Materiały konferencyjne SEP 2024

Szkoła Eksploatacji Podziemnej 2024, Materiały Konferencyjne 8 [24] Jürgens C., 1997: The modified normalized difference vegetation index (mNDVI) a new index to determine frost damages in agriculture based on Landsat TM data. International Journal of Remote Sensing, Vol.18, No. 17, pp. 3583-3594. [25] Yang C., Xu M., 1998: Discussion on Water Extraction Based on Remote Sensing Information Mech- anism. Geographical Research 7 (Suppl), pp. 86–89. [26] Xu H., 2006: Modification of normalised difference water index (NDWI) to enhance open water fea- tures in remotely sensed imagery.Int. J. Remote Sens., 27, pp. 3025–3033. [27] Wang, F., Huang J., Tang Y., Wang X., 2007: New Vegetation Index and Its Application in Estimating Leaf Area Index of Rice. Rice Science, Volume 14, Issue 3, pp. 195-203. [28] Shen L., Li C., 2010: Water body extraction from Landsat ETM+ imagery using adaboost algorithm. Proceedings of the 18th International Conference on Geoinformatics, Beijing, China, 18–20 June 2010, pp. 1–4. [29] Feyisa G., Meilby H., Fensholt R., Proud, S., 2014: Automated Water Extraction Index: A new tech- nique for surface water mapping using Landsat imagery. Remote Sens. Environ. 140, pp. 23–35. [30] Pawlik M., Rudolph T., Bernsdorf B., Benndorf J., 2023: Green Red Water Indices – vegetation indices for environmental Geomonitoring, Book of abstracts XXIII Conference of PhD Students and Young Scientists, June 13 – 15, 2023, hybrid event, pp. 1-3. Concept for long-term geo-monitoring of the post-mining environment using the example of the Prosper-Haniel mine ABSTRACT: The operation of a mining facility provides a wealth of data, starting from mining licenses, documentation of extracted deposits, tunnel reinforcement methods, to documentation regarding the cessation of mining operations, which impacts the natural environment at the local, regional, and supra-regional levels. The results of projects conducted by the Research Center for Mining at the Technical University of Georg Agricola in Bochum present the possibilities of integrating environmental geomonitoring methods to understand the processes occurring both during and after mining operations. Among the research methods used, spatiotemporal multi- spectral analyses of satellite imagery and images from drone flights stand out, and these will be presented in this reference. Additionally, attention should be paid to in-situ measurements using soil sensors, weather stations, the application of mobile GIS, and three-dimensional modeling of geological structures. A key aspect of mining process research is the implementation and inte- gration of all available geospatial data, allowing the consideration of post-mining processes as a cycle of interconnected, independent values that, through data analysis and validation, enable a comprehensive understanding. KEYWORDS: Geomonitoring, Post-Mining, Remote Sensing, Unmanned Aerial Vehicle