Influences of different environmental covariates on county-scale soil type identification using remote sensing images

Ecological Indicators

Zhiqian Guo¹  Mengqi Duan²  Chen Wang²  Xiaoguang Zhang³ 

[1] Qingdao Engineering Research Center for Remote Sensing Application in Agriculture, Qingdao 266109, China;Department of Resources and Environment, Qingdao Agricultural University, Qingdao 266109, China;Shandong Provincial NO.4 Institute of Geological and Mineral Survey, Weifang 261021, China;

关键词: Entropy parameter;    Classification features;    Environmental covariates;    Soil types mapping;    Maximum likelihood classification;    Soil groups;   

DOI  :  

来源: DOAJ

【 摘 要 】

The scientific selection of environmental covariates is an important factor that affects the accuracy of digital soil mapping (DSM).An investigation of the influences of different environmental covariates on soil type identification is vital to understand soil development and predictive soil mapping. In this study, the soil groups in the typical plain and hilly areas of the Jiaodong Peninsula in China were taken as the objects of investigation. Based on five environmental characteristics and remotely sensed indexes (texture, terrain, biology, major spectral components and moisture), 21 covariates sourced from Landsat 8 remote sensing images and a digital elevation map were utilized for the predictive mapping of soils. The identification accuracies of the soil groups were acquired through maximum likelihood classification, and the relevant environmental factors that are important for soil mapping and the influence of different environmental covariates on soil mapping were explored. The results show that texture features had the greatest influence on DSM, followed by terrain, major spectral components, moisture and biological factors. The single environmental covariate with the greatest impact on DSM was the texture entropy parameter. For areas containing plain and hilly landforms, the entropy, elevation, slope, normalized difference vegetation index (NDVI), third principal component (PC3) and normalized difference moisture index (NDMI) indicators compose the optimal covariate combination for soil identification. Clarifying the impacts of different environmental covariates on DSM can improve the soil classification applicability and accuracy. The environmental covariates discussed in this study would be useful in similar environmental areas and would provide a reference for DSM.

【 授权许可】

Unknown