Using error-in-variable regression to predict tree diameter and crown width from remotely sensed imagery

2010 ◽  
Vol 40 (6) ◽  
pp. 1095-1108 ◽  
Author(s):  
Wenhua Zhang ◽  
Yinghai Ke ◽  
Lindi J. Quackenbush ◽  
Lianjun Zhang
Keyword(s):  
Measurement Errors ◽  
Major Axis ◽  
Remotely Sensed ◽  
Ordinary Least Squares ◽  
Tree Crown ◽  
Individual Tree ◽  
Tree Diameter ◽  
Remotely Sensed Imagery ◽  
Crown Width ◽  
Response Variables

Automated individual tree detection and delineation from high spatial resolution imagery provides good opportunities for forest inventory at a large scale. However, the accuracy of delineated crown size compared with ground measurements may not be sufficient. Thus, ordinary least squares (OLS) regression is no longer an appropriate approach to estimating and predicting variables from the delineated tree crown because both response variable and regressor are subject to measurement errors. In this study, we describe the functional and structural relationships between field-measured tree variables (i.e., tree diameter and crown width) and delineated tree crown width from remotely sensed imagery. We investigated the performance of OLS and three error-in-variable regression techniques including maximum likelihood estimator (MLE), major axis (MA) regression, and reduced major axis (RMA) regression using field-measured data and simulated data under different conditions. Our results indicated that MLE was desirable for estimating unbiased model coefficients. However, the adjustment assumption of the MLE model should be checked for predicting tree variables from remotely sensed imagery. When the assumption holds, the MLE model performed better for predicting the response variables than did the OLS model. Otherwise, the MLE model produced biased predictions for the response variables.

scholarly journals A Comparison of Methods for Determining Forest Composition from High-Spatial-Resolution Remotely Sensed Imagery

Forests ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1290
Author(s):  
Benjamin T. Fraser ◽  
Russell G. Congalton
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remotely Sensed ◽  
Local Scale ◽  
Google Earth ◽  
Forest Composition ◽  
Visual Interpretation ◽  
Individual Tree ◽  
Remotely Sensed Imagery ◽  
Composition Classes

Remotely sensed imagery has been used to support forest ecology and management for decades. In modern times, the propagation of high-spatial-resolution image analysis techniques and automated workflows have further strengthened this synergy, leading to the inquiry into more complex, local-scale, ecosystem characteristics. To appropriately inform decisions in forestry ecology and management, the most reliable and efficient methods should be adopted. For this reason, our research compares visual interpretation to digital (automated) processing for forest plot composition and individual tree identification. During this investigation, we qualitatively and quantitatively evaluated the process of classifying species groups within complex, mixed-species forests in New England. This analysis included a comparison of three high-resolution remotely sensed imagery sources: Google Earth, National Agriculture Imagery Program (NAIP) imagery, and unmanned aerial system (UAS) imagery. We discovered that, although the level of detail afforded by the UAS imagery spatial resolution (3.02 cm average pixel size) improved the visual interpretation results (7.87–9.59%), the highest thematic accuracy was still only 54.44% for the generalized composition groups. Our qualitative analysis of the uncertainty for visually interpreting different composition classes revealed the persistence of mislabeled hardwood compositions (including an early successional class) and an inability to consistently differentiate between ‘pure’ and ‘mixed’ stands. The results of digitally classifying the same forest compositions produced a higher level of accuracy for both detecting individual trees (93.9%) and labeling them (59.62–70.48%) using machine learning algorithms including classification and regression trees, random forest, and support vector machines. These results indicate that digital, automated, classification produced an increase in overall accuracy of 16.04% over visual interpretation for generalized forest composition classes. Other studies, which incorporate multitemporal, multispectral, or data fusion approaches provide evidence for further widening this gap. Further refinement of the methods for individual tree detection, delineation, and classification should be developed for structurally and compositionally complex forests to supplement the critical deficiency in local-scale forest information around the world.

scholarly journals ARBOR: A new framework for assessing the accuracy of individual tree crown delineation from remotely-sensed data

2019 ◽  
Vol 231 ◽  
pp. 111256
Author(s):  
Jon Murray ◽  
David Gullick ◽  
George Alan Blackburn ◽  
James Duncan Whyatt ◽  
Christopher Edwards
Keyword(s):  
Remotely Sensed ◽  
Tree Crown ◽  
Remotely Sensed Data ◽  
Individual Tree ◽  
Crown Delineation ◽  
Tree Crown Delineation ◽  
New Framework

scholarly journals Tree Recognition on the Plantation Using UAV Images with Ultrahigh Spatial Resolution in a Complex Environment

2021 ◽  
Vol 13 (20) ◽  
pp. 4122
Author(s):  
Xuzhan Guo ◽  
Qingwang Liu ◽  
Ram P. Sharma ◽  
Qiao Chen ◽  
Qiaolin Ye ◽  
...  
Keyword(s):  
Survival Rate ◽  
Complex Terrain ◽  
Low Cost ◽  
Central China ◽  
Ground Vegetation ◽  
Tree Crown ◽  
Individual Tree ◽  
Crown Width ◽  
Aerial Vehicle ◽  
Uav Images

The survival rate of seedlings is a decisive factor of afforestation assessment. Generally, ground checking is more accurate than any other methods. However, the survival rate of seedlings can be higher in the growing season, and this can be estimated in a larger area at a relatively lower cost by extracting the tree crown from the unmanned aerial vehicle (UAV) images, which provides an opportunity for monitoring afforestation in an extensive area. At present, studies on extracting individual tree crowns under the complex ground vegetation conditions are limited. Based on the afforestation images obtained by airborne consumer-grade cameras in central China, this study proposes a method of extracting and fusing multiple radii morphological features to obtain the potential crown. A random forest (RF) was used to identify the regions extracted from the images, and then the recognized crown regions were fused selectively according to the distance. A low-cost individual crown recognition framework was constructed for rapid checking of planted trees. The method was tested in two afforestation areas of 5950 m2 and 5840 m2, with a population of 2418 trees (Koelreuteria) in total. Due to the complex terrain of the sample plot, high weed coverage, the crown width of trees, and spacing of saplings vary greatly, which increases both the difficulty and complexity of crown extraction. Nevertheless, recall and F-score of the proposed method reached 93.29%, 91.22%, and 92.24% precisions, respectively, and 2212 trees were correctly recognized and located. The results show that the proposed method is robust to the change of brightness and to splitting up of a multi-directional tree crown, and is an automatic solution for afforestation verification.

scholarly journals Individual tree measurements by means of digital aerial photogrammetry

2004 ◽  
Vol 2004 (3) ◽  
Author(s):  
Ilkka Korpela
Keyword(s):  
Measurement Errors ◽  
Aerial Photographs ◽  
Tree Level ◽  
Stem Volume ◽  
Species Classification ◽  
Mixed Stands ◽  
Individual Tree ◽  
Crown Width ◽  
The Individual ◽  
Positioning Algorithm

This study explores the plausibility of the use of multi-scale, CIR aerial photographs to conduct forest inventory at the individual tree level. Multiple digitised aerial photographs are used for manual and semi-automatic 3D positioning of tree tops, for species classification, and for measurements on tree height and crown width. A new tree top positioning algorithm is presented and tested. It incorporates template matching in a 3D search space. Also, a new method is presented for tree species classification. In it, a partition of the image space according to the continuously varying image-object-sun geometry of aerial views is performed. Discernibility of trees in aerial images is studied. The measurement accuracy and overall measurability of crown width by using manual image measurements is investigated. A simulation study is used to examine the combined effects of discernibility and photogrammetric measurement errors on stand variables. The study material contained large-scale colour and CIR image material and 7708 trees from 24 fully mapped plots in Southern Finland. The results of the discernibility analysis suggest that 88–100% of the total stem volume is measurable when using multiple aerial photographs. The structure and density of the forest were found to affect discernibility. The best hit-rates when using the semi-automatic tree top positioning algorithm ranged from 77 to 100% of the visually discernible trees. Systematic underestimation of the crown width was observed and the measurability of crown width was best near the image nadir. Species classification was tested in mixed stands of Scots pine, Norway spruce, and silver birch. The Kappa-coefficients ranged from 0.71 to 0.86. The results of the simulation suggest that very high accuracy at the individual tree level cannot be expected. However, if the photogrammetric measurements are unbiased, the aggregate stand variables can be very accurate. An accurate species recognition method is needed in the mixed stands in order to achieve unbiased estimates for the small strata.

Consistency and Efficiency of Ordinary Least Squares, Maximum Likelihood, and Three Type II Linear Regression Models

Methodology ◽  
2007 ◽  
Vol 3 (2) ◽  
pp. 81-88 ◽  
Author(s):  
João Maroco
Keyword(s):  
Maximum Likelihood ◽  
Measurement Errors ◽  
Major Axis ◽  
Ordinary Least Squares ◽  
Small Sample ◽  
Type Ii ◽  
Linear Regression Models ◽  
Sample Sizes ◽  
Reduced Major Axis ◽  
Small Sample Sizes

Abstract. Type I linear regression models, which allow for measurement errors only in the criterion variable, are frequently used in modeling research in psychology and the social sciences. Although there are frequently measurement errors and large natural variation both in the criterion and predictor variables, type II regression methods that account for these errors are seldom used in these fields of study. The consistency and efficiency of three type II regression methods (reduced major axis, Kendall's robust line-fit and Bartlett's three-group) were evaluated in comparison to ordinary least squares (OLS) and the maximum likelihood with known variance ratio used frequently in biometrics and econometrics. When predictors are measured with error, OLS slope estimates are biased toward zero, and the same bias was observed with both Kendall's and Bartlett's methods. Reduced major axis produced consistent estimates even for small sample sizes, whenever the measurement errors in X are similar in magnitude to measurement errors in Y, but there was a consistent bias when the measurement error in X was smaller/greater than in Y. Maximum likelihood estimates behaved erroneously for small sample sizes, but for larger sample sizes they converged to the expected values.

scholarly journals Improving Individual Tree Crown Delineation and Attributes Estimation of Tropical Forests Using Airborne LiDAR Data

Forests ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 759 ◽  
Author(s):  
Wan Wan Mohd Jaafar ◽  
Iain Woodhouse ◽  
Carlos Silva ◽  
Hamdan Omar ◽  
Khairul Abdul Maulud ◽  
...  
Keyword(s):  
Tropical Forests ◽  
Aboveground Biomass ◽  
Airborne Lidar ◽  
Tree Level ◽  
Lidar Data ◽  
Tree Crown ◽  
Individual Tree ◽  
Tree Crowns ◽  
Crown Width ◽  
3D Lidar

Individual tree crown (ITC) segmentation is an approach to isolate individual tree from the background vegetation and delineate precisely the crown boundaries for forest management and inventory purposes. ITC detection and delineation have been commonly generated from canopy height model (CHM) derived from light detection and ranging (LiDAR) data. Existing ITC segmentation methods, however, are limited in their efficiency for characterizing closed canopies, especially in tropical forests, due to the overlapping structure and irregular shape of tree crowns. Furthermore, the potential of 3-dimensional (3D) LiDAR data is not fully realized by existing CHM-based methods. Thus, the aim of this study was to develop an efficient framework for ITC segmentation in tropical forests using LiDAR-derived CHM and 3D point cloud data in order to accurately estimate tree attributes such as the tree height, mean crown width and aboveground biomass (AGB). The proposed framework entails five major steps: (1) automatically identifying dominant tree crowns by implementing semi-variogram statistics and morphological analysis; (2) generating initial tree segments using a watershed algorithm based on mathematical morphology; (3) identifying “problematic” segments based on predetermined set of rules; (4) tuning the problematic segments using a modified distance-based algorithm (DBA); and (5) segmenting and counting the number of individual trees based on the 3D LiDAR point clouds within each of the identified segment. This approach was developed in a way such that the 3D LiDAR points were only examined on problematic segments identified for further evaluations. 209 reference trees with diameter at breast height (DBH) ≥ 10 cm were selected in the field in two study areas in order to validate ITC detection and delineation results of the proposed framework. We computed tree crown metrics (e.g., maximum crown height and mean crown width) to estimate aboveground biomass (AGB) at tree level using previously published allometric equations. Accuracy assessment was performed to calculate percentage of correctly detected trees, omission and commission errors. Our method correctly identified individual tree crowns with detection accuracy exceeding 80 percent at both forest sites. Also, our results showed high agreement (R2 > 0.64) in terms of AGB estimates using 3D LiDAR metrics and variables measured in the field, for both sites. The findings from our study demonstrate the efficacy of the proposed framework in delineating tree crowns, even in high canopy density areas such as tropical rainforests, where, usually the traditional algorithms are limited in their performances. Moreover, the high tree delineation accuracy in the two study areas emphasizes the potential robustness and transferability of our approach to other densely forested areas across the globe.

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