Proc. SPIE. 5983, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology V
KEYWORDS: Image fusion, Data modeling, Image processing, Remote sensing, Image resolution, Geographic information systems, Finite element methods, Error control coding, Global Positioning System, Accuracy assessment
Remote sensing dynamic monitoring of land use can detect the change information of land use and update the current land use map, which is important for rational utilization and scientific management of land resources. This paper discusses the technological procedure of remote sensing dynamic monitoring of land use including the process of remote sensing images, the extraction of annual change information of land use, field survey, indoor post processing and accuracy assessment. Especially, we emphasize on comparative research on the choice of remote sensing rectifying models, image fusion algorithms and accuracy assessment methods. Taking Anning district in Lanzhou as an example, we extract the land use change information of the district during 2002-2003, access monitoring accuracy and analyze the reason of land use change.
Leaf area index (LAI) is a key variable of up-scaling or down-scaling in global climate change research. The Qinghai-Tibetan Plateau is an ideal place to study and model interactions between natural ecosystem and climate change because there are unique interactions between ecosystems and environments on the extremely high plateau where vegetation remains undisturbed. In this study, we present field data for leaf area index(LAI) in 42 field plots located along an altitudinal gradient from 2800m to 4000m around Gonghe basin in Qinghai-Tibetan plateau using a global positioning system during 2003-2004. The vegetation types of these field plots included grasslands and grass-shrub mixed lands. We also acquired MODIS data (product MOD13Q1) over the study area between January and December, 2003. These products consisted of 15-days composite of vegetation indices (EVI and NDVI) at 250m spatial resolution. We developed a MODIS-based Leaf Area Index Model of Grass land using 20 site-specific measurement data from the 42 field plots, and validated the model using other 22 field plots measurement data. Using MODIS-derived Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI), Data analyses have shown that EVI had a stronger linear relationship (R2=0.8226, n=20) with LAI at Gonghe Basin than did the NDVI (R2=0.7885, n=20). The simulation of the model was conducted at Gonghe Basin of Qinghai-Tibetan plateau. The predicted LAI values agreed well (EVI model R2=0.621, n=22) (NDVI model R2=0.612, n=22) with observed LAI of grassland at Gonghe Basin. This study demonstrated the potential of the model for scaling-up of LAI of grasslands in China.