Due to the energetic transition at course, new geological exploration technologies are needed to discover mineral deposits containing critical materials such as lithium (Li). The vast majority of European Li deposits are related to Li–Cs–Ta (LCT) pegmatites. Literature review indicates that conventional exploration campaigns are dominated by geochemical surveys and related exploration tools. However, other exploration techniques must be evaluated namely remote sensing (RS) and geophysics. This work presents the results of the INOVMINERAL4.0 project obtained through alternative approaches to traditional geochemistry that were gathered and integrated into a webGIS application. The specific objectives were to: (i) assess the potential of high-resolution elevation data; (ii) evaluate geophysical methods, particularly radiometry; (iii) establish a methodology for spectral data acquisition and build a spectral library; (iv) compare obtained spectra with Landsat 9 data for pegmatite identification; and (v) implement a user-friendly webGIS for data integration and visualization. Radiometric data acquisition using geophysical techniques effectively discriminated pegmatites from host rocks. The developed spectral library provided valuable insights for space-based exploration. Landsat 9 data accurately identified known LCT pegmatite targets, compared to Landsat 8. The user-friendly webGIS facilitated data integration, visualization, and sharing, supporting potential users in similar exploration approaches.

Annually, the oak forests of the Zagros Mountain chains, in western Iran and northeastern Iraq, face recurring challenges posed by forest fires, particularly in the Kurdo-Zagrosian forests situated in western Iran and northeastern Iraq. Assessing fire susceptibility relies significantly on vegetation condition. Integrating in situ data, Remote Sensing (RS) data, alongside the integration of Geographical Information Systems (GIS), presents a cost-effective and precise approach to capturing environmental conditions before, during, and after fire events, minimizing the need for extensive fieldwork. This study refines and applies the Zagros Grass Index (ZGI), a local vegetation index tailored to discern between grass-covered surfaces from tree canopies in Zagros forests, identifying the grass masses as the most flammable fuel type. Utilizing Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) product as input, from 2013 to 2022, the ZGI aims to mitigate the influence of tree canopies, isolating NDVI values solely attributable to grass cover. By incorporating phenological characteristics of forest trees and grass species, the ZGI outperforms NDVI in mapping grass-covered areas that are crucial for fire susceptibility assessment in the study region. Results demonstrate a strong overlap between ZGI-based maps and recorded fire occurrences, validating the efficacy of the index in fire susceptibility estimation.