Candida auris: A harbinger of further dangerous fungal species?

University of Würzburg

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Why has the yeast Candida auris become a dangerous pathogen worldwide in just a few years? This question is on the minds of many researchers. Carla Schuh and Chaitanya Gokhale from the Biocentre at the University of Würzburg have now summarised the current state of knowledge in a review article in the journal mSphere. To that end, they analysed around 180 studies by other researchers.

The two researchers have a theory: in their view, it could be that climate change is what made the fungus infectious in the first place.

Why do they think this? Of the estimated three to five million species of fungi on Earth, only a few hundred are capable of infecting humans. “This is partly because most fungi cannot grow at a body temperature of 37 degrees Celsius,” says Carla Schuh. However, climate change could lead to many fungi adapting to higher temperatures – and thus being able to colonise the human body.

Original habitat of the fungus unknown

Important: The Würzburg study does not prove that climate change has actually caused the increased infectivity of Candida auris. However, the researchers have found numerous indications in the studies reviewed that support this explanation.

They emphasise that the original habitat of Candida auris remains unknown to this day. Possible habitats include coastal areas, wetlands or other saline environments. “This is another reason why there are several possible explanations for why the fungus appeared so suddenly and spread so rapidly,” says Chaitanya Gokhale. In addition to climate change, other man-made environmental changes are also being discussed, such as the use of fungicides in agriculture.

High mortality rate in cases of blood infection

Candida auris was first discovered in 2009 in Japan, in a patient’s ear. This is where its name comes from, which literally translates as ‘yeast of the ear’. Within a few years, the fungus was then found simultaneously on several continents in different genetic lineages. In evolutionary biology, this is an indication of a globally active selective factor such as climate change.

The fungus grows readily at 37 degrees Celsius and can even withstand temperatures of up to 47 degrees Celsius. Its closest relatives cannot do this. Furthermore, it manages to survive on surfaces in hospitals for extended periods. And it is often resistant to several antifungal drugs at the same time.

All of this combined makes Candida auris life-threatening for immunocompromised people such as hospital patients or care home residents. If it enters the bloodstream, the mortality rate – depending on the study – ranges between 20 and 67 per cent. For many countries, the figure is around 52 per cent. During an outbreak in Europe with 140 confirmed cases, 41 per cent of those affected died within a month.

Fungal infections on the rise worldwide

The review article from Würzburg also examines global research findings on how environmental changes influence the spread of fungal diseases. The examples range from Cryptococcus gattii and Aspergillus fumigatus to other fungal species that are colonising new regions or frequently developing resistance.

“The research community agrees that fungal infections are becoming increasingly significant worldwide. However, the exact causes and the role of individual factors remain the subject of heated debate,” reports Carla Schuh.

Early-warning systems for new pathogens

Many researchers conclude from their findings, above all, that there is a need for better surveillance – to detect new pathogens as early as possible, before major outbreaks occur.

They propose systematically monitoring fungi in soil, water, the air and hospitals. This, they say, requires close integration of environmental monitoring, climate research, evolutionary biology and medicine.

Developing such early warning systems and preventive measures would require substantial investment in diagnostics, laboratory infrastructure and drug development. As a first pass, theoretical models could then help predict which species might become problematic in a cost-effective way.

“Given the shift in Candida infection dynamics, investing in and developing such predictive frameworks is imperative, as we expect other closely related species to follow this trajectory”, say the Würzburg researchers.

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Journal

mSphere

DOI

10.1128/msphere.00109-26

Method of Research

Literature review

Subject of Research

Not applicable

Article Title

Candidozyma auris (Candida auris) as a harbinger of novel fungal pathogens

Article Publication Date

2-Oct-2026

 

Barn owls are gradually changing color

University of Lausanne

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Barn owls

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Credit: © Quentin Fort

Over the past 30 years, Swiss barn owls have become more reddish-brown and more heavily spotted. By analysing the genomes of more than 3,000 birds, scientists at the University of Lausanne identified the genetic variants linked to these changes in plumage and revealed natural selection at work. Their findings, published in Nature Communications, show that evolution can happen rapidly in the wild.

From pure white to deep reddish-brown through all variations of golden brown, barn owl plumage varies remarkably from one bird to another. Some owls have only a few faint dark spots on their chest, while others display many more, much bolder markings.

Research at the University of Lausanne (Unil) has shown that these features are more than cosmetic. Plumage colour is linked to fertility and hunting strategies, and therefore to diet. White barn owls, for example, use moonlight to their advantage when attacking prey (see this news, in French). Spotting, meanwhile, is associated with immunity and the ability to cope with stress. Birds with large black spots appear to withstand certain stressful conditions, such as food shortages or parasites, more successfully.

Plumage is evolving

This diversity is not fixed. A study led by Tristan Cumer, a research fellow in the Department of Ecology and Evolution at Unil’s Faculty of Biology and Medicine, in the team of Professor Jérôme Goudet, shows that Swiss barn owls have gradually become more reddish-brown and more heavily spotted over the past 30 years. The findings were recently published in Nature Communications.

“Drawing on the exceptional volume of data collected by Alexandre Roulin and his group, who have monitored these birds of prey in the field for decades, we were able to sequence the complete genomes of more than 3,000 owls and identify the DNA variants associated with colour and spotting. This is one of the most in-depth genetic analyses ever conducted on a wild population,” says Jérôme Goudet, last author of the study, also affiliated with the SIB Swiss Institute of Bioinformatics.

The scientists pinpointed genetic regions that play a major role in the birds’ appearance. These include MC1R, a gene already known to affect pigmentation, and a region on a sex chromosome whose contribution to colour and spotting the researchers were able to clarify.

“This is one of the most in-depth genetic analyses ever conducted on a wild population.”
Prof. Jérôme Goudet, Department of Ecology and Evolution, Unil

Natural selection in action

The move towards redder, more heavily spotted plumage is not simply due to chance. By examining how genetic variants associated with colour and spotting became more or less common over time, the Unil biologists detected the effects of natural selection. “We compared the changes observed in the population with simulations in which evolution was entirely random. A genetic variant associated with reddish-brown plumage became far more common than chance alone could explain. Only simulations that included natural selection reproduced the changes we observed,” explains Tristan Cumer, first author of the paper. “Conducted in collaboration with the Swiss Ornithological Institute in Sempach, this study is one of the rare cases in which the genetic basis of recent evolution has been traced with such precision in the wild. This kind of demonstration is much harder in animals living in their natural environment than in a laboratory,” adds Jérôme Goudet.

“A genetic variant associated with reddish-brown plumage became far more common than chance alone could explain. Only simulations that included natural selection reproduced the changes we observed.”
Dr Tristan Cumer, Department of Ecology and Evolution, Unil

One essential question remains: why are barn owls becoming redder and more heavily spotted? Although the study clarifies the genetic basis of this evolution, the factors driving it have yet to be identified. Alexandre Roulin, professor in the Department of Ecology and Evolution and a co-author of the study, suggests several possibilities: “A change in prey availability may have favoured reddish-brown barn owls, which eat more voles, whereas white owls tend to feed more on wood mice. The increase in spotting could be linked to greater environmental stress. Despite the striking differences in colour between individuals, it remains extremely difficult to determine which ecological factors explain these variations.”

“We also do not know whether plumage colour is itself the trait directly favoured by natural selection,” says Jérôme Goudet. “MC1R plays a role in pigmentation, but it is part of the melanocortin system, which has also been linked to other traits, including stress responses and aggression. Selection may therefore be acting on one of these traits, with the change in the owls’ appearance simply arising as a consequence.”

A long-term collective effort

The findings are part of a research programme led by Jérôme Goudet and his team for almost a decade. Their goal is to decode the barn owl genome and establish the species as a model for studying evolution in the wild. The researchers have sequenced and assembled the species’ reference genome, reconstructed its evolutionary history across Europe and the Mediterranean, investigated genetic recombination, which reshuffles DNA from one generation to the next, and studied how mutations arise and are passed on within populations. “Research on this scale is possible because field teams, genomics specialists and members of the Faculty of Biology and Medicine’s core facilities work together,” says Professor Goudet.

The work is continuing. Computer analysis of photographs will allow the team to measure colour and spotting more precisely. The researchers also plan to use genetic data to reconstruct the plumage colour of birds that died very young and determine whether particular plumage features are associated with better chances of survival.

Understanding evolution is essential in a changing climate

Beyond the case of barn owls, the Nature Communications study reminds us that evolution does not take place only over millions of years. Major genetic changes can also occur within a few decades. In this case, 30 years, around ten generations, were enough for the effects of natural selection to become detectable. “The study carries an encouraging message: it shows that species living in the wild can evolve over a short period. This capacity for rapid adaptation could help them cope with environmental change, including climate change,” concludes Tristan Cumer.

 

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Journal

Nature Communications

DOI

10.1038/s41467-026-78632-4

Subject of Research

Animals

Article Title

Genomic bases of short-term evolution in wild barn owls revealed by long-term monitoring and population-scale sequencing

Article Publication Date

22-Oct-2026

 

How life on Earth experiences climate change

Stockholm University

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The Björkliden Valley showing microclimate contrasts between sunlit vs. shaded slopes. Climate change-driven changes in e.g. cloudiness or shrub cover may lead to different rates of warming on these different slopes. Photo:

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Credit: Jonas Lembrechts

Climate change can look very different at the scale of a forest, field or hillside. A new review shows how changes in vegetation and atmospheric conditions can cause local microclimates to warm at very different rates from the regional climate - and why this matters for understanding how biodiversity will respond to climate change.

Climate change is usually described using regional or global averages. But most organisms experience the climate much closer to the ground or within microhabitats, where temperature and other conditions can differ substantially from those measured by weather stations or represented in large-scale climate models.

This local climate, known as the microclimate, does not necessarily change in the same way as the surrounding regional climate. In a new review published in Global Change Biology, researchers from around the world synthesise how climate change can alter microclimates and identify the processes responsible for these differences.

“Climate change is often described as a single number for a region. But organisms live in specific places – in a particular patch of forest or on a particular slope. The climate they experience can change at a very different rate from the regional average,” says Caroline Greiser, researcher at Stockholm University and lead author of the study.

Vegetation can change the local climate

One important driver is vegetation. Climate change can alter forests, shrublands and other vegetation, which in turn changes how much sunlight reaches the ground and how easily the ground loses heat to the atmosphere.

“Vegetation is not simply responding to climate change – it can also modify the climate experienced by organisms living under canopies,” says Greiser. “When vegetation changes, it changes the local energy balance, and this can lead to very different temperature trends within the same landscape.”

In one of the model simulations presented in the review, the expansion of shrubs and trees into open steppes in Turkey substantially altered near-ground air temperatures. On the hottest days, shrub encroachment resulted in temperatures up to 16 degrees lower than those in open areas. In a regional warming scenario, the open areas would warm up much faster.

Clouds and other atmospheric changes matter too

Microclimate is also influenced directly by changes in atmospheric conditions. For example, cloud cover controls how much solar radiation reaches the Earth's surface. A reduction in cloudiness can substantially increase solar heating in sun-exposed locations, while shaded areas, such as forest interiors or pole-facing slopes, may be less affected.

The review shows how such changes can interact with topography, vegetation, soil moisture and other factors to produce increasingly different microclimatic conditions over relatively short distances.

Until recently, it has been difficult to study these changes over long periods and across large areas. New sensor networks, long-term datasets and mechanistic models are now making it possible to investigate how changes in the atmosphere and vegetation translate into the microclimates experienced by organisms.

“The processes that determine microclimate are complex and involve both atmospheric and biological systems. Understanding them therefore requires collaboration between atmospheric scientists and ecologists,” says Greiser.

Why microclimate matters for biodiversity

Understanding how microclimates change could improve predictions of how species and ecosystems respond to climate change. Regional climate data may suggest that an area is warming, while local conditions can remain relatively buffered - or change even more rapidly – because of vegetation, topography and atmospheric processes.

Accounting for this variation could improve predictions of future species distributions and ecological interactions, and help inform conservation and land management strategies that are adapted to local climate change.

“If we want to understand where species will persist and where they will be most vulnerable, we need to know not only how the regional climate is changing, but also how that change translates into the microclimates organisms actually experience,” says Greiser.

Future microclimates, in this alpine landscape, will be shaped by topography, the evolution of snow and cloud cover, and the ongoing colonisation by trees. Photo: Eva Gril

Credit

Eva Gril

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Journal

Global Change Biology

DOI

10.1111/gcb.71140

Method of Research

Literature review

Subject of Research

Not applicable

Article Title

Climate change experienced by life on Earth – drivers and mechanisms of future trends in microclimates

Article Publication Date

21-Oct-2026

 

From wastewater to fertilizer: Unlocking a hidden potassium resource

Chinese Society for Environmental Sciences

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Wastewater as a source of sustainable potassium fertilizers. Graphical overview of a scoping review examining 110 publications on potassium recovery from wastewater. The study evaluates suitable wastewater sources, recovery technologies, and fertilizer products using the Joanna Briggs Institute (JBI) framework, Scopus, and citation-network analysis. Key findings highlight ion exchange, electrodialysis, and potassium struvite precipitation as promising approaches, while revealing that most research remains at the laboratory scale, with a strong focus on solid fertilizers.

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Credit: Environmental Science and Ecotechnology

A research team has mapped how wastewater, so far an overlooked source of potassium fertilizer, offers a potential alternative to conventional potash mining. Potassium is indispensable for crop growth, yet fertilizer production depends heavily on mineral deposits concentrated in a few countries. By examining 110 publications, the researchers identified promising ways to recover this nutrient from human urine, livestock waste, industrial effluents, and municipal treatment streams. Their findings show that no single recovery method suits every source: concentrated wastewater favors solid, multi-nutrient fertilizers, while more dilute streams may be better suited to liquid products. The review offers a practical roadmap for turning nutrient losses into agricultural resources.

Farmers rely on potassium to support plant growth, water regulation, and crop resilience. Today, most agricultural potassium comes from mined potash, whose extraction can generate saline waste and damage freshwater ecosystems. Concentrated global supply also exposes fertilizer markets to geopolitical and economic disruption. Wastewater contains recoverable potassium, but wastewater treatment plants typically prioritize nitrogen and phosphorus, leaving much of this nutrient unused. Previous studies have tested precipitation, membranes, and selective separation, yet their findings remain scattered across different waste streams and experimental conditions. Many experiments use artificial solutions rather than the complex mixtures handled by wastewater treatment plants. Given these challenges, a deeper investigation is needed into which wastewater sources, recovery technologies, and fertilizer products can work together in practice.

Researchers Cora Eichholz and Heidrun Steinmetz from the Department of Resource-Efficient Wastewater Treatment at RPTU Kaiserslautern-Landau in Germany report (DOI: 10.1016/j.ese.2026.100777) their findings in Environmental Science and Ecotechnology in a paper accepted on October 8, 2026. Examining 110 publications, the review connects potassium concentrations, recovery processes, product forms, and barriers to practical deployment, showing why wastewater composition should guide fertilizer design rather than treating all nutrient-rich effluents as interchangeable sources.

The researchers followed the Joanna Briggs Institute (JBI) scoping-review framework, searching Scopus and expanding the evidence base through citation-network analysis. They classified 110 publications by wastewater origin, treatment process, technology maturity, and resulting fertilizer. The evidence revealed a field still dominated by laboratory work: 61 studies reported laboratory-scale experiments, compared with 11 at pilot scale, and approximately 34% relied on synthetic solutions. Among 94 publications examining specific technologies, potassium-struvite precipitation appeared in 40, electrodialysis in 17, and ion exchange in 15.

Wastewater composition emerged as a decisive factor. Source-separated urine contained approximately 900–2,440 milligrams of potassium per liter, while liquid digestate contained approximately 1,494–2,440 milligrams per liter. Their nutrient-rich composition favors solid compound fertilizers such as potassium struvite. By contrast, primary-settled municipal wastewater contained about 25 milligrams per liter, suggesting a possible route to liquid or single-nutrient products after suitable concentration and separation.

Promising results also came with limitations. Across individual studies, reported potassium recovery ranged from 15% to 98% for potassium-struvite precipitation and 13% to 95% for electrodialysis, but these figures were obtained under differing conditions and are not directly comparable. Ammonium and sodium can compete with potassium, while membrane fouling, mineral scaling, energy requirements, and uncertain fertilizer purity complicate real-world deployment.

The authors said the central lesson is to start with the fertilizer farmers need, then work backward to the wastewater source and recovery process. A technology that performs well with a clean laboratory mixture may behave very differently when faced with fluctuating nutrient levels and competing ions in real wastewater, they explained. They also emphasized that recovering potassium is only part of the challenge: the final product must be safe, usable, and consistent enough for agricultural markets. In their assessment, sustained pilot testing and careful product-quality evaluation are essential before wastewater-derived potassium fertilizers can become a dependable option.

The review points toward wastewater treatment plants functioning not only as pollution-control facilities but also as nutrient-recovery hubs. Concentrated side streams could support production of solid, multi-nutrient fertilizers, while selected dilute streams might supply liquid potassium formulations for controlled cultivation systems, including hydroponics. However, the authors stress that practical use depends on reliable contaminant screening, especially for heavy metals and organic micropollutants, and compliance with the European Union Fertilising Products Regulation. Future work should test integrated treatment sequences using actual, variable wastewater, assess equipment performance over extended periods, and apply life-cycle assessment to environmental impacts. These steps could establish whether potassium recovery delivers benefits beyond technical feasibility.

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References

DOI

10.1016/j.ese.2026.100777

Original Source URL

https://doi.org/10.1016/j.ese.2026.100777

Funding information

This research was funded by the Federal Ministry of Education, Research and Space (BMFTR) through the SUSKULT project (Funding No. 031B1528I) and supported by the Open Access Publication Fund of RPTU Kaiserslautern-Landau.

About Environmental Science and Ecotechnology

Environmental Science and Ecotechnology (ISSN 2666-4984) is an international, peer-reviewed, and open-access journal published by Elsevier. The journal publishes significant views and research across the full spectrum of ecology and environmental sciences, such as climate change, sustainability, biodiversity conservation, environment & health, green catalysis/processing for pollution control, and AI-driven environmental engineering. The latest impact factor of ESE is 14.3, according to the Journal Citation ReportsTM 2024.

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Journal

Environmental Science and Ecotechnology

DOI

10.1016/j.ese.2026.100777

Subject of Research

Not applicable

Article Title

Wastewater Mining for Circular Potassium Fertilisers: A Critical Scoping Review of Technologies, Product Formulations, and Up-scaling Challenges

Article Publication Date

8-Oct-2026

 

Satellite AI enables national-scale tree monitoring

Journal of Remote Sensing

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Spatial distribution of the estimated number of trees per ha in Rwanda and Tanzania in 2019. (A) An example of 0.5-m-resolution Google basemaps in a rainforest area in Rwanda (Ai), and the corresponding estimated tree counts per ha (Aii). (B) Same as (A) but for an urban/built-up area in Kigali city in Rwanda. (C) Same as (A) but for a transitional area between forest and farmlands in Tanzania. Note that the presented values in the image are extrapolated from an original pixel size of 96 by 96 m2 (32 × 32 pixels of 3 m resolution) to 100 × 100 m2 (1 ha) resolution. Also, note that block-like patterns slightly visible in the Southwestern and Northeastern part of Tanzania reflect patch-based inference artifacts arising from fixed-size input windows, and do not represent abrupt changes in tree density.

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Credit: Journal of Remote Sensing

Accurate tree counts are essential for restoration, yet conventional methods struggle across heterogeneous landscapes. Researchers developed a deep-learning framework combining multisensor satellite imagery to estimate tree abundance. Applied to Rwanda and Tanzania, it counted 5.25 billion trees and showed that more than half occur outside areas classified as tree cover.

Global tree-planting pledges have increased sharply since the Paris Agreement, creating demand for reliable baselines showing how many trees already exist and where they occur. Field inventories are costly and spatially limited, while repeated acquisition of submeter satellite or Unmanned Aerial Vehicle (UAV) imagery is difficult to scale. Object-detection methods also struggle when crowns overlap, occupy fractions of pixels, or lack clear boundaries. Coarser approaches based on vegetation indices, climate, or topography may smooth local variation and underrepresent trees in nonforest systems. Given these challenges, in-depth research is needed to develop scalable, affordable, robust, observation-based methods for counting trees across heterogeneous natural landscapes.

A research team led by the University of Copenhagen published (DOI: 10.34133/remotesensing.0970) the study on August 18, 2026, in the Journal of Remote Sensing. The team developed a multisensor deep-learning framework using PlanetScope, Sentinel-1, and Sentinel-2 imagery. Instead of detecting every crown individually, the system estimates tree numbers from spatial, spectral, and textural patterns within image patches. This approach targets a restoration-monitoring challenge: producing consistent, spatially detailed tree-count baselines across forests and nonforest landscapes without relying on expensive, repeatedly acquired submeter imagery.

The framework estimated about 5.25 billion trees across Rwanda and Tanzania: roughly 213 million in Rwanda and 5.04 billion in Tanzania after calibration. Only 48.8% fell within the WorldCover "tree cover" class; the other 51.2% occurred across shrublands, grasslands, croplands, built-up areas, wetlands, sparse vegetation, and mangroves. Unlike forest-focused inventories or coarse statistical extrapolations, the approach captures trees across heterogeneous landscapes. Its image-level regression also avoids dependence on explicit crown boundaries, allowing tree numbers to be inferred even when individual crowns are not fully resolved at 3-m pixel resolution. This provides a more direct, spatially explicit baseline for restoration planning and monitoring.

The model analyzed 96 × 96 m image patches from a 20-band composite containing four Planet optical bands, ten Sentinel-2 multispectral bands, and six Sentinel-1 radar polarization bands, all resampled to a common 3-m grid. A hybrid UNet-ResNet50 architecture predicted one tree-count value per patch. In architecture comparisons, UNet-R50 achieved a mean absolute error of 17.32 and an r² of 0.73, outperforming ResNet50, ResNeXt50, SwinT, and ViT. Sensor-ablation tests showed that Sentinel-1 plus Sentinel-2 reached an MAE of 18.42 and r² of 0.77, comparable with the three-sensor composite at 18.8 and 0.77, respectively. Training used about 12 million tree-crown centers from diverse landscapes. The national predictions were then bias-corrected using independent National Forest Inventory measurements, with country- and land-cover-specific bootstrap calibration. Final estimates indicated median densities of 37 trees per hectare in Rwanda and 42 in Tanzania, demonstrating national applicability across contrasting ecosystems. The approach therefore combines scalability with ecological and spatial detail.

The researchers emphasized the framework's practical value, noting that it "can guide the planning and optimization of resource allocation for tree restoration efforts at national scales." They further argue that repeatable satellite-based baselines could help governments track restoration outcomes, identify appropriate planting areas, and better account for trees growing beyond conventional forest boundaries.

Researchers assembled labels from manual interpretation of submeter imagery and semi-automated extraction from airborne and UAV LiDAR canopy-height models. About 12 million crown-center points covered 2,832 sample areas spanning roughly 608,483 hectares. Satellite inputs were temporally harmonized, corrected, normalized, and resampled to 3 m. The network used an 80/20 training-validation split and mean absolute error as its loss function. Predictions were subsequently evaluated and calibrated against independent National Forest Inventory plots from Rwanda and Tanzania. Large trees were used for validation.

The framework could support restoration programs by establishing repeatable national baselines, tracking tree gains and losses, and guiding planting toward ecologically appropriate locations. Future work must improve performance in dense closed-canopy forests, where crown overlap and signal saturation reduce sensitivity, and incorporate understory or smaller woody plants. The authors propose integrating forest inventories and very-high-resolution data, while exploring attention-based sensor fusion, domain adaptation, and transfer learning to extend the approach to new regions, ecosystems, and satellite sensors at larger scales.

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References

DOI

10.34133/remotesensing.0970

Original Source URL

https://spj.science.org/doi/10.34133/remotesensing.0970

Funding information
This study received funding from the European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Programme (grant agreement no. 101157661 TREEMAP) and the Danish National Research Foundation, Center for Remote Sensing and Deep Learning of Global Tree Resources (TreeSense), DNRF192. LiDAR data from Tanzania were provided by Prof. Terje Gobakken and Prof. Erik Næsset, Norwegian University of Life Sciences. The data were ac-quired as part of the project "Enhancing the measuring, reporting and verification (MRV) of forests in Tanzania through the ap-plication of advanced remote sensing techniques" funded by the Royal Norwegian Embassy in Tanzania as part of the Norwegian International Climate and Forest Initiative. G.V.L. acknowledges the Next Generation EU Mission 4, Component 2 - CUP B53C22002150006 - Project IR0000032 - ITINERIS - Italian Integrated Environmental Research Infrastructures System. The Ghana/Sierra Leone dataset has been collected and preprocessed by the Forest Ecology Lab of Prof. Valentini, DIBAF, Tuscia University, Viterbo, Italy. We also thank Prof. Andrew Davies for providing UAV LiDAR data in the Kruger National Park, South Africa. Additional South African airborne LiDAR data were funded by the Council for Scientific and Industrial Research (CSIR) Strategic Research Panel, the National Research Foundation (South Africa) Global Change Grand Challenge Grant (Grant number 110496), and the University of the Witwatersran.

About Journal of Remote Sensing

The Journal of Remote Sensing, an online-only Open Access journal published in association with AIR-CAS, promotes the theory, science, and technology of remote sensing, as well as interdisciplinary research within earth and information science.

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Journal

Journal of Remote Sensing

DOI

10.34133/remotesensing.0970

Subject of Research

Not applicable

Article Title

A Remote Sensing-Based Solution for National Scale Tree Counting