Dr. Molly E. Brown Profile

Dr. Molly E. Brown

Associate Research Professor at Univ of Maryland

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SPIE Journal Paper | 23 March 2012 Open Access

Evaluating the use of remote sensing data in the U.S. Agency for International Development Famine Early Warning Systems Network

Molly Brown, Elizabeth Brickley

JARS, Vol. 6, Issue 01, 063511, (March 2012) https://doi.org/10.1117/12.10.1117/1.JRS.6.063511

KEYWORDS: Remote sensing, Computer security, Agriculture, Information security, Satellites, Vegetation, Data modeling, Statistical analysis, Network security, Biological research

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The U.S. Agency for International Development (USAID)'s Famine Early Warning System Network (FEWS NET) provides monitoring and early warning support to decision makers responsible for responding to food insecurity emergencies on three continents. FEWS NET uses satellite remote sensing and ground observations of rainfall and vegetation in order to provide information on drought, floods, and other extreme weather events to decision makers. Previous research has presented results from a professional review questionnaire with FEWS NET expert end-users whose focus was to elicit Earth observation requirements. The review provided FEWS NET operational requirements and assessed the usefulness of additional remote sensing data. We analyzed 1342 food security update reports from FEWS NET. The reports consider the biophysical, socioeconomic, and contextual influences on the food security in 17 countries in Africa from 2000 to 2009. The objective was to evaluate the use of remote sensing information in comparison with other important factors in the evaluation of food security crises. The results show that all 17 countries use rainfall information, agricultural production statistics, food prices, and food access parameters in their analysis of food security problems. The reports display large-scale patterns that are strongly related to history of the FEWS NET program in each country. We found that rainfall data were used 84% of the time, remote sensing of vegetation 28% of the time, and gridded crop models 10% of the time, reflecting the length of use of each product in the regions. More investment is needed in training personnel on remote sensing products to improve use of data products throughout the FEWS NET system.

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Proceedings Article | 2 October 2008 Paper

Recent trends in agricultural production of Africa based on AVHRR NDVI time series

Anton Vrieling, Kirsten de Beurs, Molly Brown

Proceedings Volume 7104, 71040R (2008) https://doi.org/10.1117/12.799824

KEYWORDS: Agriculture, Molybdenum, Satellites, Vegetation, Clouds, Climate change, Statistical analysis, Raster graphics, Climatology, Remote sensing

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African agriculture is expected to be hard-hit by ongoing climate change. Effects are heterogeneous within the continent, but in some regions resulting production declines have already impacted food security. Time series of remote sensing data allow us to examine where persistent changes occur. In this study, we propose to examine recent trends in agricultural production using 26 years of NDVI data. We use the 8-km resolution AVHRR NDVI 15-day composites of the GIMMS group (1981-2006). Temporal data-filtering is applied using an iterative Savitzky-Golay algorithm to remove noise in the time series. Except for some regions with persistent cloud cover, this filter produced smooth profiles. Subsequently two methods were used to extract phenology indicators from the profiles for each raster cell. These indicators include start of season, length of season, time of maximum NDVI, maximum NDVI, and cumulated NDVI over the season. Having extracted the indicators for every year, we aggregate them for agricultural areas at sub-national level using a crop mask. The aggregation was done to focus the analysis on agriculture, and allow future comparison with yield statistics. Trend analysis was performed for yearly aggregated indicators to assess where persistent change occurred during the 26-year period. Results show that the phenology extraction method chosen has an important influence on trend outcomes. Consistent trends suggest a rising yield trend for 500-1100 mm rainfall zones ranging from Senegal to Sudan. Negative yield trends are expected for the southern Atlantic coast of West Africa, and for western Tanzania.

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