Modeling and Spatialization of Biomass and Carbon Stock Using LiDAR Metrics in Tropical Dry Forest, Brazil
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Estimation of the Biomass/Carbon Stock in the Field
2.3. Estimation of the Biomass/Carbon Stock by LiDAR Data
2.4. Modeling the Biomass/Carbon Stock Using LiDAR Data
2.5. Evaluation of Models
2.6. Generation of Stock Maps Using LiDAR Data
3. Results
Preliminary Results
4. Discussion
5. Conclusions
- Using a stepwise method to reduce the metrics proved to be more effective for better adjustment of the models;
- The LiDAR metrics which were most present in the models were: Elev.minimum, Elev.maximum, Elev.mean and Elev.P01, with the last two being found in all models;
- The most preserved area had less carbon stock than the most degraded area, this occurrence can be explained in the inventories carried out in the area that presented the largest number of bole measured at ground level (DGL: diameter at ground level) in the area degraded than in the preserved area, strongly influencing the estimated carbon values in the areas;
- The pulse density, even though it is not a variable within the models, indirectly influenced the accuracy of the models, therefore, it is recommended that data be tested with a higher pulse density in future works.
- The model is limited to the TAGB estimate in the study area and may not be suitable for application in other forests. This is due to differences in forest structure, species composition, vegetation vigor and impacts of atmospheric conditions and soil moisture and precipitation.
- New studies are recommended to assess the transferability of the model to other protected forests with same forest structure and species composition. Other studies that will test the ability of non-parametric algorithms (such as random forest) to develop TAGB estimation models for the study area, in comparison with linear regression analysis, are also recommended.
- Our preliminary results provided important information on the spatial distribution of TAGB and TAGC in the study area, which can be used to manage the reserve for optimal carbon sequestration.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Crowther, T.W.; Glick, H.B.; Covey, K.R.; Bettigole, C.; Maynard, D.S.; Thomas, S.M.; Smith, J.R.; Hintler, G.; Duguid, M.C.; Amatulli, G.; et al. Mapping Tree Density at a Global Scale. Nature 2015, 525, 201–205. [Google Scholar] [CrossRef]
- Achard, F.; House, J.I. Reporting Carbon Losses from Tropical Deforestation with Pan-Tropical Biomass Maps. Environ. Res. Lett. 2015, 10, 101002. [Google Scholar] [CrossRef]
- Brahma, B.; Nath, A.J.; Sileshi, G.W.; Das, A.K. Estimating Biomass Stocks and Potential Loss of Biomass Carbon through Clear-Felling of Rubber Plantations. Biomass Bioenergy 2018, 115, 88–96. [Google Scholar] [CrossRef]
- Coomes, D.A.; Dalponte, M.; Jucker, T.; Asner, G.P.; Banin, L.F.; Burslem, D.F.R.P.; Lewis, S.L.; Nilus, R.; Phillips, O.L.; Phua, M.-H.; et al. Area-Based vs Tree-Centric Approaches to Mapping Forest Carbon in Southeast Asian Forests from Airborne Laser Scanning Data. Remote Sens. Environ. 2017, 194, 77–88. [Google Scholar] [CrossRef] [Green Version]
- Althoff, T.D.; Menezes, R.S.C.; Pinto, A.D.S.; Pareyn, F.G.C.; de Carvalho, A.L.; Martins, J.C.R.; de Carvalho, E.X.; da Silva, A.S.A.; Dutra, E.D.; Sampaio, E.V.D.S.B. Adaptation of the Century Model to Simulate C and N Dynamics of Caatinga Dry Forest before and after Deforestation. Agric. Ecosyst. Environ. 2018, 254, 26–34. [Google Scholar] [CrossRef]
- Sampaio, E.V.; Silva, G.C. Biomass Equations for Brazilian Semiarid Caatinga Plants. Acta Bot. Bras. 2005, 19, 935–943. [Google Scholar] [CrossRef]
- Duncanson, L.; Huang, W.; Johnson, K.; Swatantran, A.; McRoberts, R.E.; Dubayah, R. Implications of Allometric Model Selection for County-Level Biomass Mapping. Carbon Balance Manag. 2017, 12, 1–11. [Google Scholar] [CrossRef]
- Somogyi, Z.; Cienciala, E.; Mäkipää, R.; Muukkonen, P.; Lehtonen, A.; Weiss, P. Indirect Methods of Large-Scale Forest Biomass Estimation. Eur. J. For. Res. 2007, 126, 197–207. [Google Scholar] [CrossRef]
- Silva, C.; Hudak, A.; Vierling, L.; Klauberg, C.; Garcia, M.; Ferraz, A.; Keller, M.; Eitel, J.; Saatchi, S. Impacts of Airborne Lidar Pulse Density on Estimating Biomass Stocks and Changes in a Selectively Logged Tropical Forest. Remote Sens. 2017, 9, 1068. [Google Scholar] [CrossRef] [Green Version]
- Silva, C.A.; Klauberg, C.; Hudak, A.T.; Vierling, L.A.; Liesenberg, V.; Carvalho, S.P.C.E.; Rodriguez, L.C.E. A Principal Component Approach for Predicting the Stem Volume in Eucalyptus Plantations in Brazil Using Airborne LiDAR Data. Forestry 2016, 89, 422–433. [Google Scholar] [CrossRef]
- Li, A.; Dhakal, S.; Glenn, N.; Spaete, L.; Shinneman, D.; Pilliod, D.; Arkle, R.; McIlroy, S. Lidar Aboveground Vegetation Biomass Estimates in Shrublands: Prediction, Uncertainties and Application to Coarser Scales. Remote Sens. 2017, 9, 903. [Google Scholar] [CrossRef] [Green Version]
- Avitabile, V.; Herold, M.; Henry, M.; Schmullius, C. Mapping Biomass with Remote Sensing: A Comparison of Methods for the Case Study of Uganda. Carbon Balance Manag. 2011, 6, 7. [Google Scholar] [CrossRef] [Green Version]
- Saatchi, S.; Malhi, Y.; Zutta, B.; Buermann, W.; Anderson, L.O.; Araujo, A.M.; Phillips, O.L.; Peacock, J.; ter Steege, H.; Lopez Gonzalez, G.; et al. Mapping Landscape Scale Variations of Forest Structure, Biomass, and Productivity in Amazonia. Biogeosci. Discuss. 2009, 6, 5461–5505. [Google Scholar] [CrossRef] [Green Version]
- Becknell, J.M.; Keller, M.; Piotto, D.; Longo, M.; dos-Santos, M.N.; Scaranello, M.A.; de Oliveira Cavalcante, R.B.; Porder, S. Landscape-Scale Lidar Analysis of Aboveground Biomass Distribution in Secondary Brazilian Atlantic Forest. Biotropica 2018, 50, 520–530. [Google Scholar] [CrossRef]
- Martinuzzi, S.; Gould, W.A.; Vierling, L.A.; Hudak, A.T.; Nelson, R.F.; Evans, J.S. Quantifying Tropical Dry Forest Type and Succession: Substantial Improvement with LiDAR. Biotropica 2013, 45, 135–146. [Google Scholar] [CrossRef] [Green Version]
- Nelson, R.; Margolis, H.; Montesano, P.; Sun, G.; Cook, B.; Corp, L.; Andersen, H.-E.; deJong, B.; Pellat, F.P.; Fickel, T.; et al. Lidar-Based Estimates of Aboveground Biomass in the Continental US and Mexico Using Ground, Airborne, and Satellite Observations. Remote Sens. Environ. 2017, 188, 127–140. [Google Scholar] [CrossRef] [Green Version]
- Mohebalian, P.M.; Aguilar, F.X. Beneath the Canopy: Tropical Forests Enrolled in Conservation Payments Reveal Evidence of Less Degradation. Ecol. Econ. 2018, 143, 64–73. [Google Scholar] [CrossRef]
- Oliveira, C.P.; Ferreira, R.L.C.; Silva, J.A.A.; Lima, R.B.; Silva, E.A.; Júnior, F.T.A.; Silva, A.F.; Lucena, J.; Santos, N.A.T.; Lopes, I.J.C.; et al. Prediction of Biomass in Dry Tropical Forests: An Approach on the Importance of Total Height in the Development of Local and Pan-tropical Models. J. Sustain. For. 2021, 31, 1–16. [Google Scholar] [CrossRef]
- Instituto Brasileiro de Geografia e Estatística-IBGE. Coordenação de Recursos Naturais e Estudos Ambientais. 2012; Manual Técnico da Vegetação Brasileira. Available online: https://biblioteca.ibge.gov.br/visualizacao/livros/liv63011.pdf (accessed on 22 March 2017).
- Empresa Brasileira de Pesquisa Agropecuária-Embrapa. Zoneamento Agroecológico do Estado de Pernambuco–ZAPE. 2007. Available online: http://www.uep.cnps.embrapa.br/zape (accessed on 15 April 2017).
- Sistema de Monitoramento Agrometeorológico-Agritempo. Estações Meteorológicas para o Estado de PE. Available online: https://www.agritempo.gov.br/agritempo/jsp/Estacao/index.jsp?siglaUF=PE&lang=pt_br (accessed on 10 April 2017).
- Lana, M.D.; Ferreira, R.L.C.; Silva, J.A.A.; Duda, G.P.; Cespedes, G.H.G. Carbon Content in Shrub-tree Species of the Caatinga. Floresta e Ambiente 2019, 26, e20170617. [Google Scholar] [CrossRef]
- Brown, S.; Lugo, A.E. The storage and production of organicmatter in tropical forests and their role in the global carboncycle. Biotropica 1982, 14, 161–187. [Google Scholar] [CrossRef]
- Roy, J.; Saugier, B.; Mooney, H.A. Terrestrial Global Productivity, 1st ed.; Academic Press: San Diego, CA, USA, 2001; pp. 559–573. [Google Scholar]
- Malhi, Y.; Baker, T.R.; Phillips, O.L.; Almeida, S.; Alvarez, E.; Arroyo, L.; Chave, J.; Czimczik, C.I.; Di Fiore, A.; Higuchi, N.; et al. The above-ground coarse wood productivity of 104 Neotropical forest plots. Glob. Chang. Biol. 2004, 10, 563–591. [Google Scholar] [CrossRef] [Green Version]
- Elias, M.; Potvin, C. Assessing inter- and intra-specific variation in trunk carbon concentration for 32 neotropical tree species. Can. J. For. Res. 2003, 33, 1039–1045. [Google Scholar] [CrossRef]
- Jensen, J.R. Introductory Digital Image Processing: A Remote Sensing Perspective, 4th ed.; Brigham Young University: Provo, Utah; Prentice Hall: Upper Saddle River, NJ, USA, 2016; pp. 1–400. [Google Scholar]
- Crookston, N.L.; Finley, A. Yaimpute: An R package for k-NN imputation. J. Stat. Softw. 2008, 23, 1–16. [Google Scholar] [CrossRef] [Green Version]
- Anderson, K.E.; Glenn, N.F.; Spaete, L.P.; Shinneman, D.J.; Pilliod, D.S.; Arkle, R.S.; McIlroy, S.K.; Derryberry, D.R. Estimating Vegetation Biomass and Cover across Large Plots in Shrub and Grass Dominated Drylands Using Terrestrial Lidar and Machine Learning. Ecol. Indic. 2018, 84, 793–802. [Google Scholar] [CrossRef]
- Hernández-Stefanoni, J.L.; Johnson, K.D.; Cook, B.D.; Dupuy, J.M.; Birdsey, R.; Peduzzi, A.; Tun-Dzul, F. Estimating Species Richness and Biomass of Tropical Dry Forests Using LIDAR during Leaf-on and Leaf-off Canopy Conditions. Appl. Veg. Sci. 2015, 18, 724–732. [Google Scholar] [CrossRef]
- Chen, Q.; Lu, D.; Keller, M.; dos-Santos, M.; Bolfe, E.; Feng, Y.; Wang, C. Modeling and Mapping Agroforestry Aboveground Biomass in the Brazilian Amazon Using Airborne Lidar Data. Remote Sens. 2015, 8, 21. [Google Scholar] [CrossRef] [Green Version]
- Kachamba, D.; Ørka, H.; Næsset, E.; Eid, T.; Gobakken, T. Influence of Plot Size on Efficiency of Biomass Estimates in Inventories of Dry Tropical Forests Assisted by Photogrammetric Data from an Unmanned Aircraft System. Remote Sens. 2017, 9, 610. [Google Scholar] [CrossRef] [Green Version]
- Naveenkumar, J.; Arunkumar, K.S.; Sundarapandian, S. Biomass and Carbon Stocks of a Tropical Dry Forest of the Javadi Hills, Eastern Ghats, India. Carbon Manag. 2017, 8, 351–361. [Google Scholar] [CrossRef]
- Chave, J.; Réjou-Méchain, M.; Búrquez, A.; Chidumayo, E.; Colgan, M.S.; Delitti, W.B.C.; Duque, A.; Eid, T.; Fearnside, P.M.; Goodman, R.C.; et al. Improved Allometric Models to Estimate the Aboveground Biomass of Tropical Trees. Glob. Chang. Biol. 2014, 20, 3177–3190. [Google Scholar] [CrossRef] [PubMed]
- Chave, J.; Andalo, C.; Brown, S.; Cairns, M.A.; Chambers, J.Q.; Eamus, D.; Fölster, H.; Fromard, F.; Higuchi, N.; Kira, T.; et al. Tree Allometry and Improved Estimation of Carbon Stocks and Balance in Tropical Forests. Oecologia 2005, 145, 87–99. [Google Scholar] [CrossRef] [PubMed]
- Silva, C.A.; Klauberg, C.; Hudak, A.T. Mapeamento de Estoques de Carbono Acima Do Solo Utilizando Dados LiDAR Em Plantações de Eucalyptus Spp No Estado de São Paulo, Brasil. Sci. For. 2014, 42, 14. [Google Scholar]
- Figueiredo, E.O.; d’Oliveira, M.V.N.; Braz, E.M.; de Almeida Papa, D.; Fearnside, P.M. LIDAR-Based Estimation of Bole Biomass for Precision Management of an Amazonian Forest: Comparisons of Ground-Based and Remotely Sensed Estimates. Remote Sens. Environ. 2016, 187, 281–293. [Google Scholar] [CrossRef] [Green Version]
- Leitold, V.; Keller, M.; Morton, D.C.; Cook, B.D.; Shimabukuro, Y.E. Airborne Lidar-Based Estimates of Tropical Forest Structure in Complex Terrain: Opportunities and Trade-Offs for REDD+. Carbon Balance Manag. 2015, 10, 1–12. [Google Scholar] [CrossRef] [PubMed]
- Magnusson, M.; Fransson, J.E.S.; Holmgren, J. Effects on Estimation Accuracy of Forest Variables Using Different Pulse Density of Laser Data. For. Sci. 2007, 53, 619–626. [Google Scholar] [CrossRef]
- Thomas, V.; Treitz, P.; McCaughey, J.H.; Morrison, I. Mapping Stand-Level Forest Biophysical Variables for a Mixedwood Boreal Forest Using Lidar: An Examination of Scanning Density. Can. J. For. Res. 2006, 36, 34–47. [Google Scholar] [CrossRef]
- Takahashi, T.; Awaya, Y.; Hirata, Y.; Furuya, N.; Sakai, T.; Sakai, A. Stand Volume Estimation by Combining Low Laser-Sampling Density LiDAR Data with QuickBird Panchromatic Imagery in Closed-Canopy Japanese Cedar (Cryptomeria Japonica) Plantations. Int. J. Remote. Sens. 2010, 31, 1281–1301. [Google Scholar] [CrossRef]
Areas | DBH (cm) | Ht (m) | AGB (Mg·ha−1) | AGC (Mg·ha−1) | N° Plots | N° Ind. | N° Stem | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Min | Max | Min | Max | Min | Max | Min | Max | ||||||||||||
Transposição | 1.91 | 29.92 | 3.85 | 2.37 | 1.3 | 9.0 | 3.97 | 0.95 | 0.162 | 206.49 | 9.327 | 5.442 | 0.077 | 99.11 | 4.66 | 27 | 40 | 1728 | 4576 |
Correntão | 2.06 | 52.20 | 11.44 | 5.4 | 1.7 | 7.5 | 3.64 | 0.92 | 0.330 | 554.81 | 24.94 | 24.95 | 0.1584 | 266.3 | 12.47 | 12.47 | 40 | 996 | 2903 |
Attribute | Values |
---|---|
LiDAR system | ALS-50 Leica |
Flight altitude (m) | 3.068 |
Data acquisition | 10 August 2014 |
Opening angle (°) | 34.5 |
Scanner frequency (Kz; kHz) | 36.8 kHz |
Pulse density (pulses·m2) | 0.5 |
Datum | Sirgas 2000 |
Category | LiDAR Metrics | Symbology |
---|---|---|
Height | Maximum height | Elev.maximum |
Minimum height | Elev.minimum | |
Mean height | Elev.mean | |
Modal height | Elev.mode | |
Standard deviation of heights | Elev.stddev | |
Height variation coefficient | Elev.CV | |
Height asymmetry | Elev.skewness | |
Kurtosis of height | Elev.kurtosis | |
Median of absolute deviations from the general mean | Elev.MAD.median | |
1st percentile of height | Elev.P01 | |
5th percentile of height | Elev.P05 | |
10th percentile of height | Elev.P10 | |
20th percentile of height | Elev.P20 | |
25th percentile of height | Elev.P25 | |
30th percentile of height | Elev.P30 | |
40th percentile of height | Elev.P40 | |
50th percentile of height | Elev.P50 | |
60th percentile of height | Elev.P60 | |
70th percentile of height | Elev.P70 | |
75th percentile of height | Elev.P75 | |
80th percentile of height | Elev.P80 | |
90th percentile of height | Elev.P90 | |
95th percentile of height | Elev.P95 | |
99th percentile of height | Elev.P99 | |
Canopy density | Canopy relief ratio 1 | Canopy.relief.ratio |
Percentage of all returns above 1.30 | Percentage.all.returns.above.1.30 |
Principal Component | Components (Eigenvectors) | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Elev. Minimum | Elev. Maximum | Elev. Mean | Elev. Mode | Elev. Stddev | Elev. CV | Elev. Skewness | Elev. Kurtosis | Elev. MAD. Median | Elev. P01 | Auto Valores | Var (%) | |
PC1 | 0.285 | 0.678 | 0.996 | 0.759 | 0.719 | 0.333 | −0.231 | −0.192 | 0.689 | 0.420 | 12.463 | 51.929 |
PC2 | −0.395 | 0.621 | 0.070 | −0.020 | 0.675 | 0.902 | 0.738 | 0.151 | 0.590 | −0.508 | 5.841 | 76.266 |
PC3 | 0.622 | 0.281 | −0.009 | −0.070 | −0.027 | 0.016 | 0.597 | 0.672 | −0.203 | 0.602 | 2.255 | 85.663 |
Principal Component | Components (Eigenvectors) | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Elev. Minimum | Elev. Maximum | Elev. Mean | Elev. Mode | Elev. Stddev | Elev. CV | Elev. Skewness | Elev. Kurtosis | Elev. MAD. Median | Elev. P01 | Auto Valores | Var (%) | |
PC1 | 0.019 | 0.825 | 0.999 | 0.709 | 0.708 | 0.158 | −0.192 | 0.170 | 0.557 | 0.305 | 13.233 | 55.136 |
PC2 | −0.130 | 0.423 | 0.018 | −0.302 | 0.675 | 0.943 | 0.708 | −0.179 | 0.633 | −0.234 | 4.119 | 72.299 |
PC3 | 0.765 | −0.283 | 0.016 | −0.106 | 0.074 | 0.110 | −0.103 | −0.805 | 0.359 | 0.636 | 2.524 | 82.815 |
Area | Biomass Predictive Models | R2ajd | RMSE | |
---|---|---|---|---|
Transposição | Multiple regression | TAGB = −86.809 − 33.295 (Elev.minimum) + 5.446 (Elev.maximum) + 195.226 (Elev.mean) + 3.774 (Elev.mode) − 92.658 (Elev.stddev) + 206.851 (Elev.CV) + 13.627 (Elev.skewness) − 1.734 (Elev.kurtosis) + 24.360 (Elev.MAD.median) + 29.676 (Elev.P01) − 25.707 (Elev.P10) − 64.704 (Elev.P20) + 49.118 (Elev.P25) − 26.958 (Elev.P30) − 44.133 (Elev.P50) − 21.226 (Elev.P60) −11.419 (Elev.P75) 2.295 (Elev.P80) − 27.855 (Elev.P90) − 15.740 (Elev.P95) + 98.142 (Canopy.relief.ratio) + 0.024 (Percentage.all.returns.above.1.30) | 0.1924 | 3.18 |
Stepwise regression | TAGB = −21.08 − 35.756 (Elev.minimum) + 119.784 (Elev.mean) + 4.582 (Elev.mode) − 63.752 (Elev.stddev) + 101.103 (Elev.CV) + 27.823 (Elev.P01) − 17.626 (Elev.P10) − 29.152 (Elev.P20) − 44.745 (Elev.P50) − 18.032 (Elev.P90) | 0.4239 | 3.51 | |
PCA regression | TAGB = 9.145 + 0.607 (Dim.1) + 1 (Dim.3) | 0.1723 | 5.99 | |
Correntão | Multiple regression | TAGB = 341.760 + 0.932 (Elev.minimum) + 123.520 (Elev.maximum) − 298.028 (Elev.mean) + 1.734 (Elev.mode) − 14.288 (Elev.stddev) + 712.426 (Elev.CV) − 8.027 (Elev.skewness)− 36.267 (Elev.kurtosis) + 103.257 (Elev.MAD.median) +114.736 (Elev.P01) + 79.665 (Elev.P10) + 54.843 (Elev.P20) + 39.873 (Elev.P25) + 90.032 (Elev.P30) + 20.564 (Elev.P50) + 5.063 (Elev.P60) − 129.573 (Elev.P75) − 33.779 (Elev.P80) + 57.286 (Elev.P90) 53.403 (Elev.P95) + 519.378 (Canopy.relief.ratio) − 0.103 (Percentage.all.returns.above.1.30) | 0.4239 | 13.61 |
Stepwise regression | TAGB = −269.86 + 145.44 (Elev. maximum) − 402.19 (Elev.mean) + 440.13 (Elev.CV) − 53.26 (Elev.kurtosis) + 88.49 (Elev.P01) + 93.09 (Elev.P10) + 165.73 (Elev.P30) − 67.6 (Elev.P75) + 673.35 (Canopy.relief.ratio) | 0.533 | 14.76 | |
PCA regression | TAGB = 30.270 – 6.465 (Dim.3) | 0.09621 | 28.45 |
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Oliveira, C.P.d.; Ferreira, R.L.C.; da Silva, J.A.A.; Lima, R.B.d.; Silva, E.A.; Silva, A.F.d.; Lucena, J.D.S.d.; dos Santos, N.A.T.; Lopes, I.J.C.; Pessoa, M.M.d.L.; et al. Modeling and Spatialization of Biomass and Carbon Stock Using LiDAR Metrics in Tropical Dry Forest, Brazil. Forests 2021, 12, 473. https://doi.org/10.3390/f12040473
Oliveira CPd, Ferreira RLC, da Silva JAA, Lima RBd, Silva EA, Silva AFd, Lucena JDSd, dos Santos NAT, Lopes IJC, Pessoa MMdL, et al. Modeling and Spatialization of Biomass and Carbon Stock Using LiDAR Metrics in Tropical Dry Forest, Brazil. Forests. 2021; 12(4):473. https://doi.org/10.3390/f12040473
Chicago/Turabian StyleOliveira, Cinthia Pereira de, Rinaldo Luiz Caraciolo Ferreira, José Antônio Aleixo da Silva, Robson Borges de Lima, Emanuel Araújo Silva, Anderson Francisco da Silva, Josias Divino Silva de Lucena, Nattan Adler Tavares dos Santos, Iran Jorge Corrêa Lopes, Mayara Maria de Lima Pessoa, and et al. 2021. "Modeling and Spatialization of Biomass and Carbon Stock Using LiDAR Metrics in Tropical Dry Forest, Brazil" Forests 12, no. 4: 473. https://doi.org/10.3390/f12040473
APA StyleOliveira, C. P. d., Ferreira, R. L. C., da Silva, J. A. A., Lima, R. B. d., Silva, E. A., Silva, A. F. d., Lucena, J. D. S. d., dos Santos, N. A. T., Lopes, I. J. C., Pessoa, M. M. d. L., & Melo, C. L. S. -M. S. d. (2021). Modeling and Spatialization of Biomass and Carbon Stock Using LiDAR Metrics in Tropical Dry Forest, Brazil. Forests, 12(4), 473. https://doi.org/10.3390/f12040473