Recognition of specified errors of individual tree detection methods based on Canopy Height Model

2022 ◽  
pp. 100690
Author(s):  
Maciej Lisiewicz ◽  
Agnieszka Kamińska ◽  
Krzysztof Stereńczak
Keyword(s):  
Canopy Height ◽  
Detection Methods ◽  
Individual Tree ◽  
Tree Detection ◽  
Canopy Height Model ◽  
Height Model

scholarly journals Comparison and Evaluation of Different Pit-Filling Methods for Generating High Resolution Canopy Height Model Using UAV Laser Scanning Data

2021 ◽  
Vol 13 (12) ◽  
pp. 2239
Author(s):  
Ying Quan ◽  
Mingze Li ◽  
Yuanshuo Hao ◽  
Bin Wang
Keyword(s):  
High Resolution ◽  
Laser Scanning ◽  
Evaluation Framework ◽  
Canopy Height ◽  
Filter Method ◽  
Individual Tree ◽  
Significant Difference ◽  
Point Interpolation ◽  
Canopy Height Model ◽  
Height Model

As a common form of light detection and ranging (LiDAR) in forestry applications, the canopy height model (CHM) provides the elevation distribution of aboveground vegetation. A CHM is traditionally generated by interpolating all the first LiDAR echoes. However, the first echo cannot accurately represent the canopy surface, and the resulting large amount of noise (data pits) also reduce the CHM quality. Although previous studies concentrate on many pit-filling methods, the applicability of these methods in high-resolution unmanned aerial vehicle laser scanning (UAVLS)-derived CHMs has not been revealed. This study selected eight widely used, recently developed, representative pit-filling methods, namely first-echo interpolation, smooth filtering (mean, medium and Gaussian), highest point interpolation, pit-free algorithm, spike-free algorithm and graph-based progressive morphological filtering (GPMF). A comprehensive evaluation framework was implemented, including a quantitative evaluation using simulation data and an additional application evaluation using UAVLS data. The results indicated that the spike-free algorithm and GPMF had excellent visual performances and were closest to the real canopy surface (root mean square error (RMSE) of simulated data were 0.1578 m and 0.1093 m, respectively; RMSE of UAVLS data were 0.3179 m and 0.4379 m, respectively). Compared with the first-echo method, the accuracies of the spike-free algorithm and GPMF improved by approximately 23% and 22%, respectively. The pit-free algorithm and highest point interpolation method also have advantages in high-resolution CHM generation. The global smooth filter method based on the first-echo CHM reduced the average canopy height by approximately 7.73%. Coniferous forests require more pit-filling than broad-leaved forests and mixed forests. Although the results of individual tree applications indicated that there was no significant difference between these methods except the median filter method, pit-filling is still of great significance for generating high-resolution CHMs. This study provides guidance for using high-resolution UAVLS in forestry applications.

scholarly journals Individual Tree Detection from Unmanned Aerial Vehicle (UAV) Derived Canopy Height Model in an Open Canopy Mixed Conifer Forest

Forests ◽  
2017 ◽  
Vol 8 (9) ◽  
pp. 340 ◽  
Author(s):  
Midhun Mohan ◽  
Carlos Silva ◽  
Carine Klauberg ◽  
Prahlad Jat ◽  
Glenn Catts ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Conifer Forest ◽  
Mixed Conifer ◽  
Individual Tree ◽  
Tree Detection ◽  
Mixed Conifer Forest ◽  
Open Canopy ◽  
Aerial Vehicle ◽  
Canopy Height Model ◽  
Height Model

scholarly journals The Influence of Vegetation Characteristics on Individual Tree Segmentation Methods with Airborne LiDAR Data

2019 ◽  
Vol 11 (23) ◽  
pp. 2880 ◽  
Author(s):  
Qiuli Yang ◽  
Yanjun Su ◽  
Shichao Jin ◽  
Maggi Kelly ◽  
Tianyu Hu ◽  
...  
Keyword(s):  
Forest Type ◽  
The United States ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Lidar Data ◽  
Individual Tree ◽  
Segmentation Methods ◽  
Vegetation Characteristics ◽  
Canopy Height Model ◽  
Height Model

This study investigated the effects of forest type, leaf area index (LAI), canopy cover (CC), tree density (TD), and the coefficient of variation of tree height (CVTH) on the accuracy of different individual tree segmentation methods (i.e., canopy height model, pit-free canopy height model (PFCHM), point cloud, and layer stacking seed point) with LiDAR data. A total of 120 sites in the Sierra Nevada Forest (California) and Shavers Creek Watershed (Pennsylvania) of the United States, covering various vegetation types and characteristics, were used to analyze the performance of the four selected individual tree segmentation algorithms. The results showed that the PFCHM performed best in all forest types, especially in conifer forests. The main forest characteristics influencing segmentation methods were LAI and CC, LAI and TD, and CVTH in conifer, broadleaf, and mixed forests, respectively. Most of the vegetation characteristics (i.e., LAI, CC, and TD) negatively correlated with all segmentation methods, while the effect of CVTH varied with forest type. These results can help guide the selection of individual tree segmentation method given the influence of vegetation characteristics.

scholarly journals FULLY AUTOMATED GIS-BASED INDIVIDUAL TREE CROWN DELINEATION BASED ON CURVATURE VALUES FROM A LIDAR DERIVED CANOPY HEIGHT MODEL IN A CONIFEROUS PLANTATION

2016 ◽  
Vol XLI-B8 ◽  
pp. 563-569
Author(s):  
R. J. L. Argamosa ◽  
E. C. Paringit ◽  
K. R. Quinton ◽  
F. A. M. Tandoc ◽  
R. A. G. Faelga ◽  
...  
Keyword(s):  
Canopy Height ◽  
Tree Crown ◽  
M Cell ◽  
Individual Tree ◽  
Automated Method ◽  
Coniferous Plantation ◽  
Crown Shape ◽  
Canopy Height Model ◽  
Crown Delineation ◽  
Height Model

The generation of high resolution canopy height model (CHM) from LiDAR makes it possible to delineate individual tree crown by means of a fully-automated method using the CHM’s curvature through its slope. The local maxima are obtained by taking the maximum raster value in a 3 m x 3 m cell. These values are assumed as tree tops and therefore considered as individual trees. Based on the assumptions, thiessen polygons were generated to serve as buffers for the canopy extent. The negative profile curvature is then measured from the slope of the CHM. The results show that the aggregated points from a negative profile curvature raster provide the most realistic crown shape. The absence of field data regarding tree crown dimensions require accurate visual assessment after the appended delineated tree crown polygon was superimposed to the hill shaded CHM.

scholarly journals FULLY AUTOMATED GIS-BASED INDIVIDUAL TREE CROWN DELINEATION BASED ON CURVATURE VALUES FROM A LIDAR DERIVED CANOPY HEIGHT MODEL IN A CONIFEROUS PLANTATION

2016 ◽  
Vol XLI-B8 ◽  
pp. 563-569
Author(s):  
R. J. L. Argamosa ◽  
E. C. Paringit ◽  
K. R. Quinton ◽  
F. A. M. Tandoc ◽  
R. A. G. Faelga ◽  
...  
Keyword(s):  
Canopy Height ◽  
Tree Crown ◽  
M Cell ◽  
Individual Tree ◽  
Automated Method ◽  
Coniferous Plantation ◽  
Crown Shape ◽  
Canopy Height Model ◽  
Crown Delineation ◽  
Height Model

The generation of high resolution canopy height model (CHM) from LiDAR makes it possible to delineate individual tree crown by means of a fully-automated method using the CHM’s curvature through its slope. The local maxima are obtained by taking the maximum raster value in a 3 m x 3 m cell. These values are assumed as tree tops and therefore considered as individual trees. Based on the assumptions, thiessen polygons were generated to serve as buffers for the canopy extent. The negative profile curvature is then measured from the slope of the CHM. The results show that the aggregated points from a negative profile curvature raster provide the most realistic crown shape. The absence of field data regarding tree crown dimensions require accurate visual assessment after the appended delineated tree crown polygon was superimposed to the hill shaded CHM.

scholarly journals Influence of Agisoft Metashape Parameters on UAS Structure from Motion Individual Tree Detection from Canopy Height Models

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 250
Author(s):  
Wade T. Tinkham ◽  
Neal C. Swayze
Keyword(s):  
Structure From Motion ◽  
Ponderosa Pine ◽  
Point Cloud ◽  
Image Resolution ◽  
Canopy Height ◽  
Computer Hardware ◽  
Forest Monitoring ◽  
Individual Tree ◽  
Ponderosa Pine Forests ◽  
Tree Detection

Applications of unmanned aerial systems for forest monitoring are increasing and drive a need to understand how image processing workflows impact end-user products’ accuracy from tree detection methods. Increasing image overlap and making acquisitions at lower altitudes improve how structure from motion point clouds represents forest canopies. However, only limited testing has evaluated how image resolution and point cloud filtering impact the detection of individual tree locations and heights. We evaluate how Agisoft Metashape’s build dense cloud Quality (image resolution) and depth map filter settings influence tree detection from canopy height models in ponderosa pine forests. Finer resolution imagery with minimal filtering provided the best visual representation of vegetation detail for trees of all sizes. These same settings maximized tree detection F-score at >0.72 for overstory (>7 m tall) and >0.60 for understory trees. Additionally, overstory tree height bias and precision improve as image resolution becomes finer. Overstory and understory tree detection in open-canopy conifer systems might be optimized using the finest resolution imagery that computer hardware enables, while applying minimal point cloud filtering. The extended processing time and data storage demands of high-resolution imagery must be balanced against small reductions in tree detection performance when down-scaling image resolution to allow the processing of greater data extents.

scholarly journals A Novel Machine Learning Method for Estimating Biomass of Grass Swards Using a Photogrammetric Canopy Height Model, Images and Vegetation Indices Captured by a Drone

Agriculture ◽  
2018 ◽  
Vol 8 (5) ◽  
pp. 70 ◽  
Author(s):  
Niko Viljanen ◽  
Eija Honkavaara ◽  
Roope Näsi ◽  
Teemu Hakala ◽  
Oiva Niemeläinen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Vegetation Indices ◽  
Canopy Height ◽  
Meat Production ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Canopy Height Model ◽  
Mean Square Errors ◽  
Height Model

Silage is the main feed in milk and ruminant meat production in Northern Europe. Novel drone-based remote sensing technology could be utilized in many phases of silage production, but advanced methods of utilizing these data are still developing. Grass swards are harvested three times in season, and fertilizer is applied similarly three times—once for each harvest when aiming at maximum yields. Timely information of the yield is thus necessary several times in a season for making decisions on harvesting time and rate of fertilizer application. Our objective was to develop and assess a novel machine learning technique for the estimation of canopy height and biomass of grass swards utilizing multispectral photogrammetric camera data. Variation in the studied crop stand was generated using six different nitrogen fertilizer levels and four harvesting dates. The sward was a timothy-meadow fescue mixture dominated by timothy. We extracted various features from the remote sensing data by combining an ultra-high resolution photogrammetric canopy height model (CHM) with a pixel size of 1.0 cm and red, green, blue (RGB) and near-infrared range intensity values and different vegetation indices (VI) extracted from orthophoto mosaics. We compared the performance of multiple linear regression (MLR) and a Random Forest estimator (RF) with different combinations of the CHM, RGB and VI features. The best estimation results with both methods were obtained by combining CHM and VI features and all three feature classes (CHM, RGB and VI features). Both estimators provided equally accurate results. The Pearson correlation coefficients (PCC) and Root Mean Square Errors (RMSEs) of the estimations were at best 0.98 and 0.34 t/ha (12.70%), respectively, for the dry matter yield (DMY) and 0.98 and 1.22 t/ha (11.05%), respectively, for the fresh yield (FY) estimations. Our assessment of the sensitivity of the method with respect to different development stages and different amounts of biomass showed that the use of the machine learning technique that integrated multiple features improved the results in comparison to the simple linear regressions. These results were extremely promising, showing that the proposed multispectral photogrammetric approach can provide accurate biomass estimates of grass swards, and could be developed as a low-cost tool for practical farming applications.

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