Change of spatial information under rescaling: A case study using multi-resolution image series

Chen, Weirong; Henebry, Geoffrey M.

doi:10.1016/j.isprsjprs.2009.05.002

Cited by 19 publications

(12 citation statements)

References 17 publications

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“…Figure 5 (preliminary published in [48]) shows the representative semi-variogram plot of the near infrared1 (NIR1) image band at six different pixel sizes, and the average mangrove feature sizes measured from field-work and the aerial photograph. It reveals similar results to those presented in other studies examining the effects of changes in image pixel size on image information content [19,24,29,55,56]. Specifically, there is a gradual loss of information detail with the increasing pixel size (Figure 4 and 5).…”

Section: Relation Of Semi-variograms To Mangrove Vegetation Structuresupporting

confidence: 89%

Characterizing the Spatial Structure of Mangrove Features for Optimizing Image-Based Mangrove Mapping

2014

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Abstract:Understanding the relationship between the size of mangrove structural features and the optimum image pixel size is essential to support effective mapping activities in mangrove environments. This study developed a method to estimate the optimum image pixel size for accurately mapping mangrove features (canopy types and features (gaps, tree crown), community, and cover types) and tested the applicability of the results. Semi-variograms were used to characterize the spatial structure of mangrove vegetation by estimating the size of dominant image features in WorldView-2 imagery resampled over a range of pixel sizes at several mangrove areas in Moreton Bay, Australia. The results show that semi-variograms detected the variations in the structural properties of mangroves in the study area and its forms were controlled by the image pixel size, the spectral-band used, and the spatial characteristics of the scene object, e.g., tree or gap. This information was synthesized to derive the optimum image pixel size for mapping mangrove structural and compositional features at specific spatial scales. Interpretation of semi-variograms combined with field data and visual image interpretation confirms that certain vegetation structural features are detectable at specific scales and can be optimally detected using a specific image pixel size. The analysis results provide a basis for multi-scale mangrove mapping using high spatial resolution image datasets.

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Section: Relation Of Semi-variograms To Mangrove Vegetation Structuresupporting

confidence: 89%

Characterizing the Spatial Structure of Mangrove Features for Optimizing Image-Based Mangrove Mapping

2014

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“…Regarding the question of evaluating the spatial heterogeneity observed from multispectral and hyperspectral images, it was revealed that the output of simulated sensors were actually the overall integration of these bands involved through rescaling the Hyperion bands to get simulated Landsat TM images (17). However, our research on two real airborne images, one multispectral image with relatively broad spectral band, one hyperspectral images with relatively narrow spectral band, provide a different answer to this question.…”

Section: Comparative Results Within Short-wave Infrared Spectramentioning

confidence: 77%

Comparative spatio-spectral heterogeneity analysis using multispectral and hyperspectral airborne images

Qiu

Zeng

Chen

2013

Geo-spatial Information Science

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Knowledge of spatio-spectral heterogeneity within multisensor remote sensing images across visible, near-infrared and short wave infrared spectra is important. Till now, little comparative research on spatio-spectral heterogeneity has been conducted on real multisensor images, especially on both multispectral and hyperspectral airborne images. In this study, four airborne images, Airborne Thematic Mapper, Compact Airborne Spectrographic Imager, Specim AISA Eagle and AISI Hawk hyperspectral airborne images of woodland and heath landscapes at Harwood, UK, were applied to quantify and evaluate the differences in spatial heterogeneity through semivariogram modelling. Results revealed that spatial heterogeneity of multisensor airborne images has a close relationship with spatial and spectral resolution and wavelength. Within the visible, near-infrared spectra and short wave infrared spectra, greater spatial heterogeneity is generally observed from the relatively longer wavelength in short wave infrared spectra. There are dramatic changes across the red and red edge spectra, and the peak value is generally examined in the red middle or red edge wavelength across the visible and near-infrared spectra for vegetation or non-vegetation landscape respectively. In all, for real multisensor airborne images, the change in spatial heterogeneity with spatial resolution will accord with the change of support theory depending on whether dramatic change exists across the corresponding wavelength. Besides, if with close spatial resolution, the spatial heterogeneity of multispectral images might be far from the overall integration of these bands from the hyperspectral images involved. A comparative assessment of spatio-spectral heterogeneity using real hyperspectral and multispectral airborne images provides practical guidance for designing the placement and width of a spectral band for different applications and also makes a contribution to the understanding of how to reconcile spatial patterns generated by multisensors.

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“…However, the problem was always ignored in the estimation of grass production by applying the satellite-based vegetation index with the spatial resolution of 250 m, even 500 m. We hope the problem on spatial scale should be considered in the future. With the increasing size of spatial resolution from 30 m to 1 km, the uncorrelated noise and the overall spatial variance were decreasing ( Figure 4 and Table 3) [14,36,[39][40][41][42]. Those results indicate that the correlation between the VI on coarse resolution and the plot-sampled AGB cannot simply be applied to estimate the AGB on the regional scale.…”

Section: Discussionmentioning

confidence: 95%

Multiscale geostatistical analysis of sampled above-ground biomass and vegetation index products from HJ-1A/B, Landsat, and MODIS

Wang

Sun

2014

SPIE Proceedings

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The spatial scaling of satellite data is faced widely and inevitably in remote sensing applications for the spatial heterogeneity of ecosystems. In this study variogram analysis was used to evaluate the spatial variability and the scale effects of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from Huanjing (that is, environment satellite sensor in Chinese, HJ-1A/B), Landsat-5 Thematic Mapper (TM), Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m, 500 m, 1 km, and the field sampled above-ground biomass (AGB). Results show that the overall spatial variance decreased when pixel size increased from 30 m (HJ and TM) to 1 km (MODIS) at the area of 10 km × 10 km. The value of 1 or 3×3 pixels approximately represent the above-ground biomass from the cyclic sampling design. This indicates that the HJ data can be used to retrieve the biomass and its scaling-up for its performance comparable with Landsat TM data, though both sensors were applicable than that of MODIS. Further the method to scale-up is a fundament approach to the validation and application of MODIS products and ecosystem model's outputs on regional scale.

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Change of spatial information under rescaling: A case study using multi-resolution image series

Cited by 19 publications

References 17 publications

Characterizing the Spatial Structure of Mangrove Features for Optimizing Image-Based Mangrove Mapping

Characterizing the Spatial Structure of Mangrove Features for Optimizing Image-Based Mangrove Mapping

Comparative spatio-spectral heterogeneity analysis using multispectral and hyperspectral airborne images

Multiscale geostatistical analysis of sampled above-ground biomass and vegetation index products from HJ-1A/B, Landsat, and MODIS

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