MAV Forest Cover Classification

<div>This image classification of forest cover in the MAV was created using Google Dynamic World (https://www.nature.com/articles/s41597-022-01307-4 - https://dynamicworld.app/) to determine what was classified as forest. This dataset is a result of an automated land classification for every Sentinel image that is released. The code used for this process is as follows. </div><div><br /></div><div>ee.ImageCollection('GOOGLE/DYNAMICWORLD/V1') \</div><div>      .filterBounds(geometry) \</div><div>      .filterDate(oldstartDate, oldendDate) \</div><div>      .select('label') \</div><div>      .mode() \</div><div>      .eq(1) \</div><div>      .updateMask(urban) </div><div><br /></div><div><div>We selected the Dynamic World dataset and filtered by our area of interest by the extents of the Lower Mississippi Joint Venture boundary (i.e. Mississippi Alluvial Valley and West Gulf Coastal Plain ecological bird conservation regions (BCRs).</div><div>We filtered the dataset based on a start and end date which is the first of 2021 and the last day of 2021.</div><div>With this dataset each class has a band that represents probability of that pixel having complete coverage of that class (https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_DYNAMICWORLD_V1#bands)</div></div><div><br /></div><div><div style='border:0px; font-variant-alternates:inherit; font-variant-numeric:inherit; font-variant-east-asian:inherit; font-variant-position:inherit; font-stretch:inherit; line-height:inherit; font-size-adjust:inherit; font-kerning:inherit; font-feature-settings:inherit; margin:0px; padding:0px;'><div style='font-variant-alternates:inherit; font-variant-numeric:inherit; font-variant-east-asian:inherit; font-variant-position:inherit; border:0px; font-stretch:inherit; line-height:inherit; font-size-adjust:inherit; font-kerning:inherit; font-feature-settings:inherit; margin:0px; padding:0px;'><font color='#000000' face='Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif, serif, EmojiFont' size='3'>Data accuracy was assessed at @82% accuracy and data resolution is 10m. </font><span style='color:rgb(0, 0, 0); font-family:Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif, serif, EmojiFont; font-size:medium;'>Each image has a ‘label’ band with a discrete classification of LULC, but also 9 probability bands with class-specific probability scores generated by the deep learning model on the basis of the pixel’s spatial context. To generate an annual LULC composite comparable with WC and Esri, we calculated the mode of the predicted LULC class in the ‘label’ band of all DW images for 2020.</span></div><div style='color:rgb(0, 0, 0) !important; font-family:Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif, serif, EmojiFont; font-size:11pt; font-style:inherit; font-variant-ligatures:inherit; font-variant-caps:inherit; font-weight:inherit;'><br /></div></div></div><div>Michael Mitchell with Ducks Unlimited Southern Regional Office led the development of this effort, in coordination and collaboration with Lower Mississippi Valley Joint Venture staff. </div><div><br /></div>