Local Maximum Algorithm Research Articles

Tree barriers height information extraction under power line based on watershed segmentation and local maximum algorithm

ABSTRACT In view of the problem that the traditional power line inspection cannot accurately obtain the height of the tree barriers under the power line, this study takes a forest area in Guilin City and a power line corridor in Yinchuan City as the research object, and combines watershed segmentation algorithm and local maximum algorithm to study the height of the tree barriers under power line. The results show that the watershed segmentation algorithm and the local maximum algorithm can accurately extract the height of tree barriers in power lines. In the Guilin experiment, there is a good correlation between the extracted height of different trees and the measured height, The correlation coefficients (R2) for osmanthus, eucalyptus, and pine trees are 0.9368, 0.8923, and 0.8227, respectively. In the experiment in Yinchuan, the tree height extracted by unmanned aerial vehicles (UAV) and LiDAR also has a good correlation with the measured height, with correlation coefficients (R2) of 0.926 and 0.952, respectively. These two experiments have verified the feasibility of watershed segmentation algorithm and local maximum algorithm in extracting the height of tree barriers under power lines, which is of great significance for monitoring and warning of tree barriers under power lines.

Fusion of airborne multimodal point clouds for vegetation parameter correction extraction in burned areas.

Most experimental studies use unimodal data for processing, the RGB image point cloud cannot separate the shrub and tree layers according to the visible vegetation index, and the airborne laser point cloud is difficult to distinguish between the ground and grass ranges, to address the above problems, a multi-band information image fusing the LiDAR point cloud and the RGB image point cloud is constructed. In this study, data collected from UAV platforms, including RGB image point clouds and laser point clouds, were used to construct a fine canopy height model (using laser point cloud data) and high-definition digital orthophotos (using image point cloud data), and the orthophotos were fused with a canopy height model (CHM) by selecting the Difference Enhancement Vegetation Index (DEVI) and Normalised Green-Blue Discrepancy Index (NGBDI) after comparing the accuracy of different indices. Morphological reconstruction of CHM + DEVI/NGBDI fusion image, remove unreasonable values; construct training samples, using classification regression tree algorithm, segmentation of the range of the burned areas and adaptive extraction of vegetation as trees, shrubs and grasslands, tree areas as foreground markers using the local maximum algorithm to detect the tree apexes, the non-tree areas are assigned to be the background markers, and the Watershed Transform is performed to obtain the segmentation contour; the original laser point cloud is divided into chunks according to the segmented single-tree contour, and the highest point is traversed to search for the highest point, and corrected for the height of the single-tree elevations one by one. Accuracy analysis of the vegetation information extracted by the method with the measured data showed that the improved method increased the overall recall rate by 4.1%, the overall precision rate by 3.7%, the overall accuracy F1 score by 3.9%, and the tree height accuracy by 8.8%, 1.4%, 1.7%, 6.4%, 1.8%, and 0.3%, respectively, in the six sampling plots. The effectiveness of the improved method is verified, while the higher the degree of vegetation mixing in the region the better the extraction effect of the improved algorithm.

Urban Treetop Detection and Tree-Height Estimation from Unmanned-Aerial-Vehicle Images

Individual tree detection for urban forests in subtropical environments remains a great challenge due to the various types of forest structures, high canopy closures, and the mixture of evergreen and deciduous broadleaved trees. Existing treetop detection methods based on the canopy-height model (CHM) from UAV images cannot resolve commission errors in heterogeneous urban forests with multiple trunks or strong lateral branches. In this study, we improved the traditional local-maximum (LM) algorithm using a dual Gaussian filter, variable window size, and local normalized correlation coefficient (NCC). Specifically, we adapted a crown model of maximum/minimum tree-crown radii and an angle strategy to detect treetops. We then removed and merged the pending tree vertices. Our results showed that our improved LM algorithm had an average user accuracy (UA) of 87.3% (SD± 4.6), an average producer accuracy (PA) of 82.8% (SD± 4.1), and an overall accuracy of 93.3% (SD± 3.9) for sample plots with canopy closures less than 0.5. As for the sample plots with canopy closures from 0.5 to 1, the accuracies were 78.6% (SD± 31.5), 73.8% (SD± 10.3), and 68.1% (SD± 12.7), respectively. The tree-height estimation accuracy reached more than 0.96, with an average RMSE of 0.61 m. Our results show that the UAV-image-derived CHM can be used to accurately detect individual trees in mixed forests in subtropical cities like Shanghai, China, to provide vital tree-structure parameters for precise and sustainable forest management.

Protection of Coastal Shelter Forests Using UAVs: Individual Tree and Tree-Height Detection in Casuarina equisetifolia L. Forests

Casuarina equisetifolia L. plays a significant role in sandy, coastal regions for sand stabilization and windbreaks. However, C. equisetifolia forests are susceptible to plant diseases and insect pests, resulting in mortality due to pure stands and a harsh natural environment. Mapping the distribution of C. equisetifolia and detecting its height can inform forest-management decisions. Unmanned aerial vehicle (UAV) imagery, coupled with the classical detection method, can provide accurate information on tree-level forest parameters. Considering that the accuracy of a forest-parameter estimation is impacted by various flight altitudes and extraction parameters, the purpose of this study is to determine the appropriate flight altitude and extraction parameters for mapping C. equisetifolia using UAV imagery and the local maxima algorithm in order to monitor C. equisetifolia more accurately. A total of 11 different flight altitudes and 36 combinations of circular smoothing window size (CSWS) and fixed circular window size (FCWS) were tested, and 796 trees with corresponding positions in the UAV image and ground–tree heights were used as reference. The results show that the combination of a 0.1 m CSWS and a 0.8 m FCWS for individual tree detection (ITD) and tree-height detection achieved excellent accuracy (with an F1 score of 91.44% for ITD and an estimation accuracy (EA) of 79.49% for tree-height detection). A lower flight altitude did not indicate a higher accuracy for individual tree and tree-height detection. The UAV image obtained within a flight altitude of 60 m–80 m can meet the accuracy requirements for the identification of C. equisetifolia tree-height estimation (F1 score > 85% for ITD; EA > 75% for tree-height estimation). This study provides a foundation for monitoring C. equisetifolia by using UAV imagery and applying the local maxima algorithm, which may help forestry practitioners detect C. equisetifolia trees and tree heights more accurately, providing more information on C. equisetifolia growth status.

Individual urban trees detection based on point clouds derived from UAV-RGB imagery and local maxima algorithm, a case study of Fateh Garden, Iran

Individual urban trees detection based on point clouds derived from UAV-RGB imagery and local maxima algorithm, a case study of Fateh Garden, Iran

An Automatic Individual Tree 3D Change Detection Method for Allometric Parameters Estimation in Mixed Uneven-Aged Forest Stands from ALS Data

Forests play a central role in the management of the Earth’s climate. Airborne laser scanning (ALS) technologies facilitate the monitoring of large and impassable areas and can be used to monitor the 3D structure of forests. While the ALS-based forest measures have been studied in depth, 3D change detection in forests is still a subject of little attention in the literature due to the challenges introduced by comparing point cloud pairs. In this study, we propose an innovative methodology to (i) automatically perform a 3D change detection of forests on an individual tree level; (ii) estimate tree parameters with allometric equations; and (iii) perform an assessment of the aboveground biomass (AGB) variation over time. The area in which the tests were carried out was hit by an ice storm that occurred in the time interval between the two LiDAR acquisitions; furthermore, field measurements were carried out and used to validate the results. The single-tree segmentation of the point clouds was automatically performed with a local maxima algorithm to detect the treetop, and a decision tree method to define the individual crowns around the local maxima. The multitemporal comparison of the point clouds was based on the identification of single trees, which were matched when there was a correlation between the position of the treetops. For each tree, the DBH (diameter at breast height) and the AGB were also estimated using allometric equations. The results are promising and allowed us to identify the uprooted trees and estimate that about 40% of the AGB of the area under examination had been destroyed, with an RMSE over the estimation ranging between 4% and 21% in four scenarios.

Comparison of single tree detection methods to extract support trees for cable road planning

For the provision of various ecosystem services in steep terrain, such as protection against natural hazards, a forest must be managed, which often requires the use of cable yarders. The design of a cable road is a complex and demanding task that also includes the search for appropriate support and anchor trees. The aim of this study was to evaluate whether and with what reliability potential support trees for cable yarding can be detected using remote sensing data. The detection of potential support trees was tested using 48 method combinations on 10 test plots of the Experimental Forest Management project in cable yarder terrain in the Swiss Alps in the Canton of Grisons. The most suitable method combinations used a Gaussian filter and a local maxima algorithm. On average, they had an extraction rate of 108.9–124.5% (root mean square, RMS) and a mean commission error of 66.0–67.2% (RMS). The correctly detected trees deviated horizontally by an average of 1.8 to 1.9 m from the position of the reference trees. The difference in tree heights was 1.1 to 1.6 m. However, for the application of single tree detection to support cable road planning in steep and complex terrain, too few potential support trees were detected. Nonetheless, the accuracy of the extracted tree parameters would already be sufficient for cable road planning.

CNN-Based Cell Analysis: From Image to Quantitative Representation

We present a novel deep learning-based quantification pipeline for the analysis of cell culture images acquired by lens-free microscopy. The image reconstruction part of the pipeline features a convolutional neural network performing phase unwrapping and accelerating the inverse problem optimization. It allows phase retrieval at the 4K level (3,840 × 2,748 pixels) in 3 s. The analysis part of the pipeline features a suite of convolutional neural networks estimating different cell metrics from the reconstructed image, that is, cell surface area, cell dry mass, cell length, and cell thickness. The networks have been trained to predict quantitative representation of the cell measurements that can be next translated into measurement lists with a local maxima algorithm. In this article, we discuss the performance and limitations of this novel deep learning-based quantification pipeline in comparison with a standard image processing solution. The main advantage brought by this method is the fast processing time, that is, the analysis rate of ∼25.000 cells measurements per second. Although our proof of principle has been established with lens-free microscopy, the approach of using quantitative cell representation in a deep learning framework can be similarly applied to other microscopy techniques.

Comparison of Classical Methods and Mask R-CNN for Automatic Tree Detection and Mapping Using UAV Imagery

Detecting and mapping individual trees accurately and automatically from remote sensing images is of great significance for precision forest management. Many algorithms, including classical methods and deep learning techniques, have been developed and applied for tree crown detection from remote sensing images. However, few studies have evaluated the accuracy of different individual tree detection (ITD) algorithms and their data and processing requirements. This study explored the accuracy of ITD using local maxima (LM) algorithm, marker-controlled watershed segmentation (MCWS), and Mask Region-based Convolutional Neural Networks (Mask R-CNN) in a young plantation forest with different test images. Manually delineated tree crowns from UAV imagery were used for accuracy assessment of the three methods, followed by an evaluation of the data processing and application requirements for three methods to detect individual trees. Overall, Mask R-CNN can best use the information in multi-band input images for detecting individual trees. The results showed that the Mask R-CNN model with the multi-band combination produced higher accuracy than the model with a single-band image, and the RGB band combination achieved the highest accuracy for ITD (F1 score = 94.68%). Moreover, the Mask R-CNN models with multi-band images are capable of providing higher accuracies for ITD than the LM and MCWS algorithms. The LM algorithm and MCWS algorithm also achieved promising accuracies for ITD when the canopy height model (CHM) was used as the test image (F1 score = 87.86% for LM algorithm, F1 score = 85.92% for MCWS algorithm). The LM and MCWS algorithms are easy to use and lower computer computational requirements, but they are unable to identify tree species and are limited by algorithm parameters, which need to be adjusted for each classification. It is highlighted that the application of deep learning with its end-to-end-learning approach is very efficient and capable of deriving the information from multi-layer images, but an additional training set is needed for model training, robust computer resources are required, and a large number of accurate training samples are necessary. This study provides valuable information for forestry practitioners to select an optimal approach for detecting individual trees.

Individual tree detection using UAV-lidar and UAV-SfM data: A tutorial for beginners

Abstract Applications of unmanned aerial vehicles (UAVs) have proliferated in the last decade due to the technological advancements on various fronts such as structure-from-motion (SfM), machine learning, and robotics. An important preliminary step with regard to forest inventory and management is individual tree detection (ITD), which is required to calculate forest attributes such as stem volume, forest uniformity, and biomass estimation. However, users may find adopting the UAVs and algorithms for their specific projects challenging due to the plethora of information available. Herein, we provide a step-by-step tutorial for performing ITD using (i) low-cost UAV-derived imagery and (ii) UAV-based high-density lidar (light detection and ranging). Functions from open-source R packages were implemented to develop a canopy height model (CHM) and perform ITD utilizing the local maxima (LM) algorithm. ITD accuracy assessment statistics and validation were derived through manual visual interpretation from high-resolution imagery and field-data-based accuracy assessment. As the intended audience are beginners in remote sensing, we have adopted a very simple methodology and chosen study plots that have relatively open canopies to demonstrate our proposed approach; the respective R codes and sample plot data are available as supplementary materials.

A Novel Vegetation Point Cloud Density Tree-Segmentation Model for Overlapping Crowns Using UAV LiDAR

Detecting and segmenting individual trees in forest ecosystems with high-density and overlapping crowns often results in bias due to the limitations of the commonly used canopy height model (CHM). To address such limitations, this paper proposes a new method to segment individual trees and extract tree structural parameters. The method involves the following key steps: (1) unmanned aerial vehicle (UAV)-scanned, high-density laser point clouds were classified, and a vegetation point cloud density model (VPCDM) was established by analyzing the spatial density distribution of the classified vegetation point cloud in the plane projection; and (2) a local maximum algorithm with an optimal window size was used to detect tree seed points and to extract tree heights, and an improved watershed algorithm was used to extract the tree crowns. The proposed method was tested at three sites with different canopy coverage rates in a pine-dominated forest in northern China. The results showed that (1) the kappa coefficient between the proposed VPCDM and the commonly used CHM was 0.79, indicating that performance of the VPCDM is comparable to that of the CHM; (2) the local maximum algorithm with the optimal window size could be used to segment individual trees and obtain optimal single-tree segmentation accuracy and detection rate results; and (3) compared with the original watershed algorithm, the improved watershed algorithm significantly increased the accuracy of canopy area extraction. In conclusion, the proposed VPCDM may provide an innovative data segmentation model for light detection and ranging (LiDAR)-based high-density point clouds and enhance the accuracy of parameter extraction.

Individual tree crown detection from high spatial resolution imagery using a revised local maximum filtering

Accurate tree density and location are important information for optimizing the management and production of forest. Combination of remote sensing techniques and local maximum (LM) filtering algorithm provides a feasible approach to individual tree crown detection, but still faces high error under complicate canopy structure. In this study, a revised LM (RLM) algorithm is presented and evaluated for identifying individual trees from four high spatial-resolution images. Instead of a moving window technique, the RLM algorithm finds crown center seeds by searching local maximal in the transects along row and column directions of the image. Each of final crown centers is then searched using a variable window centered at the crown center seed. Strategies for splitting and merging crowns are implemented in the RLM algorithm to reduce false detection. Result showed that accuracy of the RLM algorithm was more sensitive to its minimum crown length parameter (CLmin). The RLM algorithm driven by the CLmin estimates achieved high overall accuracies between 85% and 91% and low commission (9–14%) and omission errors (8–15%) for the four images. Splitting and merging strategies implemented in the RLM algorithm effectively reduced commission and omission errors. These results indicate that the RLM algorithm is a feasible method with well-defined parameters for automatically detecting individual trees with satisfactory detection accuracy.

Assessment of Stem Volume on Plots Using Terrestrial Laser Scanner: A Precision Forestry Application.

Timber volume is an important asset, not only as an ecological component, but also as a key source of present and future revenues, which requires precise estimates. We used the Trimble TX8 survey-grade terrestrial laser scanner (TLS) to create a detailed 3D point cloud for extracting total tree height and diameter at breast height (1.3 m; DBH). We compared two different methods to accurately estimate total tree heights: the first method was based on a modified version of the local maxima algorithm for treetop detection, “HTTD”, and for the second method we used the centers of stem cross-sections at stump height (30 cm), “HTSP”. DBH was estimated by a computationally robust algebraic circle-fitting algorithm through hierarchical cluster analysis (HCA). This study aimed to assess the accuracy of these descriptors for evaluating total stem volume by comparing the results with the reference tree measurements. The difference between the estimated total stem volume from HTTD and measured stems was 2.732 m3 for European oak and 2.971 m3 for Norway spruce; differences between the estimated volume from HTSP and measured stems was 1.228 m3 and 2.006 m3 for European oak and Norway spruce, respectively. The coefficient of determination indicated a strong relationship between the measured and estimated total stem volumes from both height estimation methods with an R2 = 0.89 for HTTD and R2 = 0.87 for HTSP for European oak, and R2 = 0.98 for both HTTD and HTSP for Norway spruce. Our study has demonstrated the feasibility of finer-resolution remote sensing data for semi-automatic stem volumetric modeling of small-scale studies with high accuracy as a potential advancement in precision forestry.

Assessment of an improved individual tree detection method based on local-maximum algorithm from unmanned aerial vehicle RGB imagery in overlapping canopy mountain forests

ABSTRACT Low consumer-grade cameras attached to small unmanned aerial vehicles (UAV) can easily acquire high spatial resolution images, leading to convenient forest monitoring at small-scales for forest managers. However, most studies were carried out in the low canopy density and flat ground plantations to detect individual trees. We selected overlapping canopy plantation in mountainous area in the eastern of China and acquired high spatial resolution UAV RGB images to detect individual trees. A total of 402 reference trees were located in three rectangle plots (900 m2). To enhance the confidence of the tested individual tree detection method, clear-cutting and Real-Time Kinematic (RTK) were used to obtain the truth values in the plots. A novel method for semi-automatic individual tree detection was proposed based on a local-maximum algorithm and UAV-derived DSM data (LAD) in this study. The detection accuracy of LAD was compared with commonly used methods based on UAV-derived orthophoto images, local-maximum algorithm (LAO), object-oriented feature segmentation (OFS), multiscale segmentation technique (MST) and manual visual interpretation (MVI). The overall accuracy (OA (%) decreased in the order of LAD (84.5%) > MST (69.1%) > OFS (65.1%) > MVI (64.1%) > LAO (59.1%). LAD had only 15.5%s omission errors (OM (%), which was less than half of the other four methods in comparison. It was noteworthy that MVI had 35.9% OM %, which revealed that MVI should be used carefully as the truth value. LAD showed similar repeated detection error (RP (%) and completely wrong detection (CW (%), while the other four methods had obviously higher CW % than the RP %. From our results, it can be concluded that the proposed LAD method may help improving the accuracy of individual tree detection to an acceptable accuracy (>80%) in dense mountain forests, and has practical advantages in future research direction to assess tree attributes from UAV RGB image.

Detection of Very Small Tree Plantations and Tree-Level Characterization Using Open-Access Remote-Sensing Databases

Highly fragmented land property hinders the planning and management of single species tree plantations. In such situations, acquiring information about the available resources is challenging. This study aims to propose a method to locate and characterize tree plantations in these cases. Galicia (Northwest of Spain) is an area where property is extremely divided into small parcels. European chestnut (Castanea sativa) plantations are an important source of income there; however, it is often difficult to obtain information about them due to their small size and scattered distribution. Therefore, we selected a Galician region with a high presence of chestnut plantations as a case study area in order to locate and characterize small plantations using open-access data. First, we detected the location of chestnut plantations applying a supervised classification for a combination of: Sentinel-2 images and the open-access low-density Light Detection and Ranging (LiDAR) point clouds, obtained from the untapped open-access LiDAR Spanish national database. Three classification algorithms were used: Random Forest (RF), Support Vector Machine (SVM), and XGBoost. We later characterized the plots at the tree-level using the LiDAR point-cloud. We detected individual trees and obtained their height applying a local maxima algorithm to a point-cloud-derived Canopy Height Model (CHM). We also calculated the crown surface of each tree by applying a method based on two-dimensional (2D) tree shape reconstruction and canopy segmentation to a projection of the LiDAR point cloud. Chestnut plantations were detected with an overall accuracy of 81.5%. Individual trees were identified with a detection rate of 96%. The coefficient of determination R2 value for tree height estimation was 0.83, while for the crown surface calculation it was 0.74. The accuracy achieved with these open-access databases makes the proposed procedure suitable for acquiring knowledge about the location and state of chestnut plantations as well as for monitoring their evolution.

Individual Tree Position Extraction and Structural Parameter Retrieval Based on Airborne LiDAR Data: Performance Evaluation and Comparison of Four Algorithms

Information for individual trees (e.g., position, treetop, height, crown width, and crown edge) is beneficial for forest monitoring and management. Light Detection and Ranging (LiDAR) data have been widely used to retrieve these individual tree parameters from different algorithms, with varying successes. In this study, we used an iterative Triangulated Irregular Network (TIN) algorithm to separate ground and canopy points in airborne LiDAR data, and generated Digital Elevation Models (DEM) by Inverse Distance Weighted (IDW) interpolation, thin spline interpolation, and trend surface interpolation, as well as by using the Kriging algorithm. The height of the point cloud was assigned to a Digital Surface Model (DSM), and a Canopy Height Model (CHM) was acquired. Then, four algorithms (point-cloud-based local maximum algorithm, CHM-based local maximum algorithm, watershed algorithm, and template-matching algorithm) were comparatively used to extract the structural parameters of individual trees. The results indicated that the two local maximum algorithms can effectively detect the treetop; the watershed algorithm can accurately extract individual tree height and determine the tree crown edge; and the template-matching algorithm works well to extract accurate crown width. This study provides a reference for the selection of algorithms in individual tree parameter inversion based on airborne LiDAR data and is of great significance for LiDAR-based forest monitoring and management.

Automated Extraction of Street Lights From JL1-3B Nighttime Light Data and Assessment of Their Solar Energy Potential

To realize energy conservation and environmental protection, solar street lights have been widely used in urban areas in China. To reasonably and effectively utilize solar street lights, the original street lights must be located, and the solar street light potential must be assessed. The Jilin1-03B (JL1-3B) satellite provides next-generation nighttime light data with a high spatial resolution and in three spectral bands. Consequently, the street lights can be extracted from the nighttime light data. We used the road network dataset from the open street map with a specific buffer to extract the road area as a constraint region. Next, the grayscale brightness of JL1-3B images was obtained by integrating all the three bands to locate the street light by using a local maximum algorithm. Then, the values of the original three bands were utilized to classify the types of street lights as high-pressure sodium (HPS) lamps or light-emitting diode lamps. Finally, we simulated the replacement of all the HPS lamps with solar street lights and assessed the corresponding solar energy potential by using the digital surface model data and hourly cloud cover data through the SHORTWAVE-C model. The accuracy of location of the street lights was approximately 90%. Replacing an HPS lamp by one solar street light for 20 years can save 1.85 × 104 kWh of electrical energy, 7.41 t of standard coal, 5.03 t of C emissions, 18.47 t of CO2 emissions, 0.55 t of SO2 emissions, and 0.28 t of NOX emissions.

IDENTIFYING THE NUMBER OF COMPONENTS IN GAUSSIAN MIXTURE MODELS USING NUMERICAL ALGEBRAIC GEOMETRY.

Using Gaussian mixture models for clustering is a statistically mature method for clustering in data science with numerous successful applications in science and engineering. The parameters for a Gaussian mixture model are typically estimated from training data using the iterative expectation-maximization algorithm, which requires the number of Gaussian components a priori. In this study, we propose two algorithms rooted in numerical algebraic geometry, namely an area-based algorithm and a local maxima algorithm, to identify the optimal number of components. The area-based algorithm transforms several Gaussian mixture models with varying number of components into sets of equivalent polynomial regression splines. Next, it uses homotopy continuation methods for evaluating the resulting splines to identify the number of components that results in the best fit. The local maxima algorithm forms a set of polynomials by fitting a smoothing spline to a kernel density estimate of the data. Next, it uses numerical algebraic geometry to solve the system of the first derivatives for finding the local maxima of the resulting smoothing spline, which estimates the number of mixture components. The local maxima algorithm also identifies the location of the centers of Gaussian components. Using a real-world case study in automotive manufacturing and multiple simulations, we compare the performance of the proposed algorithms with that of Akaike information criterion (AIC) and Bayesian information criterion (BIC), which are popular methods in the literature. We show the proposed algorithms are more robust than AIC and BIC when the Gaussian assumption is violated.

Optimizing individual tree detection accuracy and measuring forest uniformity in coconut (Cocos nucifera L.) plantations using airborne laser scanning

Forest inventory and monitoring is indispensable for coconut (Cocos nucifera L.) forest plantation owners as it allows them in assessing tree growth, fruit production rates and vitality of plantations, as well as assists to meet up with the rising market demands by ensuring better yields. Nonetheless, the use of remote sensing techniques for optimizing the management and production of coconut plantations is still at a latent stage. In this paper, we present an airborne laser scanning (lidar) based tree detection method applied for automatically identifying individual coconut trees in a plantation in southeast Brazil. This method locates individual trees by searching treetops on canopy height models (CHM) derived from lidar data through a moving window having fixed treetop window size (TWS). Here, an adaptive TWS approach was implemented as a function of lidar-derived canopy cover (COV, %) along with additional smoothening window sizes (SWS) for enhancing tree detection accuracy. A total of 19 plots characterized by varying levels of canopy cover were used for testing the accuracy of our framework and we were able to obtain an average tree detection accuracy of 86.22%. From a total of 341 trees, 294 trees were detected correctly by the algorithm using adaptive TWS. A low TWS (3x3) value was found to perform best in study plots having COV > 80% and for rest of the cases, a higher TWS (7x7) was perceived suitable. Results were further analyzed and compared, to evaluate the relationship of Individual Tree Detection (ITD) accuracy with varying canopy cover levels, within-plot tree distribution patterns, TWS and SWS values. Proceedings from our work show that appropriate combination of lidar-derived CHMs, local maxima (LM) algorithm and window sizes have the potential to satisfactorily (F-score ˜ 0.90) detect plantation species having irregular canopies such as coconut trees. As an extension of the ITD, estimation of forest uniformity, which gives a measure of the level of homogeneity or heterogeneity of stands, is also performed.

A hybrid method for segmenting individual trees from airborne lidar data

Segmentation of individual trees from airborne lidar data uses either the point cloud directly or canopy height models (CHMs) derived from the point cloud. Point-based segmentation is able to detect understorey trees but is sensitive to the point density and often demands a high overhead cost of computing. Conversely, CHM-based segmentation can be easily implemented but it is impractical for the detection of understorey trees. To identify highly accurate treetops as well as understorey trees, this paper presents a hybrid method by modifying a CHM-based individual tree crown delineation (ITCD) algorithm and integrating it into a point-based algorithm. A multiscale local maxima (LM) algorithm is developed to improve the accuracy of LM obtained from CHMs in different spatial resolutions. The improved LM are used as seeds to segment the lidar point cloud into individual trees. For each tree, histogram analysis is applied to investigate the presence of understorey trees. Field measurements of tree heights and crown widths are used as ground truth to evaluate how well the proposed method is performing. The mean errors of tree heights and crown widths are 0.147 m and −0.004 m, respectively. The proposed method is also compared with five conventional methods of individual tree segmentation, namely ITCD, fixed window local maxima, Popescu and Wynne’s local maxima, variable area local maxima, and Li’s point-based segmentation. The comparison results indicate that the proposed hybrid method outperforms the conventional methods in terms of detection rate, omission error, commission error, mean absolute error of tree heights and root-mean-squared-error of tree heights.