James Rowland
Jim Rowland is a physical geographer with background studies in climatology. Rowland has worked at USGS EROS since 1992, supporting geographic information systems (GIS) and remote sensing (RS) applications and capacity building in developing countries.
Jim Rowland is a physical geographer with background studies in climatology. Rowland has worked at USGS EROS since 1992, supporting geographic information systems (GIS) and remote sensing (RS) applications and capacity building in developing countries. He has extensive experience in organizing and providing GIS and RS training workshops throughout Africa, including West, East, and Southern Africa. He has also worked closely with regional Remote Sensing Centers in Africa. Rowland spent one year in Madagascar (he is fluent in French), supporting GIS and RS activities at the Malagasy national forest service and at the U.S. Agency for International Development (USAID) Mission in Antananarivo (part of an Inter-Agency Agreement between USGS EROS and USAID). For many years at EROS, he coordinated USGS participation in the Famine Early Warning Systems Network (FEWS NET) activity. For many years, Rowland managed and supervised the Early Warning and Environmental Monitoring Team at EROS, including projects such as FEWS NET, Vegetation Dynamics (U.S. drought monitoring, phenology studies), United Nations Environment Programme Global Resource Information Database (UNEP GRID), Afghanistan and Iraq spatial data infrastructure and water resources support, West Africa land use studies (land use dynamics and adapting to climate change), and low-head hydropower assessments in South America. Rowland is currently the Principal Investigator for the Early Warning for Food Security Focus Area, comprising FEWS NET, West Africa Resilience, GeoSUR, and Afghanistan activities.
Education and Certifications
MS degree from McGill University (Montreal, Canada) in Physical Geography with emphasis on Climatology.
BS degree in Mathematics from the University of New Orleans
Science and Products
Estimating actual evapotranspiration from irrigated fields using a simplified surface energy balance approach
Users Manual for the Geospatial Stream Flow Model (GeoSFM)
A linear geospatial streamflow modeling system for data sparse environments
Spatial data infrastructures in management of natural disasters
Temporal analysis of floodwater volumes in New Orleans after Hurricane Katrina
Drought monitoring techniques for famine early warning systems in Africa
Evaluation of land performance in Senegal using multi-temporal NDVI and rainfall series
Assessing land cover performance in Senegal, West Africa using 1-km integrated NDVI and local variance analysis
Interactive visualization of vegetation dynamics
A weighted least-squares approach to temporal NDVI smoothing
Estimating maize production in Kenya using NDVI: Some statistical considerations
Vegetative index for characterizing drought patterns
Science and Products
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Filter Total Items: 36
Estimating actual evapotranspiration from irrigated fields using a simplified surface energy balance approach
Food security assessment in many developing countries, such as Afghanistan, is vital because the early identification of populations at risk can enable the timely and appropriate actions needed to avert widespread hunger, destitution, or even famine. The assessment is complex, requiring the simultaneous consideration of multiple socioeconomic and environmental variables. Since large and widely disAuthorsG.B. Senay, M.E. Budde, J. P. Verdin, James D. RowlandUsers Manual for the Geospatial Stream Flow Model (GeoSFM)
The monitoring of wide-area hydrologic events requires the manipulation of large amounts of geospatial and time series data into concise information products that characterize the location and magnitude of the event. To perform these manipulations, scientists at the U.S. Geological Survey Center for Earth Resources Observation and Science (EROS), with the cooperation of the U.S. Agency for InternaAuthorsGuleid A. Artan, Kwabena Asante, Jodie Smith, Md Shahriar Pervez, Debbie Entenmann, James P. Verdin, James RowlandA linear geospatial streamflow modeling system for data sparse environments
In many river basins around the world, inaccessibility of flow data is a major obstacle to water resource studies and operational monitoring. This paper describes a geospatial streamflow modeling system which is parameterized with global terrain, soils and land cover data and run operationally with satellite‐derived precipitation and evapotranspiration datasets. Simple linear methods transfer wateAuthorsKwabena O. Asante, Guleid A. Arlan, Md Shahriar Pervez, James RowlandSpatial data infrastructures in management of natural disasters
No abstract available.AuthorsK.O. Asante, J. P. Verdin, M. P. Crane, S.A. Tokar, James D. RowlandTemporal analysis of floodwater volumes in New Orleans after Hurricane Katrina
Satellite images from multiple sensors and dates were analyzed to measure the extent of flooding caused by Hurricane Katrina in the New Orleans, La., area. The flood polygons were combined with a high-resolution digital elevation model to estimate water depths and volumes in designated areas. The multiple satellite acquisitions enabled monitoring of the floodwater volume and extent through time.AuthorsJodie Smith, James RowlandDrought monitoring techniques for famine early warning systems in Africa
No abstract available.AuthorsJames D. Rowland, J. P. Verdin, A. Adoum, G.B. SenayEvaluation of land performance in Senegal using multi-temporal NDVI and rainfall series
Time series of rainfall data and normalized difference vegetation index (NDVI) were used to evaluate land cover performance in Senegal, Africa, for the period 1982–1997, including analysis of woodland/forest, agriculture, savanna, and steppe land cover types. A strong relationship exists between annual rainfall and season-integrated NDVI for all of Senegal (r=0.74 to 0.90). For agriculture, savannAuthorsJi Li, J. Lewis, James Rowland, G. Tappan, L.L. TieszenAssessing land cover performance in Senegal, West Africa using 1-km integrated NDVI and local variance analysis
The researchers calculated seasonal integrated normalized difference vegetation index (NDVI) for each of 7 years using a time-series of 1-km data from the Advanced Very High Resolution Radiometer (AVHRR) (1992-93, 1995) and SPOT Vegetation (1998-2001) sensors. We used a local variance technique to identify each pixel as normal or either positively or negatively anomalous when compared to its surroAuthorsM.E. Budde, G. Tappan, James Rowland, J. Lewis, L.L. TieszenInteractive visualization of vegetation dynamics
Satellite imagery provides a mechanism for observing seasonal dynamics of the landscape that have implications for near real-time monitoring of agriculture, forest, and range resources. This study illustrates a technique for visualizing timely information on key events during the growing season (e.g., onset, peak, duration, and end of growing season), as well as the status of the current growing sAuthorsB. C. Reed, D. Swets, L. Bard, J. Brown, James RowlandA weighted least-squares approach to temporal NDVI smoothing
Satellite imagery provides a unique vantage point for observing seasonal dynamics of the landscape that have implications for global change issues. An objective evaluation of surface conditions may be performed using the normalized difference vegetation index (NDVI) derived from National Oceanic and Atmospheric Administration advanced very high resolution radiometer data. NDVI data are typically vAuthorsD. Swets, Bradley C. Reed, James Rowland, S.E. MarkoEstimating maize production in Kenya using NDVI: Some statistical considerations
A regression model approach using a normalized difference vegetation index (NDVI) has the potential for estimating crop production in East Africa. However, before production estimation can become a reality, the underlying model assumptions and statistical nature of the sample data (NDVI and crop production) must be examined rigorously. Annual maize production statistics from 1982-90 for 36 agriculAuthorsJ.E. Lewis, James Rowland, A. NadeauVegetative index for characterizing drought patterns
No abstract available.AuthorsJames D. Rowland, A. Nadeau, J. C. Brock, R. W. Klaver, D. G. Moore, J. Lewis