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Abstract Accurate mapping and monitoring of tropical forests aboveground biomass (AGB) is crucial to design effective carbon emission reduction strategies and improving our understanding of Earth’s carbon cycle. However, existing large-scale maps of tropical forest AGB generated through combinations of Earth Observation (EO) and forest inventory data show markedly divergent estimates, even after accounting for reported uncertainties. To address this, a network of high-quality reference data is needed to calibrate and validate mapping algorithms. This study aims to generate reference AGB datasets using field inventory plots and airborne LiDAR data for eight sites in Central Africa and five sites in South Asia, two regions largely underrepresented in global reference AGB datasets. The study provides access to these reference AGB maps, including uncertainty maps, at 100 m and 40 m spatial resolutions covering a total LiDAR footprint of 1,11,650 ha [ranging from 150 to 40,000 ha at site level]. These maps serve as calibration/validation datasets to improve the accuracy and reliability of AGB mapping for current and upcoming EO missions (viz., GEDI, BIOMASS, and NISAR).

Abstract Introduction: The advent of newborn screening across India has led to an increase in the early diagnosis of inborn errors of metabolisms (IEMs). Aminoacidopathies, the group of inherited disorders of amino acid metabolisms are of particular important because of the ease of early diagnosis and the availability of effective treatment options. Unfortunately, the biological reference intervals for amino acids vary widely between different ethnic groups and geographical locations, thereby necessitating the need to establish a population specific reference interval for optimal diagnosis. Aims and objectives: Establishment of the biological reference interval for all amino acids in the Dried Blood Spots (DBS) of term neonates belonging to coastal Karnataka using High Performance Liquid Chromatography. Methods: Heel prick blood samples were collected from 175 healthy, term neonates on a filter paper. After safe transport to the laboratory, the amino acids were extracted using an appropriate solution, derivatized, and analyzed using high performance liquid chromatography. Mean, standard deviation, and 95% confidence interval of the mean were used to establish the reference interval. Students T-test was used to compare the differences in amino acids levels among different groups. Conclusions: The biological reference intervals obtained in this study was found to have significant variations from studies conducted elsewhere in the world. This puts into perspective the need to establish a population specific reference interval for these parameters to avoid potential misdiagnosis. This reference interval may also be adopted by other labs catering to new-born screening in the same geographical area.