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Intelligent forest management is the key to mitigating climate warming, fostering a green economy, and protecting valuable habitats. Detailed knowledge about forests is a prerequisite for such management but is conventionally based on costly plot-scale data, rarely available at resolution of relevance for management strategies. Here, we present a deep learning-based framework that provides location, crown area and height for each individual tree from aerial images at country scale. We quantify and characterize all individual trees in Denmark and show that 26% of the trees grow outside forests, which is typically unrecognized in national inventories. Furthermore, we demonstrate that only marginal effort is needed to transfer our framework to Finland, despite markedly dissimilar landscapes and data sources. Our work lays the foundation for a global database, where every tree has its digital twin and is spatially traceable and manageable.

The consistent monitoring of trees both inside and outside of forests is key to mitigating climate change. However existing large-scale tree cover maps primarily quantify forest cover and do not include isolated trees, as these are not discernible in lower resolution satellite images. In many dryland countries these non-forest trees constitute the main form of tree cover, and play a vital role in ecological stability, local economies, livelihoods, and food security.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. We composite 232,053 4-band scenes from 2019 into 1x1° mosaics and apply a Convolutional Neural Network to segment canopy cover of all trees and shrubs. To train the network we use a combination of manually annotated 1 m labels and source images upsampled from 3 m to 1 m, resulting in a final model that maps tree cover at 1 m across the continent, segmenting closed canopies in forest areas and individual scattered trees in savannah areas.Our prototype map 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. This analysis lays the groundwork towards global scale studies of tree cover at individual tree level and annual temporal scale, which is crucial for improved managing of woody resources, monitoring of TOF in relation to agroforestry, tree planting and restoration efforts, and assessing land use impacts in non-forest landscapes.

Distinguishing trees on agricultural land from forests is essential for a better understanding of the relationship between forests and human farming activities. However, it is difficult to separate them with remote sensing imagery since they share similar canopy cover, especially on the edge of the amazon rain forest, which has a much-complicated agriculture pattern. Except for annual crops and pasture, there are also lots of agroforestry applications and shifting cultivation, which integrates many tree systems. And those tree systems are not well separated from the forest in the existing land cover map. Recent techniques allow for the mapping of single trees outside of forests, now we take the next step by identifying those diverse tree-involved systems in agricultural land. Here we aim to generate a robust, cost-efficient method to distinguish trees within agricultural land from the forest. We started our exploration from Peruvian Amazon, where the competition for land has increased in the last decades, causing possible adverse effects on livelihoods and ecosystem services. Deep learning models, data sampling, and fine-tuning strategies are tested and optimized with PlanetScope satellite imagery. Our research target is to provide a tool for separating tree systems in farmland from the forest. It can also be used as a base map to explore the dynamic of agriculture transition and its impact on livelihoods and ecosystem services.

Inland surface water, especially lakes and small water bodies, are essential resources and have impacts on biodiversity, greenhouse gases and health. This is particularly true in the semi-arid Sahelian region, where these resources remain largely unassessed, and little is known about their number, size and quality. Remote sensing monitoring methods remain a promising tool to address these issues at the large scale, especially in areas where field data are scarce. Thanks to technological advances, current remote sensing systems provide data for regular monitoring over time and offer a high spatial resolution, up to 10 metres. Several water detection methods have been developed, many of them using spectral information to differentiate water surfaces from soil, through thresholding on water indices (MNDWI for example), or classifications by clustering. These methods are sensitive to optical reflectance variability and are not straight forwardly applicable to regions, such as the Sahel, where the lakes and their environment are very diverse. Particularly, the presence of aquatic vegetation is an important challenge and source of error for many of the existing algorithms and available databases. Deep learning, a subset of machine learning methods for training deep neural networks, has emerged as the state-of-the-art approach for a large number of remote sensing tasks. In this study, we apply a deep learning model based on the U-Net architecture to detect water bodies in the Sahel using Sentinel-2 MSI data, and 86 manually defined lake polygons as training data. This framework was originally developed for tree mapping (Brandt et al., 2020, https://doi.org/10.1038/s41586-020-2824-5). Our preliminary analysis indicate that our models achieve a good accuracy (98 %). The problems of aquatic vegetation do not appear anymore, and each lake is thus well delimited irrespective of water type and characteristics. Using the water delineations obtained, we then classify different optical water types and thereby highlight different type of waterbodies, that appear to be mostly turbid and eutrophic waters, allowing to better understand the eco-hydrological processes in this region. This method demonstrates the effectiveness of deep learning in detecting water surfaces in the study region. Deriving water masks that account for all kind of waterbodies offer a great opportunity to further characterize different water types. This method is easily reproducible due to the availability of the satellite data/algorithm and can be further applied to detect dams and other human-made features in relation to lake environments.

Inland surface water, especially lakes and small water bodies, are essential resources and have impacts on biodiversity, greenhouse gases and health. This is particularly true in the semi-arid Sahelian region, where these resources remain largely unassessed, and little is known about their number, size and quality. Remote sensing monitoring methods remain a promising tool to address these issues at the large scale, especially in areas where field data are scarce. Thanks to technological advances, current remote sensing systems provide data for regular monitoring over time and offer a high spatial resolution, up to 10 metres. Several water detection methods have been developed, many of them using spectral information to differentiate water surfaces from soil, through thresholding on water indices (MNDWI for example), or classifications by clustering. These methods are sensitive to optical reflectance variability and are not straight forwardly applicable to regions, such as the Sahel, where the lakes and their environment are very diverse. Particularly, the presence of aquatic vegetation is an important challenge and source of error for many of the existing algorithms and available databases. Deep learning, a subset of machine learning methods for training deep neural networks, has emerged as the state-of-the-art approach for a large number of remote sensing tasks. In this study, we apply a deep learning model based on the U-Net architecture to detect water bodies in the Sahel using Sentinel-2 MSI data, and 86 manually defined lake polygons as training data. This framework was originally developed for tree mapping (Brandt et al., 2020, https://doi.org/10.1038/s41586-020-2824-5). Our preliminary analysis indicate that our models achieve a good accuracy (98 %). The problems of aquatic vegetation do not appear anymore, and each lake is thus well delimited irrespective of water type and characteristics. Using the water delineations obtained, we then classify different optical water types and thereby highlight different type of waterbodies, that appear to be mostly turbid and eutrophic waters, allowing to better understand the eco-hydrological processes in this region. This method demonstrates the effectiveness of deep learning in detecting water surfaces in the study region. Deriving water masks that account for all kind of waterbodies offer a great opportunity to further characterize different water types. This method is easily reproducible due to the availability of the satellite data/algorithm and can be further applied to detect dams and other human-made features in relation to lake environments.