Leonid Roytman

Use of remote sensing data for estimation of winter wheat yield in the United States

This paper shows the application of remote sensing data for estimating winter wheat yield in Kansas. An algorithm uses the Vegetation Health (VH) Indices (Vegetation Condition Index (VCI) and Temperature Condition Index (TCI)) computed for each week over a period of 23 years (1982–2004) from Advance Very High Resolution Radiometer (AVHRR) data. The weekly indices were correlated with the end of the season winter wheat (WW) yield. A strong correlation was found between winter wheat yield and VCI (characterizing moisture conditions) during the critical period of winter wheat development and productivity that occurs during April to May (weeks 16 to 23). Following the results of correlation analysis, the principal components regression (PCR) method was used to construct a model to predict yield as a function of the VCI computed for this period. The simulated results were compared with official agricultural statistics showing that the errors of the estimates of winter wheat yield are less than 8%. Remote sensing, therefore, is a valuable tool for estimating crop yields well in advance of harvest, and at a low cost.

Forecasting crop production using satellite-based vegetation health indices in Kansas, USA

This article shows the results of early crop yield prediction from remote-sensing data. The study was carried out in Kansas, USA. The methodology proposed allows the estimation of winter wheat (WW), sorghum and corn yields 3–4 months before harvest. The procedure uses the vegetation health (VH) indices (vegetation condition index (VCI) and temperature condition index (TCI)) computed for each pixel and week over a 21-year period (1985–2005) from the Advanced Very High Resolution Radiometer (AVHRR) data. Over this period, a strong correlation was found between crop yield and VH indices during the weather-related critical period of crop development, which controls much final crop productivity. The 3-month advanced yield forecasts were independently compared with official agricultural statistics, showing that the estimation errors for WW, sorghum and corn were 8%, 6% and 3%, respectively. Implementing the 3–4 months lead forecast in operational practice will aid farmers to mitigate weather vagaries using irrigation, diseases/insects control, application of fertilizers and so on during a growing season and will help decision-makers to regulate marketing strategies, import/export and price policies and address food security issues.

Using vegetation health indices and partial least squares method for estimation of corn yield

In this paper the possibility of predicting corn yield by the use of real time acquired satellite data from the Advance Very High Resolution Radiometer (AVHRR) sensor and Partial Least Squares (PLS) method was investigated. To test the methodology in practice, 23 years (1982–2004) of AVHRR data together with official corn yield statistics of Haskell County, Kansas, USA, were used for model development and validation. The AVHRR reflectances in the visible and thermal bands, extracted from the National Oceanic and Atmospheric Administration (NOAA)/NESDIS archive, were transformed into Vegetation Health (VH) Indices (Vegetation Condition Index (VCI) and Temperature Condition Index (TCI)). The PLS method was used to construct a model relating corn yield anomaly with VH indices. Independent model verification showed that the error of corn yield prediction in Haskell County, Kansas, USA, was less than 6%. For several reasons this approach could have excellent potential as a commercial application: satellite data are inexpensive, easily available and yield estimates can be known two to three months before harvest has been completed.

Modelling and prediction of malaria vector distribution in Bangladesh from remote-sensing data

Epidemic malaria cases and satellite-based vegetation health (VH) indices were investigated to be used as predictors of malaria vector activities in Bangladesh. The VH indices were derived from radiances, measured by the Advanced Very High Resolution Radiometer (AVHRR) on National Oceanic and Atmospheric Administration (NOAA) afternoon polar orbiting satellites. Two indices characterizing moisture and thermal conditions were investigated using correlation and regression analysis applied to the number of malaria cases recorded in the entire Bangladesh region and three administrative divisions (Chittagong, Sylhet and Dhaka) during 1992–2001. It is shown that during the cooler months (November to March), when mosquitoes are less active, the correlation between number of malaria cases and two investigated indices was near zero. From April, when the mosquito activity season starts, the correlation increased, reaching a maximum value of 0.5–0.8 by the middle of the high season (June to July), reducing thereafter to zero by the beginning of the cool season in November. Following these results, regressional equations for the number of malaria cases as a function of VH indices were built and tested independently. They showed that, in the main malaria administrative division (Chittagong) and the entire Bangladesh region, the regressional equations can be used for early prediction of malaria development.

Environmental data analysis and remote sensing for early detection of dengue and malaria

Malaria and dengue fever are the two most common mosquito-transmitted diseases, leading to millions of serious illnesses and deaths each year. Because the mosquito vectors are sensitive to environmental conditions such as temperature, precipitation, and humidity, it is possible to map areas currently or imminently at high risk for disease outbreaks using satellite remote sensing. In this paper we propose the development of an operational geospatial system for malaria and dengue fever early warning; this can be done by bringing together geographic information system (GIS) tools, artificial neural networks (ANN) for efficient pattern recognition, the best available ground-based epidemiological and vector ecology data, and current satellite remote sensing capabilities. We use Vegetation Health Indices (VHI) derived from visible and infrared radiances measured by satellite-mounted Advanced Very High Resolution Radiometers (AVHRR) and available weekly at 4-km resolution as one predictor of malaria and dengue fever risk in Bangladesh. As a study area, we focus on Bangladesh where malaria and dengue fever are serious public health threats. The technology developed will, however, be largely portable to other countries in the world and applicable to other disease threats. A malaria and dengue fever early warning system will be a boon to international public health, enabling resources to be focused where they will do the most good for stopping pandemics, and will be an invaluable decision support tool for national security assessment and potential troop deployment in regions susceptible to disease outbreaks.

Using remote sensing satellite data and artificial neural network for prediction of potato yield in Bangladesh

Potato is one of the staple foods and cash crops in Bangladesh. It is widely cultivated in all of the districts and ranks second after rice in production. Bangladesh is the fourth largest potato producer in Asia and is among the world’s top 15 potato producing countries. The weather condition for potato cultivation is favorable during the sowing, growing and harvesting period. It is a winter crop and is cultivated during the period of November to March. Bangladesh is mainly an agricultural based country with respect to agriculture’s contribution to GDP, employment and consumption. Potato is a prominent crop in consideration of production, its internal demand and economic value. Bangladesh has a big economic activities related to potato cultivation and marketing, especially the economic relations among farmers, traders, stockers and cold storage owners. Potato yield prediction before harvest is an important issue for the Government and the stakeholders in managing and controlling the potato market. Advanced very high resolution radiometer (AVHRR) based satellite data product vegetation health indices VCI (vegetation condition index) and TCI (temperature condition index) are used as predictors for early prediction. Artificial neural network (ANN) is used to develop a prediction model. The simulated result from this model is encouraging and the error of prediction is less than 10%.

Improving AVHRR-based NDVI data using a statistical technique for global climate studies

The main objective of this report is to examine the Normalized Difference Vegetation Index (NDVI) stability in the NOAA/NESDIS Global Vegetation Index (GVI) data, which was collected from five NOAA series satellites. An empirical distribution function (EDF) was developed to decrease the long-term inaccuracy of the NDVI data derived from the AVHRR sensor on NOAA polar orbiting satellite. The instability of data is a consequence of orbit degradation, and from the circuit drifts over the life of a satellite. Degradation of NDVI over time and shifts of NDVI between the satellites were estimated using the China data set, because it includes a wide variety of different ecosystems represented globally. It was found that the data for six particular years, four of which were consecutive, are not stable compared to other years because of satellite orbit drift, AVHRR sensor degradation, and satellite technical problems, including satellite electronic and mechanical satellite systems deterioration. The data for paired years for the NOAA-7, NOAA-9, NOAA-11, NOAA-14, and NOAA-16 were assumed to be standard because the crossing time of the satellite over the equator (between 13:30 and 15:00 hours) maximized the value of the coefficients. These years were considered the standard years, while in other years the quality of satellite observations significantly deviated from the standard. The deficiency of data for the affected years were normalized or corrected by using the EDF method and compared with the standard years. These normalized values were then utilized to estimate new NDVI time series that show significant improvement of NDVI data for the affected years so that the dataset is useful in climate studies.

Using artificial neural network and satellite data to predict rice yield in Bangladesh

Rice production in Bangladesh is a crucial part of the national economy and providing about 70 percent of an average citizen’s total calorie intake. The demand for rice is constantly rising as the new populations are added in every year in Bangladesh. Due to the increase in population, the cultivation land decreases. In addition, Bangladesh is faced with production constraints such as drought, flooding, salinity, lack of irrigation facilities and lack of modern technology. To maintain self sufficiency in rice, Bangladesh will have to continue to expand rice production by increasing yield at a rate that is at least equal to the population growth until the demand of rice has stabilized. Accurate rice yield prediction is one of the most important challenges in managing supply and demand of rice as well as decision making processes. Artificial Neural Network (ANN) is used to construct a model to predict Aus rice yield in Bangladesh. Advanced Very High Resolution Radiometer (AVHRR)-based remote sensing satellite data vegetation health (VH) indices (Vegetation Condition Index (VCI) and Temperature Condition Index (TCI) are used as input variables and official statistics of Aus rice yield is used as target variable for ANN prediction model. The result obtained with ANN method is encouraging and the error of prediction is less than 10%. Therefore, prediction can play an important role in planning and storing of sufficient rice to face in any future uncertainty.

Using NOAA/AVHRR based remote sensing data and PCR method for estimation of Aus rice yield in Bangladesh

Rice is a dominant food crop of Bangladesh accounting about 75 percent of agricultural land use for rice cultivation and currently Bangladesh is the world’s fourth largest rice producing country. Rice provides about two-third of total calorie supply and about one-half of the agricultural GDP and one-sixth of the national income in Bangladesh. Aus is one of the main rice varieties in Bangladesh. Crop production, especially rice, the main food staple, is the most susceptible to climate change and variability. Any change in climate will, thus, increase uncertainty regarding rice production as climate is major cause year-to-year variability in rice productivity. This paper shows the application of remote sensing data for estimating Aus rice yield in Bangladesh using official statistics of rice yield with real time acquired satellite data from Advanced Very High Resolution Radiometer (AVHRR) sensor and Principal Component Regression (PCR) method was used to construct a model. The simulated result was compared with official agricultural statistics showing that the error of estimation of Aus rice yield was less than 10%. Remote sensing, therefore, is a valuable tool for estimating crop yields well in advance of harvest, and at a low cost.

Early Detection and Monitoring of Malaria

Global Earth Observation Systems of Systems (GEOSS) are bringing vital societal benefits to people around the globe. In this research article, we engage undergraduate students in the exciting area of space exploration to improve the health of millions of people globally. The goal of the proposed research is to place students in a learning environment where they will develop their problem solving skills in the context of a world crisis (e.g., malaria). Malaria remains one of the greatest threats to public health, particularly in developing countries. The World Health Organization has estimated that over one million die of Malaria each year, with more than 80% of these found in Sub-Saharan Africa. The mosquitoes transmit malaria. They breed in the areas of shallow surface water that are suitable to the mosquito and parasite development. These environmental factors can be detected with satellite imagery, which provide high spatial and temporal coverage of the earths surface. We investigate on moisture, thermal and vegetation stress indicators developed from NOAA operational environmental satellite data. Using these indicators and collected epidemiological data, it is possible to produce a forecast system that can predict the risk of malaria for a particular geographical area with up to four months lead time. This valuable lead time information provides an opportunity for decision makers to deploy the necessary preventive measures (spraying, treated net distribution, storing medications and etc) in threatened areas with maximum effectiveness. The main objective of the proposed research is to study the effect of ecology on human health and application of NOAA satellite data for early detection of malaria.