@inproceedings{1891aeb004a0422e9e01697d7e2c7ae9,
title = "Using Landsat-8 and Sentinel-1 data for above Ground Biomass assessment in the Tamar valley and Dartmoor",
abstract = "The availability of a new generation of freely distributed Earth Observation (EO) data represents an opportunity for an affordable assessment of Above-Ground Biomass (AGB). The aim of this study is to design a new system that supports the two dimensional information provided by optical EO data with height differences derived from Sentinel-1 Radar data. The applied system combines pixel based data processing including deriving the normalized difference vegetation index (NDVI) and the NDVI multi-temporal range (NDVIR) from Landsat-8, and interferometry based Local Heights range (LHR) analysis from Sentinel-1 data, with an object based biomass assessment regression algorithm. An ANOVA analysis was applied to test the correlation strength between different data and field above-ground biomass, which showed an effective correlation of Landsat-8's band 4 (near infra-red), NDVI, and Sentinel-1 height. Multi-temporal NDVI range shows a lower significant and be excluded in the final regression. The results of AGB calculations show a significantly higher correlation with AGB, R2=0.80 compared to R2=0.71 when conventional systems applied to the same datasets.",
keywords = "AGB, Heights range, Landsat-8, NDVI, Sentinel-1",
author = "Ahmed Alboabidallah and John Martin and Samantha Lavender and Victor Abbott",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images, MultiTemp 2017 ; Conference date: 27-06-2017 Through 29-06-2017",
year = "2017",
month = sep,
day = "12",
doi = "10.1109/Multi-Temp.2017.8035247",
language = "English",
series = "2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images, MultiTemp 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images, MultiTemp 2017",
address = "United States",
}