Effects of Sample Plot Size and GPS Location Errors on Aboveground Biomass Estimates from LiDAR in Tropical Dry Forests

Hernández‐Stefanoni, José Luis; Reyes-Palomeque, Gabriela; Castillo-Santiago, Miguel Ángel; George‐Chacón, Stephanie P.; Huechacona-Ruiz, Astrid Helena; Tun-Dzul, Fernando; Rondon-Rivera, Dinosca; Dupuy, Juan Manuel

doi:10.3390/rs10101586

Cited by 36 publications

(24 citation statements)

References 42 publications

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“…Geo-location errors up to 10 m are common when using GPS receivers to measure a plot centroid location under forest canopy [61]. In our study, the circular plot with a 10 m radius may result into mismatch between field and remote-sensing data, and hence increasing the errors in the models [62,63]. This is mainly because the plot centroid may locate in a pixel that shares only partly or none of the plot area.…”

Section: Discussionmentioning

confidence: 90%

Modelling and Predicting the Growing Stock Volume in Small-Scale Plantation Forests of Tanzania Using Multi-Sensor Image Synergy

Mauya

Koskinen

Tegel

et al. 2019

Forests

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Remotely sensed assisted forest inventory has emerged in the past decade as a robust and cost efficient method for generating accurate information on forest biophysical parameters. The launching and public access of ALOS PALSAR-2, Sentinel-1 (SAR), and Sentinel-2 together with the associated open-source software, has further increased the opportunity for application of remotely sensed data in forest inventories. In this study, we evaluated the ability of ALOS PALSAR-2, Sentinel-1 (SAR) and Sentinel-2 and their combinations to predict growing stock volume in small-scale forest plantations of Tanzania. The effects of two variable extraction approaches (i.e., centroid and weighted mean), seasonality (i.e., rainy and dry), and tree species on the prediction accuracy of growing stock volume when using each of the three remotely sensed data were also investigated. Statistical models relating growing stock volume and remotely sensed predictor variables at the plot-level were fitted using multiple linear regression. The models were evaluated using the k-fold cross validation and judged based on the relative root mean square error values (RMSEr). The results showed that: Sentinel-2 (RMSEr = 42.03% and pseudo − R2 = 0.63) and the combination of Sentinel-1 and Sentinel-2 (RMSEr = 46.98% and pseudo − R2 = 0.52), had better performance in predicting growing stock volume, as compared to Sentinel-1 (RMSEr = 59.48% and pseudo − R2 = 0.18) alone. Models fitted with variables extracted from the weighted mean approach, turned out to have relatively lower RMSEr % values, as compared to centroid approaches. Sentinel-2 rainy season based models had slightly smaller RMSEr values, as compared to dry season based models. Dense time series (i.e., annual) data resulted to the models with relatively lower RMSEr values, as compared to seasonal based models when using variables extracted from the weighted mean approach. For the centroid approach there was no notable difference between the models fitted using dense time series versus rain season based predictor variables. Stratifications based on tree species resulted into lower RMSEr values for Pinus patula tree species, as compared to other tree species. Finally, our study concluded that combination of Sentinel-1&2 as well as the use Sentinel-2 alone can be considered for remote-sensing assisted forest inventory in the small-scale plantation forests of Tanzania. Further studies on the effect of field plot size, stratification and statistical methods on the prediction accuracy are recommended.

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Section: Discussionmentioning

confidence: 90%

Modelling and Predicting the Growing Stock Volume in Small-Scale Plantation Forests of Tanzania Using Multi-Sensor Image Synergy

Mauya

Koskinen

Tegel

et al. 2019

Forests

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“…Another common group of errors derives from mismatches between field and remotely sensed measurements; such mismatches include spatial location and the differences between the size of the sampled unit and the pixels of the imagery. However, these two errors decrease as plot size increases [12,68]. Here we used a sample unit of 1 ha, and adjusted the grain size of the imagery accordingly, so we expect low error values.…”

Section: Discussionmentioning

confidence: 99%

“…The ICM plots have a similar design to that of the NFI, with the addition of a nested 80 m 2 subplot where all trees with DBH between 2.5 and 7.5 cm were sampled. A total 80 ICM sample units were measured in the study area using a systematic sampling design [12] ( Fig. 1).…”

Section: Field Datamentioning

confidence: 99%

“…Within Mexico, studies by Rodriguez-Veiga et al [8] and Cartus et al [10] mapped AGB, reporting higher relative errors in the tropical dry forests of the Yucatan peninsula compared to other forest types in Mexico. A commonly used approach to map the spatial distribution of AGB or carbon density is by combining forest plot data with remotely sensed information [12]. In many cases, field data is collected by national forest inventories (NFIs), which provide extensive and detailed information of vegetation attributes [13].…”

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confidence: 99%

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Improving aboveground biomass maps of tropical dry forests by integrating LiDAR, ALOS PALSAR, climate and field data

Hernández‐Stefanoni

Castillo-Santiago

Mas

et al. 2020

Carbon Balance Manage

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Background: Reliable information about the spatial distribution of aboveground biomass (AGB) in tropical forests is fundamental for climate change mitigation and for maintaining carbon stocks. Recent AGB maps at continental and national scales have shown large uncertainties, particularly in tropical areas with high AGB values. Errors in AGB maps are linked to the quality of plot data used to calibrate remote sensing products, and the ability of radar data to map high AGB forest. Here we suggest an approach to improve the accuracy of AGB maps and test this approach with a case study of the tropical forests of the Yucatan peninsula, where the accuracy of AGB mapping is lower than other forest types in Mexico. To reduce the errors in field data, National Forest Inventory (NFI) plots were corrected to consider small trees. Temporal differences between NFI plots and imagery acquisition were addressed by considering biomass changes over time. To overcome issues related to saturation of radar backscatter, we incorporate radar texture metrics and climate data to improve the accuracy of AGB maps. Finally, we increased the number of sampling plots using biomass estimates derived from LiDAR data to assess if increasing sample size could improve the accuracy of AGB estimates. Results: Correcting NFI plot data for both small trees and temporal differences between field and remotely sensed measurements reduced the relative error of biomass estimates by 12.2%. Using a machine learning algorithm, Random Forest, with corrected field plot data, backscatter and surface texture from the L-band synthetic aperture radar (PALSAR) installed on the on the Advanced Land Observing Satellite-1 (ALOS), and climatic water deficit data improved the accuracy of the maps obtained in this study as compared to previous studies (R 2 = 0.44 vs R 2 = 0.32). However, using sample plots derived from LiDAR data to increase sample size did not improve accuracy of AGB maps (R 2 = 0.26). Conclusions: This study reveals that the suggested approach has the potential to improve AGB maps of tropical dry forests and shows predictors of AGB that should be considered in future studies. Our results highlight the importance of using ecological knowledge to correct errors associated with both the plot-level biomass estimates and the mismatch between field and remotely sensed data.

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“…Here, we tested the combined influence of only sample size and modeling algorithms, nonetheless, the influence of additional features, such as plot size, LiDAR pulse density, GPS location errors, etc., would also be interesting and helpful to the research community [52,68,69]. Another thing to keep in mind is the cost associated with LiDAR, which makes this approach economically feasible for only large study areas [10,31,70].…”

Section: Discussionmentioning

confidence: 99%

Combined Impact of Sample Size and Modeling Approaches for Predicting Stem Volume in Eucalyptus spp. Forest Plantations Using Field and LiDAR Data

Silva

Mohan

et al. 2020

Remote Sensing

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Light Detection and Ranging (LiDAR) remote sensing has been established as one of the most promising tools for large-scale forest monitoring and mapping. Continuous advances in computational techniques, such as machine learning algorithms, have been increasingly improving our capability to model forest attributes accurately and at high spatial and temporal resolution. While there have been previous studies exploring the use of LiDAR and machine learning algorithms for forest inventory modeling, as yet, no studies have demonstrated the combined impact of sample size and different modeling techniques for predicting and mapping stem total volume in industrial Eucalyptus spp. tree plantations. This study aimed to compare the combined effects of parametric and nonparametric modeling methods for estimating volume in Eucalyptus spp. tree plantation using airborne LiDAR data while varying the reference data (sample size). The modeling techniques were compared in terms of root mean square error (RMSE), bias, and R2 with 500 simulations. The best performance was verified for the ordinary least-squares (OLS) method, which was able to provide comparable results to the traditional forest inventory approaches using only 40% (n = 63; ~0.04 plots/ha) of the total field plots, followed by the random forest (RF) algorithm with identical sample size values. This study provides solutions for increasing the industry efficiency in monitoring and managing forest plantation stem volume for the paper and pulp supply chain.

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Effects of Sample Plot Size and GPS Location Errors on Aboveground Biomass Estimates from LiDAR in Tropical Dry Forests

Cited by 36 publications

References 42 publications

Modelling and Predicting the Growing Stock Volume in Small-Scale Plantation Forests of Tanzania Using Multi-Sensor Image Synergy

Modelling and Predicting the Growing Stock Volume in Small-Scale Plantation Forests of Tanzania Using Multi-Sensor Image Synergy

Improving aboveground biomass maps of tropical dry forests by integrating LiDAR, ALOS PALSAR, climate and field data

Combined Impact of Sample Size and Modeling Approaches for Predicting Stem Volume in Eucalyptus spp. Forest Plantations Using Field and LiDAR Data

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