Forecasting vegetation condition for drought early warning systems in pastoral communities in Kenya
暂无分享,去创建一个
James M. Muthoka | Edward E. Salakpi | A. Barrett | J. Mwangi | P. Rowhani | S. Duivenvoorden | Seb Oliver
[1] Gebräuchliche Fertigarzneimittel,et al. V , 1893, Therapielexikon Neurologie.
[2] A. Savitzky,et al. Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .
[3] Jonathan D. Cryer,et al. Time Series Analysis , 1986 .
[4] James D. Hamilton. Time Series Analysis , 1994 .
[5] F. Kogan. Application of vegetation index and brightness temperature for drought detection , 1995 .
[6] R. K. Dixon,et al. Mitigation and Adaptation Strategies for Global Change , 1998 .
[7] R. Boone,et al. Impacts of climate variability on East African pastoralists: linking social science and remote sensing , 2001 .
[8] J.M. Vesin,et al. Bayesian learning using Gaussian process for time series prediction , 2001, Proceedings of the 11th IEEE Signal Processing Workshop on Statistical Signal Processing (Cat. No.01TH8563).
[9] A. ADoefaa,et al. ? ? ? ? f ? ? ? ? ? , 2003 .
[10] Jin Chen,et al. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter , 2004 .
[11] Ramesh P. Singh,et al. Monitoring drought dynamics in the Aravalli region (India) using different indices based on ground and remote sensing data , 2006 .
[12] A. Nyong,et al. The value of indigenous knowledge in climate change mitigation and adaptation strategies in the African Sahel , 2007 .
[13] M. Herrero,et al. Pastoral livelihood adaptation to drought and institutional interventions in Kenya , 2007 .
[14] J. Michaelsen,et al. Warming of the Indian Ocean threatens eastern and southern African food security but could be mitigated by agricultural development , 2008, Proceedings of the National Academy of Sciences.
[15] T. Udelhoven,et al. Assessment of rainfall and NDVI anomalies in Spain (1989–1999) using distributed lag models , 2009 .
[16] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[17] S. Quiring,et al. Evaluating the utility of the Vegetation Condition Index (VCI) for monitoring meteorological drought in Texas , 2010 .
[18] V. Singh,et al. A review of drought concepts , 2010 .
[19] Etienne Piguet,et al. Migration and Climate Change: An Overview , 2011 .
[20] L. Dilling,et al. Creating usable science: Opportunities and constraints for climate knowledge use and their implications for science policy , 2011 .
[21] R. Sadiq,et al. A review of drought indices , 2011 .
[22] F. Rembold,et al. Assessing drought probability for agricultural areas in Africa with coarse resolution remote sensing imagery , 2011 .
[23] Varun Chandola,et al. A scalable gaussian process analysis algorithm for biomass monitoring , 2011, Stat. Anal. Data Min..
[24] A. Stein,et al. The chlorophyll variability in Meteosat derived NDVI in a context of drought monitoring , 2011 .
[25] F. Baret,et al. A comparison of methods for smoothing and gap filling time series of remote sensing observations - application to MODIS LAI products , 2012 .
[26] Christine J. Kirchhoff,et al. Narrowing the climate information usability gap , 2012 .
[27] R. Behnke,et al. The contribution of livestock to the Ugandan economy , 2012 .
[28] C. Prudhomme,et al. Health Effects of Drought: a Systematic Review of the Evidence , 2013, PLoS currents.
[29] Wei Guo,et al. Global and regional drought dynamics in the climate warming era , 2013 .
[30] Chong Wang,et al. Stochastic variational inference , 2012, J. Mach. Learn. Res..
[31] Joshua B. Tenenbaum,et al. Structure Discovery in Nonparametric Regression through Compositional Kernel Search , 2013, ICML.
[32] P. Kyle,et al. Climate change effects on agriculture: Economic responses to biophysical shocks , 2013, Proceedings of the National Academy of Sciences.
[33] B. Hurk,et al. Forecast-based financing: an approach for catalyzing humanitarian action based on extreme weather and climate forecasts , 2014 .
[34] Peter M. Atkinson,et al. An effective approach for gap-filling continental scale remotely sensed time-series , 2014, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.
[35] M. Verstraete,et al. Early detection of biomass production deficit hot-spots in semi-arid environment using FAPAR time series and a probabilistic approach , 2014 .
[36] J. Koenderink. Q… , 2014, Les noms officiels des communes de Wallonie, de Bruxelles-Capitale et de la communaute germanophone.
[37] Martha C. Anderson,et al. Landsat-8: Science and Product Vision for Terrestrial Global Change Research , 2014 .
[38] Molly E. Brown,et al. Weather and international price shocks on food prices in the developing world , 2015 .
[39] Arielle Tozier de la Poterie,et al. From Yokohama to Sendai: Approaches to Participation in International Disaster Risk Reduction Frameworks , 2015, International Journal of Disaster Risk Science.
[40] João Paulo Ramos Teixeira,et al. Remote sensing of drought: Progress, challenges and opportunities , 2015 .
[41] V. Mishra,et al. Prediction of vegetation anomalies to improve food security and water management in India , 2015 .
[42] N. Ramankutty,et al. Influence of extreme weather disasters on global crop production , 2016, Nature.
[43] G. Camps-Valls,et al. A Survey on Gaussian Processes for Earth-Observation Data Analysis: A Comprehensive Investigation , 2016, IEEE Geoscience and Remote Sensing Magazine.
[44] Qing Chang,et al. Evaluating an Enhanced Vegetation Condition Index (VCI) Based on VIUPD for Drought Monitoring in the Continental United States , 2016, Remote. Sens..
[45] Kees de Bie,et al. Early assessment of seasonal forage availability for mitigating the impact of drought on East African pastoralists , 2016 .
[46] Willem Waegeman,et al. A non-linear Granger-causality framework to investigate climate–vegetation dynamics , 2016 .
[47] Clement Atzberger,et al. Operational Drought Monitoring in Kenya Using MODIS NDVI Time Series , 2016, Remote. Sens..
[48] Gorjan Alagic,et al. #p , 2019, Quantum information & computation.
[49] Q. Tong,et al. Studying drought phenomena in the Continental United States in 2011 and 2012 using various drought indices , 2017 .
[50] T. Tadesse,et al. Prediction of drought-induced reduction of agricultural productivity in Chile from MODIS, rainfall estimates, and climate oscillation indices , 2018, Remote Sensing of Environment.
[51] R. Moss,et al. To co-produce or not to co-produce , 2018, Nature Sustainability.
[52] A. Weerts,et al. Towards impact-based flood forecasting and warning in Bangladesh: a case study at the local level in Sirajganj district , 2018 .
[53] Wenjiang Huang,et al. A Comparison of Hybrid Machine Learning Algorithms for the Retrieval of Wheat Biophysical Variables from Sentinel-2 , 2019, Remote. Sens..
[54] Clement Atzberger,et al. A Mixed Model Approach to Vegetation Condition Prediction Using Artificial Neural Networks (ANN): Case of Kenya's Operational Drought Monitoring , 2019, Remote. Sens..
[55] H. V. Van Lanen,et al. Moving from drought hazard to impact forecasts , 2019, Nature Communications.
[56] J. Angerer,et al. Predictive Livestock Early Warning System (PLEWS): Monitoring forage condition and implications for animal production in Kenya , 2020 .
[57] Tsuyoshi Murata,et al. {m , 1934, ACML.
[58] Erin Coughlan de Perez,et al. Bridging forecast verification and humanitarian decisions: A valuation approach for setting up action-oriented early warnings , 2020 .
[59] Danna Zhou,et al. d. , 1840, Microbial pathogenesis.
[60] P. Alam. ‘K’ , 2021, Composites Engineering.
[61] P. Alam. ‘G’ , 2021, Composites Engineering: An A–Z Guide.
[62] P. Alam. ‘A’ , 2021, Composites Engineering: An A–Z Guide.
[63] P. Alam. ‘N’ , 2021, Composites Engineering: An A–Z Guide.