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

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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Mapping forest age using National Forest Inventory, airborne laser scanning, and Sentinel-2 data

Forest Ecosystems ◽

10.1186/s40663-020-00274-9 ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Johannes Schumacher ◽

Marius Hauglin ◽

Rasmus Astrup ◽

Johannes Breidenbach

Keyword(s):

Forest Inventory ◽

Regression Models ◽

Laser Scanning ◽

Site Index ◽

National Forest Inventory ◽

Airborne Laser Scanning ◽

National Forest ◽

Practical Applications ◽

Airborne Laser ◽

Sentinel 2

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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Using GEDI lidar data and airborne laser scanning to assess height growth dynamics in fast-growing species: a showcase in Spain

Forest Ecosystems ◽

10.1186/s40663-021-00291-2 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Juan Guerra-Hernández ◽

Adrián Pascual

Keyword(s):

Pinus Radiata ◽

Pinus Pinaster ◽

Laser Scanning ◽

Forest Ecosystems ◽

Height Growth ◽

Growth Patterns ◽

Forest Growth ◽

Airborne Laser Scanning ◽

Fast Growing ◽

Airborne Laser

Abstract Background The NASA’s Global Ecosystem Dynamics Investigation (GEDI) satellite mission aims at scanning forest ecosystems on a multi-temporal short-rotation basis. The GEDI data can validate and update statistics from nationwide airborne laser scanning (ALS). We present a case in the Northwest of Spain using GEDI statistics and nationwide ALS surveys to estimate forest dynamics in three fast-growing forest ecosystems comprising 211,346 ha. The objectives were: i) to analyze the potential of GEDI to detect disturbances, ii) to investigate uncertainty source regarding non-positive height increments from the 2015–2017 ALS data to the 2019 GEDI laser shots and iii) to estimate height growth using polygons from the Forest Map of Spain (FMS). A set of 258 National Forest Inventory plots were used to validate the observed height dynamics. Results The spatio-temporal assessment from ALS surveying to GEDI scanning allowed the large-scale detection of harvests. The mean annual height growths were 0.79 (SD = 0.63), 0.60 (SD = 0.42) and 0.94 (SD = 0.75) m for Pinus pinaster, Pinus radiata and Eucalyptus spp., respectively. The median annual values from the ALS-GEDI positive increments were close to NFI-based growth values computed for Pinus pinaster and Pinus radiata, respectively. The effect of edge border, spatial co-registration of GEDI shots and the influence of forest cover in the observed dynamics were important factors to considering when processing ALS data and GEDI shots. Discussion The use of GEDI laser data provides valuable insights for forest industry operations especially when accounting for fast changes. However, errors derived from positioning, ground finder and canopy structure can introduce uncertainty to understand the detected growth patterns as documented in this study. The analysis of forest growth using ALS and GEDI would benefit from the generalization of common rules and data processing schemes as the GEDI mission is increasingly being utilized in the forest remote sensing community.

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Estimation of Total Biomass in Aleppo Pine Forest Stands Applying Parametric and Nonparametric Methods to Low-Density Airborne Laser Scanning Data

Forests ◽

10.3390/f9040158 ◽

2018 ◽

Vol 9 (4) ◽

pp. 158 ◽

Cited By ~ 13

Author(s):

Darío Domingo ◽

María Lamelas ◽

Antonio Montealegre ◽

Alberto García-Martín ◽

Juan de la Riva

Keyword(s):

Laser Scanning ◽

Nonparametric Methods ◽

Pine Forest ◽

Airborne Laser Scanning ◽

Aleppo Pine ◽

Total Biomass ◽

Low Density ◽

Forest Stands ◽

Airborne Laser

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Using low-density discrete Airborne Laser Scanning data to assess the potential carbon dioxide emission in case of a fire event in a Mediterranean pine forest

GIScience & Remote Sensing ◽

10.1080/15481603.2017.1320863 ◽

2017 ◽

Vol 54 (5) ◽

pp. 721-740 ◽

Cited By ~ 4

Author(s):

Antonio Luis Montealegre-Gracia ◽

María Teresa Lamelas-Gracia ◽

Alberto García-Martín ◽

Juan de la Riva-Fernández ◽

Francisco Escribano-Bernal

Keyword(s):

Carbon Dioxide ◽

Laser Scanning ◽

Carbon Dioxide Emission ◽

Pine Forest ◽

Airborne Laser Scanning ◽

Low Density ◽

Fire Event ◽

Airborne Laser ◽

Mediterranean Pine ◽

Potential Carbon

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Estimating above-ground biomass in young forests with airborne laser scanning

International Journal of Remote Sensing ◽

10.1080/01431160903474970 ◽

2011 ◽

Vol 32 (2) ◽

pp. 473-501 ◽

Cited By ~ 52

Author(s):

Erik Næsset

Keyword(s):

Laser Scanning ◽

Airborne Laser Scanning ◽

Above Ground Biomass ◽

Ground Biomass ◽

Airborne Laser ◽

Young Forests

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Testing the quality of forest variable estimation using dense image matching: a comparison with airborne laser scanning in a Mediterranean pine forest

International Journal of Remote Sensing ◽

10.1080/01431161.2018.1471551 ◽

2018 ◽

Vol 39 (14) ◽

pp. 4744-4760 ◽

Cited By ~ 11

Author(s):

José Antonio Navarro ◽

Alfredo Fernández-Landa ◽

José Luis Tomé ◽

María Luz Guillén-Climent ◽

Juan Carlos Ojeda

Keyword(s):

Image Matching ◽

Laser Scanning ◽

Pine Forest ◽

Airborne Laser Scanning ◽

Airborne Laser ◽

Dense Image ◽

Mediterranean Pine

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Quantification of Lichen Cover and Biomass Using Field Data, Airborne Laser Scanning and High Spatial Resolution Optical Data—A Case Study from a Canadian Boreal Pine Forest

Forests ◽

10.3390/f11060682 ◽

2020 ◽

Vol 11 (6) ◽

pp. 682

Author(s):

Ashley C. Hillman ◽

Scott E. Nielsen

Keyword(s):

High Resolution ◽

Spectral Data ◽

Laser Scanning ◽

Canopy Cover ◽

Jack Pine ◽

Pine Forest ◽

Airborne Laser Scanning ◽

Woodland Caribou ◽

Airborne Laser ◽

Pine Stands

Ground-dwelling macrolichens dominate the forest floor of mature upland pine stands in the boreal forest. Understanding patterns of lichen abundance, as well as environmental characteristics associated with lichen growth, is key to managing lichens as a forage resource for threatened woodland caribou (Rangifer tarandus caribou). The spectral signature of light-coloured lichen distinguishes it from green vegetation, potentially allowing for mapping of lichen abundance using multi-spectral imagery, while canopy structure measured from airborne laser scanning (ALS) of forest openings can indirectly map lichen habitat. Here, we test the use of high-resolution KOMPSAT (Korea Multi-Purpose Satellite-3) imagery (280 cm resolution) and forest structural characteristics derived from ALS to predict lichen biomass in an upland jack pine forest in Northeastern Alberta, Canada. We quantified in the field lichen abundance (cover and biomass) in mature jack pine stands across low, moderate, and high canopy cover. We then used generalized linear models to relate lichen abundance to spectral data from KOMPSAT and structural metrics from ALS. Model selection suggested that lichen abundance was best predicted by canopy cover (ALS points > 1.37 m) and to a lesser extent blue spectral data from KOMPSAT. Lichen biomass was low at plots with high canopy cover (98.96 g/m2), while almost doubling for plots with low canopy cover (186.30 g/m2). Overall the model fit predicting lichen biomass was good (R2 c = 0.35), with maps predicting lichen biomass from spectral and structural data illustrating strong spatial variations. High-resolution mapping of ground lichen can provide information on lichen abundance that can be of value for management of forage resources for woodland caribou. We suggest that this approach could be used to map lichen biomass for other regions.

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