Search Results - Sense data.

Inclusive agricultural insurance for sustainable wheat intensification as a pathway to smallholder resilience in Ethiopia by Ebrahim, Mohammed

“…The model will combine remote sensing, geospatial, and ground-truth agronomic data through machine learning and simulation to enable accurate yield prediction and premium estimation. …”
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Pastoral resource access and utilization: quantifying the spatial and temporal relationships between livestock mobility, density and biomass availability in southern Kenya by Butt, B.

“…The study relies on coupling remotely sensed vegetation indices with cattle Global Positioning Systems (GPS) collar data for a pastoral community in southern Kenya. …”
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Applications of satellite-based rainfall estimates in flood inundation modeling: a case study in Mundeni Aru River Basin, Sri Lanka by Yoshimoto, Shuhei, Amarnath, Giriraj

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Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity by Waldner, François, De Abelleyra, Diego, Veron, Santiago Ramón, Zhang, Miao, Wu, Bingfang, Plotnikov, Dmitry, Bartalev, Sergey, Lavreniuk, Mykola, Skakun, Sergii, Kussul, Nataliia, Le Maire, Guerric, Dupuy, Stéphane, Jarvis, Ian, Defourny, Pierre

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Drivers and impacts of land-use change in the Maasai Steppe of Northern Tanzania: An ecological-social-political analysis by Msoffe, F.U., Kifugo, S.C., Said, Mohammed Yahya, Neselle, M.O., Gardingen, P. van, Reid, Robin S., Ogutu, Joseph O., Herrero, Mario, Leeuw, Jan de

“…In this article, we discuss the drivers, causes, and impacts of land-use change in the Maasai Steppe of northern Tanzania. Remote sensing data were used to analyze land-use change, and Geographical Information Systems (GIS) was used to link-up with wildlife population dynamics and livestock distribution data derived from aerial censuses. …”
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Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests by Besnard, H., Carvalhais, N., Arain, M.A., Black, A., Brede, B., Buchmann, Nina, Chen, J., Clevers, J.G.P.W., Dutrieux, L.P., Gans, F., Herold, M., Jung, M., Kosugi, Y., Knohl, A., Law, B.E., Paul-Limoges, E., Lohila, A., Merbold, Lutz, Roupsard, O., Valentini, R., Wolf, S., Zhang, X., Reichstein, Markus

“…Here, we combined remote sensing, climate, and eddy-covariance flux data to study net ecosystem CO2 exchange (NEE) at 185 forest sites globally. …”
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Is small-scale agriculture really the main driver of deforestation in the Peruvian Amazon? Moving beyond the prevailing narrative by Ravikumar, A., Sears, R., Cronkleton, P., Menton, M., Pérez Ojeda del Arco, M.

“…We find that the narrative is based on remote sensing of deforestation patch sizes but not on field data, potentially conflating distinct drivers of deforestation under the umbrella of “migratory,” “small‐scale,” or “subsistence” agriculture. …”
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Mapping the spatial and temporal extent of floods in South Asia by CGIAR Research Program on Climate Change, Agriculture and Food Security

“…A research project by CCAFS and the International Water Management Institute (IWMI) focuses on a moderate-resolution mapping of monsoon flood risk areas in South Asia using satellite remote-sensing datasets. In the past, many studies were carried out in South Asia to investigate the hydrologic behaviour of the fl oods and post-flood event mapping using remote-sensing data. …”
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Continuous monitoring of forest change dynamics with satellite time series by Decuyper, M., Chávez, R.O., Lohbeck, M., Lastra, J.A., Tsendbazar, N.E., Hackländer, J., Herold, M., Vågen, Tor-Gunnar

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Innovating and scaling risk reducing measures for farmers and livestock keepers in the drylands by Whitbread, Anthony M., Akinseye, M. Folorunso, Banerjee, Rupsha R., Dhulipala, Ram, Diouf, Fatoumata Bintou, Faye, Aliou, Faye, Adama, Joseph, Emanuel Jacob, Kebe, Amy, Konte, Oumar, Paliwal, Ambica, Sow, Fafa, Wane, Abdrahmane, Worou, Nadine

“…At their core are the use of crop and pasture models, spatial data from remotely sensed imagery, historical time series weather datasets which, when linked to expert knowledge of the system, are used together to develop triggers for decision making or action. …”
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Current trends in River Bathymetry using UAV-borne technology to inform E-flow assessments in Southern Africa by Singh, K.

“…In situ measurements, despite their accuracy, may lack spatial representativeness and are resource intensive. Remote sensing techniques, particularly Unmanned Aerial Vehicles (UAVs), offer an alternative for bathymetric data collection driven by their ability to access challenging areas of a river and provide high-resolution data at relatively low cost. …”
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A common framework to model recovery in disturbed tropical forests by Maurent, Eliott, Delgado, Diego, Finegan, Bryan, Ngo-Bieng, Marie Ange, y otros 5 autores más

“…Testing this framework with various monitoring tools to estimate disturbance intensity and model vegetation attribute recovery, i.e., with forest monitoring or remote sensing data, will be the next step to make it widely applicable.…”
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Spatial Assessment of the Potential Impact of Infrastructure Development on Biodiversity Conservation in Lowland Nepal by Sharma, R., Rimal, B., Stork, N., Baral, H., Dhakal, M.

“…We show the application of readily available remotely sensed data and geospatial tools to assess the potential impact of these future developments on habitat quality under three protection-level scenarios. …”
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Co-occurrence and abundance of pollinators and pests in horticultural systems in Africa using an integrated Earth observation-based approach by Aduvukha, G.R., Abdel-Rahman, E.M., Mudereri, B.T., Sichangi, A.W., Makokha, G.O., Lattorff, H.M.G., Mohamed, S.A., Landmann, T., Tonnang, H.E.Z., Dubois, T.

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Classification of Active Fires and Weather Conditions in the Lower Amur River Basin by Hayasaka, H., Sokolova, G.V., Ostroukhov, A., Naito, D.

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Combining multicriteria decision making and geographic information system for assessing forest sustainability by Mendoza, G.A., Prabhu, Ravi, Purnomo, H.

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Comparison of ddRADseq and EUChip60K SNP genotyping systems for population genetics and genomic selection in Eucalyptus dunnii (Maiden) by Aguirre, Natalia Cristina, Villalba, Pamela Victoria, Garcia, Martin Nahuel, Filippi, Carla Valeria, Rivas, Juan Gabriel, Martinez, Maria Carolina, Acuña, Cintia Vanesa, Lopez, Javier Augusto, Lopez, Juan Adolfo, Pathauer, Pablo Santiago, Palazzini, Dino, Harrand, Leonel, Oberschelp, Gustavo Pedro Javier, Marco, Martin Alberto, Cisneros, Esteban F., Carreras, Rocío, Alves, Ana, Rodrigues, Jose, Hopp, Horacio Esteban, Grattapaglia, Dario, Cappa, Eduardo Pablo, Paniego, Norma Beatriz, Marcucci Poltri, Susana Noemi

“…The PAs correlated positively with narrow sense heritabilities, with the highest correlations shown by the ABLUP and GBLUP-EUChip60K. …”
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Building resilient urban water systems: emerging opportunities for solving long-lasting challenges by Nlend, B., Reimuth, A., Yang, L. E., Jampani, Mahesh, Cristiano, E., Dewals, B., Boyer, E., Cetinkaya, I. D., Diémé, L. P., Dutta, R., Feng, W., Grossi, G., Nasr, W. B., Obaitor, O. S., Olusola, A. O., Panchanathan, A., Rab, G., Sharma, S., Wang, C., Warter, M. M., Welty, C., Tetzlaf, D.

“…Public awareness and community engagement can foster behavioural change and empower residents to actively participate in urban water governance through initiatives like rainwater harvesting and participatory planning. Additionally, big data and remote sensing provide cities with the insights needed for adaptive, data-driven decision-making. …”
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The NASA hydrological forecast system for food and water security applications by Arsenault, K. R., Shukla, S., Hazra, A., Getirana, A., McNally, A., Kumar, S.V., Koster, R. D., Peters-Lidard, C. D., Zaitchik, B. F., Badr, H., Jung, H. C., Narapusetty, B., Navari, M., Wang, S., Mocko, D. M., Funk, C., Harrison, L., Husak, G. J., Adoum, A., Galu, G., Magadzire, T., Roningen, J., Shaw, M., Eylander, J., Bergaoui, K., McDonnell, Rachael, Verdin, J. P.

“…NHyFAS derives its skill from two sources: (i) accurate initial conditions, as produced by an offline land modeling system through the application and/or assimilation of various satellite data (precipitation, soil moisture, and terrestrial water storage), and (ii) meteorological forcing data during the forecast period as produced by a state-of-the-art ocean–land–atmosphere forecast system. …”
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Water quality and the Sustainable Development Goals by Zadeh, S.M., Drechsel, Pay, Salcedo, F. P.

“…The chapter also explores water quality's interlinkages with other SDGs including health, food security, and ecosystem protection, and advocates strengthened national monitoring systems, data harmonization, and use of practical technologies to bridge data gaps and guide action.…”
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