Influence of Plot Size on Efficiency of Biomass Estimates in Inventories of Dry Tropical Forests Assisted by Photogrammetric Data from an Unmanned Aircraft System
Abstract:Abstract:Applications of unmanned aircraft systems (UASs) to assist in forest inventories have provided promising results in biomass estimation for different forest types. Recent studies demonstrating use of different types of remotely sensed data to assist in biomass estimation have shown that accuracy and precision of estimates are influenced by the size of field sample plots used to obtain reference values for biomass. The objective of this case study was to assess the influence of sample plot size on effic… Show more
“…These findings concur with previous studies in both temperate [20,21,38] and tropical forests [39,40] highlighting the importance of selecting an appropriate plot size for forest inventories used for estimating forest biomass. Studies in tropical dry forests in Malawi using photogrammetric data showed the same trend of increasing accuracy with increasing sample plot size [41].…”
Section: Discussionmentioning
confidence: 69%
“…Thus, the R 2 values of cross validations increased by 0.25 (a 76% increase) from 80 m 2 (0.33) to 400 m 2 (0.58) but only by 0.17 (a 29% increase) from 400 m 2 to 1000 m 2 (0.75) in Kiuic, and by 0.28 (a 120% increase) from 80 m 2 (0.23) to 400 m 2 (0.51) but only by 0.16 (a 31% increase) from 400 m 2 to 1000 m 2 (0.67) in FCP. These results are relevant because, as plot sizes increases, the cost of field sampling also increases [41]. Therefore, the selection of an optimal plot size for AGB estimation from LiDAR data should take into account a trade-off between estimation accuracy and plot establishment cost.…”
Section: Discussionmentioning
confidence: 99%
“…Although there are alternative approaches trying to link field-surveyed tree locations with positions of trees identified by LiDAR and using an automatic procedure in order to reduce co-registration errors [23,45,46], the use of large plots has the advantage of capturing an adequate amount of structural variability in the field [22], reducing edge effects and increasing overlap area [41].…”
Accurate estimates of above ground biomass (AGB) are needed for monitoring carbon in tropical forests. LiDAR data can provide precise AGB estimations because it can capture the horizontal and vertical structure of vegetation. However, the accuracy of AGB estimations from LiDAR is affected by a co-registration error between LiDAR data and field plots resulting in spatial discrepancies between LiDAR and field plot data. Here, we evaluated the impacts of plot location error and plot size on the accuracy of AGB estimations predicted from LiDAR data in two types of tropical dry forests in Yucatán, México. We sampled woody plants of three size classes in 29 nested plots (80 m2, 400 m2 and 1000 m2) in a semi-deciduous forest (Kiuic) and 28 plots in a semi-evergreen forest (FCP) and estimated AGB using local allometric equations. We calculated several LiDAR metrics from airborne data and used a Monte Carlo simulation approach to assess the influence of plot location errors (2 to 10 m) and plot size on ABG estimations from LiDAR using regression analysis. Our results showed that the precision of AGB estimations improved as plot size increased from 80 m2 to 1000 m2 (R2 = 0.33 to 0.75 and 0.23 to 0.67 for Kiuic and FCP respectively). We also found that increasing GPS location errors resulted in higher AGB estimation errors, especially in the smallest sample plots. In contrast, the largest plots showed consistently lower estimation errors that varied little with plot location error. We conclude that larger plots are less affected by co-registration error and vegetation conditions, highlighting the importance of selecting an appropriate plot size for field forest inventories used for estimating biomass.
“…These findings concur with previous studies in both temperate [20,21,38] and tropical forests [39,40] highlighting the importance of selecting an appropriate plot size for forest inventories used for estimating forest biomass. Studies in tropical dry forests in Malawi using photogrammetric data showed the same trend of increasing accuracy with increasing sample plot size [41].…”
Section: Discussionmentioning
confidence: 69%
“…Thus, the R 2 values of cross validations increased by 0.25 (a 76% increase) from 80 m 2 (0.33) to 400 m 2 (0.58) but only by 0.17 (a 29% increase) from 400 m 2 to 1000 m 2 (0.75) in Kiuic, and by 0.28 (a 120% increase) from 80 m 2 (0.23) to 400 m 2 (0.51) but only by 0.16 (a 31% increase) from 400 m 2 to 1000 m 2 (0.67) in FCP. These results are relevant because, as plot sizes increases, the cost of field sampling also increases [41]. Therefore, the selection of an optimal plot size for AGB estimation from LiDAR data should take into account a trade-off between estimation accuracy and plot establishment cost.…”
Section: Discussionmentioning
confidence: 99%
“…Although there are alternative approaches trying to link field-surveyed tree locations with positions of trees identified by LiDAR and using an automatic procedure in order to reduce co-registration errors [23,45,46], the use of large plots has the advantage of capturing an adequate amount of structural variability in the field [22], reducing edge effects and increasing overlap area [41].…”
Accurate estimates of above ground biomass (AGB) are needed for monitoring carbon in tropical forests. LiDAR data can provide precise AGB estimations because it can capture the horizontal and vertical structure of vegetation. However, the accuracy of AGB estimations from LiDAR is affected by a co-registration error between LiDAR data and field plots resulting in spatial discrepancies between LiDAR and field plot data. Here, we evaluated the impacts of plot location error and plot size on the accuracy of AGB estimations predicted from LiDAR data in two types of tropical dry forests in Yucatán, México. We sampled woody plants of three size classes in 29 nested plots (80 m2, 400 m2 and 1000 m2) in a semi-deciduous forest (Kiuic) and 28 plots in a semi-evergreen forest (FCP) and estimated AGB using local allometric equations. We calculated several LiDAR metrics from airborne data and used a Monte Carlo simulation approach to assess the influence of plot location errors (2 to 10 m) and plot size on ABG estimations from LiDAR using regression analysis. Our results showed that the precision of AGB estimations improved as plot size increased from 80 m2 to 1000 m2 (R2 = 0.33 to 0.75 and 0.23 to 0.67 for Kiuic and FCP respectively). We also found that increasing GPS location errors resulted in higher AGB estimation errors, especially in the smallest sample plots. In contrast, the largest plots showed consistently lower estimation errors that varied little with plot location error. We conclude that larger plots are less affected by co-registration error and vegetation conditions, highlighting the importance of selecting an appropriate plot size for field forest inventories used for estimating biomass.
“…The precision and accuracy of AGB extrapolated from field plot data are affected by the size and shape of the plots [64]. Most of the used field plots in the present study exceed an area of 1000 m 2 , a size large enough to be more robust against boundary effects and less sensitive to individual trees [65][66][67]. Aside from the size, the shape affects the results of extrapolated AGB.…”
Globally available high-resolution information about canopy height and AGB is important for carbon accounting. The present study showed that Pol-InSAR data from TS-X and RS-2 could be used together with field inventories and high-resolution data such as drone or LiDAR data to support the carbon accounting in the context of REDD+ (Reducing Emissions from Deforestation and Forest Degradation) projects.
“…Nevertheless, the low cost and increased repeatability of DAP relative to ALS, for stand-level applications, shows significant potential [27,29]. Recent studies indicate that accurate DEM generation from the unsupervised classification of DAP point clouds under open forest canopies is achievable [29,31,46,47]. For example Guerra-Hernández et al [31] used 20 high precision GPS checkpoints and found RMSE of 0.046 m, 0.018 m and 0.033 m in the X, Y and Z directions respectively.…”
Detailed vertical forest structure information can be remotely sensed by combining technologies of unmanned aerial systems (UAS) and digital aerial photogrammetry (DAP). A key limitation in the application of DAP methods, however, is the inability to produce accurate digital elevation models (DEM) in areas of dense vegetation. This study investigates the terrain modeling potential of UAS-DAP methods within a temperate conifer forest in British Columbia, Canada. UAS-acquired images were photogrammetrically processed to produce high-resolution DAP point clouds. To evaluate the terrain modeling ability of DAP, first, a sensitivity analysis was conducted to estimate optimal parameters of three ground-point classification algorithms designed for airborne laser scanning (ALS). Algorithms tested include progressive triangulated irregular network (TIN) densification (PTD), hierarchical robust interpolation (HRI) and simple progressive morphological filtering (SMRF). Points were classified as ground from the ALS and served as ground-truth data to which UAS-DAP derived DEMs were compared. The proportion of area with root mean square error (RMSE) <1.5 m were 56.5%, 51.6% and 52.3% for the PTD, HRI and SMRF methods respectively. To assess the influence of terrain slope and canopy cover, error values of DAP-DEMs produced using optimal parameters were compared to stratified classes of canopy cover and slope generated from ALS point clouds. Results indicate that canopy cover was approximately three times more influential on RMSE than terrain slope.
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