scholarly journals THE EFFECTS OF COHERENCE CALCULATION ON FOREST HEIGHT ESTIMATION USING SINC MODEL

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 637-642
Author(s):  
Z. Wen ◽  
L. Zhao ◽  
W. Zhang ◽  
E. Chen ◽  
K. Xu
Keyword(s):  
Reference Data ◽  
Window Size ◽  
Airborne Lidar ◽  
Calculation Methods ◽  
Phase Information ◽  
Calculation Algorithm ◽  
Height Estimation ◽  
Forest Height ◽  
Canopy Height Model ◽  
Height Model

Abstract. In this paper, the effects of coherence on forest height estimation by SINC model based on Tandem-X InSAR data were explored. First, different coherence calculation methods and different window sizes were used to obtain interferometric coherence images. Then, the forest heights were obtained based on SINC model. Finally, the estimated forest heights were validated against reference data from airborne LIDAR CHM (Canopy height model, CHM). The results showed that the coherence calculation algorithm affect the forest height inversion results with SINC model. The algorithm using only phase information for coherence calculation show better performance than the other one using both magnitude and phase information. Meanwhile, window size selecting for coherence calculation also affect the forest height estimation results. In this study, window size with 9 × 9 shows best agreement with the forest height extracted from LiDAR CHM. The R2 and RMSE are 0.656 and 3.54 m, respectively.

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 Tree Stump Height Estimation Using Canopy Height Model at Tropical Forest in Ulu Jelai Forest Reserve, Pahang, Malaysia

2020 ◽  
Vol 540 ◽  
pp. 012015
Author(s):  
Siti Nor Maizah Saad ◽  
Khairul Nizam Abdul Maulud ◽  
Wan Shafrina Wan Mohd Jaafar ◽  
Aisyah Marliza Muhmad Kamarulzaman ◽  
Hamdan Omar
Keyword(s):  
Tropical Forest ◽  
Canopy Height ◽  
Forest Reserve ◽  
Height Estimation ◽  
Canopy Height Model ◽  
Height Model ◽  
Tree Stump

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>

Forest Height Estimation from Tandem-X InSAR Coherence Magnitude Towards Large Scale Applications

2020 ◽  
Author(s):  
Changhyun Choi ◽  
Roman Guliaev ◽  
Victor Cazcarra-Bes ◽  
Matteo Pardini ◽  
Konstantinos P. Papathanassiou
Keyword(s):  
Large Scale ◽  
Height Estimation ◽  
Forest Height

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.

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