@article{1118,
  author = {Liming Bai},
  title = {Multidimensional Information Mining of MODIS Imagery in Large-scale Forest Cover Mapping},
  journal = {Journal of Information & Systems Management},
  year = {2013},
  volume = {3},
  number = {1},
  doi = {},
  url = {http://www.dline.info/jism/fulltext/v3n1/3.pdf},
  abstract = {Geographical distribution dynamic of forest is an important basis for forest ecology and land surface process research, therefore is increasingly concerned in recent years. Coarse resolution remote sensing has provided a powerful support for global and regional scale forest cover mapping, but many problems, particularly those as to classification accuracy, are yet to be solved. This paper, taking the Moderate-resolution Imaging Spectroradiometer (MODIS) as a representative data source, proposes several methodological considerations for the application of coarse resolution MODIS data in global and regional scale forest cover mapping. Based on summarization of the potential forest-mapping associated MODIS spectral bands and standard products valuable for further detection of forest properties, it is argued that spectral bands and products reflecting unique optical, biophysical or biochemical properties of forest vegetations should be involved in forest mapping. Secondly, a frame of multidimensional information mining is proposed, including: (1) reflectance information: sub-pixel level classification with mixture model, (2) phenology information: time series of vegetation indexes, (3) biophysical information: biophysical parameters, (4) angle information: bidirectional reflectance distribution function (BRDF) model, (5) biochemical information: coupling of hyperspectral remote sensing data, and (6) landscape information: high resolution satellite image based scaling. Integration of these perspectives may provide an efficient instrument for future remotely sensed large-scale vegetation mapping. Holistic and integrated methodology combining multidimensional forest vegetation information may deepen the efficiency of coarse resolution satellite data in vegetation research.},
}