Academic literature on the topic 'Botany Bay Region (N.S.W.)'

Rapid urbanization rates impact significantly on the nature of Land Cover patterns of the environment, which has been evident in the depletion of vegetal reserves and in general modifying the human climatic systems (Henderson, et al., 2017; Kumar, Masago, Mishra, & Fukushi, 2018; Luo and Lau, 2017). This study explores remote sensing classification technique and other auxiliary data to determine LULCC for a period of 50 years (1967-2016). The LULCC types identified were quantitatively evaluated using the change detection approach from results of maximum likelihood classification algorithm in GIS. Accuracy assessment results were evaluated and found to be between 56 to 98 percent of the LULC classification. The change detection analysis revealed change in the LULC types in Minna from 1976 to 2016. Built-up area increases from 74.82ha in 1976 to 116.58ha in 2016. Farmlands increased from 2.23 ha to 46.45ha and bared surface increases from 120.00ha to 161.31ha between 1976 to 2016 resulting to decline in vegetation, water body, and wetlands. The Decade of rapid urbanization was found to coincide with the period of increased Public Private Partnership Agreement (PPPA). Increase in farmlands was due to the adoption of urban agriculture which has influence on food security and the environmental sustainability. The observed increase in built up areas, farmlands and bare surfaces has substantially led to reduction in vegetation and water bodies. The oscillatory nature of water bodies LULCC which was not particularly consistent with the rates of urbanization also suggests that beyond the urbanization process, other factors may influence the LULCC of water bodies in urban settlements. Keywords: Minna, Niger State, Remote Sensing, Land Surface Characteristics References Akinrinmade, A., Ibrahim, K., & Abdurrahman, A. (2012). Geological Investigation of Tagwai Dams using Remote Sensing Technique, Minna Niger State, Nigeria. Journal of Environment, 1(01), pp. 26-32. Amadi, A., & Olasehinde, P. (2010). Application of remote sensing techniques in hydrogeological mapping of parts of Bosso Area, Minna, North-Central Nigeria. International Journal of Physical Sciences, 5(9), pp. 1465-1474. Aplin, P., & Smith, G. (2008). Advances in object-based image classification. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(B7), pp. 725-728. Ayele, G. T., Tebeje, A. K., Demissie, S. S., Belete, M. A., Jemberrie, M. A., Teshome, W. M., . . . Teshale, E. Z. (2018). Time Series Land Cover Mapping and Change Detection Analysis Using Geographic Information System and Remote Sensing, Northern Ethiopia. Air, Soil and Water Research, 11, p 1178622117751603. Azevedo, J. A., Chapman, L., & Muller, C. L. (2016). Quantifying the daytime and night-time urban heat island in Birmingham, UK: a comparison of satellite derived land surface temperature and high resolution air temperature observations. Remote Sensing, 8(2), p 153. Blaschke, T., Hay, G. J., Kelly, M., Lang, S., Hofmann, P., Addink, E., . . . van Coillie, F. (2014). Geographic object-based image analysis–towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing, 87, pp. 180-191. Bukata, R. P., Jerome, J. H., Kondratyev, A. S., & Pozdnyakov, D. V. (2018). Optical properties and remote sensing of inland and coastal waters: CRC press. Camps-Valls, G., Tuia, D., Bruzzone, L., & Benediktsson, J. A. (2014). Advances in hyperspectral image classification: Earth monitoring with statistical learning methods. IEEE signal processing magazine, 31(1), pp. 45-54. Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., . . . Lu, M. (2015). Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing, 103, pp. 7-27. Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile networks and applications, 19(2), pp. 171-209. Cheng, G., Han, J., Guo, L., Liu, Z., Bu, S., & Ren, J. (2015). Effective and efficient midlevel visual elements-oriented land-use classification using VHR remote sensing images. IEEE transactions on geoscience and remote sensing, 53(8), pp. 4238-4249. Cheng, G., Han, J., Zhou, P., & Guo, L. (2014). Multi-class geospatial object detection and geographic image classification based on collection of part detectors. ISPRS Journal of Photogrammetry and Remote Sensing, 98, pp. 119-132. Coale, A. J., & Hoover, E. M. (2015). Population growth and economic development: Princeton University Press. Congalton, R. G., & Green, K. (2008). Assessing the accuracy of remotely sensed data: principles and practices: CRC press. Corner, R. J., Dewan, A. M., & Chakma, S. (2014). Monitoring and prediction of land-use and land-cover (LULC) change Dhaka megacity (pp. 75-97): Springer. Coutts, A. M., Harris, R. J., Phan, T., Livesley, S. J., Williams, N. S., & Tapper, N. J. (2016). Thermal infrared remote sensing of urban heat: Hotspots, vegetation, and an assessment of techniques for use in urban planning. Remote Sensing of Environment, 186, pp. 637-651. Debnath, A., Debnath, J., Ahmed, I., & Pan, N. D. (2017). Change detection in Land use/cover of a hilly area by Remote Sensing and GIS technique: A study on Tropical forest hill range, Baramura, Tripura, Northeast India. International journal of geomatics and geosciences, 7(3), pp. 293-309. Desheng, L., & Xia, F. (2010). Assessing object-based classification: advantages and limitations. Remote Sensing Letters, 1(4), pp. 187-194. Dewan, A. M., & Yamaguchi, Y. (2009). Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization. Applied Geography, 29(3), pp. 390-401. Dronova, I., Gong, P., Wang, L., & Zhong, L. (2015). Mapping dynamic cover types in a large seasonally flooded wetland using extended principal component analysis and object-based classification. Remote Sensing of Environment, 158, pp. 193-206. Duro, D. C., Franklin, S. E., & Dubé, M. G. (2012). A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sensing of Environment, 118, pp. 259-272. Elmhagen, B., Destouni, G., Angerbjörn, A., Borgström, S., Boyd, E., Cousins, S., . . . Hambäck, P. (2015). Interacting effects of change in climate, human population, land use, and water use on biodiversity and ecosystem services. Ecology and Society, 20(1) Farhani, S., & Ozturk, I. (2015). Causal relationship between CO 2 emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia. Environmental Science and Pollution Research, 22(20), pp. 15663-15676. Feng, L., Chen, B., Hayat, T., Alsaedi, A., & Ahmad, B. (2017). The driving force of water footprint under the rapid urbanization process: a structural decomposition analysis for Zhangye city in China. Journal of Cleaner Production, 163, pp. S322-S328. Fensham, R., & Fairfax, R. (2002). Aerial photography for assessing vegetation change: a review of applications and the relevance of findings for Australian vegetation history. Australian Journal of Botany, 50(4), pp. 415-429. Ferreira, N., Lage, M., Doraiswamy, H., Vo, H., Wilson, L., Werner, H., . . . Silva, C. (2015). Urbane: A 3d framework to support data driven decision making in urban development. Visual Analytics Science and Technology (VAST), 2015 IEEE Conference on. Garschagen, M., & Romero-Lankao, P. (2015). Exploring the relationships between urbanization trends and climate change vulnerability. Climatic Change, 133(1), pp. 37-52. Gokturk, S. B., Sumengen, B., Vu, D., Dalal, N., Yang, D., Lin, X., . . . Torresani, L. (2015). System and method for search portions of objects in images and features thereof: Google Patents. Government, N. S. (2007). Niger state (The Power State). Retrieved from http://nigerstate.blogspot.com.ng/ Green, K., Kempka, D., & Lackey, L. (1994). Using remote sensing to detect and monitor land-cover and land-use change. Photogrammetric engineering and remote sensing, 60(3), pp. 331-337. Gu, W., Lv, Z., & Hao, M. (2017). Change detection method for remote sensing images based on an improved Markov random field. Multimedia Tools and Applications, 76(17), pp. 17719-17734. Guo, Y., & Shen, Y. (2015). Quantifying water and energy budgets and the impacts of climatic and human factors in the Haihe River Basin, China: 2. Trends and implications to water resources. Journal of Hydrology, 527, pp. 251-261. Hadi, F., Thapa, R. B., Helmi, M., Hazarika, M. K., Madawalagama, S., Deshapriya, L. N., & Center, G. (2016). Urban growth and land use/land cover modeling in Semarang, Central Java, Indonesia: Colombo-Srilanka, ACRS2016. Hagolle, O., Huc, M., Villa Pascual, D., & Dedieu, G. (2015). A multi-temporal and multi-spectral method to estimate aerosol optical thickness over land, for the atmospheric correction of FormoSat-2, LandSat, VENμS and Sentinel-2 images. Remote Sensing, 7(3), pp. 2668-2691. Hegazy, I. R., & Kaloop, M. R. (2015). Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt. International Journal of Sustainable Built Environment, 4(1), pp. 117-124. Henderson, J. V., Storeygard, A., & Deichmann, U. (2017). Has climate change driven urbanization in Africa? Journal of development economics, 124, pp. 60-82. Hu, L., & Brunsell, N. A. (2015). A new perspective to assess the urban heat island through remotely sensed atmospheric profiles. Remote Sensing of Environment, 158, pp. 393-406. Hughes, S. J., Cabral, J. A., Bastos, R., Cortes, R., Vicente, J., Eitelberg, D., . . . Santos, M. (2016). A stochastic dynamic model to assess land use change scenarios on the ecological status of fluvial water bodies under the Water Framework Directive. Science of the Total Environment, 565, pp. 427-439. Hussain, M., Chen, D., Cheng, A., Wei, H., & Stanley, D. (2013). Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 80, pp. 91-106. Hyyppä, J., Hyyppä, H., Inkinen, M., Engdahl, M., Linko, S., & Zhu, Y.-H. (2000). Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. Forest Ecology and Management, 128(1-2), pp. 109-120. Jiang, L., Wu, F., Liu, Y., & Deng, X. (2014). Modeling the impacts of urbanization and industrial transformation on water resources in China: an integrated hydro-economic CGE analysis. Sustainability, 6(11), pp. 7586-7600. Jin, S., Yang, L., Zhu, Z., & Homer, C. (2017). A land cover change detection and classification protocol for updating Alaska NLCD 2001 to 2011. Remote Sensing of Environment, 195, pp. 44-55. Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., . . . Mitchard, E. T. (2016). A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sensing, 8(1), p 70. Kaliraj, S., Chandrasekar, N., & Magesh, N. (2015). Evaluation of multiple environmental factors for site-specific groundwater recharge structures in the Vaigai River upper basin, Tamil Nadu, India, using GIS-based weighted overlay analysis. Environmental earth sciences, 74(5), pp. 4355-4380. Koop, S. H., & van Leeuwen, C. J. (2015). Assessment of the sustainability of water resources management: A critical review of the City Blueprint approach. Water Resources Management, 29(15), pp. 5649-5670. Kumar, P., Masago, Y., Mishra, B. K., & Fukushi, K. (2018). Evaluating future stress due to combined effect of climate change and rapid urbanization for Pasig-Marikina River, Manila. Groundwater for Sustainable Development, 6, pp. 227-234. Lang, S. (2008). Object-based image analysis for remote sensing applications: modeling reality–dealing with complexity Object-based image analysis (pp. 3-27): Springer. Li, M., Zang, S., Zhang, B., Li, S., & Wu, C. (2014). A review of remote sensing image classification techniques: The role of spatio-contextual information. European Journal of Remote Sensing, 47(1), pp. 389-411. Liddle, B. (2014). Impact of population, age structure, and urbanization on carbon emissions/energy consumption: evidence from macro-level, cross-country analyses. Population and Environment, 35(3), pp. 286-304. Lillesand, T., Kiefer, R. W., & Chipman, J. (2014). Remote sensing and image interpretation: John Wiley & Sons. Liu, Y., Wang, Y., Peng, J., Du, Y., Liu, X., Li, S., & Zhang, D. (2015). Correlations between urbanization and vegetation degradation across the world’s metropolises using DMSP/OLS nighttime light data. Remote Sensing, 7(2), pp. 2067-2088. López, E., Bocco, G., Mendoza, M., & Duhau, E. (2001). Predicting land-cover and land-use change in the urban fringe: a case in Morelia city, Mexico. Landscape and urban planning, 55(4), pp. 271-285. Luo, M., & Lau, N.-C. (2017). Heat waves in southern China: Synoptic behavior, long-term change, and urbanization effects. Journal of Climate, 30(2), pp. 703-720. Mahboob, M. A., Atif, I., & Iqbal, J. (2015). Remote sensing and GIS applications for assessment of urban sprawl in Karachi, Pakistan. Science, Technology and Development, 34(3), pp. 179-188. Mallinis, G., Koutsias, N., Tsakiri-Strati, M., & Karteris, M. (2008). Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site. ISPRS Journal of Photogrammetry and Remote Sensing, 63(2), pp. 237-250. Mas, J.-F., Velázquez, A., Díaz-Gallegos, J. R., Mayorga-Saucedo, R., Alcántara, C., Bocco, G., . . . Pérez-Vega, A. (2004). Assessing land use/cover changes: a nationwide multidate spatial database for Mexico. International Journal of Applied Earth Observation and Geoinformation, 5(4), pp. 249-261. Mathew, A., Chaudhary, R., Gupta, N., Khandelwal, S., & Kaul, N. (2015). Study of Urban Heat Island Effect on Ahmedabad City and Its Relationship with Urbanization and Vegetation Parameters. International Journal of Computer & Mathematical Science, 4, pp. 2347-2357. Megahed, Y., Cabral, P., Silva, J., & Caetano, M. (2015). Land cover mapping analysis and urban growth modelling using remote sensing techniques in greater Cairo region—Egypt. ISPRS International Journal of Geo-Information, 4(3), pp. 1750-1769. Metternicht, G. (2001). Assessing temporal and spatial changes of salinity using fuzzy logic, remote sensing and GIS. Foundations of an expert system. Ecological modelling, 144(2-3), pp. 163-179. Miller, R. B., & Small, C. (2003). Cities from space: potential applications of remote sensing in urban environmental research and policy. Environmental Science & Policy, 6(2), pp. 129-137. Mirzaei, P. A. (2015). Recent challenges in modeling of urban heat island. Sustainable Cities and Society, 19, pp. 200-206. Mohammed, I., Aboh, H., & Emenike, E. (2007). A regional geoelectric investigation for groundwater exploration in Minna area, north west Nigeria. Science World Journal, 2(4) Morenikeji, G., Umaru, E., Liman, S., & Ajagbe, M. (2015). Application of Remote Sensing and Geographic Information System in Monitoring the Dynamics of Landuse in Minna, Nigeria. International Journal of Academic Research in Business and Social Sciences, 5(6), pp. 320-337. Mukherjee, A. B., Krishna, A. P., & Patel, N. (2018). Application of Remote Sensing Technology, GIS and AHP-TOPSIS Model to Quantify Urban Landscape Vulnerability to Land Use Transformation Information and Communication Technology for Sustainable Development (pp. 31-40): Springer. Myint, S. W., Gober, P., Brazel, A., Grossman-Clarke, S., & Weng, Q. (2011). Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment, 115(5), pp. 1145-1161. Nemmour, H., & Chibani, Y. (2006). Multiple support vector machines for land cover change detection: An application for mapping urban extensions. ISPRS Journal of Photogrammetry and Remote Sensing, 61(2), pp. 125-133. Niu, X., & Ban, Y. (2013). Multi-temporal RADARSAT-2 polarimetric SAR data for urban land-cover classification using an object-based support vector machine and a rule-based approach. International journal of remote sensing, 34(1), pp. 1-26. Nogueira, K., Penatti, O. A., & dos Santos, J. A. (2017). Towards better exploiting convolutional neural networks for remote sensing scene classification. Pattern Recognition, 61, pp. 539-556. Oguz, H., & Zengin, M. (2011). Analyzing land use/land cover change using remote sensing data and landscape structure metrics: a case study of Erzurum, Turkey. Fresenius Environmental Bulletin, 20(12), pp. 3258-3269. Pohl, C., & Van Genderen, J. L. (1998). Review article multisensor image fusion in remote sensing: concepts, methods and applications. International journal of remote sensing, 19(5), pp. 823-854. Price, O., & Bradstock, R. (2014). Countervailing effects of urbanization and vegetation extent on fire frequency on the Wildland Urban Interface: Disentangling fuel and ignition effects. Landscape and urban planning, 130, pp. 81-88. Prosdocimi, I., Kjeldsen, T., & Miller, J. (2015). Detection and attribution of urbanization effect on flood extremes using nonstationary flood‐frequency models. Water resources research, 51(6), pp. 4244-4262. Rawat, J., & Kumar, M. (2015). Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science, 18(1), pp. 77-84. Rokni, K., Ahmad, A., Solaimani, K., & Hazini, S. (2015). A new approach for surface water change detection: Integration of pixel level image fusion and image classification techniques. International Journal of Applied Earth Observation and Geoinformation, 34, pp. 226-234. Sakieh, Y., Amiri, B. J., Danekar, A., Feghhi, J., & Dezhkam, S. (2015). Simulating urban expansion and scenario prediction using a cellular automata urban growth model, SLEUTH, through a case study of Karaj City, Iran. Journal of Housing and the Built Environment, 30(4), pp. 591-611. Santra, A. (2016). Land Surface Temperature Estimation and Urban Heat Island Detection: A Remote Sensing Perspective. Remote Sensing Techniques and GIS Applications in Earth and Environmental Studies, p 16. Shrivastava, L., & Nag, S. (2017). MONITORING OF LAND USE/LAND COVER CHANGE USING GIS AND REMOTE SENSING TECHNIQUES: A CASE STUDY OF SAGAR RIVER WATERSHED, TRIBUTARY OF WAINGANGA RIVER OF MADHYA PRADESH, INDIA. Shuaibu, M., & Sulaiman, I. (2012). Application of remote sensing and GIS in land cover change detection in Mubi, Adamawa State, Nigeria. J Technol Educ Res, 5, pp. 43-55. Song, B., Li, J., Dalla Mura, M., Li, P., Plaza, A., Bioucas-Dias, J. M., . . . Chanussot, J. (2014). Remotely sensed image classification using sparse representations of morphological attribute profiles. IEEE transactions on geoscience and remote sensing, 52(8), pp. 5122-5136. Song, X.-P., Sexton, J. O., Huang, C., Channan, S., & Townshend, J. R. (2016). Characterizing the magnitude, timing and duration of urban growth from time series of Landsat-based estimates of impervious cover. Remote Sensing of Environment, 175, pp. 1-13. Tayyebi, A., Shafizadeh-Moghadam, H., & Tayyebi, A. H. (2018). Analyzing long-term spatio-temporal patterns of land surface temperature in response to rapid urbanization in the mega-city of Tehran. Land Use Policy, 71, pp. 459-469. Teodoro, A. C., Gutierres, F., Gomes, P., & Rocha, J. (2018). Remote Sensing Data and Image Classification Algorithms in the Identification of Beach Patterns Beach Management Tools-Concepts, Methodologies and Case Studies (pp. 579-587): Springer. Toth, C., & Jóźków, G. (2016). Remote sensing platforms and sensors: A survey. ISPRS Journal of Photogrammetry and Remote Sensing, 115, pp. 22-36. Tuholske, C., Tane, Z., López-Carr, D., Roberts, D., & Cassels, S. (2017). Thirty years of land use/cover change in the Caribbean: Assessing the relationship between urbanization and mangrove loss in Roatán, Honduras. Applied Geography, 88, pp. 84-93. Tuia, D., Flamary, R., & Courty, N. (2015). Multiclass feature learning for hyperspectral image classification: Sparse and hierarchical solutions. ISPRS Journal of Photogrammetry and Remote Sensing, 105, pp. 272-285. Tzotsos, A., & Argialas, D. (2008). Support vector machine classification for object-based image analysis Object-Based Image Analysis (pp. 663-677): Springer. Wang, L., Sousa, W., & Gong, P. (2004). Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery. International journal of remote sensing, 25(24), pp. 5655-5668. Wang, Q., Zeng, Y.-e., & Wu, B.-w. (2016). Exploring the relationship between urbanization, energy consumption, and CO2 emissions in different provinces of China. Renewable and Sustainable Energy Reviews, 54, pp. 1563-1579. Wang, S., Ma, H., & Zhao, Y. (2014). Exploring the relationship between urbanization and the eco-environment—A case study of Beijing–Tianjin–Hebei region. Ecological Indicators, 45, pp. 171-183. Weitkamp, C. (2006). Lidar: range-resolved optical remote sensing of the atmosphere: Springer Science & Business. Wellmann, T., Haase, D., Knapp, S., Salbach, C., Selsam, P., & Lausch, A. (2018). Urban land use intensity assessment: The potential of spatio-temporal spectral traits with remote sensing. Ecological Indicators, 85, pp. 190-203. Whiteside, T. G., Boggs, G. S., & Maier, S. W. (2011). Comparing object-based and pixel-based classifications for mapping savannas. International Journal of Applied Earth Observation and Geoinformation, 13(6), pp. 884-893. Willhauck, G., Schneider, T., De Kok, R., & Ammer, U. (2000). Comparison of object oriented classification techniques and standard image analysis for the use of change detection between SPOT multispectral satellite images and aerial photos. Proceedings of XIX ISPRS congress. Winker, D. M., Vaughan, M. A., Omar, A., Hu, Y., Powell, K. A., Liu, Z., . . . Young, S. A. (2009). Overview of the CALIPSO mission and CALIOP data processing algorithms. Journal of Atmospheric and Oceanic Technology, 26(11), pp. 2310-2323. Yengoh, G. T., Dent, D., Olsson, L., Tengberg, A. E., & Tucker III, C. J. (2015). Use of the Normalized Difference Vegetation Index (NDVI) to Assess Land Degradation at Multiple Scales: Current Status, Future Trends, and Practical Considerations: Springer. Yu, Q., Gong, P., Clinton, N., Biging, G., Kelly, M., & Schirokauer, D. (2006). Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogrammetric Engineering & Remote Sensing, 72(7), pp. 799-811. Zhou, D., Zhao, S., Zhang, L., & Liu, S. (2016). Remotely sensed assessment of urbanization effects on vegetation phenology in China's 32 major cities. Remote Sensing of Environment, 176, pp. 272-281. Zhu, Z., Fu, Y., Woodcock, C. E., Olofsson, P., Vogelmann, J. E., Holden, C., . . . Yu, Y. (2016). Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000–2014). Remote Sensing of Environment, 185, pp. 243-257.

The results obtained in the August and December 2003, August 2004 and January 2005 oceanographic campaigns in the northern region of the Todos os Santos Bay (lat. 12º44.5'S; long. 038º35.00'W) between the Madre de Deus and Maré islands are analyzed. Instruments of continuous and discrete samplings were used to measure hydrographic properties currents and tides. The water mass of the northern region of the bay is forced by semidiurnal and mesotides of form number 0.08 and the lunar component M2 height was estimated at 91cm. The time series of the surface currents indicated movements in the N/S direction, forced by the tide with maximum magnitudes of 0.73 m.s-1 on the December 2003 campaign. However, in August 2004 the currents were dominated by the wind stress forcing, with a maximum speed of 1.85 m.s-1 and SE direction. Near the bottom, the influence of the tide is not as evident, with a decrease in intensity due to internal and bottom friction, with a maximum velocity of 0.17 m.s-1. The thermal and haline structures were weakly horizontally, as well as vertically stratified, with extreme values varying in the intervals 23ºC (August, 2004) to 28ºC (December, 2003) and 31.0 psu (August, 2003) to 36.0 psu (December, 2003), respectively. Some conclusions may be drawn from these results: i) The signs of the dilution of the fresh water discharges of the Caípe, Mataripe and São Paulo rivers in the region under the influence of the RLAM were observed only during the winter periods, but in the summer the region was flooded by waters of oceanic origin and the salinities above 36.0 indicated TW mass intrusion; ii) The N-S circulation near the RLAM is strongly dominated by the tide, and the importance of the M2 component was unequivocal, however, the E-W component presented some tidal modulation away from abrupt bottom topographical changes, and iii) The residual series, calculated as the difference between the original and modeled, is about ¼ of the original and confirmed its semidiurnal character.

Abstract. Continuous hourly measurements of gas-phase ammonia (NH3(g)) were taken from 13 July to 7 August 2014 on a research cruise throughout Baffin Bay and the eastern Canadian Arctic Archipelago. Concentrations ranged from 30–650 ng m−3 (40–870 pptv) with the highest values recorded in Lancaster Sound (74°13' N, 84°00' W). Simultaneous measurements of total ammonium ([NHx]), pH and temperature in the ocean and in melt ponds were used to compute the compensation point (χ), which is the ambient NH3(g) concentration at which surface–air fluxes change direction. Ambient NH3(g) was usually several orders of magnitude larger than both χocean and χMP (< 0.4–10 ng m3) indicating these surface pools are net sinks of NH3(g). Flux calculations estimate average net downward fluxes of 1.4 and 1.1 ng m-2 s-1 for the open ocean and melt ponds, respectively. Sufficient NH3(g) was present to neutralize non-sea salt sulphate (nss-SO42-) in the boundary layer during most of the study. This finding was corroborated with a historical dataset of PM2.5 composition from Alert, NU (82°30' N, 62°20' W) wherein the median ratio of NH4+/nss-SO42- equivalents was greater than 0.75 in June, July and August. The GEOS-Chem chemical transport model was employed to examine the impact of NH3(g) emissions from seabird guano on boundary-layer composition and nss-SO42- neutralization. A GEOS-Chem simulation without seabird emissions underestimated boundary layer NH3(g) by several orders of magnitude and yielded highly acidic aerosol. A simulation that included seabird NH3 emissions was in better agreement with observations for both NH3(g) concentrations and nss-SO42- neutralization. This is strong evidence that seabird colonies are significant sources of NH3(g) in the summertime Arctic, and are ubiquitous enough to impact atmospheric composition across the entire Baffin Bay region. Large wildfires in the Northwest Territories were likely an important source of NH3(g), but their influence was probably limited to the Central Canadian Arctic. Implications of seabird-derived N-deposition to terrestrial and aquatic ecosystems are also discussed.

Abstract. Continuous hourly measurements of gas-phase ammonia (NH3(g)) were taken from 13 July to 7 August 2014 on a research cruise throughout Baffin Bay and the eastern Canadian Arctic Archipelago. Concentrations ranged from 30 to 650 ng m−3 (40–870 pptv) with the highest values recorded in Lancaster Sound (74°13′ N, 84°00′ W). Simultaneous measurements of total ammonium ([NHx]), pH and temperature in the ocean and in melt ponds were used to compute the compensation point (χ), which is the ambient NH3(g) concentration at which surface–air fluxes change direction. Ambient NH3(g) was usually several orders of magnitude larger than both χocean and χMP (< 0.4–10 ng m3) indicating these surface pools are net sinks of NH3. Flux calculations estimate average net downward fluxes of 1.4 and 1.1 ng m−2 s−1 for the open ocean and melt ponds, respectively. Sufficient NH3(g) was present to neutralize non-sea-salt sulfate (nss-SO42−) in the boundary layer during most of the study. This finding was corroborated with a historical data set of PM2.5 composition from Alert, Nunavut (82°30′ N, 62°20′ W) wherein the median ratio of NH4+/nss-SO42− equivalents was greater than 0.75 in June, July and August. The GEOS-Chem chemical transport model was employed to examine the impact of NH3(g) emissions from seabird guano on boundary-layer composition and nss-SO42− neutralization. A GEOS-Chem simulation without seabird emissions underestimated boundary layer NH3(g) by several orders of magnitude and yielded highly acidic aerosol. A simulation that included seabird NH3 emissions was in better agreement with observations for both NH3(g) concentrations and nss-SO42− neutralization. This is strong evidence that seabird colonies are significant sources of NH3 in the summertime Arctic, and are ubiquitous enough to impact atmospheric composition across the entire Baffin Bay region. Large wildfires in the Northwest Territories were likely an important source of NH3, but their influence was probably limited to the Central Canadian Arctic. Implications of seabird-derived N-deposition to terrestrial and aquatic ecosystems are also discussed.

Abstract. Recent satellite observations show efficient vertical transport of Asian pollutants from the surface to the upper-level anticyclone by deep monsoon convection. In this paper, we examine the transport of carbonaceous aerosols, including black carbon (BC) and organic carbon (OC), into the monsoon anticyclone using of ECHAM6-HAM, a global aerosol climate model. Further, we investigate impacts of enhanced (doubled) carbonaceous aerosol emissions on the upper troposphere and lower stratosphere (UTLS), underneath monsoon circulation and precipitation from sensitivity simulations. The model simulation shows that boundary layer aerosols are transported into the monsoon anticyclone by the strong monsoon convection from the Bay of Bengal, southern slopes of the Himalayas and the South China Sea. Doubling of emissions of both BC and OC aerosols over Southeast Asia (10° S–50° N, 65–155° E) shows that lofted aerosols produce significant warming (0.6–1 K) over the Tibetan Plateau (TP) near 400–200 hPa and instability in the middle/upper troposphere. These aerosols enhance radiative heating rates (0.02–0.03 K day−1) near the tropopause. The enhanced carbonaceous aerosols alter aerosol radiative forcing (RF) at the surface by −4.74 ± 1.42 W m−2, at the top of the atmosphere (TOA) by +0.37 ± 0.26 W m−2 and in the atmosphere by +5.11 ± 0.83 W m−2 over the TP and Indo-Gangetic Plain region (15–35° N, 80–110° E). Atmospheric warming increases vertical velocities and thereby cloud ice in the upper troposphere. Aerosol induced anomalous warming over the TP facilitates the relative strengthening of the monsoon Hadley circulation and increases moisture inflow by strengthening the cross-equatorial monsoon jet. This increases precipitation amounts over India (1–4 mm day−1) and eastern China (0.2–2 mm day−1). These results are significant at the 99 % confidence level.

AbstractDuring the Bay of Bengal (BoB) Boundary Layer Experiment (BoBBLE) in the southern BoB, time series of microstructure measurements were obtained at 8°N, 89°E from 4 to 14 July 2016. These observations captured events of barrier layer (BL) erosion and reformation. Initially, a three-layer structure was observed: a fresh surface mixed layer (ML) of thickness 10–20 m; a BL below of 30–40-m thickness with similar temperature but higher salinity; and a high salinity core layer, associated with the Summer Monsoon Current. Each of these three layers was in relative motion to the others, leading to regions of high shear at the interfaces. However, the destabilizing influence of the shear regions was not enough to overcome the haline stratification, and the three-layer structure was preserved. A salinity budget using in situ observations suggested that during the BL erosion, differential advection brought high salinity surface waters (34.5 psu) with weak stratification to the time series location and replaced the three-layer structure with a deep ML (~60 m). The resulting weakened stratification at the time series location then allowed atmospheric wind forcing to penetrate deeper. The turbulent kinetic energy dissipation rate and eddy diffusivity showed elevated values above 10−7 W kg−1 and 10−4 m2 s−1, respectively, in the upper 60 m. Later, the surface salinity decreased again (33.8 psu) through differential horizontal advection, stratification became stronger and elevated mixing rates were confined to the upper 20 m, and the BL reformed. A 1D model experiment suggested that in the study region, differential advection of temperature–salinity characteristics is essential for the maintenance of BL and to the extent to which mixing penetrates the water column.

Detailed records of methanesulphonic acid (MSA) and non-sea-salt sulphate (nss SO4 2−) have been obtained from ice cores drilled on Dolleman Island on the east coast of the Antarctic Peninsula (70°35.2′S, 60°55.5′W). Annual average concentrations of MSA are presented for the period 1652–1992. Over this time span, the mean annual concentration of MSA is 0.69 μeq l−1 (σ = 0.33, n = 340), the range is 0.13–2.35μeq l−1, and the MSA/nss-SO4 2− ratio is 0.22. The high MSA concentration reflects the proximity of the Weddell Sea, believed to be a region of high marine phytoplankton production. The overall mean nss-SO4 2− concentration is about 66% of the total sulphate deposited in the snowfall. Low-frequency variations of MSA and oxygen-isotope signals correlate closely, indicating that they may be modulated by similar atmospheric processes. Positive correlations are observed between the oxygen-isotope signature and both MSA (r = 0.41) and nss SO4 2− (r = 0.50), significant at the 99% level. A small negative correlation can be seen between both species and the annual duration of sea ice at Scotia Bay, Laurie Island in the South Orkneys, since 1902 (MSA r= –0.23, and nss SO4 2– r = –0.29; significant at 95% confidence). No significant link between high MSA concentrations and El Niño events is observed at this location.

Detailed records of methanesulphonic acid (MSA) and non-sea-salt sulphate (nss SO42−) have been obtained from ice cores drilled on Dolleman Island on the east coast of the Antarctic Peninsula (70°35.2′S, 60°55.5′W). Annual average concentrations of MSA are presented for the period 1652–1992. Over this time span, the mean annual concentration of MSA is 0.69 μeq l−1 (σ = 0.33, n = 340), the range is 0.13–2.35μeq l−1, and the MSA/nss-SO42− ratio is 0.22. The high MSA concentration reflects the proximity of the Weddell Sea, believed to be a region of high marine phytoplankton production. The overall mean nss-SO42− concentration is about 66% of the total sulphate deposited in the snowfall. Low-frequency variations of MSA and oxygen-isotope signals correlate closely, indicating that they may be modulated by similar atmospheric processes. Positive correlations are observed between the oxygen-isotope signature and both MSA (r = 0.41) and nss SO42− (r = 0.50), significant at the 99% level. A small negative correlation can be seen between both species and the annual duration of sea ice at Scotia Bay, Laurie Island in the South Orkneys, since 1902 (MSA r= –0.23, and nss SO42–r = –0.29; significant at 95% confidence). No significant link between high MSA concentrations and El Niño events is observed at this location.

I. A part of the West Greenland current flows westwards across the Davis Strait and meets the Canadian (Baffin Land) current to form the Labrador current which follows southwards the shelf and slope of Labrador Coast {figure I). Meeting the northern edge of the Newfoundland Banks, the Labrador current splits in a minor western branch running along the Avalon Peninsula and a major one proceeding south along the eastern edge of the shelf down to the Tail of the Grand Banks. There it meets the Atlantic current which very often forms a prominent westward salient at about 45°N. and 47 - 48°W. (figure IX). This salient tends to deflect and turn back east or northeastwards a variable portion of the major branch. The turning point and the volume of the deflected stream are subject to change according to the position and importance of the Atlantic salient. It is estimated that the West Greenland current and the Canadian current join respectively in a 60% and 40% ratio. The major eastern branch in the Grand Banks region amounts to 80 to 90%, splitting in turn about fifty-fifty in an eastward stream flowing just north of Flemish Cap and the current proceeding southwards along the edge of the Grand Banks. One of the most striking features of the Labrador current is its banded structure. In the area where it forms there are at least three bands (figure VII) : 1. a fresh and very cold inshore band made of polar and coastal waters ; 2. an offshore stream in the Canadian current carrying Baffin Bay waters ; and, 3. — further seawards — the West Greenland portion whose waters are mild in temperature and possess Atlantic salinity. Two cold walls mark the sharp limits between these bodies of water. Along the Labrador Coast the banding agrees pretty well with the submarine topography ; an inshore slow, cold and fresh stream is confined to the continental shelf while an offshore fast, mild and salted current follows the edge of the shelf and slope. Both exhibit shoreward and seaward salients in agreement to the shelf varying in width. In the Grand Banks area — apart from the well known Newfoundland cold wall between the contrary Labrador and Atlantic currents (see figure XII, A) — there is a marked cold wall inside the eastwards proceeding current between the bulk of the Atlantic waters and that part of Labrador waters that have been deflected by the Atlantic salient and incorporated to the eastward drift (see figures X and XIII). The point of annual cycle and long term temperature and volume variations is not discussed here in full. Some words are devoted to the vernal warming and the maintaining jar into the summer of cold waters on the central part of the Grand Banks (figure XIV). II. Three kinds of sea ice occur along the Labrador Coast and the Atlantic Coast of Newfoundland : 1. the bay or winter ice ; 2. the drift ice ; 3. the icebergs. The bay or winter ice forms every winter and disappears during spring time. The inner parts of the bays of Northern Labrador are frozen over by the middle of December while the offshore inlets and archipelagos are jammed much later, say in the course of January. A tentative map has been sketched to show the date of occurrence of the winter ice. Ice grows till it reaches a width of between 10 and 40 miles, but its thickness never exceeds three feet or so. The bay ice is subject to break during stormy weather and therefore is able to feed largely the drift ice in the heart of winter. Drift ice is made up of both local winter ice and — mainly in the first summer months —• old arctic ice. Due to age and hummockying processes the latter is characterized by heavy pieces and floes which often reach a thickness of 30 to 50 feet. Although the main track of the drift ice is governed by the Labrador current the actual short-range movements are controlled by the changing wind conditions. Four examples of drifting fields are reported. Wind control is pretty well illustrated by sketch-maps XVIII and XIX which show the appearance, deterioration and receding of ice fields carried by the Labrador current in the vicinity of Newfoundland's northern and eastern coasts. The disappearance of an ice field drifting off Southern Labrador and blocking the eastern entrance to the Strait of Belle-Isle — as shown on figure XX — is typical of the features marking the end of the ice season. Figure XXI represents heavy arctic pieces and drift ice of local origin closely packed in the central arc of the Labrador Coast before being swept away by land-breeze. Because of these changing conditions it seems difficult to de fine an average annual cycle of drift ice. The maximum limits are reached by the middle of April ; at that time ice extends as jar south as the 47 th or the 46 th parallel, but normally it keeps off the coast south of St. John s, this harbour being very seldom closed to navigation for more than ten successive days. Tentative is made to show in sketch-maps the average conditions in winter (figure XVI) as well as the receding of the drifting fields and tongues during spring and summer months (figure XVII). The story of the bergs from the parent glaciers of West Greenland down to their melting place around the Banks begins to be a well known one (see figure XXII). According to their very deep draft the bergs are less subject than the drift ice to be driven by the surface and subsurface wind wurrents. Mortality in Baffin Bay, melting and grounding on the Labrador Coast, differential velocity of inshore and offshore bands of the current, location of the point of partition between the eastern and western branches are discussed in order to explain the observed distribution of bergs in the Grand Banks region, jour typical patterns of which are shown on figure XXIII. III. An attempt has been made to show the division between ice and navigation at selected places along the coasts watered by the Labrador current. Figure XXIV is only tentative since the actual conditions due to changing weather often differ strongly from the average ones. When possible, drift ice season has been distinguished. The relatively favoured position of the St. John's harbour appears both on figure XXIV and figures XVI and XVIII. Along the Labrador Coast and in the northern part of the island of Newfoundland, dog sleigh is often the only means of transportation in winter, when fiat bay ice is wide enough and has not been hummockied or broken by stormy weather. Main sleigh tracks and their alternatives are governed by physical conditions as well as winter settlement pattern and therefore are seldom subject to change. Examples of sleigh tracks have been taken in Northern and Central Labrador (figures XXV and XXVI). In the populated areas of Northeastern Newfoundland where fast access to markets and mass transportation are needed, sleighing in winter is no more possible. It can be said that these economic conditions are the main stimulus forroad building in the island. More and more « outports » are progressively freed from backward means of transportation and yin summertime yfrom the navigation itself too. The closing of navigation in the Bay of Exploits (the innermost part of Notre Dame Bay) results in the railway between the said bay and St. John s being peculiarly overloaded in winter, since paper from Bishop's Falls and ore concentrates from Buchans can no more be shipped through Botwood on the Bay of Exploits {figure XXVII).

Macleaya microcarpa (Maxim.) Fedde, also known as smallfruit plume poppy, is a perennial herb belonging to the family Papaveraceae. The plant, together with the better-known species M. cordata (Willd.) R. Br., is native to central China and is now planted worldwide for medicinal purposes. In October 2008 and August 2009, dozens of smallfruit plume poppy planted in the Kraków Botanical Garden, Poland, were found to be severely infected with a powdery mildew. White colonies with abundant sporulation developed on both sides of leaves and young stems, forming circular to irregular patches. Infections caused leaf yellowing and premature defoliation. The damage has been observed every year since 2009. Representative voucher specimens were deposited in the fungal herbarium of the W. Szafer Institute of Botany of the Polish Academy of Sciences (KRAM) and the Korea University herbarium (KUS). Appressoria on the mycelia were lobed, often in pairs. Conidiophores composed of three to four cells arose from the upper part of creeping hyphae, 65 to 120 × 7 to 10 μm, attenuated toward the base, sub-straight or slightly flexuous in foot-cells, and produced conidia singly. Conidia were hyaline, oblong-elliptical to doliiform, 25 to 38 × 12 to 18 μm with a length/width ratio of 1.8 to 2.6; lacked fibrosin bodies; and produced germ tubes on the subterminal position with club-shaped or lobed appressoria. The conidial surface was wrinkled to irregularly reticulate. No chasmothecia were found. The structures described above match well with the anamorph of Erysiphe macleayae R.Y. Zheng & G.Q. Chen (3). To confirm the identity of the causal fungus, the internal transcribed spacer (ITS) region of rDNA from KUS-F24459 was amplified using primers ITS5 and P3 (4) and directly sequenced. The resulting sequence of 553 bp was deposited in GenBank (Accession No. JQ681217). A GenBank BLAST search using the present data revealed >99% sequence similarity of the isolate with E. macleayae on M. cordata from Japan (AB016048). Pathogenicity was confirmed through inoculation by gently pressing diseased leaves onto leaves of three healthy potted plants. Three noninoculated plants served as controls. Plants were maintained in a greenhouse at 25°C. Inoculated plants developed signs and symptoms after 7 days, whereas the control plants remained healthy. The fungus present on the inoculated plants was morphologically identical to that originally observed on diseased plants. The powdery mildew infections of M. cordata associated with E. macleayae have been recorded in China and Japan (2), and more recently in Germany (1,3). To our knowledge, this is the first report of E. macleayae on M. microcarpa globally as well as in Poland. This mildew species was described in China and is endemic to Asia, where chasmothecia of the fungus were found. Only recently have powdery mildews been found on M. cordata in Germany (1,3) and now on M. microcarpa in Poland, indicating the fungus is spreading in Europe. References: (1) N. Ale-Agha et al. Schlechtendalia 17:39, 2008. (2) D. F. Farr and A. Y. Rossman. Fungal Databases, Systematic Mycology and Microbiology Laboratory, ARS, USDA. Retrieved from http://nt.ars-grin.gov/fungaldatabases/ , February 7, 2012. (3) A. Schmidt and M. Scholler. Mycotaxon 115:287, 2011. (4) S. Takamatsu et al. Mycol. Res. 113:117, 2009.