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29 results on '"Kariyeva, Jahan"'

6. Climate Change in Turkmenistan

Author

Lioubimtseva, Elena, Kariyeva, Jahan, Henebry, Geoffrey M., Barceló, Damià, Editor-in-chief, Kostianoy, Andrey G., Editor-in-chief, and Zonn, Igor S., editor

Published
2014
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25. Data and information management for the monitoring of biodiversity in Alberta

Author

Sólymos, Péter, primary, Morrison, Shawn F., additional, Kariyeva, Jahan, additional, Schieck, Jim, additional, Haughland, Diane L., additional, Azeria, Ermias T., additional, Cobb, Tyler, additional, Hinchliffe, Robert, additional, Kittson, Jillian, additional, Mcintosh, Anne C. S., additional, Narwani, Tara, additional, Pierossi, Paola, additional, Roy, Marie-Claude, additional, Sandybayev, Turar, additional, Boutin, Stan, additional, and Bayne, Erin, additional

Published
2015
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26. LAND SURFACE PHENOLOGICAL RESPONSES TO LAND USE AND CLIMATE VARIATION IN A CHANGING CENTRAL ASIA

Author

van Leeuwen, Willem J.D., van Leeuwen, Willem J. D., Marsh, Stuart E., Woodhouse, Connie, Yool, Stephen R., Kariyeva, Jahan, van Leeuwen, Willem J.D., van Leeuwen, Willem J. D., Marsh, Stuart E., Woodhouse, Connie, Yool, Stephen R., and Kariyeva, Jahan

Abstract

During the last few decades Central Asia has experienced widespread changes in land cover and land use following the socio-economic and institutional transformations of the region catalyzed by the USSR collapse in 1991. The decade-long drought events and steadily increasing temperature regimes in the region came on top of these institutional transformations, affecting the long term and landscape scale vegetation responses. This research is based on the need to better understand the potential ecological and policy implications of climate variation and land use practices in the contexts of landscape-scale changes dynamics and variability patterns of land surface phenology responses in Central Asia. The land surface phenology responses - the spatio-temporal dynamics of terrestrial vegetation derived from the remotely sensed data - provide measurements linked to the timing of vegetation growth cycles (e.g., start of growing season) and total vegetation productivity over the growing season, which are used as a proxy for the assessment of effects of variations in environmental settings. Local and regional scale assessment of the before and after the USSR collapse vegetation response patterns in the natural and agricultural systems of the Central Asian drylands was conducted to characterize newly emerging links (since 1991) between coupled human and natural systems, e.g., socio-economic and policy drivers of altered land and water use and distribution patterns. Spatio-temporal patterns of bioclimatic responses were examined to determine how phenology is associated with temperature and precipitation in different land use types, including rainfed and irrigated agricultural types. Phenological models were developed to examine relationship between environmental drivers and effect of their altitudinal and latitudinal gradients on the broad-scale vegetation response patterns in non-cropland ecosystems of the desert, steppe, and mountainous regional landscapes of Central Asia.The

Published
2010

28. Comparing Deep Learning and Shallow Learning for Large-Scale Wetland Classification in Alberta, Canada.

Author

DeLancey, Evan R., Simms, John F., Mahdianpari, Masoud, Brisco, Brian, Mahoney, Craig, and Kariyeva, Jahan

Subjects
ARTIFICIAL neural networks, WETLANDS, IMAGE recognition (Computer vision), TAIGAS, REMOTE sensing, DEEP learning
Abstract

Advances in machine learning have changed many fields of study and it has also drawn attention in a variety of remote sensing applications. In particular, deep convolutional neural networks (CNNs) have proven very useful in fields such as image recognition; however, the use of CNNs in large-scale remote sensing landcover classifications still needs further investigation. We set out to test CNN-based landcover classification against a more conventional XGBoost shallow learning algorithm for mapping a notoriously difficult group of landcover classes, wetland class as defined by the Canadian Wetland Classification System. We developed two wetland inventory style products for a large (397,958 km2) area in the Boreal Forest region of Alberta, Canada, using Sentinel-1, Sentinel-2, and ALOS DEM data acquired in Google Earth Engine. We then tested the accuracy of these two products against three validation data sets (two photo-interpreted and one field). The CNN-generated wetland product proved to be more accurate than the shallow learning XGBoost wetland product by 5%. The overall accuracy of the CNN product was 80.2% with a mean F1-score of 0.58. We believe that CNNs are better able to capture natural complexities within wetland classes, and thus may be very useful for complex landcover classifications. Overall, this CNN framework shows great promise for generating large-scale wetland inventory data and may prove useful for other landcover mapping applications. [ABSTRACT FROM AUTHOR]

Published
2020
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29. LIGHTING EFFICIENCY FEASIBILITY STUDY OF THREE OHIO UNIVERSITY BUILDINGS

Author

Kariyeva, Jahan

Subjects
Environmental Sciences, environmental sustainability, energy efficiency, lighting fixtures, green building, sustainable building
Abstract

This thesis aims to evaluate the lighting efficiency of three Ohio University campus buildings. The primary research question is: What are the short- versus long-term costs and benefits to Ohio University of renovating the lighting systems of these older buildings? The research was conducted as a case study with examination of two subquestions: What types of lighting fixtures are currently being used and how efficient are they? How efficient can proposed lighting fixtures be? Results indicate that the cost of installing more energy-efficient lighting fixtures can be quickly recaptured in older buildings. With replacing the present lighting fixtures Ohio University would pay approximately 2.5 times less than it pays currently for the lighting utilities cost of the case study buildings. With these energy savings it would take 3 to 4 years to reclaim the money spent for reinstallation of the energy-efficient lighting fixtures.

Published
2006

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