The Canopy clumping index (CI) indicates the spatial distribution pattern of foliage. The CI<1 means foliage is clumping distribution. When CI=1, the foliage is random distribution, and when CI>1 the foliage is regular distribution. This software programmed by JavaScript in Goole Earth Engine （GEE） is to produce global CI products based on the remote sensing dataset. It is a visualization software which runs directly in the GEE to provide CI downloading at a user-defined scale.
Now, CI products are released by tif format from March 1 2000 to May 1 2020, at regional or global scale, with a temporal scale ranging from daily, montly, and yearly. Publications to which methods and technologies were referred are listed at the end of this page. The program is mainly composed of the following steps:
- filter image datasets by designated date range to corresponding image collections.
- generate multi-band image containing the required data and stack it in a image collection for CI retrieval.
- Retrieve daily CI based on band calculations and exclude low quality pixels.
- The Savitzky-Golay smoothing filter (SG-filter) was conducted for daily CI collection then to composite monthly or yearly CI image by quality indicator and provide final CI to download.
The description of software usage:
the initialized interface, the dateslider in the top of the map provides a daily CI quick view
1)After two dates input and click one of temporal scales, the desired products options are appeared.
2)select one of date point to get product.
3)You can select spatial scale to export or view product, then the new final buttons will appear.
4)The corresponding image named by its date will be added to the map after click view.
5)Because of constraints by GEE, exporting the global extent (60°S-90°N,180°E-180°W) product will be automatically clipped into 9 equal-sized pieces of image. You can set the file name and saved folder.
6)After select "draw a rectangle to export/view", you can draw one or several rectangles and select one of them to view or download. The small regional image can be downloaded to one image file in your Goole Drive.
Notes: All input datasets are provided by GEE Data Catalog expecting fraction of vegetation coverage(FCOVER) and Global land cover that. The land cover image is a single year product, and the FCOVER is the monthly product, we currently have been uploading the FCOVER to May 2020. The following FCOVER will be contiguously uploaded once it latest one is available. The land cover product is from http://bioval.jrc.ec.europa.eu/products/glc2000/glc2000.php and the FCOVER is from https://land.copernicus.vgt.vito.be/PDF/portal/Application.html#Home.
The products from 2000-03 to 2020-05 are available for download, but you can still download products not confind this date range because the program is designed to automaticallly take the approximated FCover to produce.
Some instructions as following:
- please type date with a form likes "2020-01-01".
- The CI band in export has been scaled 1000, please be a little patient for waiting result because calculating CI is not a easy work.
- You will see 9 pieces of Image in your google derive after you exported global image.
- All products including 2 bands (CI band and Quality band) named by its date, more information about products can see the reference in the end of this page;
- I give a name of exported folder ("CIFolder"), you can modify as you like.
- The fast open link of this program "https://code.earthengine.google.com/?scriptPath=users%2Flrvings%2FGMap%3AClumpingIndex%2FCAS-CI".
References:
[1] Wei, S. and H. Fang (2016). "Estimation of canopy clumping index from MISR and MODIS sensors using the normalized difference hotspot and darkspot (NDHD) method: The influence of BRDF models and solar zenith angle." Remote Sensing of Environment 187: 476-491. https://doi.org/10.1016/j.rse.2016.10.039.
[2] Wei, S., H. Fang, C. B. Schaaf, L. He and J. M. Chen (2019). "Global 500 m clumping index product derived from MODIS BRDF data (2001–2017)." Remote Sensing of Environment 232: 111296.https://doi.org/10.1016/j.rse.2019.111296.