Skip to main content

Plant cover as an estimator of above-ground biomass in semi-arid woody vegetation in Northeast Patagonia, Argentina

Abstract

The quantification of carbon storage in vegetation biomass is a crucial factor in the estimation and mitigation of CO2 emissions. Globally, arid and semi-arid regions are considered an important carbon sink. However, they have received limited attention and, therefore, it should be a priority to develop tools to quantify biomass at the local and regional scales. Individual plant variables, such as stem diameter and crown area, were reported to be good predictors of individual plant weight. Stand-level variables, such as plant cover and mean height, are also easy-to-measure estimators of above-ground biomass (AGB) in dry regions. In this study, we estimated the AGB in semi-arid woody vegetation in Northeast Patagonia, Argentina. We evaluated whether the AGB at the stand level can be estimated based on plant cover and to what extent the estimation accuracy can be improved by the inclusion of other field-measured structure variables. We also evaluated whether remote sensing technologies can be used to reliably estimate and map the regional mean biomass. For this purpose, we analyzed the relationships between field-measured woody vegetation structure variables and AGB as well as LANDSAT TM-derived variables. We obtained a model-based ratio estimate of regional mean AGB and its standard error. Total plant cover allowed us to obtain a reliable estimation of local AGB, and no better fit was attained by the inclusion of other structure variables. The stand-level plant cover ranged between 18.7% and 95.2% and AGB between about 2.0 and 70.8 Mg/hm2. AGB based on total plant cover was well estimated from LANDSAT TM bands 2 and 3, which facilitated a model-based ratio estimate of the regional mean AGB (approximately 12.0 Mg/hm2) and its sampling error (about 30.0%). The mean AGB of woody vegetation can greatly contribute to carbon storage in semi-arid lands. Thus, plant cover estimation by remote sensing images could be used to obtain regional estimates and map biomass, as well as to assess and monitor the impact of land-use change on the carbon balance, for arid and semi-arid regions.

This is a preview of subscription content, access via your institution.

References

  • Baccini A, Laporte N, Goetz S J, et al. 2008. A first map of tropical Africa’s above-ground biomass derived from satellite imagery. Environmental Research Letters, 3(4): 045011, Doi: https://doi.org/10.1088/1748-9326/3/4/045011.

    Article  Google Scholar 

  • Bertiller M B, Bisigato A J, Carrera A L, et al. 2004. Structure of the vegetation and functioning of the ecosystems of Monte Chubutense. Bulletin of the Argentine Botanical Society, 39(3–4): 139–158. (in Spanish)

    Google Scholar 

  • Burnham K P, Anderson D R. 2002. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. New York, NY: Springer, 261–303.

    Google Scholar 

  • Cartus O, Kellndorfer J, Walker W, et al. 2014. A national, detailed map of forest aboveground carbon stocks in Mexico. Remote Sensing, 6(6): 5559–5588.

    Article  Google Scholar 

  • Chave J, Andalo C, Brown S, et al. 2005. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia, 145(1): 87–99.

    Article  Google Scholar 

  • Chen W, Cao C X, He Q S, et al. 2010. Quantitative estimation of the shrub canopy LAI from atmosphere-corrected HJ-1 CCD data in Mu Us Sandland. Science China Earth Sciences, 53: 26–33.

    Article  Google Scholar 

  • Chen W, Zhao J, Cao C, et al. 2018. Shrub biomass estimation in semi-arid sandland ecosystem based on remote sensing technology. Global Ecology and Conservation, 16: e00479, doi: https://doi.org/10.1016/j.gecco.2018.e00479.

    Article  Google Scholar 

  • Chen Y, Gillieson D. 2009. Evaluation of Landsat TM vegetation indices for estimating vegetation cover on semi-arid rangelands: A case study from Australia. Canadian Journal of Remote Sensing, 35(5): 435–446.

    Article  Google Scholar 

  • Chojnacky D C, Milton M. 2008. Measuring carbon in shrubs. In: Hoover C M. Field measurements for forest carbón monitoring. New York: Springer, 45–72.

    Chapter  Google Scholar 

  • Conti G, Enrico L, Casanoves F, et al. 2013. Shrub biomass estimation in the semiarid Chaco forest: A contribution to the quantification of an underrated carbon stock. Annals of Forest Science, 70: 515–524.

    Article  Google Scholar 

  • Conti G, Gorné L D, Zeballos S R, et al. 2019. Developing allometric models to predict the individual aboveground biomass of shrubs worldwide. Global Ecology and Biogeography, 28(7): 961–975.

    Article  Google Scholar 

  • Dengsheng L. 2006. The potential and challenge of remote sensing-based biomass estimation, International Journal of Remote Sensing, 27 (7): 1297–y1328

    Article  Google Scholar 

  • di Gregorio A, Jansen L J M. 2000. Land Cover Classification System (LCCS): classification concepts and user manual. FAO/UNEP/Cooperazione Italiana, Rome, 20–31.

  • di Rienzo J A, Casanoves F, Balzarini M G, et al. 2016. InfoStat Versión 2016. Grupo InfoStat, FCA, National University of Córdoba, Argentina. http://www.infostat.com.ar.

    Google Scholar 

  • Dong J, Kaufmann R K, Myneni R B, et al. 2003. Remote sensing estimates of boreal and temperate forest woody biomass: carbon pools, sources, and sinks. Remote Sensing of Environment, 84(3): 393–410.

    Article  Google Scholar 

  • Eisfelder C, Kuenzer C, Dech S. 2012. Derivation of biomass information for semi-arid areas using remote-sensing data. International Journal of Remote Sensing, 33(9): 2937–2984.

    Article  Google Scholar 

  • Fensholt R, Langanke T, Rasmussen K, et al. 2012. Greenness in semi-arid areas across the globe 1981–2007—an Earth Observing Satellite based analysis of trends and drivers. Remote Sensing of Environment, 121: 144–158.

    Article  Google Scholar 

  • Flombaum P, Sala O E. 2007. Cover is a good predictor of aboveground biomass in arid systems. Journal of Arid Environments, 73(6): 597–598.

    Google Scholar 

  • Foley J A, DeFries R, Asner G P, et al. 2005. Global consequences of land use. Science, 309(5734): 570–574.

    Article  Google Scholar 

  • Fonseca W G, Alice F G, Rey J M. 2009. Models to estimate the biomass of native species in plantations and secondary forests in the Caribbean zone of Costa Rica. Bosque, 30(1): 36–47. (in Spanish)

    Article  Google Scholar 

  • Fusco E J, Rau B M, Falkowski M, et al. 2019. Accounting for aboveground carbon storage in shrubland and woodland ecosystems in the Great Basin. Ecosphere, 10(8): e02821, doi: https://doi.org/10.1002/ecs2.2821.

    Article  Google Scholar 

  • Gabella J, Campo A M. 2016. Fragility and environmental degradation in rural areas of the temperate arid Argentinian diagonal. Estudios Geográficos, 77 (281): 491–519. (in Spanish)

    Article  Google Scholar 

  • Galidaki G, Zianis D, Gitas I, et al. 2017. Vegetation biomass estimation with remote sensing: focus on forest and other wooded land over the Mediterranean ecosystem. International Journal of Remote Sensing, 38(7): 1940–1966.

    Article  Google Scholar 

  • Gasparri N I, Parmuchi M G, Bono J, et al. 2010. Assessing multi-temporal Landsat 7 ETM+ images for estimating above-ground biomass in subtropical dry forests of Argentina. Journal Arid Environments, 74(10): 1262–1270.

    Article  Google Scholar 

  • Gasparri N I, Baldi G. 2013. Regional patterns and controls of biomass in semiarid woodlands: lessons from the Northern Argentina Dry Chaco. Regional Environmental Change, 13(6): 1131–1144.

    Article  Google Scholar 

  • Gasparri N I, Grau H R, Gutierrez-Angonese J. 2013. Linkages between soybean and neotropical deforestation: Coupling and transient decoupling dynamics in a multi-decadal analysis. Global Environmental Change-Human and Policy Dimensions, 23(6): 1605–1614.

    Article  Google Scholar 

  • Godagnone R E, Bran D E. 2009. Integrated inventory of the natural resources of the province of Río Negro. Buenos Aires: INTA, 319–363. (in Spanish)

    Google Scholar 

  • González-Iturbe Ahumada J A. 2004. Introduction to remote sensing: sampling techniques for natural resource managers. Mexico: Autonomous University of Mexico, Autonomous University of Yucatán National Council of Science and Technology, and National Institute of Ecology, 455–471. (in Spanish)

    Google Scholar 

  • González-Roglich M, Swenson J. 2016. Tree cover and carbon mapping of Argentine savannas: Scaling from field to region. Remote Sensing of Environment, 172: 139–147.

    Article  Google Scholar 

  • Grainger A. 1999. Constraints on modelling the deforestation and degradation of tropical open woodlands. Global Ecology and Biogeography, 8: 179–190.

    Google Scholar 

  • Gregoire T G, Salas C. 2009. Ratio estimation with measurement error in the auxiliary variate. Biometrics, 65(2): 590–598.

    Article  Google Scholar 

  • Grünzweig J M, Lin T, Rotenberg E, et al. 2003. Carbon sequestration in arid-land forest. Global Change Biology, 9(5): 791–799.

    Article  Google Scholar 

  • GTOS. 2010. A framework for terrestrial climate-related observations and development of standards for the terrestrial essential climate variables: proposed work plan. [2016-11-20]. http://www.fao.org/gtos/doc/pub78.pdf.

  • Hansen M C, Potapov P V, Moore R, et al. 2013. High-resolution global maps of 21st-century forest cover change. Science, 342(6160): 850–853.

    Article  Google Scholar 

  • Hengeveld G M, Didion M, Clerkx S, et al. 2015. The landscape-level effect of individual-owner adaptation to climate change in Dutch forests. Regional Environmental Change, 15: 1515–1529.

    Article  Google Scholar 

  • Hierro J L, Branch L C, Villarreal D, et al. 2000. Predictive equations for biomass and fuel characteristics of Argentine shrubs. Journal of Range Management, 53: 617–621.

    Article  Google Scholar 

  • Hofstad O. 2005. Review of biomass and volume functions for individual trees and shrubs in southeast Africa. Journal of Tropical Forest Science, 17(1): 151–162.

    Google Scholar 

  • Houghton R A. 2005. Aboveground forest biomass and the global carbon balance. Global Change Biology, 11(6): 945–958.

    Article  Google Scholar 

  • Houghton R A. 2007. Balancing the global carbon budget. Annual Review of Earth and Planetary Sciences, 35: 313–347.

    Article  Google Scholar 

  • Huete A R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25: 295–309.

    Article  Google Scholar 

  • Huete A, Didan K, Miura T, et al. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1–2): 195–213.

    Article  Google Scholar 

  • Issa S M, Dahy B S, Saleous N. 2020. Accurate mapping of date palms at different age-stages for the purpose of estimating their biomass. Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume 3. XXIVth International Society for Photogrammetry and Remote Sensing Congress. 4 July-10 July 2021. Nice, France, 461–467.

  • Jenkins J C, Chojnacky D C, Heath L S, et al. 2004. Comprehensive database of diameter-based biomass regressions for North American trees species. Delaware: US Department of Agriculture, Forest Service and Northeastern Research Station, 1–45.

    Book  Google Scholar 

  • Kangas A, Maltamo M. 2006. Forest Inventory: Methodology & Applications. Berlin: Springer, 357.

    Book  Google Scholar 

  • Kaufman Y J. 1989. The atmospheric effect on remote sensing and its correction. In: Asrar G. Theory and Application of Optical Remote Sensing. New York: Wiley Publication, 336–428.

    Google Scholar 

  • Kindermann G, Obersteiner M, Sohngen B, et al. 2008. Global cost estimates of reducing carbon emissions through avoided deforestation. Proceedings of the National Academy of Sciences, 105(30): 10302–10307.

    Article  Google Scholar 

  • León R J C, Bran D, Collantes M, et al. 1998. Mean vegetation units of extra-Andean Patagonia. Austral Ecology, 8: 125–144. (in Spanish)

    Google Scholar 

  • Le Polain de Waroux Y, Lambin E F. 2012. Monitoring degradation in arid and semi-arid forests and woodlands: the case of the argan woodlands (Morocco). Applied Geography, 32(2): 777–786.

    Article  Google Scholar 

  • Lopez Serrano P M, Cárdenas Domínguez J L, Corral-Rivas J J, et al. 2020. Modeling of aboveground biomass with landsat 8 oli and machine learning in temperate forests. Forests, 11(1): 11, https://doi.org/10.3390/f11010011.

    Article  Google Scholar 

  • Mageto T, Motubwa J. 2018. Bootstrap confidence interval for model based sampling. American Journal of Theoretical and Applied Statistics, 7(4): 147–155.

    Article  Google Scholar 

  • Malagnoux M, Sène E H, Atzmon N. 2007. Forests, trees and water in arid lands: a delicate balance. Unasylva, 58: 24–29.

    Google Scholar 

  • Morello J, Matteucci S D, Rodríguez A F, et al. 2012. Argentine ecoregions and ecosystem complexes. Buenos Aires: Graphic Orientation, 309–347. (in Spanish)

    Google Scholar 

  • Navone S M. 2003. Remote Sensors Applied to the Study of Natural Resources. Buenos Aires: Faculty of Agronomy, University of Buenos Aires, 81–95. (in Spanish)

    Google Scholar 

  • Nosetto M D, Jobbágy E G, Paruelo J M. 2006. Carbon sequestration in semi-arid rangelands: Comparison of Pinus ponderosa plantations and grazing exclusion in NW Patagonia. Journal Arid Environments, 67(1): 142–156.

    Article  Google Scholar 

  • Oñatibia G R, Aguiar M R, Cipriotti P A, et al. 2010. Individual plant and population biomass of dominant shrubs in Patagonian grazed fields. Ecología Austral, 20: 269–279.

    Google Scholar 

  • Oyarzabal M, Clavijo J, Oakley L, et al. 2018. Vegetation units of Argentina. Austral Ecology, 28: 040–063. (in Spanish)

    Article  Google Scholar 

  • Pearce H G, Anderson W R, Fogarty L G, et al. 2010. Linear mixed-effects models for estimating biomass and fuel loads in shrublands. Canadian Journal of Forest Research, 40(10): 2015–2026.

    Article  Google Scholar 

  • Peri P L. 2011. Carbon storage in cold temperate ecosystems in Southern Patagonia, Argentina. In: Islam Atazadeh. Biomass and Remote Sensing of Biomass. London, In Tech, 213–225.

    Google Scholar 

  • Pordel F, Ebrahimi A, Azizi Z. 2018. Canopy cover or remotely sensed vegetation index, explanatory variables of above-ground biomass in an arid rangeland, Iran. Journal Arid Land, 10(5): 767–780.

    Article  Google Scholar 

  • Roig F A, Roig-Juñent S, Corbalán V. 2009. Biogeography of the Monte Desert. Journal of Arid Environments, 73(2): 164–172.

    Article  Google Scholar 

  • Rouse J W, Haas H R, Deering D W, et al. 1974. Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. NASA/GSFC Type III Final Report, Greenbelt, Md, 371.

  • Saatchi S S, Harris N L, Brown S, et al. 2011. Benchmark map of forest carbon stocks in tropical regions across three continents. Proceedings of the National Academy of Sciences, 108(24): 9899–9904.

    Article  Google Scholar 

  • Sankarán N M, Hanan N P, Scholes R J, et al. 2005. Determinants of woody cover in African savannas. Nature, 438(7069): 846–849.

    Article  Google Scholar 

  • Shoshany M, Karnibad L. 2015. Remote sensing of shrubland drying in the south-east Mediterranean, 1995–2010: Water-Use-Efficiency-Based mapping of biomass change. Remote Sensor, 7(3): 2283–2301.

    Article  Google Scholar 

  • Ståhl G, Saarela S, Schnell S, et al. 2016. Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation. Forest Ecosystems, 3(5): 1–11.

    Google Scholar 

  • Torres Robles S S, Arturi M, Contreras C, et al. 2015. Geographical variations of the structure and composition of the woody vegetation in the limit between the spinal and the mount in the Northeast of Patagonia (Argentina). Bulletin of the Argentine Botanical Society, 50 (2): 209–215. (in Spanish)

    Google Scholar 

  • Yan F, Wu B, Wang Y. 2013. Estimating aboveground biomass in Mu Us Sandy Land using Landsat spectral derived vegetation indices over the past 30 years. Journal Arid Land, 5: 521–530.

    Article  Google Scholar 

  • Zeberio J M, Torres Robles S, Calabrese G M. 2018. Land use and conservation status of the woody vegetation of the Monte in the Northeast of Patagonia. Austral Ecology, 28: 543–552. (in Spanish)

    Article  Google Scholar 

  • Zeberio J M, Pérez C A. 2020. Rehabilitation of degraded areas in northeastern Patagonia, Argentina: Effects of environmental conditions and plant functional traits on performance of native woody species. Journal of Arid Land, 12: 653–665.

    Article  Google Scholar 

  • Zhang W, Brandt M, Wang Q, et al. 2019. From woody cover to woody canopies: How Sentinel-1 and Sentinel-2 data advance the mapping of woody plants in savannas. Remote Sensing of Environment, 234: 111465, doi: https://doi.org/10.1016/j.rse.2019.111465.

    Article  Google Scholar 

  • Zivkovic L, Martínez Carretero E, Dalmasso A, et al. 2013. Carbon accumulated in the plant biomass of the Villavicencio reserve (Mendoza - Argentina). Bulletin of the Argentine Botanical Society, 48(3–4): 543–551. (in Spanish)

    Google Scholar 

Download references

Acknowledgments

This research was funded by the National University of Río Negro Research Project (40-C-658) and the Research Project National Institute of Agricultural Technology, University Association of Higher Agricultural Education and National Council of Veterinary Deans (Proyect 940175). This work is part of Laura B RODRIGUEZ’s Ph.D. thesis supported by a scholarship from National Council of Scientific and Technical Research, Argentina. Special thanks to Dr. Timothy GREGOIRE, who kindly helped us to define the procedure for sampling error estimation.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Silvia S. Torres Robles.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Rodriguez, L.B., Torres Robles, S.S., Arturi, M.F. et al. Plant cover as an estimator of above-ground biomass in semi-arid woody vegetation in Northeast Patagonia, Argentina. J. Arid Land 13, 918–933 (2021). https://doi.org/10.1007/s40333-021-0083-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40333-021-0083-4

Keywords

  • above-ground biomass
  • shrublands
  • ratio estimation
  • carbon storage
  • remote sensing
  • Patagonia