Influences of vegetation, model, and data parameters on forest aboveground biomass assessment using an area-based approach

Brovkina, Olga; Navrátilová, Barbora; Novotný, Jan; Albert, Jan; Slezák, Lukáš; Cienciala, Emil

doi:10.1016/j.ecoinf.2022.101754

Cited by 12 publications

(8 citation statements)

References 30 publications

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“…They can be computationally expensive, but they can be effective for high-dimensional datasets. The most common embedded methods include principal component analysis (PCA), Lasso Regression, Ridge Regression, Elastic Net, Tree-Based Models, and Boosting models [207,207,[219][220][221]. Figure 19 shows the frequency distribution of different feature selection methods used for above-ground biomass (AGB) estimation.…”

Section: Feature Selectionmentioning

confidence: 99%

An Overview of Using Unmanned Aerial System Mounted Sensors to Measure Plant Above-Ground Biomass

et al. 2023

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Conventional measurement methods for above-ground biomass (AGB) are time-consuming, inaccurate, and labor-intensive. Unmanned aerial systems (UASs) have emerged as a promising solution, but a standardized procedure for UAS-based AGB estimation is lacking. This study reviews recent findings (2018–2022) on UAS applications for AGB estimation and develops a vegetation type-specific standard protocol. Analysis of 211 papers reveals the prevalence of rotary-wing UASs, especially quadcopters, in agricultural fields. Sensor selection varies by vegetation type, with LIDAR and RGB sensors in forests, and RGB, multispectral, and hyperspectral sensors in agricultural and grass fields. Flight altitudes and speeds depend on vegetation characteristics and sensor types, varying among crop groups. Ground control points (GCPs) needed for accurate AGB estimation differ based on vegetation type and topographic complexity. Optimal data collection during solar noon enhances accuracy, considering image quality, solar energy availability, and reduced atmospheric effects. Vegetation indices significantly affect AGB estimation in vertically growing crops, while their influence is comparatively less in forests, grasses, and horizontally growing crops. Plant height metrics differ across vegetation groups, with maximum height in forests and vertically growing crops, and central tendency metrics in grasses and horizontally growing crops. Linear regression and machine learning models perform similarly in forests, with machine learning outperforming in grasses; both yield comparable results for horizontally and vertically growing crops. Challenges include sensor limitations, environmental conditions, reflectance mixture, canopy complexity, water, cloud cover, dew, phenology, image artifacts, legal restrictions, computing power, battery capacity, optical saturation, and GPS errors. Addressing these requires careful sensor selection, timing, image processing, compliance with regulations, and overcoming technical limitations. Insights and guidelines provided enhance the precision and efficiency of UAS-based AGB estimation. Understanding vegetation requirements aids informed decisions on platform selection, sensor choice, flight parameters, and modeling approaches across different ecosystems. This study bridges the gap by providing a standardized protocol, facilitating widespread adoption of UAS technology for AGB estimation.

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Section: Feature Selectionmentioning

confidence: 99%

An Overview of Using Unmanned Aerial System Mounted Sensors to Measure Plant Above-Ground Biomass

et al. 2023

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“…CBD is the dry mass of available canopy fuel per unit volume and is the bulk property of the stand and not of individual trees (Scott and Reinhardt 2001) and was calculated based on the point cloud data with the following series of steps: 1/total aboveground biomass (AGB) was calculated with the area-based approach when we applied a general model trained for the mountainous forests in the Czech Republic (Brovkina et al 2022); 2/AGB estimates in tons per hectare from the previous step were recalculated into CBD estimates in kilograms per m 3 using an empirical value of the ratio between crown biomass and total tree biomass and a crown volume estimate based on the point cloud.…”

Section: Model Description and Input Data Formatmentioning

confidence: 99%

Using FlamMap to assess wildfire behavior in Bohemian Switzerland National Park

Kudláčková,

Poděbradská,

Bláhová

et al. 2023

Nat Hazards

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The 2022 summer re in Bohemian Switzerland National Park (BSNP) is ranked as the largest in the Czech Republic´s 30-year recorded history, with an affected area over 1000 ha. The FlamMap re modeling system was used to investigate the re behavior associated with the BSNP and to evaluate scenarios under a range of fuel types, fuel moistures, and weather conditions. We used the model to simulate re conditions, propagation, and extent. We focused on matching the observed re perimeter and on re behavior characteristics. The re took place in a region of BSNP that was heavily affected by the Spruce bark beetle (Ips typographus L.) infestation; hence, most of the burned area was in dead spruce forest (Picea abies Karst.). The best FlamMap simulations of observed re behavior and progression were compared with several created scenarios that differed in various input conditions. Scenarios included a re in a healthy spruce forest, in clearcuts, or under different meteorological conditions. We were able to calibrate and use FlamMap to recreate a recent wild re under observed conditions. We found that the re would have likely spread to its observed nal perimeter even if standing dead trees were removed. However, at lower re intensity and with a considerably shorter duration. Alternatively, if healthy standing vegetation with a closed canopy had been present, the wild re perimeter would have been approximately half. Similar results were obtained for both the nonnative spruce forest and the deciduous forest, which is a native alternative.

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“…As a basis for such scaling can serve, firstly, the stability of spectral-brightness and spatial-frequency characteristics over time for the considered scene [15], and secondly, a reasonable choice of the specific method for processing ERS data and determining the desired characteristics of the environmental state. Currently, the task on selecting a method is most often solved by qualified researchers applying their own experience in a non-operative manner [16,17,18]. However, the widespread use of ERS data processing methods, expanding range of processing tasks and the necessity to introduce prospective methods for analyzing the NTS and environmental state into practical activities make it necessary to automate this stage of ERS data processing.…”

Section: Introductionmentioning

confidence: 99%

Multicriteria selection of a method for processing multispectral Earth remote sensing data

Zelentsov,

Andrianov,

Mochalov

2023

J Appl Eng Science

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The article is devoted to the use of qualimetry methods for models and poly model complexes in order to solve one of the relevant engineering problems-automation of selecting methods for calculating Earth remote sensing (ERS) data processing when analyzing the state of complex natural and technical systems. The proposed approach was discussed using the example of choosing methods for calculating forest sustainability indicators. A typical situation was considered when alternative methods and models can be applied at each stage of data processing. The essence of the proposed approach is to formulate and solve the task on multi criteria comparative analysis of processing methods based on a set of indicators, which include costs, required for implementation one or another method, efficiency, which refers to calculation duration of the analyzed sustainability indicator, and an indicator reflecting the quality of the solution-accuracy of calculations result. The solution algorithm was illustrated within an example of choosing the method for assessing consequences of the forest fire. The selection results were presented in the form of a table, which allows the user to evaluate losses and gains in the values of partial indicators when moving from applying one method to another. The proposed algorithmization of the selection task determines possibility for its automation and, thereby, simplifying application of complex methods for processing ERS data for the end user. In addition, the possibilities and degree of validity for scaling the results of processing ERS data from individual areas to large forest areas are expanding.

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Influences of vegetation, model, and data parameters on forest aboveground biomass assessment using an area-based approach

Cited by 12 publications

References 30 publications

An Overview of Using Unmanned Aerial System Mounted Sensors to Measure Plant Above-Ground Biomass

An Overview of Using Unmanned Aerial System Mounted Sensors to Measure Plant Above-Ground Biomass

Using FlamMap to assess wildfire behavior in Bohemian Switzerland National Park

Multicriteria selection of a method for processing multispectral Earth remote sensing data

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