More than one quarter of Africa's tree cover is found outside areas previously classified as forest.

The consistent monitoring of trees both inside and outside of forests is key to sustainable land management. Current monitoring systems either ignore trees outside forests or are too expensive to be applied consistently across countries on a repeated basis. Here we use the PlanetScope nanosatellite constellation, which delivers global very high-resolution daily imagery, to map both forest and non-forest tree cover for continental Africa using images from a single year. Our prototype map of 2019 (RMSE = 9.57%, bias = -6.9%). demonstrates that a precise assessment of all tree-based ecosystems is possible at continental scale, and reveals that 29% of tree cover is found outside areas previously classified as tree cover in state-of-the-art maps, such as in croplands and grassland. Such accurate mapping of tree cover down to the level of individual trees and consistent among countries has the potential to redefine land use impacts in non-forest landscapes, move beyond the need for forest definitions, and build the basis for natural climate solutions and tree-related studies.

Resistance of termite mounds to variation in long-term fire regimes across semi-arid African savannas.

Fire regimes are expected to change with climate change, resulting in a crucial need to understand the specific ways in which variable fire regimes impact important contributors to ecosystem functioning, such as mound-building termites. Termite mounds and fire are both important agents of savanna ecosystem heterogeneity and functioning, but there is little understanding of how they interact across savanna types. We used very high-resolution LiDAR remote sensing to measure the size and distribution of termite mounds across approximately 1300 ha of experimental burn plots in four South African savanna landscapes representing a wide range of fire treatments differing in seasonality and frequency of burning. In nutrient-poor granitic savannas, fire had no impact on termite mound size, densities and spatial distributions. In nutrient-rich basaltic savannas with high mammalian herbivore abundance and intermediate rainfall, very frequent fires caused a decrease in termite mound size, whereas in arid nutrient-rich basaltic savannas, fires that occurred at intermediate frequencies and in transitional seasons (i.e. late dry season and late wet season) decreased the degree of spatial overdispersal exhibited by mounds. Overall, our results suggest that termite mounds are resistant to variation in fire seasonality and frequency, likely indicating that ecosystem services provided by mound-building termites will be unaffected by changing fire regimes. However, consideration of changes to termite mound size and distribution could be necessary for land managers in specific savanna types, such as nutrient-rich soils with high mammalian herbivore abundance.

Limited increases in savanna carbon stocks over decades of fire suppression.

Savannas cover a fifth of the land surface and contribute a third of terrestrial net primary production, accounting for three-quarters of global area burned and more than half of global fire-driven carbon emissions1-3. Fire suppression and afforestation have been proposed as tools to increase carbon sequestration in these ecosystems2,4. A robust quantification of whole-ecosystem carbon storage in savannas is lacking however, especially under altered fire regimes. Here we provide one of the first direct estimates of whole-ecosystem carbon response to more than 60 years of fire exclusion in a mesic African savanna. We found that fire suppression increased whole-ecosystem carbon storage by only 35.4 ± 12% (mean ± standard error), even though tree cover increased by 78.9 ± 29.3%, corresponding to total gains of 23.0 ± 6.1 Mg C ha-1 at an average of about 0.35 ± 0.09 Mg C ha-1 year-1, more than an order of magnitude lower than previously assumed4. Frequently burned savannas had substantial belowground carbon, especially in biomass and deep soils. These belowground reservoirs are not fully considered in afforestation or fire-suppression schemes but may mean that the decadal sequestration potential of savannas is negligible, especially weighed against concomitant losses of biodiversity and function.

Sampling forests with terrestrial laser scanning.

BACKGROUND AND AIMS: Terrestrial laser scanners (TLSs) have successfully captured various properties of individual trees and have potential to further increase the quality and efficiency of forest surveys. However, TLSs are limited to line of sight observations, and forests are complex structural environments that can occlude TLS beams and thereby cause incomplete TLS samples. We evaluate the prevalence and sources of occlusion that limit line of sight to forest stems for TLS scans, assess the impacts of TLS sample incompleteness, and evaluate sampling strategies and data analysis techniques aimed at improving sample quality and representativeness. METHODS: We use a large number of TLS scans (761), taken across a 255 650-m2 area of forest with detailed field survey data: the Harvard Forest Global Earth Observatory (ForestGEO) (MA, USA). Sets of TLS returns are matched to stem positions in the field surveys to derive TLS-observed stem sets, which are compared with two additional stem sets derived solely from the field survey data: a set of stems within a fixed range from the TLS and a set of stems based on 2-D modelling of line of sight. Stem counts and densities are compared between the stem sets, and four alternative derivations of area to correct stem densities for the effects of occlusion are evaluated. Representation of diameter at breast height and species, drawn from the field survey data, are also compared between the stem sets. KEY RESULTS: Occlusion from non-stem sources was the major influence on TLS line of sight. Transect and point TLS samples demonstrated better representativeness of some stem properties than did plots. Deriving sampled area from TLS scans improved estimates of stem density. CONCLUSIONS: TLS sampling efforts should consider alternative sampling strategies and move towards in-progress assessment of sample quality and dynamic adaptation of sampling.

Classifying ecosystems with metaproperties from terrestrial laser scanner data.

In this study, we introduce metaproperty analysis of terrestrial laser scanner (TLS) data, and demonstrate its application through several ecological classification problems. Metaproperty analysis considers pulse level and spatial metrics derived from the hundreds of thousands to millions of lidar pulses present in a single scan from a typical contemporary instrument. In such large aggregations, properties of the populations of lidar data reflect attributes of the underlying ecological conditions of the ecosystems.In this study, we provide the Metaproperty Classification Model to employ TLS metaproperty analysis for classification problems in ecology. We applied this to a proof-of-concept study, which classified 88 scans from rooms and forests with 100% accuracy, to serve as a template.We then applied the Metaproperty Classification Model in earnest, to separate scans from temperate and tropical forests with 97.09% accuracy (N = 224), and to classify scans from inland and coastal tropical rainforests with 84.07% accuracy (N = 270).The results demonstrate the potential for metaproperty analysis to identify subtle and important ecosystem conditions, including diseases and anthropogenic disturbances. Metaproperty analysis serves as an augmentation to contemporary object reconstruction applications of TLS in ecology, and can characterize regional heterogeneity.