An unexpectedly large count of trees in the West African Sahara and Sahel

Brandt, Martin; Tucker, Compton J.; Kariryaa, Ankit; Rasmussen, Kjeld; Abel, Christin; Small, Jennifer; Chave, Jérôme; Rasmussen, Laura Vang; Hiernaux, Pierre; Diouf, Alioune; Kergoat, Laurent; Mertz, Ole; Igel, Christian; Gieseke, Fabian; Schöning, Johannes; Li, Sizhuo; Melocik, K. A.; Meyer, Jesse G.; Sinno, Scott; Romero, Enrique Azpra; Glennie, Erin; Montagu, Amandine; Dendoncker, Morgane; Fensholt, Rasmus

doi:10.1038/s41586-020-2824-5

Cited by 286 publications

(277 citation statements)

References 36 publications

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“…Apart from a different definition of forest loss, this can also be attributed to lower amounts of forest present in Hansen et al [63]. Based on a recent study of Brandt et al [64], tree cover over the Sahel is highly underestimated in several datasets. This is also the case for Hansen et al [63], while more realistic values are obtained in our cover fraction maps.…”

Section: Discussionmentioning

confidence: 98%

Thirty Years of Land Cover and Fraction Cover Changes over the Sudano-Sahel Using Landsat Time Series

et al. 2020

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Historical land cover maps are of high importance for scientists and policy makers studying the dynamic character of land cover change in the Sudano-Sahel, including anthropogenic and climatological drivers. Despite its relevance, an accurate high resolution record of historical land cover maps is currently lacking over the Sudano-Sahel. In this study, 30 m resolution historically consistent land cover and cover fraction maps are provided over the Sudano-Sahel for the period 1986–2015. These land cover/cover fraction maps are achieved based on the Landsat archive preprocessed on Google Earth Engine and a random forest classification/regression model, while historical consistency is achieved using the hidden Markov model. Using these historical maps, a multitude of variability in the dynamic Sudano-Sahel region over the past 30 years is revealed. On the one hand, Sahel-wide cropland expansion and the re-greening of the Sahel is observed in the discrete land cover classification. On the other hand, subtle changes such as forest degradation are detected based on the cover fraction maps. Additionally, exploiting the 30 m spatial resolution, fine-scale changes, such as smallholder or subsistence farming, can be detected. The historical land cover/cover fraction maps presented in this study are made available via an open-access platform.

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Section: Discussionmentioning

confidence: 98%

Thirty Years of Land Cover and Fraction Cover Changes over the Sudano-Sahel Using Landsat Time Series

et al. 2020

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“…While most prior studies have used UAVs to estimate FVC in grasslands, shrublands, or agricultural environments (e.g., Reference [63,64,66,67]), this study uses UAV imagery to quantify woody vegetation cover in a desert riparian forest. Such research contributes to the growing understanding of the extent of dryland woody cover, which recent studies have shown to be much higher than previously estimated by using very high-resolution remotely sensed imagery [68,69].…”

Section: Introductionmentioning

confidence: 91%

Spatiotemporal Analysis of Vegetation Cover Change in a Large Ephemeral River: Multi-Sensor Fusion of Unmanned Aerial Vehicle (UAV) and Landsat Imagery

et al. 2020

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Ephemeral rivers in arid regions act as linear oases, where corridors of vegetation supported by accessible groundwater and intermittent surface flows provide biological refugia in water-limited landscapes. The ecological and hydrological dynamics of these systems are poorly understood compared to perennial systems and subject to wide variation over space and time. This study used imagery obtained from an unmanned aerial vehicle (UAV) to enhance satellite data, which were then used to quantify change in woody vegetation cover along the ephemeral Kuiseb River in the Namib Desert over a 35-year period. Ultra-high resolution UAV imagery collected in 2016 was used to derive a model of fractional vegetation cover from five spectral vegetation indices, calculated from a contemporaneous Landsat 8 Operational Land Imager (OLI) image. The Normalized Difference Vegetation Index (NDVI) provided the linear best-fit relationship for calculating fractional cover; the model derived from the two 2016 datasets was subsequently applied to 24 intercalibrated Landsat images to calculate fractional vegetation cover for the Kuiseb extending back to 1984. Overall vegetation cover increased by 33% between 1984 and 2019, with the most highly vegetated reach of the river exhibiting the greatest positive change. This reach corresponds with the terminal alluvial zone, where most flood deposition occurs. The spatial and temporal trends discovered highlight the need for long-term monitoring of ephemeral ecosystems and demonstrate the efficacy of a multi-sensor approach to time series analysis using a UAV platform.

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“…However, delineation requires a distinct set of technical background and computational approaches and thus many ecological applications skip an explicit delineation step entirely (Williams et al, 2020a). In addition, the growing availability of continental scale datasets of high resolution remote sensing imagery opens up the possibility for broad scale forest monitoring of individual trees (Brandt et al, 2020; Schneider et al, 2020) that can be supported by this dataset. Just as we used weak annotations generated from unsupervised LiDAR algorithms, future developers can use this dataset to train in the multiple data types provided by the NEON Airborne Observation Platform across a broad range of forest types.…”

Section: Evaluation and Validationmentioning

confidence: 99%

Individual Tree-Crown Detection in RGB Imagery Using Semi-Supervised Deep Learning Neural Networks

Marconi

Bohlman

Zare

et al. 2019

Preprint

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Remote sensing can transform the speed, scale, and cost of biodiversity and forestry surveys. Data acquisition currently outpaces the ability to identify individual organisms in high resolution imagery. We outline an approach for identifying tree-crowns in true color, or red/green blue (RGB) imagery using a deep learning detection network. Individual crown delineation is a persistent challenge in studies of forested ecosystems and has primarily been addressed using three-dimensional LIDAR. We show that deep learning models can leverage existing lidar-based unsupervised delineation approaches to initially train an RGB crown detection model, which is then refined using a small number of hand-annotated RGB images. We validate our proposed approach using an open-canopy site in the National Ecological Observation Network (NEON). Our results show that combining LIDAR and RGB methods in a self-supervised model improves predictions of trees in natural landscapes. The addition of a small number of hand-annotated trees improved performance over the initial self-supervised model. While undercounting of individual trees in complex canopies remains an area of development, deep learning can increase the performance of remotely sensed tree surveys.

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An unexpectedly large count of trees in the West African Sahara and Sahel

Cited by 286 publications

References 36 publications

Thirty Years of Land Cover and Fraction Cover Changes over the Sudano-Sahel Using Landsat Time Series

Thirty Years of Land Cover and Fraction Cover Changes over the Sudano-Sahel Using Landsat Time Series

Spatiotemporal Analysis of Vegetation Cover Change in a Large Ephemeral River: Multi-Sensor Fusion of Unmanned Aerial Vehicle (UAV) and Landsat Imagery

Individual Tree-Crown Detection in RGB Imagery Using Semi-Supervised Deep Learning Neural Networks

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