Satellite-Based Carbon Estimation in Scotland: AGB and SOC
نویسندگان
چکیده
The majority of state-of-the-art research employs remote sensing on AGB (Above Ground Biomass) and SOC (Soil Organic Carbon) separately, although some studies indicate a positive correlation between the two. We intend to combine two domains in our improve total carbon estimation. begin by establishing baseline model study area Scotland, using methodologies domains. effects feature engineering techniques such as variance inflation factor selection machine learning models are then investigated. This is extended combining predictor variables from Finally, we leverage possible establish relationship propose novel an attempt outperform results. compared three techniques, boosted regression tree, random forest, xgboost. These have been demonstrated be most effective both makes contributions: (i) Including Digital Elevation Map (DEM) variable improves result 13.5 % average across experimented, implying that DEM should considered for estimation well, despite fact it has previously used exclusively (ii) Using Density prediction significant 14.2% (When comparing R2 value all modeling Model B H, there increase 0.5016 0.5604 BRT, 0.4958 0.5925 RF 0.5161 0.5750 XGB), which strengthens experiment results suggests future direction joint domain. (iii) performance Random Forest, but reduced Boosted Regression tree XG Boost, indicating specific ML more required space techniques. Additionally, method estimating data Sentinel 1, 2, Landsat 8, Elevation, Forest Inventory.
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ژورنال
عنوان ژورنال: Land
سال: 2023
ISSN: ['2073-445X']
DOI: https://doi.org/10.3390/land12040818