Remote sensing has been used as an important tool for disaster monitoring and disaster scope extraction, especially for the analysis of temporal and spatial disasters patterns of large-scale long time series. In order to find out a rapid and effective method to monitor disaster in a wide range, based on the Google Earth Engine cloud platform, this study used MODIS vegetation index products of 250 meter spatial resolution synthesized in 16 days during the year 2005-2019 and three kinds of disaster monitoring and scope extraction models are proposed: normalized vegetation index median time standardization (RNDVI_TM(i)) model, the normalized vegetation index median phenology Standardization(RNDVI_AM(i)(j)) model, normalized vegetation index median spatiotemporal Standardization (RNDVI_ZM(i)(j)) model. The optimal threshold of disaster extraction for each model in different time phases was determined by Otsu method, and the extraction results were verified by Medium resolution image and ground measured data of the same or quasi-same period. Finally, the disaster scope of cultivated land in Heilongjiang province from 2010 to 2019 was extracted and the temporal and spatial pattern of disasters was analyzed based on the meteorological data. It shows that the three above-mentioned models have high disaster monitoring and range extraction capabilities with the verification accuracy of RNDVI_TM(i) 97.46%, RNDVI_AM(i)(j) 96.90%, and RNDVI_ZM(i)(j) 96.67% respectively. The spatial and temporal distribution of disasters is consistent with the disaster of the insured plots and meteorological data in the whole province. Meanwhile, it turns out that different monitoring and extraction methods are used in different disasters, among which wind hazard and insect disasters often need to be delayed for 16 days to observe. Each model also has various sensitivity and applicability to different disasters. Compared with other methods, this method is fast, and convenient, which allows it to be used for large-scale agricultural disaster monitoring and is easy to be applied into other research areas. The research provides a new idea for large-scale agricultural disaster monitoring.

The giant Tizert copper deposit is considered as the largest copper resource in the western Anti-Atlas (Morocco). The site is characterized by Cu mineralization carried by malachite, chalcocite, covellite, bornite and chalcopyrite; azurite is not observed. The host rocks are mainly limestones (Formation of Tamjout Dolomite) and sandstones/siltstones (Basal Series) of the Ediacaran/Cambrian transition. The supergene enrichment is most likely related to episodes of uplift/doming (last event since 30 Ma), which triggered the exhumation of primary/hypogene mineralization (chalcopyrite, pyrite, galena, chalcocite I and bornite I), generating their oxidation and the precipitation of secondary/supergene sulfides, carbonates and Fe-oxyhydroxides. The Tizert supergene deposit mainly consists in i) a residual patchwork of laterite rich in Fe-oxyhydroxides, ii) a saprolite rich in malachite, or « green oxide zone » where primary structures such as stratification are preserved, and iii) a cementation zone containing secondary sulfides (covellite, chalcocite II and bornite II). The abundance of Cu carbonates results from the rapid neutralization of acidic meteoric fluids, due to oxidation of primary sulfides, by carbonate host rocks. Chlorite is also involved in the neutralization processes in the sandstones/siltstones of the Basal Series, in which supergene clays such as kaolinite and smectites subsequently precipitated. At Tizert, as it can be highlighted in other supergene Cu-deposits around the world, azurite is absent due to low pCO2 and relatively high pH conditions. In addition to copper, Ag enrichment is also observed in weathered rocks; Fe-oxyhydroxides contain high Zn, As, and Pb contents. However, these secondary enrichments are quite low compared to Cu in the whole Tizert site, therefore considered as relatively homogeneous.

Due to increase demand of food grain in the world, assessment of yield before actual production is important in making policies and decisions in agricultural production system. For a large area, forecast models developed from vegetation indices derived from remote sensing satellite data possesses the potential to give quantitative and timely information on crops over large areas. Different vegetation indices are being made used for this purpose, however, their efficiency in estimating crop yield is needed to be certainly tested. In this study, wheat yield forecast was derived by regressing ground truthing yield data against time series of spatial vegetation indices for the 2013 to 2019 growing seasons. These spatial vegetation indices derived from Landsat 8 image data: Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) were compared to evaluate the most appropriate index that performs better in forecasting wheat production at Karcag, Kunhegyes and Ecsegfalva settlements in Jász-Nagykun-Szolnok county, in the Northern Great Plain region of central Hungary. The best time for making wheat yield prediction with Landsat 8- SAVI and NDVI was found to be the beginning of ripening period (160th day of the year) with higher correlation between the vegetation indices and the wheat yield. The validation results revealed that the model from SAVI provides more consistent and accurate forecasts yield compared to NDVI. The SAVI model forecast yield for the validation years, 2018 and 2019 were within 6.00% and 4.41% of the final reported values while that of NDVI model were within 8.31% and 6.27%. Nash-Sutcliffe efficiency index is positive with E1= 0.99 for the model from SAVI and for NDVI, E1=0.57, which connote that the forecasting method developed and evaluated performs acceptable forecast efficiency.