Lifewatch-FWB : UCL

Choose a legend :

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Draw a polygon where you notice an error in the landcover classification
Complete the form that will show

You can click on the map to view the landcover at that location

Polygons are visible at high zoom level

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detail in the ecotope:

{{selectedFeature[type] / 10 | number:0}}% {{selectedFeature[type] / 10 | number:0}}

250m around the ecotope:

{{selectedFeature[type+'250'] / 10 | number:0}}% {{selectedFeature[type+'250'] / 10 | number:0}}

500m around the ecotope:

{{selectedFeature[type+'500'] / 10 | number:0}}% {{selectedFeature[type+'500'] / 10 | number:0}}

1km around the ecotope:

{{selectedFeature[type+'1km'] / 10 | number:0}}% {{selectedFeature[type+'1km'] / 10 | number:0}}

Drainage:

{{selectedFeature[type] / 10 | number:0}}% {{selectedFeature[type] / 10 | number:0}}

Depth:

{{selectedFeature[type] / 10 | number:0}}% {{selectedFeature[type] / 10 | number:0}}

Elevation mean: {{selectedFeature.elev_mean}} m

Elevation min: {{selectedFeature.elev_min}} m

Elevation max: {{selectedFeature.elev_max}} m

Slope mean: {{selectedFeature.slope_mean}} °

Slope max: {{selectedFeature.slope_max}} °

Topographic Position Index 1km: {{selectedFeature.tpi_1km}}

Topographic Position Index 250m: {{selectedFeature.tpi_250m}}

Potential incident light (spring): {{selectedFeature.sunspring}} W/m²

Height of vegetation:

{{selectedFeature[type] / 10 | number:0}}% {{selectedFeature[type] / 10 | number:0}}

Mean height of vegetation: {{selectedFeature.mn_treehcm / 100 | number:1}} m

Artificial night light: {{selectedFeature.nightli / 2.55 | number:1}} %

Pseudo-Distance to roads: {{selectedFeature.dst_road/100 |number:1}} m

Distance to railway tracks: {{selectedFeature.dst_rail/1000 |number:1}} km

Pseudo-Distance to rivers: {{selectedFeature.dst_river}} m

Distance to forest boundaries: {{selectedFeature.dst_forest}} m

Distance to the sea: {{selectedFeature.dst_sea}} m

Ecotope database v4.19 :

Ecotope documentation is available here

The landscape has been divided into ecotopes based on automated delineation taking remote sensing data and topography as input variables. More details are available in this paper .

The v2.7 dataset had been validated. The results have been published in "Good practices for Object-based Accuracy assessment" (2017) , which also documents the thematic legend.

The land cover proportions inside ecotopes are based on a 2-m pixel based classification. The v4.17 validation has not been published but a point-based accuracy assessment was performed on the input land cover data. The overall accuracy was 93.1%, 92.6%, 94.8% and 93.9% +/− 1.3% for the years 2006, 2015, 2018 and 2019, respectively. More details in this data paper .

The raw pixel-based classifications can be downloaded for 2006 , 2010 , 2015 , 2018 , 2019 , 2020 , 2021 and 2022 .

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