Spatial time-series measurements of forest degradation rates are important for estimating national greenhouse gas emissions but have been challenging for open forests and woodlands. This lack of quantitative data on forest degradation rates, location and biomass is an important constraint to developing national REDD+ policy. In Malawi, and in most countries in Africa, most assessments of forest cover change for carbon emissions monitoring tend to report only deforestation in the public forest estate managed by the government, even when important forest degradation also occurs in agricultural areas, such as customary forests and other tree-based systems. This study has resulted in: (a) a new robust forest map for Malawi, (b) spatial and quantitative measurements of both forest degradation and deforestation, and (c) a demonstration of the approach through the introduction of a tool that maps across the broad landscape of forests and trees outside of forests. The results can be used to support REDD+ National Forest Monitoring Systems. This analysis produces new estimates of landscape-wide deforestation rates between 2000–2009 (22,410 ha yr⁻¹) and 2009–2015 (38,937 ha yr⁻¹). We further produce new estimates of the rate of forest degradation between 2000–2009 (42,961 ha yr⁻¹) and 2009–2015 (71,878 ha yr⁻¹). The contribution of these new tools and estimates to capacities for calculating carbon emissions are important, increasing prospects for full REDD+ readiness across semi-arid Africa.

While closed canopy forests have been an important focal point for land cover change monitoring and climate change mitigation, less consideration has been given to methods for large scale measurements of trees outside of forests. Trees outside of forests are an important but often overlooked natural resource throughout sub-Saharan Africa, providing benefits for livelihoods as well as climate change mitigation and adaptation. In this study, the development of an individual tree cover map using very high-resolution remote sensing and a comparison with a new automated machine learning mapping product revealed an important contribution of trees outside of forests to landscape tree cover and carbon stocks in a region where trees outside of forests are important components of livelihood systems. Here, we test and demonstrate the use of allometric scaling from remote sensing crown area to provide estimates of landscape-scale carbon stocks. Prominent biomass and carbon maps from global-scale remote sensing greatly underestimate the “invisible” carbon in these sparse tree-based systems. The measurement of tree cover and carbon in these landscapes has important application in climate change mitigation and adaptation policies.

To reduce emissions of carbon and other greenhouse gases on a pathway that does not overshoot and keeps global average temperature increase to below the 1.5 °C target stipulated by the Paris Agreement, it shall be necessary to rely on nature-based solutions with atmospheric removals. Without activities that create removals by carbon sequestration it will not be possible to balance residual emissions. Policies that focus solely on reducing deforestation will only lower future emissions. On the other hand, activities that include regeneration or regrowth of tree biomass can be used to create net-zero emissions through carbon sequestration and atmospheric removals now. New methods demonstrated here using high resolution remote sensing and deep machine learning enable analyses of carbon stocks of individual trees outside of forests (TOF). Allometric scaling models based on tree crowns at very high spatial resolution (<0.5 m) can map carbon stocks across large landscapes of millions of trees outside of forests. In addition to carbon removals, these landscapes are also important to livelihoods for millions of rural farmers and most TOF activities have the capacity to bring more countries into climate mitigation while also providing adaptation benefits. Here were present a multi-resolution, multi-sensor method that provides a way to measure carbon at the individual tree level in TOF landscapes in India. The results of this analysis show the effectiveness of mapping trees outside of forest across a range of satellite data resolution from 0.5 m to 10 m and for measuring carbon across large landscapes at the individual tree scale.

The project trained forest managers from Kesatuan Pengelolaan Hutan (KPH) and Social Forestry units in Aceh Province in forest resource data collection and the use of three tools that report important forest ecosystem services. Data were collected in systematic forest plots by local KPH staff and social forestry community members for measuring forest carbon, tree biodiversity and forest ecosystem health. Teams represented nine different forest ecosystems in Aceh Province, Indonesia. Average forest carbon estimates range from a low of 27.14 Mg C ha-1 in a coffee agroforestry system to a high of 446.93 Mg C ha-1 in a tropical forest area managed under social forestry. Tree biodiversity ranged from a low of one species as expected in a coastal mangrove forest to a high of 35 species in the tropical forest area managed under social forestry. Forest health conditions on average for the nine areas were mostly healthy, with a few noted in fair condition and one considered to be in poor condition. Resources provided at the local level would enable mainstreaming of field data collection for measuring these forest ecosystem services, and continued and expanded training should be considered to meet national demand.

Accurate biomass and carbon estimation in tropical plantation forests requires species-specific allometric models. Silver Oak (Grevillea robusta A. Cunn. ex R. Br.), cultivar “AVAONE,” is widely planted in northeastern Thailand, yet locally calibrated equations remain limited. This study developed species- and site-specific allometric models using destructive sampling of eight trees (n = 8) aged 2–9 years from a single plantation in Pak Chong District, Nakhon Ratchasima Province, without independent validation. Each tree was separated into stem, branches, leaves, and roots to determine fresh and dry biomass, and carbon concentrations were measured using a LECO CHN628 analyzer in an ISO/IEC 17025-accredited laboratory. Aboveground biomass increased from 17.49 kg at age 2 to 860.42 kg at age 9, with the most rapid gains occurring between ages 6 and 9. Tree height stabilized at approximately 19–20 m after age 7, while diameter continued to increase. Stems accounted for the largest proportion of dry biomass, followed by branches and roots. Carbon concentrations ranged from 45.561% to 48.704%, close to the IPCC default value of 47%. Power-law models based on D²H showed clear relationships with biomass, with R² values ranging from 0.7365 to 0.9372 for individual components and 0.8409 for aboveground biomass. These locally derived equations provide preliminary, site-specific relationships for estimating biomass and carbon stocks in Silver Oak AVAONE plantations and offer a baseline for future studies with expanded sampling and independent validation.