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

Abstract

The consistent monitoring of trees both inside and outside of forests is key to mitigating climate change. Current monitoring systems either ignore trees outside forests or are too expensive to be applied consistently across countries on a repeated basis. Here we make use of 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 demonstrates that a precise assessment of all tree-based systems is possible at continental scale, and reveals that 29% of tree cover is found outside areas previously classified as tree cover, such as in croplands and grassland. Such accurate mapping of tree cover at metric resolution down to the level of individual trees and consistent among countries has the potential to redefine land use impacts, move beyond the need for forest definitions, build the basis for natural climate solutions, and provide a new scientific basis for tree related studies.

The overlooked contribution of trees outside forests to tree cover and woody biomass across Europe

“…Third, LiDAR and aerial imagery differ between countries with regard to spectral, spatial, and temporal resolution, making it difficult to create biomass maps that are consistent between countries and years at continental scale. Although this was not demonstrated in this study, PlanetScope-based maps have a certain consistency in space and time [36,37], allowing for large-scale and multi-year biomass assessments. Fourth, integrating trees outside forests in national inventories is not only interesting from a carbon stock perspective, but also allows to quantify a variety of ecosystem services provided by these trees.…”

“…For areas with frequent cloud cover, the time window was progressively extended towards earlier dates until the whole tile was fully covered with scenes. To reduce differences resulting from the mosaicking scenes collected from different sensors and times, we applied a histogram matching to Landsat and Sentinel-2 surface reflectance images from the same date range to produce homogeneous mosaics [37]. Airborne LiDAR canopy height maps.…”

“…1). The modified U-Net has proven to be able to delineate tree canopies in very high resolution imagery [11,37,45]. EfficientNets have shown to perform well with small parameter size by a pre-designed network depth, width, and resolution.…”

Mega-cities dominate China’s urban greening

“…We used 3-m resolution PlanetScope satellite imagery from 2019 29 covering all Chinese cities with an urban area larger than 50 km 2 (242 cities; see Methods for definition of urban areas), summing up to a total area of 51,882 km 2 . We trained a deep learning segmentation model 30 with labels corresponding to an area of 209 km 2 (Supplementary Table 1, Supplementary Fig.…”

“…The advent of images from the PlanetScope nano-satellite constellation, which provide daily global imagery at a resolution high enough to identify single trees (~3 m), represents an emerging alternative for such large scale mapping. It has been shown that these images can support mapping of individual trees at continental scales 29 , but the short period of data availability (since 2017) makes it unfit to study changes over longer time periods. Here, we complement the PlanetScope satellite constellation with data from RapidEye (~5 m), providing a comparable product since 2010, and uncover how China's urban greening policies have been implemented in regards to changes in tree cover across all major cities of China between 2010 and 2019.…”