Tree Counts Research Articles

Combining OBIA, CNN, and UAV imagery for automated detection and mapping of individual olive trees

The identification of individual trees is an important research topic in forestry, remote sensing and computer vision. It represents a tool for effectively and efficiently managing and maintaining forests and orchards. However, this task is not as simple as it seems; tree detection and counting can be time consuming, cost-prohibitive and accuracy-limited, especially if performed manually on a large scale.The availability of very high-resolution UAV imagery with remote sensing can make the counting process easier, faster and more precise. With the development of technology, this process can be made more automated by using intelligent algorithms such as CNN.This work presents an OBIA-CNN (Object Based Image Analysis-Convolution Neural Network) approach that combines CNNs with OBIA to automatically detect and count olive trees from Phantom4 advanced drone imagery. Initially, The CNN-based classifier was created, trained, validated, and applied to generate the Olive trees probability maps on the ortho-photo. The post-classification refinement based on OBIA was then conducted. A super-pixel segmentation and the Excess Green index were performed and a detailed accuracy analysis has been carried out to establish the suitability of the proposed method.The application to a RGB ortho-mosaic of an olive grove, in the east region of Morocco was successful using a manually elaborated training dataset of 4500 images of 24×24 pixels. Finally, the CNN detected and counted 2934 olive trees on the ortho-photo, achieving an overall accuracy of 97 % and 99 % after the OBIA refinement. The results of the proposed OBIA-CNN method were also compared with the classification results of using the Template matching technique, CNN method alone, and OBIA analysis alone to evaluate the performance of the approach. Our findings suggest the use of very high resolution images with object-based deep learning is promising for automatic detection and counting of olive trees to support the accurate and sustainable agricultural monitoring.

Measuring ornamental tree canopy attributes for precision spraying using drone technology and self-supervised segmentation

Tree canopy attributes or characteristics, such as canopy volume and density, are important parameters for calculating the precise quantity of agrochemicals required in each tree. Assessing the agrochemical needs in a nursery can help growers effectively estimate expenses, mitigate the risks of overuse or underuse, enhance resource utilization, and promote sustainable agricultural practices. This study aimed to develop an unmanned aerial vehicle (UAV)-based system to measure tree canopy attributes like tree height, tree count, canopy area and canopy volume using an image processing and self-supervised zero-shot segmentation approach. A high-resolution red-green-blue (RGB) sensor mounted on a drone captured three aerial image datasets, D1 and D2 from the first experimental plot and D3 from the second experimental plot. The acquired aerial images were stitched together and processed to generate a digital surface model (DSM) and a digital terrain model (DTM). A self-supervised zero-shot segmentation model, Segment Anything Model (SAM), was applied for tree segmentation and counting. The tree canopy area was calculated by segmenting individual trees and applying a conversion factor to convert pixelwise areas into square meters. Height calculation was done using elevation data of each pixel from DSM-DTM imagery within SAM-derived bounding box coordinates, and the highest pixel was considered as the height of the tree. The drone-calculated heights of 24 randomly selected trees were compared with manually measured heights in all datasets. The results showed an average absolute error of 2.43% in D1, 7.09% in D2 and 12.58% in D3. The Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were noted to be 0.09 m and 0.23 m in D1, 0.13 m and 0.25 m in D2, and 0.16 m and 0.21 m in D3, respectively. In D3, manual area and volume calculations were performed for validation. The RMSE and MAE for area calculation were calculated as 0.18 m2 and 0.15 m2, respectively, and the RMSE and MAE for volume measurements were calculated as 0.33 m3 and 0.26 m3 respectively, which indicated the level of agreement between manual and drone measurements. Using canopy volume information, calculated by summing pixel heights within each tree’s bounding box and multiplying by the ground sample distance (GSD) of 0.025 m per pixel, the average quantity of agrochemicals needed for a Maple tree was estimated at 0.20 L. These results underscore the high potential of this method for accurate canopy characteristic calculations and precision spray applications in the ornamental industry.

The Impact of Tree Density on Automated Oil Palm Tree Counting Accuracy

The rapid development of oil palm plantations in Kualuh Leidong Subdistrict, Labuhanbatu Utara District, North Sumatra Province has led to an increased need for effective and efficient monitoring and supervision of oil palm trees. One method that supports such monitoring and supervision is the use of an automatic counting method using orthophoto data. This orthophoto data was used for automatic tree counting using a deep learning method with the Faster R-CNN algorithm. The study considered two planting patterns: regular planting patterns with spacing of 4 to 9 meters, and random planting patterns with varying spacing. Data processing involved an epoch value of 80 and a batch size value of 4. The accuracy of the automatic oil palm tree counting was evaluated based on the density of the spacing between trees with reference to the ground truth. The findings indicated that the deep learning Faster R-CNN algorithm achieved higher accuracy in automatic calculations for regular planting patterns.

Hyperfiniteness for group actions on trees

We identify natural conditions for a countable group acting on a countable tree which imply that the orbit equivalence relation of the induced action on the Gromov boundary is Borel hyperfinite. Examples of this condition include acylindrical actions. We also identify a natural weakening of the aforementioned conditions that implies measure hyperfiniteness of the boundary action. We then document examples of group actions on trees whose boundary action is not hyperfinite.

AdaTreeFormer: Few shot domain adaptation for tree counting from a single high-resolution image

The process of estimating and counting tree density using only a single aerial or satellite image is a difficult task in the fields of photogrammetry and remote sensing. However, it plays a crucial role in the management of forests. The huge variety of trees in varied topography severely hinders tree counting models to perform well. The purpose of this paper is to propose a framework that is learnt from the source domain with sufficient labeled trees and is adapted to the target domain with only a limited number of labeled trees. Our method, termed as AdaTreeFormer, contains one shared encoder with a hierarchical feature extraction scheme to extract robust features from the source and target domains. It also consists of three subnets: two for extracting self-domain attention maps from source and target domains respectively and one for extracting cross-domain attention maps. For the latter, an attention-to-adapt mechanism is introduced to distill relevant information from different domains while generating tree density maps; a hierarchical cross-domain feature alignment scheme is proposed that progressively aligns the features from the source and target domains. We also adopt adversarial learning into the framework to further reduce the gap between source and target domains. Our AdaTreeFormer is evaluated on six designed domain adaptation tasks using three tree counting datasets, i.e. Jiangsu, Yosemite, and London. Experimental results show that AdaTreeFormer significantly surpasses the state of the art, e.g. in the cross domain from the Yosemite to Jiangsu dataset, it achieves a reduction of 15.9 points in terms of the absolute counting errors and an increase of 10.8% in the accuracy of the detected trees’ locations. The codes and datasets are available at https://github.com/HAAClassic/AdaTreeFormer.

Estimation of Oil Palm Tree Quantity Using Oil Palm Ecognition and SAGA GIS Based on UAV Imagery (Case Study: Dumai, Riau)

Abstract Oil palm is a plantation commodity in Indonesia that drives the growth of the national economy. The growth of oil palm is rapidly expanding every year, thus requiring efficient land monitoring strategies. Tree counting is one technique used to monitor current conditions in vast plantation areas. Utilizing UAV technology in agricultural mapping is the best option as a tool for capturing high-resolution aerial photos. The objective of this research is to automatically count the number of oil palm trees by comparing two methods and algorithms. GEOBIA (Geography-Based Image Analysis) and Deep Learning classification are methods used for automatic object counting. In this study, high-resolution aerial photo data was utilized, making object detection much more feasible. The research applied the Template Matching and Watershed Segmentation algorithm models in a sample area of 32.5 hectares divided into two blocks: Unknown-2 and E-1. It can be concluded that both methods, with their respective algorithm models, are sufficiently relevant for use in the automatic counting process of oil palm trees. This is evidenced by the accuracy test error values, which are not more than 15%, indicating that the counted oil palm tree results can be used for further data analysis.

Examination of Drone Usage in Estimating Hardwood Plantations Structural Metrics

Planting hardwood trees on retired marginal agricultural land is one of the main strategies used to restore forested wetlands. Evaluating effectiveness of wetland restoration requires efficient monitoring to evaluate recovery trajectories and desired conditions. Recent advancements in unmanned aerial system (UAS) technologies have prompted wide-scale adoption of UAS platforms in providing a range of ecological data. In this study, we examined the use of UAS Structure from Motion (SfM) derived point clouds in estimating tree density, canopy height, and percent canopy cover for bottomland hardwood plantations within four wetland reserve easements. Using a local maxima approach for individual tree detection produced plantation level estimates with mean absolute errors of 150 trees per hectare, 0.5 m, and 18.4% for tree density, canopy height, and percent canopy cover, respectively. At the plot level, UAS-derived tree counts (r = 0.53, p < 0.01) and canopy height (r = 0.57, p < 0.01) were significantly correlated with ground-based estimates. We demonstrate that UAS-SfM is a viable method of assessing bottomland hardwood plantations for applications that require precision levels congruent with the mean absolute errors reported here. The accuracy of tree density estimates was reliant upon specific local maxima window parameters relative to stand conditions. Therefore, acquisition of leaf-off and leaf-on imagery may allow for better individual tree detection and subsequently more accurate tree density and other structural attributes.

The Number of Topological Types of Trees

Two graphs are of the same topological type if they can be mutually embedded into each other topologically. We show that there are exactly ℵ1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\aleph _1$$\\end{document} distinct topological types of countable trees. In general, for any infinite cardinal κ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\kappa $$\\end{document} there are exactly κ+\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\kappa ^+$$\\end{document} distinct topological types of trees of size κ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\kappa $$\\end{document}. This solves a problem of van der Holst from 2005.

Provisioning services decline for both people and Critically Endangered wildlife in a rainforest transformation landscape

Abstract The loss and degradation of forests and other ecosystems worldwide threaten both global biodiversity and the livelihoods of people who use natural resources. Understanding how natural resource use impacts landscape provisioning services for both people and wildlife is thus critical for designing comprehensive resource management strategies. We used data from community focus groups, botanical plots and an inventory of plant species consumed by the Critically Endangered red‐ruffed lemur (Varecia rubra) to assess the availability of key provisioning services for people and endemic wildlife on the Masoala Peninsula, a rainforest transformation landscape, in northeastern Madagascar (Masoala National Park and 13 surrounding communities). We constructed Poisson regression mixed models to evaluate the impact of community factors (i.e. community population size, plot distance to community) and changes over time on the count and species richness of timber trees, medicinal plants and red‐ruffed lemur food trees within botanical plots. Over three‐quarters of all plant species could be used for at least one purpose by local communities (n = 238 species). Of the 59 V. rubra food tree species, only 15% had no reported human use. Timber and ruffed lemur food tree availability declined both with community population size and time and were predicted to be lower outside of Masoala National Park. In contrast, medicinal plant availability was not strongly predicted by any tested factors. Provisioning service availability also differed strongly across sites, suggesting that additional, untested proxies of human pressure likely also have an effect. Our results highlight the importance of evaluating natural resource availability from a community‐based perspective and by resource purpose to inform forest landscape restoration efforts that can support both people and wildlife. Read the free Plain Language Summary for this article on the Journal blog.

Application of Image Analytics for Tree Enumeration for Diversion of Forest Land

Accurate tree enumeration is essential for responsible forest land diversion in development projects. Conventional manual surveys are slow, costly, and prone to errors. This paper introduces a cutting-edge image analytics solution that leverages satellite imagery and aerial photos to automate tree counting. The primary objective is to develop a robust system that identifies and categorizes trees by crown size, and environmental conditions. Advanced computer vision algorithms are integrated with machine learning models to analyze the imagery. Rigorous validation processes, including comparisons with ground-truth data from manual surveys, ensure high accuracy and reliability. The results are impressive. This solution significantly accelerates tree enumeration, eliminating resource-intensive manual efforts. It consistently demonstrates precision with minimal false positives and negatives. Moreover, it categorizes trees efficiently by species, offering a comprehensive view of the forested area. This project significance lies in its contribution to responsible and sustainable land development practices. By automating tree enumeration, it equips stakeholders with timely, precise data for informed decisions about land usage, conservation, and environmental impact assessments. The solution strikes a balance between development and ecological preservation, optimizing resource allocation while minimizing environmental impact in forested regions. In conclusion, this innovative image analytics solution revolutionizes forest land diversion, enabling efficient and ecologically conscious decision-making. It addresses the critical need for accurate tree enumeration in the face of developmental challenges, fostering responsible land use and environmental stewardship.

A comparative study on the efficacy of dual-pol and full-pol ASAR data in radiative transfer modeling for forest above-ground biomass estimation

ABSTRACT In this study, a comprehensive evaluation of the potential of dual-polarization and full-polarization L- and S-band airborne SAR (LS-ASAR) data (ISRO’s dual-frequency airborne SAR mounted on NASA’s Gulfstream III aircraft) in forest above-ground biomass (AGB) estimation has been carried out. The study area comprises the temperate forests of the Lenoir landing site in Southwest Alabama, United States of America. A trunk scattering model based on Vector Radiative Transfer (VRT) theory is used in this study to estimate the AGB. Full-polarization ASAR data inversion retrieves three critical biophysical parameters: tree height, diameter at breast height (DBH), and tree count, whereas with dual-polarization ASAR data, the model is limited to retrieving only two parameters (tree height and DBH). These parameters are then employed in a suitable allometric equation to estimate the AGB over the study area. Validation of the retrieval results is performed through comparison with ground-truth measurements taken from the study site. The findings demonstrate that the inversion of L-band full-polarized data yields the highest correlation (R2 = 0.87, RMSE = 20.37 t/ha), revealing high accuracy in AGB estimation. The L-band dual-polarized data shows a high correlation (R2 = 0.83, RMSE = 30.66 t/ha), indicating reasonable accuracy. However, the S-band data, both full and dual-polarized, reveals weaker correlations with higher RMSE values (R2 = 0.59, RMSE = 55.07 t/ha, and R2 = 0.49, RMSE = 58.31 t/ha, respectively), suggesting a lower degree of reliability. This study emphasizes the pre-eminence of L-band fully polarized data inversion for reliable and accurate AGB estimation while also showing the utility of relatively ubiquitous dual-pol data (L-band) to achieve reasonable accuracy for the same.

An Open Benchmark Dataset for Forest Characterization from Sentinel-1 and -2 Time Series

Earth observation satellites offer vast opportunities for quantifying landscapes and regional land cover composition and changes. The integration of artificial intelligence in remote sensing is essential for monitoring significant land cover types like forests, demanding a substantial volume of labeled data for effective AI model development and validation. The Wald5Dplus project introduces a distinctive open benchmark dataset for mid-European forests, labeling Sentinel-1/2 time series using data from airborne laser scanning and multi-spectral imagery. The freely accessible satellite images are fused in polarimetric, spectral, and temporal domains, resulting in analysis-ready data cubes with 512 channels per year on a 10 m UTM grid. The dataset encompasses labels, including tree count, crown area, tree types (deciduous, coniferous, dead), mean crown volume, base height, tree height, and forested area proportion per pixel. The labels are based on an individual tree characterization from high-resolution airborne LiDAR data using a specialized segmentation algorithm. Covering three test sites (Bavarian Forest National Park, Steigerwald, and Kranzberg Forest) and encompassing around six million trees, it generates over two million labeled samples. Comprehensive validation, including metrics like mean absolute error, median deviation, and standard deviation, in the random forest regression confirms the high quality of this dataset, which is made freely available.

Early-Stage Pine Wilt Disease Detection via Multi-Feature Fusion in UAV Imagery

Pine wilt disease (PWD) is a highly contagious and devastating forest disease. The timely detection of pine trees infected with PWD in the early stage is of great significance to effectively control the spread of PWD and protect forest resources. However, in the spatial domain, the features of early-stage PWD are not distinctly evident, leading to numerous missed detections and false positives when directly using spatial-domain images. However, we found that frequency domain information can more clearly express the characteristics of early-stage PWD. In this paper, we propose a detection method based on deep learning for early-stage PWD by comprehensively utilizing the features in the frequency domain and the spatial domain. An attention mechanism is introduced to further enhance the frequency domain features. Employing two deformable convolutions to fuse the features in both domains, we aim to fully capture semantic and spatial information. To substantiate the proposed method, this study employs UAVs to capture images of early-stage pine trees infected with PWD at Dahuofang Experimental Forest in Fushun, Liaoning Province. A dataset of early infected pine trees affected by PWD is curated to facilitate future research on the detection of early-stage infestations in pine trees. The results on the early-stage PWD dataset indicate that, compared to Faster R-CNN, DETR and YOLOv5, the best-performing method improves the average precision (AP) by 17.7%, 6.2% and 6.0%, and the F1 scores by 14.6%, 3.9% and 5.0%, respectively. The study provides technical support for early-stage PWD tree counting and localization in the field in forest areas and lays the foundation for the early control of pine wood nematode disease.

Coupling an Autonomous UAV With a ML Framework for Sustainable Environmental Monitoring and Remote Sensing

Many countries face problems in monitoring plant problems and monitoring large environments, as until now, there is no accurate means of aerial monitoring through which concerned parties can benefit from watersheds, monitor large agricultural areas, and make wise environmental decisions regarding them. This paper describes a pioneering approach to develop smart agriculture using multimission drones equipped with dual cognitive modules (brains) that are powered by a machine learning (ML) framework. The first brain uses deep reinforcement learning (DRL) principles to enable autonomous flight, allowing drones to navigate complex agricultural terrain with agility and flexibility. The second brain is responsible for precise and crucial agricultural tasks: counting trees, detecting water locations, and observing and analyzing plants using the Faster R‐CNN algorithm. The system is linked with the ground station for control and command, as well as it includes an Internet of Things (IoT) infrastructure equipped with sensors that collect soil parameters, which then get sent via 5G Wi‐Fi. The dual architecture of the drones, combined with the ground‐level IoT system, creates a comprehensive framework that not only enhances agricultural technologies but also aligns with environmental conservation goals, embodying a paradigm shift towards a greener and more sustainable future. Obtained results show reasonable results with an accuracy rate of 98%.

Template matching technique for smart tree detection and counting with UAV imagery

The identification of individual trees is an important research topic in forestry, remote sensing, and computer vision. It is a requirement in forest management and monitoring because it provides key forest inventory information, vegetation distribution mapping, vegetation density estimation, change monitoring, and species classification.However, counting trees can be challenging due to the crowded environment, time-consumption, and expensive operation. Remote Sensing methods such as UAV imagery and the development of efficient algorithms can be adapted to estimate and detect individual tree counts in orchards. This paper aims to use the template matching technique to automatically detect olive trees from high resolution drone imagery in the eastern part of Morocco. The algorithm successfully detected and counted 2719 olive trees with a difference of less than 233 trees with manual detection. The results of detecting and counting the individual olive trees were evaluated using several parameters: an Fscore of 94%, with a recall of 92% and a precision of 98%, which are satisfactory.

Oil palm tree counting and abnormality assessment using deep learning and index vegetation

Oil palm tree counting and abnormality assessment using deep learning and index vegetation

Tree-CRowNN: A Network for Estimating Forest Stand Density from VHR Aerial Imagery

Estimating the number of trees within a forest stand, i.e., the forest stand density (FSD), is challenging at large scales. Recently, researchers have turned to a combination of remote sensing and machine learning techniques to derive these estimates. However, in most cases, the developed models rely heavily upon additional data such as LiDAR-based elevations or multispectral information and are mostly applied to managed environments rather than natural/mixed forests. Furthermore, they often require the time-consuming manual digitization or masking of target features, or an annotation using a bounding box rather than a simple point annotation. Here, we introduce the Tree Convolutional Row Neural Network (Tree-CRowNN), an alternative model for tree counting inspired by Multiple-Column Neural Network architecture to estimate the FSD over 12.8 m × 12.8 m plots from high-resolution RGB aerial imagery. Our model predicts the FSD with very high accuracy (MAE: ±2.1 stems/12.8 m2, RMSE: 3.0) over a range of forest conditions and shows promise in linking to Sentinel-2 imagery for broad-scale mapping (R2: 0.43, RMSE: 3.9 stems/12.8 m2). We believe that the satellite imagery linkage will be strengthened with future efforts, and transfer learning will enable the Tree-CRowNN model to predict the FSD accurately in other ecozones.

Mapping the number of mangrove trees in the Guangdong-Hong Kong-Macao Greater Bay Area

Mangroves are vital components of coastal ecosystems. Due to the complex canopy morphology and dense distribution of mangroves, it is challenging to accurately estimate the density based on satellite data. In this study, a density regression-based mangrove mapping network is proposed. The network can capture the multi-scale characteristics of mangroves through the combination of an attention mechanism and a parallel segmentation path, and its performance is better than existing methods. We then apply it to mapping the Greater Bay Area (GBA) the number of mangrove trees. The results show about 2.55 million mangrove trees in the GBA, with an average density of 782 trees per hectare. The tree number of mangroves on the beach is significantly higher than those distributed along the riverbank. This study is the first to achieve mangrove tree count mapping, opening up new prospects for applying satellite-based mangrove monitoring.

The Effects of Neem Tree (Azadirachta indica A. Juss) Invasion on the Growth of Indigenous Flora in Katsina State, Northwest Nigeria

Invasive or alien species are increasing in number, extent, and influence worldwide. They are both passengers and drivers of change, and they interact synergistically with many other facets of global change. The Neem trees have been grown successfully in all parts of Nigeria. Neem has become an invasive and naturalized species in various parts of Nigeria in quite diverse ecological and climatic conditions. The tree is adaptable to a wide range of climatic and topographic conditions. The objective of the study is to determine the effect of neem invasion on the growth of indigenous flora in Katsina state. The information on farmers' perception about Neem tree was obtained using semi semi-structured questionnaire. About 200 informants were interviewed. Direct counting of trees was conducted to determine the effect of Neem invasion on the indigenous flora. A total of 58 species of trees belonging to 47 genera within 15 families, of which family Fabaceae has the highest number of tree species, 44 affected by neem invasion in the study area. The result showed the effects of having neem in the farms. Some of the respondents believed that Neem spread easily and nothing grew near it. Others believed that it's difficult to kill and grow back easily. The result indicated a number of tree species found in farms compared to the number of farmers needed for that tree. A lot of farmers preferred indigenous trees then Neem in their farms. In conclusion, in Katsina and from casual observations in the study areas, we have found that Neem is spreading in a way characteristic of an invasive species. They are spread by birds and other animals, becoming numerous under native trees. The trees themselves may become 'weeds. Since farmers in Katsina do not like Neem in their fields, it can be considered a weed, and because it can reproduce in large quantities at a considerable distance from the parent plant, it is, by definition, an invasive plant.

Meteorological influences on airborne pollen and spores in Johannesburg (Gauteng), South Africa

Airborne fungal spores and pollen (aerospora), synergistic with air pollution, are key triggers of allergic respiratory diseases. Effective diagnosis and treatment requires up-to-date location-specific knowledge on the temporal variability of aerospora types and levels. Johannesburg is the largest city in South Africa and has grown substantially in three decades, with changes in ground cover, population density and air pollution, yet until now, no continuous aerospora sampling has occurred. We present a daily two-year (August 2019–July 2021) aerospora assemblage for Johannesburg and explore temporal characteristics of 13 dominant aerospora in relation to daily meteorological variables (pressure, rainfall, relative humidity, temperature and wind characteristics). February–July, July–September and January-July represent high-risk periods for fungal spores [(Alternaria alternata (Fries. ex Keissler), Ascospores, Aspergillus niger (Van Tieghem), Penicillium chrysogenum (Thom), Cladosporium graminum (Corda), Epicoccum nigrum (Link), Helminthosporium solani (Durieu and Montagne) Nigrospora sphaerica (Saccardo ex. Mason), Smuts Ustilago nuda (Jensen ex. Rostrup) and Torula herbarum (Link)], trees (Cupressus, Morus and Platanus) and grass (Poaceae), respectively. Using a generalised additive model, results show that daily meteorological characteristics explained 7–32% of daily aerospora variability, with the largest effect on tree pollen. Rainfall, relative humidity and temperature influenced daily fungal spore and Poaceae counts, with moderate/low rainfall (< 20 mm), higher/mid-ranging relative humidity (~ 40–60%) and temperatures of ~ 15–20 °C associated with higher counts during high-risk periods. Rainfall predominantly influenced tree counts during high-risk periods, with higher counts occurring on low rainfall (<10 mm) days. These results update the aerospora profile of Johannesburg, South Africa, providing important information to inform allergy care.