Evaluating tropical forest classification and field sampling stratification from lidar to reduce effort and enable landscape monitoring

Papa, Daniel de Almeida; Almeida, Danilo Roberti Alves de; Silva, Carlos Alberto; Figueiredo, E. O.; Stark, Scott C.; Valbuena, Rubén; Rodriguez, Luiz Carlos Estraviz; Oliveira, M. V. N. d'.

doi:10.1016/j.foreco.2019.117634

Cited by 15 publications

(12 citation statements)

References 71 publications

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“…Pre-stratification based on aerial images did not improve the model accuracies, as the optical imagery was not able to detect the small variations at the plot level, although the ALS-based pre-stratification (into mature and young stands) resulted in a slight improvement. Similarly, ALS-based pre-stratification can also reduce the sampling efforts by up to 41 % ( de Almeida Papa et al 2020).…”

Section: Aboveground Biomass Predictions In Als-based Direct Fst (Iii)mentioning

confidence: 99%

Improvements in forest structural type assessment using airborne laser scanning

Adnan¹

2020

Diss. For.

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Accurate forest structural type (FST) assessment provides a valuable support tool to distinguish the different structures in forest stands, achieve sustainable forest management and formulate effective decisions. Data from four research sites within three biogeographical regions-Boreal, Mediterranean and Atlanticwere used in this study, and reliable methodologies were developed for FST assessment. First, the Gini coefficient () of tree size inequality was used for the structural characterisation, and the effects of plot size, stand density and point density of airborne laser scanning (ALS) on the ALS-assisted estimations were evaluated for the Boreal region. Second, four forest structural attributesquadratic mean diameter (), , basal area larger than the mean () and stand density ()from the three biogeographical regions were used to develop regionindependent methods for FST assessment. Lastly, a threshold value to represent maximum entropy was determined and was used to classify the various FST directly from ALS data using L-coefficient of variation and L-skewness of ALS echo heights. Aboveground biomass (AGB) was predicted for each FST and was compared with the AGB predictions without prestratification. The results showed that (a) plot size had a greater effect on the ALS-assisted estimation compared to stand size and point density, and that 250-450 m 2 plot size (radius 9-12 m for circular plots) is the optimal plot size for reliable ALS-assisted estimations, (b) and are the most reliable bivariate descriptors for FST assessment, and single storey, multi-storey and reversed-J type forest structures can be separated by lower, medium and upper and values, respectively, while and are relevant for the separation of young/mature and sparse/dense subtypes, and (c) based on the mathematical proofs, the threshold values calculated from ALS echo heights and tree basal areas to represent maximum entropy should be 0.33 and 0.50, respectively. Moderate improvements were observed in the AGB predictions from FST classified directly from ALS data compared to the full dataset but critical differences were identified in the selection of ALS metrics by the prediction models. For example, higher percentiles were more relevant in uneven-sized structures and open canopy areas, while cover metrics and average percentiles were important in the even-sized structures and closed canopy areas. Thus, these results are very useful in improving our understanding of the relationships that underpin the choice of ALS predictors in structurally complex forests.

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Section: Aboveground Biomass Predictions In Als-based Direct Fst (Iii)mentioning

confidence: 99%

Improvements in forest structural type assessment using airborne laser scanning

Adnan¹

2020

Diss. For.

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“…Other authors have shown ALS data to enable forest stratification which led to a reduction of 41% in the required sampling intensity [24]. This significant reduction in sampling units (from 46 to 27-1 ha plots) saved US $28,500.00 by reducing the field work, which paid for the ALS data collection (US $26,400.00) [24]. We believe that following the approach that reduces sampling intensity [24] with the approach proposed in this paper could be a future object of study.…”

Section: Discussionmentioning

confidence: 99%

“…Rather than supplanting existing approaches, ALS data can be integrated into current forest management processes [23] by providing information about the vertical structure that can be linked with the horizontal structure from field plots [19,24]. Studies have shown that ALS data can already provide information about the terrain characteristics [25], drainage network [26], forest characteristics [24,27], gaps dynamics [28,29], vegetation structure [30][31][32], coarse woody debris [33], tree life stage [34], diameter distribution [35,36], and others. However, ALS can only provide limited information related to forest composition, i.e., tree species [37].…”

Section: Introductionmentioning

confidence: 99%

Qualifying the Information Detected from Airborne Laser Scanning to Support Tropical Forest Management Operational Planning

Reis

Görgens

Almeida

et al. 2021

Forests

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(1) Background: Forests throughout the world are managed to fulfil a range of commercial and ecosystem services. The same applies to managed areas of the Amazon forest. We explore a method of sustainable forest management (SFM) which anticipates the result of processes of natural mortality of large, mature trees that could fall and damage their neighbors. Collecting all the information required for planning logging in the Brazilian Amazon is, currently, a hard, time-consuming and expensive task. (2) Methods: This information can be obtained more quickly, accurately and objectively by including airborne laser scanning (ALS) products in the operational plan. We used ALS point clouds to isolate emergent crowns from the canopy height model. Then, we performed field work to validate the existence of these trees, and to understand how many commercial trees (tree diameter ≥ 50 cm) we identified by orienting the trees search through the emergent canopy model. (3) Results: We were able to detect 184 (54.4%) trees from 338 field-recorded individuals in 20 plots (totaling 8 ha). Of the detected trees, 66 individuals were classified as having potential for commerce. Furthermore, 58 individuals presented the best stem quality for logging, which represents more than seven high quality commercial trees per hectare. The logistic regression showed that the effects that positively influence the emergent crown formation are strongly presented in the commercial species. (4) Conclusions: Using airborne laser scanning can improve the SFM planning in a structurally complex, dense and mixed composition tropical forest by reducing field work in the initial stages of management. Therefore, we propose that ALS operational planning can be used to more efficiently direct field surveys without the need for a full census.

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“…Airborne LiDAR has been used to characterize vertical canopy structure, crown shape, and aboveground biomass for entire ecosystems (Lefsky et al 2002;Parker et al 2004;Coops et al 2007;Stark et al 2012;Harding et al 2001;Cao et al 2014;Ellsworth and Reich 1993;Papa, 2020). It has also been used to identify tree species Koch 2011, 2012;Vaughn et al 2012;Axelsson et al 2018;Hovi et al 2016;Fassnacht et al 2016;Fedrigo et al 2018) and to study stand characteristics such as age, crown cover, and basal area (Korhonen et al 2011;Racine et al 2014;White et al 2013;Karna et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Tree species, crown cover, and age as determinants of the vertical distribution of airborne LiDAR returns

Racine

Coops

Bégin

et al. 2021

Trees

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Key message We assessed even-aged stand vertical distributions of LiDAR returns and found that tree species, age, and crown cover each have a distinct pattern that together explains up to 47% of the variation. Abstract Light detection and ranging (LiDAR) provides information on the vertical structure of forest stands enabling detailed and extensive ecosystem study. The vertical structure is often summarized by scalar features and data-reduction techniques that limit the interpretation of results. Instead, we quantified the influence of three variables, species, crown cover, and age, on the vertical distribution of airborne LiDAR returns from forest stands. We studied 5428 regular, even-aged stands in Quebec (Canada) with five dominant species: balsam fir [Abies balsamea (L.) Mill.], paper birch (Betula papyrifera Marsh), black spruce [Picea mariana (Mill.) BSP], white spruce (Picea glauca Moench) and aspen (Populus tremuloides Michx.). We modeled the vertical distribution against the three variables using a functional general linear model and a novel nonparametric graphical test of significance. Results indicate that LiDAR returns from aspen stands had the most uniform vertical distribution. Balsam fir and white birch distributions were similar and centered at around 50% of the stand height, and black spruce and white spruce distributions were skewed to below 30% of stand height ($$p$$ p <0.001). Increased crown cover concentrated the distributions around 50% of stand height. Increasing age gradually shifted the distributions higher in the stand for stands younger than 70-years, before plateauing and slowly declining at 90–120 years. Results suggest that the vertical distributions of LiDAR returns depend on the three variables studied.

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Evaluating tropical forest classification and field sampling stratification from lidar to reduce effort and enable landscape monitoring

Cited by 15 publications

References 71 publications

Improvements in forest structural type assessment using airborne laser scanning

Improvements in forest structural type assessment using airborne laser scanning

Qualifying the Information Detected from Airborne Laser Scanning to Support Tropical Forest Management Operational Planning

Tree species, crown cover, and age as determinants of the vertical distribution of airborne LiDAR returns

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