38 research outputs found
Application of Satellite Remote Sensing for Detection of Abiotic Stress in Coniferous Landscapes
No full textIn the article is made an overview of the application of different satellite remote sensing methods and technologies in detection of the abiotic stress in coniferous landscapes. The review paper is discussing in short the application of different remote sensing technologies such as: satellite multispectral and infrared (thermal), imaging spectrometry and fluorescence imaging. The studied period spans from the onset of the satellite remote sensing in the 1960s until present day. In conclusion, are drawn requirements for the perspective technologies in satellite remote sensing which should address the fast and reliable detection of the manifestation of abiotic stress in coniferous landscapes
Review of the Applications of Satellite Remote Sensing in Organic Farming (Part I)
No full textOrganic farming is a much more sustainable farming system than conventional farming. It is part of humanity's efforts to preserve biodiversity and provides healthy and safe food to humans. Remote sensing methods are widely used in agriculture. Their use will help the transition from conventional to organic farming. They can help farmers choose the most suitable place to build an organic farm. Remote sensing methods are a very powerful tool for weed control in organic farming. They can be used to determine the level of stress that crops experience. They provide a good opportunity to forecast yields on organic farms. Remote sensing methods can optimize fertilization on organic farms. They can be used to distinguish between organic and conventional agriculture, as well as to monitor biodiversity in agricultural areas. Remote sensing methods can help organic farmers make timely and adequate decisions in managing their farms
Mineralogical Mapping of Pyroxene and Anorthosite in Dryden Crater Using M3 Hyperspectral Data
No full textThis study investigates the mineral composition of the lunar Dryden Crater using Moon Mineralogy Mapper (M3) data. A RGB false-color composite reveals distinct pyroxene, anorthosite, and possibly spinel distribution patterns. Orthopyroxenes, excavated from deep crustal layers, dominate steep slopes, while plagioclase-rich materials align with magma ocean models of lunar crustal formation. Minor clinopyroxenes indicate impact melt origins. While space weathering and shock metamorphism pose analytical challenges, integrating spectral data with geological context elucidates the crater’s complex history. The resulting mineral distribution map supports targeted exploration during upcoming lunar missions, resource prospecting and resource utilization initiatives within this geologically complex region
Transition Metal Elemental Mapping of Fe, Ti, and Cr in Lunar Dryden Crater Using Moon Mineralogy Mapper Data
No full textThis study investigates the spatial distribution of transition metals—iron (Fe), titanium (Ti), and chromium (Cr)—within the Dryden crater on the Moon using hyperspectral data from the Moon Mineralogy Mapper (M3). By applying spectral parameters and false color composite techniques, geospatial maps of chromite distribution and FeO, TiO2 wt.% distribution were generated at a resolution of ~140 m. The findings reveal distinct elemental enrichments along geomorphologically active regions such as crater walls, terraces, and central peaks, highlighting impact-driven material differentiation, the influence of morphology, degradation, and space weathering. These results enhance our understanding of lunar crustal evolution and support future exploration and resource utilization efforts
DETECTION AND ASSESSMENT OF ABIOTIC STRESS OF CONIFEROUS LANDSCAPES CAUSED BY URANIUM MINING (USING MULTITEMPORAL HIGH RESOLUTION LANDSAT DATA)
No full textRemote sensing have become one of decisive technologies for detection and assessment of abiotic stress situations, such as snowstorms, forest fires, drought, frost, technogenic pollution etc. Present work is aiming at detection and assessment of abiotic stress of coniferous landscapes caused by uranium mining using high resolution satellite data from Landsat. To achieve the aim, ground-based geochemical data and were coupled with the satellite data for two periods, i.e. prior and after uranium mining decommissioning, into a file geodatabase in ArcGIS/ArcInfo 9.2, where spatial analyses were carried out. As a result, weak and very weak relationships were found between the factor of technogenic pollution—Zc and vegetation indices NDVI, NDWI, MSAVI, TVI, and VCI. The TVI performs better compared to other indices in terms of separability among classes, whereas the NDVI and VCI correlate well than other indices with Zc
Assessment of the land surface temperature dynamics in the city of Sofia using Landsat satellite data
No full textPossibilities of forecasting the yield of organic wheat using aerospace methods
No full textWith climate change, adverse natural phenomena, such as floods and droughts, are becoming more common, which in turn are a major threat to wheat yields. Almost all regions of the planet are vulnerable to such climatic events. Remote sensing methods can help farmers by giving them up-to-date information on the condition and yield forecasting of wheat crops, thus minimizing the risk of climate change
REVIEW OF THE APPLICATIONS OF SATELLITE REMOTE SENSING IN ORGANIC FARMING – PART II
No full textThe use of remote sensing methods for monitoring, managing, and decision support in
agriculture is increasingly intensifying. With the advancement of technologies, they become more
accessible, while the quality and security of the obtained data are improving. Striving to improve
the quality of the environment and its preservation, expanding the areas occupied by organic farming
will allow us to achieve these goals. At the same time, this type of agriculture provides healthy and safe
food. For this reason, it is of great importance to start applying satellite data in organic farming as
quickly as possible. In Part II of the "Review of the applications of satellite remote sensing in organic
farming," we examine the various areas of satellite data application in organic farming. Five different
areas of satellite data application in organic farming have been identified, including satellite remote
sensing monitoring of weeds, remote sensing of crop stress and irrigation needs, yield forecasting using
remote sensing methods and remote sensing monitoring of plant nutrition. From the review conducted,
we found that satellite data can significantly support and facilitate the transition to organic farming,
adequate fertilization, application in phytosanitary monitoring of crops, and assessment of crop stress
Opportunities for Remote Sensing Applications in Organic Cultivation of Cereals – a Review
No full textIn recent years, a number of studies have proven that the conventional agricultural system is not sustainable, toxic to the environment, human health, and its potential to feed humanity is limited to the next 50 years. With this in mind, as well as the increasing demand for healthy and safe foods, and the increase in the proportion of people who care about how the food they consume was produced, how much it does not harm the environment and health, farmers are starting to reorient their production into organic. Over the past 40 years, remote sensing methods and technologies have increasingly been used in agriculture. They have proved extremely useful for optimizing the working processes in the sector, as well as solving many of the problems in it. With this report, we aim to draw the scientific community's attention to the possibilities provided by remote sensing methods and technologies to solve a range of problems related to organic cultivation of cereals
Evaluation of Sentinel-2 Deep Resolution 3.0 Data for Winter Crop Identification and Organic Barley Yield Prediction
No full textBarley is an ecologically adaptable crop widely used in agriculture and well suited for organic farming. Satellite imagery from Sentinel-2 can support crop monitoring and yield prediction, optimising production processes. This study compares two types of Sentinel-2 data—standard (S2) data with 10 m and 20 m resolution and Sentinel-2 Deep Resolution 3 (S2DR3) data with 1 m resolution—to assess their (i) relationship with yield in organically grown barley and (ii) utility for winter crop mapping. Vegetation indices were generated and analysed across different phenological phases to determine the most suitable predictors of yield. The results indicate that using 10 × 10 m data, the BBCH-41 phase is optimal for yield prediction, with the Green Chlorophyll Vegetation Index (GCVI; r = 0.80) showing the strongest correlation with yield. In contrast, S2DR3 data with a 1 × 1 m resolution demonstrated that Transformed the Chlorophyll Absorption in Reflectance Index (TCARI), TO, and Normalised Difference Red Edge Index (NDRE1) were consistently reliable across all phenological stages, except for BBCH-51, which showed weak correlations. These findings highlight the potential of remote sensing in organic barley farming and emphasise the importance of selecting appropriate data resolutions and vegetation indices for accurate yield prediction. With the use of three-date spectral band stacks, the Random Forest (RF) and Support Vector Classification (SVC) methods were used to differentiate between wheat, barley, and rapeseed. A five-fold cross-validation approach was applied, training data were stratified with 200 points per crop, and classification accuracy was assessed using the User’s and Producer’s accuracy metrics through pixel-by-pixel comparison with a reference raster. The results for S2 and S2DR3 were very similar to each other, confirming the significant potential of S2DR3 for high-resolution crop mapping
