Modeling and Forecasting the Phytocenosis Transformation Using the Tangential Photodocumentation
- 10.2991/aer.k.200202.043How to use a DOI?
- phytocenoses, agro-ecosystems, anthropogenic transformation, ecological monitoring, forecast model, remote sensing
The work set its task to develop and verify a possibility to use in practice the method of remote analysis of phytocenoses based on tangential shooting in usual color format and as simple as possible transformation of the obtained digital information. We implemented this idea by developing a technique of shooting with the calculation of the index of phytocenosis diversity. The first step of algorithm consisted of obtaining tangential images of plant communities and its transformation into two original indexes. Then we compared these indexes with the results of real field observations and analysis of native and transformed hyperspectral space images on the same territories, including technogenic intrusions on the border with agro-ecosystem. As it turned out, although the method is somewhat inferior to NDVI mapping in its sensitivity, the use of simple equipment and ground-based nature of the photo shooting makes it prospective for express analysis of territories. This is useful for identifying ‘area of interest’ and ‘risk territories’ in the analysis of various phytocenoses both in ecology and agriculture.
- © 2020, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Valery Novochadov AU - Alexander Shiroky AU - Artyom Isakov PY - 2020 DA - 2020/02/08 TI - Modeling and Forecasting the Phytocenosis Transformation Using the Tangential Photodocumentation BT - Proceedings of the IV International Scientific and Practical Conference 'Anthropogenic Transformation of Geospace: Nature, Economy, Society' (ATG 2019) PB - Atlantis Press SP - 213 EP - 217 SN - 2352-5401 UR - https://doi.org/10.2991/aer.k.200202.043 DO - 10.2991/aer.k.200202.043 ID - Novochadov2020 ER -