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Wildfires have become increasingly prevalent and destructive in forest ecosystems worldwide, necessitating a comprehensive understanding of post-fire recovery dynamics for effective conservation and management. Remote sensing technology, coupled with vegetation indices such as Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), Green Red Vegetation Index (GRVI), and Red Vegetation Index (RVI), offers a powerful means to investigate these processes. In this study, we utilize remote sensing techniques to conduct a comparative analysis of secondary ecological succession following wildfires in three distinct forest types (Coniferous, Sclerophyll, and Mixed) of a forest affected by fire near Moguer, Spain. Through the acquisition and analysis of multispectral satellite imagery, we monitored changes in vegetation health and recovery across the region of interest. The NBR index allowed us to assess the severity and extent of wildfire damage, while NDVI quantified vegetation greenness and regrowth. GRVI and RVI provided insights into subtle variations in vegetation composition and health. We identified distinct temporal and spatial patterns in post-fire recovery among the different forest types by applying these indices for the period between 2017 and 2021. Our findings underscore the significance of understanding the diverse responses of these ecosystems to wildfires. While common recovery patterns emerged, such as an initial decrease in NDVI followed by regeneration, variations were observed in the timing and magnitude of recovery. These distinctions are attributed to differences in species composition, fire adaptations, and ecological processes specific to each forest type. In conclusion, the utilization of NBR, NDVI, GRVI, and RVI indices allows for a more nuanced evaluation of post-fire recovery dynamics. [ABSTRACT FROM AUTHOR]

Monitoring post-fire forest disturbances and subsequent recovery is vital for the management and preservation purposes of the forest ecosystems. This study aimed to assess forests' damages and regrowth dynamics after fire using remotely sensed data and to compare its reliability for post-fire monitoring in different forest environments. This paper compared forest regrowth dynamics applying selected spectral indices - Differenced Normalized Difference Vegetation Index (dNDVI), Differenced Normalized Burn Ratio (dNBR), and Differenced Disturbance Index (dDI). The post-fire environmental impact and recovery processes were performed on the territory of the three fires in Bulgaria - Ardino, Bistrishko branishte, and Perperek. [ABSTRACT FROM AUTHOR]

The coal industry is the largest global emitter of carbon dioxide (CO2). However, recent data suggests that coal mine methane (CH4) emissions worldwide are higher than those of the oil and gas industry. Furthermore, the coal industry is less active in reducing methane emissions than the oil and gas sectors due to lower profitability. Although uncertainties remain in quantifying methane emissions from mines, the use of satellite observations is revolutionizing the process of monitoring and improving the accuracy of emission accounting. The methodology presented here allows us to determine background CH4 concentrations and improve our ability to detect emission events using Sentinel-5P data. Knowing the background concentrations for the area of interest provides us the opportunity to track seasonal and annual variations and trends, as well as quickly detect periodic or accidental emissions from unregulated sources, etc. The methodology and systematic research applied in this paper for the period of May 2018 to the end of 2022 enables us to detect hundreds of large-scale emissions of CH4, NO2, and CO from the coal mines in the Kuznetsk Basin (the Kemerovo region), the largest coal mining area in Russia. We estimated that the amount of these emissions is significantly higher than the emissions reported by various authors for other coal mining regions such as Poland and Australia. We found that in cases of high methane quantity there is a positive correlation between NO2 and CO emissions in time and location. The source of emissions in the study area is homogeneous, which allows it to be used as a benchmark for building models to estimate and track emissions in heterogeneous areas. [ABSTRACT FROM AUTHOR]

Agriculture is one of the leading sectors, directly affected by the negative impacts of climate change and in particular of drought. Water stress is amongst the key growth limiting factors in crop production. It is expected that in the next decades Bulgaria will be affected by warming of air temperatures and reduction of precipitation sum that will significantly increase the risk of droughts. Although, sunflower (Helianthus annuus L.) is known to be a drought tolerant crop, sufficient and optimized agrotechnical activities are needed to achieve yield increase under changing environmental conditions and depletion of natural resources. The proper irrigation scheduling, for example, requires determination of biologically optimal health and water status for different sunflower varieties, at different stages of ontogeny. The research suggests a remote sensing methodology, integrating vegetation indices and orthogonalization of multispectral satellite data for studying phenological development of selected sunflower hybrids under drought conditions. Two indices -- Normalized Differential Greenness Index (NDGI), and Normalized Differential Wetness Index (NDWNI), based on Tasseled Cap Transformation are introduced for agricultural assessments. The results show the dynamics of water and health status of the studied agroecosystems in the different growth stages, defined according to BBCH scale. NDGI and NDWNI appear to be especially suitable for determination of timing and duration of growth stages and for monitoring of the status of the studied crops during these stages. The methodology possesses a great potential for better understanding of drought impact on crop dynamics and functioning. [ABSTRACT FROM AUTHOR]