Detecção de solos erodidos pela avaliação de dados espectrais

Demattê, José Alexandre Melo; Focht, D. D.

doi:10.1590/s0100-06831999000200023

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…Actually, on this part of the map, PE, PV and C occur, but they are in eroded areas that made the spectral method determine a different map unit. This is in agreement with Demattê and Focht (1999) that verified different soil pattern between eroded and not eroded soils.…”

Section: Spectral Data and Soil Map And Tillagesupporting

confidence: 93%

Visible–NIR reflectance: a new approach on soil evaluation

et al. 2004

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Section: Spectral Data and Soil Map And Tillagesupporting

confidence: 93%

Visible–NIR reflectance: a new approach on soil evaluation

et al. 2004

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“…These methods are based on classification techniques or use different mathematical and statistical procedures to assess the correlation between erosion signs and their spectral reflectance [15,26,27]. Soil erosion and accumulation affect chemical and physical properties of upper layer of soils; thus, spectral reflectance is changed and eroded soils have different spectral response from non-eroded "healthy" soils [28][29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Soil Degradation by Erosion Based on Analysis of Soil Properties Using Aerial Hyperspectral Images and Ancillary Data, Czech Republic

2017

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Abstract:The assessment of the soil redistribution and real long-term soil degradation due to erosion on agriculture land is still insufficient in spite of being essential for soil conservation policy. Imaging spectroscopy has been recognized as a suitable tool for soil erosion assessment in recent years. In our study, we bring an approach for assessment of soil degradation by erosion by means of determining soil erosion classes representing soils differently influenced by erosion impact. The adopted methods include extensive field sampling, laboratory analysis, predictive modelling of selected soil surface properties using aerial hyperspectral data and the digital elevation model and fuzzy classification. Different multivariate regression techniques (Partial Least Square, Support Vector Machine, Random forest and Artificial neural network) were applied in the predictive modelling of soil properties. The properties with satisfying performance (R 2 > 0.5) were used as input data in erosion classes determination by fuzzy C-means classification method. The study was performed at four study sites about 1 km 2 large representing the most extensive soil units of the agricultural land in the Czech Republic (Chernozems and Luvisols on loess and Cambisols and Stagnosols on crystalline rocks). The influence of site-specific conditions on prediction of soil properties and classification of erosion classes was assessed. The prediction accuracy (R 2 ) of the best performing models predicting the soil properties varies in range 0.8-0.91 for soil organic carbon content, 0.21-0.67 for sand content, 0.4-0.92 for silt content, 0.38-0.89 for clay content, 0.73-089 for Fe ox , 0.59-0.78 for F ed and 0.82 for CaCO 3 . The performance and suitability of different properties for erosion classes' classification are highly variable at the study sites. Soil organic carbon was the most frequently used as the erosion classes' predictor, while the textural classes showed lower applicability. The presented approach was successfully applied in Chernozem and Luvisol loess regions where the erosion classes were assessed with a good overall accuracy (82% and 67%, respectively). The model performance in two Cambisol/Stagnosol regions was rather poor (51%-52%). The results showed that the presented method can be directly and with a good performance applied in pedologically and geologically homogeneous areas. The sites with heterogeneous structure of the soil cover and parent material will require more precise local-fitted models and use of further auxiliary information such as terrain or geological data. The future application of presented approach at a regional scale promises to produce valuable data on actual soil degradation by erosion usable for soil conservation policy purposes.

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“…The basic principle of the identification of eroded and accumulated (AC) soils is based on the assumption that the spectral reflectance of eroded or AC soils has different attributes than non-eroded (NE) "healthy" soils. These differences are caused by changes in the chemical and physical properties of the upper layer of soils due to the processes connected with soil removal, soil transportation and accumulation (Chabrillat et al, 2014;Chappell, Zobeck, & Brunner, 2005;Conforti et al, 2013;Demattê & Focht, 1999;Lin, Zhou, Wu, Zhu, & Dang, 2014;Schmid et al, 2012Schmid et al, , 2016Žížala, Zádorová, & Kapička, 2017). Soil properties affected by erosion processes that have a spectral response in soil spectra at the same time can be identified as spectral soil erosion indicators.…”

Section: Introductionmentioning

confidence: 99%

Mapping soil degradation using remote sensing data and ancillary data: South-East Moravia, Czech Republic

Žížala

Juřicová

Zádorová

et al. 2018

European Journal of Remote Sensing

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Data on the real extent of soil that is degraded by erosion represent important information for the purposes of conservation policy. However, this type of data is rarely available for large areas. A remote-sensing-based method for identifying of eroded areas at the regional scale has been tested using a combination of time series of free access Sentinel-2 image data, airborne orthoimages and ground-truth data. The unsupervised classification ISODATA of the Sentinel-2A images has been performed. The minimum distance method has been applied for the assignment of unsupervised classes to four erosion classes using the ground-truth data. The automatic classification of eroded soils achieved an overall accuracy of 55.2% for three distinguished classes. An accumulated class has been eliminated as no unsupervised classes were assigned to this erosion class. A simplified classification of two classes (strongly eroded and other soils) reached an accuracy of 80.9%. The overall accuracy of the simplified classification increased to 86.9% after the visual refinement using orthoimages. This study shows the potential of the tested approach to produce valuable data on actual soil degradation by erosion. The limitations of the method are related to the soil cover variability, masking effect of clouds, vegetation or litter and the spectral separability of individual classes.

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Detecção de solos erodidos pela avaliação de dados espectrais

Cited by 5 publications

References 9 publications

Visible–NIR reflectance: a new approach on soil evaluation

Visible–NIR reflectance: a new approach on soil evaluation

Assessment of Soil Degradation by Erosion Based on Analysis of Soil Properties Using Aerial Hyperspectral Images and Ancillary Data, Czech Republic

Mapping soil degradation using remote sensing data and ancillary data: South-East Moravia, Czech Republic

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