Relationship between spatial heterogeneity and wavelength in multisensor airborne images

Abstract

Variogram has been utilized to exploring the spatial heterogeneity of remote sensing images, especially its association with spatial resolution. However, very few attentions have been drawn on evaluating the spatial heterogeneity of multisensor airborne imagery and its relationship with spectral wavelength. Therefore, an investigation was carried out on multisensor airborne images to determine the relation between spatial heterogeneity and spectral wavelength for woodland, grass, and urban landscapes by applying variogram modeling. The airborne thematic mapper (ATM), compact airborne spectrographic imager (CASI), and Specim AISA Eagle airborne images at Harwood Forest, Monks wood, Cambridge, and River Frome areas, UK, were utilized. Results revealed that (1) the red band contained greater spatial variability than near-infrared wavelengths and other visible wavebands; (2) there was a steep gradient at the red edge in reference to its spatial variability of multisensor airborne images; (3) only for natural landscape such as woodland and grass, near-infrared waveband contains greater within-scene variations than the blue and green bands; (4) compared with the discrepancy of spatial resolution introduced by multisensor images (ATM, CASI, and Eagle), the specific landscape and spectral bands were more important in determining heterogeneity by means of original visible, near-infrared bands, and normalized difference vegetation index (NDVI). These findings remained us to be caution when combining and interpreting spatial variability and spatial structures derived from airborne images with different spatial resolution and spectral wavelength. Additionally, the outcomes of this study also have considerable implications in terms of designing and choosing suitable images for different applications.

Construction and Optimization of Green Infrastructure Network Based on Space Syntax: A Case Study of Suining County, Jiangsu Province

“…Li Kongming et al [14] Conduct MSPA Lin Hongyu et al [15] Lucía Pascual-Hortal et al [16] Build a new index Bingwen Qiu et al [17] Apply variogram modeling Investigation of the relation between spatial heterogeneity and spectral wavelength for objects Demuzere M et al [10] Carry out statistical analysis Significance of GI Rafael Calderón-Contreras et al [18] Utilize remote sensing technology and the normalized difference vegetation index (NDVI) combined with fieldwork verification F ňukalová Eliška et al [19] Conduct analysis of ecological corridor maintenance capacity Identification of GI networks in different areas Huang He et al [20] Analyze GI pattern based on the MSPA method Hu Tinghao et al [21] Use literature analysis and case analysis methods An overview of the concept of GI Liya [22] Conduct case analysis Hana Skokanová et al [23] Conduct MSPA and equivalent connected area (ECA) calculation…”