International Journal of Applied Earth Observations and Geoinformation

Reference cloud data over NASA GSFC for validating cloud masking algorithm for moderate spatial resolution satellite data, namely Sentinel-2 and Landsat 8.

This repository contains the data used to generate the results for the paper: Yuri Shendryk, Jeremy Sofonia, Robert Garrard, Yannik Rist, Danielle Skocaj, and Peter Thorburn (2020). Fine-scale Prediction of Leaf Nitrogen Content and Yield in Sugarcane using UAV LiDAR and Multispectral Imaging. The data to reproduce results could be also found here: https://github.com/RobGarrard/Fine-scale-prediction-of-leaf-nitrogen-content-and-yield-in-sugarcane. For any queries regarding these codes please contact robert.garrard@csiro.au or yuri.shendryk@csiro.au.

These data provide forest structure diversity information collected in the field and from a rare combination of leaf-on and –off airborne LiDAR surveys. Field surveys incorporate forest plots containing a wide range of species, age and silvicultural practises, including areas of ancient woodland. The leaf-on LiDAR dataset was collected during 2009 and the leaf-off dataset was collected during 2011. Allometric measurements were collected from each tree (DBH ≥ 8) within 30 field sites benefitting from leaf-on and –off LiDAR coverage. Analysis includes normalisation of point clouds to height above ground and calculation of a wide variety of point distribution metrics for each survey plot. L-CV of field derived variables (tree height, DBH, crown length and crown width) was calculated for each plot as a corresponding field diversity reference measurement. Those who can benefit from these data include data scientists looking for a dataset collected from a complex natural system, individuals looking to model and map forest structure diversity and biodiversity from airborne LiDAR, those interested in LiDAR representations of forestry metrics and those with an interest in forest growth and change detection. This article is a co-submission with the article “The effect of leaf-on and leaf-off forest canopy conditions on LiDAR derived estimations of forest structural diversity”

The datasets consist of the method incorporated for miscellaneous fusion techniques. i) Actual_ESTARFM__STARFM file contains the results generated by implementing the actual STARFM and ESTARFM method with code (Actual_ESTARFM.py) and (Actual_STARFM.py) ii) IMAGE_FUSION_STATISTICS file contains the results generated by implementing the code for estimating (RMSE, Correlation Coefficient, ERGAS and Absolute Average Difference) iii)MODIFIED_ESTARFM__STARFM file contains the results generated by implementing the modified STARFM and ESTARFM method with code and datasets (Landsat and VIIRS in tiff format) for specific channels (NIR, SWIR1 and SWIR2)

All the necessary file types and associated .tiff files have been attached including fused images and actual Landsat-8 OLI Images. Also, the scatterplots have been attached for the manuscript.

The measured dry and wet biomass, measured soil moisture, satellite observations including HH and HV backscatters and incidence angles are reported for all the 66 points (including calibration and validation points) in a table. This table was sorted such that the first 23 points are the calibration points and the rest of the points were used as the validation points.

General spectral vectors derived by this study as well as example codes.