info text changes

src/containers/datasets/alerts/info.mdx

@@ -4,15 +4,15 @@ import LayoutMdx from 'layouts/mdx';
 
 ## Overview:
 
-This data set shows a heatmap representing points were there is high confidence of a change in mangrove forest cover. Note this is an experimental layer, presently only available for Africa, under going validation, and will be updated in the future. Changes in mangrove coverage were identified using a combination of USGS Landsat 8 (LS8), ESA Sentinel-1 (S1) and ESA Sentinel-2 (S2) data. For the optical S2 and LS8 sensors, only scenes where cloud cover is less than 20 % were used. In general scenes were avalaible about every 14-16 days. However, for the optical sensors, cloud cover can substantially reduce data availability in some regions. Potential change features were identified within pixels masked by the 2016 GMW mangrove extent layer; where NDVI values were < 0.2, and backscatering values were < -18 dB for the VV and < -23 dB for the VH polarisation channels, respectively. To combine the scene based potential change features and to filter false positives a scoring system was used where pixels were scored based on the number of times they have been identified as a change. Where S1 identifies a change 1 is added to the score, if LS8 or S2 identify a change then 2 is added to the score. Changes identified within the LS8 and S2 sensors were considered to be more reliable and less frequent (due to cloud cover). If no change was identified for a pixel, which was previously identified as a change, and has a score > 0, then 1 was removed from the score. If the score has a value of 5 or greater then the pixel was deemed to be a ‘True’ change.  The score cannot go below 0 or above 5. Processing was undertaken on a 20 m pixel grid, and then resampled to 60 m for presentation.
+This data set shows a heatmap representing points were there is high confidence of a loss of mangrove forest cover. This layer has a spatial resolution of 20 m and is updated on a monthly basis. The analysis is undertaken using Sentinel-2 imagery and updated on a monthly basis. The mangrove loss alert system is based on optimised normalised difference vegetation index (NDVI) thresholds used to identify mangrove losses and a temporal scoring system to filter false positives. The mangrove loss alert system was found to have an estimated overall accuracy of 92.1%, with the alert commission and omission estimated to be 10.4% and 20.6%, respectively. 
 
 ## DataLink: 
 
 More info available soon
 
 ## Reference:
 
-Bunting et al., (2020). In Prep. Code is available at https://github.com/globalmangrovewatch/gmw_monitoring_demo
+Bunting, P.; Hilarides, L.; Rosenqvist, A.; Lucas, R.M.; Kuto, E.; Gueye, Y.; Ndiaye, L. Global Mangrove Watch: Monthly Alerts of Mangrove Loss for Africa. Remote Sens. 2023, 15, 2050. [https://doi.org/10.3390/rs15082050](https://doi.org/10.3390/rs15082050)
 
 ## Date of content:
 

src/containers/datasets/habitat-change/info.mdx

@@ -4,12 +4,13 @@ import LayoutMdx from 'layouts/mdx';
 
 ## Overview:
 
-This data set shows a ranking of locations with the largest change in areal extent of mangrove habitat between different times. Where location is a defined geometry, such as a country, protected area or area-of-interest. The difference in mangrove habitat extent was calculated between 2 consecutive time periods (t1 and t2); gain and loss are defined as the increase and decrease in extent between t1 and t2, respectively. Net change for the period t1-t2 is the sum of gain and loss. Locations are then ranked by their total gain or loss (km<sup>2</sup>) within the specified time-period. Data on the areal extent of mangrove habitat was generated by Aberystwyth University and soloEO within the framework of the Global Mangrove Watch (GMW) project, which is a part of the Japan Aerospace Exploration Agency’s (JAXA) Kyoto & Carbon Initiative and the Mangrove Capital Africa Programme coordinated by Wetlands International and financed by DOB Ecology. The map (v2.0) depicts the global extent of mangrove forests for the year 2010, derived by Random Forest Classification of a combination of L-band radar (ALOS PALSAR) and optical (Landsat-5, -7) satellite data. All satellite data and software used to derive the GMW mangrove maps are available in the public domain. Approximately 15,000 Landsat scenes and 1,500 ALOS PALSAR (1 x 1 degree) mosaic tiles were used to create optical and radar image composites covering the coastlines along the tropical and sub-tropical coastlines in the Americas, Africa, Asia and Oceania. The classification was confined using a mangrove habitat mask, which defined regions where mangrove ecosystems can be expected to exist. The mangrove habitat definition was based on geographical parameters such as latitude, elevation and distance from oceans. Training for the habitat mask and classification of the 2010 mangrove mask was based on randomly sampling 38 million points using the mangrove masks (for the year 2000) of Giri et al. (2011) and Spalding et al. (2010) and the water occurrence layer defined by Pekel et al. (2017). The data set is available for download at, <a href="http://data.unep-wcmc.org/datasets/45" target="_blank" rel="noopener noreferrer">http://data.unep-wcmc.org/datasets/45</a>
+This data set shows a ranking of locations with the largest change in areal extent of mangrove habitat between different times. Where location is a defined geometry, such as a country, protected area or area-of-interest. The difference in mangrove habitat extent was calculated between 2 consecutive time periods (t1 and t2); gain and loss are defined as the increase and decrease in extent between t1 and t2, respectively. Net change for the period t1-t2 is the sum of gain and loss. Locations are then ranked by their total gain or loss (km<sup>2</sup>) within the specified time-period. Data on the areal extent of mangrove habitat was generated by Aberystwyth University and soloEO within the framework of the Global Mangrove Watch (GMW) project, which is a part of the Japan Aerospace Exploration Agency’s (JAXA) Kyoto & Carbon Initiative and the Mangrove Capital Africa Programme coordinated by Wetlands International and financed by DOB Ecology. The map (v2.0) depicts the global extent of mangrove forests for the year 2010, derived by Random Forest Classification of a combination of L-band radar (ALOS PALSAR) and optical (Landsat-5, -7) satellite data with some regions updated using Sentinel-2 to create the GMW v2.5 baseline (Bunting et al., 2022). All satellite data and software used to derive the GMW mangrove maps are available in the public domain. The classification was confined using a mangrove habitat mask, which defined regions where mangrove ecosystems can be expected to exist [(10.5281/zenodo.7478491)](https://doi.org/10.5281/zenodo.7478491). The mangrove habitat definition was based on geographical parameters such as latitude, elevation and distance from oceans. The data set is available for download at, <a href="http://data.unep-wcmc.org/datasets/45" target="_blank" rel="noopener noreferrer">http://data.unep-wcmc.org/datasets/45</a>
 
-## Reference:
+## References:
 
-Bunting P., Rosenqvist A., Lucas R., Rebelo L-M., Hilarides L., Thomas N., Hardy A., Itoh T., Shimada M. and Finlayson C.M. (2018). <a href="https://www.mdpi.com/2072-4292/10/10/1669" target="_blank" rel="noopener noreferrer">The Global Mangrove Watch – a New 2010 Global Baseline of Mangrove Extent.</a> Remote Sensing, 2018, 10, 1669; doi:10.3390/rs10101669
+Bunting, P.; Rosenqvist, A.; Hilarides, L.; Lucas, R.M.; Thomas, N.; Tadono, T.; Worthington, T.A.; Spalding, M.; Murray, N.J.; Rebelo, L.-M. Global Mangrove Extent Change 1996–2020: Global Mangrove Watch Version 3.0. Remote Sens. 2022, 14, 3657. [https://doi.org/10.3390/rs14153657](https://doi.org/10.3390/rs14153657) 
 
+Bunting, P.; Rosenqvist, A.; Hilarides, L.; Lucas, R.M.; Thomas, N. Global Mangrove Watch: Updated 2010 Mangrove Forest Extent (v2.5). Remote Sens. 2022, 14, 1034. [https://doi.org/10.3390/rs14041034](https://doi.org/10.3390/rs14041034)
 ## Date of content:
 
 1996, 2007, 2008, 2009, 2010, 2015, 2016, 2017, 2018, 2019, 2020

src/containers/datasets/habitat-extent/info.mdx

@@ -6,7 +6,7 @@ import LayoutMdx from 'layouts/mdx';
 This layer depicts the global extent and change of mangrove forests in the years 1996, 2007 - 2010 and 2015 - 2020. The data set was generated within the framework of the Global Mangrove Watch (GMW) , an initiative convened by Aberystwyth University, soloEO, The Nature Conservancy and Wetlands International.
 
 The paper describing the mangrove extent and change dataset is available at:
-[https://doi.org/10.3390/rs14153657](https://doi.org/10.1080/1755876X.2018.1529714)
+[https://doi.org/10.3390/rs14153657](https://doi.org/10.3390/rs14153657)
 The proportional length of coastline covered by mangroves was calculated by intersecting the mangrove extent, buffered by 200 m, with the Global Shoreline Vector dataset (Sayre, 2019) within each geometry. The percentage (%) covered was then calculated as the sum of the length of coastline occupied by mangrove habitat (km) divided by the total length of coastline (km) within the geometry multiplied by 100. Please note coverage calculations are still under validation.
 The paper describing the Global Coastline Vector dataset:
 [https://doi.org/10.1080/1755876X.2018.1529714](https://doi.org/10.1080/1755876X.2018.1529714)

src/containers/datasets/national-dashboard/info.mdx

@@ -4,19 +4,19 @@ import LayoutMdx from 'layouts/mdx';
 
 ## Overview:
 
-Pending
+This widget allows users to explore mangrove extent data and reports that have been developed for a country or region. Datasets and reports provided through this widget have been selected to provide the most recent and high resolution data available at the national scale, and is not intended to provide an exhaustive list of mangrove data resources for a given geography.  There may be discrepancies between the global and national maps due to differences in mapping methodologies and timeframes. Datasets are provided as is, and users are encouraged to consult the metadata for each dataset to better understand whether the data are appropriate for their intended use. 
 
 ## Reference:
 
-Pending
+See Widget
 
 ## Date of content:
 
-Pending
+See Widget
 
 ## License:
 
-Pending
+Various
 
 
 export default ({ children, ...props }) => (