carbonate weathering is also higher than the silicates and evaporates. Similarly, the source controlling mechanisms indicated that the hydrochemistry of the Ramaroshan area is determined by both geogenic weathering and precipitation. Water quality assessment for irrigation parameters confirmed its suitability for irrigational purposes. The finding of this study could be the baseline dataset for assessing the future status and management of water resources of the Ramaroshan area.