Canopy Height Model (CHM) Derived From a TanDEM-X InSAR DSM and an Airborne Lidar DTM in Boreal Forest

2016 ◽  
Vol 9 (1) ◽  
pp. 381-397 ◽  
Author(s):  
Yaser Sadeghi ◽  
Benoit St-Onge ◽  
Brigitte Leblon ◽  
Marc Simard
Keyword(s):  
Boreal Forest ◽  
Airborne Lidar ◽  
Canopy Height ◽  
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 Extrapolating a spatially explicit tree root reinforcement model with field and LiDAR-derived stand data

2020 ◽  
Author(s):  
Edoardo Alterio ◽  
Andrea Rizzi ◽  
Paolo Fogliata ◽  
Niccolò Marchi ◽  
Alessio Cislaghi ◽  
...  
Keyword(s):  
Stand Structure ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Soil Reinforcement ◽  
Spatially Explicit ◽  
Spatial And Temporal Variability ◽  
Mixed Stands ◽  
Root Reinforcement ◽  
Canopy Height Model ◽  
Height Model

<p>Protection from landslides is one of the most important regulating services provided by forest ecosystems. Tree roots provide an increase in tensile strength, compression and shear resistance, compared to that uniquely due to the soil properties. This additional effect is known as root reinforcement. The degree of soil reinforcement given by roots have been modeled using laboratory and field data. The great spatial and temporal variability of root distribution is one of the main sources of uncertainty for the development of accurate and reliable models to quantify root reinforcement. The relative importance of stand structure remains poorly known. Here, we analyze the relationships between observed stand structure from a sample of spruce, beech, chestnut and mixed stands of the Southeastern Alps, and a spatially explicit model of root reinforcement. Data were collected in 20-m radius sampling units inclined 15-40° and covered by a low-resolution airborne LiDAR-derived canopy height model. Tree size and position were used to calculate root reinforcement through commonly used and calibrated models. Then, we studied the relationships between root reinforcement, stand structural indexes and area-based stand metrics from canopy height model. In specific conditions, the three groups of variables were correlated. Therefore, root reinforcement values might be spatially extrapolated through available canopy height models. Final step is to integrate the extrapolated values into a landslide susceptibility model, which combines other data available from forest plans, digital elevation models, geological and meteorological data. This study provides managers with a tool to periodically update maps of the service given by forest trees to protection of humans from landslides.</p>

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 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.

Effect of slope on treetop detection using a LiDAR Canopy Height Model

2015 ◽  
Vol 104 ◽  
pp. 44-52 ◽  
Author(s):  
Anahita Khosravipour ◽  
Andrew K. Skidmore ◽  
Tiejun Wang ◽  
Martin Isenburg ◽  
Kourosh Khoshelham
Keyword(s):  
Canopy Height ◽  
Canopy Height Model ◽  
Height Model
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