Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019: Globe

Buchhorn, Marcel; Smets, Bruno; Bertels, Luc; Roo, De; Lesiv, Myroslava; Tsendbazar, Nandin‐Erdene; Herold, Martin; Fritz, Steffen

doi:10.5281/zenodo.3939050

Cited by 110 publications

(91 citation statements)

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“…To assess peatland degradation, we intersected the estimates of Tanneberger et al, [18] with the land cover maps of the Copernicus Land Monitoring Service (Corine Land Cover-CLC-European seamless vector database RELEASE v18_5), mainly based on PROBA-V satellite observations, organized into Sentinel-2 equivalent tiles of 110 x 110 km with UTM projection [33]. All features in the original vector database were classified and digitised based on satellite images with 100 m positional accuracy (according to CLC specifications) and 25 ha minimum mapping unit (5 ha MMU for change layer) into the standardized CLC nomenclature (44 CLC classes).…”

Section: Peatland Conditionmentioning

confidence: 99%

Mires in Europe—Regional Diversity, Condition and Protection

et al. 2021

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In spite of the worldwide largest proportional loss of mires, Europe is a continent with important mire diversity. This article analyses the condition and protection status of European mire ecosystems. The overview is based on the system of European mire regions, representing regional variety and ecosystem biodiversity. We combined peatland distribution data with land cover maps of the Copernicus Land Monitoring Service as well as with the World Database on Protected Areas to assess the extent of degraded peatlands and the proportion of peatlands located in protected areas in each European mire region. The total proportion of degraded peatlands in Europe is 25%; within the EU it is 50% (120,000 km2). The proportion of degradation clearly increases from north to south, as does the proportion of peatlands located within protected areas. In more than half of Europe’s mire regions, the target of at least 17% of the area located in protected areas is not met with respect to peatlands. Data quality is discussed and the lessons learned from Europe for peatland conservation are presented.

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Section: Peatland Conditionmentioning

confidence: 99%

Mires in Europe—Regional Diversity, Condition and Protection

et al. 2021

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“…Tree plantations (oil palm, coconut) are explicitly excluded from the HCS classification by overlaying the corresponding masks 35,36 , which were also derived from Sentinel-2. Finally, urban regions are overlaid using the latest existing global land cover product 37 .…”

Section: Large-scale Indicative Hcs Mapsmentioning

confidence: 99%

“…This work uses three major data sources: Sentinel-2 optical images, sparse canopy top height estimates from GEDI L1B waveforms 32 , and a carbon density product from an airborne lidar campaign in Sabah, northern Borneo 34,39 . In addition, three existing semantic map layers are overlaid as additional filters on the high carbon stock classification: oil palm 35 , coconut 36 , and urban regions 37 .…”

Section: Datamentioning

confidence: 99%

“…To mask urban (built-up) regions, we fall back to the latest existing map layer for the year 2019 provided by the Copernicus Global Land Service 37 , which has a resolution of 100 m. In addition, two types of tall crop plantations are explicitly masked, oil palm and coconut. Semantic binary maps of oil palm plantations are created by thresholding an existing oil palm tree density map for the year 2019 35 .…”

Section: /12mentioning

confidence: 99%

“…In a second step the carbon density map is regressed from the canopy top height map with an ensemble of small CNNs that is trained on carbon density data from an airborne lidar campaign in Borneo Sabah 34 . The last step then applies the carbon thresholds defined by the HCSA, and overlays the HCS classification with additional map layers to explicitly identify tall crops 35,36 (i.e., oil palm and coconut plantations) and urban regions 37 .…”

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confidence: 99%

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High carbon stock mapping at large scale with optical satellite imagery and spaceborne LIDAR

Lang

Schindler²,

Wegner³

2021

Preprint

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The increasing demand for commodities is leading to changes in land use worldwide. In the tropics, deforestation, which causes high carbon emissions and threatens biodiversity, is often linked to agricultural expansion. While the need for deforestation-free global supply chains is widely recognized, making progress in practice remains a challenge. Here, we propose an automated approach that aims to support conservation and sustainable land use planning decisions by mapping tropical landscapes at large scale and high spatial resolution following the High Carbon Stock (HCS) approach. A deep learning approach is developed that estimates canopy height for each 10 m Sentinel-2 pixel by learning from sparse GEDI LIDAR reference data, achieving an overall RMSE of 6.3 m. We show that these wall-to-wall maps of canopy top height are predictive for classifying HCS forests and degraded areas with an overall accuracy of 86 % and produce a first high carbon stock map for Indonesia, Malaysia, and the Philippines.b Aboveground biomass consists of ≈47% carbon 15 , a value also adopted for the Intergovernmental Panel on Climate Change (IPCC) guidelines 16 .

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A utilization distribution for the global population of Cape Vultures (Gypscoprotheres) to guide wind energy development

Cervantes

Murgatroyd

Allan³

et al. 2023

Ecological Applications

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The rapid development of wind energy in southern Africa represents an additional threat to the already fragile populations of African vultures. The distribution of the vulnerable Cape Vulture Gyps coprotheres overlaps considerably with wind energy development areas in South Africa, creating conflicts that can hinder both vulture conservation and sustainable energy development. To help address this conflict and aid in the safe placement of wind energy facilities, we map the utilization distribution (UD) of this species across its distributional range. Using tracking data from 68 Cape Vultures collected over the last 20 years, we develop a spatially explicit habitat use model to estimate the expected UDs around known colonies. Scaling the UDs by the number of vultures expected to use each of the colonies, we estimate the Cape Vulture population utilization distribution (PUD) and determine its exposure to wind farm impacts. To complement our results, we model the probability of a vulture flying within the rotor sweep area of a wind turbine throughout the species range and use this to identify areas that are particularly prone to collisions. Overall, our estimated PUD correlates well with reporting rates of the species from the Southern African Bird Atlas Project, currently used to assess potential overlap between Cape Vultures and wind energy developments, but it adds important benefits, such as providing a spatial gradient of activity estimates over the entire species range. We illustrate the application of our maps by analyzing the exposure of Cape Vultures in the Renewable Energy Development Zones (REDZs) in South Africa. This application is a scalable procedure that can be applied at different planning phases, from strategic, nationwide planning to project-level assessments.

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Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019: Globe

Cited by 110 publications

References 0 publications

Mires in Europe—Regional Diversity, Condition and Protection

Mires in Europe—Regional Diversity, Condition and Protection

High carbon stock mapping at large scale with optical satellite imagery and spaceborne LIDAR

A utilization distribution for the global population of Cape Vultures (Gypscoprotheres) to guide wind energy development

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