4.5 Article

terraGIS - a web GIS for delivery of digital soil maps in cotton-growing areas of Australia

期刊

SOIL USE AND MANAGEMENT
卷 33, 期 4, 页码 568-582

出版社

WILEY
DOI: 10.1111/sum.12383

关键词

Electromagnetic induction; gamma-ray spectrometry; GIS; digital soil mapping; Google Maps; soil conservation

资金

  1. Australian Cotton Research and Development Corporation
  2. Australian Cotton Cooperative Research Centre
  3. Natural Heritage Trust Program
  4. New South Wales State Governments Salt Action Program
  5. NHT National Competitive Component

向作者/读者索取更多资源

Effective management of soil requires the spatial distribution of its various physical, chemical and hydrological properties. This is because properties, for example clay content, determine the ability of soil to hold cations and retain water. However, data acquisition is labour intensive and time-consuming. To add value to the limited soil data, remote sensing (e.g. airborne gamma-ray spectrometry) and proximal sensing, such as electromagnetic (EM) induction, are being used as ancillary data. Here, we provide examples of developing Digital Soil Maps (DSM) of soil physical, chemical and hydrological properties, for seven cotton-growing areas of southeastern Australia, by coupling soil data with remote and proximal sensed ancillary data. A greater challenge is how to get these DSM to a stakeholder in a way that is useful for practical soil use and management. This study describes how we facilitate access to the DSMs, using a simple-to-use web GIS platform, called terraGIS. The platform is underpinned by Google Maps API, which is an open-source development environment for building spatially enabled Internet applications. In conclusion, we consider that terraGIS and the supporting information, available on the sister web page (), allow easy access to explanation of DSM of soil properties, which are relevant to cotton growers, farm managers, consultants, extension staff, researchers, state and federal government agency personnel and policy analysts. Future work should be aimed at developing error budget maps to identify where additional soil and/or ancillary data is required to improve the accuracy of the DSMs.

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