The Moderate Resolution Imaging Spectroradiometer (MODIS) data offers a unique combination of spectral, temporal, and spatial resolution in comparison to other global sensors. The MODIS Enhanced Vegetation Index (EVI) product has several advantages, which make it suitable for regional land cover mapping. This paper investigates the application of MODIS EVI time-series data for mapping temperate arid and semi-arid land cover at a moderate resolution (500 m), for regional land-cover/land-use monitoring purposes. A 16-day composite EVI time-series data for 2003 (22 March 2003 - 30 September 2003) was adopted for the study. A land cover map was generated for the Inner Mongolia Autonomous Region using 7 tiles of MODIS EVI time-series data and Self-Organizing Map (SOM) neural network classification. Land-use GIS data, Landsat TM/ETM, and ASTER data were employed as reference data. The results show that the overall accuracy of land cover classification is about 84% with a Kappa coefficient of 0.8170. These results demonstrate that the SOM neural network model could work well for the multi-temporal MODIS EVI data, and suggest a potential of using MODIS EVI time-series remote sensing data to monitor desertification in Inner Mongolia with limited ancillary data and little labor-input in comparison with using high-spatial resolution remote sensing data.
KEYWORDS: Earth observing sensors, High resolution satellite images, Satellites, Satellite imaging, Image fusion, Airborne laser technology, Data fusion, Data modeling, 3D modeling, 3D image processing
High-resolution satellite imagery has become widely available recently and it enables urban remote sensing to not only
classify land-use, but also map the details in urban environment. However, due to high object density and scene complexity, normally it is extremely difficult to automatically extract urban objects solely based on images. This paper describes our approach to detect buildings by fusing high-resolution IKONOS satellite images and airborne laser scanning data. With the high spatial resolution, rich spectral signature of IKONOS images and the very accurate positioning information of laser data, our data fusion methods show an efficient way to exploit the complementary characteristics of these two kinds of dataset for the purpose of building detection. In order to simplify the complexity of processing, a top to down strategy is generally applied to extract features of objects from coarsely to finely, and multiple cues are also derived and fused at different processing levels. The paper describes the developed framework and experimental results in detail, and also discusses both the advantage and deficiencies of the approach.
Carbon absorption o f plant is one of the essential parameters in assessing terrestrial ecosystem functions with respect to global warning. It is however, not easy to estimate carbon absorption directly on the ground. In this study, an experiment method was designed to estimate the saturated Amax from hyperspectral data in the laboratory and in the field scale. First, we measure the relationship between biochemical concentrations and parameters of 'Blackman' photosynthetic rate model. Secondly, we measure the relationship between biochemical concentration and hyperspectral characteristics. High-resolution reflectance over a range of 333-2507 nm with resolution of about 1.5-10 nm and net Amax-photon flux density (PFD) were measured respectively by the GER 2600 and Li-6400. Also, chlorophyll a, chlorophyll b, chlorophyll a + b and nitrogen concentration were quantitative analyzed from in situ measurement of cucumber's fresh leaves that were cultivated for different biochemical concentration in a greenhouse chamber. Correlation between saturated Amax and chlorophyll a and nitrogen concentration was r2 equals 0.90, and 0.91 respectively. Chlorophyll b did not show any correlation with saturated Amax. Chlorophyll a and nitrogen concentrations were estimated by using the first derivative spectral reflectance of fresh leaf. RF' at 678.011 correlated best with chlorophyll a concentration. RF' at 732.122nm correlated best with nitrogen concentration. Finally net Amax at given PFD was estimated by the photosynthetic rate model. A correlation between the actual net Amax and the estimated net Amax was r2 equals 0.74. In this study, both chlorophyll a and nitrogen concentrations show good correlation with saturated Amax.
A method is presented for extracting wetland areas in northern high-latitude zones using Normalized Difference Vegetation Index (NDVI) and land surface temperature (Ts) calculated from midday NOAA/AVHRR data. Wetland areas have been distinguished from other land-cover types using signatures on a scattergram of NDVI vs. Ts. The method was applied for extracting wetland areas in the basin of the Ob River in the west Siberian lowland. The result have been verified with ground-truth data and land-cover classification results obtained from high-resolution satellite images.
AVNIR is a high spatial resolution imager on ADEOS with four multispectral bands and one panchromatic band covering the visible and the near-infrared range. AVNIR also has multi- view angle observation (pointing) function with the range of plus or minus 40 degrees from nadir. These two characteristics of AVNIR, high spatial resolution observation and stereoscopic observation, enable it to produce high quality land cover/use maps and digital elevation models (DEM). Besides these two practical products AVNIR is expected to observe scientific parameters including space reflectance and surface reflectance which would play an important role in assessing the radiation budget at the earth surface. Also AVNIR is expected to produce several science data sets for monitoring coastal environment, volcanic activities as well as terrestrial ecosystems.
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