A Web service based application serving vegetation condition indices for Flood Crop Loss Assessment
Bei Zhang, Liping Di, Genong Yu, Yuanzheng Shao, Ranjay Man Shrestha, Lingjun Kang · 2013
Vegetation condition assessment is very useful and helpful for researchers and decision makers to evaluate crop loss and value, and identify and manage risks in the flood hazard areas. Crop responses to flooding vary with crop types, crop growing stages, soil characteristics, weather condition, flood duration and depth, etc. How to measure and understand crop response is a challenging and important research topic in agriculture. The availability and integration of high spatial and temporal resolution remote sensing data facilitates crop type identification, crop condition monitoring, soil moisture measurement, crop yield estimation, and crop damage evaluation. Remote sensing based vegetation condition indices are widely used by researchers in these fields. In this paper, an integrated Web geospatial application named Remote-sensing-based Flood Crop Loss Assessment Service System (RF-CLASS) is developed to automate the “data-information-knowledge-decision” process of downloading near real time 250m resolution MODIS land surface reflectance data from NASA website, re-projecting, reformatting, and mosaicking these data using geospatial software packages, calculating various daily, weekly, and bi-weekly vegetation condition indices through efficient computing method, visualizing and analyzing these indices in an interactive way, assessing crop progress and condition, and evaluating crop damages in the flood areas. Currently, the vegetation indices for the whole continental United States from the year of 2000 have been generated routinely. These indices and other geospatial data like the boundary layers, the road layers, and the latest cropland data layer (CDL) have been served in the prototype system of RF-CLASS. RF-CLASS not only provides basic map operations, area of interest definition, geospatial data customization and downloading, and geospatial analytics functions for decision making through its interactive and intuitive user interface, but also offers standard Web services to query, visualize, disseminate, and analyze various types of vegetation condition indices for integration in other applications or invocation in scientific workflows.