A Novel Approach to Estimate Diffuse Attenuation Coefficients for QuickBird Satellite Images: A Case Study at Kish Island, the Persian Gulf

Diffuse attenuation coefficient (k _d ) is a critical parameter for benthic habitat mapping using remotely sensed data. This research attempted to develop a new approach to estimate k _d in blue and green bands of QuickBird satellite image based on the integration of Lyzenga’s method and updated NASA-k _d ⁴⁹⁰ algorithm. To do this, the Lyzenga’s method was utilized to determine the ratio of k _d in different bands of QuickBird satellite image. Additionally, NASA-k _d ⁴⁹⁰ algorithm was applied to determine k _d ⁴⁹⁰ by using remotely sensed reflectance values of blue (R _rs ^Blue ) and green (R _rs ^Green ) bands in each pixel of QuickBird satellite image. Since the aforementioned algorithm has been developed for other types of sensors, an approach using weighted mean value of parameters for SeaWiFS, MERIS, VIIRS, and OCTS sensors were employed to estimate parameter values for QuickBird image. After determining the k _d ⁴⁹⁰ values as k _d for blue band, the k _d values for green and red bands were subsequently obtained by using Lyzenga’s method. Then, Mumby and Edwards’ method was employed as evidence to evaluate the accuracy of the results achieved from newly developed approach. Eventually, the maximum likelihood classifier was implemented during pre and post correction steps to examine the capability of the proposed approach. The final results proved to be consistent in the areas deeper than 2 m between estimated k _d values using the proposed approach and the results obtained from Mumby and Edwards’ method. On the other hand, the values estimated for extremely shallow areas seem to be overestimated. Furthermore, results demonstrated an increment of ~16 % in the overall accuracy of the classification.     

Potential fish habitat mapping using MODIS-derived sea surface salinity,temperature and chlorophyll-a data: South China Sea Coastal areas,Malaysia

This paper demonstrates the use of moderate resolution imaging spectro-radiometer (MODIS) data for fish forecasting mapping of seasonal spatial distribution of sea surface salinity (SSS), temperature (SST) and chlorophyll-a in the ocean waters off the coast of Semporna, Malaysia. Multi-linear regression analysis was performed to estimate SSS and the Brown and Minnet algorithm was used for the SST. The extracted parameters were validated using in situ measurement taken with Hydro-Lab equipment. The extracted parameters from MODIS images reveal the signature values which establish the relationships between these parameters, and thus delineating the potential fish zonation (PFZ) map. These developed models will help for accurate monitoring of large coverage areas at low cost and within short period of time. Furthermore, such models will allow the prediction of the total fish catch in different seasons, thus contributing to fish industry management and marketing. This research recommends the use of PFZ map for mass scale fish harvesting in short time for larger areas. Finally, the research has developed a potential fish zone model amalgamating all the above parameters. The PFZ mapping was carried out off the coast of Semporna, Sabah as there were sufficient fish catch data for accuracy assessment. The R was computed as 0.93 and the higher fish catch areas have coincided very well with the higher PFZ values, meaning the tool is ready for use for operational near real-time fish forecasting.     

Delineating a recharge area for a spring using numerical modeling, Monte Carlo techniques, and geochemical investigation

Recharge areas of spring systems can be hard to identify, but they can be critically important for protection of a spring resource. A recharge area for a spring complex in southern Wisconsin was delineated using a variety of complementary techniques. A telescopic mesh refinement (TMR) model was constructed from an existing regional-scale ground water flow model. This TMR model was formally optimized using parameter estimation techniques; the optimized "best fit" to measured heads and fluxes was obtained by using a horizontal hydraulic conductivity 200% larger than the original regional model for the upper bedrock aquifer and 80% smaller for the lower bedrock aquifer. The uncertainty in hydraulic conductivity was formally considered using a stochastic Monte Carlo approach. Two-hundred model runs used uniformly distributed, randomly sampled, horizontal hydraulic conductivity values within the range given by the TMR optimized values and the previously constructed regional model. A probability distribution of particles captured by the spring, or a "probabilistic capture zone," was calculated from the realistic Monte Carlo results (136 runs of 200). In addition to portions of the local surface watershed, the capture zone encompassed areas outside of the watershed--demonstrating that the ground watershed and surface watershed do not coincide. Analysis of water collected from the site identified relatively large contrasts in chemistry, even for springs within 15 m of one another. The differences showed a distinct gradation from Ordovician-carbonate-dominated water in western spring vents to Cambrian-sandstone-influenced water in eastern spring vents. The difference in chemistry was attributed to distinctive bedrock geology as demonstrated by overlaying the capture zone derived from numerical modeling over a bedrock geology map for the area. This finding gives additional confidence to the capture zone calculated by modeling.     

Generic rule-sets for automated detection of urban tree species from very high-resolution satellite data

The sustainable management and monitoring of urban forests is an important activity in the urbanized world, and operational approaches require information about the status of urban trees to determine the best strategy. One limitation in urban forest studies is the detection and discrimination of tree species using limited training data. Thus, this study focuses on developing generic rule sets from high-resolution WorldView-2 imagery in conjunction with spectral, spatial, colour and textural information for automated urban tree species detection. The object-based image analysis and its combination with statistical analysis of object features is utilized for this purpose. Results of attribute selection indicated that from 55 attributes, only 26 were useful to discriminate urban tree species, namely Messua ferrea L., Samanea saman and Casuarina sumatrana. Finally, the high overall accuracy, approximately 86.87% with kappa of 0.75 confirmed the transferability of the generic model.     

FRESHWATER ALGAE COMMUNITY OF A MELTWATER RUNNEL,REEVE HILL, CASEY STATION, ANTARCTICA

Researches on the Antarctic microalgal diversityand distributions in the freshwater environment ran-ging from snow to large lakes and well-definedstreams are extensive.For instance,studies on thestream microalgal community have been conducted onthe Pervay…     

Precursory signals associated with the 2010 M8.8 Bio-Bio earthquake (Chile) and the 2010 M7.2 Baja California earthquake (Mexico)

Recently, new theories on underground geophysical and geochemical interactions which had been reported to occur during the preparation stages of earthquakes and the remotely measurable variations have been put to test and some warning factors were suggested as earthquake precursors. Data vendors are providing daily basis information from the earth's surface by combining remote sensing data and in situ observations. In this paper, we analyze atmospheric, oceanic, and surface changes in the ocean, coast, and land lying near the epicenters of two recent major earthquakes. The changes are studied in terms of the regional fault locations which have been reported by the U.S. Geological Survey as the shake triggering geological structures. Our detailed analyses showed anomalous increases of surface latent heat flux (SLHF) for both the earthquakes. Meaningfully limited to the geographical extents of the regional active faults, the SLHF variation patterns suggest pre-seismic activity 2–3 weeks before the main events. The agreement of these variations with abnormalities in other climatic and surface factors like relative humidity and temperature represents an unusual situation during the same period as well. Spatiotemporal variations of chlorophyll-a was also studied as another earthquake indicator. Abnormal rises in these factors are possibly caused by the formation of micro-cracks, heat production, evaporation, ionization, and upwelling of nutrient-rich water produced by pre-seismic activity prior to the main events.     

A hybrid model using machine learning methods and GIS for potential rockfall source identification from airborne laser scanning data

The main objectives of this paper are to design and evaluate a hybrid approach based on Gaussian mixture model (GMM) and random forest (RF) for detecting rockfall source areas using airborne laser scanning data. The former model was used to calculate automatically slope angle thresholds for different type of landslides such as shallow, translational, rotational, rotational-translational, complex, debris flow, and rockfalls. After calculating the slope angle thresholds, a homogenous morphometric land use area (HMLA) was constructed to improve the performance of the model computations and reduce the sensitivity of the model to the variations in different conditioning factors. After that, the support vector machine (SVM) was applied in addition to backward elimination (BE) to select and rank the conditioning factors considering the type of landslides. Then, different machine learning methods [artificial neural network (ANN), logistic regression (LR), and random forest (RF) were trained with the selected best factors and previously prepared inventory datasets. The best fit method (RF) was then used to generate the probability maps and then the source areas were detected by combining the slope raster (reclassified according to the thresholds found by the GMM model) and the probability maps. The accuracy assessment shows that the proposed hybrid model could detect the potential rockfalls with an accuracy of 0.92 based on training data and 0.96 on validation data. Overall, the proposed model is an efficient model for identifying rockfall source areas in the presence of other types of landslides with an accepted generalization performance.     

3D visualizing of volumetric surface movement at karstic limestone area

Limestone bedrock topography has complex phenomena and highly relief subsurface topography due to the presence of karstic features. Geotechnical and environmental problems arise whenever foundations are established on the surface of the limestone bedrock or within the overburden soils. Geographical information system (GIS) and remote sensing are emerging as powerful techniques widely applicable in natural resources management to detect land use changes and devise strategies based on these changes. The study focuses on using aerial photography for the detection of changes and effects of mining on geomorphology, especially the use of sequential images that allows to detect changes taken place from time to time, by using. Volumetric Surface Movement Spatiotemporal Data Model (VSMSDM) application has been employed to create karst terrain surface movements and visualized 3D information in the Virtual Geographical Information Systems (VGIS). VSMSDM application was implemented by developing prototype of visualization system using with integrated time in the TIN structure. The data have been collected from aerial photography in 1981 and 2004, and the results displayed that the proposed data model is able to view the changing in karst topography to detect significant landscapes and landforms changes.     

Rainfall–runoff modelling and water balance analysis for Al-Hindiyah barrage,Iraq using remote sensing and GIS

Investigation of rainfall–run-off modelling is an important subject to develop any available means to water supply, which maintains human life such as run-off harvesting method. This study aims to analyse and understand the rainfall–run-off relationship in a part of Babil city, Iraq. Curve number which is a function of land use, soil texture, soil moisture and land slope is used in this study. Remote sensing and GIS are used to analyse the data and to produce the run-off depth map for the study area. Then, the run-off depth is used with rainfall to investigate the relationship between them using linear correlation. This study showed a strong linear relationship between rainfall and run-off (R² = 0.992). It indicates that in the absence of rainfall data, run-off data can be used to estimate rainfall amount. Also, the study revealed through water balance analysis that there is an average monthly change in storage.