Geo-spatial modelling of carbon stock assessment of date palm at different age stages: An integrated approach of fieldwork, remote sensing and GIS

Basam Dahy, Salem Issa and Nazmi Saleous

Ecological Modelling, 2023, vol. 481, issue C

Abstract: The aim of this study is to develop a geospatial model for the assessment and mapping of carbon stock (CS) of date palms (DP) in Abu Dhabi, at three age stages: mature, medium and young. The approach relied on the use of correlated remote sensing predictors tested and derived from Landsat-8 imagery over the study period to estimate CS. Multiple predictors including single bands, vegetation indices (VIs) and their combination were used in linear and non-linear regression models to identify the best performing predictors and regression models for DP. Mature DP, older than 10 years, attained the most significant correlation when using a second-order polynomial model with the tasseled cap for wetness as a predictor, yielding R² of 0.7643. For non-mature DP, less than 10 years, the exponential regression model using renormalized difference vegetation index (RDVI) as a predictor provided the most significant correlation for AGB estimation with R2 of 0.4987. The study demonstrated that moderate-resolution Landsat-8 OLI imagery has the potential to estimate the CS in DP of drylands at different age stages. This is the first attempt to estimate CS in DP using geospatial technologies with minimal fieldwork hence, expanding our approach to other remote and less-studied drylands.

Keywords: Climate change; Landsat; Remote sensing predictors; Spatial modeller; Stepwise multiple regression (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:481:y:2023:i:c:s0304380023001084

DOI: 10.1016/j.ecolmodel.2023.110377

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