A high-resolution bathymetry dataset for global reservoirs using multi-source satellite imagery and altimetry

Shallow marine waters comprise diverse benthic types forming habitats for reef fish community, which important for the livelihood of coastal and small island inhabitants. Satellite imagery provide synoptic map of benthic habitat and further utilized to estimate reef fish stock. The objective of this research was to estimate reef fish stock in complex coral reef of Pulau Pari, by utilizing high resolution satellite imagery of the WorldView-2 in combination with field data such as visual census of reef fish. Field survey was conducted between May-August 2013 with 160 sampling points representing four sites (north, south, west, and east). The image was analy-zed and grouped into five classes of benthic habitats i.e., live coral (LC), dead coral (DC), sand (Sa), seagrass (Sg), and mix (Mx) (combination seagrass+coral and seagrass+sand). The overall accuracy of benthic habitat map was 78%. Field survey revealed that the highest live coral cover (58%) was found at the north site with fish density 3.69 and 1.50 ind/m2at 3 and 10 m depth, respectively. Meanwhile, the lowest live coral cover (18%) was found at the south site with fish density 2.79 and 2.18 ind/m2 at 3 and 10 m depth, respectively. Interpolation on fish density data in each habitat class resulted in standing stock reef fish estimation: LC (5,340,698 ind), DC (56,254,356 ind), Sa (13,370,154 ind), Sg (1,776,195 ind) and Mx (14,557,680 ind). Keywords: mapping, satellite imagery, benthic habitat, reef fish, stock estimation

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Estimations for Percentage of Impervious Area by the Use of Satellite Remote Sensing Imagery

Water Science & Technology ◽

10.2166/wst.1994.0659 ◽

1994 ◽

Vol 29 (1-2) ◽

pp. 135-144 ◽

Cited By ~ 21

Author(s):

C. Deguchi ◽

S. Sugio

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Satellite Imagery ◽

Satellite Remote Sensing ◽

Aerial Photographs ◽

Estimation Errors ◽

Remote Sensing Imagery ◽

Impervious Area ◽

Urbanized Areas

This study aims to evaluate the applicability of satellite imagery in estimating the percentage of impervious area in urbanized areas. Two methods of estimation are proposed and applied to a small urbanized watershed in Japan. The area is considered under two different cases of subdivision; i.e., 14 zones and 17 zones. The satellite imageries of LANDSAT-MSS (Multi-Spectral Scanner) in 1984, MOS-MESSR(Multi-spectral Electronic Self-Scanning Radiometer) in 1988 and SPOT-HRV(High Resolution Visible) in 1988 are classified. The percentage of imperviousness in 17 zones is estimated by using these classification results. These values are compared with the ones obtained from the aerial photographs. The percent imperviousness derived from the imagery agrees well with those derived from aerial photographs. The estimation errors evaluated are less than 10%, the same as those obtained from aerial photographs.

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Identifying Urban Poverty Using High-Resolution Satellite Imagery and Machine Learning Approaches: Implications for Housing Inequality

Land ◽

10.3390/land10060648 ◽

2021 ◽

Vol 10 (6) ◽

pp. 648

Author(s):

Guie Li ◽

Zhongliang Cai ◽

Yun Qian ◽

Fei Chen

Keyword(s):

Machine Learning ◽

High Resolution ◽

Satellite Imagery ◽

Low Income ◽

Urban Poverty ◽

Image Features ◽

Housing Affordability ◽

Learning Approaches ◽

Housing Inequality ◽

High Resolution Satellite Imagery

Enriching Asian perspectives on the rapid identification of urban poverty and its implications for housing inequality, this paper contributes empirical evidence about the utility of image features derived from high-resolution satellite imagery and machine learning approaches for identifying urban poverty in China at the community level. For the case of the Jiangxia District and Huangpi District of Wuhan, image features, including perimeter, line segment detector (LSD), Hough transform, gray-level cooccurrence matrix (GLCM), histogram of oriented gradients (HoG), and local binary patterns (LBP), are calculated, and four machine learning approaches and 25 variables are applied to identify urban poverty and relatively important variables. The results show that image features and machine learning approaches can be used to identify urban poverty with the best model performance with a coefficient of determination, R2, of 0.5341 and 0.5324 for Jiangxia and Huangpi, respectively, although some differences exist among the approaches and study areas. The importance of each variable differs for each approach and study area; however, the relatively important variables are similar. In particular, four variables achieved relatively satisfactory prediction results for all models and presented obvious differences in varying communities with different poverty levels. Housing inequality within low-income neighborhoods, which is a response to gaps in wealth, income, and housing affordability among social groups, is an important manifestation of urban poverty. Policy makers can implement these findings to rapidly identify urban poverty, and the findings have potential applications for addressing housing inequality and proving the rationality of urban planning for building a sustainable society.

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e-Navigating in highly-constrained waters: a case study of the Vistula Lagoon

Journal of Navigation ◽

10.1017/s0373463320000661 ◽

2021 ◽

pp. 1-10

Author(s):

Adam Weintrit ◽

Jacek Pietraszkiewicz ◽

Wiesław Piotrzkowski ◽

Wojciech Tycholiz

Keyword(s):

High Resolution ◽

Global Solution ◽

Transition Process ◽

Practical Implementation ◽

Vistula Lagoon ◽

Digital Era ◽

High Resolution Bathymetry ◽

Marine Navigation ◽

The Vistula Lagoon

Abstract In recent years the transition of marine navigation to the digital era has been gaining momentum. Implementation of e-Navigation solutions varies from country to country in terms of their priorities, goals, levels and effects. Maritime authorities in Poland have been setting the pace in this transition process, not only in Poland but also in general as a global solution. The most recent example is the planned deployment of a variety of e-Navigation tools in the Vistula Lagoon: from GNSS-RTK Ground-Based Augmentation System, to virtual and synthetic aids to navigation, high-resolution bathymetry and advanced navigational software for piloting. The major objectives of this paper are, first, to summarise recent dynamics in the e-Navigation field, and second, to present a practical implementation of the e-Navigation concept in the Vistula Lagoon area.

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Lake-to-Lake Cloud Bands: Frequencies and Locations

Monthly Weather Review ◽

10.1175/2007mwr1960.1 ◽

2007 ◽

Vol 135 (12) ◽

pp. 4202-4213 ◽

Cited By ~ 24

Author(s):

Yarice Rodriguez ◽

David A. R. Kristovich ◽

Mark R. Hjelmfelt

Keyword(s):

High Resolution ◽

Great Lakes ◽

Satellite Imagery ◽

Lake Superior ◽

Great Lakes Region ◽

Lake Effect ◽

Time Period ◽

High Resolution Satellite Imagery

Abstract Premodification of the atmosphere by upwind lakes is known to influence lake-effect snowstorm intensity and locations over downwind lakes. This study highlights perhaps the most visible manifestation of the link between convection over two or more of the Great Lakes lake-to-lake (L2L) cloud bands. Emphasis is placed on L2L cloud bands observed in high-resolution satellite imagery on 2 December 2003. These L2L cloud bands developed over Lake Superior and were modified as they passed over Lakes Michigan and Erie and intervening land areas. This event is put into a longer-term context through documentation of the frequency with which lake-effect and, particularly, L2L cloud bands occurred over a 5-yr time period over different areas of the Great Lakes region.

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