Snail Density Prediction for Schistosomiasis Control Using Ikonos and ASTER Images

Schistosomiasis is a water-borne parasitic disease endemic in tropical and subtropical areas. Its transmission depends upon the presence of snails, which serve as intermediate hosts for the parasite. Some efforts have been made to classify snail habitats with remotely sensed data, but not to estimate snail abundance that is an important parameter in schistosomiasis transmission modeling. In this research, snail density was predicted by integrating the field survey and satellite images of different spatial resolution. A mountainous environment near Xichang city, in southwest Sichuan province, China, was chosen as the test site. Land-cover and land-use information extracted from 4 m resolution Ikonos data and elevation data derived from ASTER (Advanced Space-borne Thermal Emission and Reflection Radiometer) data were used as reference for scaling up to greater spatial extents. Therefore, we estimated land-cover and land-use fraction data at the 30 m resolution level based on classification results from the Ikonos data. Snail abundance for each 30 m resolution grid was then predicted by regressing field survey data with land-cover and land-use fractions. Subsequently, a snail density map was generated using the territory of each of the over 200 residential groups as a mapping unit. An R of 0.87 was obtained between the average snail density predicted and that surveyed for 19 groups. With such a model, we were able to extrapolate scattered snail abundance surveyed at a limited number of sites to the entire area. Spatial autocorrelation of snail distribution was considered as one of the possible factors in predicting snail density and tested for further model calibration. Introduction Schistosomiasis is an environmentally-mediated disease that depends upon the presence of suitable habitats for snails, Snail Density Prediction for Schistosomiasis Control Using Ikonos and ASTER Images Bing Xu, Peng Gong, Greg Biging, Song Liang, Edmond Seto, and Robert Spear which serve as intermediate hosts for the parasite. Inadequate sanitation and contact with contaminated water that together facilitate the lifecycle of the parasite. In a mountainous region of Sichuan province, China, snails of the species Oncomelania hupensis live along the edges of irrigation ditches in crop fields. The size of the snails is tiny. Adult snails are 6–8 mm in length. Humans or cattle become infected from skin contact with water contaminated by a form of the Schistosoma japonicum parasite called cercaria (Davis, et al., 1999). After infection, parasites migrate to the liver where they mature into adult worms, mate, and lay eggs. A fraction of the eggs are released in the stool which is used as fertilizer in rural China. When this fertilizer is spread in the fields, the eggs hatch into a free-swimming form called miracidia, which penetrates the amphibious snails. After 30 days in adequate temperature, infected snails subsequently release the cercarial form of the parasite, which complete the lifecycle by reinfecting humans or cattle. Snail population is crucial for more refined analysis of schistosomiasis transmission and for the development of a control model. Such information is traditionally obtained through field survey. Research has been carried out on the use of remote sensing and geographic information system (GIS) in modeling of such infectious diseases as tryponosomiasis (Rogers and Randolph, 1991; Rogers, et al., 1997; Kitron, et al., 1996), rift valley fever (Linthicum, et al., 1987; Linthicum, et al., 1999), malaria (Thomson, et al., 1997, 2003; Beck, et al., 1997; Hay, et al., 1998), lyme disease (Dister, et al., 1997; Kitron and Kazmierczak, 1997) and schistosomiasis (Malone, 1997; Zhou, et al., 2002; Seto, et al., 2002; Xu, 2003). Some of the studies directly correlate remotely sensed data with intermediate host abundance or disease outbreaks. The NOAA satellite series launched since the 1970s carrying on board the AVHRR (Advanced Very High Resolution Radiometer) sensor has been continuously providing data for green-leaf biomass monitoring on a daily basis and has facilitated land surface mapping by adding seasonal dimensions (Tucker, et al., 1985). Correlation analysis was explored between normalized difference vegetation index (NDVI) generated from the first two channels of AVHRR data and potential virus activity for rift valley fever outbreaks transmitted by mosquitoes in Kenya (Linthicum, et al., 1987). Similarly correlation was established between NDVI obtained from AVHRR data and mortality rate and abundance of intermediate host of tsetse fly in tryponosomiasis transmission in West Africa (Rogers and Randolph, 1991). Time series analysis was applied by building an autoregressive integrated moving average (ARIMA) model combining both sea surface temperature and NDVI P H OTO G R A M M E T R I C E N G I N E E R I N G & R E M OT E S E N S I N G November 2004 1 2 8 5 Bing Xu was formerly with Department of Geography, Texas State University, 601 University Drive, San Marcos, TX 78666. She is currently with the Department of Geography, University of Utah, 260 S. Central Campus Drive, Salt Lake City, UT 84112-9155 ([email protected]). Peng Gong is with Center for Assessment and Monitoring of Forest and Environmental Resources (CAMFER), 151 Hilgard Hall, University of California, Berkeley, CA 94720, and the International Institute for Earth System Science, Nanjing University, China, 210093, and State Key Lab of Remote Sensing Science, Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing, 100101. Greg Biging is with the Center for Assessment and Monitoring of Forest and Environmental Resources (CAMFER), 151 Hilgard Hall, University of California, Berkeley, CA 94720-3110. Song Liang, Edmund Seto, and Robert Spear are with the Center for Occupational and Environmental Health, School of Public Health, University of California, 140 Earl Warren Hall, Berkeley, CA 94720-7360. Photogrammetric Engineering & Remote Sensing Vol. 70, No. 11, November 2004, pp. 1285–1294. 0099-1112/04/7011–1285/$3.00/0 © 2004 American Society for Photogrammetry and Remote Sensing 04-076.qxd 10/11/04 14:29 Page 1285