Plantations established in highly-pollutant industrial areas have a crucial role to absorb greenhouse gases, particularly CO2. A thorough monitoring of their aboveground biomass and carbon balance is essential to ensure their beneficial effects. This can be operationally supported by using a combination of field and multispectral stereo remote sensing data to provide surface height information with high resolution and wide coverage. We estimated the fresh and dry aboveground biomass and the carbon sequestration from pairs of Pléiades satellite imagery of 25-year-old monoculture plantations of Pinus eldarica Medw., Cupressus arizonica Greene, Morus alba L. and Robinia pseudoacacia L., around the Mobarakeh Steel Complex near the megacity Isfahan. This complex is the largest-scale of its kind in semi-arid Iran. Tree heights were derived from a Canopy height model (CHM) at plantation management unit level. Parsimonious regression models were developed, and the accuracy was assessed by the coefficient of determination, bias and root mean square errors (RMSEs) at plot level. This resulted in R2 of total biomass, dry biomass, carbon sequestration, tree height and tree count of 0.90, 0.90, 0.91, 0.89, and 0.88, respectively. Moreover, mixed bias (with lowest value of -0.12 m for tree height) and NRMSE% (with lowest value of 5.93 % for tree carbon sequestration) values were obtained. The results demonstrated that pairs of stereo imageries can be effectively used for predicting forest biomass and carbon sequestration across semi-arid plantations, hence enabling a continuous monitoring of vegetation established around pollutant industrial areas.
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Citation
Hosseini Z, Naghavi H, Latifi H, Bakhtiari Bakhtiarvand S (2019). Estimating biomass and carbon sequestration of plantations around industrial areas using very high resolution stereo satellite imagery. iForest 12: 533-541. - doi: 10.3832/ifor3155-012
Academic Editor
Alessio Collalti
Paper history
Received: May 26, 2019
Accepted: Sep 17, 2019
First online: Dec 12, 2019
Publication Date: Dec 31, 2019
Publication Time: 2.87 months
© SISEF - The Italian Society of Silviculture and Forest Ecology 2019
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This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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List of the papers citing this article based on CrossRef Cited-by.
(1)
Bakhtiarvand Bakhtiari S, Sohrabi H (2012)Estimation of atmospheric CO2 absorption by plantation around Mobarakeh Steel Complexes. In: Proceedings of the “6th National Conference on Environmental Engineering”. Tehran University (Tehran, Iran) 17-21 Nov 2012, pp. 1-9.
Online |
Gscholar
(2)
Bakhtiarvand Bakhtiari S (2011a)Estimating above and below-ground carbon storage of four broadleaved and coniferous trees in Mobarakeh Steel complex. Master Thesis, Faculty of Natural Resources and Earth Science, University of Shahrekord, Iran, pp. 35-44.
Gscholar
(3)
Bakhtiarvand Bakhtiari S (2011b)The ability to trade carbon through plantation projects around industrial areas. In: Proceedings of the “First International Conference on New Approaches to Energy Conservation”. Amirkabier University (Tehran, Iran), 21-22 Nov 2011, pp. 1-10.
Online |
Gscholar
(4)
Banharnsakun A, Tanathong S (2014)Object detection based on template matching through use of best-so-far ABC. Computational Intelligence and Neuroscience 2014: 1-9.
CrossRef |
Gscholar
(5)
Bhattarai N, Quackenbush LJ, Calandra L, Im J, Teale SA (2012)An automated object_based approch to detect sirex_infestation in points. In: Proceedings of the “ASPRS 2012 Annual Conference”. Sacramento (CA, USA) 19-23 Mar 2012, pp. 1-13.
CrossRef |
Gscholar
(6)
Cairns MA, Olmsted I, Granados J, Argaez J (2003)Composition and aboveground tree biomass of a dry semi-evergreen forest on Mexico’s Yucatan Peninsula. Forest Ecology and Management 186: 125-132.
CrossRef |
Gscholar
(7)
Coyle DR, Coleman MD, Aubrey DP (2008)production, and distribution of sweetgum and loblolly pine grown with irrigation and fertilization. Canadian Journal of Forest Research 38: 1335-1348.
CrossRef |
Gscholar
(8)
Ebuy J, Lokombe JP, Ponette Q, Snwa D, Picard N (2011)Allometric equations for predicting above ground biomass of three tree species. Journal of Tropical Forest Science 23: 125-132.
Gscholar
(9)
Fassnacht FE, Mangold D, Schäfer J, Immitzer M, Kattenborn T, Koch B, Latifi H (2017)Estimating stand density, biomass and tree species from very high resolution stereo-imagery - towards an all-in-one sensor for forestry applications? Forestry 90: 613-631.
CrossRef |
Gscholar
(10)
Guo Y, Ni J, Wu Y, Guo C, Xu X, Zhong Q (2018)Estimating aboveground biomass using Pléiades satellite image in a karst watershed of Guizhou Province, Southwestern China. Journal of Mountain Science 15: 1020-1034.
CrossRef |
Gscholar
(11)
Hunt CAG (2009)Carbon sinks and climate change. Edward Elgar Publishing Limited, Cheltenham, UK, pp. 207-212.
CrossRef |
Gscholar
(12)
Jenkins JC, Chojnacky DC, Heath LS, Birdsey RA (2003)National-scale biomass estimators for United States tree species. Forest Science 49: 12-35.
Online |
Gscholar
(13)
Kanniah KD, Muhamad N, Kang CS (2014)Remote sensing assessment of carbon storage by urban forest. IOP Conference Series: Earth and Environmental Science 18: 012151.
CrossRef |
Gscholar
(14)
Karsenty A, Blanco C, Dufour T (2003)Forest and climate change. Instruments related to the United Nations Framework Convention on Climate Change and their Potential for Sustainable Forest Management in Africa. FAO, Rome, Italy, pp. 44.
Gscholar
(15)
Khare S, Latifi H, Ghosh SK (2017)Training module for point cloud data generation and then DSM using high spatial resolution optical stereo pairs. Technical report, web site, pp. 28. [unpublished]
CrossRef |
Gscholar
(16)
Laclau P, Andenmatten E, Letourneau FJ, Loguercio G (2017)Carbon sequestration of ponderosa pine plantations in Northwestern Patagonia. Managing Forest Ecosystems 34: 329-349.
CrossRef |
Gscholar
(17)
Larsen M, Eriksson M, Descombes X, Perrin G, Brandt Berg T, Gougeon F (2011)Comparison of six individual tree crown detection algorithms evaluated under varying forest conditions. International Journal of Remote Sensing 32: 5827-5852.
CrossRef |
Gscholar
(18)
Latifi H, Nothdurft A, Koch B (2010)Non-parametric prediction and mapping of standing timber volume and biomass in a temperate forest: application of multiple optical/LiDAR-derived predictors. Forestry 83: 395-407.
CrossRef |
Gscholar
(19)
Li W, Niu Z, Chen H, Li D, Wu M, Zhao W (2016)Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system. Ecological Indicators 67: 637-648.
CrossRef |
Gscholar
(20)
Lucas R, Bunting P, Paterson M, Chisholm L (2008)Classification of Australian forest communities using aerial photography, CASI and HyMap data. Remote Sensing of Environment 112: 2088-2103.
CrossRef |
Gscholar
(21)
Maack J, Kattenborn T, Fassnacht FE, Enle F (2015)Modeling forest biomass using very-high-resolution data - Combining textural, spectral and photogrammetric predictors derived from spaceborne stereo images. European Journal of Remote Sensing 48: 245-261.
CrossRef |
Gscholar
(22)
Maillard P, Gomes MF (2016)Detection and counting of orchard trees from VHR IMAGES using a geometrical-optical model and marked template matching. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences III-7: 75-83.
CrossRef |
Gscholar
(23)
Malabanan MV, Paringit EC, Zaragosa GP, Ibañez CAG, Faelga RAG, Argamosa RJL, Posilero MAV, Tandoc FAM, Palmon A, Maralit AR (2010)Extracting tree count and individual tree crown from lidar-derived canopy height model using object-based image analysis. Phil-LiDAR 2, Project 3: “Forest Resources Extraction from LiDAR Surveys”. University of the Philippines Training Center for Applied Geodesy and Photogrammetry, Philippines, pp. 1-9.
Gscholar
(24)
Miguelito FI, Eliza EC, Guiller BD, Ronaldo TA (2018)Estimation of Mango tree count and crown cover delineation using template matching algorithm. International Journal for Research in Applied Science and Engineering Technology 6: 1955-1960.
Online |
Gscholar
(25)
Narimani H, Iran Nezhad Parizi M, Kiani B, Ghorbali R (2015)Effects of plantation with conifers on carbon sequestration (case study: Zob-e-Ahan company, Isfahan). Iranian Journal of Forest and Poplar Research 23: 53-63.
Online |
Gscholar
(26)
Nichol JE, Sarker LR (2011)Improved biomass estimation using the texture parameters of two high-resolution optical sensors. Transactions on Geoscience and Remote Sensing 49: 930-948.
CrossRef |
Gscholar
(27)
Parresol BR (2001)Additivity of nonlinear biomass equations. Canadian Journal of Forest Research 31 (5): 865-878.
CrossRef |
Gscholar
(28)
Patthanaissaranukoola W, Polpraserta C, Englande A (2013)Potential reduction of carbon emissions from crude palm oil production based on energy and carbon balances. Applied Energy 102: 710-717.
CrossRef |
Gscholar
(29)
Persson H, Wallerman J, Olsson H, Fransson JES, Fransson JES, Persson H (2013)Estimating forest biomass and height using optical stereo satellite data and a DTM from laser scanning data Estimating forest biomass and height using optical stereo satellite data and a DTM from laser scanning data. Canadian Journal of Remote Sensing 39: 251-262.
CrossRef |
Gscholar
(30)
Poli D, Caravaggi I (2012)Digital surface modelling and 3D information extraction from spaceborne very high resolution stereo pairs. Publication Office of the European Union, Luxembourg, pp. 18-20.
CrossRef |
Gscholar
(31)
Poorter H, Niklas KJ, Reich PB, Oleksyn J, Poot P, Mommer L (2012)Biomass allocation to leaves, stems and roots: meta-analyses of interspecific variation and environmental control. New Phytologist 193: 30-50.
CrossRef |
Gscholar
(32)
Psomas A, Huber S, Itten K (2011)Hyperspectral remote sensing for estimating aboveground biomass and for exploring species richness patterns of grassland habitats. International Journal of Remote Sensing 32: 9007-9031.
CrossRef |
Gscholar
(33)
R Development Core Team (2009)A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
Online |
Gscholar
(34)
Rashtiyan S, Khatoon Abadi A, Jokhari M (2013)Investigation of pollution in Isfahan city and ways to prevent it. In: Proceedings of the “First National Conference on Urban and Environmental Services”. Ferdousi University (Mashhad, Iran), 9-10 October 2013. pp. 1-13. [online]
Online |
Gscholar
(35)
Reed D, Tomé M (1998)Total aboveground biomass and net dry matter accumulation by plant component in young
Eucalyptus globulus in response to irrigation. Forest Ecology and Management 103 (1): 21-32.
CrossRef |
Gscholar
(36)
Salih A, Mohamed A, Abahussain A, Tashtoosh F (2017)Use of some trees to mitigate air and soil pollution around oil refinery, Kingdom of Bahrain. Journal of Environmental Science and Pollution Research 3: 167-170.
Gscholar
(37)
Sammartano G, Spanò A (2016)DEM generation based on UAV photogrammetry data in critical areas. In: Proceedings of the “2nd International Conference on Geographical Information Systems Theory, Applications and Management”, vol. 1, GISTAM, Rome, Italy, pp. 92-98.
CrossRef |
Gscholar
(38)
Schulze ED, Beck E, Müller Hohenstein K (2005)Plant ecology. Springer, Berlin, Germany, pp. 131-134.
Gscholar
(39)
Socha J, Wezyk P (2007)Allometric equations for estimating the foliage biomass of Scots pine. European Journal of Forest Research 126: 263-270.
CrossRef |
Gscholar
(40)
Sohrabi H, Bakhtiarvand Bakhtiari S, Ahmadi K (2016)Above- and below-ground biomass and carbon stocks of different tree plantations in central Iran. Journal of Arid Land 8: 138-145.
CrossRef |
Gscholar
(41)
Straub C, Tian J, Seitz R, Reinartz P (2013)Assessment of Cartosat-1 and WorldView-2 stereo imagery in combination with a LiDAR-DTM for timber volume estimation in a highly structured forest in Germany. Forestry 86: 463-473.
CrossRef |
Gscholar
(42)
Tonolli S, Dalponte M, Neteler M, Rodeghiero M, Vescovo L, Gianelle D (2011)Remote sensing of environment fusion of airborne LiDAR and satellite multispectral data for the estimation of timber volume in the Southern Alps. Remote Sensing of Environment 115: 2486-2498.
CrossRef |
Gscholar
(43)
Verwijst T, Telenius B (1999)Biomass estimation procedures in short rotation forestry. Forest Ecology and Management 121: 137-146.
CrossRef |
Gscholar
(44)
Vosselman G (2000)Slope based filtering of laser altimetry data. International Archives of Photogrammetry and Remote Sensing 33: 935-942.
Gscholar
(45)
Wang X, Li Z, Liu X, Deng G, Jiang Z (2007)Estimating stem volume using QuickBird imagery and allometric relationships for open Populus xiaohei plantations. Journal of Integrative Plant Biology 49: 1304-1312.
CrossRef |
Gscholar
(46)
Wichmann V (2010)SAGA-GIS module library documentation (v2.2.3). Module DTM Filter (slope-based). Web site
Online |
Gscholar
(47)
Yu X, Hyyppä J, Karjalainen M, Nurminen K, Karila K, Kukko A, Jaakkola A, Liang X, Wang Y, Hyyppä H (2015)Comparison of laser and stereo optical, SAR and InSAR point clouds from air- and space-borne sources in the retrieval of forest inventory attributes. Remote Sensing 7: 15933-15954.
CrossRef |
Gscholar
(48)
Zinn YL, Resck DVS (2002)Soil organic carbon as affected by afforestation with
Eucalyptus and
Pinus in the Cerrado region of Brazil. Forest Ecology and Management 166: 285-294.
CrossRef |
Gscholar