## 6 Pan-Tropical AGB Mapping
Primarily, the ambiguities arose due to the exclusion of post deforestation carbon content in the estimation reports. Recent studies submitted to FREL/FRL made sure that carbon content must be included in the post deforested land in their carbon emission estimations [167]. Uncertainty analysis is important in assessing the estimations of carbon emissions by deforestation studies. Three steps are important in uncertainty analysis, including the identification of the estimation uncertainty source and quantifying the sources of uncertainty and aggregation of all uncertainty to conclude the final uncertainty value. IPCC guidelines Tier 1 document the uncertainty analysis and different organizations and countries apply this methodology. The Monte Carlo simulation approach is recommended for carbon estimation studies [168]. IPCC Tier 1 maps are still the primary source of AGB estimation maps which are based on \"stratify and multiply\" mapping technique and represent the year 2000 and provide a detailed wall-to-wall product but advancement in recent technologies such as high-resolution maps and detailed field surveys showed that this method of estimation of AGB increases the error and uncertainties. [PERSON] et al. [165] developed a methodology using 300 m spatial resolution datasets for the AGB map of 2010 but were advised not to use it in comparison with the IPCC Tier 1 AGB map because of a significant difference in methodologies and that it should only be used for global studies. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study** & **Year** & **Data Used** & **Attribute** \\ \hline [PERSON] et al., 2020 [165] & 2010 & Harmonization of existing biomass products & Above and below-ground biomass carbon density at 300 m \\ \hline [PERSON] and [PERSON], 2019 [164] & 2017 & PALSAR-2 Sentinel-1 & Global AGB biomass of 2017 at 100 m resolution \\ \hline [PERSON] et al., 2016 [163] & 2010 & Updated from [PERSON] [156] and [PERSON] [105] & Improved pan-tropical biomass at 1 km \\ \hline [PERSON] et al., 2015 [169] & 2010 & Envisat ASAR & Topical forest AGB at 1.1 km \\ \hline [PERSON] et al., 2011 [105] & 2010 & GLAS LiDAR, MODIS, SRTM assessment 2000-2010 \\ \hline [PERSON] et al., 2010 [156] & 2001 & GLAS LiDAR, MODIS, QSCAT, SRTM & Pan-tropical total biomass carbon at 1 km \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Remote-sensing-based datasets of Pan-tropical biomass mapping (arranged by the year of publication). Figure 5: Spatial distribution of total pan-tropical biomass [163], tropical forest AGB for 2010 [169] (Another dataset for 2017 is recently released by the ESA [164]), and net emissions of CO\({}_{2}\) from tropical areas for 2000–2015 [170]. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Study** & **Period** & **Data Used** & **Gross Loss** & **Gross Gain** & **Net Change** & **Remarks** \\ \hline [PERSON] et al., 2011 [4] & 1990–1990–2007 & Multiple remote sensing 1990–2007 & Multiple remote sensing products & 3030 2820 2940 2830 & 290 & 130 & Gross deforestation emissions. Include soil respirations or carbon \\ \hline [PERSON] et al., 2012 [105] & 2000–2010 & MODIS, GLAS LiDAR, SRTM DEM & 810 & 480 & 330 & – \\ \hline [PERSON] et al., 2012 [92] & 2000–2005 & MODIS, Landsat, LiDAR & 810 & – & – & \begin{tabular}{} \end{tabular} & \
\begin{tabular}{} \end{tabular} \\ \hline [PERSON] et al., 2014 [145] & 1990–1990 200–2010 & Landsat & 887 80 & 115 97 & 772 & Gain is calculated from 880 & regenerated forest \\ \hline [PERSON] et al., 2015 [157] & 2002–2012 & Landsat, LiDAR & 1022 & – & – &
\begin{tabular}{} \end{tabular} \\ \hline [PERSON] et al., 2017 [159] & 2003–2014 & MODIS, LiDAR, Landsat & 861.7 & 436.5 & 425.2 & – \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Remote-sensing-based assessment changes in pan-tropical biomass (TgC year\({}^{-1}\)) (arranged by the year of publication).