High-resolution Satellite Research Articles

Quantifying greenspace with satellite images in Karachi, Pakistan using a new data augmentation paradigm

Greenspaces in communities are critical for mitigating effects of climate change and have important impacts on health. Today, the availability of satellite imagery data combined with deep learning methods allows for automated greenspace analysis at high resolution. We propose a novel green color augmentation for deep learning model training to better detect and delineate types of greenspace (trees, grass) with satellite imagery. Our method outperforms gold standard methods, which use vegetation indices, by 33.1% (accuracy) and 77.7% (intersection-over-union; IoU). The proposed augmentation technique also shows improvement over state-of-the-art deep learning-based methods by 13.4% (IoU) and 3.11% (accuracy) for greenspace segmentation. We apply the method to high-resolution (0.27 m /pixel) satellite images covering Karachi, Pakistan and illuminates an important need; Karachi has 4.17 m 2 of greenspace per capita, which significantly lags World Health Organization recommendations. Moreover, greenspaces in Karachi are often in areas of economic development (Pearson’s correlation coefficient shows a 0.352 correlation between greenspaces and roads, p < 0.001), and corresponds to higher land surface temperature in localized areas. Our greenspace analysis and how it relates to infrastructure and climate is relevant to urban planners, public health and government professionals, and ultimately the public, for improved allocation and development of greenspaces.

Antarctica in 2025: Drivers of deep uncertainty in projected ice loss.

Antarctica is a vital component of Earth's climate system, influencing global sea level, ocean circulation, and planetary albedo. Major knowledge gaps in critical processes-spanning the atmosphere, ocean, ice sheets, underlying beds, ice shelves, and sea ice-create uncertainties in future projections, hindering climate adaptation and risk assessments of ice intervention strategies. Antarctica's ice sheet could contribute 28 centimeters to sea level by 2100, and potentially more if we surpass warming thresholds that trigger instabilities and rapid retreat. We review recent advances in understanding the changing stability of the ice sheet margins and identify key processes that require further research. Progress requires high-resolution satellite data, targeted field campaigns, improved modeling, and refined theory. Increased investment and interdisciplinary collaboration are essential to uncovering Antarctica's hidden processes and reducing uncertainties in future projections.

Integration of remote sensing and machine learning algorithm for agricultural drought early warning over Genale Dawa river basin, Ethiopia.

Drought remains a menace in the Horn of Africa; as a result, the Ethiopia's Genale Dawa River Basin is one of the most vulnerable to agricultural drought. Hence, this study integrates remote sensing and machine learning algorithm for early warning identification through assessment and prediction of index-based agricultural drought over the basin. To track the severity of the drought in the basin from 2003 to 2023, a range of high-resolution satellite imagery output indexes were used, including the Vegetation Condition Index (VCI), Thermal Condition Index (TCI), and Vegetation Health Index (VHI). Additionally, the Artificial Neural Network machine learning technique was used to predict agricultural drought VHI for the period of 2028 and 2033. Results depict that during the 2023 period, 25% of severe drought and 18% of extreme drought countered at the lower part of the basin at Dolo ado and Chereti regions. A high TCI value was found that around 23.24% under extreme drought and low precipitation countered in areas of Moyale, Dolo ado, Dolobay, Afder, and Bure lower than 3.57mm per month. Similarly, increment of severe drought from 24.26% to 24.58% and 16.53% to 16.58% of extreme drought value of VHI might be experienced during the 2028 and 2033 period respectively in the area of Mada Wolabu, Dolo ado, Dodola, Gore, Gidir, and Rayitu. The findings of this study are significantly essential for the institutes located particularly in the basin as they will allow them to adapt drought-coping mechanisms and decision-making easily.

Spatiotemporal evolution and driving forces of landscape structure and habitat quality in river corridors with ceased flow: A case study of the Yongding River corridor in Beijing, China.

Flow cessation leads to severe degradation of river corridor landscape structure, habitat quality, and ecological functions. This study focuses on the representative river with ceased flow in northern China, the Yongding River plain section. Utilizing long-term, high-resolution satellite remote sensing imagery and the InVEST model, we analyzed the spatiotemporal evolution of landscape structure and habitat quality (HQ) before and after river corridor flow cessation over the past 50 years. The study further employs partial least squares regression (PLSR) to explore the impact of landscape structural changes on HQ and uses generalized additive models (GAMs) and geographical detector (GeoDetector) to quantitatively identify key factors affecting habitat degradation and their interactive effects. Results indicate that from 1967 to 2018, mid-channel bar, floodplain, and waterbody decreased sharply from 37.4% to 3.8%. The mean HQ value dropped from 0.58 to 0.34 after flow cessation. Although HQ slightly recovered post-2004, high-quality habitat areas remain absent. Different landscape structures significantly influence HQ, with increased size and area of the waterbody and forest patches positively contributing, while cultivated land, barren land, and built-up land generally have negative impacts. PLAND, LPI, MPS, and AWMPFD are key metrics for optimizing landscape structure and implementing habitat restoration in river management. Anthropogenic activities emerged as the primary driver of river corridor habitat degradation post-flow cessation. Different drivers exhibit complex linear and nonlinear effects on HQ. Based on these findings, we propose ecological management strategies for river corridors with ceased flow. This study is essential for a deeper understanding of river corridors' structural dynamics and degradation mechanisms, providing a scientific basis for effective ecological restoration and management.

War-induced soil disturbances in north-eastern Ukraine (Kharkiv region): Physical disturbances, soil contamination and land use change.

The war in Ukraine is having a dramatic impact on the physical, chemical and biological soil properties. A comprehensive study of the war-affected soils during the ongoing war is a challenging task owing to the many constrains that arise during fieldworks. Remote sensing data is the best solution for overall analysis of physical soil disturbances. With the advancement in the very-high spatial resolution satellite imagery, it has become feasible to identify precise indicators of the war-induced physical soil disturbances. Here, we present a comprehensive methodology for assessing the war-affected soils tested in the Kharkiv region (north-eastern Ukraine). The methodology combines analysis of remote sensing data, soil mapping and subsequent soil modelling, field research and laboratory measurements. High-resolution satellite images, which acquired via WorldView and GeoEye, have enabled the identification of 420,615 craters and 3411ha of soils, which impacted by the movement of heavy military vehicles. Soil bombturbation covers 4212ha and is represented by entire or partial soil mixing, as well as the soil burial under the ejected material. The research showed that craters become the sites of secondary geomorphic processes that lead to crater filling and degradation of the surrounded areas. Furthermore, all sampled craters showed enrichment in heavy metals, especially cadmium, copper, lead and zinc. However, their concentration is predominantly only 1.1-1.5 times more than background value and almost does not exceed threshold limit values in Ukraine. Conversely, the sites of military helicopter crash and 'fresh' missile falling site are displayed critical contamination with heavy metals. The paper also discusses the feasibility of combining data from soil physical disturbances with land use change in order to assess the risk of landmines and the presence of unexploded ordnance, environmental effect of which is remain unclear in Ukraine.

Microphytobenthos spatio-temporal dynamics across an intertidal gradient in a tropical estuary using Sentinel-2 imagery.

Intertidal mudflats are among the most productive coastal ecosystems, largely because of the activity of the photosynthetic microbial community on the sediment surface, known as microphytobenthos (MPB). While the dynamics of MPB have been extensively studied in temperate estuaries, there is limited research in tropical estuaries. To address this knowledge gap, we investigated the spatio-temporal dynamics of MPB in the Nicoya Gulf (Costa Rica), one of the world's most productive tropical estuaries, using Sentinel-2 images at 10m spatial resolution from 2018 to 2022. We used Normalized Difference Vegetation Index (NDVI) values ranging from 0 to 0.4 to monitor MPB cover and growth rates (μNDVI, days-1). Analysis of data using Generalized Additive Models (GAM) showed that up to 62% of the temporal variability in average MPB NDVI, peaking in late wet and early dry seasons (November-December), can be explained by years and months. The high temperatures and irradiance during the dry season (December to April) in the Nicoya estuary may inhibit MPB growth, contrasting with patterns observed in temperate estuaries. We observed higher MPB NDVI in the upper intertidal zone (mean sea level>0.5m) as usually occurs in temperate estuaries. This research highlights the importance of high-resolution satellite imagery for long-term monitoring of MPB dynamics in tropical tidal flats, offering a valuable tool for estimating ecosystem services provided by intertidal MPB, such as primary production, climate regulation, and nutrient cycling on a global scale.

Improving the spatial prediction of topsoil properties in a typical grazing area using multi-season PlanetScope spectral covariates and data mining techniques

Understanding the spatial distribution of topsoil properties in grassland ecosystems is essential for improving soil ecosystem services, quality, and erosion resilience. The availability of free, high-resolution satellite imagery and advanced data mining techniques offers new opportunities for efficient soil property assessment. This study aimed to evaluate the potential utility of multi-season PlanetScope imagery to predict soil organic carbon (SOC), pH, and calcium carbonate (CaCO3). Using random sampling, 121 topsoil samples (0–30 cm depth) were collected with an auger across grasslands, bare soil, and eroded areas within a typical grazing land use. Three data mining techniques: random forest (RF), extreme gradient boosting (XGB), and support vector machines (SVM), were applied and evaluated using a 10-fold cross-validation. The results indicated that multi-season spectral covariates considerably improved the accuracy of the target soil properties compared to single-season imagery. SVM was the most effective algorithm for predicting SOC, achieving a root mean square error (RMSE) of 0.52%, mean absolute error (MAE) of 0.24%, and R² of 0.92. RF was the best-performing algorithm for predicting soil pH (RMSE = 0.22, MAE = 0.17, and R² = 0.97) and CaCO3 (RMSE = 0.55%, MAE = 0.42%, and R² = 0.96). While XGB failed to capture the variability in soil pH, the other models generated interpretable maps that accurately represented the distribution of soil properties across different land cover categories. The green-red vegetation index (GRVI) was the most critical covariate for predicting SOC, while elevation and the topographic wetness index (TWI) were key predictors for soil pH and CaCO3, respectively. This study underscores the potential of multi-season PlanetScope imagery for accurately predicting soil properties and recommends conducting similar studies in diverse geographical settings to validate these findings and develop more generalizable models.

Variable vertical land motion and its impacts on sea level rise projections.

Coastal vertical land motion (VLM), including uplift and subsidence, can greatly alter relative sea level projections and flood mitigations plans. Yet, current projection frameworks, such as the IPCC Sixth Assessment Report, often underestimate VLM by relying on regional linear estimates. Using high-resolution (90-meter) satellite data from 2015 to 2023, we provide local VLM estimates for California and assess their contribution to sea level rise both now and in future. Our findings reveal that regional estimates substantially understate sea level rise in parts of San Francisco and Los Angeles, projecting more than double the expected rise by 2050. Additionally, temporally variable (nonlinear) VLM, driven by factors such as hydrocarbon and groundwater extraction, can increase uncertainties in 2050 projections by up to 0.4 meters in certain areas of Los Angeles and San Diego. This study highlights the critical need to include local VLM and its uncertainties in sea level rise assessments to improve coastal management and ensure effective adaptation efforts.

Effect of Pandemic Covid-19 on Tropical Forest Cover in Small Island: Case Mansinam Island Papua using Google Earth Imagery

Covid-19 became a global pandemic in 2020 and has multiple impacts on the economy, social, culture, food systems, and also the environment, especially on tropical forest cover in small island. This study aims to determine the impact of the Covid-19 pandemic on the forest cover of small populated islands. Using remote sensing and geographic information system technology based on high-resolution satellite imagery from google earth imagery, land cover was observed in 2018, 2020, and 2023. This study found that during the 2020 pandemic, tropical forest cover on small populated islands has decreased. Mansinam's forest cover decreased by 4.3%, bare land increased by 80.6%, agricultural land increased by 75.3%, and shrubs increased by 54.9%. Another important finding is that 78.9% of the total deforestation was due to forest conversion to bare land and agricultural land. Land and forest utilization on small islands will increase when accessibility is limited, especially during the pandemic. Future studies will be on the structure and composition of species in locations that experienced deforestation during the pandemic and detailed studies related to changes in the occupations of Mansinam people due to the pandemic that have a relationship with forests.

The long multiphase trunk–tributary surge history of the high-Arctic Chapman Glacier, 1959–2023

ABSTRACT Surge-type glaciers have been identified throughout the Canadian Arctic, but detailed surge behavior has been sparsely studied. Recent high spatiotemporal resolution satellite products enable such studies, allowing for better process-based understanding of surging in this region. Here, we present a multidecadal record (1999–2023) of flow velocities, strain rates, and elevation changes for Chapman Glacier, a 40-km-long land-terminating glacier on Umingmak Nuna (Ellesmere Island), using ITS_LIVE (Inter-mission Time Series of Land Ice Velocity and Elevation), Sentinel-1, and elevation time series data products. We further use historical remote sensing data to analyze surface morphology and displacement from 1959 to 2023. After an inferred century-long or longer quiescent phase, Chapman Glacier surged for at least twenty-two years in two successive phases. Phase 1 occurred from 2001 to 2012 and was spatially limited to the trunk, and Phase 2 started around 2012 and continues to 2024, impacting the main tributary and the lower part of the trunk. In each surge phase flow speeds increased from ~25 to >200 m a−1 over approximately ten years, propagated from up-glacier to down-glacier, and involved notable mass displacements. Our record of this high-Arctic surge and first account of a tributary–trunk surge in the Canadian Arctic contributes to the characterization of the spectrum of dynamic ice flow instabilities worldwide.

Leveraging Deep Learning in Remote Sensing: A Novel Approach for Agricultural Greenhouse Detection and Innovation Management

Innovation management plays a pivotal role in harnessing advanced technologies to drive progress across diverse fields. In this context, integrating deep learning models within remote sensing technologies presents transformative potential for monitoring, change detection, analysis, and decision-making in fields such as agriculture, urban planning, and environmental studies. This study examines the role of sophisticated deep learning approaches in analyzing high-resolution satellite imagery to improve the detection of agricultural greenhouses. Using MMSegmentation (DeepLabv3Plus) with multispectral data at 0.7-meter resolution, the research addresses the limitations of traditional methods by substantially enhancing detection accuracy and efficiency. To address data scarcity and increase model robustness, advanced data augmentation techniques—such as rotations, scaling, and flipping - expand dataset diversity, fostering adaptability and performance under diverse conditions. The study also assesses the impact of environmental factors, including seasonal variations and weather, on model performance. Suggested improvements include expanding the dataset to encompass a wider variety of greenhouse types and conditions, incorporating high-resolution or hyperspectral imagery for finer details, and building multi-temporal datasets to capture dynamic environmental changes. The findings underscore the importance of advanced innovation management in enhancing remote sensing applications, offering actionable insights for agricultural management in Albania and similar regions. This research contributes to the broader field of innovation management by showcasing how deep learning can revolutionize practical applications in agriculture

Robustness of Actual Evapotranspiration Predicted by Random Forest Model Integrating Remote Sensing and Meteorological Information: Case of Watermelon (Citrullus lanatus, (Thunb.) Matsum. & Nakai, 1916)

Water scarcity, exacerbated by climate change and increasing agricultural water demands, highlights the necessity for efficient irrigation management. This study focused on estimating actual evapotranspiration (ETa) in watermelons under semi-arid Mediterranean conditions by integrating high-resolution satellite imagery and agro-meteorological data. Field experiments were conducted in Rutigliano, southern Italy, over a 2.80 ha area. ETa was measured with the eddy covariance (EC) technique and predicted using machine learning models. Multispectral reflectance data from Planet SuperDove satellites and local meteorological records were used as predictors. Partial least squares, the generalized linear model and three machine learning algorithms (Random Forest, Elastic Net, and Support Vector Machine) were evaluated. Random Forest yielded the highest predictive accuracy with an average R2 of 0.74, RMSE of 0.577 mm, and MBE of 0.03 mm. Model interpretability was performed through permutation importance and SHAP, identifying the near-infrared and red spectral bands, average daily temperature, and relative humidity as key predictors. This integrated approach could provide a scalable, precise method for watermelon ETa estimation, supporting data-driven irrigation management and improving water use efficiency in Mediterranean horticultural systems.

Identifying narco-trafficking landing zones using satellite imagery and geospatial indicators in Costa Rica

Transnational cocaine trafficking, or ‘narco-trafficking’, networks often move large shipments of harmful and illegal drugs by sea through the use of unregulated boats in remote maritime spaces. This study presents a framework for identifying narco-trafficking drop-off zones by detecting and analyzing Unreported and Unregulated Boats (UUBs) potentially linked to narco-trafficking in Costa Rica’s Guanacaste and Puntarenas regions, utilizing a combination of high-resolution satellite imagery, machine learning, and spatial analysis. By training a YoloV5 convolutional neural network model, we detected boat wakes in PlanetScope satellite images, which were then cross-referenced with legal vessel traffic data (Automatic Identification System and Vessel Monitoring System) to isolate UUBs. A spatial autocorrelation analysis revealed a positive association between UUB locations and narco-trafficking activity indicators, such as drug-related media reports, court arrest records, and cocaine seizure data. High-high clusters of UUBs and trafficking indicators suggested that particular coastal districts may serve as primary landing zones for illicit shipments, a finding consistent with secondary data on cocaine trafficking in the region. By integrating geospatial intelligence with contextual data sources, this study advances methodology for identifying narco-trafficking drop-off zones and contributes a spatially explicit perspective to the broader understanding of cocaine and other illicit supply chains. Despite the limitations of cloud cover and restricted nighttime visibility, this framework offers a proof-of-concept approach for identifying UUB concentrations and cocaine drop-off shipment zones. Future work should consider expanding temporal coverage and multiple imagery to further enhance the identification of narco-trafficking zones.

Evaluating Methane Emission Estimates from Intergovernmental Panel on Climate Change Compared to Sentinel-Derived Air–Methane Data

This study compares the methane emission estimates from the Intergovernmental Panel on Climate Change with satellite-based measurements from Sentinel-5P to assess trends in global methane emissions and concentrations. Focusing on the countries listed in Annex I of the United Nations Framework Convention on Climate Change and the key sectors of Agriculture, Energy, industrial processes and product use, land use, land use change, forestry, and Waste, this analysis uses data from 1990 to 2021, evaluated through the Mann–Kendall trend test. The findings reveal a decline in methane emissions reported by the IPCC, particularly in the Energy and Waste sectors, driven by stricter environmental regulations and technological advancements in these regions. However, the satellite data from the TROPOspheric Monitoring Instrument for 2019–2024 indicate an increasing trend in atmospheric methane concentrations, suggesting that the reductions reported in the inventories may be insufficient to offset ongoing or previously accumulated emissions. The discrepancies between the IPCC inventories and the satellite observations highlight the challenges in methane source attribution and the limitations of relying solely on inventory-based methods. This study demonstrates the potential of integrating high-resolution satellite data with the traditional methodologies to improve the accuracy of methane emission estimates. Such an approach provides a more comprehensive understanding of methane dynamics, particularly in regions where natural and anthropogenic sources overlap. The findings of this study contribute to a better understanding of global methane emission trends and their implications for climate change. Integrating satellite observations into national inventories has practical applications for enhancing methane monitoring, improving emission reporting, and supporting global climate goals through the more effective and sustainable management of methane emissions.

SPS-UNet: a super-pixel sampling UNet for extracting buildings from high-resolution satellite images

SPS-UNet: a super-pixel sampling UNet for extracting buildings from high-resolution satellite images

Prediction of Winter Wheat Parameters with Planet SuperDove Imagery and Explainable Artificial Intelligence

This study investigated the application of high-resolution satellite imagery from SuperDove satellites combined with machine learning algorithms to estimate the spatiotemporal variability of some winter wheat parameters, including the relative leaf chlorophyll content (RCC), relative water content (RWC), and aboveground dry matter (DM). The research was carried out within an experimental field in Southern Italy during the 2024 growing season. Different machine learning (ML) algorithms were trained and compared using spectral band data and calculated vegetation indices (VIs) as predictors. Model performance was assessed using R2 and RMSE. The ML models tested were random forest (RF), support vector regressor (SVR), and extreme gradient boosting (XGB). RF outperformed the other ML algorithms in the prediction of RCC when using VIs as predictors (R2 = 0.81) and in the prediction of the RWC and DM when using spectral bands data as predictors (R2 = 0.71 and 0.87, respectively). Model explainability was assessed with the SHAP method. A SHAP analysis highlighted that GNDVI, Cl1, and NDRE were the most important VIs for predicting RCC, while yellow and red bands were the most important for DM prediction, and yellow and nir bands for RWC prediction. The best model found for each target was used to model its seasonal trend and produce a variability map. This approach highlights the potential of integrating ML and high-resolution satellite imagery for the remote monitoring of wheat, which can support sustainable farming practices.

Mass wasting reveals ongoing asymmetric retreat of the martian north polar ice cap

Ongoing mass wasting through ice block falls is intensive at the north polar ice cap of Mars. We monitored how this activity is currently shaping the marginal steep scarps of the ice cap, which holds a record of the planet’s climate history. With AI-driven change detection between multi-temporal high-resolution satellite images, we created a comprehensive map of mass wasting across the entire North Polar Layered Deposits (NPLD). Our results show a more active erosion process than previously thought, with scarps retreating by up to ~3 m every kiloyear. The distribution of the active scarps indicates an ongoing asymmetric retreat of the already subcircular ice cap. The active scarps and the interior dune fields correlate strongly with exposures of the underlying, sandier Basal Unit (BU), providing evidence that erosion of the BU undermines the base of the NPLD. Moreover, ice block fall activity suggests potential areas where gypsum is released, given that the interior gypsum-bearing dune fields are located adjacent to these active scarps. Here, our study reveals the rates of present-day topographic change of the north polar ice cap, providing a valuable constraint for study of its past evolution.

Intercomparison of satellite-derived SST with logger data in the Caribbean—Implications for coral reef monitoring

Since the early 1980s measurements of Sea Surface Temperature (SST) derived from satellite-borne instruments have provided a wide range of global gridded products documenting changes in SST. However, there are many sources of uncertainty in these records and significant differences exist among them. One use of these products is identification of coral bleaching events, and the predictions of the impact of future warming on coral reefs. This relies on an understanding of how temperatures near reefs as recorded by SST products differ from the in-situ SST experienced by the corals. This difference is a combination of real spatio-temporal variations, inadequate in product resolution and errors in the products. This paper investigates the relationship between the local temperature measured in-situ by loggers at coral sites in the western tropical Atlantic and two high resolution satellite SST products. Using differences among ESA SST CCI v2.1 (CCI analysis SST), NOAA CoralTemp SST products and in-situ logger data from coral reefs, an assessment of the satellite products with focus on coral reef monitoring is carried out. Discrepancies between the two products can be large, especially in coastal areas and for the warmest and coldest months when there is a particular risk of bleaching. By comparison to the stable CCI analysis SST product, CoralTemp was found to overestimate the rise in SST by as much as 0.20°C per decade. In almost all cases SSTs from CCI analysis SST were more consistent with temperatures measured near the corals than those from CoralTemp.

Fast Expansion of Surface Water Extent in Coastal Chinese Mainland from the 1980s to 2020 Based on Remote Sensing Monitoring

High-resolution satellite imagery providing long-term, continuous information on surface water extent in highly developed regions is paramount for elucidating the spatiotemporal dynamics of water bodies. The landscape of water bodies is a key indicator of water quality and ecological services. In this study, we analyzed surface water dynamics, including rivers, lakes, and reservoirs, using Landsat images spanning from the 1980s to 2020, with a focus on the highly developed Coastal Chinese Mainland (CCM) region. Our objectives were to investigate the temporal and spatial variations in surface water area extent and landscape characteristics, to explore the driving forces behind these variations, to gain insights into the complex interactions between water bodies and evolving environmental conditions, and ultimately to support sustainable development in coastal regions. Our findings revealed that reservoirs constitute the largest proportion of surface water, while lakes occupy the smallest share. Notably, a trend of expansion in surface water extent in the CCM was observed, mainly from the construction of new reservoirs. These reservoirs primarily gained new areas from agricultural land and river floodplains in the early stages (1980s–2000), while a greater proportion of construction land was encroached upon by reservoirs in later periods (2001–2020). At the landscape level, a tendency toward fragmentation and complexity in surface water, particularly in reservoirs, was evident. Human interference, particularly urbanization, played a pivotal role in driving the expansion of water surfaces. While reservoir construction benefits water resource assurance, flood control, and prevention, it also poses eco-hydrological challenges, including water quality deterioration, reduced hydrological connectivity, and aquatic ecosystem degradation. The findings of this study provide essential data support for sustainable water resource development. These insights underscore the urgency and importance of integrated water resource management strategies, particularly in efforts aimed at conservation and restoration of natural water bodies and the scientific regulation of artificial water bodies. Balancing human development needs with the preservation of ecological integrity is crucial to facilitating a water resource management strategy that integrates climatic and socio-economic dimensions, ensuring sustainable water use and protection for future generations.

Climate Change-Induced Decline in Succulent Euphorbia in Namibia's Arid Regions.

The global rise in temperatures due to climate change has made it difficult even for specialised desert-adapted plant species to survive on sandy desert soils. Two of Namibia's iconic desert-adapted plant species, Welwitschia mirabilis and the quiver tree Aloidendron dichotomum, have recently been shown to be under threat because of climate change. In the current study, three ecologically important Namibian Euphorbia milk bushes were evaluated for their climate change response. By comparing good-quality aerial photographs from the 1960s and recent 2020s high-resolution satellite images, it was determined by QGIS remote sensing techniques that very high percentages of the large succulents E. damarana, E. gummifera, and E. gregaria have died during the last 50 years in arid areas of Namibia. Areas like Brandberg (northern Namibia), Klein Karas (south-east), and Garub (south-west), with a high sandy-textured ground cover, have seen the loss of around 90% of E. damarana and E. gregaria and about 61% of E. gummifera in this period. This is alarming, as it could threaten the survival of several animal species adapted to feed on them, especially during droughts. This study focused on large succulent euphorbias, distinguishable in satellite images and historical photographs. It was observed that many other plant species are also severely stressed in arid sandy areas. The obtained results were ground-truthed and species identification was confirmed by the chemical analysis of remaining dead twigs using GC-MS and metabolomics. The ERA5 satellite's 2 m above-ground temperature data show a 2 °C rise in annual average noon temperatures since 1950 at the three locations analysed. Annual daily temperatures increased by 1.3 °C since 1950, exceeding the global average rise of about 1.0 °C since 1900. This suggests that euphorbias and other plants on low-water-capacity sandy soils in Namibia face greater climate change pressure than plants globally.