Development and evaluation of an automated tree detection–delineation algorithm for monitoring regenerating coniferous forests

2005 ◽  
Vol 35 (10) ◽  
pp. 2332-2345 ◽  
Author(s):  
D A Pouliot ◽  
D J King ◽  
D G Pitt
Keyword(s):  
Absolute Error ◽  
Tree Regeneration ◽  
Tree Size ◽  
Detection Accuracy ◽  
Individual Tree ◽  
Commission Errors ◽  
Tree Detection ◽  
Crown Diameter ◽  
Accuracy Index ◽  
The Individual

An algorithm is presented for automated detection–delineation of coniferous tree regeneration that combines strategies of several existing algorithms, including image processing to isolate conifer crowns, optimal image scale determination, initial crown detection, and crown boundary segmentation and refinement. The algorithm is evaluated using 6-cm pixel airborne imagery in operational regeneration conditions typically encountered in the boreal forest 5–10 years after harvest. Detection omission and commission errors as well as an accuracy index combining both error types were assessed on a tree by tree basis, on an aggregated basis for each study area, in relation to tree size and the amount of woody competition present. Delineation error was assessed in a similar manner using field-measured crown diameters as a reference. The individual tree detection accuracy index improved with increasing tree size and was >70% for trees larger than 30 cm crown diameter. Crown diameter absolute error measured from automated delineations was <23%. Large crown diameters tended to be slightly underestimated. The presence of overtopping woody competition had a negligible effect on detection accuracy and only reduced estimates of crown diameter slightly.

scholarly journals Individual Detection of Citrus and Avocado Trees Using Extended Maxima Transform Summation on Digital Surface Models

2020 ◽  
Vol 12 (10) ◽  
pp. 1633 ◽  
Author(s):  
Daniel G. García-Murillo ◽  
J. Caicedo-Acosta ◽  
G. Castellanos-Dominguez
Keyword(s):  
Low Cost ◽  
False Negative ◽  
False Negative Rate ◽  
Seed Selection ◽  
Individual Tree ◽  
Local Maxima ◽  
Commission Errors ◽  
Tree Detection ◽  
Surface Models ◽  
Cumulative Summation

Individual tree detection (ITD) locates plants from images to estimate monitoring parameters, helping the management of forestry and agriculture systems. As a low-cost solution to help farm monitoring, digital surface models are increasingly involved together with mathematical morphology techniques within the framework of ITD tasks. However, morphology-based approaches are prone to omission and commission errors due to the shape and size of structuring elements. To reduce the error rate in ITD tasks, we introduce a morphological transform that is based on the local maxima segmentation (Cumulative Summation of Extended Maxima transform (SEMAX)) with the aim to enhance the seed selection by extracting information collected from different heights. Validation is performed on data collected from the plantations of citrus and avocado using different measures of precision. The results obtained by the SEMAX approach show that the devised ITD algorithm provides enough accuracy, and achieves the lowest false-negative rate than other compared state-of-art approaches do.

scholarly journals Individual Tree Diameter Growth Models of Larch–Spruce–Fir Mixed Forests Based on Machine Learning Algorithms

Forests ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 187 ◽  
Author(s):  
Qiangxin Ou ◽  
Xiangdong Lei ◽  
Chenchen Shen
Keyword(s):  
Machine Learning ◽  
Tree Growth ◽  
Growth Models ◽  
Tree Size ◽  
Diameter Growth ◽  
Individual Tree ◽  
Mixed Forests ◽  
Tree Diameter ◽  
Individual Tree Growth ◽  
The Individual

Individual tree growth models are flexible and commonly used to represent growth dynamics for heterogeneous and structurally complex uneven-aged stands. Besides traditional statistical models, the rapid development of nonparametric and nonlinear machine learning methods, such as random forest (RF), boosted regression tree (BRT), cubist (Cubist) and multivariate adaptive regression splines (MARS), provides a new way for predicting individual tree growth. However, the application of these approaches to individual tree growth modelling is still limited and short of a comparison of their performance. The objectives of this study were to compare and evaluate the performance of the RF, BRT, Cubist and MARS models for modelling the individual tree diameter growth based on tree size, competition, site condition and climate factors for larch–spruce–fir mixed forests in northeast China. Totally, 16,619 observations from long-term sample plots were used. Based on tenfold cross-validation, we found that the RF, BRT and Cubist models had a distinct advantage over the MARS model in predicting individual tree diameter growth. The Cubist model ranked the highest in terms of model performance (RMSEcv [0.1351 cm], MAEcv [0.0972 cm] and R2cv [0.5734]), followed by BRT and RF models, whereas the MARS ranked the lowest (RMSEcv [0.1462 cm], MAEcv [0.1086 cm] and R2cv [0.4993]). Relative importance of predictors determined from the RF and BRT models demonstrated that the competition and tree size were the main drivers to diameter growth, and climate had limited capacity in explaining the variation in tree diameter growth at local scale. In general, the RF, BRT and Cubist models are effective and powerful modelling methods for predicting the individual tree diameter growth.

scholarly journals Prediction error aggregation behaviour for remote sensing augmented forest inventory approaches

2021 ◽  
Author(s):  
Eetu Kotivuori ◽  
Matti Maltamo ◽  
Lauri Korhonen ◽  
Jacob L Strunk ◽  
Petteri Packalen
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
Stem Volume ◽  
Prediction Errors ◽  
Individual Tree ◽  
Commission Errors ◽  
Tree Detection ◽  
Fundamental Properties ◽  
Aggregation Behaviour ◽  
Quartet Level

Abstract In this study we investigated the behaviour of aggregate prediction errors in a forest inventory augmented with multispectral Airborne Laser Scanning and airborne imagery. We compared an Area-Based Approach (ABA), Edge-tree corrected ABA (EABA) and Individual Tree Detection (ITD). The study used 109 large 30 × 30 m sample plots, which were divided into four 15 × 15 m subplots. Four different levels of aggregation were examined: all four subplots (quartet), two diagonal subplots (diagonal), two edge-adjacent subplots (adjacent) and subplots without aggregation. We noted that the errors at aggregated levels depend on the selected predictor variables, and therefore, this effect was studied by repeating the variable selection 200 times. At the subplot level, EABA provided the lowest mean of root mean square error ($\overline{\mathrm{RMSE}}$) values of 200 repetitions for total stem volume (EABA 21.1 percent, ABA 23.5 percent, ITD 26.2 percent). EABA also fared the best for diagonal and adjacent aggregation ($\overline{\mathrm{RMSE}}$: 17.6 percent, 17.4 percent), followed by ABA ($\overline{\mathrm{RMSE}}$: 19.3 percent, 18.2 percent) and ITD ($\overline{\mathrm{RMSE}}$: 21.8, 21.9 percent). Adjacent subplot errors of ABA were less correlated than errors of diagonal subplots, which resulted also in clearly lower RMSEs for adjacent subplots. This appears to result from edge tree effects, where omission and commission errors cancel for trees leaning from one subplot into the other. The best aggregate performance was achieved at the quartet level, as expected from fundamental properties of variance. ABA and EABA had similar RMSEs at the quartet level ($\overline{\mathrm{RMSE}}$ 15.5 and 15.3 percent), with poorer ITD performance ($\overline{\mathrm{RMSE}}$ 19.4 percent).

Comparing individual tree detection and the area-based statistical approach for the retrieval of forest stand characteristics using airborne laser scanning in Scots pine stands

2011 ◽  
Vol 41 (3) ◽  
pp. 583-598 ◽  
Author(s):  
Jussi Peuhkurinen ◽  
Lauri Mehtätalo ◽  
Matti Maltamo
Keyword(s):  
Laser Scanning ◽  
Statistical Approach ◽  
Basal Area ◽  
Airborne Laser Scanning ◽  
Tree Size ◽  
Size Distributions ◽  
Individual Tree ◽  
Tree Detection ◽  
Airborne Laser ◽  
Number Of Stems

Airborne laser scanning based forest inventories employ two major methods: individual tree detection (ITD) and the area-based statistical approach (ABSA). ITD is based on the assumption that trees are of a certain form and can be delineated using airborne laser scanning techniques, whereas ABSA is an empirical method based on the relations between area-level forest attributes and laser echo height distributions. These two methods are compared here within the same test area in terms of their usefulness for estimating mean forest stand characteristics and tree size distributions. All evaluations were performed using leave-one-out cross validation. The average errors in volume and basal area did not differ significantly between the methods. ABSA resulted in overall better accuracies when estimating the diameter and height of the basal area median tree and the number of stems, whereas ITD produced significantly biased estimates for the number of stems and the mean tree size. Tree size distributions were estimated with slightly better accuracy using ABSA. More comprehensive investigations revealed that both methods were not able to estimate forest structure (tree size distribution and spatial distribution of tree locations), which in turn, affected the estimation accuracies.

scholarly journals Predicting forest stand variables from airborne LiDAR data using a tree detection method in Central European forests

2019 ◽  
Vol 65 (3-4) ◽  
pp. 191-197 ◽  
Author(s):  
Ivan Sačkov ◽  
Ľubomír Scheer ◽  
Tomáš Bucha
Keyword(s):  
Accuracy Assessment ◽  
A Priori ◽  
Forest Stand ◽  
Airborne Lidar ◽  
Individual Tree ◽  
Tree Detection ◽  
Detection Approach ◽  
Mean Diameter ◽  
Priori Information ◽  
The Individual

Abstract In this study, the individual tree detection approach (ITD) was used to estimate forest stand variables, such as mean height, mean diameter, and total volume. Specifically, we applied the multisource-based method implemented in reFLex software (National Forest Centre, Slovakia) which uses all the information contained in the original point cloud and a priori information. For the accuracy assessment, four reference forest stands with different types of species mixture and the area of 7.5 ha were selected and measured. Furthermore, independent measurements of 1 372 trees were made for the construction of allometric models. The author’s ITD-based method provided slightly more accurate estimations for stands with substantial or moderate dominance of coniferous trees. However, no statistically significant effect of species mix on the overall accuracy was confirmed (p < 0.05). The root mean square error did not exceed 1.9 m for mean height, 3.0 cm for mean diameter, and 12.88 m3 ha−1 for total volume.

scholarly journals A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data

2021 ◽  
Vol 13 (2) ◽  
pp. 322
Author(s):  
Melissa Latella ◽  
Fabio Sola ◽  
Carlo Camporeale
Keyword(s):  
Point Clouds ◽  
Individual Tree ◽  
Tree Distribution ◽  
Tree Detection ◽  
Forest Modeling ◽  
Point Density ◽  
Potential Applications ◽  
Field Campaigns ◽  
The Individual ◽  
Individual Trees

Nowadays, LiDAR is widely used for individual tree detection, usually providing higher accuracy in coniferous stands than in deciduous ones, where the rounded-crown, the presence of understory vegetation, and the random spatial tree distribution may affect the identification algorithms. In this work, we propose a novel algorithm that aims to overcome these difficulties and yield the coordinates and the height of the individual trees on the basis of the point density features of the input point cloud. The algorithm was tested on twelve deciduous areas, assessing its performance on both regular-patterned plantations and stands with randomly distributed trees. For all cases, the algorithm provides high accuracy tree count (F-score > 0.7) and satisfying stem locations (position error around 1.0 m). In comparison to other common tools, the algorithm is weakly sensitive to the parameter setup and can be applied with little knowledge of the study site, thus reducing the effort and cost of field campaigns. Furthermore, it demonstrates to require just 2 points·m−2 as minimum point density, allowing for the analysis of low-density point clouds. Despite its simplicity, it may set the basis for more complex tools, such as those for crown segmentation or biomass computation, with potential applications in forest modeling and management.

Individual tree detection in digital aerial images by combining locally adaptive binarization and local maxima methods

2001 ◽  
Vol 31 (5) ◽  
pp. 832-844 ◽  
Author(s):  
Juho Pitkänen
Keyword(s):  
Digital Camera ◽  
Adaptive Methods ◽  
Aerial Images ◽  
Detection Accuracy ◽  
Image Smoothing ◽  
Individual Tree ◽  
Local Maxima ◽  
Tree Detection ◽  
Locally Adaptive ◽  
Tree Data

Locating local maxima of grey levels in aerial images was used for individual tree detection in boreal, closed forest conditions in southern Finland. Image smoothing and binarization were used as preprocessing steps. Binarization was used to restrict the local maxima searching to the bright areas of the images, which were assumed to be tree crowns. Because brightness variations are typical of aerial images, both within and among images, locally adaptive methods were suggested for binarization. Aerial digital camera images and mapped tree data of eight stands in three field plots were used. Four adaptive binarization methods were compared. Differences in tree detection accuracy were small even though the appearance of the binarized images were different. Image smoothing improved the results of tree detection in the three stands that had the largest mean tree size. Tree detection worked fairly well in all seven stands with a density of less than 1500 trees/ha. In these stands, 70–95% of the trees were detected, whereas only 54% were detected in the last stand, which had a density of approximately 1900 trees/ha.

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

2019 ◽  
Vol 409 ◽  
pp. 108736 ◽  
Author(s):  
Midhun Mohan ◽  
Bruno Araujo Furtado de Mendonça ◽  
Carlos Alberto Silva ◽  
Carine Klauberg ◽  
Acauã Santos de Saboya Ribeiro ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Cocos Nucifera ◽  
Airborne Laser Scanning ◽  
Detection Accuracy ◽  
Individual Tree ◽  
Tree Detection ◽  
Airborne Laser ◽  
Cocos Nucifera L
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