Land cover of oases and forest in XinJiang, China retrieved from ASTER data

Aosier, B.; Tsuchiya, K.; Kaneko, Masami; Ohtaishi, Noriyuki; Halik, Mahmut

doi:10.1016/j.asr.2006.02.056

Cited by 23 publications

(15 citation statements)

References 6 publications

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“…Due to inconsistency in Band 9 of the AST07 product, the image spectra could be unreliable to estimate and map surface soil properties with reasonable accuracy [82,83]. On the other hand, AST L1B atmospherically corrected with ATCOR software was successfully used in various mapping applications [82,84,85]; therefore, the atmospherically corrected Level 1B dataset was applied also in this study to model selected soil properties.…”

Section: Evaluation Of Aster Datamentioning

confidence: 99%

Digital Mapping of Soil Properties Using Multivariate Statistical Analysis and ASTER Data in an Arid Region

2015

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Modeling and mapping of soil properties has been identified as key for effective land degradation management and mitigation. The ability to model and map soil properties at sufficient accuracy for a large agriculture area is demonstrated using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. Soil samples were collected in the El-Tina Plain, Sinai, Egypt, concurrently with the acquisition of ASTER imagery, and measured for soil electrical conductivity (ECe), clay content and soil organic matter (OM). An ASTER image covering the study area was preprocessed, and two predictive models, multivariate adaptive regression splines (MARS) and the partial least squares regression (PLSR), were constructed based on the ASTER spectra. For all three soil properties, the results of MARS models were better than those of the respective PLSR models, with cross-validation estimated R

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Section: Evaluation Of Aster Datamentioning

confidence: 99%

Digital Mapping of Soil Properties Using Multivariate Statistical Analysis and ASTER Data in an Arid Region

2015

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“…L is an empirical constant to minimize or separate the vegetation reflectance of the soil brightness (Baret and Guyot 1991;Buhe et al 2007). L is an empirical constant to minimize or separate the vegetation reflectance of the soil brightness (Baret and Guyot 1991;Buhe et al 2007).…”

Section: Study Areamentioning

confidence: 99%

“…where, R NIR is the surface reflectance in the near-infrared and R RED the surface reflectance of the red band. L is an empirical constant to minimize or separate the vegetation reflectance of the soil brightness (Baret and Guyot 1991;Buhe et al 2007). This study applies the standard value of L = 0.5, which is the best adjustment for intermediate unknown vegetation cover (Huete 1988).…”

Section: Aster Data and Image Pre-processingmentioning

confidence: 99%

Application of satellite remote sensing for mapping wind erosion risk and dust emission‐deposition in Inner Mongolia grassland, China

et al. 2012

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Intensive grazing leads to land degradation and desertification of grassland ecosystems followed by serious environmental and social problems. The Xilingol steppe grassland in Inner Mongolia, China, which has been a sink area for dust for centuries, is strongly affected by the negative effects of overgrazing and wind erosion. The aim of this study is the provision of a wind erosion risk map with a spatial high resolution of 25 m to identify actual source and sink areas. In an integrative approach, field measurements of vegetation features and surface roughness length z0 were combined with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image data for a land use classification. To determine the characteristics of the different land use classes, a field observation (ground truth) was performed in April 2009. The correlation of vegetation height and z0 (R2 = 0.8, n = 55) provided the basis for a separation of three main classes, “grassland”, “non‐vegetation” and “other”. The integration of the soil‐adjusted vegetation index (SAVI) and the spectral information from the atmospheric corrected ASTER bands 1, 2 and 3 (visible to near‐infrared) led to a classification of the overall accuracy (OA) of 0.79 with a kappa () statistic of 0.74, respectively. Additionally, a digital elevation model (DEM) was used to identify topographical effects in relation to the main wind direction, which enabled a qualitative estimation of potential dust deposition areas. The generated maps result in a significantly higher description of the spatial variability in the Xilingol steppe grassland reflecting the different land use intensities on the current state of the grassland – less, moderately and highly degraded. The wind erosion risk map enables the identification of characteristic mineral dust sources, sinks and transition zones.

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“…Given the fragile environments in DOTA, it is beneficial to strengthen the ecological barrier effect if handled properly while the land will degrade rapidly if treated improperly. However, aeolian environments in desert regions have well been studied and documented in many literatures in the last several decades except for those in DOTA (Thomas, 2004;Aosier et al, 2007;Faria et al, 2011;Warren, 2013). Thus, it is critical to master the characteristics of aeolian environments and sand transport which will be beneficial to adopt applicable strategies and comprehensive sand control systems to combat aeolian desertification in DOTA.…”

Section: Introductionmentioning

confidence: 99%

Key Role of Desert–Oasis Transitional Area in Avoiding Oasis Land Degradation from Aeolian Desertification in Dunhuang, Northwest China

Zhang

Dan

et al. 2016

Land Degrad Dev

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The survival of oasis is partially determined by the evolution of desert–oasis transitional area (abbr. DOTA) characterized by fragile and unstable environments. This study reveals the function of DOTA in avoiding oasis land degradation from its aeolian environments based on the detailed wind data, in situ observation of wind‐blown sand and granular characteristics of surface sediments from desert to oasis. Results indicate that the DOTA has buffering function in slowing down aeolian desertification in oasis. Additionally, the annual mean wind speed reduces 40·8% from desert to DOTA area but up to 92·8% from desert to oasis. The frequency of sand‐laden wind, drift potential and sand transport all decrease following the section from desert to oasis while surface roughness increases. And the granular characteristics of surface sediments show that the weight percentage of coarse sand decreases but fine sand increases along the section from desert to oasis. This paper reveals that the aeolian environments are of great difference between desert and DOTA and the significant role of DOTA in protecting oasis. Integrated sand control system needs to be settled in the DOTA to strengthen its buffering function. Copyright © 2016 John Wiley & Sons, Ltd.

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Land cover of oases and forest in XinJiang, China retrieved from ASTER data

Cited by 23 publications

References 6 publications

Digital Mapping of Soil Properties Using Multivariate Statistical Analysis and ASTER Data in an Arid Region

Digital Mapping of Soil Properties Using Multivariate Statistical Analysis and ASTER Data in an Arid Region

Application of satellite remote sensing for mapping wind erosion risk and dust emission‐deposition in Inner Mongolia grassland, China

Key Role of Desert–Oasis Transitional Area in Avoiding Oasis Land Degradation from Aeolian Desertification in Dunhuang, Northwest China

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