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Trees sustain livelihoods and mitigate climate change but a predominance of trees outside forests and limited resources make it difficult for many tropical countries to conduct automated nation-wide inventories. Here, we propose an approach to map the carbon stock of each individual overstory tree at the national scale of Rwanda using aerial imagery from 2008 and deep learning. We show that 72% of the mapped trees are located in farmlands and savannas and 17% in plantations, accounting for 48.6% of the national aboveground carbon stocks. Natural forests cover 11% of the total tree count and 51.4% of the national carbon stocks, with an overall carbon stock uncertainty of 16.9%. The mapping of all trees allows partitioning to any landscapes classification and is urgently needed for effective planning and monitoring of restoration activities as well as for optimization of carbon sequestration, biodiversity and economic benefits of trees.

Trees outside of forests are numerous and can be important carbon sinks, while also providing ecosystem services and benefits to livelihoods. New monitoring tools highlight the crucial contribution they can make to strategies for both mitigation and adaptation.

Leucaena leucocephala(leucaena) is an important agroforestry tree of tropics and subtropics due to its protein rich foliages, ability to grow on poor soils, and high level of tolerance to various stresses including drought and diseases. We hypothesized that leucaena have a unique set of genes which confer tolerance to various abiotic and biotic stresses and these genes are either absent or not expressed in other legumes that are susceptible to these stress conditions. Interspecies suppression subtractive hybridization (iSSH) was performed to identify some unique genes from leucaena using cDNAs from leucaena and Acacia confusa (acacia) as the tester and driver, respectively. We identified 100 leucaena genes showing homology to various plant genes with known functions, of which 23 genes had homology to various stress-related proteins including chitinase, PR-10 protein, wound-stress protein, type-2 metallothionein, thaumatin-like protein, disease resistance response protein, cysteine proteinase, and kunitz trypsin inhibitor. We also identified 50 leucaena genes which either had homology to hypothetical proteins (HyPs) or had no homology at all. Putative functions of 15 HyPs were predicted using conserved domain and PSI-BLAST analyses. Linear motif analyses identified 17 HyPs with MAPK recognition motif, which is involved in signaling pathways, including stress responses. This study identified 23 known and some putative unique stress-related genes from leucaena which may serve as a useful source of stress-tolerance genes for tree and crop improvement in the future.

We determined stocks of C and N for soils under undisturbed vegetation across the Brazilian Amazon Basin based on 1162 soil profiles of the RADAMBRASIL survey and a digitized Brazilian soil survey map. Mean basin soil C density was 10.3 kg C m−2. Forty‐seven petagrams C and 4.4 Pg N were contained in the top 1 m of soil. Forty‐five percent of total basin soil C (21 Pg C) and 41% of total soil N (1.8 Pg N) were contained in the top 20 cm across a ≈ 5 000 000‐km2area. Mean C/N ratio for the basin to a depth of 1 m was 10.7. Because these data represent sites with forest vegetation in the absence of significant disturbances, they represent a valuable baseline for evaluating the effects of land‐use changes on soil C stocks in the Amazon Basin.

A signal processing technique is presented and applied to annual patterns of the Global Vegetation Index (GVI) derived from the Advanced Very High Resolution Radiometer (AVHRR) to examine the frequency distribution of the multi-temporal signal. It is shown that frequencies of the signal are linked to integrated GVI, seasonal variability and subseasonal variability of the land cover type. These characteristics are used to derive a land cover classification.