Beyond trees: Mapping total aboveground biomass density in the Brazilian savanna using high-density UAV-lidar data

Costa, Máira Beatriz Teixeira da; Silva, Carlos Alberto; Broadbent, Eben N.; Leite, Rodrigo Vieira; Mohan, Midhun; Liesenberg, Veraldo; Stoddart, Jaz; Amaral, Cibele Hummel do; Almeida, Danilo Roberti Alves de; Silva, Anne Laura da; Goya, Lucas Ruggeri Ré Y.; Cordeiro, Victor Almeida; Rex, Franciel Eduardo; Hirsch, André; Marcatti, Gustavo Eduardo; Cardíl, Adrián; Mendonça, Bruno Araújo Furtado de; Hamamura, Caio; Corte, Ana Paula Dalla; Matricardi, Eraldo Aparecido Trondoli; Hudak, Andrew T.; Zambrano, Angélica M. Almeyda; Valbuena, Rubén; Faria, Bruno Lopes de; Silva, Celso; Aragão, L. E.; Ferreira, Manuel Eduardo; Liang, Jingjing; Klauberg, Carine

doi:10.1016/j.foreco.2021.119155

Cited by 30 publications

(33 citation statements)

References 82 publications

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“…Other studies have used repeated ALS flights to assess relative growth or damage following disturbances [47,48]. Most recently, unmanned aerial vehicles (UAVs) and drones have been used to assess similar individual tree structural attributes as a potential lower cost alternative to ALS [49][50][51][52]. However, the delay that was observed between initial research applications and the widespread use of ALS in supporting operational forest products supply chains and resource management suggests that it may take at least a decade for UAV-and drone-based approaches to replace ALS as the standard approach to acquire these measurements [2,7].…”

Section: Introductionmentioning

confidence: 99%

Accuracy of a LiDAR-Based Individual Tree Detection and Attribute Measurement Algorithm Developed to Inform Forest Products Supply Chain and Resource Management

Sparks

Smith

2021

Forests

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Individual Tree Detection (ITD) algorithms that use Airborne Laser Scanning (ALS) data can provide accurate tree locations and measurements of tree-level attributes that are required for stand-to-landscape scale forest inventory and supply chain management. While numerous ITD algorithms exist, few have been assessed for accuracy in stands with complex forest structure and composition, limiting their utility for operational application. In this study, we conduct a preliminary assessment of the ability of the ForestView® algorithm created by Northwest Management Incorporated to detect individual trees, classify tree species, live/dead status, canopy position, and estimate height and diameter at breast height (DBH) in a mixed coniferous forest with an average tree density of 543 (s.d. ±387) trees/hectare. ITD accuracy was high in stands with lower canopy cover (recall: 0.67, precision: 0.8) and lower in stands with higher canopy cover (recall: 0.36, precision: 0.67), mainly owing to omission of suppressed trees that were not detected under the dominant tree canopy. Tree species that were well-represented within the study area had high classification accuracies (producer’s/user’s accuracies > ~60%). The similarity between the ALS estimated and observed tree attributes was high, with no statistical difference in the ALS estimated height and DBH distributions and the field observed height and DBH distributions. RMSEs for tree-level height and DBH were 0.69 m and 7.2 cm, respectively. Overall, this algorithm appears comparable to other ITD and measurement algorithms, but quantitative analyses using benchmark datasets in other forest types and cross-comparisons with other ITD algorithms are needed.

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Section: Introductionmentioning

confidence: 99%

Accuracy of a LiDAR-Based Individual Tree Detection and Attribute Measurement Algorithm Developed to Inform Forest Products Supply Chain and Resource Management

Sparks

Smith

2021

Forests

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“…We assessed the data from the Fazenda Cauaxi in Pará state, Brazil. The region is humid and tropical with the average total precipitation annually being 2200 mm and with predominantly flat topology and an elevation ranging between 74 and 150 m above sea level [32]. The primary form of vegetation in the region is ombrophilous dense forest with the upper canopy having a mean height between 30 and 40 m and emergent trees reaching above the canopy up to 50 m tall [20,32].…”

Section: Study Areamentioning

confidence: 99%

“…The region is humid and tropical with the average total precipitation annually being 2200 mm and with predominantly flat topology and an elevation ranging between 74 and 150 m above sea level [32]. The primary form of vegetation in the region is ombrophilous dense forest with the upper canopy having a mean height between 30 and 40 m and emergent trees reaching above the canopy up to 50 m tall [20,32]. The site consisted of a region split into 12 blocks of 100 ha which were either left unlogged or were subject to selective logging by similar methods at different times in the years 2006-2013 [59].…”

Section: Study Areamentioning

confidence: 99%

“…Models using data from airborne LiDAR can be used to generate maps of aboveground biomass (AGB) estimates over large areas [16][17][18][21][22][23][24][25][26][27][28][29][30][31][32]. Multitemporal LiDAR can be used to evaluate small-scale changes [33,34] but the capacity of these maps to detect smallscale changes is contingent upon AGB estimation models being accurate enough to detect forest disturbance [35].…”

Section: Introductionmentioning

confidence: 99%

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A Conceptual Model for Detecting Small-Scale Forest Disturbances Based on Ecosystem Morphological Traits

et al. 2022

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Current LiDAR-based methods for detecting forest change use a host of statistically selected variables which typically lack a biological link with the characteristics of the ecosystem. Consensus of the literature indicates that many authors use LiDAR to derive ecosystem morphological traits (EMTs)—namely, vegetation height, vegetation cover, and vertical structural complexity—to identify small-scale changes in forest ecosystems. Here, we provide a conceptual, biological model for predicting forest aboveground biomass (AGB) change based on EMTs. We show that through use of a multitemporal dataset it is possible to not only identify losses caused by logging in the period between data collection but also identify regions of regrowth from prior logging using EMTs. This sensitivity to the change in forest dynamics was the criterion by which LiDAR metrics were selected as proxies for each EMT. For vegetation height, results showed that the top-of-canopy height derived from a canopy height model was more sensitive to logging than the average or high percentile of raw LiDAR height distributions. For vegetation cover metrics, lower height thresholds for fractional cover calculations were more sensitive to selective logging and the regeneration of understory. For describing the structural complexity in the vertical profile, the Gini coefficient was found to be superior to foliage height diversity for detecting the dynamics occurring over the years after logging. The subsequent conceptual model for AGB estimation obtained a level of accuracy which was comparable to a model that was statistically optimised for that same area. We argue that a widespread adoption of an EMT-based conceptual approach would improve the transferability and comparability of LiDAR models for AGB worldwide.

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“…Unfortunately, efficiently summarizing and extracting all the information on forest structure from LiDAR 3D point cloud data can be challenging. One approach is to calculate summary statistics for grid cells at a given spatial resolution, such as mean and maximum return height, standard deviation of the return heights and height percentiles (Andersen et al, 2013; Costa et al, 2021; de Almeida, Galvao, et al, 2019; Jucker et al, 2018; Rex et al, 2020; Silva et al, 2017). Another approach consists of first characterizing the vertical structure of forests by calculating leaf area density (LAD) to then describe the vertical and horizontal heterogeneity in LAD with summary statistics (e.g.…”

Section: Introductionmentioning

confidence: 99%

The Latent Dirichlet Allocation model applied to airborne LiDAR data: A case study on mapping forest degradation associated with fragmentation and fire in the Amazon region

et al. 2022

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LiDAR data are being increasingly used to provide a detailed characterization of the vertical profile of forests. This characterization enables the generation of new insights on the influence of environmental drivers and anthropogenic disturbances on forest structure as well as on how forest structure influences important ecosystem functions and services. Unfortunately, extracting information from LiDAR data in a way that enables the spatial visualization of forest structure, as well as its temporal changes, is challenging due to the high dimensionality of these data. We show how the Latent Dirichlet Allocation model applied to LiDAR data (LidarLDA) can be used to identify forest structural types and how the relative abundance of these forest types changes throughout the landscape. The code to fit this model is made available through the open‐source r package LidarLDA in github. We illustrate the use of LidarLDA both with simulated data and data from a large‐scale fire experiment in the Brazilian Amazon region. Using simulated data, we demonstrate that LidarLDA accurately identifies the number of forest types as well as their spatial distribution and absorptance probabilities. For the empirical data, we found that LidarLDA detects both landscape‐level patterns in forest structure as well as the strong interacting effect of fire and forest fragmentation on forest structure based on the experimental fire plots. More specifically, LidarLDA reveals that proximity to forest edge exacerbates the impact of fires, and that burned forests remain structurally different from unburned areas for at least 7 years, even when burned only once. Importantly, LidarLDA generates insights on the 3D structure of forest that cannot be obtained using more standard approaches that just focus on top‐of‐the‐canopy information (e.g. canopy height models based on LiDAR data). By enabling the mapping of forest structure and its temporal changes, we believe that LidarLDA will be of broad utility to the ecological research community.

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Beyond trees: Mapping total aboveground biomass density in the Brazilian savanna using high-density UAV-lidar data

Cited by 30 publications

References 82 publications

Accuracy of a LiDAR-Based Individual Tree Detection and Attribute Measurement Algorithm Developed to Inform Forest Products Supply Chain and Resource Management

Accuracy of a LiDAR-Based Individual Tree Detection and Attribute Measurement Algorithm Developed to Inform Forest Products Supply Chain and Resource Management

A Conceptual Model for Detecting Small-Scale Forest Disturbances Based on Ecosystem Morphological Traits

The Latent Dirichlet Allocation model applied to airborne LiDAR data: A case study on mapping forest degradation associated with fragmentation and fire in the Amazon region

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