Search Results (141)

Drought-induced forest mortality threatens biodiversity globally, particularly in arid, and semi-arid woodlands. The continual development of remote sensing approaches enables enhanced monitoring of forest health. Herein, we investigate the ability of a limited ground-truthed canopy dieback dataset and satellite image derived Normalised Difference Vegetation Index (NDVI) to make inferences about forest health as temporal and spatial extent from its collection increases. We used ground-truthed observations of relative canopy mortality from the Pinus edulis-Juniperus osteosperma woodlands of southeastern Utah, United States of America, collected after the 2017–2018 drought, and PlanetScope satellite imagery. Through assessing different modelling approaches, we found that NDVI is significantly associated with sitewide mean canopy dieback, with beta regression being the most optimal modelling framework due to the bounded nature of the variable relative canopy dieback. Model performance was further improved by incorporating the proportion of J. osteosperma as an interaction term, matching the reports of species-specific differential dieback. A time-series analysis revealed that NDVI retained its predictive power for our whole testing period; four years after the initial ground-truthing, thus enabling retrospective inference of defoliation and regreening. A spatial random forest model trained on our ground-truthed observations accurately predicted dieback across the broader landscape. These findings demonstrate that modest field campaigns combined with high-resolution satellite data can generate reliable, scalable insights into forest health, offering a cost-effective method for monitoring drought-impacted ecosystems under climate change. Full article

Gypsy (or spongy) moth (Lymantria dispar) outbreaks have imposed significant threats to European forests for centuries. While traditional field-based research has provided detailed insights, it remains time-consuming, labour-intensive, and spatially limited. With the advancement of Earth observation satellite technology, forest monitoring has become more efficient and flexible. This study examined the impact of the most extensive gypsy moth outbreak (2003–2006) on the forest dynamics in Hungary using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived indices: the Normalised Difference Vegetation Index (NDVI), Standardised NDVI (Z NDVI), and Leaf Area Index (LAI). Our results show that while the gypsy moth population in Hungary peaked in 2004, based on light trap data, and in 2005, according to field damage reports, the most severe defoliation occurred in 2005 and 2006, as detected by satellite-based decreases in the NDVI and LAI. MODIS-based vegetation indices proved effective in quantifying the extent and severity of defoliation, showing temporal and spatial patterns that aligned with ground observations. The LAI and NDVI metrics also captured varying degrees of defoliation and partial recovery. These findings underscore the value of integrating satellite data with field observations to improve early warning systems and enhance the forecasting and management of gypsy moth outbreaks. Full article

Accurate soil organic carbon (SOC) estimation is critical for assessing ecosystem services, carbon budgets, and informing sustainable land management, particularly in ecologically sensitive mountainous regions. This study focuses on modelling the spatial distribution of SOC within the heterogeneous volcanic landscape of the Nevado de Toluca (NdT), central Mexico, an area spanning 535.9 km² and characterised by diverse land uses, altitudinal gradients, and climatic regimes. Using 29 machine learning algorithms, we evaluated the predictive capacity of three key variables: land use, elevation, and the Normalised Difference Vegetation Index (NDVI) derived from satellite imagery. Complementary analyses were performed using the Bare Soil Index (BSI) and the Modified Soil-Adjusted Vegetation Index 2 (MSAVI2) to assess their relative performance. Among the tested models, the Quadratic Support Vector Machine (SVM) using NDVI, elevation, and land use emerged as the top-performing model, achieving a coefficient of determination (R²) of 0.84, indicating excellent predictive accuracy. Notably, 14 models surpassed the R² threshold of 0.80 when using NDVI and BSI as predictor variables, whereas MSAVI2-based models consistently underperformed (R² < 0.78). Validation plots demonstrated strong agreement between observed and predicted SOC values, confirming the robustness of the best-performing models. This research highlights the effectiveness of integrating multispectral remote sensing indices with advanced machine learning frameworks for SOC estimation in mountainous volcanic ecosystems Full article

Spatial variability in vine vigour and yield components is a major challenge for vineyard management and consistent grape quality, particularly in hilly landscapes. This study evaluates the impact of on-the-go variable-rate fertilisation (VRA) in reducing within-vineyard variability in an 8.5 hectares commercial vineyard in the Conegliano Valdobbiadene Prosecco DOCG region (Italy). Over two growing seasons, a proximal NDVI sensor (GreenSeeker) guided real-time fertiliser applications without prescription maps. Vine vigour, yield components, and grape quality were evaluated using geostatistical analysis and coefficient of variation (CV) metrics. VRA reduced total spatial variability (sill) by 55% and erratic variance (nugget effect) by 39% for NDVI measurements. Variability in yield components also decrease (−21.1% for cluster number, −6.25% for cluster weight), while grape composition parameters (total soluble solids, titratable acidity, and pH) was not significantly altered despite a slightly higher variability (in titratable acidity and pH), indicating that fertiliser modulation did not compromise grape quality. Nitrogen input was reduced by 50%, highlighting economic and environmental benefits (−302 kg CO₂). These results show that simplified, sensor-based, on-the-go VRA is a practical and sustainable precision viticulture tool, even in small and heterogeneous vineyards typical of the Conegliano Valdobbiadene Prosecco DOCG area. Full article

Climate change has intensified the frequency and severity of forest disturbances globally, including windthrow, which poses substantial risks for both forest productivity and ecosystem stability. Rapid and precise assessment of wind-induced tree damage is essential for effective management, yet many injuries remain visually undetectable in the early stages. This study employed drone-based multispectral imaging and a simulated wind stress experiment (static pulling) on Norway spruce (Picea abies (L.) Karst.) to investigate the detectability of physiological and structural changes over four years. Multispectral data were collected at multiple time points (2023–2024), and a suite of vegetation indices (the Normalised Difference Vegetation Index (NDVI), the Structure Insensitive Pigment Index (SIPI), the Difference Vegetation Index (DVI), and Red Edge-based indices) were calculated and analysed using mixed-effects models. Our results demonstrate that trees subjected to mechanical bending (“Bent”) exhibit substantial reductions in the near-infrared (NIR)-based indices, while healthy trees maintain higher and more stable index values. Structure- and pigment-sensitive indices (e.g., the Modified Chlorophyll Absorption Ratio Index (MCARI 2), the Transformed Chlorophyll Absorption in Reflectance Index/Optimised Soil-Adjusted Vegetation Index (TCARI/OSAVI), and RDVI) showed the highest diagnostic value for differentiating between damaged and healthy trees. We found the clear identification of group- and season-specific patterns, revealing that the most pronounced physiological decline in Bent trees emerged only several seasons after the disturbance. Full article

Modern cities are rapidly evolving in terms of urban morphology, driven by exponential population growth that accelerates the urbanisation process. The changes in land use have increased urban area and density, intensifying the urban heat island (UHI) effect, which poses one of the biggest threats to human health and well-being, especially in metropolitan regions. One of the most effective strategies to counter urban heat is the implementation of green infrastructure and the use of suitable building materials that help reduce heat stress. However, access to green spaces and the affordability of efficient building materials are not the same among citizens. This paper aims to identify the socio-economic characteristics of communities in Melbourne, Australia, that contribute to their vulnerability to urban heat under local conditions. This study employs remote sensing and geographical information systems (GIS) to conduct a macro-scale analysis, to investigate the correlation between urban heat patterns and socio-economic characteristics, taking into account factors such as vegetation cover, built-up areas, and land use types. The results from the satellite images and the geospatial data reveal that Deer Park, located in the western suburbs of Melbourne, has the highest land surface temperature (LST) at 32.54 °C, a UHI intensity of 1.84 °C, a normalised difference vegetation index (NDVI) of 0.11, and a normalised difference moisture index (NDMI) of −0.081. The LST and UHI intensity indicate a strong negative correlation with the NDVI (r = −0.42) and NDMI (r = −0.6). In contrast, the NDVI and NDMI have a positive correlation with the index of economic resources (IER) with r values of 0.29 and 0.24, indicating that the areas with better finance resources tend to have better vegetation coverage or plant health with less water stress, leading to lower LST and UHI intensity. This study helps to identify the most critical areas in the Greater Melbourne region that are vulnerable to the risk of urban heat and extreme heat events, providing insights for the local city councils to develop effective mitigation strategies and urban development policies that promote a more sustainable and liveable community. Full article

Extreme weather events are increasing the frequency and intensity of forest fires, generating serious environmental and socio-economic impacts. These fires cause soil loss through erosion, organic matter depletion, increased surface runoff and the release of greenhouse gases, intensifying climate change. They also affect biodiversity, terrestrial and aquatic ecosystems, and soil quality. The assessment of forest fires by remote sensing, such as the use of the Normalised Difference Vegetation Index (NDVI), allows rapid analysis of damaged areas, monitoring of vegetation changes and the design of restoration strategies. On the other hand, models such as RUSLE are key tools for calculating soil erosion and planning conservation measures. A study of the impacts on soils and vegetation in the south of Salamanca, where one of the worst fires in the province took place in 2022, has been carried out using RUSLE and NDVI models, respectively. The study confirms that fires significantly affect soil properties, increase erosion and hinder vegetation recovery, highlighting the need for effective restoration strategies. It was observed that erosion intensifies after fires (the maximum rate of soil loss before is 1551.85 t/ha/year, while after it is 4899.42 t/ha/year) especially in areas with steeper slopes, which increases soil vulnerability, according to the RUSLE model. The NDVI showed a decrease in vegetation recovery in the most affected areas (with a maximum value of 0.3085 after the event and 0.4677 before), indicating a slow regeneration process. The generation of detailed cartographies is essential to identify critical areas and prioritise conservation actions. Furthermore, the study highlights the importance of implementing restoration measures, designing sustainable agricultural strategies and developing environmental policies focused on the mitigation of land degradation and the recovery of fire-affected ecosystems. Full article

During urbanisation, extensive production and construction activities encroach on ecological spaces, leading to changes in environmental structures and soil erosion. The issue of yellow muddy water caused by rainfall in cities with high construction intensity has garnered significant attention. Taking Guangzhou City as the research area, this study is the first to propose a risk assessment model for yellow muddy water in cities with high construction intensity, and the influence of construction sites on yellow muddy water was fully considered. Rainfall and construction sites were used as indicators to assess the hazards of yellow muddy water. Elevation, slope, normalised difference vegetation index (NDVI), soil erosion modulus, stream power index (SPI), surface permeability, and roads represent the exposure evaluation indicators. Population number and GDP (Gross Domestic Product) were used as vulnerability evaluation indicators. Based on the analytic hierarchy process (AHP) method, the weights of each evaluation indicator were determined, and a risk assessment system for yellow muddy water was established. By overlaying the weighted layers of different evaluation indicators on the geographic information system (GIS) platform, a risk degree distribution map of yellow muddy water disasters was generated. The evaluation results demonstrated that the disaster risk levels within the study area exhibited spatial differentiation, with areas of higher risk accounting for 14.76% of the total. The evaluation results were compared with historical yellow muddy water event information from Guangzhou, and the effectiveness of the model was verified by the receiver operating characteristic (ROC) curve. The validation results indicate that this model provides high accuracy in assessing the degree of risk of yellow muddy water in high-construction-intensity cities, offering effective technical support for precise disaster prevention and mitigation. Full article

Soil salinisation is a critical problem in northern China’s arid and semi-arid irrigated regions, posing a substantial impediment to the sustainable advancement of agriculture in these areas. This research utilises the Donghaixin Irrigation District, located on the southern bank of the Yellow River in Inner Mongolia, as a case study. This study examines the spatial distribution and determinants of soil salinisation through macro-environmental variables and micro-ion composition, integrating regression models and groundwater ion characteristics to elucidate the patterns and causes of soil salinisation systematically. The findings demonstrate that soil salinisation in the study region displays notable spatial clustering, with surface water-irrigated regions exhibiting greater salinisation levels than groundwater-irrigated areas. More than 80% of the land exhibits moderate salinity, predominantly characterised by the ions Cl⁻, HCO₃⁻, and SO₄²⁻. The hierarchy of ion concentration variation with escalating soil salinity is as follows: Na⁺ > K⁺ > SO₄²⁻ > Cl⁻ > Mg²⁺ > HCO₃⁻ + CO₃²⁻ > Ca²⁺. The susceptibility of ions to soil salinisation is ordered as follows: Ca²⁺ > Na⁺ > HCO₃⁻ + CO₃²⁻ > Mg²⁺ > K⁺ > Cl⁻ > SO₄²⁻. In contrast to the ordinary least squares (OLS) model, the geographic weighted regression (GWR) model more effectively elucidates the geographical variability of salinity, evidenced by an adjusted R² of 0.68, particularly in high-salinity regions, where it more precisely captures the trend of observed values. Ecological driving elements such as organic matter (OM), pH, groundwater depth (GD), total dissolved solids (TDS), digital elevation model (DEM), normalised difference vegetation index (NDVI), soil moisture (SM), and potential evapotranspiration (PET) govern the distribution of salinisation. In contrast, anthropogenic activities affect the extent of salinisation variation. Piper’s trilinear diagram demonstrates that Na cations mainly characterise groundwater and soil water chemistry. In areas irrigated by surface water, the concentration of SO₄²⁻ is substantially elevated and significantly affected by agricultural practises; conversely, in groundwater-irrigated regions, Cl⁻ and HCO₃⁻ are more concentrated, primarily driven by evaporation and ion exchange mechanisms. Full article

The extraction, processing and transport of crude oil in the Niger Delta region of Nigeria has long been associated with collateral environmental damage to the largest mangrove ecosystem in Africa. Oil pollution is impacting not only one of the planet’s most ecologically diverse regions but also the health, livelihoods, and social cohesion of the Delta region inhabitants. Quantifying and directly associating localised oil pollution events to specific petrochemical infrastructure is complicated by the difficulty of monitoring such vast and complex terrain, with documented concerns regarding the thoroughness and impartiality of reported oil pollution events. Earth Observation (EO) offers a means to deliver such a monitoring and assessment capability using Normalised Difference Vegetation Index (NDVI) measurements as a proxy for mangrove biomass health. However, the utility of EO can be impacted by persistent cloud cover in such regions. To overcome such challenges here, we present a workflow that leverages EO-derived high-resolution (10 m) synthetic aperture radar data from the Sentinel-1 satellite constellation combined with machine learning to conduct observations of the spatial land cover changes associated with oil pollution-induced mangrove mortality proximal to pipeline networks in a 9000 km² region of Rivers State located near Port Harcourt. Our analysis identified significant deforestation from 2016–2024, with an estimated mangrove mortality rate of 5644 hectares/year. Using our empirically derived Pipeline Impact Indicator (PII), we mapped the oil pipeline network to 1 km resolution, highlighting specific pipeline locations in need of immediate intervention and restoration, and identified several new pipeline sites showing evidence of significant oil spill damage that have yet to be formally reported. Our findings emphasise the critical need for the continuous and comprehensive monitoring of oil extractive regions using satellite remote sensing to support decision-making and policies to mitigate environmental and societal damage from pipeline oil spills, particularly in ecologically vulnerable regions such as the Niger Delta. Full article

The widespread oil extraction in the Niger Delta and the impacts on different types of vegetation are poorly understood due to lack of ground access. This study aims to determine the impact of oil spills on vegetation in the Niger Delta using a Landsat satellite-derived normalised difference vegetation index (NDVI). The impact of oil spill volume and time after an oil spill on the health of different types of vegetation were evaluated, and the time series of the changes in NDVI were analysed to determine the medium- and long-term responses of vegetation to oil spill exposure, using a combination of linear regression and paired t-tests. With regards to the relationship between spill volume and NDVI, a moderate correlation (R² = 0.5018) was observed for spill volumes in the range of 401–1000 barrels for sparse vegetation, for volumes greater than 1000 barrels for dense vegetation (R² = 0.4356), whilst no correlation was found for mangrove vegetation at any range of spill volume. Similarly, the results of the paired t-test confirmed a significant difference (p-value < 0.05) between the change in NDVI values for spill sites and non-spill sites for all vegetation types, with the sparse vegetation being the most affected of the three types. However, the impact of the oil spill on vegetation over a period is not statistically significant. The outcomes of this study provide insights into how different types of vegetation in the Niger Delta respond to oil spills, which could ultimately help in designing an oil spill clean-up program to reduce the impact on the environment. Full article

The rainfed cereal growing regions of Northern Kazakhstan experience significant yield fluctuations due to dependence on weather conditions. Early detection and monitoring of droughts is crucial for effective mitigation strategies in this region. This study emphasises the following objectives: (1) description of the current vegetation condition with a possible separation of short-term weather effects and (2) analysing trends of changes with their directionality and quantification. Terra MODIS satellite images from 2000 to 2023 are used. Differential indices—Normalised Difference Vegetation Index (NDVI) and Vegetation Condition Index (VCI)—are used to determine the characteristics of each current season. A key component is the comparison of the current NDVI values with historical maximum, minimum, and average values to identify early indicators of drought. NDVI deviations from multiyear norms and VCI values below 0.3 visually reflect changing vegetation conditions influenced by seasonal weather patterns. The results show that the algorithm effectively detects early signs of drought through observed deviations in NDVI values, showing a trend towards increasing drought frequency and intensity in Northern Kazakhstan. The algorithm was particularly effective in detecting severe drought seasons in advance, as was the case in June 2010 and May 2012, thus supporting early recognition of drought onset. The Integrated Vegetation Index (IVI) and Integrated Vegetation Condition Index (IVCI) time series are used for integrated multiyear assessments, in analysing temporal changes in vegetation cover, determining trends in these changes, and ranking the weather conditions of each growing season in the multiyear series. Areas with high probability of drought based on low IVCI values are mapped. The present study emphasises the value of remote sensing as a tool for drought monitoring, offering timely and spatially detailed information on vulnerable areas. This approach provides critical information for agricultural planning, environmental management and policy making, especially in arid and semi-arid regions. The study emphasises the importance of multiyear data series for accurate drought forecasting and suggests that this methodology can be adapted to other drought-sensitive regions. Emphasising the socio-economic benefits, this study suggests that the early detection of drought using satellite data can reduce material losses and facilitate targeted responses. Full article

Wildfires are affecting natural ecosystems worldwide, causing economic and human losses and exacerbated by climate change. Models of fire severity and fire susceptibility are crucial tools for fire monitoring. This case study analyses a fire event on 3 September 2019 in Vilcabamba parish, Loja province, Ecuador. This article aims to assess the severity and susceptibility of a fire through spectral indices and multi-criteria methods for establishing a fire action plan proposal. The methodology comprises the following: (i) the acquisition of Sentinel-2A products for the calculation of spectral indices; (ii) a fire severity model using differentiated indices (dNBR and dNDVI) and a fire susceptibility model using the Analytic Hierarchy Process (AHP) method; (iii) model validation using Logistic Regression (LR) and Non-metric Multidimensional Scaling (NMDS) algorithms; (iv) the proposal of an action plan for fire management. The Normalised Burn Ratio (NBR) index revealed that 10.98% of the fire perimeter has burned areas with moderate-high severity in post-fire scenes (2019) and decreased to 0.01% for post-fire scenes in 2021. The Normalised Difference Vegetation Index (NDVI) identified 67.28% of the fire perimeter with null photosynthetic activity in the post-fire scene (2019) and 5.88% in the post-fire scene (2021). The Normalised Difference Moisture Index (NDMI) applied in the pre-fire scene identified that 52.62% has low and dry vegetation (northeast), and 8.27% has high vegetation cover (southwest). The dNDVI identified 10.11% of unburned areas and 7.91% using the dNBR. The fire susceptibility model identified 11.44% of the fire perimeter with null fire susceptibility. These results evidence the vegetation recovery after two years of the fire event. The models demonstrated excellent performance for fire severity models and were a good fit for the AHP model. We used the Root Mean Square Error (RMSE) and area under the curve (AUC); dNBR and dNDVI have an RMSE of 0.006, and the AHP model has an RMSE of 0.032. The AUC = 1.0 for fire severity models and AUC = 0.6 for fire susceptibility. This study represents a holistic approach by combining Google Earth Engine (GEE), Geographic Information System (GIS), and remote sensing tools for proposing a fire action plan that supports decision making. This study provides escape routes that considered the most significant fire triggers, the AHP, and fire severity approaches for monitoring wildfires in Andean regions. Full article

The rise in inexpensive Unmanned Aerial Systems (UAS) and accessible processing software offers several advantages in forest ecosystem monitoring and management. The increase in usability of such tools can result in the simplification of workflows, potentially impacting the quality of the generated data. This study offers insights into the precision and reliability of the DJI Phantom 4 Multispectral (P4MS) UAS for mapping shrublands using the Agisoft Metashape (AM) for image processing. Geometric accuracy was evaluated using ground control points (GCPs) and different configurations. The best configuration was then used to produce orthomosaics. Subsequently, the orthomosaics were transformed into reflectance orthomosaics using various radiometric correction methods. These methods were further assessed using reference panels. The method producing the most accurate reflectance values was then chosen to create the final reflectance and Normalised Difference Vegetation Index (NDVI) maps. Radiometric accuracy was assessed through a multi-step process. Initially, precision was measured by comparing reflectance orthomosaics and NDVI derived from images taken on consecutive days. Finally, reliability was evaluated by comparing the NDVI with NDVI from a reference camera, the MicaSense Altum AL0, produced with images acquired on the same days. The results demonstrate that the P4MS is both precise and reliable for shrubland mapping. Reflectance maps and NDVI generated in AM exhibit acceptable geometric and radiometric accuracy when geometric calibration is performed with at least one GCP and radiometric calibration utilises images of reflectance panels captured at flight height, without relying on incident light sensor (ILS) data. Full article

The values of vegetation indices can provide a new source of data for use in the estimation of land to be consolidated. The results of research work carried out so far indicate a significant advantage of low-volume imaging over satellite methods when it comes to calculating vegetation index values. This paper analyses multispectral images for the areas of selected croplands acquired via the Sentinel-2 satellite and an unmanned aerial vehicle (UAV) equipped with a multispectral camera. The research work consisted of evaluating NDVI (Normalised Difference Vegetation Index) and SAVI (Soil Adjusted Vegetation Index) values depending on the type of crop grown, the size of the cultivated area and the method of data acquisition. The data obtained were used to assess their potential use in the estimation of land to be consolidated. The effect of land consolidation is primarily to create more favourable living conditions and increase agricultural productivity. The results of the study showed that it would be preferable to use multispectral images acquired using UAVs rather than those from Sentinel satellites. This is due to the insufficient resolution of the satellite data, the correlation of NDVI and SAVI values at only a satisfactory level and the low accuracy of the data obtained for small registered plots of land. Full article