Started changing 'waterbodies' to 'water bodies' except in the brand …

…name of the product

docs/data/product/dea-waterbodies-landsat/_details.md

@@ -2,11 +2,11 @@
 
 DEA Waterbodies provides up-to-date information about the extent and location of surface water in Australia to enable us to understand of this valuable and increasingly scarce resource.
 
-It uses Geoscience Australia’s archive of over 30 years of Landsat satellite imagery to identify the locations of over 300,000 waterbodies on the Australian landscape and it estimates the wet surface area of these waterbodies.
+It uses Geoscience Australia’s archive of over 30 years of Landsat satellite imagery to identify the locations of over 300,000 water bodies on the Australian landscape and it estimates the wet surface area of these water bodies.
 
-The tool applies a [water classification scheme](/data/product/dea-water-observations-landsat/) to each available Landsat satellite image and maps the locations of waterbodies across Australia. It provides a time series of wet surface areas for all waterbodies that are present for over 10% of the time between 1987 and 2020, and are larger than 2,700 m<sup>2</sup> (the size of three Landsat pixels).
+The tool applies a [water classification scheme](/data/product/dea-water-observations-landsat/) to each available Landsat satellite image and maps the locations of water bodies across Australia. It provides a time series of wet surface areas for all water bodies that are present for over 10% of the time between 1987 and 2020, and are larger than 2,700 m<sup>2</sup> (the size of three Landsat pixels).
 
-The tool allows you to see changes in the wet surface area of waterbodies over time. This can be used to identify when waterbodies are increasing or decreasing in wet surface area.
+The tool allows you to see changes in the wet surface area of water bodies over time. This can be used to identify when water bodies are increasing or decreasing in wet surface area.
 
 % ## Data description
 
@@ -21,7 +21,7 @@ The tool allows you to see changes in the wet surface area of waterbodies over t
 ## Technical information
 
 The DEA Waterbodies product is comprised of two key components:
-* a polygon dataset of automatically mapped waterbody outlines, and
+* a polygon dataset of automatically mapped water body outlines, and
 * a csv time series for each polygon capturing the surface area of water within each polygon at every available, clear Landsat observation. 
 
 ### Data Specification Tables
@@ -37,37 +37,37 @@ The DEA Waterbodies v3.0 Shapefile and CSV contain the following data, as specif
 
 |Field name |Description |Update Frequency |Data Availability*** |Status^ |Type |
 |:----|:----|:----|:----|:----|:----|
-|uid |A unique identifier determined from waterbody location and data version |Once per version |Shapefile, DEA Maps, WMS, csv |Existing |String |
-|perimetr_m |Perimeter of the defined waterbody (m) |Once per version |Shapefile, DEA Maps, WMS |Existing |Real |
-|area_m2 |Area of the defined waterbody (m2) |Once per version |Shapefile, DEA Maps, WMS |Existing |Real |
-|dt_wetobs |The last date any water was observed. This is subject to the satellite having clear visibility of the waterbody. The satellite must view 80% of a waterbody to have a valid wet observation recorded.   |As scene input data is available* |DEA Maps, WMS |New |DateTime (UTC) |
-|wet_sa_m2 |The total estimated wet surface area calculated from the last clear satellite observation of the waterbody. Calculated as the wet percentage (pc_wet, see timeseries) multiplied by the waterbody area (area_m2) divided by 100.** Any area estimates should be compared to additional data for verification. |As scene input data is available* |DEA Maps, WMS |New |Real |
-|dt_satpass |The most recent date that the satellite passed over the waterbody. |As scene input data is available* |DEA Maps, WMS |New |DateTime (UTC) |
+|uid |A unique identifier determined from water body location and data version |Once per version |Shapefile, DEA Maps, WMS, csv |Existing |String |
+|perimetr_m |Perimeter of the defined water body (m) |Once per version |Shapefile, DEA Maps, WMS |Existing |Real |
+|area_m2 |Area of the defined water body (m2) |Once per version |Shapefile, DEA Maps, WMS |Existing |Real |
+|dt_wetobs |The last date any water was observed. This is subject to the satellite having clear visibility of the water body. The satellite must view 80% of a water body to have a valid wet observation recorded.   |As scene input data is available* |DEA Maps, WMS |New |DateTime (UTC) |
+|wet_sa_m2 |The total estimated wet surface area calculated from the last clear satellite observation of the water body. Calculated as the wet percentage (pc_wet, see timeseries) multiplied by the water body area (area_m2) divided by 100.** Any area estimates should be compared to additional data for verification. |As scene input data is available* |DEA Maps, WMS |New |Real |
+|dt_satpass |The most recent date that the satellite passed over the water body. |As scene input data is available* |DEA Maps, WMS |New |DateTime (UTC) |
 |dt_updated |The date that the dt_wetobs, wet_sa_m2 and dt_satpass attributes were last updated. |As scene input data is available* |DEA Maps, WMS |New |DateTime (UTC) |
 |dt_created |The date the polygons were created |Once per version |Shapefile, DEA Maps, WMS |New |DateTime (UTC) |
 |meta_url |The metadata url for this dataset |Once per version |Shapefile, DEA Maps, WMS |New |String |
-|timeseries |The Amazon S3 location of the wet percentage time series for this waterbody. The timeseries data is stored in a CSV file with the following columns: </br></br> (DateTime UTC) – date of observation </br></br> pc_wet (Float) – percentage of the waterbody recorded as wet (0-100) </br></br> px_wet (Integer) – number of 30m Landsat pixels recorded as wet  |Value is static, but the csv contents are updated as scene input data becomes available* |Shapefile, DEA Maps, WMS |Existing |String |
+|timeseries |The Amazon S3 location of the wet percentage time series for this water body. The timeseries data is stored in a CSV file with the following columns: </br></br> (DateTime UTC) – date of observation </br></br> pc_wet (Float) – percentage of the water body recorded as wet (0-100) </br></br> px_wet (Integer) – number of 30m Landsat pixels recorded as wet  |Value is static, but the csv contents are updated as scene input data becomes available* |Shapefile, DEA Maps, WMS |Existing |String |
 
 
 #### Data specification table for DEA Waterbodies 3.0 Timeseries CSV
 
 |Field name |Description |Update Frequency |Data Availability |Status |Type |
 |:----|:----|:----|:----|:----|:----|
 |date |date of observation (UTC) |Value is static, but the csv contents are updated as scene input data becomes available* |DEA Maps, csv |Existing |DateTime (UTC) |
-|pc_wet |percentage of the waterbody recorded as wet (0-100) |Value is static, but the csv contents are updated as scene input data becomes available* |DEA Maps, csv |Existing |Float |
+|pc_wet |percentage of the water body recorded as wet (0-100) |Value is static, but the csv contents are updated as scene input data becomes available* |DEA Maps, csv |Existing |Float |
 |px_wet |number of 30m Landsat pixels recorded as wet |Value is static, but the csv contents are updated as scene input data becomes available* |DEA Maps, csv |Existing |Integer |
 
-</p><p><small>* Scene data is available approximately two weeks from the satellite overpass for the Water Observations feature layers used to process Waterbodies. Waterbodies scenes are processed as Water Observations feature layer scenes become available in the DEA datacube. It takes approximately ten minutes to process Waterbodies per scene.  One Landsat scene measures approximately 190 x 180 km https://www.nasa.gov/wp-content/uploads/2015/04/landsat_9_fast_facts.pdf  
+</p><p><small>* Scene data is available approximately two weeks from the satellite overpass for the Water Observations feature layers used to process water bodies. Water bodies scenes are processed as Water Observations feature layer scenes become available in the DEA datacube. It takes approximately ten minutes to process Waterbodies per scene.  One Landsat scene measures approximately 190 x 180 km https://www.nasa.gov/wp-content/uploads/2015/04/landsat_9_fast_facts.pdf  
 
-** Larger waterbodies are easier to detect and smaller or narrower waterbodies are harder to detect. Area estimates should be compared to additional data for verification. 
+** Larger water bodies are easier to detect and smaller or narrower water bodies are harder to detect. Area estimates should be compared to additional data for verification. 
 
 *** Data fields empty in shapefile (dt_wetobs, wet_sa_m2, dt_satpass, dt_updated) are available for the latest relevant observations only via DEA Maps and WMS 
 
 ^Data fields introduced in v3.0 are ‘New’ </small></p>
 
 ### Producing DEA Waterbodies
 
-DEA Waterbodies is a polygon-based view of DEA Water Observations (DEA WO), derived through the automatic processing of DEA WO to identify the outlines of persistent waterbodies across Australia (Figure 1). 
+DEA Waterbodies is a polygon-based view of DEA Water Observations (DEA WO), derived through the automatic processing of DEA WO to identify the outlines of persistent water bodies across Australia (Figure 1). 
 
 :::{figure} /_files/cmi/V2Workflow.JPG
 :alt: DEA Waterbodies workflow

docs/data/product/dea-waterbodies-landsat/_overview_1.md

@@ -1,6 +1,6 @@
 ## About
 
-Locate over 300,000 waterbodies across Australia and look back at their changes over three decades with Digital Earth Australia (DEA) Waterbodies. Monitor critical lakes and dams, including hard-to-reach waterbodies in remote areas and on large properties.
+Locate over 300,000 water bodies across Australia and look back at their changes over three decades with Digital Earth Australia (DEA) Waterbodies. Monitor critical lakes and dams, including hard-to-reach water bodies in remote areas and on large properties.
 
 :::{admonition} Notes
 :class: note

docs/data/product/dea-waterbodies-landsat/_quality.md

@@ -2,19 +2,19 @@
 
 For a full discussion of the accuracies and limitations of DEA Waterbodies, please refer to [Krause et al. 2021](https://doi.org/10.3390/rs13081437). 
 
-This product shows the wet surface area of waterbodies as estimated from satellites. It does not show depth, volume, purpose of the waterbody, nor the source of the water. Larger waterbodies are easier to detect and smaller or narrower waterbodies are harder to detect. Area estimates should be compared to additional data for verification.  
+This product shows the wet surface area of water bodies as estimated from satellites. It does not show depth, volume, purpose of the water body, nor the source of the water. Larger water bodies are easier to detect and smaller or narrower water bodies are harder to detect. Area estimates should be compared to additional data for verification.  
 
 ### Inaccuracies inherited from DEA Water Observations (WO)
 
-Many of the inaccuracies and limitations of the waterbody analysis are inherited from WO, with this product a reanalysis and mapping product built off the WO datasets. WO has a number of known limitations, and these manifest as misclassified waterbodies within this analysis. WO uses the spectral signature of water to classify wet pixels, and is known to be suboptimal in locations where water and vegetation are mixed. This includes locations such as rivers with vegetated riparian zones and vegetated wetlands. The effect of this can be seen by the discontinuity of narrower river features identified within this analysis, and an under representation of water within vegetated wetlands, such as the Macquarie Marshes, NSW.
+Many of the inaccuracies and limitations of the water body analysis are inherited from WO, with this product a reanalysis and mapping product built off the WO datasets. WO has a number of known limitations, and these manifest as misclassified water bodies within this analysis. WO uses the spectral signature of water to classify wet pixels, and is known to be suboptimal in locations where water and vegetation are mixed. This includes locations such as rivers with vegetated riparian zones and vegetated wetlands. The effect of this can be seen by the discontinuity of narrower river features identified within this analysis, and an under representation of water within vegetated wetlands, such as the Macquarie Marshes, NSW.
 
-Other known WO limitations have been limited through the filtering processes used to produce the map of waterbodies. Issues with mixed water and vegetation pixels around features like small farm dams have been avoided by limiting the size of mapped waterbodies to at least three Landsat pixels. Misclassification of water in deep shadows in high density cities has been handled by removing any waterbody polygons identified within CBDs. Intermittently misclassified features, which return valid results only a handful of times over the 32 year study period, are also filtered out by testing for the number of valid observations returned for each pixel.
+Other known WO limitations have been limited through the filtering processes used to produce the map of water bodies. Issues with mixed water and vegetation pixels around features like small farm dams have been avoided by limiting the size of mapped water bodies to at least three Landsat pixels. Misclassification of water in deep shadows in high density cities has been handled by removing any water body polygons identified within CBDs. Intermittently misclassified features, which return valid results only a handful of times over the 32 year study period, are also filtered out by testing for the number of valid observations returned for each pixel.
 
-Despite this, some errors remain in the final waterbodies dataset. Steep terrain shadows present a known difficulty for the WO classifier, due to the shadows produced. While WO has attempted to mitigate this issue, some misclassification remains. We have not specifically attempted to address these errors within this workflow, and as such, a negligible number of the identified waterbodies may in fact be artifacts caused by terrain shadow. The signal to noise ratio over deeper water has also not been addressed here, and may result in some pixels missing from the centre of deeper waterbodies, resulting in doughnut-shaped mapped polygons. Similarly, different water colours may interfere with the decision-tree classifier, resulting in very turbid or coloured waterbodies being misclassified.
+Despite this, some errors remain in the final water bodies dataset. Steep terrain shadows present a known difficulty for the WO classifier, due to the shadows produced. While WO has attempted to mitigate this issue, some misclassification remains. We have not specifically attempted to address these errors within this workflow, and as such, a negligible number of the identified water bodies may in fact be artifacts caused by terrain shadow. The signal to noise ratio over deeper water has also not been addressed here, and may result in some pixels missing from the centre of deeper water bodies, resulting in doughnut-shaped mapped polygons. Similarly, different water colours may interfere with the decision-tree classifier, resulting in very turbid or coloured water bodies being misclassified.
 
-The automatic cloud masking algorithm used in this analysis can misclassify bright, white sands seen on the bottom of some waterbodies as clouds. This issue is particularly problematic where these bright sands are only exposed when the waterbody begins to empty, resulting in the bright sands being seen inconsistently over time. It is very difficult to accurately cloud mask these sands, as they are seen in some scenes but not others, in the same way that clouds come and go between scenes. In this version of DEA Waterbodies, we have not addressed this issue, and note it as a limitation that results in short or missing timeseries; the sands are incorrectly classified as cloud, and the scene is thrown out as being unsuitable, resulting in very few ‘clear’ scenes. We hope to address this issue in the next release of the DEA Waterbodies product.
+The automatic cloud masking algorithm used in this analysis can misclassify bright, white sands seen on the bottom of some water bodies as clouds. This issue is particularly problematic where these bright sands are only exposed when the water body begins to empty, resulting in the bright sands being seen inconsistently over time. It is very difficult to accurately cloud mask these sands, as they are seen in some scenes but not others, in the same way that clouds come and go between scenes. In this version of DEA Waterbodies, we have not addressed this issue, and note it as a limitation that results in short or missing timeseries; the sands are incorrectly classified as cloud, and the scene is thrown out as being unsuitable, resulting in very few ‘clear’ scenes. We hope to address this issue in the next release of the DEA Waterbodies product.
 
-Some larger salt lakes in Australia have very few records currently available. If less than 90% of the total waterbody is observed on any one day, due to cloud cover or missing data, then that observation is marked as a missing value. For larger bodies, which may cross multiple swath boundaries or suffer from misclassifications (salt lakes can be misclassified as cloud due to their brightness) this can be problematic. 
+Some larger salt lakes in Australia have very few records currently available. If less than 90% of the total water body is observed on any one day, due to cloud cover or missing data, then that observation is marked as a missing value. For larger bodies, which may cross multiple swath boundaries or suffer from misclassifications (salt lakes can be misclassified as cloud due to their brightness) this can be problematic. 
 
 For a full discussion of the limitations and accuracy of WO, see [Mueller et al. (2016)](https://doi.org/10.1016/j.rse.2015.11.003).