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Items: 1 to 20 of 95

1.

A global dataset of crowdsourced land cover and land use reference data.

Fritz S, See L, Perger C, McCallum I, Schill C, Schepaschenko D, Duerauer M, Karner M, Dresel C, Laso-Bayas JC, Lesiv M, Moorthy I, Salk CF, Danylo O, Sturn T, Albrecht F, You L, Kraxner F, Obersteiner M.

Sci Data. 2017 Jun 13;4:170075. doi: 10.1038/sdata.2017.75.

2.

Mapping global cropland and field size.

Fritz S, See L, McCallum I, You L, Bun A, Moltchanova E, Duerauer M, Albrecht F, Schill C, Perger C, Havlik P, Mosnier A, Thornton P, Wood-Sichra U, Herrero M, Becker-Reshef I, Justice C, Hansen M, Gong P, Abdel Aziz S, Cipriani A, Cumani R, Cecchi G, Conchedda G, Ferreira S, Gomez A, Haffani M, Kayitakire F, Malanding J, Mueller R, Newby T, Nonguierma A, Olusegun A, Ortner S, Rajak DR, Rocha J, Schepaschenko D, Schepaschenko M, Terekhov A, Tiangwa A, Vancutsem C, Vintrou E, Wenbin W, van der Velde M, Dunwoody A, Kraxner F, Obersteiner M.

Glob Chang Biol. 2015 May;21(5):1980-92. doi: 10.1111/gcb.12838. Epub 2015 Jan 16.

PMID:
25640302
3.

Assessment and statistical modeling of the relationship between remotely sensed aerosol optical depth and PM2.5 in the eastern United States.

Paciorek CJ, Liu Y; HEI Health Review Committee.

Res Rep Health Eff Inst. 2012 May;(167):5-83; discussion 85-91.

PMID:
22838153
4.

Comparing the quality of crowdsourced data contributed by expert and non-experts.

See L, Comber A, Salk C, Fritz S, van der Velde M, Perger C, Schill C, McCallum I, Kraxner F, Obersteiner M.

PLoS One. 2013 Jul 31;8(7):e69958. doi: 10.1371/journal.pone.0069958. Print 2013.

5.

A global reference database of crowdsourced cropland data collected using the Geo-Wiki platform.

Laso Bayas JC, Lesiv M, Waldner F, Schucknecht A, Duerauer M, See L, Fritz S, Fraisl D, Moorthy I, McCallum I, Perger C, Danylo O, Defourny P, Gallego J, Gilliams S, Akhtar IUH, Baishya SJ, Baruah M, Bungnamei K, Campos A, Changkakati T, Cipriani A, Das K, Das K, Das I, Davis KF, Hazarika P, Johnson BA, Malek Z, Molinari ME, Panging K, Pawe CK, Pérez-Hoyos A, Sahariah PK, Sahariah D, Saikia A, Saikia M, Schlesinger P, Seidacaru E, Singha K, Wilson JW.

Sci Data. 2017 Sep 26;4:170136. doi: 10.1038/sdata.2017.136.

6.

Evaluating the role of land cover and climate uncertainties in computing gross primary production in Hawaiian Island ecosystems.

Kimball HL, Selmants PC, Moreno A, Running SW, Giardina CP.

PLoS One. 2017 Sep 8;12(9):e0184466. doi: 10.1371/journal.pone.0184466. eCollection 2017. Erratum in: PLoS One. 2018 Jan 25;13(1):e0192041.

7.

Land cover classification from multi-temporal, multi-spectral remotely sensed imagery using patch-based recurrent neural networks.

Sharma A, Liu X, Yang X.

Neural Netw. 2018 Sep;105:346-355. doi: 10.1016/j.neunet.2018.05.019. Epub 2018 Jun 2.

PMID:
29933156
8.

Opportunities for the application of advanced remotely-sensed data in ecological studies of terrestrial animal movement.

Neumann W, Martinuzzi S, Estes AB, Pidgeon AM, Dettki H, Ericsson G, Radeloff VC.

Mov Ecol. 2015 May 4;3(1):8. doi: 10.1186/s40462-015-0036-7. eCollection 2015.

9.

Land use/cover classification in the Brazilian Amazon using satellite images.

Lu D, Batistella M, Li G, Moran E, Hetrick S, Freitas CD, Dutra LV, Sant'anna SJ.

Pesqui Agropecu Bras. 2012 Sep;47(9). doi: 10.1590/S0100-204X2012000900004.

10.

Geostatistical modelling of the malaria risk in Mozambique: effect of the spatial resolution when using remotely-sensed imagery.

Giardina F, Franke J, Vounatsou P.

Geospat Health. 2015 Nov 26;10(2):333. doi: 10.4081/gh.2015.333.

11.

A simple semi-automatic approach for land cover classification from multispectral remote sensing imagery.

Jiang D, Huang Y, Zhuang D, Zhu Y, Xu X, Ren H.

PLoS One. 2012;7(9):e45889. doi: 10.1371/journal.pone.0045889. Epub 2012 Sep 26.

12.

Linking remote sensing, land cover and disease.

Curran PJ, Atkinson PM, Foody GM, Milton EJ.

Adv Parasitol. 2000;47:37-80. Review.

PMID:
10997204
13.

Multisensor earth observations to characterize wetlands and malaria epidemiology in Ethiopia.

Midekisa A, Senay GB, Wimberly MC.

Water Resour Res. 2014 Nov;50(11):8791-8806. Epub 2014 Nov 17.

14.

Assessing the use of global land cover data for guiding large area population distribution modelling.

Linard C, Gilbert M, Tatem AJ.

GeoJournal. 2011 Oct;76(5):525-538. Epub 2010 May 25.

15.

A dataset mapping the potential biophysical effects of vegetation cover change.

Duveiller G, Hooker J, Cescatti A.

Sci Data. 2018 Feb 20;5:180014. doi: 10.1038/sdata.2018.14.

16.

Crowdsourcing: It Matters Who the Crowd Are. The Impacts of between Group Variations in Recording Land Cover.

Comber A, Mooney P, Purves RS, Rocchini D, Walz A.

PLoS One. 2016 Jul 26;11(7):e0158329. doi: 10.1371/journal.pone.0158329. eCollection 2016.

17.

High-resolution satellite imagery is an important yet underutilized resource in conservation biology.

Boyle SA, Kennedy CM, Torres J, Colman K, Pérez-Estigarribia PE, de la Sancha NU.

PLoS One. 2014 Jan 23;9(1):e86908. doi: 10.1371/journal.pone.0086908. eCollection 2014.

18.

Completing the Picture: Importance of Considering Participatory Mapping for REDD+ Measurement, Reporting and Verification (MRV).

Beaudoin G, Rafanoharana S, Boissière M, Wijaya A, Wardhana W.

PLoS One. 2016 Dec 15;11(12):e0166592. doi: 10.1371/journal.pone.0166592. eCollection 2016.

19.

GIS based mapping of land cover changes utilizing multi-temporal remotely sensed image data in Lake Hawassa Watershed, Ethiopia.

Nigatu Wondrade, Dick ØB, Tveite H.

Environ Monit Assess. 2014 Mar;186(3):1765-80. doi: 10.1007/s10661-013-3491-x. Epub 2013 Dec 6.

PMID:
24310365
20.

Monitoring global change: Comparison of forest cover estimates using remote sensing and inventory approaches.

Turner DP, Koerper G, Gucinski H, Peterson C, Dixon RK.

Environ Monit Assess. 1993 Jul;26(2-3):295-305. doi: 10.1007/BF00547506.

PMID:
24220843

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