English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

During the recent six decades, numerous check dams have been constructed for soil erosion control and agricultural production, and have become the key measure for soil and water conservation in the Loess Plateau. Obtaining check dam information is very important for soil erosion control and river basin management. This study utilizes remote-sensing images in conjunction with Google Earth images and field survey to derive the spatial distribution of the check dams in the Huangfuchuan catchment. Multisource data including topographic maps, Landsat images, and images from Google Earth are collected for check dam identification. The results are verified based on a field survey. The water surface area of the check dams derives from in situ measurement and images present good relationship with the high correlation coefficient of 0.96. Furthermore, the area extent and number of check dams derived from the remote sensing images are similar to those from Google Earth images. Historically, an increase in the check dam number, controlled area, and storage capacity indicate their substantial trapping effects on streamflow and sediment load in the Huangfuchuan catchment. This study may be a good reference for proposing an efficient approach to identify the check dams and provide decision supports for soil and water conservation in the Loess Plateau. © The Authors 2013.

Check dam identification using multisource data and their effects on streamflow and sediment load in a Chinese Loess Plateau catchment