Spatial variations in salinity stress across a coastal landscape using vegetation indices derived from hyperspectral imagery

Abstract

Chlorophyll fluorescence and landscapelevel reflectance imagery were used to evaluate spatial variations in stress in Myrica cerifera and Iva frutescens during a severe drought and compared to an extremely wet year. Measurements of relative water content and the water band index (WBI 970 ) indicated that the water stress did not vary across the island. In contrast, there were significant differences in tissue chlorides across sites for both species. Using the physiological reflectance index (PRI), we were able to detect salinity stress across the landscape. For M. cerifera, PRI did not differ between wet and dry years, while for I. frutescens, there were differences in PRI during the 2 years, possibly related to flooding during the wet year. There was a positive relationship between PRI and DF=F 0 m for M. cerifera (r 2 = 0.79) and I. frutescens (r 2 = 0.72). The normalized difference vegetation index (NDVI), the chlorophyll index (CI), and WBI 970 were higher during the wet summer for M. cerifera, but varied little across the island. CI and WBI 970 were higher during 2004 for I. frutescens, while there were no differences in NDVI during the 2 years. PRI was not significantly related to NDVI, suggesting that the indices are spatially independent. These results suggest that PRI may be used for early identification of salt stress that may lead to changes in plant distributions at the landscape level, as a result of rising sea level. Comparsions between the two species indicate that variations in PRI and other indices may be species specific.

Landscape position and habitat polygons in a dynamic coastal environment

“…Hyperspectral and LIDAR imagery were used to identify species distributions and generate landscape positions on barrier islands for A. breviligulata, M. cerifera and J. virginiana using standard methods for processing airborne imagery (Young et al 2007, Jensen 1996, Leica Geosystems 2003, Naumann et al 2009, Brantley et al 2011. Hyperspectral data (450-2,450 nm, 3 nm band width, 2 m per pixel) were post processed to correct for geometric and radiometric effects.…”

Mapping and Modelling Spatial Variation in Soil Salinity in the Al Hassa Oasis Based on Remote Sensing Indicators and Regression Techniques

“…Image enhancement is data processing that aims to increase the overall visual quality of an image or to enhance the visibility and interpretability of certain features of interest in it [53]. Several studies have shown that image enhancement techniques consisting of spectral indices (e.g., NDVI, SI, NDSI, TNDVI) have a great potential in enhancing and delineating soil salinity detail in an image [29,31,[54][55][56][57][58][59]. For example, Tripathi et al [49] found and emphasized that identifying salt-affected soils based on the image enhancement method, represented by the salinity index, yields better results than individual bands, due to its ability to enhance the saline patches by suppressing the vegetation.…”

Connection of the Photochemical Reflectance Index (PRI) with the Photosystem II Quantum Yield and Nonphotochemical Quenching Can Be Dependent on Variations of Photosynthetic Parameters among Investigated Plants: A Meta-Analysis

“…Connections between PRI and other photosynthetic parameters, including a quantum yield of photosystem II (∆F/F m ') [38,41,42,[45][46][47][48][49], photosynthetic light use efficiency (LUE) [3,48,[50][51][52][53][54][55], and net CO 2 uptake [47,[56][57][58][59], are actively being investigated. However, the results of these different works vary considerably, e.g., the linear correlation coefficients between PRI and NPQ can range from −0.90 [38,49,60] to +0.86 [41] in different investigations.…”