Analysis Of Satellite Imagery Research Articles

Geostatistical Analysis of Lineament Domains: The Study Case of the Apennine Seismic Province of Italy

Regional-scale swarms of subparallel linear topographic features, known as lineament domains, are a common feature of planetary surfaces. Lineament domains are superficial manifestations of the crustal stress field trajectory. Notably, one of the effects of active tectonics is seismicity. Italy is one of the most seismically active regions in the Mediterranean, with many destructive earthquakes that have occurred in past centuries. Here, we assess the seismic meaning of the main lineament domain in the tectonically active region of Central Italy. We describe the use of an automated analysis of satellite imagery coupled with spatial grid analysis to identify three lineament domains of the Central Apennines. Spatial and azimuthal comparisons of the main lineament domain (i.e., the Apennine Domain), with the known locations of earthquakes (moment magnitude of Mw > 5.5) that occurred during the past century, revealed the most seismically active tectonic areas and their spatial distributions. Further, we present a conceptual seismo-geodynamic model for the Central Apennines, which is characterized by regional arching and explains the presence of an extensional tectonic regime in the upper crustal layer of the active Apennines fold-and-thrust belt.

Integrated approach to soil salinity assessment using SEM in Sirdarya province, Uzbekistan

The majority of irrigated land in the Sirdarya area in Uzbekistan is susceptible to salinization. Yet, due to the benefits of its geographical and temporal data, satellite imaging has the ability to efficiently monitor territorial resources. The objectives of this paper are (1) to find how strong surface water mineralization used for irrigation correlated with soil salinity, and (2) to introduce a simplified approach for the soil salinity assessment using SEM based on satellite imagery analysis. The main focus of this research is to explore the potential of using SEM methodology for soil salinity assessment in the study area by streamlining spatial image analysis processes. Throughout data collection of 261 surface water samples and 206 soil samples in Sirdaria irrigation district, while satellite images and other official datasets were our secondary material. We used the Inverse Distance Weighting interpolation method to map soil salinity in ArcGIS, achieving an interpolation accuracy of 72% and a Kappa value of 81%. Statistical packages in R software served as analyst tools to validate our findings. Our findings imply that while MODIS satellite imagery has a lesser connection with EC values, however, the EC values in terms of soil salinization in Sirdarya Province can be predicted using several Landsat bands without using remote sensing indices with correlation coefficients of R ≥ 0.8. We proved that the wavelength ranges of 0.64–0.67 μm and 2.11–2.29 μm are acceptable for mapping soil salinity. Nevertheless, continuous research is needed to understand the mechanisms behind this relationship and the potential applications of these findings in other drylands, land management, and environmental monitoring.

Remote Sensing and Machine Learning for Accurate Fire Severity Mapping in Northern Algeria

Forest fires pose a significant threat worldwide, with Algeria being no exception. In 2020 alone, Algeria witnessed devastating forest fires, affecting over 16,000 hectares of land, a phenomenon largely attributed to the impacts of climate change. Understanding the severity of these fires is crucial for effective management and mitigation efforts. This study focuses on the Akfadou forest and its surrounding areas in Algeria, aiming to develop a robust method for mapping fire severity. We employed a comprehensive approach that integrates satellite imagery analysis, machine learning techniques, and geographic information systems (GIS) to assess fire severity. By evaluating various remote sensing attributes from the Sentinel-2 and Planetscope satellites, we compared different methodologies for fire severity classification. Specifically, we examined the effectiveness of reflectance indices-based metrics such as Relative Burn Ratio (RBR) and Difference Burned Area Index for Sentinel-2 (dBIAS2), alongside machine learning algorithms including Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), implemented in ArcGIS Pro 3.1.0. Our analysis revealed promising results, particularly in identifying high-severity fire areas. By comparing the output of our methods with ground truth data, we demonstrated the robust performance of our approach, with both SVM and CNN achieving accuracy scores exceeding 0.84. An innovative aspect of our study involved semi-automating the process of training sample labeling using spectral indices rasters and masks. This approach optimizes raster selection for distinct fire severity classes, ensuring accuracy and efficiency in classification. This research contributes to the broader understanding of forest fire dynamics and provides valuable insights for fire management and environmental monitoring efforts in Algeria and similar regions. By accurately mapping fire severity, we can better assess the impacts of climate change and land use changes, facilitating proactive measures to mitigate future fire incidents.

Investigation of changes in land use/land cover using principal component analysis and supervised classification from operational land imager satellite data: a case study of under developed regions, Pakistan

Monitoring and understanding Land Use/Land Cover (LU/LC) is critical for sustainable development, as it can impact various environmental, social, and economic systems. For example, deforestation and land degradation can lead to soil erosion, loss of biodiversity, and greenhouse gas emissions, affecting the quality of soil, air, and water resources. The present research examined changes in (LU/LC) within the underdeveloped regions of Balochistan and Sindh provinces, which are situated in Pakistan. In order to monitor temporal variations of LU/LC, we employed Geographic Information System (GIS) technique, to conduct an analysis of satellite imagery obtained from the Landsat 8 Operational Land Imager (OLI) during the time period spanning from 2013 to 2023. In order to obtain an accurate LU/LC classification, we used principal component analysis (PCA) and a supervised classification approach using the maximum likelihood algorithm (MLC). According to the results of our study, there was a decrease in the extent of water bodies (− 593.24 km2) and vegetation (− 68.50 km2) by − 3.43% and − 0.40% respectively. In contrast, the area occupied by settlements in the investigated region had a 2.23% rise, reaching a total of 385.66 square kilometers. Similarly, the extent of barren land also expanded by 1.60%, encompassing a total area of 276.04 square kilometers, during the course of the last decade. The overall accuracy (94.25% and 95.75%) and K value (91.75% and 93.50%) were achieved during the year 2013 and 2023 respectively. The enhancement of agricultural output in Pakistan is of utmost importance in order to improve the income of farmers, mitigate food scarcity, stimulate economic growth, and facilitate the expansion of exports. To enhance agricultural productivity, it is recommended that the government undertake targeted initiatives that aimed at enhancing water infrastructure and optimizing land use to foster a sustainable ecological framework. Integrating the sustainable ecological framework provides a foundation for informed decision-making and effective resource management. By identifying areas of urban expansion, agricultural intensification, or alterations in natural LU/LC, stakeholders can design targeted conservation strategies, mitigating potential environmental degradation and promoting biodiversity conservation. In conclusion, the integration of GIS and Remote Sensing (RS) may effectively facilitate the monitoring of land use patterns over a period of time. This combined approach offers valuable insights and recommendations for the judicious and optimal management of land resources, as well as informing policy decisions.

Kurgan Phenomenon in the Southern Caucasus: Results of an Interdisciplinary Multi‐Method Remote Sensing Survey Along the Kurekçay Valley (Goranboy Province, Western Azerbaijan)

ABSTRACTThis paper presents the results of a pilot study that combines findings from satellite remote sensing analyses and an intensive interdisciplinary survey conducted in November 2021 on kurgans along the Kurekçay Valley, in western Azerbaijan. Through the integration of various methods, including historical and contemporary satellite and aerial imagery analysis, topographic mapping and geophysical prospections, this research remotely maps and quantifies the density, size, morphology and distribution of the burial mounds in the area. Unlike previous studies focusing solely on individual burial mounds, this investigation adopts a broader approach by contextualizing the kurgans within their respective landscapes. The results contribute to a deeper understanding of the physical and symbolic aspects and relationships of these structures, providing valuable insights into the enduring burial practices that shaped the funerary traditions of the Southern Caucasus during the 4th and 1st millennium BCE. Furthermore, this approach supports an assessment of the condition of the individual kurgans, thereby opening new perspectives to develop innovative plans to protect these burial mounds and their settings.

Damage to medical complexes in the Gaza Strip during the Israel–Hamas war: a geospatial analysis

IntroductionMedical facilities are civilian objects specially protected during armed conflict by international humanitarian law (IHL). These protections are customarily applied regardless of the conflict, parties or contexts involved. Attacks on...

Probability of Erosion Utilizing Google Earth Engine and the RUSLE Method in the Tuntang Watershed

The Tuntang Watershed is an important watershed in Central Java. Management of watersheds in the Tuntang stream is a priority for various parties to carry out. One of the things that threatens the sustainability of the Tuntang watershed is erosion. The erosion rate can lead to sediment accumulation and siltation in the Tuntang River reservoir, which can cause catastrophic flooding. Flood disaster mitigation caused by erosion needs to be done, one of which is by calculating the erosion rate per year that occurs in the Tuntang watershed. This study calcultated the predicted erosion rate (per year in the Tuntang watershed) using the Revised Universal Soil Loss Equation (RUSLE) method, processed using the Google Earth Engine (GEE). Google offers a cloud-storage technology called GEE. Programming in JavaScript is required to operate GEE. GEE is a petabyte-scale data-based tool that can be used to analyze and archive geospatial data that is open source. The computing environment is designed for the processing of geospatial data, including the depiction of spatial analysis of satellite imagery. Data for RUSLE is obtained from the database in GEE, and the results can be imaged on a map. According to the study’s findings, the degree of soil erosion throughout the Tuntang Watershed was essentially constant, with Moderate erosion predominating in the majority of locations. Senjoyo Sub Watershed, Rowopening Sub Watershed, and Tuntang Hilir Sub Watershed are the primary locations with severe erosion. Rowopening Sub Watershed is the region that is the worst.

Understanding the transition of community land use from shifting cultivation to cash cropping in southern Tanintharyi, Myanmar

Abstract Many tropical landscapes have experienced the loss of traditional cultivation practices as they have transitioned to other land use systems. The Tanintharyi Region of southern Myanmar is a landscape experiencing a rapid land use regime shift from traditional subsistence farming to permanent cash crop agriculture. Despite previous research in this region on the expansion of large‐scale agribusiness, such as oil palm and rubber plantations, little is known about how the small‐scale shifting cultivation system practiced by the local Karen ethnic people in Tanintharyi has changed over recent decades or the underlying reasons for this transition. Our study explores this transition process and its drivers from a social and land system change perspective in four villages of Bokpyin Township in Tanintharyi. We investigated the drivers of land cover and land use change by collecting information through focus groups and individual interviews with community members who previously practised shifting cultivation. We also quantified the overall change in land cover and land use through remote sensing analysis of Landsat 8 (2020) and declassified KH‐9 satellite imagery (1976). Prior to a period of civil war in the 1980s, cultivators used a traditional ‘slash‐and‐burn’ agricultural system for subsistence use. The present land use system is dominated by permanent betel nut cultivation, which represents the primary income source for farming households. The reported drivers of the transition to cash crop farming are greater income potential and livelihood security, risk of losing fallow land due to the needs for new cultivation areas for in‐migration and new centralized land use policies and administration after the national ceasefire agreement in 2012. The documented change in land use systems has been paired with a concurrent net loss of forest that, given the conflicted governance within the landscape, seems likely to continue without additional land use planning efforts. Our study contributes to an increased understanding of changes in shifting cultivation systems that are being documented globally. Furthermore, our example of combining qualitative interviews with analysis of historical satellite imagery to detect and explain land use regime shifts can serve as a model for future work in different landscapes. Read the free Plain Language Summary for this article on the Journal blog.

Megafires and koala occurrence: a comparative analysis of field data and satellite imagery

Megafires can have a devastating effect on koala populations. With climate change increasing habitat vulnerability to wildfires, understanding how to efficiently measure the impact of these events on koalas is essential. We analysed the relationship between the 2019-2020 megafires and the probability of koala occurrence in Mid North Coast NSW. We found that two on-field and one satellite-derived variables measuring fire severity equally explained koala occurrence. The probability of koala occurrence decreased with increasing fire severity. This supports the use of remote sensing imagery to efficiently monitor the impact of future fire events on koala populations in the region.

The pace of global river meandering influenced by fluvial sediment supply

Meandering rivers move gradually across the floodplains, and this river movement presents socioeconomic risks along river corridors and regulates terrestrial biogeochemical cycles. Experimental and field studies suggest that fluvial sediment supply can exert a primary control on lateral migration rates of rivers. However, we lack an understanding of the relative importance of environmental boundary conditions, such as floodplain vegetation and sediment supply, in setting the pace of river meandering across different environmental settings. Here, we combine the analysis of satellite imagery and global-in-scale sediment and water discharge models to evaluate the controls on lateral migration rates of 139 meandering rivers that span a wide range in size, climate, and bank vegetation. We show that migration rates normalized by the channel width monotonically increase with the volumetric sediment flux normalized by the characteristic size of the river. This relation is consistent across rivers in vegetated and unvegetated catchments, indicating that enhanced lateral migration rates in unvegetated basins is likely not only facilitated by lower bank mechanical strength, but also by higher normalized sediment supply in ephemeral rivers. Using three case examples, we also demonstrate that width-normalized meander migration rates respond to spatial gradients in sediment supply caused by river impoundments, highlighting the prominent role of sediment supply in setting the pace of meander migration. Our results suggest that sediment-supply variations caused by climate, land-cover and land-use changes can lead to predictable changes in meandering river evolution and ultimately drive architectural changes in sedimentary stratigraphy.

Temporal analysis of settlement areas and city footprints on construction and demolition waste quantification using Landsat satellite imagery

Municipal solid waste management has seen a surge in the use of satellite imagery in decision-making processes, yet its application to analyze quantitative variations in construction and demolition (C&D) waste remains under-investigated. This study employs satellite imagery and multivariate analysis to comprehensively assess and predict C&D waste generation in four diverse urban jurisdictions of Canada (Regina) and the USA (Seattle, Buffalo, and Philadelphia). Factors such as settlement area expansion, economic activities, and population growth significantly influence C&D waste rates. Stepwise multivariate regression models tailored to different city types, such as moderately populated (Group 1) and highly populated (Group 2), showcase acceptable predictive capabilities. For moderately populated cities, settlement area, average humidity, and GDP are identified as key predictors, while for highly populated cities, settlement area, unemployment rate, and building permit value prove effective indicators. These models, characterized by R² values from 0.70 to 0.94, provide tailored insights for distinct demographic conditions, aiding waste management planning. This research underscores the importance of satellite imagery and multivariate analysis in understanding C&D waste dynamics and empowers policymakers and waste management agencies with evidence-based strategies for effective waste management in urban centers.

Assessing lake shoreline change and prediction for 2030 by physical drivers: A Case Study from Lake Batur, Batur UNESCO Global Geopark, Bali

Over ten years, the water level of Lake Batur has increased. Agriculture area and settlements around Lake Batur are threatened by rising water level. This study aims to analyze the shoreline change of Lake Batur, located in the Batur UNESCO Global Geopark (BUGG), during the period of 2007 – 2018 and design a prediction for 2030. Understanding the shoreline change is very important for lake management and planning. Shoreline changes were analyzed in Geographic Information System (GIS) application. The data were obtained based on Remote Sensing (RS) data, Landsat ETM+ imagery on September 24, 2007, and Landsat OLI imagery on October 24th, 2018. Predictions of lake shoreline in 2030 result from modelling by integrating ASTER-GDEM V2 data, lake water level data for 2007–2018, annual average rainfall data for 2007–2018, and bathymetry data for 2013 and 2015. The results of the satellite imagery analysis show that there has been a change in the length of the shoreline, which has increased from 20.47 km in 2007 to 21.28 km (3.96%) in 2018. The lake surface area changed from 15.34 km2 in 2007 to 16.16 km2 in 2018 (5.35%). The prediction of lake shoreline changes in 2030 showed that Lake Batur will increase to 26.90 km (26.41%), and lake surface area is predicted to increase by 17.67 km2 (9.34%) from 2018. This is because of the morphological change of Lake Bottom.

Vegetation enhances curvature-driven dynamics in meandering rivers

Stabilization of riverbanks by vegetation has long been considered necessary to sustain single-thread meandering rivers. However, observation of active meandering in modern barren landscapes challenges this assumption. Here, we investigate a globally distributed set of modern meandering rivers with varying riparian vegetation densities, using satellite imagery and statistical analyses of meander-form descriptors and migration rates. We show that vegetation enhances the coefficient of proportionality between channel curvature and migration rates at low curvatures, and that this effect wanes in curvier channels irrespective of vegetation density. By stabilizing low-curvature reaches and allowing meanders to gain sinuosity as channels migrate laterally, vegetation quantifiably affects river morphodynamics. Any causality between denser vegetation and higher meander sinuosity, however, cannot be inferred owing to more frequent avulsions in modern non-vegetated environments. By illustrating how vegetation affects channel mobility and floodplain reworking, our findings have implications for assessing carbon stocks and fluxes in river floodplains.

Assessing water quality at Jakkur Lake: a comparative analysis of Landsat-8 OLI and Sentinel-2 Satellite Imagery

Jakkur lake is one of the biggest lakes of Bangalore and is located on the northern side of the city. It derives its name from the name of the locality, Jakkur. The waterbody area is about 160 acres and length is about 1.5 km with several islands. Because of urbanization and increase in population, the catchment location is highly encroached. The amount of the stormwater achieving the lake has decreased making the streams dry. Remote sensing is a tool used for monitoring water bodies to present better spatial and temporal resolution. Remote sensing is also used for continuous monitoring and assessment of water bodies. Landsat-8 OLI and Sentinel-2 are recently launched multispectral sensors that have the potential to be used for the effective monitoring of the Jakkur Lake's water quality. Landsat-8 OLI and Sentinel-2 are similar in many approaches. Sentinel-2, however, has higher spatial resolution. The aim of this research was to find out if Sentinel-2 produces better results than Landsat-8 OLI in testing water quality parameters in the Jakkur lake. The parameters of interest were chlorophyll_a and turbidity. The results show that Sentinel-2 generates better results. For Sentinel-2, the R2 values for chlorophyll_a and turbidity were 0.975 and 0.845 respectively whilst for Landsat-8, the chlorophyll_a and turbidity R2 values were 0.911 and 0.747 respectively.

Enhancing risk communication and environmental crisis management through satellite imagery and AI for air quality index estimation

Due to climate change, the air pollution problem has become more and more prominent [23]. Air pollution has impacts on people globally, and is considered one of the leading risk factors for premature death worldwide; it was ranked as number 4 according to the website [24]. A study, ‘The Global Burden of Disease,’ reported 4,506,193 deaths were caused by outdoor air pollution in 2019 [22,25]. The air pollution problem is become even more apparent when it comes to developing countries [22], including Thailand, which is considered one of the developing countries [26]. In this research, we focus and analyze the air pollution in Thailand, which has the annual average PM2.5 (particulate matter 2.5) concentration falls in between 15 and 25, classified as the interim target 2 by 2021′s WHO AQG (World Health Organization's Air Quality Guidelines) [27]. (The interim targets refer to areas where the air pollutants concentration is high, with 1 being the highest concentration and decreasing down to 4 [27,28]). However, the methodology proposed here can also be adopted in other areas as well.During the winter in Thailand, Bangkok and its surrounding metroplex have been facing the issue of air pollution (e.g., PM2.5) every year. Currently, air quality measurement is done by simply implementing physical air quality measurement devices at designated—but limited number of locations. In this work, we propose a method that allows us to estimate the Air Quality Index (AQI) on a larger scale by utilizing Landsat 8 images with machine learning techniques. We propose and compare hybrid models with pure regression models to enhance AQI predictionbased on satellite images. Our hybrid model consists of two parts as follows:•The classification part and the estimation part, whereas the pure regressor model consists of only one part, which is a pure regression model for AQI estimation.•The two parts of the hybrid model work hand in hand such that the classification part classifies data points into each class of air quality standard, which is then passed to the estimation part to estimate the final AQI.From our experiments, after considering all factors and comparing their performances, we conclude that the hybrid model has a slightly better performance than the pure regressor model, although both models can achieve a generally minimum R2 (R2 > 0.7). We also introduced and tested an additional factor, DOY (day of year), and incorporated it into our model. Additional experiments with similar approaches are also performed and compared. And, the results also show that our hybrid model outperform them.Keywords: climate change, air pollution, air quality assessment, air quality index, AQI, machine learning, AI, Landsat 8, satellite imagery analysis, environmental data analysis, natural disaster monitoring and management, crisis and disaster management and communication.

Recent changes in riparian and floodplain vegetation in England and Wales and its geomorphic implications

AbstractRecent river studies in the United Kingdom have observed an increase in riparian vegetation and its potential impact on river channel change and sedimentation. Here, we quantify changes in floodplain and riparian vegetation during the first two decades of the 21st century along reaches of eight gravel‐bed rivers in England and Wales that exhibit varied active and stable, single and multi‐thread planforms. The analysis employs information drawn from open‐access sources including national LiDAR surveys and the photographic analysis of repeat aerial and satellite imagery. Most reaches show an increase in woody vegetation across their floodplains reflecting both natural colonisation and deliberate planting, and all but one of the reaches show an increase in woody riparian vegetation cover along their riverbanks. Of the six reaches that have exposed riverine sediments, five show a reduction in exposed sediment area as a result of vegetation encroachment. Contrasts in the rate, extent and location of riparian change were associated with the stability of the channel planform, with differences seen between stable and more active reaches. The significance of riparian woodland in promoting riverbank stability is demonstrated. Riparian vegetation development varies between river reaches, largely in response to differing rates of channel movement but also because of multiple and diverse local decisions regarding the management of in‐channel and channel‐adjacent vegetation by landowners rather than a single national policy. We consider the relevance of our findings and the value of remote monitoring for future river management interventions.

Aspects of the ecosystem services threatened by desertification in Algerian steppe rangelands: concepts, status and stakes

This study focuses on the imminent threat of desertification within Djelfa's steppe grazing lands, emphasizing its implications for land management and the sustainability of livestock practices. The methodology adopted employs a stringent approach that commences with a precise definition of desertification as a critical risk. It then proceeds to thoroughly assess the susceptibility of the local ecosystem to this phenomenon and delineates its consequential impact on both the human inhabitants and the surrounding environment. This comprehensive analysis effectively contextualizes human activities within the sphere of desertification's influence. Methodologically, the study employs a multidimensional framework to categorize the array of environmental goods and services rendered by these grazing lands. By identifying the beneficiaries associated with each service, the research aims to elucidate the complex threat posed at various levels. Crucially, the findings highlight the severe jeopardy that desertification imposes, not only endangering essential resources vital for extensive livestock production but also triggering a decline in invaluable environmental goods pivotal for the sustainability of the ecosystem and activities supporting human welfare. Statistically substantiated through an integration of diverse methodologies such as field surveys, satellite imagery analysis, and stakeholder consultations, this study validates the correlations between desertification and the degradation of ecosystem services. It provides empirical evidence showcasing the gradual decline of grazing lands, thereby compelling an urgent call for intervention strategies. In summary, this research underscores the urgent need for holistic strategies to mitigate the adverse effects of desertification. Its findings provide critical insights into the complex dynamics between human activities, ecosystem vulnerabilities, and the looming threat of desertification. It emphasizes the necessity for immediate collective action and sustainable resource management practices to safeguard ecosystems, ensure long-term sustainability, and protect the well-being of communities in Djelfa and beyond.

Landslides and growing folds: A lesson from the Kura fold-and-thrust belt (Azerbaijan, Georgia)

Active fold-and-thrust belts create new landslide-prone slopes during tectonic deformation propagation. However, studies on landslide distribution in newly formed fold-and-thrust belts are limited. In this study, we present a new inventory of landslides in the Kura fold-and-thrust belt, a tectonically active, but relatively low-altitude southern margin of the Greater Caucasus. The area has been tectonically framed in the last ~2–3 Ma and is represented by folds and thrusts deforming Miocene to Quaternary sediments. Through satellite imagery analysis, we mapped nearly 1600 landslides, with a quarter currently active. While landslides cover <1 % of the area, they tend to cluster at higher elevations and in regions with relatively high local relief. Landslides predominantly occur in tectonically uplifted areas, affecting the highest and steepest parts of growing anticlines and the steep slopes of incising valleys intersecting active thrust faults. Based on observed landslide distribution in folds at different stages of development, we propose a conceptual model for the temporal evolution of landslide patterns in weak sediment-based fold-and-thrust belts: 1) In the initial stages, slow-moving slope deformations affect incipient thrust fronts. With the flanks of the growing anticline lacking sufficient steepness, landslides tend to concentrate in deep valleys intersecting the uplifting hanging walls. 2) With ongoing thrust uplift, growing and steepening anticlines become more prone to planar sliding when dip slopes exceed friction angle, and valley development creates additional dip slopes, resulting in widespread landslides. 3) In the final stage, erosional decay reduces topographic relief, leading to badland formation with gully erosion and decreased landslide occurrence.

COMPARATIVE ANALYSES OF SECONDARY ECOLOGICAL SUCCESSION FOLLOWING WILDFIRES IN THREE DISTINCT FOREST TYPES. A STUDY CASE FROM MOGUER, SPAIN

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.

Concept for long-term geo-monitoring of the post-mining environment using the example of the Prosper-Haniel mine

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 termination 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 of Post-Mining at the Technical University of Georg Agricola in Bochum present the possibilities of integrating environmental geo-monitoring methods to understand the processes occurring both during and after mining operations. Among the research methods used, spatiotemporal multispectral analyses of satellite imagery and images from drone flights stand out, and these will be presented in this paper. Additionally, in-situ measurements using soil sensors, weather stations, the application of mobile GIS, and three-dimensional modelling of geological structures should be noted. A key aspect of mining process research is the implementation and integration 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.