Treemap Research Articles

Mapping Tree Species Using CNN from Bi-Seasonal High-Resolution Drone Optic and LiDAR Data

As the importance of forests has increased, continuously monitoring and managing information on forest ecology has become essential. The composition and distribution of tree species in forests are essential indicators of forest ecosystems. Several studies have been conducted to classify tree species using remote sensing data and machine learning algorithms because of the constraints of the traditional approach for classifying tree species in forests. In the machine learning approach, classification accuracy varies based on the characteristics and quantity of the study area data used. Thus, applying various classification models to achieve the most accurate classification results is necessary. In the literature, patch-based deep learning (DL) algorithms that use feature maps have shown superior classification results than point-based techniques. DL techniques substantially affect the performance of input data but gathering highly explanatory data is difficult in the study area. In this study, we analyzed (1) the accuracy of tree classification by convolutional neural networks (CNNs)-based DL models with various structures of CNN feature extraction areas using a high-resolution LiDAR-derived digital surface model (DSM) acquired from a drone platform and (2) the impact of tree classification by creating input data via various geometric augmentation methods. For performance comparison, the drone optic and LiDAR data were separated into two groups according to the application of data augmentation, and the classification performance was compared using three CNN-based models for each group. The results demonstrated that Groups 1 and CNN-1, CNN-2, and CNN-3 were 0.74, 0.79, and 0.82 and 0.79, 0.80, and 0.84, respectively, and the best mode was CNN-3 in Group 2. The results imply that (1) when classifying tree species in the forest using high-resolution bi-seasonal drone optical images and LiDAR data, a model in which the number of filters of various sizes and filters gradually decreased demonstrated a superior classification performance of 0.95 for a single tree and 0.75 for two or more mixed species; (2) classification performance is enhanced during model learning by augmenting training data, especially for two or more mixed tree species.

Interpretation and Mapping Tree Crown Diameter Using Spatial Heterogeneity in Relation to the Radiative Transfer Model Extracted from GF-2 Images in Planted Boreal Forest Ecosystems

Tree crown diameter (CD) values, relating to the rate of material exchange between the forest and the atmosphere, can be used to evaluate forest biomass and carbon stock. To map tree CD values using meter-level optical remote sensing images, we propose a novel method that interprets the relationships between the spectral reflectance of pixels and the CD. The approach employs the spectral reflectance of pixels in the tree crown to express the diversity of inclination angles of leaves based on the radiative transfer model and the spatial heterogeneity of these pixels. Then, simulated and acquired GF-2 images are applied to verify the relationships between spatial heterogeneity and the tree CD. Meanwhile, filter-based and object-based methods are also employed to extract three types of variables (spectral features, texture features, and spatial heterogeneity). Finally, the tree CD values are mapped by four models (random forest (RF), K-nearest neighbor (K-NN), support vector machine (SVM), and multiple linear regression (MLR)), using three single types of variables and combinations of variables with different strategies. The results imply that the spatial heterogeneity of spectral reflectance is significantly positively correlated with tree CD values and is more sensitive to tree CD values than traditional spectral features and textural features. Furthermore, the ability of spatial heterogeneity to map tree CD values is significantly higher than traditional variable sets after obtaining stable features with appropriate filter window sizes. The results also demonstrate that the accuracy of mapped tree CD values is significantly improved using combined variable sets with different feature extraction methods. For example, in our experiments, the R2 and rRMSE values of the optimal results ranged from 0.60 to 0.66, and from 15.76% to 16.68%, respectively. It is confirmed that spatial heterogeneity with high sensitivity can effectively map tree CD values, and the accuracy of mapping tree CD values can be greatly improved using a combination of spectral features extracted by an object-based method and spatial heterogeneity extracted by a filter-based method.

A Comparison of Machine Learning Models for Mapping Tree Species Using WorldView-2 Imagery in the Agroforestry Landscape of West Africa

Farmland trees are a vital part of the local economy as trees are used by farmers for fuelwood as well as food, fodder, medicines, fibre, and building materials. As a result, mapping tree species is important for ecological, socio-economic, and natural resource management. The study evaluates very high-resolution remotely sensed WorldView-2 (WV-2) imagery for tree species classification in the agroforestry landscape of the Kano Close-Settled Zone (KCSZ), Northern Nigeria. Individual tree crowns extracted by geographic object-based image analysis (GEOBIA) were used to remotely identify nine dominant tree species (Faidherbia albida, Anogeissus leiocarpus, Azadirachta indica, Diospyros mespiliformis, Mangifera indica, Parkia biglobosa, Piliostigma reticulatum, Tamarindus indica, and Vitellaria paradoxa) at the object level. For every tree object in the reference datasets, eight original spectral bands of the WV-2 image, their spectral statistics (minimum, maximum, mean, standard deviation, etc.), spatial, textural, and color-space (hue, saturation), and different spectral vegetation indices (VI) were used as predictor variables for the classification of tree species. Nine different machine learning methods were used for object-level tree species classification. These were Extra Gradient Boost (XGB), Gaussian Naïve Bayes (GNB), Gradient Boosting (GB), K-nearest neighbours (KNN), Light Gradient Boosting Machine (LGBM), Logistic Regression (LR), Multi-layered Perceptron (MLP), Random Forest (RF), and Support Vector Machines (SVM). The two top-performing models in terms of highest accuracies for individual tree species classification were found to be SVM (overall accuracy = 82.1% and Cohen’s kappa = 0.79) and MLP (overall accuracy = 81.7% and Cohen’s kappa = 0.79) with the lowest numbers of misclassified trees compared to other machine learning methods.

Assessing the Performance of a Handheld Laser Scanning System for Individual Tree Mapping—A Mixed Forests Showcase in Spain

The use of mobile laser scanning to survey forest ecosystems is a promising, scalable technology to describe the 3D structure of forests at a high resolution. We use a structurally complex, mixed-species Mediterranean forest to test the performance of a mobile Handheld Laser Scanning (HLS) system to estimate tree attributes within a forest patch in central Spain. We describe the different stages of the HLS approach: field position, ground data collection, scanning path design, point cloud processing, alignment between detected trees and measured reference trees, and finally, the assessment of main tree structural attributes diameter at breast height (DBH) and tree height considering species and tree size as control factors. We surveyed 418 reference trees to account for omission and commission error rates over a 1 ha plot divided into 16 sections and scanned using two different scanning paths. The HLS-based approach reached a high of 88 and 92% tree detection rate for the best combination of scanning path and point cloud processing modes for the HLS system. The root mean squared errors for DBH estimates varied between species: errors for Pinus pinaster were below 2 cm for Scan 02. Quercus pyrenaica, and Alnus glutinosa showed higher error rates. We observed good agreement between ALS and HLS estimates for tree height, highlighting differences to field measurements. Despite the complexity of the mixed forest area surveyed, our results show that HLS is highly efficient at detecting tree locations, estimating DBH, and supporting tree height measurements as confirmed with airborne laser data used for validation. This study is one of the first HLS-based studies conducted in the Mediterranean mixed forest region, where variability in tree allometries and spacing and the presence of natural regeneration pose challenges for the HLS approach. HLS is a feasible, time-efficient, scalable technology for tree mapping in mixed forests with potential to support forest monitoring programmes such as national forest inventories lacking three-dimensional, remote sensing data to support field measurements.

Individual structure mapping over six million trees for New York City USA

Individual tree structure mapping in cities is important for urban environmental studies. Despite mapping products for tree canopy cover and biomass are reported at multiple spatial scales using various approaches, spatially explicit mapping of individual trees and their three-dimensional structure is sparse. Here we produced an individual tree dataset including tree locations, height, crown area, crown volume, and biomass over the entire New York City, USA for 6,005,690 trees. Individual trees were detected and mapped from remotely sensed datasets along with their height and crown size information. Tree biomass in 296 field plots was measured and modelled using i-Tree Eco. Wall-to-wall tree biomass was mapped using relationships between field measurements and remotely sensed datasets and downscaled to individual trees. Validation using field-plot measurements indicated that our mapping products overestimated tree number, mean tree height and maximum tree height by 11.1%, 8.6%, and 5.3%, respectively. These overestimations were mainly due to the spatial and temporal mis-match between field measurements and remote sensing observations and uncertainties in tree segmentation algorithms. This dataset enables the evaluation of urban forest ecosystem services including regulating urban heat and promoting urban health, which can provide valuable insights for urban forest management and policy making.

Topological sequence entropy and topological dynamics of tree maps

We prove that a zero topological entropy continuous tree map always displays zero topological sequence entropy when it is restricted to its non-wandering and chain recurrent sets. In addition, we show that a similar result is not possible when the phase space is a dendrite even when we consider only the restriction on the set of periodic points.

Mapping Diverse Paddy Rice Cropping Patterns in South China Using Harmonized Landsat and Sentinel-2 Data

Paddy rice cropping patterns (PRCPs) play important roles in both agroecosystem modeling and food security. Although paddy rice maps have been generated over several regions using satellite observations, few studies have focused on mapping diverse smallholder PRCPs, which include crop rotation and are dominant cropping structures in South China. Here, an approach called the feature selection and hierarchical classification (FSHC) method was proposed to effectively identify paddy rice and its rotation types. Considering the cloudy and rainy weather in South China, a harmonized Landsat and Sentinel-2 (HLS) surface reflectance product was employed to increase high-quality observations. The FSHC method consists of three processes: cropping intensity mapping, feature selection, and decision tree (DT) model development. The FSHC performance was carefully evaluated using crop field samples obtained in 2018 and 2019. Results suggested that the derived cropping intensity map based on the Savitzky–Golay (S-G) filtered normalized difference vegetation index (NDVI) time series was reliable, with an overall accuracy greater than 93%. Additionally, the optimal spectral (i.e., normalized difference water index (NDWI) and land surface water index (LSWI)) and temporal (start-of-season (SOS) date) features for distinguishing different PRCPs were successfully identified, and these features are highly related to the critical growth stage of paddy rice. The developed DT model with three hierarchical levels based on optimal features performed satisfactorily, and the identification accuracy of each PRCP can be achieved approximately 85%. Furthermore, the FSHC method exhibited similar performances when mapping PRCPs in adjacent years. These results demonstrate that the proposed FSHC approach with HLS data can accurately extract diverse PRCPs over fragmented croplands; thus, this approach represents a promising opportunity for generating refined crop type maps.

Synergism of Multi-Modal Data for Mapping Tree Species Distribution—A Case Study from a Mountainous Forest in Southwest China

Accurately mapping tree species is crucial for forest management and conservation. Most previous studies relied on features derived from optical imagery, and digital elevation data and the potential of synthetic aperture radar (SAR) imagery and other environmental factors have, generally, been underexplored. Therefore, the aim of this study is to evaluate the potential of fusing freely available multi-modal data for accurately mapping tree species. Sentinel-2, Sentinel-1, and various environmental datasets over a large mountainous forest in Southwest China were obtained and analyzed using Google Earth Engine (GEE). Seven data cases considering the individual or joint performance of different features, and four additional cases considering a novel clustering-based feature selection method, were analyzed. All 11 cases were assessed using three machine learning algorithms, including random forest (RF), support vector machine (SVM), and extreme gradient boosting tree (XGBoost). The best performance, with an overall accuracy of 77.98%, was attained from the case with all features and the random forest classifier. Sentinel-2 data alone exhibited similar performance as environmental data in terms of overall accuracy. Similar species, such as oak and birch, cannot be spectrally discriminated based on Sentinel-2-based features alone. The addition of SAR features improved discrimination, especially when distinguishing between some coniferous and deciduous species, but also decreased accuracy for oak. The analysis based on different data cases and feature importance rankings indicated that environmental features are important. The random forest outperformed other models, and a better prediction was achieved for planted tree species compared to that for the natural forest. These results suggest that accurately mapping tree species over large mountainous areas is feasible with freely accessible multi-modal data, especially when considering environmental factors.

Identifying the drawbacks of joint patents: An evaluation of IPR sharing in R&D alliances in Biotechnology Firms in Singapore

R&D alliances in the biotechnology industry have always been common in order to fulfill the demands and needs of the involved parties to carry out their innovation processes. The increasing trend of R&D alliances in the biotechnology industry in Singapore has also promoted IPR sharing in R&D alliances. This mechanism has promoted the concept of joint patents in this regard. Thus, the present study focused on the drawbacks of joint patents in R&D alliances in biotech firms in Singapore. For this purpose, semi-structured interviews were conducted with five employers from different biotech firms in Singapore. The collected data was then analyzed using NVivo software, and project map and tree maps were formulated for each research objective. The results obtained from this study showed that R&D alliances are effective in improving the financial and innovation performance of biotechnology firms. It has also been observed that IPR sharing in these alliances is effective in promoting technology and knowledge transfer. However, various drawbacks of joint patents were also observed, including financial issues, technology-related issues, and legal issues.

Mountain Tree Species Mapping Using Sentinel-2, PlanetScope, and Airborne HySpex Hyperspectral Imagery

Europe’s mountain forests, which are naturally valuable areas due to their high biodiversity and well-preserved natural characteristics, are experiencing major alterations, so an important component of monitoring is obtaining up-to-date information concerning species composition, extent, and location. An important aspect of mapping tree stands is the selection of remote sensing data that vary in temporal, spectral, and spatial resolution, as well as in open and commercial access. For the Tatra Mountains area, which is a unique alpine ecosystem in central Europe, we classified 13 woody species by iterative machine learning methods using random forest (RF) and support vector machine (SVM) algorithms of more than 1000 polygons collected in the field. For this task, we used free Sentinel-2 multitemporal satellite data (10 m pixel size, 12 spectral bands, and 21 acquisition dates), commercial PlanetScope data (3 m pixel size, 8 spectral bands, and 3 acquisitions dates), and airborne HySpex hyperspectral data (2 m pixel size, 430 spectral bands, and a single acquisition) with fusion of the data of topographic derivatives based on Shuttle Radar Topography Mission (SRTM) and airborne laser scanning (ALS) data. The iterative classification method achieved the highest F1-score with HySpex (0.95 RF; 0.92 SVM) imagery, but the multitemporal Sentinel-2 data cube, which consisted of 21 scenes, offered comparable results (0.93 RF; 0.89 SVM). The three images of the high-resolution PlanetScope produced slightly less accurate results (0.89 RF; 0.87 SVM).

Apricot Tree Detection from UAV-Images Using Mask R-CNN and U-Net

Monitoring trees is necessary to manage and take inventory of forests, monitor plants in urban areas, distribute vegetation, monitor change, and establish sensitive and renewable agricultural systems. This study aims to automatically detect, count, and map apricot trees in an orthophoto, covering an area of approximately 48 ha on the ground surface using two different algorithms based on deep learning. Here, Mask region-based convolutional neural network (Mask R-CNN) and U-Net models were run together with a dilation operator to detect apricot trees in UAV images, and the performances of the models were compared. Results show that Mask R-CNN operated in this way performs better in tree detection, counting, and mapping tasks compared to U-Net. Mask R-CNN with the dilation operator achieved a precision of 98.7%, recall of 99.7%, F1 score of 99.1%, and intersection over union (IoU) of 74.8% for the test orthophoto. U-Net, on the other hand, has achieved a recall of 93.3%, precision of 97.2%, F1 score of 95.2%, and IoU of 58.3% when run with the dilation operator. Mask R-CNN was able to produce successful results in challenging areas. U-Net, on the other hand, showed a tendency to overlook existing trees rather than generate false alarms.

Quality improvement tools to manage deceased organ donation processes: a scoping review

ObjectiveTo collate and summarise the literature on the quality improvement tools that have been developed for deceased organ donation processes after circulatory determination of death and neurological determination of death.DesignScoping...

Mapping tree species composition in a Caspian temperate mixed forest based on spectral-temporal metrics and machine learning

Mapping tree species composition in a Caspian temperate mixed forest based on spectral-temporal metrics and machine learning

The use of i-THINK Mapping in teaching reading comprehension among ESL teachers

This study investigated the use of i-THINK Mapping in teaching reading comprehension by ESL teachers to a group of Form Five students, and the factors and challenges the ESL teachers faced in teaching reading comprehension using i-THINK Mapping. A qualitative approach, specifically a case study design, was employed in this study. Classroom observations, semi-structured interviews, and document analysis of their lesson plans were used to collect the data. Four ESL teachers with a minimum of 5-year teaching experience in a rural secondary school in Hulu Selangor, Malaysia, were selected using a purposive sampling technique to participate in this study. Thematic analysis was used to analyse the data obtained from classroom observations and interviews. The findings show that the teachers had applied five i-THINK maps, including a Circle Map to define in context, a Bubble Map to describe, a Double Bubble Map to compare and contrast, a Flow Map to show the sequence of an event, and a Tree Map to classify different ideas. The i-THINK Mapping motivated the students to discuss, brainstorm, and cooperate with their peers to detect the details from the given reading texts. The teachers’ challenges include the time constraints in preparing the lesson with i-THINK Mapping and a low level of student English proficiency. One implication of this study is that teachers’ use of i-THINK Mapping to teach reading comprehension could assist students in generating ideas, expanding ideas, and expressing them orally.

Large-Scale Date Palm Tree Segmentation from Multiscale UAV-Based and Aerial Images Using Deep Vision Transformers

The reliable and efficient large-scale mapping of date palm trees from remotely sensed data is crucial for developing palm tree inventories, continuous monitoring, vulnerability assessments, environmental control, and long-term management. Given the increasing availability of UAV images with limited spectral information, the high intra-class variance of date palm trees, the variations in the spatial resolutions of the data, and the differences in image contexts and backgrounds, accurate mapping of date palm trees from very-high spatial resolution (VHSR) images can be challenging. This study aimed to investigate the reliability and the efficiency of various deep vision transformers in extracting date palm trees from multiscale and multisource VHSR images. Numerous vision transformers, including the Segformer, the Segmenter, the UperNet-Swin transformer, and the dense prediction transformer, with various levels of model complexity, were evaluated. The models were developed and evaluated using a set of comprehensive UAV-based and aerial images. The generalizability and the transferability of the deep vision transformers were evaluated and compared with various convolutional neural network-based (CNN) semantic segmentation models (including DeepLabV3+, PSPNet, FCN-ResNet-50, and DANet). The results of the examined deep vision transformers were generally comparable to several CNN-based models. The investigated deep vision transformers achieved satisfactory results in mapping date palm trees from the UAV images, with an mIoU ranging from 85% to 86.3% and an mF-score ranging from 91.62% to 92.44%. Among the evaluated models, the Segformer generated the highest segmentation results on the UAV-based and the multiscale testing datasets. The Segformer model, followed by the UperNet-Swin transformer, outperformed all of the evaluated CNN-based models in the multiscale testing dataset and in the additional unseen UAV testing dataset. In addition to delivering remarkable results in mapping date palm trees from versatile VHSR images, the Segformer model was among those with a small number of parameters and relatively low computing costs. Collectively, deep vision transformers could be used efficiently in developing and updating inventories of date palms and other tree species.

Trends and Implications of Caring Economics Research in Indonesia: a Bibliometric Analysis from 2016 to 2022

Purpose: The aim of this study is to explain the trends of published research on Caring Economics and their implications for the development of future research. Theoritical Framework: Caring Economics is a novel approach to considering human prosperity. According to conventional economics, prosperity is determined by consumption, income, and wealth. Caring Economics, in contrast, conceives of prosperity in terms of more fundamental causes of long-term human wellbeing. From this perspective, individuals are prosperous when their lives are going well. This feeling of success is not limited primarily to the satisfaction of materialistic demands and desires. Moreover, it contains an interpersonal component. Design/methodology/approach: This research method is a bibliographic analysis using data from the Google Scholar database, the most popular indexer in Indonesia. To collect data, Harzing Publish or Perish was used with the central database of Google Scholar. VosViewer is also used as a tool to get a map of the relationship between authors and between searched keywords. Furthermore, Microsoft Excel is used to process the data and tree mapping from the data obtained. Findings: This research is essential to drive the development of a sustainable caring economics paradigm in realizing collective economic prosperity. The results of this study indicate that the type of research with the theme of Caring economics is not widely published, namely 32 publications of research results and conceptual scientific ideas. Focused and Interdisciplinary Journal of Partnership Studies is the most influential journal in Caring Economics research, namely H-index 4. The most cited Caring Economics researcher from 2015 to 2022 is Indra Darmawan, with 4 citations. Silvia Hedenigg followed in second place with 4 citations and Mit Witjaksono with 2 citations. After being traced, Indra Darmawan and Mit Witjaksono came from the State University of Malang. Researcher Silvia Hedenigg comes from Friedensau Adventist University institution. Research, Practical & Social implications: The results and findings indicate that caring economics research has not been widely studied in economics, economic education, community economics, health, and services related to humans and the economy. Empirically, problems related to concerns in economics, financial education, community economics, health, and services related to humans and the economy must be investigated to solve the issues faced. It is essential to implement caring economics to build the Indonesian economy towards social justice for all Indonesians by realizing a prosperous Indonesia as a whole. Originality/value: This finding has implications for efforts to encourage academics and researchers in Indonesia to discover caring economics to revitalize the economy from the micro to macro level so that Indonesia's economic fundamentals are getting stronger. In addition, it is recommended for collaborative research between institutions to enrich scientific wealth and develop a more comprehensive research study on Caring Economics.

Ensemble Machine Learning for Mapping Tree Species Alpha-Diversity Using Multi-Source Satellite Data in an Ecuadorian Seasonally Dry Forest

The increased variety of satellite remote sensing platforms creates opportunities for estimating tropical forest diversity needed for environmental decision-making. As little as 10% of the original seasonally dry tropical forest (SDTF) remains for Ecuador, Peru, and Colombia. Remnant forests show high rates of species endemism, but experience degradation from climate change, wood-cutting, and livestock-grazing. Forest census data provide a vital resource for examining remote sensing methods to estimate diversity levels. We used spatially referenced trees ≥5 cm in diameter and simulated 0.10 ha plots measured from a 9 ha SDTF in southwestern Ecuador to compare machine learning (ML) models for six α-diversity indices. We developed 1 m tree canopy height and elevation models from stem mapped trees, at a scale conventionally derived from light detection and ranging (LiDAR). We then used an ensemble ML approach comparing single- and combined-sensor models from RapidEye, Sentinel-2 and interpolated canopy height and topography surfaces. Validation data showed that combined models often outperformed single-sensor approaches. Combined sensor and model ensembles for tree species richness, Shannon’s H, inverse Simpson’s, unbiased Simpson’s, and Fisher’s alpha indices typically showed lower root mean squared error (RMSE) and increased goodness of fit (R2). Piélou’s J, a measure of evenness, was poorly predicted. Mapped tree species richness (R2 = 0.54, F = 27.3, p = <0.001) and Shannon’s H′ (R2 = 0.54, F = 26.9, p = <0.001) showed the most favorable agreement with field validation observations (n = 25). Small-scale model experiments revealed essential relationships between dry forest tree diversity and data from multiple satellite sensors with repeated global coverage that can help guide larger-scale biodiversity mapping efforts.

Code Generation by Example Using Symbolic Machine Learning

Code generation is a key technique for model-driven engineering (MDE) approaches of software construction. Code generation enables the synthesis of applications in executable programming languages from high-level specifications in UML or in a domain-specific language. Specialised code generation languages and tools have been defined; however, the task of manually constructing a code generator remains a substantial undertaking, requiring a high degree of expertise in both the source and target languages, and in the code generation language. In this paper, we apply novel symbolic machine learning techniques for learning tree-to-tree mappings of software syntax trees, to automate the development of code generators from source–target example pairs. We evaluate the approach on several code generation tasks, and compare the approach to other code generator construction approaches. The results show that the approach can effectively automate the synthesis of code generators from examples, with relatively small manual effort required compared to existing code generation construction approaches. We also identified that it can be adapted to learn software abstraction and translation algorithms. The paper demonstrates that a symbolic machine learning approach can be applied to assist in the development of code generators and other tools manipulating software syntax trees.

POINTNET++ TRANSFER LEARNING FOR TREE EXTRACTION FROM MOBILE LIDAR POINT CLOUDS

Abstract. Trees are an essential part of the natural and urban environment due to providing crucial benefits such as increasing air quality and wildlife habitats. Therefore, various remote sensing and photogrammetry technologies, including Mobile Laser Scanner (MLS), have been recently introduced for precise 3D tree mapping and modeling. The MLS provides densely 3D LiDAR point clouds from the surrounding, which results in measuring applicable information of trees like stem diameter or elevation. In this paper, a transfer learning procedure on the PointNet++ has been proposed for tree extraction. Initially, two steps of converting the MLS point clouds into same-length smaller sections and eliminating ground points have been conducted to overcome the massive volume of MLS data. The algorithm was tested on four LiDAR datasets ranging from challengeable urban environments containing multiple objects like tall buildings to railway surroundings. F1-Score accuracy was gained at around 93% and 98%, which showed the feasibility and efficiency of the proposed algorithm. Noticeably, the algorithms also measured geometrical information of extracted trees such as 2D coordinate space, height, stem diameter, and 3D boundary tree locations.

Evaluating Factors Impacting Fallen Tree Detection from Airborne Laser Scanning Point Clouds

Fallen tree mapping provides valuable information regarding the ecological value of boreal forests. Airborne laser scanning (ALS) enables mapping fallen trees on a large scale. We compared the performance of line-detection-based individual fallen tree detection when using moderate point density ALS data (15 points/m2) and high-point-density unmanned aerial vehicle-based laser scanning (ULS) data (285 points/m2). Furthermore, we inspected the dataset and detection methodology-related factors impacting performance in each case. The results of this study showed that increasing the point density of the laser scanning dataset enables the detection of a larger proportion of fallen trees. However, based on our experiment, a line-detection-based fallen tree detection approach is sensitive to noise, thus generating a large number of false detections, especially with high-point-density data. Different types of filters, such as a simple height-based filter and machine-learning-based filters, can be used for reducing noise. However, using such filters is always a compromise, as in addition to reducing noise and thus false detections, they also reduce the number of true detections. Hence, a less noise-sensitive fallen tree detection method utilizing the finer details visible in high-density point clouds could be more suitable for high-point-density laser scanning data.