The Wide Field View (WFV), a space borne multi-spectral sensor onboard the Chinese GaoFen-1 (GF-1) satellite from the China High-resolution Earth Observation System, is operating in orbit dedicating to providing Earth observation with decametric spatial resolution, high temporal resolution and wide coverage for environment monitoring purpose. The objective of this study is to present an integrated image processing and environment monitoring platform specifically for GF-1 WFV data. The platform is developed with a multi-layer architecture and C/S structure, which primarily consists of image pre-processing, environment monitoring, data visualization, and results output modules. The client application was created by using C# whereas IDL was used to develop image processing and other relevant algorithms. This paper focuses mainly on the overall design of the platform and related key techniques. The platform has been implemented as a stand-alone application, and successfully implemented in real world environment monitoring studies.
Normalized Difference Vegetation Index (NDVI) is one of the most widely used indicators for monitoring the vegetation coverage in land surface. The time series features of NDVI are capable of reflecting dynamic changes of various ecosystems. Calculating NDVI via Moderate Resolution Imaging Spectrometer (MODIS) and other wide-swath remotely sensed images provides an important way to monitor the spatial and temporal characteristics of large-scale NDVI. However, difficulties are still existed for ecologists to extract such information correctly and efficiently because of the problems in several professional processes on the original remote sensing images including radiometric calibration, geometric correction, multiple data composition and curve smoothing. In this study, we developed an efficient and convenient online toolbox for non-remote sensing professionals who want to extract NDVI time series with a friendly graphic user interface. It is based on Java Web and Web GIS technically. Moreover, Struts, Spring and Hibernate frameworks (SSH) are integrated in the system for the purpose of easy maintenance and expansion. Latitude, longitude and time period are the key inputs that users need to provide, and the NDVI time series are calculated automatically.