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3. STUDY OF EMULSION PRODUCTS STABILIZED WITH SURFACTANTS BASED ON RHAMNOLIPIDS PSEUDOMONAS SP. PS-17

Author

PELEKH-BONDARUK I. R., VILDANOVA R. I., KOBYLINSKA L. I., BILA Y. Y., and BILOUS S. B.

Subjects
Pharmaceutical Science, Pharmacology, Toxicology and Pharmaceutics (miscellaneous)
Abstract

Objective: To study the effectiveness of the biocomplex of surfactants based on rhamnolipids Pseudomonas sp. PS-17 (biocomplex PS) as an emulsifier in emulsions for use in dermatology. Methods: Technological methods of o/w emulsions manufacturing, ultrasonic dispersion, as well as microscopic studies of manufactured emulsions have been used. Results: To select the concentration of biocomplex PS as an emulsifier, studies of the emulsifying properties of bio complex PS in comparison with polysorbate 80 have been performed, taking into account the similarity of the hydrophilic-lipophilic balance of these substances. Several compositions of emulsions have been studied in which the biocomplex PS has been used as an independent emulsifier in a concentration from 4 to 10%, as well as in combination with other emulsifiers-lanolin and glycerol monostearate. Conclusion: The creation of stable o/w emulsions requires the use of high concentrations of biocomplex PS as an emulsifier, more than 10%, which is impractical and economically unreasonable. The use of biocomplex PS as a co-emulsifier with emulsifiers of the second type allows obtaining stable emulsions at a total concentration of complex emulsifier 7-10%.

Published
2022
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4. Drivers of tropical forest loss between 2008 and 2019

Author

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

5. A crowdsourced global data set for validating built-up surface layers

Author

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

6. Global forest management data for 2015 at a 100 m resolution

Author

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.

Published
2022

9. A Crowdsourced Global Data Set for Validating Built-up Surface Layers V.2

Author

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.

Published
2021
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11. Global forest management data at a 100m resolution for the year 2015

Author

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.

Published
2021

12. A Crowdsourced Global Data Set for Validating Built-up Surface Layers

Author

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

13. Crowdsourcing deforestation in the tropics during the last decade: Data sets from the “Driver of Tropical Forest Loss” Geo-Wiki campaign

Author

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

15. Methodology for generating a global forest management layer

Author

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

16. The Forest Observation System, building a global reference dataset for remote sensing of forest biomass [data paper]

Author

Schepaschenko, D., Chave, J., Phillips, O. L., Lewis, S. L., Davies, S. J., Réjou-Méchain, Maxime, Sist, P., Scipal, K., Perger, C., Herault, B., Labriere, N., Hofhansl, F., Affum-Baffoe, K., Aleinikov, A., Alonso, A., Amani, C., Araujo-Murakami, A., Armston, J., Arroyo, L., Ascarrunz, N., Azevedo, C., Baker, T., Balazy, R., Bedeau, C., Berry, N., Bilous, A. M., Bilous, S. Y., Bissiengou, P., Blanc, L., Bobkova, K. S., Braslavskaya, T., Brienen, R., Burslem, Dfrp, Condit, R., Cuni-Sanchez, A., Danilina, D., Torres, D. D., Derroire, G., Descroix, L., Sotta, E. D., d'Oliveira, M. V. N., Dresel, C., Erwin, T., Evdokimenko, M. D., Falck, J., Feldpausch, T. R., Foli, E. G., Foster, R., Fritz, S., Garcia-Abril, A. D., Gornov, A., Gornova, M., Gothard-Bassebe, E., Gourlet-Fleury, S., Guedes, M., Hamer, K. C., Susanty, F. H., Higuchi, N., Coronado, E. N. H., Hubau, W., Hubbell, S., Ilstedt, U., Ivanov, V. V., Kanashiro, M., Karlsson, A., Karminov, V. N., Killeen, T., Koffi, J. C. K., Konovalova, M., Kraxner, F., Krejza, J., Krisnawati, H., Krivobokov, L. V., Kuznetsov, M. A., Lakyda, I., Lakyda, P. I., Licona, J. C., Lucas, R. M., Lukina, N., Lussetti, D., Malhi, Y., Manzanera, J. A., Marimon, B., Martinez, R. V., Martynenko, O. V., Matsala, M., Matyashuk, R. K., Mazzei, L., Memiaghe, H., Mendoza, C., Mendoza, A. M., Moroziuk, O. V., Mukhortova, L., Musa, S., Nazimova, D. I., Okuda, T., Oliveira, L. C., Ontikov, P. V., Osipov, A. F., Pietsch, S., Playfair, M., Poulsen, J., Radchenko, V. G., Rodney, K., Rozak, A. H., Ruschel, A., Rutishauser, E., See, L., Shchepashchenko, M., Shevchenko, N., Shvidenko, A., Silveira, M., Singh, J., Sonke, B., Souza, C., Sterenczak, K., Stonozhenko, L., Sullivan, M. J. P., Szatniewska, J., Aedoumg, H. T., Ter Steege, H., Tikhonova, E., Toledo, M., Trefilova, O. V., Valbuena, R., Gamarra, L. V., Vasiliev, S., Vedrova, E. F., Verhovets, S. V., Vidal, E., Vladimirova, N. A., Vleminckx, J., Vos, V. A., Vozmitel, F. K., Wanek, W., West, T. A. P., Woell, H., Woods, J. T., Wortel, V., Yamada, T., Hajar, Z. S. N., and Zo-Bi, I. C.

Abstract

Forest biomass is an essential indicator for monitoring the Earth's ecosystems and climate. It is a critical input to greenhouse gas accounting, estimation of carbon losses and forest degradation, assessment of renewable energy potential, and for developing climate change mitigation policies such as REDD+, among others. Wall-to-wall mapping of aboveground biomass (AGB) is now possible with satellite remote sensing (RS). However, RS methods require extant, up-to-date, reliable, representative and comparable in situ data for calibration and validation. Here, we present the Forest Observation System (FOS) initiative, an international cooperation to establish and maintain a global in situ forest biomass database. AGB and canopy height estimates with their associated uncertainties are derived at a 0.25 ha scale from field measurements made in permanent research plots across the world's forests. All plot estimates are geolocated and have a size that allows for direct comparison with many RS measurements. The FOS offers the potential to improve the accuracy of RS- based biomass products while developing new synergies between the RS and ground-based ecosystem research communities.

Published
2019

20. Исследования по фармацевтической разработке лекарственных форм с наночастицами металлов для применения в стоматологии и хирурги

Author

Bilous, S. B., Rieznichenko, L. S., Dybkova, S. M., Rybachuk, A. V., and Kalyniuk, T. H.

Subjects
UDC: 615.454/.456.281:546.57/.59, наночастинки срібла, наночастинки золота, розчин для промивання ран, мазь, гель, наночастицы серебра, наночастицы золота, раствор для промывания ран, УДК: 615.454/.456.281:546.57/.59, silver nanoparticles, gold nanoparticles, solution for wound cleansing, ointment, gel
Abstract

In dentistry and surgery a special role belongs to medications of the local action that possess the antimicrobial and anti-inflammatory properties and can stimulate the tissue regeneration.Aim. To study the antimicrobial activity of colloidal solutions of silver and gold nanoparticles in vitro, estimate their pharmacological activity in vivo and substantiate the composition and technology of 3 dosage forms based on them – a hydrophilic ointment, gel and solution for wound cleansing.Materials and methods. Colloidal solutions of spherical silver and gold nanoparticles with the particle size of 30 nm and the concentration of 8.0 mg/ml and 77.2 μg/ml nanoparticles, respectively, were used to develop dosage forms. The antimicrobial activity of colloidal solutions of silver and gold nanoparticles was determined using the test strains of Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli, Bacillus subtilis, Enterococcus faecalis, Candida albicans, Proteus vulgaris, as well as clinical isolates of pathogenic strains of microorganisms isolated from patients with purulent inflammatory diseases of the maxillofacial area. The therapeutic efficacy of colloidal solutions of silver and gold nanoparticles in vivo was studied on the model of the jaw abscess of the experimental animals (Wistar rats). Technological methods were used to make dosage forms in accordance with the general rules for the manufacture of liquid and semi-solid medicines.Results and discussion. It has been found that silver nanoparticles in the concentration of 0.16 mg/ml and their combination with gold nanoparticles in the concentration of 0.08 mg/ml for Ag and 1.93 μg/ml for Au exhibit a high level of the antimicrobial activity against all test microorganisms. Gold nanoparticles as independent antimicrobial agents within the concentration range of 1.93-38.6 μg/ml exhibit a weak antimicrobial activity. The combination of silver nanoparticles with gold nanoparticles exhibit the marked anti-inflammatory action and contribute to decontamination and healing of the wound in the studies in vivo compared to the effect of silver nanoparticles only. The studies conducted made it possible to substantiate the choice of the concentration of colloidal solutions in dosage forms.Conclusions. According to the results obtained the marked complex action (antimicrobial, anti-inflammatory, regenerative) of the сombination of colloidal solutions of silver and gold nanoparticles in the treatment of purulent-inflammatory diseases of the maxillofacial area has been determined. The composition and technology of 3 dosage forms based on them – an ointment, gel, solution for wound cleansing, which can be promising for application in dentistry and surgery, have been substantiated., У стоматології і хірургії особлива роль належить засобам місцевої дії, які володіють антимікробними та протизапальними властивостями, а також здатні стимулювати процеси репарації.Мета роботи. Вивчити антимікробну дію колоїдних розчинів наночастинок срібла і золота в дослідженнях in vitro, їх фармакологічну активність in vivo та обґрунтувати склад і технологію 3-х лікарських форм на їх основі – гідрофільної мазі, гелю та розчину для промивання ран.Матеріали та методи. Для розробки лікарських засобів використані колоїдні розчини наночастинок срібла і золота сферичної форми із середнім розміром частинок 30 нм та концентрацією наночастинок 8,0 мг/мл і 77,2 мкг/мл відповідно. Антимікробну активність колоїдних розчинів наночастинок срібла і золота визначали щодо музейних штамів мікроорганізмів Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli, Bacillus subtilis, Enterococcus faecalis, Candida albicans, Proteus vulgaris, а також клінічних ізолятів патогенних штамів мікроорганізмів, виділених від хворих на гнійно-запальні захворювання щелепно-лицевої ділянки. Ефективність фармакологічної дії in vivo колоїдних розчинів наночастинок срібла і золота досліджували на моделі абсцесу щелепної ділянки дослідних тварин. Для виготовлення лікарських форм застосовували технологічні методи відповідно до загальних правил виготовлення рідких і м’яких лікарських засобів.Результати та їх обговорення. Встановлено, що наночастинки срібла у концентрації 0,16 мг/мл та їх комбінація з наночастинками золота у концентрації 0,08 мг/мл за Ag та 1,93 мкг/мл за Au виявляють високий рівень антимікробної активності відносно усіх досліджуваних тест-культур мікроорганізмів. Наночастинки золота як самостійні антимікробні агенти в концентраційному діапазоні 1,93-38,6 мкг/мл за металом виявляють слабку антимікробну активність. У дослідженнях in vivo встановлено, що комбінація колоїдного розчину наночастинок срібла і золота виявляє виражену протизапальну дію і сприяє знезараженню та загоєнню рани порівняно із характером впливу монодисперсії наночастинок срібла. Проведені дослідження дали можливість обґрунтувати вибір концентрації колоїдних розчинів наночастинок у лікарських формах.Висновки. За результатами проведених досліджень встановлена виражена комплексна дія (антимікробна, протизапальна, регенеративна) комбінації колоїдних розчинів наночастинок срібла і золота при лікуванні гнійно-запальних захворювань щелепно-лицевої ділянки. Обґрунтовано склад і технологію 3-х лікарських форм на їх основі – мазі, гелю, розчину для промивання ран, що можуть бути перспективними для застосування у стоматології і хірургії., В стоматологии и хирургии особая роль отведена средствам местного действия, которые обладают антимикробными и противовоспалительными свойствами, а также способны влиять на процессы репарации.Цель работы. Изучить антимикробное действие коллоидных растворов наночастиц серебра и золота в исследованиях in vitro, их фармакологическую активность in vivo и обосновать состав и технологию 3-х лекарственных форм на их основании – гидрофильной мази, геля и раствора для промывания ран.Материалы и методы. Для разработки лекарственных средств использованы коллоидные растворы наночастиц серебра и золота сферической формы со средним размером частиц 30 нм и концентрацией наночастиц 8,0 мг/мл и 77,2 мкг/мл соответственно. Антимикробную активность коллоидных растворов наночастиц серебра и золота определяли на музейных штаммах микроорганизмов Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli, Bacillus subtilis, Enterococcus faecalis, Candida albicans, Proteus vulgaris, а также клинических изолятах патогенных штаммов микроорганизмов, выделенных от больных гнойно-воспалительными заболеваниями челюстно-лицевой области. Эффективность фармакологического действия in vivo коллоидных растворов наночастиц серебра и золота исследовали на модели абсцесса челюстного участка подопытных животных. Для изготовления лекарственных форм применяли технологические методы в соответствии с общими правилами изготовления жидких и мягких лекарственных средств.Результаты и их обсуждение. Установлено, что наночастицы серебра в концентрации 0,16 мг/мл и их комбинация с наночастицами золота в концентрации 0,08 мг/мл по Ag и 1,93 мкг/мл по Au проявляют высокий уровень антимикробной активности в отношении всех исследуемых тест-культур микроорганизмов. Наночастицы золота как самостоятельные антимикробные агенты в диапазоне концентраций наночастиц 1,93-38,6 мкг/мл по металлу проявляют слабую антимикробную активность. В исследованиях in vivo установлено, что комбинация наночастиц серебра и золота оказывает выраженное противовоспалительное действие и способствует обеззараживанию и заживлению раны по сравнению с характером влияния монодисперсии наночастиц серебра. Проведенные исследования позволили обосновать выбор концентрации коллоидных растворов наночастиц в лекарственных формах.Выводы. По результатам проведенных исследований установлено выраженное комплексное воздействие (антимикробное, противовоспалительное, регенеративное) комбинации коллоидных растворов наночастиц серебра и золота при лечении гнойно-воспалительных заболеваний челюстно-лицевой области. Обоснованы состав и технология 3-х лекарственных форм на их основе – мази, геля, раствора для промывания ран, которые могут быть перспективными для применения в стоматологии и хирургии.

Published
2018

22. Estimating the Global Distribution of Field Size using Crowdsourcing

Author

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

23. The Forest Observation System, building a global reference dataset for remote sensing of forest biomass

Author

Schepaschenko, D., Chave, J., Phillips, O.L., Lewis, S.L., Davies, S.J., Réjou-Méchain, M., Sist, P., Scipal, K., Perger, C., Herault, B., Labrière, N., Hofhansl, F., Affum-Baffoe, K., Aleinikov, A., Alonso, A., Amani, C., Araujo-Murakami, A., Armston, J., Arroyo, L., Ascarrunz, N., Azevedo, C., Baker, T., Bałazy, R., Bedeau, C., Berry, N., Bilous, A.M., Bilous, S., Bissiengou, P., Blanc, L., Bobkova, .S., Braslavskaya, T., Brienen, R., Burslem, D., Condit, R., Cuni-Sanchez, A., Danilina, D., del Castillo Torres, D., Derroire, G., Descroix, L., Sotta, E.D., d’Oliveira, M.V.N., Dresel, C., Erwin, T., Evdokimenko, M.D., Falck, J., Feldpausch, T.R., Foli, E.G., Foster, R., Fritz, S., Garcia-Abril, A.D., Gornov, A., Gornova, M., Gothard-Bassébé, E., Gourlet-Fleury, S., Guedes, M., Hamer, K.C., Susanty, F.H., Higuchi, N., Coronado, E.N.H., Hubau, W., Hubbell, S., Ilstedt, U., Ivanov, V.V., Kanashiro, M., Karlsson, A., Karminov, V.N., Killeen, T., Koffi, J.-C., Konovalova, M., Kraxner, F., Krejza, J., Krisnawati, H., Krivobokov, L.V., Kuznetsov, M.A., Lakyda, I., Lakyda, P.I., Licona, J.C., Lucas, R.M., Lukina, N., Lussetti, D., Malhi, Y., Manzanera, J.A., Marimon, B., Marimon, B.H., Martinez, R.V., Martynenko, O.V., Matsala, M., Matyashuk, R.K., Mazzei, L., Memiaghe, H., Mendoza, C., Mendoza, A.M., Moroziuk, Olga V., Mukhortova, L., Musa, S., Nazimova, D.I., Okuda, T., Oliveira, L.C., Ontikov, P.V., Osipov, A., Pietsch, S., Playfair, M., Poulsen, J., Radchenko, V.G., Rodney, K., Rozak, A.H., Ruschel, A., Rutishauser, E., See, L., Shchepashchenko, M., Shevchenko, N., Shvidenko, A., Silveira, M., Singh, J., Sonké, B., Souza, C., Stereńczak, K., Stonozhenko, L., Sullivan, M., Szatniewska, J., Taedoumg, H., ter Steege, H., Tikhonova, E., Toledo, M., Trefilova, O.V., Valbuena, R., Gamarra, L.V., Vasiliev, S., Vedrova, E.F., Verhovets, S.V., Vidal, E., Vladimirova, N.A., Vleminckx, J., Vos, V.A., Vozmitel, F.K., Wanek, W., West, T., Woell, H., Woods, J.T., Wortel, V., Yamada, T., Nur Hajar, Z., Zo-Bi, I., Schepaschenko, D., Chave, J., Phillips, O.L., Lewis, S.L., Davies, S.J., Réjou-Méchain, M., Sist, P., Scipal, K., Perger, C., Herault, B., Labrière, N., Hofhansl, F., Affum-Baffoe, K., Aleinikov, A., Alonso, A., Amani, C., Araujo-Murakami, A., Armston, J., Arroyo, L., Ascarrunz, N., Azevedo, C., Baker, T., Bałazy, R., Bedeau, C., Berry, N., Bilous, A.M., Bilous, S., Bissiengou, P., Blanc, L., Bobkova, .S., Braslavskaya, T., Brienen, R., Burslem, D., Condit, R., Cuni-Sanchez, A., Danilina, D., del Castillo Torres, D., Derroire, G., Descroix, L., Sotta, E.D., d’Oliveira, M.V.N., Dresel, C., Erwin, T., Evdokimenko, M.D., Falck, J., Feldpausch, T.R., Foli, E.G., Foster, R., Fritz, S., Garcia-Abril, A.D., Gornov, A., Gornova, M., Gothard-Bassébé, E., Gourlet-Fleury, S., Guedes, M., Hamer, K.C., Susanty, F.H., Higuchi, N., Coronado, E.N.H., Hubau, W., Hubbell, S., Ilstedt, U., Ivanov, V.V., Kanashiro, M., Karlsson, A., Karminov, V.N., Killeen, T., Koffi, J.-C., Konovalova, M., Kraxner, F., Krejza, J., Krisnawati, H., Krivobokov, L.V., Kuznetsov, M.A., Lakyda, I., Lakyda, P.I., Licona, J.C., Lucas, R.M., Lukina, N., Lussetti, D., Malhi, Y., Manzanera, J.A., Marimon, B., Marimon, B.H., Martinez, R.V., Martynenko, O.V., Matsala, M., Matyashuk, R.K., Mazzei, L., Memiaghe, H., Mendoza, C., Mendoza, A.M., Moroziuk, Olga V., Mukhortova, L., Musa, S., Nazimova, D.I., Okuda, T., Oliveira, L.C., Ontikov, P.V., Osipov, A., Pietsch, S., Playfair, M., Poulsen, J., Radchenko, V.G., Rodney, K., Rozak, A.H., Ruschel, A., Rutishauser, E., See, L., Shchepashchenko, M., Shevchenko, N., Shvidenko, A., Silveira, M., Singh, J., Sonké, B., Souza, C., Stereńczak, K., Stonozhenko, L., Sullivan, M., Szatniewska, J., Taedoumg, H., ter Steege, H., Tikhonova, E., Toledo, M., Trefilova, O.V., Valbuena, R., Gamarra, L.V., Vasiliev, S., Vedrova, E.F., Verhovets, S.V., Vidal, E., Vladimirova, N.A., Vleminckx, J., Vos, V.A., Vozmitel, F.K., Wanek, W., West, T., Woell, H., Woods, J.T., Wortel, V., Yamada, T., Nur Hajar, Z., and Zo-Bi, I.

Abstract

Forest biomass is an essential indicator for monitoring the Earth’s ecosystems and climate. It is a critical input to greenhouse gas accounting, estimation of carbon losses and forest degradation, assessment of renewable energy potential, and for developing climate change mitigation policies such as REDD+, among others. Wall-to-wall mapping of aboveground biomass (AGB) is now possible with satellite remote sensing (RS). However, RS methods require extant, up-to-date, reliable, representative and comparable in situ data for calibration and validation. Here, we present the Forest Observation System (FOS) initiative, an international cooperation to establish and maintain a global in situ forest biomass database. AGB and canopy height estimates with their associated uncertainties are derived at a 0.25 ha scale from field measurements made in permanent research plots across the world’s forests. All plot estimates are geolocated and have a size that allows for direct comparison with many RS measurements. The FOS offers the potential to improve the accuracy of RS-based biomass products while developing new synergies between the RS and ground-based ecosystem research communities.

Published
2019

29. Estimating the Global Distribution of Field Size using Crowdsourcing

Author

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

30. Mapping growing stock volume and forest live biomass: a case study of the Polissya region of Ukraine

Author

Bilous, A., Myroniuk, V., Holiaka, D., Bilous, S., See, L., Schepaschenko, D., Bilous, A., Myroniuk, V., Holiaka, D., Bilous, S., See, L., and Schepaschenko, D.

Abstract

Forest inventory and biomass mapping are important tasks that require inputs from multiple data sources. In this paper we implement two methods for the Ukrainian region of Polissya: random forest (RF) for tree species prediction and k-nearest neighbors (k-NN) for growing stock volume and biomass mapping. We examined the suitability of the five-band RapidEye satellite image to predict the distribution of six tree species. The accuracy of RF is quite high: ~99% for forest/non-forest mask and 89% for tree species prediction. Our results demonstrate that inclusion of elevation as a predictor variable in the RF model improved the performance of tree species classification. We evaluated different distance metrics for the k-NN method, including Euclidean or Mahalanobis distance, most similar neighbor (MSN), gradient nearest neighbor, and independent component analysis. The MSN with the four nearest neighbors (k = 4) is the most precise (according to the root-mean-square deviation) for predicting forest attributes across the study area. The k-NN method allowed us to estimate growing stock volume with an accuracy of 3 m3 ha−1 and for live biomass of about 2 t ha−1 over the study area.

Published
2017

31. Hygienic estimation of occupational risk and substantiation of regulations on Orvego Fungicide safe application in agricultural sector.

Author

Bilous, S. V., Vavrinevych, E. P., Omelchuk, S. T., Bilous, S. V., Vavrinevych, E. P., and Omelchuk, S. T.

Abstract

Hygienic evaluation of labour conditions during Orvego pesticide application on grapes and onion was carried out and content of ametoctradin and dimethomorp in the working zone air, atmospheric air, and the soil was studied. The findings allow to substantiate ametoctradin hygienic standards in the working zone air, atmospheric air, and the soil and to elaborate regulations of Orvego pesticide safe application in agriculture. Estimation of complex risk of ametoctradin and dimethomorp effects via different routes of exposure during airblast and boom spraying treatment showed that the values of the risks were within the ranges of allowable (<1). Values of combined risk during simultaneous effect of the both active ingredients do not exceed allowable level (<1) either.

Published
2015

43. Antifungal Activity and Effect of Plant-Associated Bacteria on Phenolic Synthesis of Quercus robur L.

Author

Bilous S, Likhanov A, Boroday V, Marchuk Y, Zelena L, Subin O, and Bilous A

Abstract

Europe's forests, particularly in Ukraine, are highly vulnerable to climate change. The maintenance and improvement of forest health are high-priority issues, and various stakeholders have shown an interest in understanding and utilizing ecological interactions between trees and their associated microorganisms. Endophyte microbes can influence the health of trees either by directly interacting with the damaging agents or modulating host responses to infection. In the framework of this work, ten morphotypes of endophytic bacteria from the tissues of unripe acorns of Quercus robur L. were isolated. Based on the results of the sequenced 16S rRNA genes, four species of endophytic bacteria were identified: Bacillus amyloliquefaciens , Bacillus subtilis , Delftia acidovorans , and Lelliottia amnigena . Determining the activity of pectolytic enzymes showed that the isolates B. subtilis and B. amyloliquefaciens could not cause maceration of plant tissues. Screening for these isolates revealed their fungistatic effect against phytopathogenic micromycetes, namely Fusarium tricinctum , Botrytis cinerea , and Sclerotinia sclerotiorum . Inoculation of B. subtilis , B. amyloliquefaciens , and their complex in oak leaves, in contrast to phytopathogenic bacteria, contributed to the complete restoration of the epidermis at the sites of damage. The phytopathogenic bacteria Pectobacterium and Pseudomonas caused a 2.0 and 2.2 times increase in polyphenol concentration in the plants, respectively, while the ratio of antioxidant activity to total phenolic content decreased. Inoculation of Bacillus amyloliquefaciens and Bacillus subtilis isolates into oak leaf tissue were accompanied by a decrease in the total pool of phenolic compounds. The ratio of antioxidant activity to total phenolic content increased. This indicates a qualitative improvement in the overall balance of the oak leaf antioxidant system induced by potential PGPB. Thus, endophytic bacteria of the genus Bacillus isolated from the internal tissues of unripe oak acorns have the ability of growth biocontrol and spread of phytopathogens, indicating their promise for use as biopesticides.

Published
2023
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44. Global forest management data for 2015 at a 100 m resolution.

Author

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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45. Drivers of tropical forest loss between 2008 and 2019.

Author

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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46. A crowdsourced global data set for validating built-up surface layers.

Author

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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47. Estimating the global distribution of field size using crowdsourcing.

Author

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
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