Estimation of surface roughness and stone cover in burnt soils using SAR images

Duarte, Rosana Menéndez; Woźniak, Edyta; Recondo, Carmen; Cabo, Carlos; Marquínez, J.; Fernández, Susana

doi:10.1016/j.catena.2008.05.003

Cited by 14 publications

(6 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…TOC and OC maximum values were relatively high (greater than 35%) and related to the high amount of organic matter accumulated in the existing paleo surfaces. The minimum values of TOC (4.3%) and OC (2.7%) occurred in soils located in burned slopes, which are characterized by very thin organic horizons as a result of the frequent fires [79]. Higher TOC and OC values were obtained in the north and center of the study area, and lower values were obtained in the south.…”

Section: Descriptive Statistics Of Socmentioning

confidence: 84%

Prediction of Topsoil Organic Carbon Using Airborne and Satellite Hyperspectral Imagery

et al. 2017

View full text Add to dashboard Cite

Abstract:The Airborne Hyperspectral Scanner (AHS) and the Hyperion satellite hyperspectral sensors were evaluated for their ability to predict topsoil organic carbon (C) in burned mountain areas of northwestern Spain slightly covered by heather vegetation. Predictive models that estimated total organic C (TOC) and oxidizable organic C (OC) content were calibrated using two datasets: a ground observation dataset with 39 topsoil samples collected in the field (for models built using AHS data), and a dataset with 200 TOC/OC observations predicted by AHS (for models built using Hyperion data). For both datasets, the prediction was performed by stepwise multiple linear regression (SMLR) using reflectances and spectral indices (SI) obtained from the images, and by the widely-used partial least squares regression (PLSR) method. SMLR provided a performance comparable to or even better than PLSR, while using a lower number of channels. SMLR models for the AHS were based on a maximum of eight indices, and showed a coefficient of determination in the leave-one-out cross-validation R 2 = 0.60-0.62, while models for the Hyperion sensor showed R 2 = 0.49-0.61, using a maximum of 20 indices. Although slightly worse models were obtained for the Hyperion sensor, which was attributed to its lower signal-to-noise ratio (SNR), the prediction of TOC/OC was consistent across both sensors. The relevant wavelengths for TOC/OC predictions were the red region of the spectrum (600-700 nm), and the short wave infrared region betweeñ 2000-2250 nm. The use of SMLR and spectral indices based on reference channels at~1000 nm was suitable to quantify topsoil C, and provided an alternative to the more complex PLSR method.

show abstract

Section: Descriptive Statistics Of Socmentioning

confidence: 84%

Prediction of Topsoil Organic Carbon Using Airborne and Satellite Hyperspectral Imagery

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Secondly, fires increased the microbial metabolism after burning [69], increased degradation of soil organic matter and mortality of microbes, resulting in increased carbon mineralization, ammonium nitrogen (NH 4 -N), available phosphorus (P), and exchangeable potassium (K) and calcium (Ca) into the soil [70]. Notwithstanding, frequent burning of crop residues in highland can cause soil degradation [71], resulting in the loss of top SOCD due to surface runoff, water erosion [72], and sedimentation processes [73] as well as fires increase CO 2 , oxides of nitrogen, and black carbon into the atmosphere [74].…”

Section: Effect Of Burning Crop Residues On Top Socdmentioning

confidence: 99%

Factors Controlling Soil Organic Carbon Sequestration of Highland Agricultural Areas in the Mae Chaem Basin, Northern Thailand

et al. 2020

View full text Add to dashboard Cite

Understanding the effect of the environment, crop types, and land management practices on the organic carbon sequestration of top soil is crucial for adopting management strategies in highland agricultural areas. The objectives of this study are: (1) to estimate top soil organic carbon density (SOCD) of different crop types and (2) to analyze the factors controlling top SOCD in highland agricultural areas. The top soil layers from 0 to 30 cm depths were collected from the Mae Chaem basin, Northern Thailand. The results showed that the highest top SOCD was found soil used for growing upland rice, which contained an average of 58.71 Mg C ha−1. A significant difference between the top SOCD was detected between areas where minimum tillage and conventional tillage of various crops, with average of values 59.17 and 41.33 Mg C ha−1, respectively, for areas growing strawberries; 61.14 and 37.58 Mg C ha−1, respectively, for cabbage, and 71.15 and 39.55 Mg C ha−1, respectively, for maize. At higher elevation, the top SOCD was high, which may be due to high clay content and low temperature. Increased use of chemical fertilizers lead to increases in top SOCD, resulting in increased crop yields. Elevation, bulk density, N and K2O fertilizers were the main factors controlling the top SOCD at all sites.

show abstract

“…On the other hand, the lowest values of TOC and OC (4·3% and 2·7%, respectively) are related to the erosive degradation suffered by burned slopes in which the soil parent material corresponds mainly to coarse regolithic with grain-supported fabric. Frequent fires in these slopes caused very intense soil degradation, and as a result these soils have extremely thin organic horizons (Fernandez et al, 2005;Duarte et al, 2008).…”

Section: Soil Samples Descriptive Analysismentioning

confidence: 99%

Spatial Modelling of Organic Carbon in Burned Mountain Soils Using Hyperspectral Images, Field Datasets, and NIR Spectroscopy (Cantabrian Range; NW Spain)

et al. 2015

View full text Add to dashboard Cite

Soil organic matter is seriously affected by fires and suffers changes in stock, composition, and distribution. In the North‐West side of the Cantabrian Range (northern Spain) fires are very common. In order to develop a cartographic technique to map areas with high carbon stocks caused by fire, we test a technique based on calibrated VIS‐NIR soil organic carbon models and hyperspectral images. Total (TOC) and oxidizable carbon (OC) were measured in 89 soil samples. The samples were scanned with VIS‐NIR spectrometer (400–2,500 nm), and the spectra were resampled to the hyperspectral image channels. Spectroscopic models for TOC and OC were fitted (R2 > 0·81) using partial least squares regression (PLSR). The predictions were regionalized to the hyperspectral image and the results validated with a new soil population consisting of 12 Valeri plots collected in burned slopes of the study area under heather vegetation. In soil samples, TOC and OC values are highly correlated (R = 0·92), and the coefficients of the PLSR models have a similar pattern, which suggests similar organic components. Nevertheless, there are significant differences in the values of the regression coefficients, much higher in the TOC model except at 560 and 2,054 nm that might be interpreted as labile carbon components, and at 1,590 nm. At this wavelength the coefficient of TOC is positive and OC is negative, and it could be interpreted as hydrocarbons components present in the TOC model. Copyright © 2015 John Wiley & Sons, Ltd.

show abstract

Estimation of surface roughness and stone cover in burnt soils using SAR images

Cited by 14 publications

References 32 publications

Prediction of Topsoil Organic Carbon Using Airborne and Satellite Hyperspectral Imagery

Prediction of Topsoil Organic Carbon Using Airborne and Satellite Hyperspectral Imagery

Factors Controlling Soil Organic Carbon Sequestration of Highland Agricultural Areas in the Mae Chaem Basin, Northern Thailand

Spatial Modelling of Organic Carbon in Burned Mountain Soils Using Hyperspectral Images, Field Datasets, and NIR Spectroscopy (Cantabrian Range; NW Spain)

Contact Info

Product

Resources

About