31 results on '"Prestele, R."'
Search Results
2. Agent‐Based Modeling of Alternative Futures in the British Land Use System
- Author
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Brown, C., primary, Seo, B., additional, Alexander, P, additional, Burton, V., additional, Chacón‐Montalván, E. A., additional, Dunford, R., additional, Merkle, M., additional, Harrison, P. A., additional, Prestele, R., additional, Robinson, E. L., additional, and Rounsevell, M., additional
- Published
- 2022
- Full Text
- View/download PDF
3. Agent‐Based Modeling of Alternative Futures in the British Land Use System
- Author
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Brown, C., Seo, B., Alexander, P, Burton, V., Chacón‐Montalván, E. A., Dunford, R., Merkle, M., Harrison, P. A., Prestele, R., Robinson, E. L., Rounsevell, M., 1 Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK‐IFU) Karlsruhe Institute of Technology Garmisch‐Partenkirchen Germany, 2 School of Geosciences University of Edinburgh Edinburgh UK, 3 Forest Research Northern Research Station Midlothian UK, 5 Mathematics and Statistics Department Fylde College, Lancaster University Lancaster UK, 6 UK Centre for Ecology & Hydrology Maclean Building Wallingford UK, and 8 UK Centre for Ecology & Hydrology Lancaster UK
- Subjects
ddc:333.7 ,land use change ,model evaluation ,socio‐economic scenario ,land use model ,TRACE ,scenario analysis - Abstract
Socio‐economic scenarios such as the Shared Socioeconomic Pathways (SSPs) have been widely used to analyze global change impacts, but representing their diversity is a challenge for the analytical tools applied to them. Taking Great Britain as an example, we represent a set of stakeholder‐elaborated UK‐SSP scenarios, linked to climate change scenarios (Representative Concentration Pathways), in a globally‐embedded agent‐based modeling framework. We find that distinct model components are required to account for divergent behavioral, social and societal conditions in the SSPs, and that these have dramatic impacts on land system outcomes. From strong social networks and environmental sustainability in SSP1 to land consolidation and technological intensification in SSP5, scenario‐specific model designs vary widely from one another and from present‐day conditions. Changes in social and human capitals reflecting social cohesion, equality, health and education can generate impacts larger than those of technological and economic change, and comparable to those of modeled climate change. We develop an open‐access, transferrable model framework and provide UK‐SSP projections to 2080 at 1 km2 resolution, revealing large differences in land management intensities, provision of a range of ecosystem services, and the knowledge and motivations underlying land manager decision‐making. These differences suggest the existence of large but underappreciated areas of scenario space, within which novel options for land system sustainability could occur., Key Points: A national‐scale agent‐based model is developed to represent paired climatic and socio‐economic scenarios in the land system. Key scenario characteristics relate to forms of human behavior, interactions and societal preferences. Large differences emerge between scenarios in terms of land management intensities, ecosystem service provision and land sparing., Helmholtz Association http://dx.doi.org/10.13039/501100009318, Natural Environment Research Council http://dx.doi.org/10.13039/501100000270, Climate Resilience Programme, Forestry Commission UK Forestry Commission http://dx.doi.org/10.13039/100017497, UKRI, Engineering and Physical Sciences Research Council http://dx.doi.org/10.13039/501100000266, Global Food Security Programme, DAAD, German Academic Exchange Service London http://dx.doi.org/10.13039/501100001654, Government of the United Kingdom http://dx.doi.org/10.13039/100013986, Deutscher Akademischer Austauschdienst http://dx.doi.org/10.13039/501100001655, Bundesministerium für Bildung und Forschung http://dx.doi.org/10.13039/501100002347, Karlsruhe Institute of Technology http://dx.doi.org/10.13039/100009133, Leibniz‐Gemeinschaft http://dx.doi.org/10.13039/501100001664, https://landchange.earth/CRAFTY, https://doi.org/10.17605/OSF.IO/CY8WE
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- 2022
4. Agent‐based modelling of alternative futures in the British land use system
- Author
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Brown, C, Seo, B, Alexander, P, Burton, V, Chacón‐montalván, Ea, Dunford, R, Merkle, M, Harrison, Pa, Prestele, R, Robinson, El, and Rounsevell, M
- Abstract
Socio-economic scenarios such as the Shared Socioeconomic Pathways (SSPs) have been widely used to analyse global change impacts, but representing their diversity is a challenge for the analytical tools applied to them. Taking Great Britain as an example, we represent a set of stakeholder-elaborated UK-SSP scenarios, linked to climate change scenarios (Representative Concentration Pathways), in a globally-embedded agent-based modelling framework. We find that distinct model components are required to account for divergent behavioural, social and societal conditions in the SSPs, and that these have dramatic impacts on land system outcomes. From strong social networks and environmental sustainability in SSP1 to land consolidation and technological intensification in SSP5, scenario-specific model designs vary widely from one another and from present-day conditions. Changes in social and human capitals reflecting social cohesion, equality, health and education can generate impacts larger than those of technological and economic change, and comparable to those of modelled climate change. We develop an open-access, transferrable model framework and provide UK-SSP projections to 2080 at 1km2 resolution, revealing large differences in land management intensities, provision of a range of ecosystem services, and the knowledge and motivations underlying land manager decision-making. These differences suggest the existence of large but underappreciated areas of scenario space, within which novel options for land system sustainability could occur.
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- 2022
5. Drivers of tropical forest loss between 2008 and 2019
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Laso Bayas, J.C., See, L., Georgieva, I., Shchepashchenko, D., Danylo, O., Dürauer, M., Bartl, H., Hofhansl, F., Zadorozhniuk, R., Burianchuk, M., Sirbu, F., Magori, B., Blyshchyk, K., Blyshchyk, V., Rabia, A.H., Pawe, C.K., Su, Y.-F., Ahmed, M., Panging, K., Melnyk, O., Vasylyshyn, O., Vasylyshyn, R., Bilous, A., Bilous, S., Das, K., Prestele, R., Pérez-Hoyos, A., Bungnamei, K., Lashchenko, A., Lakyda, Maryna., Lakyda, I., Serediuk, O., Domashovets, G., Yurchuk, Y., Koper, M., Fritz, S., Laso Bayas, J.C., See, L., Georgieva, I., Shchepashchenko, D., Danylo, O., Dürauer, M., Bartl, H., Hofhansl, F., Zadorozhniuk, R., Burianchuk, M., Sirbu, F., Magori, B., Blyshchyk, K., Blyshchyk, V., Rabia, A.H., Pawe, C.K., Su, Y.-F., Ahmed, M., Panging, K., Melnyk, O., Vasylyshyn, O., Vasylyshyn, R., Bilous, A., Bilous, S., Das, K., Prestele, R., Pérez-Hoyos, A., Bungnamei, K., Lashchenko, A., Lakyda, Maryna., Lakyda, I., Serediuk, O., Domashovets, G., Yurchuk, Y., Koper, M., and Fritz, S.
- Abstract
During December 2020, a crowdsourcing campaign to understand what has been driving tropical forest loss during the past decade was undertaken. For 2 weeks, 58 participants from several countries reviewed almost 115 K unique locations in the tropics, identifying drivers of forest loss (derived from the Global Forest Watch map) between 2008 and 2019. Previous studies have produced global maps of drivers of forest loss, but the current campaign increased the resolution and the sample size across the tropics to provide a more accurate mapping of crucial factors leading to forest loss. The data were collected using the Geo-Wiki platform (www.geo-wiki.org) where the participants were asked to select the predominant and secondary forest loss drivers amongst a list of potential factors indicating evidence of visible human impact such as roads, trails, or buildings. The data described here are openly available and can be employed to produce updated maps of tropical drivers of forest loss, which in turn can be used to support policy makers in their decision-making and inform the public.
- Published
- 2022
6. A crowdsourced global data set for validating built-up surface layers
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See, L., Georgieva, I., Dürauer, M., Kemper, T., Corbane, C., Maffenini, L., Gallego, J., Pesaresi, M., Sirbu, F., Ahmed, R., Blyshchyk, K., Magori, B., Blyshchyk, V., Melnyk, O., Zadorozhniuk, R., Mandici, M.-T., Su, Y.-F., Rabia, A.H., Pérez-Hoyos, A., Vasylyshyn, R., Pawe, C.K., Bilous, S., Kovalevskyi, S.B., Kovalevskyi, S.S., Bordoloi, K., Bilous, A., Panging, K., Bilous, V., Prestele, R., Sahariah, D., Deka, A., Nath, N., Neves, R., Myroniuk, V., Karner, M., Fritz, S., See, L., Georgieva, I., Dürauer, M., Kemper, T., Corbane, C., Maffenini, L., Gallego, J., Pesaresi, M., Sirbu, F., Ahmed, R., Blyshchyk, K., Magori, B., Blyshchyk, V., Melnyk, O., Zadorozhniuk, R., Mandici, M.-T., Su, Y.-F., Rabia, A.H., Pérez-Hoyos, A., Vasylyshyn, R., Pawe, C.K., Bilous, S., Kovalevskyi, S.B., Kovalevskyi, S.S., Bordoloi, K., Bilous, A., Panging, K., Bilous, V., Prestele, R., Sahariah, D., Deka, A., Nath, N., Neves, R., Myroniuk, V., Karner, M., and Fritz, S.
- Abstract
Several global high-resolution built-up surface products have emerged over the last five years, taking full advantage of open sources of satellite data such as Landsat and Sentinel. However, these data sets require validation that is independent of the producers of these products. To fill this gap, we designed a validation sample set of 50 K locations using a stratified sampling approach independent of any existing global built-up surface products. We launched a crowdsourcing campaign using Geo-Wiki (https://www.geo-wiki.org/) to visually interpret this sample set for built-up surfaces using very high-resolution satellite images as a source of reference data for labelling the samples, with a minimum of five validations per sample location. Data were collected for 10 m sub-pixels in an 80 × 80 m grid to allow for geo-registration errors as well as the application of different validation modes including exact pixel matching to majority or percentage agreement. The data set presented in this paper is suitable for the validation and inter-comparison of multiple products of built-up areas.
- Published
- 2022
7. Global forest management data for 2015 at a 100 m resolution
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Lesiv, M., Shchepashchenko, D., Buchhorn, M., See, L., Dürauer, M., Georgieva, I., Jung, M., Hofhansl, F., Schulze, K., Bilous, A., Blyshchyk, V., Mukhortova, L., Brenes, C., Krivobokov, L., Ntie, S., Tsogt, K., Pietsch, S., Tikhonova, E., Kim, M., Di Fulvio, F., Su, Y.-F., Zadorozhniuk, R., Sirbu, F., Panging, K., Bilous, S., Kovalevskii, S., Kraxner, F., Rabia, A.H., Vasylyshyn, R., Ahmed, R., Diachuk, P., Kovalevskyi, S., Bungnamei, K., Bordoloi, K., Churilov, A., Vasylyshyn, O., Sahariah, D., Tertyshnyi, A., Saikia, A., Malek, Å., Singha, K., Feshchenko, R., Prestele, R., Akhtar, I., Sharma, K., Domashovets, G., Spawn-Lee, S., Blyshchyk, O., Slyva, O., Ilkiv, M., Melnyk, O., Sliusarchuk, V., Karpuk, A., Terentiev, A., Bilous, V., Blyshchyk, K., Bilous, M., Bogovyk, N., Blyshchyk, I., Bartalev, S., Yatskov, M., Smets, B., Visconti, P., McCallum, I., Obersteiner, M., Fritz, S., Lesiv, M., Shchepashchenko, D., Buchhorn, M., See, L., Dürauer, M., Georgieva, I., Jung, M., Hofhansl, F., Schulze, K., Bilous, A., Blyshchyk, V., Mukhortova, L., Brenes, C., Krivobokov, L., Ntie, S., Tsogt, K., Pietsch, S., Tikhonova, E., Kim, M., Di Fulvio, F., Su, Y.-F., Zadorozhniuk, R., Sirbu, F., Panging, K., Bilous, S., Kovalevskii, S., Kraxner, F., Rabia, A.H., Vasylyshyn, R., Ahmed, R., Diachuk, P., Kovalevskyi, S., Bungnamei, K., Bordoloi, K., Churilov, A., Vasylyshyn, O., Sahariah, D., Tertyshnyi, A., Saikia, A., Malek, Å., Singha, K., Feshchenko, R., Prestele, R., Akhtar, I., Sharma, K., Domashovets, G., Spawn-Lee, S., Blyshchyk, O., Slyva, O., Ilkiv, M., Melnyk, O., Sliusarchuk, V., Karpuk, A., Terentiev, A., Bilous, V., Blyshchyk, K., Bilous, M., Bogovyk, N., Blyshchyk, I., Bartalev, S., Yatskov, M., Smets, B., Visconti, P., McCallum, I., Obersteiner, M., and Fritz, S.
- Abstract
Spatially explicit information on forest management at a global scale is critical for understanding the status of forests, for planning sustainable forest management and restoration, and conservation activities. Here, we produce the first reference data set and a prototype of a globally consistent forest management map with high spatial detail on the most prevalent forest management classes such as intact forests, managed forests with natural regeneration, planted forests, plantation forest (rotation up to 15 years), oil palm plantations, and agroforestry. We developed the reference dataset of 226 K unique locations through a series of expert and crowdsourcing campaigns using Geo-Wiki (https://www.geo-wiki.org/). We then combined the reference samples with time series from PROBA-V satellite imagery to create a global wall-to-wall map of forest management at a 100 m resolution for the year 2015, with forest management class accuracies ranging from 58% to 80%. The reference data set and the map present the status of forest ecosystems and can be used for investigating the value of forests for species, ecosystems and their services.
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- 2022
8. A Crowdsourced Global Data Set for Validating Built-up Surface Layers V.2
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See, L., Georgieva, I., Duerauer, M., Kemper, T., Corbane, C., Maffenini, L., Gallego, J., Pesaresi, M., Sirbu, F., Ahmed, R., Blyshchyk, K., Magori, B., Blyshchyk, V., Melnyk, O., Zadorozhniuk, R., Mandici, M.-T., Su, Y.-F., Rabia, A.H., Pérez-Hoyos, A., Vasylyshyn, R., Pawe, C.K., Bilous, S., Kovalevskyi, S.B., Kovalevskyi, S.S., Bordoloi, K., Bilous, A., Panging, K., Bilous, V., Prestele, R., Sahariah, D., Deka, A., Nath, N., Neves, R., Myroniuk, V., Karner, M., and Fritz, S.
- Abstract
This collection contains data that were collected during a crowdsourcing campaign using Geo-Wiki (https://www.geo-wiki.org/). The campaign involved visual interpretation of a sample that is designed for validating any existing global built-up surface product. A zipped shapefile (ValidationGrids.zip) contains the random stratified sample of 50K locations, which consist of 80x80m grids further sub-divided into 10m cells so there are 64 cells per grid. These locations were provided to the crowd, who used very high-resolution satellite images to label the grids as built-up (i.e., containing a building), non-built-up or unsure. The file (Geo-WikiBuilt-upCentroidsAll.csv) contains the data collected in the campaign summarized by the centroid (or central point of each 80m grid location). It also contains fields for quality control, one that indicates if the change information matches the control points (see below) or the majority answer from the crowd, and another that indicates whether the presence/absence of built-up matches the control points (see below) or the majority answer from the crowd. The data collected for all 64 cells per grid can be found in Geo-WikiBuilt-upCellsAll.csv. The Geo-Wiki campaign uses visually interpreted grid locations called control points as part of the scoring mechanism of Geo-Wiki for quality control. These control points are provided by centroid (Geo-WikiBuilt-upCentroidsControls.csv) and for all cells in the 80m grid (Geo-WikiBuilt-upCellsControls.csv). In addition to the raw data, two additional quality-controlled files have been produced. The first file (Geo-WikiBuilt-upCentroidsChangeQualityControlled.csv) provides a single record for each location on change in built-up (if built-up is present) that lists either the control point answer or the majority answer from the crowd. The second file (Geo-WikiBuilt-upCellsQualityControlled.csv) contains a single record for each of the 64 cells in each grid, listing either the control point answer or the majority answer from the crowd. Finally, the file Strata.csv contains the mapping between the grid location and the sampling stratum used in the design of the sample.
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- 2021
- Full Text
- View/download PDF
9. Global forest management data at a 100m resolution for the year 2015
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Lesiv, M., Shchepashchenko, D., Buchhorn, M., See, L., Dürauer, M., Georgieva, I., Jung, M., Hofhansl, F., Schulze, K., Bilous, A., Blyshchyk, V., Mukhortova, L., Brenes, C., Krivobokov, L., Ntie, S., Tsogt, K., Pietsch, S., Tikhonova, E., Kim, M., Di Fulvio, F., Su, Y.-F., Zadorozhniuk, R., Sirbu, F.S., Pangin, K., Bilous, S., Kovalevskii, S.B., Kraxner, F., Rabia, A., Vasylyshyn, R., Ahmed, R., Diachuk, P., Kovalevskyi, S., Bungnamei, K., Bordoloi, K., Churilov, A., Vasylyshyn, O., Sahariah, D., Tertyshnyi, A., Saikia, A., Malek, Ž., Singha, K., Feshchenko, R., Prestele, R., ul Hassan Akhtar, I., Sharma, K., Domashovets, G., Spawn-Lee, S., Blyshchyk, O., Slyva, O., Ilkiv, M., Melnyk, O., Sliusarchuk, V., Karpuk, A., Terentiev, A., Bilous, V., Blyshchyk, K., Bilous, M., Bogovyk, N., Blyshchyk, I., Bartalev, S., Yatskov, M., Smets, B., Visconti, P., McCallum, I., Obersteiner, M., Fritz, S., Lesiv, M., Shchepashchenko, D., Buchhorn, M., See, L., Dürauer, M., Georgieva, I., Jung, M., Hofhansl, F., Schulze, K., Bilous, A., Blyshchyk, V., Mukhortova, L., Brenes, C., Krivobokov, L., Ntie, S., Tsogt, K., Pietsch, S., Tikhonova, E., Kim, M., Di Fulvio, F., Su, Y.-F., Zadorozhniuk, R., Sirbu, F.S., Pangin, K., Bilous, S., Kovalevskii, S.B., Kraxner, F., Rabia, A., Vasylyshyn, R., Ahmed, R., Diachuk, P., Kovalevskyi, S., Bungnamei, K., Bordoloi, K., Churilov, A., Vasylyshyn, O., Sahariah, D., Tertyshnyi, A., Saikia, A., Malek, Ž., Singha, K., Feshchenko, R., Prestele, R., ul Hassan Akhtar, I., Sharma, K., Domashovets, G., Spawn-Lee, S., Blyshchyk, O., Slyva, O., Ilkiv, M., Melnyk, O., Sliusarchuk, V., Karpuk, A., Terentiev, A., Bilous, V., Blyshchyk, K., Bilous, M., Bogovyk, N., Blyshchyk, I., Bartalev, S., Yatskov, M., Smets, B., Visconti, P., McCallum, I., Obersteiner, M., and Fritz, S.
- Abstract
We provide four data records: 1.The reference data set as a comma-separated file ("reference_data_set.csv") with the following attributes: “ID” is a unique location identifier “Latitude, Longitude” are centroid coordinates of a 100m x 100m pixel. “Land_use_ID “is a land use class: 11 - Naturally regenerating forest without any signs of human activities, e.g., primary forests. 20 - Naturally regenerating forest with signs of human activities, e.g., logging, clear cuts etc. 31 - Planted forest. 32 - Short rotation plantations for timber. 40 - Oil palm plantations. 53 - Agroforestry. “Flag” identifies a data origin: 1- the crowdsourced locations, 2- the control data set, 0 – the additional experts' classifications following the opportunistic approach. 2. The 100 m forest management map in a geoTiff format with the classes presented - "FML_v3.2.tif ". 3. The predicted class probability from the Random Forest classification in a geoTiff format - "ProbaV_LC100_epoch2015_global_v2.0.3_forest-management--layer-proba_EPSG-4326.tif" 4. Validation data set as a comma-separated file ("validation_data_set.csv) with the following attributes: “ID” is a unique location identifier “pixel_center_x” , “pixel_center_y ” are centroid coordinates of a 100m x 100m pixel in lat/lon projection “first_landuse_class “is a land use class, as in (1). “second_landuse_class “is a second possible land use class, as in (1), identified in case it was difficult to assign one class with high confidence.
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- 2021
10. A Crowdsourced Global Data Set for Validating Built-up Surface Layers
- Author
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See, L., Georgieva, I., Dürauer, M., Kemper, T., Corbane, C., Maffenini, L., Gallego, J., Pesaresi, M., Sirbu, F., Ahmed, R., Blyshchyk, K., Magori, B., Blyshchyk, V., Melnyk, O., Zadorozhniuk, R., Mandici, M.-T., Su, Y.-F., Rabia, A.H., Pérez-Hoyos, A., Vasylyshyn, R., Pawe, C.K., Bilous, S., Kovalevskyi, S.B., Kovalevskyi, S.S., Bordoloi, K., Bilous, A., Panging, K., Bilous, V., Prestele, R., Sahariah, D., Deka, A., Nath, N., Neves, R., Myroniuk, V., Karner, M., Fritz, S., See, L., Georgieva, I., Dürauer, M., Kemper, T., Corbane, C., Maffenini, L., Gallego, J., Pesaresi, M., Sirbu, F., Ahmed, R., Blyshchyk, K., Magori, B., Blyshchyk, V., Melnyk, O., Zadorozhniuk, R., Mandici, M.-T., Su, Y.-F., Rabia, A.H., Pérez-Hoyos, A., Vasylyshyn, R., Pawe, C.K., Bilous, S., Kovalevskyi, S.B., Kovalevskyi, S.S., Bordoloi, K., Bilous, A., Panging, K., Bilous, V., Prestele, R., Sahariah, D., Deka, A., Nath, N., Neves, R., Myroniuk, V., Karner, M., and Fritz, S.
- Abstract
This collection contains data that were collected during a crowdsourcing campaign using Geo-Wiki (https://www.geo-wiki.org/). The campaign involved visual interpretation of a sample that is designed for validating any existing global built-up surface product. A zipped shapefile (ValidationGrids.zip) contains the random stratified sample of 50K locations, which consist of 80x80m grids further sub-divided into 10m cells so there are 64 cells per grid. These locations were provided to the crowd, who used very high-resolution satellite images to label the grids as built-up (i.e., containing a building), non-built-up or unsure. The file (Geo-WikiBuilt-upCentroidsAll.csv) contains the data collected in the campaign summarized by the centroid (or central point of each 80m grid location). The data collected for all 64 cells per grid can be found in Geo-WikiBuilt-upCellsAll.csv. The Geo-Wiki campaign uses visually interpreted grid locations called control points as part of the scoring mechanism of Geo-Wiki for quality control. These control points are provided by centroid (Geo-WikiBuilt-upCentroidsControls.csv) and for all cells in the 80m grid (Geo-WikiBuilt-upCellsControls.csv). Finally, the file Strata.csv contains the mapping between the grid location and the sampling stratum used in the design of the sample.
- Published
- 2021
11. Crowdsourcing deforestation in the tropics during the last decade: Data sets from the “Driver of Tropical Forest Loss” Geo-Wiki campaign
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Laso Bayas, J.C., See, L., Georgieva, I., Shchepashchenko, D., Danylo, O., Dürauer, M., Bartl, H., Hofhansl, F., Lesiv, M., Zadorozhniuk, R., Burianchuk, M., Sirbu, F., Magori, B., Blyshchyk, K., Blyshchyk, V., Rabia, A.H., Pawe, C.K., Su, Y.-F., Ahmed, M., Panging, K., Melnyk, O., Vasylyshyn, O., Vasylyshyn, R., Bilous, A., Bilous, S., Das, K., Prestele, R., Pérez-Hoyos, A., Bungnamei, K., Lashchenko, A., Lakyda, M., Lakyda, I., Serediuk, O., Domashovets, G., Yurchuk, Y., Fritz, S., Laso Bayas, J.C., See, L., Georgieva, I., Shchepashchenko, D., Danylo, O., Dürauer, M., Bartl, H., Hofhansl, F., Lesiv, M., Zadorozhniuk, R., Burianchuk, M., Sirbu, F., Magori, B., Blyshchyk, K., Blyshchyk, V., Rabia, A.H., Pawe, C.K., Su, Y.-F., Ahmed, M., Panging, K., Melnyk, O., Vasylyshyn, O., Vasylyshyn, R., Bilous, A., Bilous, S., Das, K., Prestele, R., Pérez-Hoyos, A., Bungnamei, K., Lashchenko, A., Lakyda, M., Lakyda, I., Serediuk, O., Domashovets, G., Yurchuk, Y., and Fritz, S.
- Abstract
The data set is the result of the Drivers of Tropical Forest Loss crowdsourcing campaign. The campaign took place in December 2020. A total of 58 participants contributed validations of almost 120k locations worldwide. The locations were selected randomly from the Global Forest Watch tree loss layer (Hansen et al 2013), version 1.7. At each location the participants were asked to look at satellite imagery time series using a customized Geo-Wiki user interface and identify drivers of tropical forest loss during the years 2008 to 2019 following 3 steps: Step 1) Select the predominant driver of forest loss visible on a 1 km square (delimited by a blue bounding box); Step 2) Select any additional driver(s) of forest loss and; Step 3) Select if any roads, trails or buildings were visible in the 1 km bounding box. The Geo-Wiki campaign aims, rules and prizes offered to the participants in return for their work can be seen here: https://application.geo-wiki.org/Application/modules/drivers_forest_change/drivers_forest_change.html . The record contains 3 files: One “.csv” file with all the data collected by the participants during the crowdsourcing campaign (1158021 records); a second “.csv” file with the controls prepared by the experts at IIASA, used for scoring the participants (2001 unique locations, 6157 records) and a ”.docx” file describing all variables included in the two other files. A data descriptor paper explaining the mechanics of the campaign and describing in detail how the data was generated will be made available soon.
- Published
- 2021
12. Methodology for generating a global forest management layer
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Lesiv, M., Shchepashchenko, D., Buchhorn, M., See, L., Dürauer, M., Georgieva, I., Jung, M., Hofhansl, F., Schulze, K., Bilous, A., Blyshchyk, V., Mukhortova, L., Muñoz Brenes, C., Krivobokov, L.V., Ntie, S., Tsogt, K., Pietsch, S., Tikhonova, E., Kim, M., Su, Y.-F., Zadorozhniuk, R., Sirbu, F., Panging, K., Bilous, S., Kovalevskii, S.B., Harb Rabia, A., Vasylyshyn, R., Ahmed, R., Diachuk, P., Kovalevskyi, S.S., Bungnamei, K., Bordolo, K., Churilov, A., Vasylyshyn, O., Sahariah, D., Tertyshnyi, A.P., Saikia, A., Malek, Ž., Singha, K., Feshchenko, R., Prestele, R., Akhtar, I.H., Sharma, K., Domashovets, G., Spawn, S., Blyshchyk, O., Slyva, O., Ilkiv, M., Melnyk, O., Sliusarchuk, V., Karpuk, A., Terentiev, A., Bilous, V., Blyshchyk, K., Bilous, M., Bogovyk, N., Blyshchyk, I., Lesiv, M., Shchepashchenko, D., Buchhorn, M., See, L., Dürauer, M., Georgieva, I., Jung, M., Hofhansl, F., Schulze, K., Bilous, A., Blyshchyk, V., Mukhortova, L., Muñoz Brenes, C., Krivobokov, L.V., Ntie, S., Tsogt, K., Pietsch, S., Tikhonova, E., Kim, M., Su, Y.-F., Zadorozhniuk, R., Sirbu, F., Panging, K., Bilous, S., Kovalevskii, S.B., Harb Rabia, A., Vasylyshyn, R., Ahmed, R., Diachuk, P., Kovalevskyi, S.S., Bungnamei, K., Bordolo, K., Churilov, A., Vasylyshyn, O., Sahariah, D., Tertyshnyi, A.P., Saikia, A., Malek, Ž., Singha, K., Feshchenko, R., Prestele, R., Akhtar, I.H., Sharma, K., Domashovets, G., Spawn, S., Blyshchyk, O., Slyva, O., Ilkiv, M., Melnyk, O., Sliusarchuk, V., Karpuk, A., Terentiev, A., Bilous, V., Blyshchyk, K., Bilous, M., Bogovyk, N., and Blyshchyk, I.
- Abstract
The first ever global map of forest management was generated based on remote sensing data. To collect training data, we launched a series of Geo-Wiki (https://www.geo-wiki.org/) campaigns involving forest experts from different world regions, to explore which information related to forest management could be collected by visual interpretation of very high-resolution images from Google Maps and Microsoft Bing, Sentinel time series and normalized difference vegetation index (NDVI) profiles derived from Google Earth Engine. A machine learning technique was then used with the visually interpreted sample (280K locations) as a training dataset to classify PROBA-V satellite imagery. Finally, we obtained a global wall-to-wall map of forest management at a 100m resolution for the year 2015. The map includes classes such as intact forests; forests with signs of management, including logging; planted forests; woody plantations with a rotation period up to 15 years; oil palm plantations; and agroforestry. The map can be used to deliver further information about forest ecosystems, protected and observed forest status changes, biodiversity assessments, and other ecosystem-related aspects.
- Published
- 2020
13. Linking land use and climate: the key role of uncertainty and spatial location
- Author
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Prestele, R. and Prestele, R.
- Published
- 2019
14. Estimating the Global Distribution of Field Size using Crowdsourcing
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Lesiv, M., Laso Bayas, J.C., See, L., Dürauer, M., Dahlia, D., Durando, N., Hazarika, R., Sahariah, P.K., Vakolyuk, M., Blyshchyk, V., Bilous, A., Perez-Hoyos, A., Gengler, S., Prestele, R., Bilous, S., Akhtar, I.H., Singha, K., Choudhury, S.B., Chetri, T., Malek, Z., Bungnamei, K., Saikia, A., Sahariah, D., Narzary, W., Danylo, O., Sturn, T., Karner, M., McCallum, I., Schepaschenko, D., Moltchanova, E., Fraisl, D., Moorthy, I., Fritz, S., Lesiv, M., Laso Bayas, J.C., See, L., Dürauer, M., Dahlia, D., Durando, N., Hazarika, R., Sahariah, P.K., Vakolyuk, M., Blyshchyk, V., Bilous, A., Perez-Hoyos, A., Gengler, S., Prestele, R., Bilous, S., Akhtar, I.H., Singha, K., Choudhury, S.B., Chetri, T., Malek, Z., Bungnamei, K., Saikia, A., Sahariah, D., Narzary, W., Danylo, O., Sturn, T., Karner, M., McCallum, I., Schepaschenko, D., Moltchanova, E., Fraisl, D., Moorthy, I., and Fritz, S.
- Abstract
There is increasing evidence that smallholder farms contribute substantially to food production globally yet spatially explicit data on agricultural field sizes are currently lacking. Automated field size delineation using remote sensing or the estimation of average farm size at subnational level using census data are two approaches that have been used. However, both have limitations, e.g. automatic field size delineation using remote sensing has not yet been implemented at a global scale while the spatial resolution is very coarse when using census data. This paper demonstrates a unique approach to quantifying and mapping agricultural field size globally using crowdsourcing. A campaign was run in June 2017 where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo-Wiki application. During the campaign, participants collected field size data for 130K unique locations around the globe. Using this sample, we have produced the most accurate global field size map to date and estimated the percentage of different field sizes, ranging from very small to very large, in agricultural areas at global, continental and national levels. The results show that smallholder farms occupy up to 40% of agricultural areas globally, which means that, potentially, there are many more smallholder farms in comparison with the two different current global estimates of 12% and 24%. The global field size map and the crowdsourced data set are openly available and can be used for integrated assessment modelling, comparative studies of agricultural dynamics across different contexts, for training and validation of remote sensing field size delineation, and potential contributions to the Sustainable Development Goal of Ending hunger, achieve food security and improved nutrition and promote sustainable agriculture.
- Published
- 2019
15. Estimating the Global Distribution of Field Size using Crowdsourcing
- Author
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Lesiv, M., Bayas, J.C.L., See, L., Duerauer, M., Dahlia, D., Durando, N., Hazarika, R., Sahariah, P.K., Vakolyuk, M., Blyshchyk, V., Bilous, A., Perez-Hoyos, A., Gengler, S., Prestele, R., Bilous, S., Akhtar, I., Singha, K., Choudhury, S.B., Chetri, T., Malek, Z., Bungnamei, K., Saikia, A., Sahariah, D., Narzary, W., Danylo, O., Sturn, T., Karner, M., McCallum, I., Schepaschenko, D., Molchanova, E., Fraisl, D., Moorthy, I., Fritz, S., Lesiv, M., Bayas, J.C.L., See, L., Duerauer, M., Dahlia, D., Durando, N., Hazarika, R., Sahariah, P.K., Vakolyuk, M., Blyshchyk, V., Bilous, A., Perez-Hoyos, A., Gengler, S., Prestele, R., Bilous, S., Akhtar, I., Singha, K., Choudhury, S.B., Chetri, T., Malek, Z., Bungnamei, K., Saikia, A., Sahariah, D., Narzary, W., Danylo, O., Sturn, T., Karner, M., McCallum, I., Schepaschenko, D., Molchanova, E., Fraisl, D., Moorthy, I., and Fritz, S.
- Abstract
There is increasing evidence that smallholder farms contribute substantially to food production globally yet spatially explicit data on agricultural field sizes are currently lacking. Automated field size delineation using remote sensing or the estimation of average farm size at subnational level using census data are two approaches that have been used but both have limitations, e.g. limited geographical coverage by remote sensing or coarse spatial resolution when using census data. This paper demonstrates another approach to quantifying and mapping field size globally using crowdsourcing. A campaign was run in June 2017 where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo-Wiki application. During the campaign, participants collected field size data for 130K unique locations around the globe. Using this sample, we have produced an improved global field size map (over the previous version) and estimated the percentage of different field sizes, ranging from very small to very large, in agricultural areas at global, continental and national levels. The results show that smallholder farms occupy no more than 40% of agricultural areas, which means that, potentially, there are much more smallholder farms in comparison with the current global estimate of 12%. The global field size map and the crowdsourced data set are openly available and can be used for integrated assessment modelling, comparative studies of agricultural dynamics across different contexts and contribute to SDG 2, among many others.
- Published
- 2018
16. Assessing uncertainties in land cover projections
- Author
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Alexander, P., Prestele, R., Verburg, P.H., Arneth, A., Baranzelli, C., Batista e Silva, F., Brown, C., Butler, A., Calvin, K., Dendoncker, N., Doelman, J., Dunford, R., Engström, K., Eitelberg, D.A., Fujimori, S., Harrison, P. A., Hasegawa, T., Havlik, P., Holzhauer, S., Humpenöder, F., Jacobs-Crisioni, C., Jain, A. K., Krisztin, T., Kyle, P., Lavalle, C., Lenton, T., Liu, Jiayi, Meiyappan, P., Popp, A., Powell, T., Sands, R.D., Schaldach, R., Stehfest, E., Steinbuks, J., Tabeau, A., van Meijl, H., Wise, M.A., Rounsevell, M.D.A., Alexander, P., Prestele, R., Verburg, P.H., Arneth, A., Baranzelli, C., Batista e Silva, F., Brown, C., Butler, A., Calvin, K., Dendoncker, N., Doelman, J., Dunford, R., Engström, K., Eitelberg, D.A., Fujimori, S., Harrison, P. A., Hasegawa, T., Havlik, P., Holzhauer, S., Humpenöder, F., Jacobs-Crisioni, C., Jain, A. K., Krisztin, T., Kyle, P., Lavalle, C., Lenton, T., Liu, Jiayi, Meiyappan, P., Popp, A., Powell, T., Sands, R.D., Schaldach, R., Stehfest, E., Steinbuks, J., Tabeau, A., van Meijl, H., Wise, M.A., and Rounsevell, M.D.A.
- Abstract
Understanding uncertainties in land cover projections is critical to investigating land-based climate mitigation policies, assessing the potential of climate adaptation strategies and quantifying the impacts of land cover change on the climate system. Here, we identify and quantify uncertainties in global and European land cover projections over a diverse range of model types and scenarios, extending the analysis beyond the agro-economic models included in previous comparisons. The results from 75 simulations over 18 models are analysed and show a large range in land cover area projections, with the highest variability occurring in future cropland areas. We demonstrate systematic differences in land cover areas associated with the characteristics of the modelling approach, which is at least as great as the differences attributed to the scenario variations. The results lead us to conclude that a higher degree of uncertainty exists in land use projections than currently included in climate or earth system projections. To account for land use uncertainty, it is recommended to use a diverse set of models and approaches when assessing the potential impacts of land cover change on future climate. Additionally, further work is needed to better understand the assumptions driving land use model results and reveal the causes of uncertainty in more depth, to help reduce model uncertainty and improve the projections of land cover.
- Published
- 2017
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- View/download PDF
17. Hotspots of uncertainty in land-use and land-cover change projections: a global-scale model comparison
- Author
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Prestele, R., Alexander, P., Rounsevell, M., Arneth, A., Calvin, K., Doelman, J., Eitelberg, D.A., Engström, K., Fujimori, S., Hasegawa, T., Havlik, P., Humpenöder, F., Jain, A. K., Krisztin, T., Kyle, P., Meiyappan, P., Popp, A., Sands, R.D., Schaldach, R., Schüngel, J., Stehfest, E., Tabeau, A., van Meijl, H., van Vliet, J., Verburg, P.H., Prestele, R., Alexander, P., Rounsevell, M., Arneth, A., Calvin, K., Doelman, J., Eitelberg, D.A., Engström, K., Fujimori, S., Hasegawa, T., Havlik, P., Humpenöder, F., Jain, A. K., Krisztin, T., Kyle, P., Meiyappan, P., Popp, A., Sands, R.D., Schaldach, R., Schüngel, J., Stehfest, E., Tabeau, A., van Meijl, H., van Vliet, J., and Verburg, P.H.
- Abstract
Model-based global projections of future land-use and land-cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global-scale LULC change models representing a wide range of assumptions of future biophysical and socioeconomic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios, we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g., boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process and improving the allocation mechanisms of LULC change models remain important challenges. Current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches, and many studies ignore the uncertainty in LULC projections in assessments of LULC cha
- Published
- 2016
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18. Investigation of neutron deficient nuclei in the region 28<N, Z <50 with the help of heavy ion compound reactions
- Author
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Nolte, E., Shida, Y., Kutschera, W., Prestele, R., and Morinaga, H.
- Published
- 1974
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19. Fifty-month CEP-Experience in an Area of a Half-million People
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Schneider, Bernd Michael, primary, Beck, A., additional, Erdmann, J., additional, Fromme, R., additional, Gerstacker, H., additional, Prestele, R., additional, Seidl, W.D., additional, Koehle, W., additional, Kinzl, L., additional, and Wipp, A., additional
- Published
- 2001
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- View/download PDF
20. Investigation of neutron deficient nuclei in the region 28< N, Z <50 with the help of heavy ion compound reactions.
- Author
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Nolte, E., Shida, Y., Kutschera, W., Prestele, R., and Morinaga, H.
- Abstract
In-beam γ ray spectroscopy with γ singles spectra, γγ coincidences and γ ray angular distributions has been performed with several target-projectile combinations:Ca +S,Ca +Ca,Fe +N,Ni +C,Ni +N,Zn +O,Zn +O. Level schemes ofGe,Ge,As,Se,Kr,Sr andSr have been deduced. The following level energies and spin-parity assignments have been found:Ge: 957.4 keV, 2; 1693.7; 2174.7, (4); 3685.7; 4207.5;Ge: 1015.8, 2; 1777.9, 2; 2267.9, 4; 2428.8, 3(); 2649.1, 3; 3582.3, (5); 3649.3, (5); 3696.2, (6); 3883.3, (6); 4054.4, (7); 4454.6; 4837.3;As: 98.2; 164.6;789.6;863.2; 1306;2160;2210.4;2831.5;3258.3;3263.5;3991.1;5195.7;Se: 945.4, 2; 1600.9; 2039.4, (4);Kr: 455.7, 2; 1013.5, (4); 1781.5;Sr: 385.4, 2; 980.2, (4); 1763.2, (6);Sr: 573.4, 2; 1328.5,(4); 2229.6,(6). γ ray activity spectra have been measured after the bombardment of natural Ca withS. The half-life of the new isotopeSe has been found to be 27±3 sec. Recoil distance Doppler-shift measurements have been performed with the reactionsNi,zn(O, 2 n) Kr,Sr. The following half-lives have been determined:Kr: 423.8 keV, 2, 37±5 psec; 1034.2,4, 5.7±1.6;Kr: 455.3, 2, 25±3; 1120.0, 4, 3.8±1;Sr: 385.4, 2, 44±6. The energy and half-life systematics of the first excited state of even-even nuclei suggest a maximum of nuclear deformation in the region 28≦N, Z≦50 nearSr orZr. [ABSTRACT FROM AUTHOR]
- Published
- 1974
- Full Text
- View/download PDF
21. Investigation of neutron deficient nuclei in the region 28<50 with the help of heavy ion compound reactions
- Author
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Nolte, E., primary, Shida, Y., additional, Kutschera, W., additional, Prestele, R., additional, and Morinaga, H., additional
- Published
- 1974
- Full Text
- View/download PDF
22. Global forest management data for 2015 at a 100 m resolution.
- Author
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Lesiv M, Schepaschenko D, Buchhorn M, See L, Dürauer M, Georgieva I, Jung M, Hofhansl F, Schulze K, Bilous A, Blyshchyk V, Mukhortova L, Brenes CLM, Krivobokov L, Ntie S, Tsogt K, Pietsch SA, Tikhonova E, Kim M, Di Fulvio F, Su YF, Zadorozhniuk R, Sirbu FS, Panging K, Bilous S, Kovalevskii SB, Kraxner F, Rabia AH, Vasylyshyn R, Ahmed R, Diachuk P, Kovalevskyi SS, Bungnamei K, Bordoloi K, Churilov A, Vasylyshyn O, Sahariah D, Tertyshnyi AP, Saikia A, Malek Ž, Singha K, Feshchenko R, Prestele R, Akhtar IUH, Sharma K, Domashovets G, Spawn-Lee SA, Blyshchyk O, Slyva O, Ilkiv M, Melnyk O, Sliusarchuk V, Karpuk A, Terentiev A, Bilous V, Blyshchyk K, Bilous M, Bogovyk N, Blyshchyk I, Bartalev S, Yatskov M, Smets B, Visconti P, Mccallum I, Obersteiner M, and Fritz S
- Subjects
- Ecosystem, Conservation of Natural Resources, Forests
- Abstract
Spatially explicit information on forest management at a global scale is critical for understanding the status of forests, for planning sustainable forest management and restoration, and conservation activities. Here, we produce the first reference data set and a prototype of a globally consistent forest management map with high spatial detail on the most prevalent forest management classes such as intact forests, managed forests with natural regeneration, planted forests, plantation forest (rotation up to 15 years), oil palm plantations, and agroforestry. We developed the reference dataset of 226 K unique locations through a series of expert and crowdsourcing campaigns using Geo-Wiki ( https://www.geo-wiki.org/ ). We then combined the reference samples with time series from PROBA-V satellite imagery to create a global wall-to-wall map of forest management at a 100 m resolution for the year 2015, with forest management class accuracies ranging from 58% to 80%. The reference data set and the map present the status of forest ecosystems and can be used for investigating the value of forests for species, ecosystems and their services., (© 2022. The Author(s).)
- Published
- 2022
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23. Drivers of tropical forest loss between 2008 and 2019.
- Author
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Laso Bayas JC, See L, Georgieva I, Schepaschenko D, Danylo O, Dürauer M, Bartl H, Hofhansl F, Zadorozhniuk R, Burianchuk M, Sirbu F, Magori B, Blyshchyk K, Blyshchyk V, Rabia AH, Pawe CK, Su YF, Ahmed M, Panging K, Melnyk O, Vasylyshyn O, Vasylyshyn R, Bilous A, Bilous S, Das K, Prestele R, Pérez-Hoyos A, Bungnamei K, Lashchenko A, Lakyda M, Lakyda I, Serediuk O, Domashovets G, Yurchuk Y, Koper M, and Fritz S
- Abstract
During December 2020, a crowdsourcing campaign to understand what has been driving tropical forest loss during the past decade was undertaken. For 2 weeks, 58 participants from several countries reviewed almost 115 K unique locations in the tropics, identifying drivers of forest loss (derived from the Global Forest Watch map) between 2008 and 2019. Previous studies have produced global maps of drivers of forest loss, but the current campaign increased the resolution and the sample size across the tropics to provide a more accurate mapping of crucial factors leading to forest loss. The data were collected using the Geo-Wiki platform ( www.geo-wiki.org ) where the participants were asked to select the predominant and secondary forest loss drivers amongst a list of potential factors indicating evidence of visible human impact such as roads, trails, or buildings. The data described here are openly available and can be employed to produce updated maps of tropical drivers of forest loss, which in turn can be used to support policy makers in their decision-making and inform the public., (© 2022. The Author(s).)
- Published
- 2022
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24. A crowdsourced global data set for validating built-up surface layers.
- Author
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See L, Georgieva I, Duerauer M, Kemper T, Corbane C, Maffenini L, Gallego J, Pesaresi M, Sirbu F, Ahmed R, Blyshchyk K, Magori B, Blyshchyk V, Melnyk O, Zadorozhniuk R, Mandici MT, Su YF, Rabia AH, Pérez-Hoyos A, Vasylyshyn R, Pawe CK, Bilous S, Kovalevskyi SB, Kovalevskyi SS, Bordoloi K, Bilous A, Panging K, Bilous V, Prestele R, Sahariah D, Deka A, Nath N, Neves R, Myroniuk V, Karner M, and Fritz S
- Abstract
Several global high-resolution built-up surface products have emerged over the last five years, taking full advantage of open sources of satellite data such as Landsat and Sentinel. However, these data sets require validation that is independent of the producers of these products. To fill this gap, we designed a validation sample set of 50 K locations using a stratified sampling approach independent of any existing global built-up surface products. We launched a crowdsourcing campaign using Geo-Wiki ( https://www.geo-wiki.org/ ) to visually interpret this sample set for built-up surfaces using very high-resolution satellite images as a source of reference data for labelling the samples, with a minimum of five validations per sample location. Data were collected for 10 m sub-pixels in an 80 × 80 m grid to allow for geo-registration errors as well as the application of different validation modes including exact pixel matching to majority or percentage agreement. The data set presented in this paper is suitable for the validation and inter-comparison of multiple products of built-up areas., (© 2022. The Author(s).)
- Published
- 2022
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25. Large variability in response to projected climate and land-use changes among European bumblebee species.
- Author
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Prestele R, Brown C, Polce C, Maes J, and Whitehorn P
- Subjects
- Animals, Bees, Climate Change, Ecosystem
- Abstract
Bumblebees (Bombus ssp.) are among the most important wild pollinators, but many species have suffered from range declines. Land-use change, agricultural intensification, and the associated loss of habitat have been identified as drivers of the observed dynamics, amplifying pressures from a changing climate. However, these drivers are still underrepresented in continental-scale species distribution modeling. Here, we project the potential distribution of 47 European bumblebee species in 2050 and 2080 from existing European-scale distribution maps, based on a set of climate and land-use futures simulated through a regional integrated assessment model and consistent with the RCP-SSP scenario framework. We compare projections including (1) dynamic climate and constant land use (CLIM); (2) constant climate and dynamic land use (LU); and (3) dynamic climate and dynamic land use (COMB) to disentangle the effects of land use and climate change on future habitat suitability, providing the first rigorous continental-scale assessment of linked climate-land-use futures for bumblebees. We find that direct climate impacts, although variable across species, dominate responses for most species, especially under high-end climate change scenarios (up to 99% range loss). Land-use impacts are highly variable across species and scenarios, ranging from severe losses (up to 75% loss) to considerable gains (up to 68% gain) of suitable habitat extent. Rare species thereby tend to be disproportionally affected by both climate and land-use change. COMB projections reveal that land use may amplify, attenuate, or offset changes to suitable habitat extent expected from climate impact depending on species and scenario. Especially in low-end climate change scenarios, land use has the potential to become a game changer in determining the direction and magnitude of range changes, indicating substantial potential for targeted conservation management., (© 2021 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)
- Published
- 2021
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26. The overlooked spatial dimension of climate-smart agriculture.
- Author
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Prestele R and Verburg PH
- Subjects
- Africa South of the Sahara, Carbon Sequestration, Conservation of Natural Resources, Food Supply, Agriculture, Climate Change
- Abstract
Climate-smart agriculture (CSA) and sustainable intensification (SI) are widely claimed to be high-potential solutions to address the interlinked challenges of food security and climate change. Operationalization of these promising concepts is still lacking and potential trade-offs are often not considered in the current continental- to global-scale assessments. Here we discuss the effect of spatial variability in the context of the implementation of climate-smart practices on two central indicators, namely yield development and carbon sequestration, considering biophysical limitations of suggested benefits, socioeconomic and institutional barriers to adoption, and feedback mechanisms across scales. We substantiate our arguments by an illustrative analysis using the example of a hypothetical large-scale adoption of conservation agriculture (CA) in sub-Saharan Africa. We argue that, up to now, large-scale assessments widely neglect the spatially variable effects of climate-smart practices, leading to inflated statements about co-benefits of agricultural production and climate change mitigation potentials. There is an urgent need to account for spatial variability in assessments of climate-smart practices and target those locations where synergies in land functions can be maximized in order to meet the global targets. Therefore, we call for more attention toward spatial planning and landscape optimization approaches in the operationalization of CSA and SI to navigate potential trade-offs., (© 2020 John Wiley & Sons Ltd.)
- Published
- 2020
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27. Estimating the global distribution of field size using crowdsourcing.
- Author
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Lesiv M, Laso Bayas JC, See L, Duerauer M, Dahlia D, Durando N, Hazarika R, Kumar Sahariah P, Vakolyuk M, Blyshchyk V, Bilous A, Perez-Hoyos A, Gengler S, Prestele R, Bilous S, Akhtar IUH, Singha K, Choudhury SB, Chetri T, Malek Ž, Bungnamei K, Saikia A, Sahariah D, Narzary W, Danylo O, Sturn T, Karner M, McCallum I, Schepaschenko D, Moltchanova E, Fraisl D, Moorthy I, and Fritz S
- Subjects
- Agriculture, Crowdsourcing statistics & numerical data, Farms, Satellite Imagery
- Abstract
There is an increasing evidence that smallholder farms contribute substantially to food production globally, yet spatially explicit data on agricultural field sizes are currently lacking. Automated field size delineation using remote sensing or the estimation of average farm size at subnational level using census data are two approaches that have been used. However, both have limitations, for example, automatic field size delineation using remote sensing has not yet been implemented at a global scale while the spatial resolution is very coarse when using census data. This paper demonstrates a unique approach to quantifying and mapping agricultural field size globally using crowdsourcing. A campaign was run in June 2017, where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo-Wiki application. During the campaign, participants collected field size data for 130 K unique locations around the globe. Using this sample, we have produced the most accurate global field size map to date and estimated the percentage of different field sizes, ranging from very small to very large, in agricultural areas at global, continental, and national levels. The results show that smallholder farms occupy up to 40% of agricultural areas globally, which means that, potentially, there are many more smallholder farms in comparison with the two different current global estimates of 12% and 24%. The global field size map and the crowdsourced data set are openly available and can be used for integrated assessment modeling, comparative studies of agricultural dynamics across different contexts, for training and validation of remote sensing field size delineation, and potential contributions to the Sustainable Development Goal of Ending hunger, achieve food security and improved nutrition and promote sustainable agriculture., (© 2018 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.)
- Published
- 2019
- Full Text
- View/download PDF
28. Modelled biophysical impacts of conservation agriculture on local climates.
- Author
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Hirsch AL, Prestele R, Davin EL, Seneviratne SI, Thiery W, and Verburg PH
- Subjects
- Conservation of Natural Resources, Models, Biological, Soil, Temperature, Agriculture methods, Climate Change
- Abstract
Including the parameterization of land management practices into Earth System Models has been shown to influence the simulation of regional climates, particularly for temperature extremes. However, recent model development has focused on implementing irrigation where other land management practices such as conservation agriculture (CA) has been limited due to the lack of global spatially explicit datasets describing where this form of management is practiced. Here, we implement a representation of CA into the Community Earth System Model and show that the quality of simulated surface energy fluxes improves when including more information on how agricultural land is managed. We also compare the climate response at the subgrid scale where CA is applied. We find that CA generally contributes to local cooling (~1°C) of hot temperature extremes in mid-latitude regions where it is practiced, while over tropical locations CA contributes to local warming (~1°C) due to changes in evapotranspiration dominating the effects of enhanced surface albedo. In particular, changes in the partitioning of evapotranspiration between soil evaporation and transpiration are critical for the sign of the temperature change: a cooling occurs only when the soil moisture retention and associated enhanced transpiration is sufficient to offset the warming from reduced soil evaporation. Finally, we examine the climate change mitigation potential of CA by comparing a simulation with present-day CA extent to a simulation where CA is expanded to all suitable crop areas. Here, our results indicate that while the local temperature response to CA is considerable cooling (>2°C), the grid-scale changes in climate are counteractive due to negative atmospheric feedbacks. Overall, our results underline that CA has a nonnegligible impact on the local climate and that it should therefore be considered in future climate projections., (© 2018 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.)
- Published
- 2018
- Full Text
- View/download PDF
29. A spatially explicit representation of conservation agriculture for application in global change studies.
- Author
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Prestele R, Hirsch AL, Davin EL, Seneviratne SI, and Verburg PH
- Subjects
- Ecosystem, Geography, Models, Theoretical, Agriculture, Climate Change, Conservation of Natural Resources
- Abstract
Conservation agriculture (CA) is widely promoted as a sustainable agricultural management strategy with the potential to alleviate some of the adverse effects of modern, industrial agriculture such as large-scale soil erosion, nutrient leaching and overexploitation of water resources. Moreover, agricultural land managed under CA is proposed to contribute to climate change mitigation and adaptation through reduced emission of greenhouse gases, increased solar radiation reflection, and the sustainable use of soil and water resources. Due to the lack of official reporting schemes, the amount of agricultural land managed under CA systems is uncertain and spatially explicit information about the distribution of CA required for various modeling studies is missing. Here, we present an approach to downscale present-day national-level estimates of CA to a 5 arcminute regular grid, based on multicriteria analysis. We provide a best estimate of CA distribution and an uncertainty range in the form of a low and high estimate of CA distribution, reflecting the inconsistency in CA definitions. We also design two scenarios of the potential future development of CA combining present-day data and an assessment of the potential for implementation using biophysical and socioeconomic factors. By our estimates, 122-215 Mha or 9%-15% of global arable land is currently managed under CA systems. The lower end of the range represents CA as an integrated system of permanent no-tillage, crop residue management and crop rotations, while the high estimate includes a wider range of areas primarily devoted to temporary no-tillage or reduced tillage operations. Our scenario analysis suggests a future potential of CA in the range of 533-1130 Mha (38%-81% of global arable land). Our estimates can be used in various ecosystem modeling applications and are expected to help identifying more realistic climate mitigation and adaptation potentials of agricultural practices., (© 2018 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.)
- Published
- 2018
- Full Text
- View/download PDF
30. Assessing uncertainties in land cover projections.
- Author
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Alexander P, Prestele R, Verburg PH, Arneth A, Baranzelli C, Batista E Silva F, Brown C, Butler A, Calvin K, Dendoncker N, Doelman JC, Dunford R, Engström K, Eitelberg D, Fujimori S, Harrison PA, Hasegawa T, Havlik P, Holzhauer S, Humpenöder F, Jacobs-Crisioni C, Jain AK, Krisztin T, Kyle P, Lavalle C, Lenton T, Liu J, Meiyappan P, Popp A, Powell T, Sands RD, Schaldach R, Stehfest E, Steinbuks J, Tabeau A, van Meijl H, Wise MA, and Rounsevell MD
- Subjects
- Climate, Earth, Planet, Forecasting, Plants, Climate Change, Uncertainty
- Abstract
Understanding uncertainties in land cover projections is critical to investigating land-based climate mitigation policies, assessing the potential of climate adaptation strategies and quantifying the impacts of land cover change on the climate system. Here, we identify and quantify uncertainties in global and European land cover projections over a diverse range of model types and scenarios, extending the analysis beyond the agro-economic models included in previous comparisons. The results from 75 simulations over 18 models are analysed and show a large range in land cover area projections, with the highest variability occurring in future cropland areas. We demonstrate systematic differences in land cover areas associated with the characteristics of the modelling approach, which is at least as great as the differences attributed to the scenario variations. The results lead us to conclude that a higher degree of uncertainty exists in land use projections than currently included in climate or earth system projections. To account for land use uncertainty, it is recommended to use a diverse set of models and approaches when assessing the potential impacts of land cover change on future climate. Additionally, further work is needed to better understand the assumptions driving land use model results and reveal the causes of uncertainty in more depth, to help reduce model uncertainty and improve the projections of land cover., (© 2016 John Wiley & Sons Ltd.)
- Published
- 2017
- Full Text
- View/download PDF
31. Hotspots of uncertainty in land-use and land-cover change projections: a global-scale model comparison.
- Author
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Prestele R, Alexander P, Rounsevell MD, Arneth A, Calvin K, Doelman J, Eitelberg DA, Engström K, Fujimori S, Hasegawa T, Havlik P, Humpenöder F, Jain AK, Krisztin T, Kyle P, Meiyappan P, Popp A, Sands RD, Schaldach R, Schüngel J, Stehfest E, Tabeau A, Van Meijl H, Van Vliet J, and Verburg PH
- Subjects
- Biodiversity, Uncertainty, Conservation of Natural Resources, Ecosystem, Models, Theoretical
- Abstract
Model-based global projections of future land-use and land-cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global-scale LULC change models representing a wide range of assumptions of future biophysical and socioeconomic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios, we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g., boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process and improving the allocation mechanisms of LULC change models remain important challenges. Current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches, and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity., (© 2016 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.)
- Published
- 2016
- Full Text
- View/download PDF
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