Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2015 Nov;2(2):83-97.
doi: 10.1002/gdj3.32. Epub 2016 Feb 19.

Datasets Related to In-Land Water for Limnology and Remote Sensing Applications: Distance-To-Land, Distance-To-Water, Water-Body Identifier and Lake-Centre Co-Ordinates

Affiliations
Free PMC article

Datasets Related to In-Land Water for Limnology and Remote Sensing Applications: Distance-To-Land, Distance-To-Water, Water-Body Identifier and Lake-Centre Co-Ordinates

Laura Carrea et al. Geosci Data J. .
Free PMC article

Abstract

Datasets containing information to locate and identify water bodies have been generated from data locating static-water-bodies with resolution of about 300 m (1/360) recently released by the Land Cover Climate Change Initiative (LC CCI) of the European Space Agency. The LC CCI water-bodies dataset has been obtained from multi-temporal metrics based on time series of the backscattered intensity recorded by ASAR on Envisat between 2005 and 2010. The new derived datasets provide coherently: distance to land, distance to water, water-body identifiers and lake-centre locations. The water-body identifier dataset locates the water bodies assigning the identifiers of the Global Lakes and Wetlands Database (GLWD), and lake centres are defined for in-land waters for which GLWD IDs were determined. The new datasets therefore link recent lake/reservoir/wetlands extent to the GLWD, together with a set of coordinates which locates unambiguously the water bodies in the database. Information on distance-to-land for each water cell and the distance-to-water for each land cell has many potential applications in remote sensing, where the applicability of geophysical retrieval algorithms may be affected by the presence of water or land within a satellite field of view (image pixel). During the generation and validation of the datasets some limitations of the GLWD database and of the LC CCI water-bodies mask have been found. Some examples of the inaccuracies/limitations are presented and discussed. Temporal change in water-body extent is common. Future versions of the LC CCI dataset are planned to represent temporal variation, and this will permit these derived datasets to be updated.

Keywords: global; lake; limnology; satellite; surface water temperature.

Figures

Figure 1
Figure 1
A portion of the global LC CCI water‐bodies map represented at 1/20 resolution. The colour shows the number of 1/360×1/360 cells identified as water per 1/20×1/20 latitude–longitude grid box, with a maximum value of 18×18 = 324.
Figure 2
Figure 2
The area around Lake Winnipeg in Canada in the full resolution LC CCI water‐bodies dataset.
Figure 3
Figure 3
Extract of the distance‐to‐land dataset resampled at 1/20 for plotting. The colour scale relates to global distances.
Figure 4
Figure 4
Lake Victoria in Tanzania: example of the distance‐to‐land dataset for inland water (plotted at full resolution).
Figure 5
Figure 5
Global plot of the distance‐to‐land dataset at coarse resolution.
Figure 6
Figure 6
Extract of the distance‐to‐water dataset resampled at 1/20 for plotting. The colour scale relates to global distances.
Figure 7
Figure 7
Global plot of the distance‐to‐water dataset at coarse resolution.
Figure 8
Figure 8
The area around Lake Victoria in Tanzania: extract of the distance‐to‐water dataset for inland water plotted at full resolution.
Figure 9
Figure 9
LC CCI cells labeled as Lake Winnipeg, and the corresponding GLWD polygon for ID 13.
Figure 10
Figure 10
Lake San Martin in Chile/Argentina: the GLWD polygon shown together with the labeled pixels (a) and with the Landsat image from 2015, accessed through Google Earth ©2015 TerraMetrics (b).
Figure 11
Figure 11
Lake Titicaca in Peru/Bolivia: the GLWD polygon shown together with the labeled pixels (a) and with the Landsat image from 2015, accessed through Google Earth ©2015 TerraMetrics (b). Note that land can be seen through the red overlay colour filling the GLWD polygon.
Figure 12
Figure 12
Lake Titicaca in Peru/Bolivia: a portion of the lake where the receding of the water level can be clearly seen. This fact is captured in the labeled pixels of the presented dataset. Landsat image from 2015, accessed through Google Earth ©2015 Landsat.
Figure 13
Figure 13
The Arctic lagoon in the United States: the GLWD polygon shown together with the labeled pixels (a) and with the Landsat image from 2015, accessed through Google Earth ©2015 TerraMetrics (b).
Figure 14
Figure 14
Lago de Serra da Mesa in Brazil: the GLWD polygon shown together with the labeled pixels (a) and with the Landsat image from 2015, accessed through Google Earth ©2015 TerraMetrics (b).
Figure 15
Figure 15
Lake Taymyr in Russia: different identifiers have been assigned to different portion of the lake. Landsat image from 2015, accessed through Google Earth ©2015 TerraMetrics.
Figure 16
Figure 16
Lake Tymyr in Russia: the GLWD polygon shown together with the labeled pixels for the different portions of the lake. In (a) the pixels labeled with 43 are shown, in (b) with 1398, in (c) with 796 and in (d) with 2483.
Figure 17
Figure 17
Lake Rukwa in Tanzania: the GLWD polygon shown together with the labeled pixels (a) and with the Landsat image from 2015, accessed through Google Earth ©2015 TerraMetrics (b).
Figure 18
Figure 18
Lake Tuz in Turkey: the GLWD polygon shown together with the labeled pixels (a) and with the Landsat image from 2015, accessed through Google Earth ©2015 TerraMetrics (b).
Figure 19
Figure 19
The Caniapiscau reservoir in Canada: the GLWD polygon shown together with the labeled pixels (a) and with the Landsat image from 2015, accessed through Google Earth ©2015 TerraMetrics (b).
Figure 20
Figure 20
Lake Sarygamyş in Turkmenistan/Uzbekistan: the GLWD polygon shown together with the labeled pixels (a) and with the Landsat image from 2015, accessed through Google Earth ©2015 TerraMetrics (b).
Figure 21
Figure 21
Lake of the Woods in Canada: the GLWD polygon shown together with the labeled pixels (a) and with the Landsat image from 2015, accessed through Google Earth ©2015 TerraMetrics (b).
Figure 22
Figure 22
The sea around Scotland. The blue pixels are labeled as ocean, the white pixels are land or inland water.
Figure 23
Figure 23
Extract of the water‐body IDs dataset around Lake Winnipeg in Canada. To each ID a unique color has been assigned. The color white corresponds to ‘land’ and the black color to ‘other inland water’. Each of the other colors corresponds to a specific classified lake.
Figure 24
Figure 24
Extract of the water‐body IDs dataset in the south of Sweden. To each ID, a unique color has been assigned. The color white corresponds to ‘land’, the dark blue to ‘sea’ and the red to ‘other inland water’. Each of the other color corresponds to the ID of a specific classified lake.

Similar articles

See all similar articles

Cited by 1 article

References

    1. Alder J. 2003. Putting the coast in the Sea Around Us. The Sea Around Us Newsletter 15:1–2.
    1. Bontemps S, Boettcher M, Brockmann C, Kirches G, Lamarche C, Radoux J, Santoro M, Van Bogaert E, Wegmüller U, Herold M, Achard F, Ramoino F, Arino O, Defourny P. 2015. Multi‐year global land cover mapping at 300 m and characterization for climate modeling: achievements of the Land Cover component of the ESA Climate Change Initiative. The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences XL‐7/W3.
    1. Carrea L, Embury O, Merchant CJ. 2015. Distance to land dataset. NERC Centre for Environmental Data Analysis (CEDA), Dataset, Version 1.0. 10.5285/6be871bc‐9572‐4345‐bb9a‐2c42d9d85ceb - DOI
    1. Carrea L, Embury O, Merchant CJ. 2015. Distance to water dataset. NERC Centre for Environmental Data Analysis (CEDA), Dataset, Version 1.0. 10.5285/6be871bc‐9572‐4345‐bb9a‐2c42d9d85ceb - DOI
    1. Carrea L, Embury O, Merchant CJ. 2015. Water bodies identifiers dataset. NERC Centre for Environmental Data Analysis (CEDA), Dataset, Version 1.0. 10.5285/6be871bc‐9572‐4345‐bb9a‐2c42d9d85ceb - DOI

LinkOut - more resources

Feedback