Wiki source code of 2.3.8 Normalised Difference Vegetation index
Last modified by Emily West on 2022/03/08 10:16
Hide last authors
| author | version | line-number | content |
|---|---|---|---|
 |
9.1 | 1 | = 2.3.8 Normalised Difference Vegetation index = |
 |
1.1 | 2 | |
| 3 | (% class="table-bordered" %) | ||
| 4 | |(% style="width:211px" %)((( | ||
| 5 | ====== **Social challenge/ impact linked to:** ====== | ||
| |
4.1 | 6 | )))|(% style="width:730px" %)Environmental |
| |
1.1 | 7 | |(% style="width:211px" %)((( |
| 8 | ====== **Co-creation phase linked to:** ====== | ||
| 9 | )))|(% style="width:730px" %)((( | ||
| 10 | E) Co-monitoring | ||
| 11 | ))) | ||
| 12 | |(% style="width:211px" %)((( | ||
| 13 | ====== **Level of importance** ====== | ||
| 14 | )))|(% style="width:730px" %)((( | ||
| |
4.1 | 15 | 2 - Medium Importance / good to have |
| |
1.1 | 16 | ))) |
| 17 | |(% style="width:211px" %)((( | ||
| 18 | ====== **Description** ====== | ||
| |
10.1 | 19 | )))|(% style="width:730px" %)There are many vegetation indices which can be calculated based only on a satellite image, all of which vary in their resolution and accuracy as they are calculated using different electromagnetic bands. Of these, the NDVI is the most commonly used and can be widely regarded as a measure of the health of vegetation. The chlorophyll present in plants strongly absorbs visible light and the cellular structure strongly reflects light in the near-infrared. It has been found to saturate at high biomasses (for example when calculated over forested land), but is highly effective for delineating the presence of greenery in urban environments. It produces a score between -1 and 1, a higher score corresponds to a greener environment. |
| |
1.1 | 20 | |(% style="width:211px" %)((( |
| 21 | ====== **Scale as linked to D2.2 framework** ====== | ||
| 22 | )))|(% style="width:730px" %)((( | ||
| |
10.1 | 23 | Scale is dependent on the resolution of the satellite imagery used. Most ubiquitous satellites include MODIS (250m x 250m resolution), the Landsat series (30 x 30 m) and Sentinel-2 (10 x 10 m but only launched in 2015 so long time series are not available). |
| |
1.1 | 24 | |
| 25 | * National | ||
| 26 | * City | ||
| 27 | * Neighborhood | ||
| 28 | * Project Area | ||
| 29 | ))) | ||
| 30 | |(% style="width:211px" %)((( | ||
| 31 | ====== **Data source and input type** ====== | ||
| 32 | )))|(% style="width:730px" %)((( | ||
| |
4.1 | 33 | * Satellite image repository |
| |
10.1 | 34 | * for example Copernicus Open Access (Sentinel-2) hub: [[https:~~/~~/scihub.copernicus.eu/dhus/>>url:https://scihub.copernicus.eu/dhus/#/home]] |
| 35 | * or NASA Earth Explorer [[EarthExplorer (usgs.gov)>>url:https://earthexplorer.usgs.gov/]] (Landsat 1-9 and MODIS terra and aqua) | ||
| |
1.1 | 36 | ))) |
| 37 | |(% style="width:211px" %)((( | ||
| 38 | ====== **Connection with SDGs** ====== | ||
| 39 | )))|(% style="width:730px" %)((( | ||
| 40 | * Goal 11: Sustainable cities and communities | ||
| 41 | * Goal 13: Climate action | ||
| 42 | * Goal 15: Life on land | ||
| 43 | ))) | ||
| 44 | |(% style="width:211px" %)((( | ||
| 45 | ====== **Required Data** ====== | ||
| 46 | )))|(% style="width:730px" %)((( | ||
| |
5.1 | 47 | * Satellite image which is largely cloud-free or cloud-masked, of an appropriate resolution (for example from freely available satellite image repositories from Sentinel-2 (10x10m resolution) or Landsat (30x30m resolution)). |
| |
1.1 | 48 | ))) |
| 49 | |(% style="width:211px" %)((( | ||
| 50 | ====== **Expected Outputs** ====== | ||
| 51 | )))|(% style="width:730px" %)((( | ||
| |
4.1 | 52 | * A value between -1.0 and 1.0 |
| 53 | * The lower the value the poorer health the land is in. | ||
| 54 | * This can also be displayed as an image based on the satellite image from which the index has been calculated. | ||
| |
1.1 | 55 | ))) |
| 56 | |(% style="width:211px" %)((( | ||
| 57 | ====== **Calculation methods** ====== | ||
| 58 | )))|(% style="width:730px" %)((( | ||
| |
4.1 | 59 | * There are many simple tools in GIS software which are able to automatically calculate the NDVI based on a satellite image, provided the image does not contain too many clouds and cloud shadows which impact the result. ArcGIS is one software example which has an NDVI tool. |
| 60 | * It can also be done manually, for example using Google Earth Engine, using the following formula: | ||
| 61 | * [[image:1646143507415-927.png||height="82" width="226"]] | ||
| |
1.1 | 62 | * |
| 63 | ))) | ||
| 64 | |(% style="width:211px" %)((( | ||
| 65 | ====== **Level of expertise required** ====== | ||
| 66 | )))|(% style="width:730px" %)((( | ||
| |
6.1 | 67 | * Medium - basic GIS skills required, and ability to process satellite images. |
| |
1.1 | 68 | ))) |
| 69 | |(% style="width:211px" %)((( | ||
| 70 | ====== **Links and references** ====== | ||
| 71 | )))|(% style="width:730px" %)((( | ||
| |
6.1 | 72 | * [[NDVI: Normalized Difference Vegetation Index For Agriculture (eos.com)>>url:https://eos.com/make-an-analysis/ndvi/]] |
| |
1.1 | 73 | ))) |
| 74 | |||
| 75 | (% class="lead" %) | ||
| 76 | Revisions | ||
| 77 | |||
| 78 | (% class="success" %)|(% style="width:68px" %)**Version**|(% style="width:179px" %)**Name**|(% style="width:112px" %)**Date**|(% style="width:636px" %)**Changes made** | ||
| |
6.1 | 79 | (% class="active" %)|(% style="width:68px" %)**0.1**|(% style="width:179px" %)Emily|(% style="width:112px" %)01/03/2022|(% style="width:636px" %)Factsheet created |
| |
1.1 | 80 | (% class="active" %)|(% style="width:68px" %) |(% style="width:179px" %) |(% style="width:112px" %) |(% style="width:636px" %) |
| 81 | |||
| 82 |
