Comparison of regression models to estimate biomass losses and CO2 emissions using low-density airborne laser scanning data in a burnt Aleppo pine forest

Abstract Background The age of forest stands is critical information for forest management and conservation, for example for growth modelling, timing of management activities and harvesting, or decisions about protection areas. However, area-wide information about forest stand age often does not exist. In this study, we developed regression models for large-scale area-wide prediction of age in Norwegian forests. For model development we used more than 4800 plots of the Norwegian National Forest Inventory (NFI) distributed over Norway between latitudes 58° and 65° N in an 18.2 Mha study area. Predictor variables were based on airborne laser scanning (ALS), Sentinel-2, and existing public map data. We performed model validation on an independent data set consisting of 63 spruce stands with known age. Results The best modelling strategy was to fit independent linear regression models to each observed site index (SI) level and using a SI prediction map in the application of the models. The most important predictor variable was an upper percentile of the ALS heights, and root mean squared errors (RMSEs) ranged between 3 and 31 years (6% to 26%) for SI-specific models, and 21 years (25%) on average. Mean deviance (MD) ranged between − 1 and 3 years. The models improved with increasing SI and the RMSEs were largest for low SI stands older than 100 years. Using a mapped SI, which is required for practical applications, RMSE and MD on plot level ranged from 19 to 56 years (29% to 53%), and 5 to 37 years (5% to 31%), respectively. For the validation stands, the RMSE and MD were 12 (22%) and 2 years (3%), respectively. Conclusions Tree height estimated from airborne laser scanning and predicted site index were the most important variables in the models describing age. Overall, we obtained good results, especially for stands with high SI. The models could be considered for practical applications, although we see considerable potential for improvements if better SI maps were available.

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Assessment of Low Density Full-Waveform Airborne Laser Scanning for Individual Tree Detection and Tree Species Classification

Forests ◽

10.3390/f5051011 ◽

2014 ◽

Vol 5 (5) ◽

pp. 1011-1031 ◽

Cited By ~ 29

Author(s):

Xiaowei Yu ◽

Paula Litkey ◽

Juha Hyyppä ◽

Markus Holopainen ◽

Mikko Vastaranta

Keyword(s):

Tree Species ◽

Laser Scanning ◽

Airborne Laser Scanning ◽

Low Density ◽

Species Classification ◽

Individual Tree ◽

Tree Detection ◽

Tree Species Classification ◽

Full Waveform ◽

Airborne Laser

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Model-based inference for biomass estimation in a LiDAR sample survey in Hedmark County, NorwayThis article is one of a selection of papers from Extending Forest Inventory and Monitoring over Space and Time.

Canadian Journal of Forest Research ◽

10.1139/x10-161 ◽

2011 ◽

Vol 41 (1) ◽

pp. 96-107 ◽

Cited By ~ 108

Author(s):

Göran Ståhl ◽

Sören Holm ◽

Timothy G. Gregoire ◽

Terje Gobakken ◽

Erik Næsset ◽

...

Keyword(s):

Regression Models ◽

Laser Scanning ◽

Model Error ◽

Airborne Laser Scanning ◽

Total Variance ◽

Sample Survey ◽

Biomass Estimation ◽

Model Errors ◽

Model Based ◽

Airborne Laser

In forest inventories, regression models are often applied to predict quantities such as biomass at the level of sampling units. In this paper, we propose a model-based inference framework for combining sampling and model errors in the variance estimation. It was applied to airborne laser (LiDAR) data sets from Hedmark County, Norway, where the model error proportion of the total variance was found to be large for both scanning (airborne laser scanning) and profiling LiDAR when biomass was estimated. With profiling LiDAR, the model error variance component for the entire county was as large as 71% whereas for airborne laser scanning, it was 43% of the total variance. Partly, this reflects the better accuracy of the pixel-based regression models estimated from scanner data as compared with the models estimated from profiler data. The framework proposed in our study can be applied in all types of sample surveys where model-based predictions are made at the level of individual sampling units. Especially, it should be useful in cases where model-assisted inference cannot be applied due to the lack of a probability sample from the target population or due to problems of correctly matching observations of auxiliary and target variables.

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Retrieving Suppressed Trees from Model-Based Height Distribution by Combining High- and Low-Density Airborne Laser Scanning Data

Canadian Journal of Remote Sensing ◽

10.1080/07038992.2014.935933 ◽

2014 ◽

Vol 40 (3) ◽

pp. 233-242 ◽

Cited By ~ 3

Author(s):

Qing Xu ◽

Zhengyang Hou ◽

Matti Maltamo ◽

Timo Tokola

Keyword(s):

Laser Scanning ◽

Airborne Laser Scanning ◽

Low Density ◽

Height Distribution ◽

Model Based ◽

Airborne Laser

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Temporal Transferability of Pine Forest Attributes Modeling Using Low-Density Airborne Laser Scanning Data

Remote Sensing ◽

10.3390/rs11030261 ◽

2019 ◽

Vol 11 (3) ◽

pp. 261 ◽

Cited By ~ 10

Author(s):

Darío Domingo ◽

Rafael Alonso ◽

María Teresa Lamelas ◽

Antonio Luis Montealegre ◽

Francisco Rodríguez ◽

...

Keyword(s):

Laser Scanning ◽

Growth Models ◽

Model Performance ◽

Basal Area ◽

Stand Density ◽

Airborne Laser Scanning ◽

Support Vector ◽

Low Density ◽

Airborne Laser ◽

Forest Attributes

This study assesses model temporal transferability using airborne laser scanning (ALS) data acquired over two different dates. Seven forest attributes (i.e. stand density, basal area, squared mean diameter, dominant diameter, tree dominant height, timber volume, and total tree biomass) were estimated using an area-based approach in Mediterranean Aleppo pine forests. Low-density ALS data were acquired in 2011 and 2016 while 147 forest inventory plots were measured in 2013, 2014, and 2016. Single-tree growth models were used to generate concomitant field data for 2011 and 2016. A comparison of five selection techniques and five regression methods were performed to regress field observations against ALS metrics. The selection of the best regression models fitted for each stand attribute, and separately for both 2011 and 2016, was performed following an indirect approach. Model performance and temporal transferability were analyzed by extrapolating the best fitted models from 2011 to 2016 and inversely from 2016 to 2011 using the direct approach. Non-parametric support vector machine with radial kernel was the best regression method with average relative % root mean square error differences of 2.13% for 2011 models and 1.58% for 2016 ones.

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Comparison of Airborne Laser Scanning (3D) with Sentinel-2 (2D) for Estimating Above Ground Biomass/Carbon Stock in a Subtropical Chir Pine Forest of Balakot, Pakistan

10.21203/rs.3.rs-173795/v1 ◽

2021 ◽

Author(s):

Sami Ullah ◽

Tahir Saeed ◽

Muhammad Shafique ◽

Muhammad Saad ◽

Adnan Khan

Keyword(s):

Carbon Stock ◽

Laser Scanning ◽

Forest Ecosystems ◽

Pine Forest ◽

Airborne Laser Scanning ◽

Biomass Carbon ◽

Above Ground Biomass ◽

Airborne Laser ◽

Chir Pine ◽

Sentinel 2

Abstract Forest ecosystems act as a sink of atmospheric carbon dioxide in the form of biomass, and plays one of the crucial role for carbon sequestration and in regulating the global carbon cycle. Few studies based on ground sample plots were conducted for estimating forest biomass/carbon stock across Pakistan. This study comparing the first time the potential of three dimensional (3D) airborne laser scanning (ALS) with two dimensional (2D) Sentinel-2 to estimate above-ground biomass/carbon stock (AGB/C) in a Subtropical Chir Pine forest of Balakot, Pakistan. We derived height and density metrics from the ALS canopy height model (CHM), and different metrics from Sentinel-2 images, and were regressed with field measured AGB/C at sample plots locations. We found R2 = 0.86 with RMSE% = 25.70, and R2 = 0.62 with RMSE% = 43.92 for ALS and for Sentinel-2 respectively with ground measured AGB/C at sample plots locations. Our study demonstrated that 3D ALS technology has greater potential and is the most accurate option as compared to 2D Sentinel-2 for regular planning and monitoring of AGB/C in the context of the national forest inventory of Pakistan. Our study will be useful for the accomplishment of the REDD+ in measuring, reporting, and verification of forest resources, and future sustainable utilization of forest, safeguarding the livelihoods of forest-dependent people, and reducing pressure on forest ecosystems.

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COMPARISON OF HIGH AND LOW DENSITY AIRBORNE LIDAR DATA FOR FOREST ROAD QUALITY ASSESSMENT

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iii-8-167-2016 ◽

2016 ◽

Vol III-8 ◽

pp. 167-172

Author(s):

K. Kiss ◽

J. Malinen ◽

T. Tokola

Keyword(s):

Laser Scanning ◽

High Density ◽

Road Surface ◽

Airborne Laser Scanning ◽

Low Density ◽

Surface Wear ◽

Forest Roads ◽

Density Data ◽

The Road ◽

Airborne Laser

Good quality forest roads are important for forest management. Airborne laser scanning data can help create automatized road quality detection, thus avoiding field visits. Two different pulse density datasets have been used to assess road quality: high-density airborne laser scanning data from Kiihtelysvaara and low-density data from Tuusniemi, Finland. The field inventory mainly focused on the surface wear condition, structural condition, flatness, road side vegetation and drying of the road. Observations were divided into poor, satisfactory and good categories based on the current Finnish quality standards used for forest roads. Digital Elevation Models were derived from the laser point cloud, and indices were calculated to determine road quality. The calculated indices assessed the topographic differences on the road surface and road sides. The topographic position index works well in flat terrain only, while the standardized elevation index described the road surface better if the differences are bigger. Both indices require at least a 1 metre resolution. High-density data is necessary for analysis of the road surface, and the indices relate mostly to the surface wear and flatness. The classification was more precise (31–92%) than on low-density data (25–40%). However, ditch detection and classification can be carried out using the sparse dataset as well (with a success rate of 69%). The use of airborne laser scanning data can provide quality information on forest roads.

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