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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

6. 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

7. 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

8. A global reference dataset for remote sensing of forest biomass. The Forest Observation System approach

Author

Shchepashchenko, 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., Banki, O., Bedeau, C., Berry, N., Bilous, A.M., Bilous, S.Y., Bissiengou, P., Blanc, L., Bobkova, K.S., Braslavskaya, T., Brienen, R., Burslem, D., Condit, R., Cuni-Sanchez, A., Danilina, D., del Castillo Torres, D., Derroire, G., Descroix, L., Doff Sotta, E., 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., Susanty, F.H., Higuchi, N., Honorio Coronado, E.N., Hubau, W., Hubbell, S., Ilstedt, U., Ivanov, V., Kanashiro, M., Karlsson, A., Karminov, V.N., Killeen, T., Konan, J.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., Marimon Junior, B.H., Martinez, R.V., Martynenko, O.V., Matsala, M.S., Matyashuk, R.K., Mazzei, L., Memiaghe, H., Mendoza, C., Monteagudo-Mendoza, A., Morozyuk, O.V., Mukhortova, L., Musa, S., Nazimova, D.I., Okuda, T., Oliveira, L.C., Ontikov, P.V., Osipov, A.F., Gutierrez, A.P., Pietsch, S., Playfair, M., Poulsen, J., Radchenko, V., Rodney, K., Rozak, A.H., Ruschel, A., Rutishauser, E., See, L., Shchepashchenko, M., Shevchenko, N., Shvidenko, A., Silva-Espejo, J.E., Silveira, M., Singh, J., Sonké, B., Souza, C., Stereńczak, K., Sullivan, M.J.P., Szatniewska, J., Taedoumg, H., ter Steege, H., Tikhonova, E., Toledo, M., Trefilova, O.V., Valbuena, R., Valenzuela Gamarra, L.V., 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., Zamah Shari, N.H., Zo-Bi, I.C., Shchepashchenko, 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., Banki, O., Bedeau, C., Berry, N., Bilous, A.M., Bilous, S.Y., Bissiengou, P., Blanc, L., Bobkova, K.S., Braslavskaya, T., Brienen, R., Burslem, D., Condit, R., Cuni-Sanchez, A., Danilina, D., del Castillo Torres, D., Derroire, G., Descroix, L., Doff Sotta, E., 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., Susanty, F.H., Higuchi, N., Honorio Coronado, E.N., Hubau, W., Hubbell, S., Ilstedt, U., Ivanov, V., Kanashiro, M., Karlsson, A., Karminov, V.N., Killeen, T., Konan, J.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., Marimon Junior, B.H., Martinez, R.V., Martynenko, O.V., Matsala, M.S., Matyashuk, R.K., Mazzei, L., Memiaghe, H., Mendoza, C., Monteagudo-Mendoza, A., Morozyuk, O.V., Mukhortova, L., Musa, S., Nazimova, D.I., Okuda, T., Oliveira, L.C., Ontikov, P.V., Osipov, A.F., Gutierrez, A.P., Pietsch, S., Playfair, M., Poulsen, J., Radchenko, V., Rodney, K., Rozak, A.H., Ruschel, A., Rutishauser, E., See, L., Shchepashchenko, M., Shevchenko, N., Shvidenko, A., Silva-Espejo, J.E., Silveira, M., Singh, J., Sonké, B., Souza, C., Stereńczak, K., Sullivan, M.J.P., Szatniewska, J., Taedoumg, H., ter Steege, H., Tikhonova, E., Toledo, M., Trefilova, O.V., Valbuena, R., Valenzuela Gamarra, L.V., 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., Zamah Shari, N.H., 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 0.25ha 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. Live, most up-to-date dataset is available at https://forest-observation-system.net

Published
2019

9. 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

10. Impact of Disturbances on the Carbon Cycle of Forest Ecosystems in Ukrainian Polissya

Author

Lakyda, P., Shvidenko, A., Bilous, A., Myroniuk, V., Matsala, M., Zibtsev, S., Schepaschenko, D., Holiaka, D., Vasylyshyn, R., Lakyda, I., Diachuk, P., Kraxner, F., Lakyda, P., Shvidenko, A., Bilous, A., Myroniuk, V., Matsala, M., Zibtsev, S., Schepaschenko, D., Holiaka, D., Vasylyshyn, R., Lakyda, I., Diachuk, P., and Kraxner, F.

Abstract

Climate change continues to threaten forests and their ecosystem services while substantially altering natural disturbance regimes. Land cover changes and consequent management entail discrepancies in carbon sequestration provided by forest ecosystems and its accounting. Currently there is a lack of sufficient and harmonized data for Ukraine that can be used for the robust and spatially explicit assessment of forest provisioning and regulation of ecosystem services. In the frame of this research, we established an experimental polygon (area 45 km2) in Northern Ukraine aiming at estimating main forest carbon stocks and fluxes and determining the impact caused by natural disturbances and harvest for the study period of 2010–2015. Coupled field inventory and remote sensing data (RapidEye image for 2010 and SPOT 6 image for 2015) were used. Land cover classification and estimation of biomass and carbon pools were carried out using Random Forest and k-Nearest Neighbors (k-NN) method, respectively. Remote sensing data indicates a ca. 16% increase of carbon stock, while ground-based computations have shown only a ca. 1% increase. Net carbon fluxes for the study period are relatively even: 5.4 Gg C·year−1 and 5.6 Gg C C·year−1 for field and remote sensing data, respectively. Stand-replacing wildfires, as well as insect outbreaks and wind damage followed by salvage logging, and timber harvest have caused 21% of carbon emissions among all C sources within the experimental polygon during the study period. Hence, remote sensing data and non-parametric methods coupled with field data can serve as reliable tools for the precise estimation of forest carbon cycles on a regional spatial scale. However, featured land cover changes lead to unexpected biases in consistent assessment of forest biophysical parameters, while current management practices neglect natural forest dynamics and amplify negative impact of disturbances on ecosystem services.

Published
2019

12. A dataset of forest biomass structure for Eurasia

Author

Schepaschenko, D., Shvidenko, A., Usoltsev, V., Lakyda, P., Luo, Y., Vasylyshyn, R., Lakyda, I., Myklush, Y., See, L., McCallum, I., Fritz, S., Kraxner, F., Obersteiner, M., Schepaschenko, D., Shvidenko, A., Usoltsev, V., Lakyda, P., Luo, Y., Vasylyshyn, R., Lakyda, I., Myklush, Y., See, L., McCallum, I., Fritz, S., Kraxner, F., and Obersteiner, M.

Abstract

The most comprehensive dataset of in situ destructive sampling measurements of forest biomass in Eurasia have been compiled from a combination of experiments undertaken by the authors and from scientific publications. Biomass is reported as four components: live trees (stem, bark, branches, foliage, roots); understory (above- and below ground); green forest floor (above- and below ground); and coarse woody debris (snags, logs, dead branches of living trees and dead roots), consisting of 10,351 unique records of sample plots and 9,613 sample trees from ca 1,200 experiments for the period 1930–2014 where there is overlap between these two datasets. The dataset also contains other forest stand parameters such as tree species composition, average age, tree height, growing stock volume, etc., when available. Such a dataset can be used for the development of models of biomass structure, biomass extension factors, change detection in biomass structure, investigations into biodiversity and species distribution and the biodiversity-productivity relationship, as well as the assessment of the carbon pool and its dynamics, among many others.

Published
2017

13. Biomass plot data base

Author

Schepaschenko, D., Shvidenko, A., Usoltsev, V.A., Lakyda, P., Luo, Y., Vasylyshyn, R., Lakyda, I, Myklush, Y., See, L., McCallum, I., Fritz, S., Kraxner, F., Obersteiner, M., Schepaschenko, D., Shvidenko, A., Usoltsev, V.A., Lakyda, P., Luo, Y., Vasylyshyn, R., Lakyda, I, Myklush, Y., See, L., McCallum, I., Fritz, S., Kraxner, F., and Obersteiner, M.

Published
2017

14. Growth and Bioproductivity of Urban Forests

Author

Lakyda, I. and Lakyda, I.

Abstract

This paper provides information about the background, process and results of growth modeling, yield and bioproductivity of artificial (planted) pine stands in urban forests in the city of Kyiv. This topic is exceptionally important to ecological functions of urban forest ecosystems around Kyiv, like any other urban forests, in terms of maintaining sustainability of the internal environment of cities, improving the environmental situation and reducing harmful effects of industry, transport, etc. A characteristic feature of urban forests is a substantial anthropogenic pressure on forest ecosystems, which is much higher compared to production forests. As a rule, reference information, growth models, etc. for urban forests are either weak or completely absent. This work is designed to eliminate some of such shortcomings and problems for forests around the biggest city in Ukraine. As a major result of the study, models of growth and biological productivity dynamics of live biomass, Net Primary Production have been developed. We show specific features of growth and functioning of urban forests and therefore a need for the development of a special reference and normative base for inventory and management of this category of forests.

Published
2012

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

Author

Schepaschenko D, Chave J, Phillips OL, Lewis SL, Davies SJ, 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 AM, Bilous SY, Bissiengou P, Blanc L, Bobkova KS, Braslavskaya T, Brienen R, Burslem DFRP, Condit R, Cuni-Sanchez A, Danilina D, Del Castillo Torres D, Derroire G, Descroix L, Sotta ED, d'Oliveira MVN, Dresel C, Erwin T, Evdokimenko MD, Falck J, Feldpausch TR, Foli EG, Foster R, Fritz S, Garcia-Abril AD, Gornov A, Gornova M, Gothard-Bassébé E, Gourlet-Fleury S, Guedes M, Hamer KC, Susanty FH, Higuchi N, Coronado ENH, Hubau W, Hubbell S, Ilstedt U, Ivanov VV, Kanashiro M, Karlsson A, Karminov VN, Killeen T, Koffi JK, Konovalova M, Kraxner F, Krejza J, Krisnawati H, Krivobokov LV, Kuznetsov MA, Lakyda I, Lakyda PI, Licona JC, Lucas RM, Lukina N, Lussetti D, Malhi Y, Manzanera JA, Marimon B, Junior BHM, Martinez RV, Martynenko OV, Matsala M, Matyashuk RK, Mazzei L, Memiaghe H, Mendoza C, Mendoza AM, Moroziuk OV, Mukhortova L, Musa S, Nazimova DI, Okuda T, Oliveira LC, Ontikov PV, Osipov AF, Pietsch S, Playfair M, Poulsen J, Radchenko VG, Rodney K, Rozak AH, 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 MJP, Szatniewska J, Taedoumg H, Ter Steege H, Tikhonova E, Toledo M, Trefilova OV, Valbuena R, Gamarra LV, Vasiliev S, Vedrova EF, Verhovets SV, Vidal E, Vladimirova NA, Vleminckx J, Vos VA, Vozmitel FK, Wanek W, West TAP, Woell H, Woods JT, Wortel V, Yamada T, Nur Hajar ZS, and Zo-Bi IC

Subjects
Conservation of Natural Resources, Environmental Monitoring methods, Biomass, Forests, Remote Sensing Technology
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
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17. A dataset of forest biomass structure for Eurasia.

Author

Schepaschenko D, Shvidenko A, Usoltsev V, Lakyda P, Luo Y, Vasylyshyn R, Lakyda I, Myklush Y, See L, McCallum I, Fritz S, Kraxner F, and Obersteiner M

Subjects
Asia, Ecosystem, Europe, Biomass, Forests
Abstract

The most comprehensive dataset of in situ destructive sampling measurements of forest biomass in Eurasia have been compiled from a combination of experiments undertaken by the authors and from scientific publications. Biomass is reported as four components: live trees (stem, bark, branches, foliage, roots); understory (above- and below ground); green forest floor (above- and below ground); and coarse woody debris (snags, logs, dead branches of living trees and dead roots), consisting of 10,351 unique records of sample plots and 9,613 sample trees from ca 1,200 experiments for the period 1930-2014 where there is overlap between these two datasets. The dataset also contains other forest stand parameters such as tree species composition, average age, tree height, growing stock volume, etc., when available. Such a dataset can be used for the development of models of biomass structure, biomass extension factors, change detection in biomass structure, investigations into biodiversity and species distribution and the biodiversity-productivity relationship, as well as the assessment of the carbon pool and its dynamics, among many others.

Published
2017
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