Description: <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Detailed information about the data, including but not limited to: </SPAN></P><UL><LI><P><SPAN>Wetlands were mapped using a combination of LiDAR (2018) and RGBI (2014-2016) imagery captured by Northland and Auckland Councils. Wetlands were validated using a combination of Photoblique and Biospatial_KMR Historic Imagery layer derived from Retrolens. </SPAN></P></LI><LI><P><SPAN>Slope, Spectral signatures, Topographic wetness index and vegetation structure were inputs into the machine learning model. </SPAN></P></LI><LI><P><SPAN>Information is current to the datasets available. </SPAN></P></LI><LI><P><SPAN>Data was derived using a combination of machine learning model training and manual output validation. </SPAN></P></LI><LI><P><SPAN>Wetlands have been manually selected using a combination of aerial surveys, GIS desktop surveys and Photoblique validation. As such data is limited to wetlands which have been manually validated and may not be representative of all wetlands present. </SPAN></P></LI><LI><P><SPAN>Data is presented using a polygon approach for ease of display. </SPAN></P></LI><LI><P><SPAN>Elevation data has been attached for data overlay in Photoblique </SPAN></P></LI></UL><P><SPAN>Data Limitations</SPAN></P><UL><LI><P><SPAN>Data may be subject to false positive and/or false negative results. </SPAN></P></LI><LI><P><SPAN>The spatial extents can be used as a guide for where waterbodies exist </SPAN></P></LI><LI><P><SPAN>Data is a representation of the capture date of the imagery and LiDAR </SPAN></P></LI></UL><P><SPAN>Version Date: 2024/05/22</SPAN></P></DIV></DIV></DIV>
Service Item Id: 5f0fede0094e419290b7e3737b5bdf3a
Copyright Text: Biospatial Limited Photoblique Limited Auckland Council Northland Regional Council
Description: <DIV STYLE="text-align:Left;"><DIV><DIV><UL><LI><P><SPAN STYLE="font-size:10pt">Wetlands were mapped using a combination of LiDAR (2018) and RGBI (2014-2016) imagery captured by Northland and Auckland Councils. Waterbodies were validated using a combination of Photoblique and GIS desktop techniques. </SPAN></P></LI><LI><P><SPAN STYLE="font-size:10pt">Slope, Spectral signatures, Topographic wetness index and vegetation structure were were inputs into the machine learning model. </SPAN></P></LI><LI><P><SPAN STYLE="font-size:10pt">Information is current to the datasets available. </SPAN></P></LI><LI><P><SPAN STYLE="font-size:10pt">The spatial extents can be used as a guide for where a waterbody exists</SPAN></P></LI><LI><P><SPAN STYLE="font-size:10pt">Data was derived using a combination of machine learning model training and manual output validation. </SPAN></P></LI><LI><P><SPAN STYLE="font-size:10pt">Elevation data has been attached for data overlay in Photoblique. </SPAN></P></LI><LI><P><SPAN>Data is presented using a polygon approach for ease of display.</SPAN></P></LI></UL><P><SPAN>Data Limitations: </SPAN></P><UL><LI><P><SPAN>Data may be subject to some false positive and/or false negative results. </SPAN></P></LI><LI><P><SPAN>The spatial extents can be used as a guide for where waterbodies exist </SPAN></P></LI><LI><P><SPAN>Scale is created to a 1m resolution </SPAN></P></LI><LI><P><SPAN>Data is a representation of the capture date of the imagery and LiDAR </SPAN></P></LI></UL></DIV></DIV></DIV>
Service Item Id: 5f0fede0094e419290b7e3737b5bdf3a
Copyright Text: Biospatial Limited Photoblique Limited Auckland Council Northland Regional Council
Description: <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Detailed information about the data, including but not limited to: </SPAN></P><UL><LI><P><SPAN>Gumlands were mapped using a combination of LiDAR (2018) and RGBI (2014-2016) imagery captured by Northland and Auckland Councils. Gumlands were validated using a combination of Photoblique and Biospatial_KMR Historic Imagery layer derived from retrolens. </SPAN></P></LI><LI><P><SPAN>Slope, Spectral signatures, Topographic wetness index and vegetation structure were inputs into the machine learning model. </SPAN></P></LI><LI><P><SPAN>Information is current to the datasets available. </SPAN></P></LI><LI><P><SPAN>Data was derived using a combination of AI model training and manual output validation. </SPAN></P></LI><LI><P><SPAN>Data is presented using a polygon approach for ease of display. </SPAN></P></LI><LI><P><SPAN>Elevation data has been attached for data overlay in Photoblique</SPAN></P><P><SPAN /></P></LI></UL><P><SPAN>Data Limitations: </SPAN></P><UL><LI><P><SPAN> Data may be subject to false positive and/or false negative results. </SPAN></P></LI><LI><P><SPAN>The spatial extents can be used as a guide for where saltmarsh exists </SPAN></P></LI><LI><P><SPAN>Scale is created to a 1m resolution </SPAN></P></LI><LI><P><SPAN>Data is representation of the capture date of the imagery and LiDAR. </SPAN></P></LI></UL><P><SPAN>Version Date: 2024/04/08</SPAN></P></DIV></DIV></DIV>
Service Item Id: 5f0fede0094e419290b7e3737b5bdf3a
Copyright Text: Biospatial Limited Photoblique Limited Auckland Council Northland Regional Council
Description: <DIV STYLE="text-align:Left;"><DIV><DIV><UL><LI><P><SPAN>Saltmarsh was mapped using a combination of LiDAR (2018) and RGBI (2014-2016) imagery captured by Northland and Auckland Councils. </SPAN></P></LI><LI><P><SPAN>Data was derived using a combination of machine learning model training and manual output validation. </SPAN></P></LI><LI><P><SPAN>Saltmarsh was validated using a combination of Photoblique and GIS desktop surveys. </SPAN></P></LI><LI><P><SPAN>Slope, Spectral signatures, Topographic wetness index and vegetation structure were inputs into the machine learning model. </SPAN></P></LI><LI><P><SPAN>Data is presented using a polygon approach for ease of display. </SPAN></P></LI><LI><P><SPAN>Elevation data has been attached for data overlay in Photoblique. </SPAN></P></LI></UL><P><SPAN>Data Limitations: </SPAN></P><UL><LI><P><SPAN>Data has a high accuracy rate but may be subject to some false positive and/or false negative results. </SPAN></P></LI><LI><P><SPAN>The spatial extents can be used as a guide for where saltmarsh exists </SPAN></P></LI><LI><P><SPAN>Scale is created to a 1m resolution </SPAN></P></LI><LI><P><SPAN>Data is representation of the capture date of the imagery and LiDAR. </SPAN></P></LI></UL><P><SPAN>Version Date: 2024/05/29</SPAN></P></DIV></DIV></DIV>
Service Item Id: 5f0fede0094e419290b7e3737b5bdf3a
Copyright Text: Biospatial Limited Photoblique Limited Auckland Council Northland Regional Council
Description: <DIV STYLE="text-align:Left;"><DIV><DIV><UL><LI><P><SPAN>Mangroves were mapped using a combination of LiDAR (2018) and RGBI (2014-2016) imagery captured by Northland and Auckland Councils. </SPAN></P></LI><LI><P><SPAN>Data was derived using a combination of machine learning model training and manual output validation. </SPAN></P></LI><LI><P><SPAN>Validated was conducted using a combination of Photoblique and GIS desktop surveys. </SPAN></P></LI><LI><P><SPAN>Slope, Spectral signatures, Topographic wetness index and vegetation structure were inputs into the machine learning model. </SPAN></P></LI><LI><P><SPAN>Mangrove height of 0.5m and vegetation cover of 50% were used as guides for mapping. </SPAN></P></LI><LI><P><SPAN>Data is presented using a polygon approach for ease of display. </SPAN></P></LI><LI><P><SPAN>Elevation data has been attached for overlay in Photoblique. </SPAN></P></LI></UL><P><SPAN>Data Limitations: </SPAN></P><UL><LI><P><SPAN>Data may be subject to false positive and/or false negative results. </SPAN></P></LI><LI><P><SPAN>The spatial extents can be used as a guide for where saltmarsh exists </SPAN></P></LI><LI><P><SPAN>Scale is created to a 1m resolution </SPAN></P></LI><LI><P><SPAN>Data is representation of the capture date of the imagery and LiDAR. </SPAN></P></LI></UL></DIV></DIV></DIV>
Service Item Id: 5f0fede0094e419290b7e3737b5bdf3a
Copyright Text: Biospatial Limited Photoblique Limited Auckland Council Northland Regional Council