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Hadi, Yowargana P, Zulkarnain MT, Mohamad F, Goib BK, Hultera P, Sturn T, Karner M, Dürauer M, See L, Fritz S, Hendriatna A, Nursafingi A, Melati DN, Prasetya FVAS, Carolita I, Kiswanto, Firdaus MI, Rosidi M, Kraxner F. A national-scale land cover reference dataset from local crowdsourcing initiatives in Indonesia. Sci Data 2022; 9:574. [PMID: 36115866 PMCID: PMC9482649 DOI: 10.1038/s41597-022-01689-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 09/07/2022] [Indexed: 11/13/2022] Open
Abstract
Here we present a geographically diverse, temporally consistent, and nationally relevant land cover (LC) reference dataset collected by visual interpretation of very high spatial resolution imagery, in a national-scale crowdsourcing campaign (targeting seven generic LC classes) and a series of expert workshops (targeting seventeen detailed LC classes) in Indonesia. The interpreters were citizen scientists (crowd/non-experts) and local LC visual interpretation experts from different regions in the country. We provide the raw LC reference dataset, as well as a quality-filtered dataset, along with the quality assessment indicators. We envisage that the dataset will be relevant for: (1) the LC mapping community (researchers and practitioners), i.e., as reference data for training machine learning algorithms and map accuracy assessment (with appropriate quality-filters applied), and (2) the citizen science community, i.e., as a sizable empirical dataset to investigate the potential and limitations of contributions from the crowd/non-experts, demonstrated for LC mapping in Indonesia for the first time to our knowledge, within the context of complementing traditional data collection by expert interpreters. Measurement(s) | land cover | Technology Type(s) | visual interpretation of satellite imagery | Sample Characteristic - Location | Indonesia |
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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, Fritz S. Estimating the global distribution of field size using crowdsourcing. Glob Chang Biol 2019; 25:174-186. [PMID: 30549201 PMCID: PMC7379266 DOI: 10.1111/gcb.14492] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Accepted: 09/16/2018] [Indexed: 05/07/2023]
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.
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Affiliation(s)
- Myroslava Lesiv
- International Institute for Applied Systems Analysis, ESMLaxenburgAustria
| | | | - Linda See
- International Institute for Applied Systems Analysis, ESMLaxenburgAustria
| | - Martina Duerauer
- International Institute for Applied Systems Analysis, ESMLaxenburgAustria
| | - Domian Dahlia
- International Institute for Applied Systems Analysis, ESMLaxenburgAustria
| | | | | | | | - Mar'yana Vakolyuk
- Department of Energy and Mass Exchange in GeosystemsState Institution Scientific Centre for Aerospace Research of the Earth Institute of Geological Science National Academy of Sciences of UkraineKyivUkraine
| | - Volodymyr Blyshchyk
- Forest ManagementNacional'nyj Universytet Bioresursiv i Pryrodokorystuvannya UkrayinyKyivUkraine
| | - Andrii Bilous
- Department of Energy and Mass Exchange in GeosystemsState Institution Scientific Centre for Aerospace Research of the Earth Institute of Geological Science National Academy of Sciences of UkraineKyivUkraine
| | - Ana Perez‐Hoyos
- European Commission Joint Research Centre Ispra SectorIspraItaly
| | - Sarah Gengler
- Environmental SciencesUniversité catholique de Louvain, Earth and Life InstituteLouvain‐la‐NeuveBelgium
| | - Reinhard Prestele
- Department of Earth Sciences, Environmental Geography GroupVrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Svitlana Bilous
- Forest ManagementNacional'nyj Universytet Bioresursiv i Pryrodokorystuvannya UkrayinyKyivUkraine
| | - Ibrar ul Hassan Akhtar
- Department of MeteorologyCOMSATS UniversityIslamabadPakistan
- Pakistan Space and Upper Atmosphere Research CommissionIslamabadPakistan
| | | | | | | | - Žiga Malek
- Vrije Universiteit Amsterdam Faculteit Economische wetenschappen en BedrijfskundeAmsterdamThe Netherlands
| | | | | | | | | | - Olha Danylo
- International Institute for Applied Systems Analysis, ESMLaxenburgAustria
| | - Tobias Sturn
- International Institute for Applied Systems Analysis, ESMLaxenburgAustria
| | - Mathias Karner
- International Institute for Applied Systems Analysis, ESMLaxenburgAustria
| | - Ian McCallum
- International Institute for Applied Systems Analysis, ESMLaxenburgAustria
| | - Dmitry Schepaschenko
- International Institute for Applied Systems Analysis, ESMLaxenburgAustria
- Soil ScienceMoscow State Forest UniversityMoscowRussia
| | | | - Dilek Fraisl
- International Institute for Applied Systems Analysis, ESMLaxenburgAustria
| | - Inian Moorthy
- International Institute for Applied Systems Analysis, ESMLaxenburgAustria
| | - Steffen Fritz
- International Institute for Applied Systems Analysis, ESMLaxenburgAustria
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