Abstract
Biomass in karst terrain has rarely been measured because the steep mountainous limestone terrain has limited the ability to sample woody plants. Satellite observation, especially at high spatial resolution, is an important surrogate for the quantification of the biomass of karst forests and shrublands. In this study, an artificial neural network (ANN) model was built using Pléiades satellite imagery and field biomass measurements to estimate the aboveground biomass (AGB) in the Houzhai River Watershed, which is a typical plateau karst basin in Central Guizhou Province, Southwestern China. A back-propagation ANN model was also developed. Seven vegetation indices, two spectral bands of Pléiades imagery, one geomorphological parameter, and land use/land cover were selected as model inputs. AGB was chosen as an output. The AGB estimated by the allometric functions in 78 quadrats was utilized as training data (54 quadrats, 70%), validation data (12 quadrats, 15%), and testing data (12 quadrats, 15%). Data-model comparison showed that the ANN model performed well with an absolute root mean square error of 11.85 t/ha, which was 9.88% of the average AGB. Based on the newly developed ANN model, an AGB map of the Houzhai River Watershed was produced. The average predicted AGB of the secondary evergreen and deciduous broadleaved mixed forest, which is the dominant forest type in the watershed, was 120.57 t/ha. The average AGBs of the large distributed shrubland, tussock, and farmland were 38.27, 9.76, and 11.69 t/ha, respectively. The spatial distribution pattern of the AGB estimated by the new ANN model in the karst basin was consistent with that of the field investigation. The model can be used to estimate the regional AGB of karst landscapes that are distributed widely over the Yun-Gui Plateau.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Arrogante-Funes P, Novillo CJ, Romero-Calcerrada R, et al. (2017) Relationship between MRPV Model Parameters from MISRL2 Land Surface Product and Land Covers: A Case Study within Mainland Spain. International Journal of Geo-Information 6(11): 353. https://doi.org/10.3390/ijgi6110353
Arsanjani JJ, Tayyebi A, Vaz E (2016) GlobeLand30 as an alternative fine-scale global land cover map: Challenges, possibilities, and implications for developing countries. Habitat International 55: 25–31. https://doi.org/10.1016/j.habitatint.2016.2.003
Barrachina M, Cristóbal J, Tulla AF (2015) Estimating aboveground biomass on mountain meadows and pastures through remote sensing. International Journal of Applied Earth Observations and Geoinformation 38: 184–192. https://doi.org/10.1016/j.jag.2014.12.002
Brovkina O, Novotny J, Cienciala E, et al. (2017) Mapping forest aboveground biomass using airborne hyperspectral and LiDAR data in the mountainous conditions of Central Europe. Ecological Engineering 100: 219–230. https://doi.org/10.1016/j.ecoleng.2016.12.004
Caldwell IM, Maclaren VW, Chen JM, et al. (2007) An integrated assessment model of carbon sequestration benefits: A case study of Liping county, China. Journal of Environmental Management 85(3): 757–773. https://doi.org/10.1016/j.jenvman.2006.08.020
Clark ML, Roberts DA, Ewel JJ, et al. (2011) Estimation of tropical rain forest aboveground biomass with small-footprint lidar and hyperspectral sensors. Remote Sensing of Environment 115(11): 2931–2942. https://doi.org/10.1016/j.rse.2010.08.029
Clerici N, Rubiano K, Abd-Elrahman A, et al. (2016) Estimating aboveground biomass and carbon stocks in Periurban Andean secondary forests using very high resolution imagery. Forests 7(7): 138. https://doi.org/10.3390/f7070138
Chopping M, Moisen GG, Su L, et al. (2009) Large area mapping of southwestern forest crown cover, canopy height, and biomass using the NASA Multiangle Imaging Spectro-Radiometer. Remote Sensing of Environment 112(5): 2051–2063. https://doi.org/10.1016/j.rse.2007.07.024
Chopping M, North M, Chen JQ, et al. (2012) Forest canopy cover and height from MISR in topographically complex Southwestern US landscapes assessed with high quality reference data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5(1): 44–58. https://doi.org/10.1109/JSTARS.2012.2184270
Deng SQ, Katoh M, Guan QW, et al. (2014) Estimating forest aboveground biomass by combining ALOS PALSAR and WorldView-2 data: A case study at Purple Mountain National Park, Nanjing, China. Remote Sensing 6(9): 7878–7910. https://doi.org/10.3390/rs6097878
Drake JB, Knox RG, Dubayah RO, et al. (2003) Above-ground biomass estimation in closed canopy Neotropical forests using lidar remote sensing: factors affecting the generality of relationships. Global Ecology and Biogeography 12(2): 147–159. https://doi.org/10.1046/j.1466-822X.2003.00010.x
Du YX, Pan GX, Li LQ, et al. (2010) Partitioning of vegetation biomass, nutrient storage and cycling of degraded ecosystems from moutainous karst region, central Guizhou, China. Acta Ecologica Sinica 30(23): 6338–6347. (In Chinese)
Eckert, S (2012) Improved forest biomass and carbon estimations using texture measures from WorldView-2 satellite data. Remote Sensing 4(4): 810–829. https://doi.org/10.3390/rs4040810
Fan WJ, Rong L, Zhang ZQ (2015) Carbon distribution of main vegetation types of Mengzhai watershed in south China karst canyon area. Journal of Mountain Science 33(4): 457–464. (In Chinese) https://doi.org/10.16089/j.cnki.1008-2786.000057
Fassnacht FE, Hartig F, Latifi H, et al. (2014) Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass. Remote Sensing of Environment 154: 102–114. https://doi.org/10.1016/j.rse.2014.07.028
Gao HJ, Han HQ, Zhang CQ, et al. (2016) Eeffects of land use change on carbon storage in Wujiang river of Guizhou province from 2000 to 2010. Journal of Sichuan Agricultural University 34(1): 48–53. (In Chinese) https://doi.org/10.16036/j.issn.1000-2650.2016.01.009
Günlü A, Ercanli I, Bas¸Kent E Z, et al. (2014) Estimating aboveground biomass using Landsat TM imagery: a case study of Anatolian Crimean pine forests in Turkey. Annals of Forest Research 57(2): 289–298. https://doi.org/10.15287/afr.2014.278
Guo K, Liu CC, Qiao ZP, et al. (2013) Vegetation map of the Houzhai River Watershed in Puding County, Guizhou Province. Institute of Botany, the Chinese Academy of Sciences, Beijing, China. (In Chinese)
Hassan AM, Alrashdan A, Hayajneh MT, et al. (2009) Prediction of density, porosity and hardness in aluminum–copper-based composite materials using artificial neural network. Journal of Materials Processing Technology 209(2): 894–899. https://doi.org/10.1016/j.jmatprotec.2008.02.066
Jesúsa A, Emilio C, Alicia PO (2009) Aboveground biomass assessment in Colombia: A remote sensing approach. Forest Ecology and Management 257(4): 1237–1246. https://doi.org/10.1016/j.foreco.2008.11.016
Jiang ZC, Lian YQ, Qin XQ (2014) Rocky desertification in Southwest China: Impacts, causes, and restoration. Earth-Science Reviews 132(3): 1–12. https://doi.org/10.1016/j.earscirev.2014.01.005
Kumar R, Aggarwal RK, Sharma JD, et al. (2015) Comparison of regression and artificial neural network models for estimation of global solar radiations. Renewable and Sustainable Energy Reviews 52: 1294–1299. https://doi.org/10.13140/RG.2.1.4454.5762
Laliberte AS, Fredrickson EL, Rango A (2007) Combining decision trees with hierarchical object-oriented image analysis for mapping arid rangelands. Photogrammetric Engineering and Remote Sensing 73(2): 197–207. https://doi.org/10.14358/PERS.73.2.197
Laurin GV, Chen Q, Lindsell JA, et al. (2014) Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data. ISPRS Journal of Photogrammetry and Remote Sensing 89(26): 49–58. https://doi.org/10.1016/j.isprsjprs.2014.01.001
Laurin GV, Puletti N, Chen Q, et al. (2016) Above ground biomass and tree species richness estimation with airborne lidar in tropical Ghana forests. International Journal of Applied Earth Observation and Geoinformation 52: 371–379. https://doi.org/10.1016/j.jag.2016.07.008
Liu CC, Liu YG, Guo K, et al. (2016a) Aboveground carbon stock, allocation and sequestration potential during vegetation recovery in the karst region of southwestern China: A case study at a watershed scale. Agriculture, Ecosystems and Environment 235: 91–100. https://doi.org/10.1016/j.agee.2016.10.003
Liu CC, Wei YF, Liu YG, et al. (2009) Biomass of canopy and shrub layers of karst forests in Puding, Guizhou, China. Chinese Journal of Plant Ecology 33(4): 698–705. (In Chinese) https://doi.org/10.3773/j.issn.1005-264x.2009.04.008
Liu LB, Wu YY, Hu G, et al. (2016b) Biomass of karst evergreen and deciduous broad-leaved mixed forest in central Guizhou province, southwestern China: a comprehensive inventory of a 2 ha plot. Silva Fennica 50(3): 1492. https://doi.org/10.14214/sf.1492
Liu YG, Liu CC, Wang SJ, et al. (2013) Organic carbon storage in four ecosystem types in the karst region of southwestern China. Plos One 8(2): e56443. https://doi.org/10.1371/journal.pone.0106876
Lu DS (2006) The potential and challenge of remote sensingbased biomass estimation. International Journal of Remote Sensing. 27(7): 1297–1328. https://doi.org/10.1080/01431160500486732
Maack J, Kattenborn T, Fassnacht FE, et al. (2015) Modeling forest biomass using Very-High-Resolution data -Combining textural, spectral and photogrammetric predictors derived from space borne stereo images. European Journal of Remote Sensing 48(1): 245–261. http://dx.doi.org/10.5721/EuJRS20154814
Ma J, Xiao XM, Qin YW, et al. (2017) Estimating aboveground biomass of broadleaf, needle leaf, and mixed forests in Northeastern China through analysis of 25-m ALOS/PALSAR mosaic data. Forest Ecology and Management 389(1): 199–210. https://doi.org/10.1016/j.foreco.2016.12.020
Main-Knorn M, Cohen WB, Kennedy RE, et al. (2013) Monitoring coniferous forest biomass change using a Landsat trajectory-based approach. Remote Sensing of Environment 139(4): 277–290. https://doi.org/10.1016/j.rse.2013.08.010
Menlik T, Özdemir MB, Kirmaci V (2010) Determination of freeze-drying behaviors of apples by artificial neural network. Expert Systems with Applications 37(12): 7669–7677. https://doi.org/10.1016/j.eswa.2010.04.075
Montesano PM, Cook BD, Sun G, et al. (2013) Achieving accuracy requirements for forest biomass mapping: a space borne data fusion method for estimating forest biomass and lidar sampling error. Remote Sensing of Environment 130: 153–170. https://doi.org/10.1016/j.rse.2012.11.016
Mutanga O, Adam E, Cho MA (2012) High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm. International Journal of Applied Earth Observations and Geoinformation 18(1): 399–406. https://doi.org/10.1016/j.jag.2012.03.012
Muukkonen P, Heiskanen J (2005) Estimating biomass for boreal forests using ASTER satellite data combined with stand wise forest inventory data. Remote Sensing of Environment 99(4): 434–447. https://doi.org/10.1016/j.rse.2005.09.011
Özc¸elik, R, Diamantopoulou MJ, Brooks JR, et al. (2010) Estimating tree bole volume using artificial neural network models for four species in Turkey. Journal of Environmental Management 91(3): 742–753. https://doi.org/10.1016/j.jenvman.2009.10.002
Peña-Barragán JM, Ngugi MK, Plant RE, et al. (2011) Objectbased crop identification using multiple vegetation indices, textural features and crop phenology. Remote Sensing of Environment 115(6): 1301–1316. https://doi.org/10.1016/j.rse.2011.01.009
Powell SL, Cohen WB, Healey SP, et al. (2010) Quantification of live aboveground forest biomass dynamics with Landsat timeseries and field inventory data: A comparison of empirical modeling approaches. Remote Sensing of Environment 114(5): 1053–1068. https://doi.org/10.1016/j.rse.2009.12.018
Pradhan B, Saro L (2010) Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environmental Modelling and Software 25(6): 747–759. https://doi.org/10.1016/j.envsoft.2009.10.016
Quan XW, He BB, Yebra M, et al. (2017) A radiative transfer model-based method for the estimation of grassland aboveground biomass. International Journal of Applied Earth Observation and Geoinformation. 54: 159–168. https://doi.org/10.1016/j.jag.2016.10.002
Sedano F, Gómez D, Gong P, et al. (2008) Tree density estimation in a tropical woodland ecosystem with multiangular MISR and MODIS data. Remote Sensing of Environment 112(5): 2523–2537. https://doi.org/10.1016/j.rse.2007.11.009
Shu GN, Kanninen M, Atyi RE, et al. (2014) Assessment and prediction of above-ground biomass in selectively logged forest concessions using field measurements and remote sensing data: Case study in South East Cameroon. Forest Ecology and Management 329(1): 177–185. https://doi.org/10.1016/j.foreco.2014.06.018
Soenen SA, Peddle DR, Hall RJ, et al. (2010) Estimating aboveground forest biomass from canopy reflectance model inversion in mountainous terrain. Remote Sensing of Environment 114(7): 1325–1337. https://doi.org/10.1016/j.rse.2009.12.012
Stagakis S, Markos N, Sykioti O, et al. (2010) Monitoring canopy biophysical and biochemical parameters in ecosystem scale using satellite hyperspectral imagery: an application on a Phlomis fruticosa Mediterranean ecosystem using multiangular CHRIS/PROBA observations. Remote Sensing of Environment 114(5): 977–994. https://doi.org/10.1016/j.rse.2009.12.006
Temesgen H, Affleck D, Poudel K, et al. (2015) A review of the challenges and opportunities in estimating above ground forest biomass using tree-level models. Scandinavian Journal of Forest Research 30(4): 326–335. https://doi.org/10.1080/02827581.2015.1012114
Tian X, Su ZB, Chen EX, et al. (2012) Estimation of forest above-ground biomass using multi-parameter remote sensing data over a cold and arid area. International Journal of Applied Earth Observation and Geoinformation 14(1): 160–168. https://doi.org/10.1016/j.jag.2011.09.010
Tiryaki S, Aydin A (2014) An artificial neural network model for predicting compression strength of heat treated woods and comparison with a multiple linear regression model. Construction and Building Materials 62(2): 102–108. https://doi.org/10.1016/j.conbuildmat.2014.03.041
Vahedi AA (2016) Artificial neural network application in comparison with modeling allometric equations for predicting above-ground biomass in the Hyrcanian mixed-beech forests of Iran. Biomass and Bioenergy 88: 66–76. https://doi.org/10.1016/j.biombioe.2016.03.020
Wang C, Nie S, Xi XH, et al. (2017) Estimating the biomass of maize with hyperspectral and LiDAR data. Remote Sensing 9(1): 11. https://doi.org/10.3390/rs9010011
Wang LA, Zhou XD, Zhu XK, et al. (2016) Estimation of biomass in wheat using random forest regression algorithm and remote sensing data. The Crop Journal 4(3): 212–219. https://doi.org/10.1016/j.cj.2016.01.008
Wu ZY (1980) Vegetation of China. Science Press, Beijing, China. (In Chinese).
Xie YC, Sha ZY, Yu M, et al. (2009) A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia, China. Ecological Modelling 220(15): 1810–1818. https://doi.org/10.1016/j.ecolmodel.2009.04.025
Zeng SB, Jiang YJ, Liu ZH (2016) Assessment of climate impacts on the karst-related carbon sink in SW China using MPD and GIS. Global and Planetary Change 144: 171–181. https://doi.org/10.1016/j.gloplacha.2016.07.015
Zheng DL, Rademacher J, Chen JQ, et al. (2004) Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA. Remote Sensing of Environment 93(3): 402–411. https://doi.org/10.1016/j.rse.2004.08.008
Zhang SY, Bai XY, Wang SJ, et al. (2015) Spatial distribution patterns and the evolution process of carbon storage in a typical karst canyon area. Journal of Resources and Ecology 6(4): 199–207. https://doi.org/10.5814/j.issn.1674-764x.2015.04.002
Zhang Z, Tian X, Chen ER, et al. (2011) Review of methods on estimating forest above ground biomass. Journal of Beijing Forestry University 33(5): 144–150. (In Chinese) https://doi.org/10.13332/j.1000-1522.2011.05.026
Zheng G, Chen JM, Tian QJ, et al. (2007) Combining remote sensing imagery and forest age inventory for biomass mapping. Journal of Environmental Management 85(3): 616–623. https://doi.org/10.1016/j.jenvman.2006.07.015
Zhou XN, Lai AH, Zhou CJ, et al. (2015) Advances in ecological logging of mountain forest. Journal of Forest and Environment 27(2): 180–185. (In Chinese) https://doi.org/10.13324/j.cnki.jfcf.2015.02.016
Zhu SQ, Wei LM, Chen ZR, et al. (1995) A preliminary study on biomass components of karst forest in Maolan of Guizhou Province, China. Acta hytoecologica Sinica 19(4): 358–367. (In Chinese)
Zhu YH, Liu K, Liu L, et al. (2015) Retrieval of mangrove aboveground biomass at the individual species level with WorldView-2 Images. Remote Sensing 7(9): 12192–12214. https://doi.org/10.3390/rs70912192
Zolkos SG, Goetz SJ, Dubayah R (2013) A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing. Remote Sensing of Environment 128(1): 289–298. https://doi.org/10.1016/j.rse.2012.10.017
Acknowledgements
This work was supported by the National Key R and D Program of China (2016YFC0502101) and the National Basic Research Program of China (2013CB956704). This is also a contribution to the Opening Fund of the State Key Laboratory of Environmental Geochemistry (SKLEG2017911). We thank Changcheng Liu, Yuguo Liu, and Ke Guo for providing the quadrat data.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Guo, Ym., Ni, J., Liu, Lb. et al. Estimating aboveground biomass using Pléiades satellite image in a karst watershed of Guizhou Province, Southwestern China. J. Mt. Sci. 15, 1020–1034 (2018). https://doi.org/10.1007/s11629-017-4760-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11629-017-4760-x