ABSTRACT
The distribution of dryland trees and their density, cover, size, mass and carbon content are not well known at sub-continental to continental scales1-14. This information is important for ecological protection, carbon accounting, climate mitigation and restoration efforts of dryland ecosystems15-18. We assessed more than 9.9 billion trees derived from more than 300,000 satellite images, covering semi-arid sub-Saharan Africa north of the Equator. We attributed wood, foliage and root carbon to every tree in the 0-1,000 mm year-1 rainfall zone by coupling field data19, machine learning20-22, satellite data and high-performance computing. Average carbon stocks of individual trees ranged from 0.54 Mg C ha-1 and 63 kg C tree-1 in the arid zone to 3.7 Mg C ha-1 and 98 kg tree-1 in the sub-humid zone. Overall, we estimated the total carbon for our study area to be 0.84 (±19.8%) Pg C. Comparisons with 14 previous TRENDY numerical simulation studies23 for our area found that the density and carbon stocks of scattered trees have been underestimated by three models and overestimated by 11 models, respectively. This benchmarking can help understand the carbon cycle and address concerns about land degradation24-29. We make available a linked database of wood mass, foliage mass, root mass and carbon stock of each tree for scientists, policymakers, dryland-restoration practitioners and farmers, who can use it to estimate farmland tree carbon stocks from tablets or laptops.
Subject(s)
Carbon , Desert Climate , Ecosystem , Trees , Carbon/analysis , Carbon/metabolism , Trees/anatomy & histology , Trees/chemistry , Trees/metabolism , Desiccation , Satellite Imagery , Africa South of the Sahara , Machine Learning , Wood/analysis , Plant Roots , Agriculture , Environmental Restoration and Remediation , Databases, Factual , Biomass , ComputersABSTRACT
A systematic calibration approach is presented to correlate the digital output of an infrared camera and the scene temperature. Aided by the optoelectronic properties of the camera, as few as two experimental data points are needed to establish this correlation. This approach can readily include the effects of atmospheric transmission, scene emissivity, and different background subtractions. Hence, the temperature conversion in flight can be reliably obtained from laboratory calibration. The conversion function can also be used to identify the camera's thermal sensitivity and temperature resolution, which are important information in different space missions. In applying this calibration procedure to a laboratory camera and the compact thermal imager onboard the International Space Station, its validity is confirmed.
