Health Status Of Trees Research Articles

Exploring forest changes in an Ips typographus L. outbreak area: insights from multi-temporal multispectral UAS remote sensing

AbstractUncrewed Aerial Systems (UAS) offer a versatile solution for monitoring forest ecosystems. This study aimed to develop and assess an individual tree-based methodology using multi-temporal, multispectral UAS images to track changes caused by the European spruce bark beetle (Ips typographus L.). The approach encompassed four key steps: (1) individual tree detection using structure-from-motion point clouds, (2) tree species classification, (3) health classification of spruce trees as healthy, declined, or dead, and (4) change detection, identifying fallen/removed trees and alterations in tree health status. The developed methodology was employed to quantify changes in a bark beetle outbreak area covering 215 hectares in southeastern Finland during 2019–2021. The dataset included two managed and two conserved forest areas. The uncertainty estimation demonstrated the overall accuracies ranging from 0.58 to 0.91 for individual tree detection, 0.84 for species classification, and 0.83–0.96 for health classification, and a F1-score of 0.91 for the fallen or removed tree detection. Maps and statistics were produced, containing information on the health of the spruce trees in the area and information on changes, including trees that died during monitoring and those that fell or were removed from the forest. The results demonstrated successful control of the outbreak in the managed stands, evidenced by moderate tree mortality. Conversely, in the conserved stands, the outbreak resulted in dramatic tree mortality. This method serves stakeholders by enabling large-scale outbreak impact monitoring, facilitating timely risk assessment, and validating bark beetle outbreak management strategies.

Urban tree health assessment using multifaceted remote sensing datasets: A case study in Hong Kong

Although climate change is impacting various aspects of our environment, it is important to note that the overall risk to trees remains low, especially in urban areas like Hong Kong where the benefits of trees to society are significant. The trees planted in an urban setting are isolated and have several limiting factors including, excessive run-off, urban pollution, physical damage and limited root growth, which sometimes lead for tree failure incidents. The conventional on-site tree health assessment method is time consuming thus, requiring a remote sensing based method to effectively and routinely monitor the health status of urban trees. In this study several types of remote sensing datasets have been exploited to assess the health status of more than 700 Old and Valuable Trees (OVTs) and Stone Wall Trees (SWTs) around Hong Kong. These datasets include the data from Terrestrial LiDAR (Light Detection and Ranging) Surveys (TLS), Handheld Laser Scanner (HLS), Airborne LiDAR Surveys (ALS) and airborne multispectral data. For validation purpose, the in situ tree parameters data was also obtained from the Tree Management Office (TMO) of the Greening, Landscape & Tree Management Section (GLTMS) under the Development Bureau of the Hong Kong SAR Government. The results have indicated that over the period of four years (2017–2020) there has been a decline in the health of some target trees which can be attributed to the increased infestation rate in trees and severe weather conditions. The usage of LiDAR data has supported the fact that different tree structural forms can effectively be extracted and can help making informed decisions on the precise health conditions of urban trees.

Culture-Independent Characterization of Citrus Rhizospheric Bacterial and Fungal Microbiota from Piura, Peru

The “limón sutil” (Citrus aurantifolia) has been widely cultivated and well established for many years in Piura, northwestern Peru, because of its exceptional climate and soil conditions. However, decline and death of C. aurantifolia trees caused by different phytopathogens remain a common problem which has been observed in the last decades. It is known that the microbiota of soil plays an important role with their host and could be the starting point to understand the causes of citrus decline. In this study, we identified through culture-independent methods the bacterial and fungal microbiota associated to C. aurantifolia, C. jambhiri and C. volkameriana rhizospheres in the main areas of Piura. By using a 16S rRNA and ITS-metabarcoding analysis, we evaluated the taxonomic diversity between healthy trees and with decline symptoms and how this diversity could influence the health status of citrus trees. More than 600 and 200 bacterial and fungal ASVs were identified, respectively. Our metabarcoding analysis was able to identify Proteobacteria, Cyanobacteria, Firmicutes, Bacteroidota and Acidobacteriota prokaryotic phyla, while fungal phyla included Ascomycota, Basidiomycota and Glomeromycota. In addition, there were differences between microbial diversity indices in rhizospheres evaluated. Finally, bacterial and fungal genera were shared among the different citrus rhizospheres. These results have allowed us to obtain a preliminary identification of microbiota in the citrus rhizospheres of healthy trees and with decline symptoms.

Determining the health status of natural monument trees using acoustic tomography method

Areas with natural beauty and rich biodiversity have had a different significance for people since ancient times. With the latest developments in industry, technology, and urbanization, the demand for natural areas has increased considerably. Accordingly, the need to protect such private areas has become even more significant. In order to protect areas with such natural characteristics effectively, problems and threat factors must be correctly revealed. For this purpose; a study was conducted to determine the health status of some trees, which are natural monuments in the Eastern Black Sea Region of Türkiye, using the acoustic tomography method. The general appearance of six trees in the study area was examined, and measurements were made with the Arbor Sonic 3D Acoustic Tomography device. In the research, it was found that the trunk of one of the natural monument trees was hollow. The other five trees were found to be in good general condition. According to the results of the tomography, it was determined that one of the six trees measured was in good health, three of them had partial hollows and decay, and two of them were completely hollow. According to the data obtained, it has been revealed that the acoustic tomography method is an effective tool in determining the health status of nature monument trees.

A novel framework of smart monitoring to face the challenges of tree management in historic gardens

Historic gardens are green spaces characterised by tree stands with several veteran specimens of high artistic and cultural value. Such valuable plant components have to cope with biotic and abiotic stress factors as well as ongoing senescence processes. Maintaining tree health is therefore crucial to preserve their ecosystem services, but also to protect the monument and visitor health. In this context, finding smart, fast and cost-effective management solutions to monitor health and detect critical conditions for both stands and individual veteran trees can promote garden conservation. For this reason, we developed a novel framework based on Sentinel2 imagery, LiDAR sources and automatic cameras to identify risk spots regarding trees in historic gardens. The pilot study area consists of two closed Italian gardens from the 16th century, which were analysed as a unique Historic Garden System (HGS). The tree health status at stand level was assessed using a criterion based on the Normalized Difference Vegetation Index weighed on tree volume (NDVIt) and validated by a visual crown defoliation assessment. At the tree level, the health status of four veteran trees defined by the NDVIt was also evaluated using green chromatic coordinates (GCC) obtained from digital images acquired by cameras at daily intervals during one growing season. The 33% of the tree population was classified as being in poor health, i.e. “at risk”. Veteran trees classified as “at risk” showed an anticipation of phenological phases and a lower GCC compared to reference trees. Despite variability determined by Sentinel medium resolution, the proposed framework showed good accuracy (0.74) for monitoring historical gardens. The semi-automatic risk point mapping system tested here proved to be effective in facilitating the management of historic gardens, which in turn could be applied in the wider context of urban greening.

A citizen science method to monitor a polyphagous shot hole borer infestation in Johannesburg's urban forest

Citizen science has become a powerful tool for gathering data, and the validity of these data should be tested. The accuracy of a citizen science database used to monitor the polyphagous shothole borer (PSHB) (Euwallacea fornicatus) (Coleoptera: Curculionidae: Scolytinae) infestation in a suburb of the city of Johannesburg was assessed. Out of 785 trees surveyed by the citizen scientists, 157 trees (20%) were selected as a validation data set to confirm the tree identity, tree health status and level of PSHB infestation. A chi-square (χ2) test was performed to examine whether there was a significant agreement between the citizen scientists’ and researcher’s data used for the classification of the tree infestation, tree health status and PSHB infestation holes. A Cohen’s kappa coefficient was calculated to determine the level of agreement between the two different sources of data, for the infested parts (trunk, main branches, and canopy branches) of the trees surveyed and PSHB infestation signs. The two surveys showed no significance difference for classifying tree health status and PSHB infestation holes. There was a high level of agreement between citizen scientists and the researcher ratings for measures of PSHB infestation (kappa values ranging between 0.71 and 0.89) and the level of agreement was significant. In conclusion, citizen science allowed us to obtain a large quantity of data at a low cost that correlated with the researchers’ validation dataset. Citizen science is a useful method for monitoring PSHB in a diverse urban forest in trees that show obvious signs of infestation.

Genomic prediction of resistance to Hymenoscyphus fraxineus in common ash (Fraxinus excelsior L.) populations.

The increase in introduced insect pests and pathogens due to anthropogenic environmental changes has become a major concern for tree species worldwide. Common ash (Fraxinus excelsior L.) is one of such species facing a significant threat from the invasive fungal pathogen Hymenoscyphus fraxineus. Some studies have indicated that the susceptibility of ash to the pathogen is genetically determined, providing some hope for accelerated breeding programs that are aimed at increasing the resistance of ash populations. To address this challenge, we used a genomic selection strategy to identify potential genetic markers that are associated with resistance to the pathogen causing ash dieback. Through genome-wide association studies (GWAS) of 300 common ash individuals from 30 populations across Poland (ddRAD, dataset A), we identified six significant SNP loci with a p-value ≤1 × 10-4 associated with health status. To further evaluate the effectiveness of GWAS markers in predicting health status, we considered two genomic prediction scenarios. Firstly, we conducted cross-validation on dataset A. Secondly, we trained markers on dataset A and tested them on dataset B, which involved whole-genome sequencing of 20 individuals from two populations. Genomic prediction analysis revealed that the top SNPs identified via GWAS exhibited notably higher prediction accuracies compared to randomly selected SNPs, particularly with a larger number of SNPs. Cross-validation analyses using dataset A showcased high genomic prediction accuracy, predicting tree health status with over 90% accuracy across the top SNP sets ranging from 500 to 10,000 SNPs from the GWAS datasets. However, no significant results emerged for health status when the model trained on dataset A was tested on dataset B. Our findings illuminate potential genetic markers associated with resistance to ash dieback, offering support for future breeding programs in Poland aimed at combating ash dieback and bolstering conservation efforts for this invaluable tree species.

Assessment of the Health Status of Old Trees of Platycladus orientalis L. Using UAV Multispectral Imagery

Assessing the health status of old trees is crucial for the effective protection and health management of old trees. In this study, we utilized an unmanned aerial vehicle (UAV) equipped with multispectral cameras to capture images for the rapid assessment of the health status of old trees. All trees were classified according to health status into three classes: healthy, declining, and severe declining trees, based on the above-ground parts of the trees. Two traditional machine learning algorithms, Support Vector Machines (SVM) and Random Forest (RF), were employed to assess their health status. Both algorithms incorporated selected variables, as well as additional variables (aspect and canopy area). The results indicated that the inclusion of these additional variables improved the overall accuracy of the models by 8.3% to 13.9%, with kappa values ranging from 0.166 and 0.233. Among the models tested, the A-RF model (RF with aspect and canopy area variables) demonstrated the highest overall accuracy (75%) and kappa (0.571), making it the optimal choice for assessing the health condition of old trees. Overall, this research presents a novel and cost-effective approach to assessing the health status of old trees.

Average weighted estimate of health of forest trees as a criterion for the sanitary condition of a stand

The issue of using a weighted average assessment of the health of trees condition Kср as a criterion for the sanitary condition of a stand on the example of spruce and birch forests. Based on the growth tables, Kср developed for five categories of tree health status with differentiation of results by age groups and assessment methods (by stock volume or by number of trees). It showed that exceeding Kср of 1.5 is not a sufficient criterion for the unsatisfactory sanitary condition of the stand. For a stand with natural thinning by age the estimations Kср are increase from Ia to V quality of locality and are decrease with age groups. The estimates Kср by number of trees may exceed similar estimates by stock volume by 0.1-0.3 points. For a young and middle age of healthy stands with natural thinning, the evaluations Kср usually exceed 1.5 points. Proposed the criteria Kср with the differentiation on age groups and assessment methods. As rough criterion of unsatisfactory sanitary state it is possible to accept excess of average weighted of estimations (on a stock volume/on number of trees): Kср :in middle-aged stands - 1,7/2,0, in ripening stand - 1,5/1,7, in ripe and maturity and old-growth stand 1,2/1,4. Keywords: CONDITION OF FOREST STAND, WEIGHTED AVERAGE ESTIMATE

Oil Palm Leaves Phenotyping using Biomarkers Derived from Raman Spectra

The efficient production of oil from oil palm trees is heavily dependent on their health status, reflected in the oil extraction rate (OER). The 17th frond of the oil palm trees contains a significant amount of organic compounds that directly influence the overall health of the tree. Achieving an optimal balance of essential nutrients such as nitrogen (N), phosphorus (P), and potassium (K) is crucial for classifying a tree as healthy, as it results in an increased oil to bunch and fruit to bunch ratio. To accurately assess the health level of oil palm trees, this study explores the application of Raman spectroscopy, a non-invasive technique in determining the molecular fingerprint of an organic sample. In this research, Raman spectroscopy is employed to determine the health level of oil palm trees, and a machine learning-based health level classification algorithm is developed. The algorithm analyzes the organic compounds found in oil palm leaves, which were collected from 20 different trees. The extracted spectral features from these leaves are used to classify them into two health levels: healthy and not healthy. For this purpose, 31 machine learning models are tested to identify the most accurate classifier. The findings reveal that the Tree and fine K-Nearest Neighbors (KNN) classifier demonstrates the highest overall accuracy of 95% using three significant features, namely the Raman intensity, Full Width at Half Maximum (FWHM), and area under the curve. This result signifies the potential of Raman spectroscopy as a reliable and promising method for non-invasively phenotyping oil palm leaves, enabling precise prediction of the health status of oil palm trees.

Health status of the field protective forest belts in Dobrudzha – results from the monitoring carried out in 2022

In 2022, the health status of the field protective forest belts on the territory of the State Hunting Enterprise Balchik and State Forest Enterprises General Toshevo and Dobrich was monitored. The assessment of tree crown condition covered 7069.3 ha (66% of all field protective belts in Bulgaria). The results showed that 70% of monitored belts were in good condition, 23% - in moderate condition, and 7% - in poor condition. Since 2014 the area of the protective forest belts in poor condition has increased 2.6 times, and 76% of them were situated in Dobrudzha region. Ash belts – Fraxinus excelsior (66% of all belts) and Fraxinus americana (10%) were in the worst conditon. In ash belts a process of crown dieback, premature falling of leaves and drying of whole trees were observed. In individual ash belts, drying reached up to 80% of tree crowns, regardless of their age and origin. The drying was mostly due to the biotic factors – insect pests and fungal pathogens. The protective forest belts of Quercus cerris, Q. petraea, Q., rubra and Gleditschia triacanthos stand out as sustainable tree species. It is necessary to significantly upgrade the methodology by which the monitoring of the health status of trees in the field protective forest belts is currently carried out. In the new methodology a detailed description of the type of data that need to be collected, the methods of assessments, and data reporting formats, have to be included.

Long‐term effects of intercropping on multi‐trophic structure and bio‐thermodynamic health of mixed Eucalyptus‐native tree plantations

Abstract The intercropping approach of Eucalyptus and native trees has been widely recommended, as an ideal replacement for monoculture Eucalyptus plantations (EUs), to ameliorate global biodiversity loss and mitigate environmental change. However, both suitable native tree species (NS) and the best intercropping ratio between Eucalyptus and native trees have not been determined. To fill this gap, a four‐level intercropping gradient of Eucalyptus urophylla planted with eight NS was set up (i.e., 20%NS, 30%NS, 40%NS, 50%NS), monitored and compared with a monoculture E. urophylla plantation (EU) and a randomly mixed plantation of nine NS in southern China. The results showed that, the intercropping ratio of Eucalyptus and native trees had a long‐term effect on tree layer structure and health status, and a cascading effect on the thermodynamic health state of soil microbes. Shade‐tolerant woody species are more suitable for intercropping with Eucalyptus. Intercropping plantations with not less than 30% native trees were more favourable for long‐term survival and growth of both planted Eucalyptus and native trees, and provided much more favourable conditions for the natural immigration of other native trees, which leads to a healthy plant community with significantly higher eco‐exergy than EU. The initial mixing ratio between Eucalyptus and high diversity native trees affected soil fertility through its long‐term effects on the biodiversity and bio‐thermodynamic state of trees and soil microbes. Synthesis and applications. Our results highlight the long‐term positive effect of the intercropping ratio of Eucalyptus and high diversity native trees on multi‐trophic biodiversity conservation, bio‐thermodynamic health development and soil fertility conservation. In the conversion of monoculture EUs to multi‐species plantations, it is recommended to mix more than 30% NS, which have different ecological niches with Eucalyptus.

Assessing the benefits and management of urban forest in supporting low carbon city in Jakarta, Indonesia

Abstract. Aulia R, Kaswanto, Arifin HS, Mosyaftiani A, Syasita N, Wahyu A, Wiyoga H. 2023. Assessing the benefits and management of urban forest in supporting low carbon city in Jakarta, Indonesia. Biodiversitas 24: 6151-6159. Climate change is a phenomenon that has become a global concern including in urban areas. Jakarta is one of the most populated cities in Indonesia, reaching 10.56 million people in 2020 with high emissions levels due to high human activities. Thus, efforts to reduce carbon emissions to mitigate climate change are necessary in Jakarta one of which is through the concept of low carbon city and manifested in the establishment of Green Open Space (GOS), including urban forests. Urban forests aim to sequester and store carbon, and improve the urban microclimate. This research aimed to assess the benefits of urban forests in Jakarta in term of their capacity in carbon sequestration using i-Tree Eco tool, and analyse the management of urban forests related to the applicable regulations. To collect data on trees, this study created 49 plots scattered across Srengseng Urban Forest, Cipayung Urban Forest, Munjul Urban Forest, Rawa Malang Urban Forest, and Pondok Labu Urban Forest. Tree species, Diameter at Breast Height (DBH), total tree height, live tree height, crown base height, crown width, percent crown loss, and crown light exposure were all gathered. Furthermore, in-depth interviews with urban forest managers were performed to gather additional information on present management, including questions derived from Jakarta's green open space legislation to assess management efficacy. The results of the analysis of urban forest quality show that the health status of trees is one of the factors that indicate the quality of urban forests at the five study sites, with more than 50% of the total trees in fair condition (75- 90% health). The gross carbon sequestration of trees in five urban forests Jakarta is about 184.8 metric tons of carbon per year. Additionally, the in-depth interviews also test the mortality of the urban forest, a decent indicator of current management with estimates over the next 10 years, showing a slight increase in carbon sequestration and storage capacity. However, the mortality scenario leads to a trend toward a decrease in leaf area and the number of tree categories, especially in the Srengseng Urban Forest.

Proportions of Green Area and Tree Health on University Campus: The Impact of Pavement Presence

Sustainable urban ecosystems require healthy green spaces that provide ecological services to meet social and environmental needs. This study evaluates the proportion of green areas in the urban campus and assesses the effects of pavement on the health of Samanea saman. The MAPIR Normalised Difference Vegetation Index (NDVI) camera mounted on a drone captured a proportion of green areas of approximately 27.80%, which is roughly a quarter of the total study sites. However, the significance of green areas depends on the health of the trees. Therefore, the pavement effects on the health status of trees in green areas were studied using Visual Tree Assessment (VTA) and aerial image analysis using the MAPIR (NDVI) camera on a drone. Although both methods evaluate health status, the final outputs differed. VTA produced categorical outputs, which assigned trees into health categories based on a visual assessment of factors. In contrast, NDVI produced linear outputs, which provided a numerical value to demonstrate tree health. Both methods indicate that trees in non-paved areas are healthier, particularly for the excellent trees identified by the VTA, which suggests that pavement negatively impacts tree health. However, the effects of paved and non-paved areas on tree health status analysis using the aerial image are not significant (p>0.05), which may be due to the low quality and accuracy of the images. The study provides insights into the importance of green areas and tree health in creating sustainable urban ecosystems.

Determining the Optimal Sample Size for Assessing Crown Damage on Color Infrared (CIR) Aerial Photographs

One of the priorities in sustainable forest management is monitoring the health status of trees and stands. From the aspect of remote sensing (RS), the best way of doing this is by interpreting color infrared (CIR) aerial photographs; however, this raises the issue of sample size. For this reason, to apply this method in practice, it is indispensable to determine an appropriate sample size to ensure sufficient reliability of the health status assessment of trees in CIR aerial photographs. This research was conducted in lowland forests of pedunculate oak in Croatia. To determine damage in the photographs of the main tree species, a systematic sample with varying dot grid densities—100 × 100 m, 200 × 200 m, 300 × 300 m, 500 × 500 m, 1000 × 1000 m—was used with combinations of different numbers of interpreted trees per sample. Damage indicators were also calculated based on tree distributions obtained by interpreting four trees, two trees and one tree in different sample sizes. The results of the testing showed that there were no statistically significant differences between different sample densities and numbers of interpreted trees in relation to mean damage assessment. Regardless of the fact that there were no statistically significant differences during damage assessment, it was found that by lowering sample densities, starting with 200 × 200 m, the number of trees and the number of sample points per particular sub-compartment significantly decreased, and so did the desired accuracy. Consequently, the participation (distribution) of particular species and damage degrees in the sample were lost, which significantly affected the overall tree health assessment. In contrast, grid densities of 100 × 100 m with one interpreted tree at the raster point proved to be the optimal sample size. This confirms the fact found in earlier research, that is, that the selected sample should have several spatially well-distributed points with a smaller number of trees in the point, and samples with larger numbers of trees in a smaller number of points should be avoided.

Comparison of Deep Neural Networks in the Classification of Bark Beetle-Induced Spruce Damage Using UAS Images

The widespread tree mortality caused by the European spruce bark beetle (Ips typographus L.) is a significant concern for Norway spruce-dominated (Picea abies H. Karst) forests in Europe and there is evidence of increases in the affected areas due to climate warming. Effective forest monitoring methods are urgently needed for providing timely data on tree health status for conducting forest management operations that aim to prepare and mitigate the damage caused by the beetle. Unoccupied aircraft systems (UASs) in combination with machine learning image analysis have emerged as a powerful tool for the fast-response monitoring of forest health. This research aims to assess the effectiveness of deep neural networks (DNNs) in identifying bark beetle infestations at the individual tree level from UAS images. The study compares the efficacy of RGB, multispectral (MS), and hyperspectral (HS) imaging, and evaluates various neural network structures for each image type. The findings reveal that MS and HS images perform better than RGB images. A 2D-3D-CNN model trained on HS images proves to be the best for detecting infested trees, with an F1-score of 0.759, while for dead and healthy trees, the F1-scores are 0.880 and 0.928, respectively. The study also demonstrates that the tested classifier networks outperform the state-of-the-art You Only Look Once (YOLO) classifier module, and that an effective analyzer can be implemented by integrating YOLO and the DNN classifier model. The current research provides a foundation for the further exploration of MS and HS imaging in detecting bark beetle disturbances in time, which can play a crucial role in forest management efforts to combat large-scale outbreaks. The study highlights the potential of remote sensing and machine learning in monitoring forest health and mitigating the impacts of biotic stresses. It also offers valuable insights into the effectiveness of DNNs in detecting bark beetle infestations using UAS-based remote sensing technology.

Canary in the forest?—Tree mortality and canopy dieback of western redcedar linked to drier and warmer summers

AbstractAimForest dieback is increasing from unfavourable climate conditions. Western redcedar (WRC)—a culturally, ecologically and economically important species—has recently experienced anomalously high mortality rates and partial canopy dieback. We investigated how WRC tree growth and dieback responded to climate variability and drought using tree‐ring methods.LocationPacific Northwest, USA.TaxonWestern redcedar (Thuja plicata).MethodsWe collected tree cores from three tree health status groups (no canopy dieback, partial canopy dieback, and dead trees) at 11 sites in coastal (maritime climate) and interior (continental climate) WRC populations. From growth rates, we computed four growth indices that assessed the resilience to drought and estimated the year of death.ResultsWarmer and drier climate conditions in May/June that extended the annual July‐to‐September dry season reduced radial growth in 9 of 11 sites (1975–2020). WRC trees recovered growth to pre‐drought rates within 3 years when post‐drought climate conditions were cooler/wetter than average. However, recovery from drought was slower or absent when warmer/drier conditions occurred during the post‐drought recovery period, possibly leading to the recent and widespread mortality across the coastal population. WRC mortality was portended by 4–5 years of declining growth. Annually‐resolved mortality in coastal populations predominately occurred in 2017–2018 (80% of sampled dead trees), a period that coincided with exceedingly hot temperatures and the longest regionally dry period from May to September (1970–2020). In interior populations, mortality was dispersed among years but associated with warmer and drier conditions from August to September.Main conclusionsOur findings forewarn that a warming climate and more frequent and severe summer droughts, especially in consecutive years, will likely increase the vulnerability of WRC to canopy dieback and mortality and possibly other drought‐sensitive trees in one of the world's largest forest carbon sinks.

Early symptom detection of basal stem rot disease in oil palm trees using a deep learning approach on UAV images

The oil palm tree (Elaeis guineensis Jacq.) is a vital and economically important plant, as the oil from its fruit is a primary export for many Southeast Asian countries. Hence the health of these trees is directly linked to their yield and the income of the growers. Unfortunately, basal stem rot (BSR) disease has been identified as the main threat to oil palm trees, causing infected trees to reduce their production and ultimately leading to death. While prior research has shown high accuracy in detecting oil palm trees, there has been limited focus on automating the observation of individual tree health status, particularly in identifying early symptoms of BSR disease within densely populated regions. This study introduces an enhanced approach for early symptom detection of BSR disease in densely populated oil palm tree areas. We present a modified U-Net architecture called the Multi-Convolution Residual (M-CR) U-Net, coupled with an image postprocessing method known as the overlapped contour separation (OVCS) algorithm. The M-CR U-Net differs from the original U-Net as it leverages on multiple convolution kernels of various sizes while incorporating skip connections. This design improvement results in superior segmentation performance compared to approaches utilizing fixed kernel sizes. Our proposed M-CR U-Net outperforms alternative architectures across a range of metrics, demonstrating improved training efficiency and delivering enhanced quantitative and qualitative segmentation outcomes. These achievements underscore its proficiency in accurately delineating the intricate features within input images. The M-CR U-Net excels in capturing the fine details while minimizing overlapping regions, as evidenced by its dice coefficient of 0.978, intersection over union of 0.871, precision of 0.989, and recall of 0.982. Additionally, the OVCS algorithm enables effective segmentation of individual oil palm trees within highly crowded regions. By adeptly separating the boundaries of overlapped areas, this algorithm enhances segmentation results. This innovation ensures precise identification of the health and boundaries of oil palm trees, leading to an average performance improvement of 2.716% across various evaluation metrics. The proposed methodology demonstrates significant potential in detecting early symptoms of BSR disease in oil palm trees, even within crowded regions, leading to more effective management of oil palm plantations.

Multi-Parameter Health Assessment of Jujube Trees Based on Unmanned Aerial Vehicle Hyperspectral Remote Sensing

To address the current difficult problem of scientifically assessing the health status of date palm trees due to a single parameter for date palm health assessment, an imperfect index system, and low precision. In this paper, using jujube trees in 224 regiment of the 14th division of Xinjiang Production and Construction Corps “Kunyu city” as the research object, we carried out the inversion study of various physicochemical parameters of jujube trees (canopy chlorophyll content, leaf area index (LAI), tree height, canopy area) using the unmanned aerial vehicle (UAV) hyperspectral imagery of jujube trees during the period of fruit expansion, and put forward a model for assessing the health of jujube trees based on multiple physicochemical parameters. First, we calculated six spectral indices for inversion of chlorophyll content and four spectral index for inversion of LAI, analyzed the spectral index with high correlation with chlorophyll content and LAI of jujube trees canopy, and constructed the inversion models of chlorophyll content and LAI. Second, the Mask R-CNN model was used to achieve jujube trees’ canopy segmentation and area extraction, and the segmented canopy was matched with the Canopy Height Model (CHM) for jujube trees’ height extraction. Finally, based on the four physicochemical parameters of inversion, we construct four jujube trees’ health assessment models, namely, Partial Least Squares Regression Analysis (PLSR), Random Forest (RF), Support Vector Machines (SVM), and Decision Tree (DT). The results showed that the R2 of the PLSR tree health assessment model constructed based on the multi-physical and chemical parameters of chlorophyll content, LAI, tree height, and canopy area was 0.853, and the RMSE was 0.3. Compared with the jujube trees’ health assessment models constructed by RF, SVM, and DT, the R2 increased by 0.127, 0.386, and 0.165, and the RMSE decreased by 0.04, 0.175, and 0.063, respectively. This paper can achieve rapid and accurate inversion of multi-physical and chemical parameters of jujube trees with the help of UAV hyperspectral images, and the PLSR model constructed based on multi-physical and chemical parameters can accurately assess the health status of jujube trees and provide a reference for a scientific and reasonable assessment of jujube trees’ health.

Biochar-biofertilizer combinations enhance growth and nutrient uptake in silver maple grown in an urban soil.

Declining tree health status due to pollutant impacts and nutrient imbalance is widespread in urban forests; however, chemical fertilizer use is increasingly avoided to reduce eutrophication impacts. Biochar (pyrolyzed organic waste) has been advocated as an alternative soil amendment, but biochar alone generally reduces plant N availability. The combination of biochar and either organic forms of N or Plant Growth Promoting Microbes (PGPMs) as biofertilizers may address these challenges. We examined the effects of two wood biochar types with Bacillus velezensis and an inactivated yeast (IY) biofertilizer in a three-month factorial greenhouse experiment with Acer saccharinum L. (silver maple) saplings grown in a representative urban soil. All treatments combining biochars with biofertilizers significantly increased sapling growth, with up to a 91% increase in biomass relative to controls. Growth and physiological responses were closely related to nutrient uptake patterns, with nutrient vector analyses indicating that combined biochar and biofertilizer treatments effectively addressed nutrient limitations of both macronutrients (N, P, K, Mg, Ca), and micronutrients (B, Fe, Mn, Mo, Na, S, and Zn). Biochar-biofertilizer treatments also reduced foliar concentrations of Cu, suggesting potential to mitigate toxic metal impacts common in urban forestry. We conclude that selected combinations of biochar and biofertilizers have substantial promise to address common soil limitations to tree performance in urban settings.