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iForest - Biogeosciences and Forestry

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Estimation of above-ground biomass using machine learning approaches with InSAR and LiDAR data in tropical peat swamp forest of Brunei Darussalam

Elaheh Zadbagher (1), Aycan Murat Marangoz (1)   , Kazimierz Becek (2)

iForest - Biogeosciences and Forestry, Volume 17, Issue 3, Pages 172-179 (2024)
doi: https://doi.org/10.3832/ifor4434-017
Published: Jun 17, 2024 - Copyright © 2024 SISEF

Research Articles


Forest above-ground biomass (AGB) is one of the critical measures of forest resources. Therefore, it is crucial to identify a reliable method to estimate the AGB, especially in the tropics, where forest ecosystems are exposed to several depleting factors, including deforestation, climate change and replacing natural forests with palm oil tree plantations. We investigated the digital elevation data over the forest and uses an artificial intelligence-based approach to develop a method for quick and cost-effective assessment of the AGB. The study was conducted in the tropical peatland rainforest of Brunei Darussalam. The Shuttle Radar Topography Mission (SRTM) elevation data product and Light Detection and Ranging (LiDAR) digital elevation data were used. A linear regression (LR) model and three different machine learning (ML) algorithms, i.e., Random Forest (RF), Artificial Neural Network (ANN) and Support Vector Machines (SVM), were tested and compared. As model inputs, the SRTM elevation and distance from the peat dome’s center, a feature of a peatland swamp forest, were used. ML methods were trained on the samples taken from the LiDAR elevations. The validation results showed that the SVM was the best method to predict AGB in the study area with R2 = 0.70, RMSE = 83.65 Mg ha-1, and MAE = 74.43 Mg ha-1, which in relative terms corresponds to approximately 6% of the AGB of the forest of interests. This study demonstrated the potential of ML algorithms in AGB estimation based on canopy height derived from the InSAR-based DEM in tropical forests.

  Keywords


Above-Ground Biomass, Machine Learning, Tropical Forest, InSAR, Badas Peatland Forest

Authors’ address

(1)
Elaheh Zadbagher
Aycan Murat Marangoz 0000-0003-4409-6000
Geomatics Engineering Department, Zonguldak Bulent Ecevit University, Zonguldak (Turkey)
(2)
Kazimierz Becek 0000-0003-1532-9725
Faculty of Geoengineering, Mining, and Geology, Wroclaw University of Science and Technology, Wroclaw (Poland)

Corresponding author

 
Aycan Murat Marangoz
aycanmarangoz@hotmail.com

Citation

Zadbagher E, Marangoz AM, Becek K (2024). Estimation of above-ground biomass using machine learning approaches with InSAR and LiDAR data in tropical peat swamp forest of Brunei Darussalam. iForest 17: 172-179. - doi: 10.3832/ifor4434-017

Academic Editor

Carlotta Ferrara

Paper history

Received: Jul 22, 2023
Accepted: Mar 05, 2024

First online: Jun 17, 2024
Publication Date: Jun 30, 2024
Publication Time: 3.47 months

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List of the papers citing this article based on CrossRef Cited-by.

 
(1)
Anderson J, Marsden J (1984)
Brunei forest resources and strategic planning study, Final report: The Forest Resources of Negara Brunei Darussalam. The Government of His Majesty the Sultan and Yang Di Pertuan of Negara Brunei Darussalam, vol. 1, Brunei Darussalam.
Gscholar
(2)
Baloloy AB, Blanco AC, Candido CG, Argamosa RJL, Dumalag JBLC, Dimapilis LLC, Paringit EC (2018)
Estimation of mangrove forest above-ground biomass using multispectral bands, vegetation indices and biophysical variables derived from optical satellite imageries: rapideye, planetscope and sentinel-2. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences 4 (3): 29-36.
CrossRef | Gscholar
(3)
Becek K, Yong GYV, Sukri RS, Lai DTC (2022)
Shorea albida Sym. does not regenerate in the Badas peat swamp forest, Brunei Darussalam -An assessment using remote sensing technology. Forest Ecology and Management 504: 119816.
CrossRef | Gscholar
(4)
Brandić I, Pezo L, Bilandzija N, Peter A, Surić J, Voća N (2023)
Comparison of different machine learning models for modelling the higher heating value of biomass. Mathematics 11 (9): 2098.
CrossRef | Gscholar
(5)
Chave J, Réjou-Méchain M, Búrquez A, Chidumayo E, Colgan MS, Delitti WB, Duque A, Eid T, Fearnside PM, Goodman RC (2014)
Improved allometric models to estimate the above-ground biomass of tropical trees. Global Change Biology 20 (10): 3177-3190.
CrossRef | Gscholar
(6)
Chen L, Ren C, Zhang B, Wang Z, Xi Y (2018)
Estimation of forest above-ground biomass by geographically weighted regression and machine learning with Sentinel imagery. Forests 9 (10): 582.
CrossRef | Gscholar
(7)
Chen L, Wang Y, Ren C, Zhang B, Wang Z (2019)
Optimal combination of predictors and algorithms for forest above-ground biomass mapping from sentinel and SRTM data. Remote Sensing 11 (4): 414.
CrossRef | Gscholar
(8)
Dai S, Zheng X, Gao L, Xu C, Zuo S, Chen Q, Wei X, Ren Y (2021)
Improving plot-level model of forest biomass: a combined approach using machine learning with spatial statistics. Forests 12(12): 1663.
CrossRef | Gscholar
(9)
Dang ATN, Nandy S, Srinet R, Luong NV, Ghosh S, Kumar AS (2019)
Forest above-ground biomass estimation using machine learning regression algorithm in Yok Don National Park, Vietnam. Ecological Informatics 50: 24-32.
CrossRef | Gscholar
(10)
Fagua JC, Jantz P, Rodriguez-Buritica S, Duncanson L, Goetz SJ (2019)
Integrating LiDAR, multispectral and SAR data to estimate and map canopy height in tropical forests. Remote Sensing 11 (22): 2697.
CrossRef | Gscholar
(11)
García-Gutiérrez J, Martínez-Alvarez F, Troncoso A, Riquelme JC (2015)
A comparison of machine learning regression techniques for LiDAR-derived estimation of forest variables. Neurocomputing 167: 24-31.
CrossRef | Gscholar
(12)
Ghosh SM, Behera M (2018)
Above-ground biomass estimation using multi-sensor data synergy and machine learning algorithms in a dense tropical forest. Applied Geography 96: 29-40.
CrossRef | Gscholar
(13)
Gleason CJ, Im J (2012)
Forest biomass estimation from airborne LiDAR data using machine learning approaches. Remote Sensing of Environment 125: 80-91.
CrossRef | Gscholar
(14)
Grohmann CH (2018)
Evaluation of TanDEM-X DEMs on selected Brazilian sites: Comparison with SRTM, ASTER GDEM and ALOS AW3D30. Remote Sensing of Environment 212: 121-133.
CrossRef | Gscholar
(15)
Guo Y, Li Z, Chen E, Yu X, He Q (2017)
Application of RF-KNN optimal technology for the estimation of forest above-ground biomass using multisource remote sensing data. DEStech Transactions on Computer Science and Engineering, pp. 67-76.
Online | Gscholar
(16)
Hunter M, Keller M, Victoria D, Morton D (2013)
Tree height and tropical forest biomass estimation. Biogeosciences 10 (12): 8385-8399.
CrossRef | Gscholar
(17)
Hu Z, Peng J, Hou Y, Shan J (2017)
Evaluation of recently released open global digital elevation models of Hubei, China. Remote Sensing 9 (3): 262.
CrossRef | Gscholar
(18)
Jachowski NR, Quak MS, Friess DA, Duangnamon D, Webb EL, Ziegler AD (2013)
Mangrove biomass estimation in Southwest Thailand using machine learning. Applied Geography 45: 311-321.
CrossRef | Gscholar
(19)
Jiang X, Li G, Lu D, Chen E, Wei X (2020)
Stratification-based forest aboveground biomass estimation in a subtropical region using airborne lidar data. Remote Sensing 12 (7): 1101.
CrossRef | Gscholar
(20)
Kappas M (2020)
Estimating the above-ground biomass of an evergreen broadleaf forest in Xuan Lien Nature Reserve, Thanh Hoa, Vietnam, using SPOT-6 data and the random forest algorithm. International Journal of Forestry Research 2020: 4216160.
CrossRef | Gscholar
(21)
Karakoc A, Karabulut M (2019)
Ratio-based vegetation indices for biomass estimation depending on grassland characteristics. Turkish Journal of Botany 43 (5): 619-633.
CrossRef | Gscholar
(22)
Lee J, Im J, Kim K, Quackenbush LJ (2018)
Machine learning approaches for estimating forest stand height using plot-based observations and airborne LiDAR data. Forests 9 (5): 268.
CrossRef | Gscholar
(23)
Lee WJ, Lee CW (2018)
Forest canopy height estimation using multiplatform remote sensing dataset. Journal of Sensors 2018: 1593129.
CrossRef | Gscholar
(24)
Li M, Im J, Quackenbush LJ, Liu T (2014)
Forest biomass and carbon stock quantification using airborne LiDAR data: a case study over Huntington Wildlife Forest in the Adirondack Park. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 (7): 3143-3156.
CrossRef | Gscholar
(25)
Lu D, Chen Q, Wang G, Liu L, Li G, Moran E (2016)
A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems. International Journal of Digital Earth 9 (1): 63-105.
CrossRef | Gscholar
(26)
López-Serrano P, Cárdenas Dominguez J, Corral-Rivas J, Jiménez E, López-Sánchez C, Vega-Nieva D (2020)
Modeling of above-ground biomass with Landsat 8 OLI and machine learning in temperate forests. Forests 11 (1): 11.
CrossRef | Gscholar
(27)
Malhi RKM, Anand A, Srivastava PK, Chaudhary SK, Pandey MK, Behera MD, Kumar A, Singh P, Kiran GS (2022)
Synergistic evaluation of Sentinel 1 and 2 for biomass estimation in a tropical forest of India. Advances in Space Research 69 (4): 1752-1767.
CrossRef | Gscholar
(28)
Mangla R, Kumar S, Nandy S (2016)
Random forest regression modeling for forest above-ground biomass estimation using RISAT-1 PolSAR and terrestrial LiDAR data. In: Proceedings of the Conference “SPIE Asia-Pacific Remote Sensing - Lidar Remote Sensing for Environmental Monitoring XV”. New Delhi (India) 4-7 Apr 2016. International Society for Optics and Photonics, vol. 9879, pp. 98790Q.
CrossRef | Gscholar
(29)
Marchesan J, Alba E, Schuh MS, Favarin JAS, Pereira RS (2020)
Above-ground biomass estimation in a tropical forest with selective logging using Random Forest and LiDAR data. Floresta 50 (4): 1873-1882.
CrossRef | Gscholar
(30)
Montaño RANR, Sanquetta CR, Wojciechowski J, Mattar E, Corte APD, Todt E (2017)
Artificial intelligence models to estimate biomass of tropical forest trees. Polibits 56: 29-37.
Online | Gscholar
(31)
Ncibi K, Sadraoui T, Faycel M, Djenina A (2017)
A multilayer perceptron artificial neural networks based a preprocessing and hybrid optimization task for data mining and classification. International Journal of Economics, Finance and Management Sciences 5 (1): 12-21.
CrossRef | Gscholar
(32)
Pham TD, Le NN, Ha NT, Nguyen LV, Xia J, Yokoya N, To TT, Trinh HX, Kieu LQ, Takeuchi W (2020)
Estimating mangrove above-ground biomass using extreme gradient boosting decision trees algorithm with fused sentinel-2 and ALOS-2 PALSAR-2 data in Can Gio biosphere reserve, Vietnam. Remote Sensing 12(5): 777.
CrossRef | Gscholar
(33)
Potapov P, Li X, Hernandez-Serna A, Tyukavina A, Hansen MC, Kommareddy A, Pickens A, Turubanova S, Tang H, Silva CE (2021)
Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sensing of Environment 253: 112165.
CrossRef | Gscholar
(34)
Pourshamsi M, Garcia M, Lavalle M, Balzter H (2018)
A machine-learning approach to PolInSAR and LiDAR data fusion for improved tropical forest canopy height estimation using NASA AfriSAR Campaign data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11 (10): 3453-3463.
CrossRef | Gscholar
(35)
Pourshamsi M, Xia J, Yokoya N, Garcia M, Lavalle M, Pottier E, Balzter H (2021)
Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning. ISPRS Journal of Photogrammetry and Remote Sensing 172: 79-94.
CrossRef | Gscholar
(36)
Rex FE, Silva CA, Dalla Corte AP, Klauberg C, Mohan M, Cardil A, Silva Sd V, Almeida Ad DR, Garcia M, Broadbent EN (2020)
Comparison of statistical modeling approaches for estimating tropical forest above-ground biomass stock and reporting their changes in low-intensity logging areas using multi-temporal LiDAR data. Remote Sensing 12 (9): 1498.
CrossRef | Gscholar
(37)
Rocha MG, Barros FMM, Oliveira SR, Amaral LR (2019)
Biometric characteristics and canopy reflectance association for early-stage sugarcane biomass prediction. Scientia Agricola 76: 274-280.
CrossRef | Gscholar
(38)
Saatchi SS, Harris NL, Brown S, Lefsky M, Mitchard ET, Salas W, Zutta BR, Buermann W, Lewis SL, Hagen S (2011)
Benchmark map of forest carbon stocks in tropical regions across three continents. Proceedings of the National Academy of Sciences USA 108 (24): 9899-9904.
CrossRef | Gscholar
(39)
Santi E, Paloscia S, Pettinato S, Cuozzo G, Padovano A, Notarnicola C, Albinet C (2020)
Machine-learning applications for the retrieval of forest biomass from airborne P-Band SAR data. Remote Sensing 12 (5): 804.
CrossRef | Gscholar
(40)
Su H, Shen W, Wang J, Ali A, Li M (2020)
Machine learning and geostatistical approaches for estimating above-ground biomass in Chinese subtropical forests. Forest Ecosystems 7 (1): 1-20.
CrossRef | Gscholar
(41)
Vafaei S, Soosani J, Adeli K, Fadaei H, Naghavi H, Pham TD, Tien Bui D (2018)
Improving accuracy estimation of forest above-ground biomass based on incorporation of ALOS-2 PALSAR-2 and Sentinel-2A imagery and machine learning: A case study of the Hyrcanian forest area (Iran). Remote Sensing 10 (2): 172.
CrossRef | Gscholar
(42)
Were K, Bui DT, Dick OB, Singh BR (2015)
A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape. Ecological Indicators 52: 394-403.
CrossRef | Gscholar
(43)
Zhang W, Zhao L, Li Y, Shi J, Yan M, Ji Y (2022)
Forest above-ground biomass inversion using optical and SAR images based on a multi-step feature optimised inversion model. Remote Sensing 14 (7): 1608.
CrossRef | Gscholar
(44)
Zhang Y, Ma J, Liang S, Li X, Li M (2020)
An evaluation of eight machine learning regression algorithms for forest above-ground biomass estimation from multiple satellite data products. Remote Sensing 12 (24): 4015.
CrossRef | Gscholar
 

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