KEYWORDS: Data modeling, Remote sensing, Data conversion, Soil science, Instrument modeling, Agriculture, Injuries, Systems modeling, Decision support systems, Databases
Production management of winter wheat is more complicated than other crops since its growth period is covered all four seasons and growth environment is very complex with frozen injury, drought, insect or disease injury and others. In traditional irrigation and fertilizer management, agricultural technicians or farmers mainly make decision based on phenology, planting experience to carry out artificial fertilizer and irrigation management. For example, wheat needs more nitrogen fertilizer in jointing and booting stage by experience, then when the wheat grow to the two growth periods, the farmer will fertilize to the wheat whether it needs or not. We developed a spatial decision support system for optimizing irrigation and fertilizer measures based on WebGIS, which monitoring winter wheat growth and soil moisture content by combining a crop model, remote sensing data and wireless sensors data, then reasoning professional management schedule from expert knowledge warehouse. This system is developed by ArcIMS, IDL in server-side and JQuery, Google Maps API, ASP.NET in client-side. All computing tasks are run on server-side, such as computing 11 normal vegetable indexes (NDVI/ NDWI/ NDWI2/ NRI/ NSI/ WI/ G_SWIR/ G_SWIR2/ SPSI/ TVDI/ VSWI) and custom VI of remote sensing image by IDL; while real-time building map configuration file and generating thematic map by ArcIMS.
Along with the rapid development of urbanization since 1980s, immense changes of the land use/cover have been caused a series of problems of ecological environment, such as farmland decreasing, natural vegetation damage, construction land expansion, land desertification and salinization, and so on. The research on the changes and driving forces of spatial pattern of land use/cover by remote sensing is conducive to master the influences on ecosystem from natural factors and human factors and accelerate sustainable development of ecological environment. The LandSat MSS/TM/ETM+ images were used in the paper. Taken support vector machine (SVM) as classifier, the supervised classification was carried out to extract the spatial distribution of each land cover types in 1978, 1992, 2000 and 2010. By calculating the transition matrix among four result images, the changes of spatial patterns of land cover in Beijing in recent thirty years was analyzed from numeral and spatial dynamics. Result showed that the land use/cover in Beijing region had changed a great deal from 1978 to 2010. The farmland area and unused land area were decreasing with a range more than 40% in recent 32 years, while the urban area and forest area were increasing with a range more than 35%. Most of the farmland was transformed into urban land and forest, while the grassland was transformed into farmland. The input urban area was mainly originated from farmland, while the output was grassland. It indicated that the urbanization and afforestation were the two primary drivers of land use/cover change in Beijing region in recent thirty years.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.