Characterization of semivegetated salt-affected soils by means of field remote sensing

Tóth, Tibor; Csillag, Ferenc; Biehl, L. L.; Michéli, Erika

doi:10.1016/0034-4257(91)90079-l

Cited by 41 publications

(19 citation statements)

References 6 publications

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“…These conditions, together with the poor soil hydrophysical properties, favoured the development of salt-affected soils, mainly solonetz and solonchak types (Schofield et al 2001). The native vegetation of the area is dominated by grasses and the mean annual temperature and precipitation approach 10.2°C and 540 mm, respectively (Béla 1972;Toth et al 1991).…”

Section: The Regionmentioning

confidence: 98%

“…A detailed topographic map of the area and direct observations in the field confirmed that both stands in each site were located at the same topographic situation. The differences in topographic levels between sampling sites were very small (<0.25 m), which is of particular importance for this region considering that groundwater depth played a dominant role in the development of these soils (Szabolcs 1989;Toth et al 1991). In addition, soil sampling transects in paired stands were separated by less than 80 m, which, together with the homogeneity of the loessic sediments of the Hortobagy region (Á brahám and Bocskai 1971), hinders strong initial pedogenic variation across stands.…”

Section: Study Sitesmentioning

confidence: 99%

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The effects of tree establishment on water and salt dynamics in naturally salt-affected grasslands

et al. 2007

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Plants, by influencing water fluxes across the ecosystem-vadose zone-aquifer continuum, can leave an imprint on salt accumulation and distribution patterns. We explored how the conversion of native grasslands to oak plantations affected the abundance and distribution of salts on soils and groundwater through changes in the water balance in naturally salt-affected landscapes of Hortobagy (Hungary), a region where artificial drainage performed approximately 150 years ago lowered the water table (from -2 to -5 m) decoupling it from the surface ecosystem. Paired soil sampling and detailed soil conductivity transects revealed consistently different salt distribution patterns between grasslands and plantations, with shallow salinity losses and deep salinity gains accompanying tree establishment. Salts accumulated in the upper soil layers during pre-drainage times have remained in drained grasslands but have been flushed away under tree plantations (65 and 83% loss of chloride and sodium, respectively, in the 0 to -0.5 m depth range) as a result of a five- to 25-fold increase in infiltration rates detected under plantations. At greater depth, closer to the current water table level, the salt balance was reversed, with tree plantations gaining 2.5 kg sodium chloride m(-2) down to 6 m depth, resulting from groundwater uptake and salt exclusion by tree roots in the capillary fringe. Diurnal water table fluctuations, detected in a plantation stand but not in the neighbouring grasslands, together with salt mass balances suggest that trees consumed approximately 380 mm groundwater per year, re-establishing the discharge regime and leading to higher salt accumulation rates than those interrupted by regional drainage practices more than a century ago. The strong influences of vegetation changes on water dynamics can have cascading consequences on salt accumulation and distribution, and a broad ecohydrological perspective that explicitly considers vegetation-groundwater links is needed to anticipate and manage them.

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Section: The Regionmentioning

confidence: 98%

Section: Study Sitesmentioning

confidence: 99%

The effects of tree establishment on water and salt dynamics in naturally salt-affected grasslands

et al. 2007

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“…Approaches have included, the use of multiple discriminant analysis to predict community type on the basis of spectral variables [27][28][29][30][31], statistical association between the distributions of spectral classes and independently mapped vegetation classes [32], and statistical comparisons between spectral and either numeric or subjective vegetation classifications [33][34][35]. Thomas et al [15] explored the application of ordination techniques and various clustering methods within a peatland complex with mixed results, particularly within regard to the ability to find spectrally distinct units.…”

Section: Introductionmentioning

confidence: 99%

Arctic Ecological Classifications Derived from Vegetation Community and Satellite Spectral Data

Atkinson

Treitz

2012

Remote Sensing

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Abstract:As a result of the warming observed at high latitudes, there is significant potential for the balance of ecosystem processes to change, i.e., the balance between carbon sequestration and respiration may be altered, giving rise to the release of soil carbon through elevated ecosystem respiration. Gross ecosystem productivity and ecosystem respiration vary in relation to the pattern of vegetation community type and associated biophysical traits (e.g., percent cover, biomass, chlorophyll concentration, etc.). In an arctic environment where vegetation is highly variable across the landscape, the use of high spatial resolution imagery can assist in discerning complex patterns of vegetation and biophysical variables. The research presented here examines the relationship between ecological and spectral variables in order to generate an ecologically meaningful vegetation classification from high spatial resolution remote sensing data. Our methodology integrates ordination and image classifications techniques for two non-overlapping Arctic sites across a 5° latitudinal gradient (approximately 70° to 75°N). Ordination techniques were applied to determine the arrangement of sample sites, in relation to environmental variables, followed by cluster analysis to create ecological classes. The derived classes were then used to classify high spatial resolution IKONOS multispectral data. The results demonstrate moderate levels of success. Classifications had overall accuracies between 69%-79% and Kappa values of 0.54-0.69. Vegetation classes were generally distinct at each site with the exception of sedge wetlands. Based on the results presented here, the combination of ecological and remote sensing techniques can produce classifications that have ecological meaning and are spectrally separable in an arctic environment. These

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“…moisture, surface roughness, organic matter) on soil reflectance (Ben-Dor, et al, 2002;Shrestha, et al, 2005). Earlier, monitoring and mapping of salt affected lands in relatively large areas were widely and successfully undertaken using broadband multispectral data (Toth, et al, 1991;Mougenot and Pouget, 1993). But nowadays, use of broadband multispectral data for soil salinity studies is very less and restricted because of spatial and spectral resolution limitations that mask details in the spectral signatures of various kinds of salt-affected lands (Allbed and Kumar, 2013).…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Remote Sensing in Characterizing Soil Salinity Severity using SVM Technique - A Case Study of Alluvial Plains

K¹,

Kumar²

2015

IJARSG

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Hyperspectral remote sensing is widely used for analyzing and estimating the severity of soil salinity in arid and semi-arid regions, throughout the world. The present study is an attempt to map the various soil salinity severity classes using different hyperspectral indices generated using EO-1 Hyperion data and Support Vector Machine (SVM) method, in the Mathura region of Indo-Gangetic plain of India. Various hyperspectral indices such as Soil Adjusted Vegetation Index (SAVI), Desertification Soil Index (DSI), Salinity Index (SI) and Normalized Difference Water Index (NDWI) were chosen, generated and effectively used for characterizing and mapping soil salinity severity. Salt infestation in the study area was categorized into four classes of normal, slight, moderate, high soil salinity. Hyperspectral indices helped in identification of various features like vegetation, waterlogged area and soil areas under various classes of soil salinity. The salinity index and desertification soil indices were found to respond well to varying degrees of soil salinity. The SVM technique generated soil salinity map with overall classification accuracy of 78.13 percent, with a kappa statistic of 0.71. The results indicated highest accuracy in high soil salinity class in comparison to other classes, attaining producers and users accuracies of 85.71% and 90.0% respectively. Slight saline class showed poor producers and users accuracy. The result showed high accuracy for mapping soil salinity severity with machine learning classifier like SVM using various indices generated from hyperspectral remote sensing data. These generated images can be effectively used in planning of various management practices and effective reclamation measures of salt affected soils.

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Characterization of semivegetated salt-affected soils by means of field remote sensing

Cited by 41 publications

References 6 publications

The effects of tree establishment on water and salt dynamics in naturally salt-affected grasslands

The effects of tree establishment on water and salt dynamics in naturally salt-affected grasslands

Arctic Ecological Classifications Derived from Vegetation Community and Satellite Spectral Data

Hyperspectral Remote Sensing in Characterizing Soil Salinity Severity using SVM Technique - A Case Study of Alluvial Plains

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