This paper evaluated the spatiotemporal non-stationarity in the vegetation dynamic based on 1-km resolution 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) datasets in China during 2001-2011 through a wavelet transform method. First, it revealed from selected pixels that agricultural crops, natural forests, and meadows were characterized by their distinct intra-annual temporal variation patterns in different climate regions. The amplitude of intra-annual variability generally increased with latitude. Second, parameters calculated using a per-pixel strategy indicated that the natural forests had the strongest variation pattern from seasonal to semiannual scales, and the multiple-cropping croplands typically showed almost equal variances distributed at monthly, seasonal, and semiannual scales. Third, spatiotemporal non-stationarity induced from cloud cover was also evaluated. It revealed that the EVI temporal profiles were significantly distorted with regular summer cloud cover in tropical and subtropical regions. Nevertheless, no significant differences were observed from those statistical parameters related to the interannual and interannual components between the de-clouded and the original MODIS EVI datasets across the whole country. Finally, 12 vegetation zones were proposed based on spatiotemporal variability, as indicated by the magnitude of interannual and intra-annual dynamic components, normalized wavelet variances of detailed components from monthly to semiannual scale, and proportion of cloud cover in summer. This paper provides insightful solutions for addressing spatiotemporal non-stationarity by evaluating the magnitude and frequency of vegetation variability using monthly, seasonal, semiannual to interannual scales across the whole study area.
Most evaluation of the consistency of multisensor images have focused on Normalized Difference Vegetation Index (NDVI) products for natural landscapes, often neglecting less vegetated urban landscapes. This gap has been filled through quantifying and evaluating spatial heterogeneity of urban and natural landscapes from QuickBird, Satellite pour l'observation de la Terre (SPOT), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Landsat Thematic Mapper (TM) images with variogram analysis. Instead of a logarithmic relationship with pixel size observed in the corresponding aggregated images, the spatial variability decayed and the spatial structures decomposed more slowly and complexly with spatial resolution for real multisensor images. As the spatial resolution increased, the proportion of spatial variability of the smaller spatial structure decreased quickly and only a larger spatial structure was observed at very coarse scales. Compared with visible band, greater spatial variability was observed in near infrared band for both densely and less densely vegetated landscapes. The influence of image size on spatial heterogeneity was highly dependent on whether the empirical semivariogram reached its sill within the original image size. When the empirical semivariogram did not reach its sill at the original observation scale, spatial variability and mean characteristic length scale would increase with image size; otherwise they might decrease. This study could provide new insights into the knowledge of spatial heterogeneity in real multisensor images with consideration of their nominal spatial resolution, image size and spectral bands.
Spatial heterogeneity of airborne remote sensing images is critical for surface character delineation. The purpose of this paper is to quantify and evaluate the spatial variability and characteristic scales of Coniferous trees from multi-sensor airborne images by applying variogram modelling. The Airborne Thematic Mapper (ATM) Compact Airborne Spectrographic Imager (CASI-2), Specim AISA Eagle airborne images at Harwood, Northumberland, UK, were utilized, with spatial resolutions of 9m, 7.2m and 2.5m respectively. We demonstrate that variogram properties provide a robust assessment of the differences in spatial variability and characteristic scale between multi-sensor airborne datasets. Spatial variability of Coniferous trees in ATM airborne imagery is consistently larger than CASI airborne imagery in blue, green, red and infrared bands. The spatial variability of Eagle airborne images is strongest in red and near infrared bands but weakest in the blue band. For the blue, green, red and near infrared bands utilized, results indicate that the total within-scene variation of multi-sensor airborne images increases with wavelength. Moreover, the mean characteristic length scale consistently decreases with the nominal spatial resolution and spectral bands. It is recommended that applications of one type of tree development observations could take advantage of Eagle images in the near infrared band to gain more within-species information of spatial structure and its variability. Other applications like mapping tree species might exploit ATM images to obtain more information about spatial structure and its variability between different tree species.
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