Journal of Forest and Environmental Science

Institute of Forest Science, kangwon National University (강원대학교 산림과학연구소)
권호 표지
DOI QR코드

DOI QR Code

Aboveground Net Primary Productivity and Spatial Distribution of Chaco Semi-Arid Forest in Copo National Park, Santiago del Estero, Argentina

  • Jose Luis Tiedemann (National University of Santiago del Estero, Faculty of Forestry Sciences)
  • Received : 2024.02.14
  • Accepted : 2024.06.02
  • Published : 2024.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

According to the REDD+ program, it is necessary to monitor, quantify, and report forest conditions in protected land areas. The objectives of this work were to quantify the average monthly aerial net primary productivity (ANPPMONTH) of semi-arid Chaco Forest at Copo National Park (CNP), Santiago del Estero, Argentina, during the period 2000-2023, as well as its spatial distribution and relationship, and its use efficiency (RUE) of average monthly rainfall (AMR). The ANPPMONTH model accounted for 90% of the seasonal variability from October to May, the average seasonal ANPPMONTH was 145 tons of dry matter per hectare (t dm/ha), being the maximum in January with 192 t dm/ha and the minimum in May with 91 t dm/ha. The surface area covered by ANPPMONTH exhibited a consistent positive trend from October to May (t test=15.65, p<0.01). Strong and significant direct relationships were found between ANPPMONTH and AMRs, linear models explaining 90% and 96% of the variability, respectively. The results obtained become reference values for assessing the capacity of the forest systems to stock carbon for global warming mitigation and for monitoring and controlling their response to extreme climatic adversities. The average ANPPMONTH reduces uncertainty when defining the thresholds to monitor and quantify ANPP and forest area, thus facilitating the detection of negative changes in land use in CNP. Such results evidence the National Parks Administration's high effectiveness for the maintenance of protected area and for the high ANPP of the FCHS of CNP in the period 2000-2023.

Keywords

Acknowledgement

To the Temporal-Vegetation Analysis System of the Space Research Institute /dsr.inpe.br/laf/series/. To the LP-DAAC/EOS-NASA Project. To the NASA Prediction of Worldwide Energy Resource Project- https://power.larc.nasa.gov/. To Sociedad Rural del Noreste Santiagueño (Mr. Silvio Vicente) for his contribution on rainfall data-sociedadruralquimili@hotmail.com

References

  1. Baldassini P, Paruelo J. 2020. Agricultural and silvopastoral systems in the semi-arid Chaco. Impacts on primary productivity. Ecol Aust 30: 45-62. https://doi.org/10.25260/EA.20.30.1.0.961
  2. Baldassini P. 2018. Provision of Ecosystem Services in the semi-arid Chaco: effects of land use change and climate variability in carbon dynamics. PhD thesis. University of Buenos Aires, Buenos Aires, Argentina. (in Spanish)
  3. Clark Labs. 2020. TerrSet 2020 Geospatial Monitoring and Modeling Software. https://clarklabs.org/terrset/. Accessed 23 Jul 2022.
  4. Di Rienzo JA, Casanoves F, Balzarini, Gonzalez MG. 2022. User manual. InfoStat Group, FCA, National University of Cordoba, Argentina. https://www.researchgate.net/publication/319875201. Accessed 14 Aug 2023. (in Spanish)
  5. Didan K. 2015. MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006 [Data set]. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD13Q1.006. Accessed 25 Aug 2023.
  6. Didan K. 2015a. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MYD13Q1.006. Accessed 26 Aug 2023.
  7. Dieguez H, Paruelo JM. 2017. Disentangling the signal of climatic fluctuations from land use: changes in ecosystem functioning in South American protected areas (1982-2012). Remote Sens Ecol Conserv 3: 177-189. https://doi.org/10.1002/rse2.39
  8. Duan P, Wang Y, Yin P. 2020. Remote Sensing Applications in Monitoring of Protected Areas: A Bibliometric Analysis. Remote Sens 12: 772.
  9. Fensholt R. 2004. Assessment of primary production in a semi-arid environment from satellite data - exploiting capabilities of new sensors. PhD thesis. University of Copenhagen, Copenhagen, Denmark. (in English)
  10. Food and Agriculture Organization. 2020. The state of the world forests. http://www.fao.org/3/ca8642es/CA8642ES.pdf. Accessed 1 Mar 2022. (in Spanish)
  11. Gao Y, Skutsch M, Paneque-Galvez J, Ghilardi A. 2020. Remote sensing of forest degradation: a review. Environ Res Lett 15: 103001.
  12. Garbulsky MF, Paruelo JM. 2004. Remote sensing of protected areas to derive baseline vegetation functioning characteristics. J Veg Sci 15: 711-720. https://doi.org/10.1111/j.1654-1103.2004.tb02313.x
  13. Gasparri NI, Baldi G. 2013. Regional patterns and controls of biomass in semiarid woodlands: lessons from the Northern Argentina Dry Chaco. Reg Environ Chang 13: 1131-1144. https://doi.org/10.1007/s10113-013-0422-x
  14. Gasparri NI, Grau HR, Manghi E. 2008. Carbon Pools and Emissions from Deforestation in Extra-Tropical Forests of Northern Argentina between 1900 and 2005. Ecosystems 11: 1247-1261. https://doi.org/10.1007/s10021-008-9190-8
  15. Gasparri NI, Manghi E. 2004. Estimation of volume, biomass and carbon content of the Argentine Forest Regions. Final Report. Department of Environment and Sustainable Development, Forests Directorate, Management Unit of the Forest Evaluation System. http://surl.li/incxm. Accessed 1 Apr 2022. (in Spanish)
  16. Gasparri NI, Parmuchi MG, Bono J, Karszenbaum H, Montenegro CL. 2010. Assessing multi-temporal Landsat 7 ETM++ images for estimating above-ground biomass in subtropical dry forests of Argentina. J Arid Environ 74: 1262-1270. https://doi.org/10.1016/j.jaridenv.2010.04.007
  17. Gillespie TW, Ostermann-Kelm S, Dong C, Willis KS, Okin GS, Macdonald GM. 2018. Monitoring changes of NDVI in protected areas of southern California. Ecol Indic 88: 485-494. https://doi.org/10.1016/j.ecolind.2018.01.031
  18. Gillespie TW, Willis KS, Ostermann-Kelm S. 2015. Spaceborne remote sensing of the world's protected areas. Prog Phys Geog 39(3):388-404. https://doi.org/10.1177/0309133314561648.
  19. Gobbi B, Van Rompaey A, Loto D, Gasparri I, Vanacker V. 2020. Comparing Forest Structural Attributes Derived from UAV-Based Point Clouds with Conventional Forest Inventories in the Dry Chaco. Remote Sens 12: 4005.
  20. Le Houerou HN. 1984. Rain use efficiency: a unifying concept in arid-land ecology. J Arid Environ 7: 213-247. https://doi.org/10.1016/S0140-1963(18)31362-4
  21. Mao L, Li M, Shen W. 2020. Remote Sensing Applications for Monitoring Terrestrial Protected Areas: Progress in the Last Decade. Sustainability 12: 5016.
  22. Ministerio de Ambiente y Desarrollo Sostenible de la Nacion. 2020. Second National Inventory of Native Forests: Chaco Park Report: First Revision. Buenos Aires: Ministry of Environment and Sustainable Development of the Nation. https://acortar.link/VE1cPm. Accessed 6 Feb 2022. (in Spanish)
  23. Mitchell AL, Rosenqvist A, Mora B. 2017. Current remote sensing approaches to monitoring forest degradation in support of countries measurement, reporting and verification (MRV) systems for REDD+. Carbon Balance Manag 12: 9.
  24. Monteith JL. 1972. Solar Radiation and Productivity in Tropical Ecosystems. J Appl Ecol 9: 747-766. https://doi.org/10.2307/2401901
  25. Myneni RB, Williams DL. 1994. On the relationship between FAPAR and NDVI. Remote Sens Environ 49: 200-211. https://doi.org/10.1016/0034-4257(94)90016-7
  26. NASA (National Aeronautics and Space Administration). 2021. Prediction of Worldwide Energy Resource (POWER) Project. https://power.larc.nasa.gov/. Accessed 7 Mar 2022.
  27. National Weather Service. 2021. Meteorological Information Center, Weather Services. https://www.smn.gob.ar/descarga-de-datos. Accessed 1 May 2023. (in Spanish)
  28. Noy-Meir I. 1973. Desert Ecosystems: Environment and Producers. Annu Rev Ecol Syst 4: 25-51. https://doi.org/10.1146/annurev.es.04.110173.000325
  29. Rouse JW, Haas RH, Schell JA, Deering DW. 1973. Monitoring Vegetation Systems in the Great Plains with ERTS. 3rd ERTS Symposium, NASA SP-351, Washington DC, 10-14 December 1973, 309-317..
  30. Sala OE, Parton WJ, Joyce LA, Lauenroth WK. 1988. Primary Production of the Central Grassland Region of the United States. Ecology 69: 40-45. https://doi.org/10.2307/1943158
  31. Seaquist JW, Olsson L, Ardo J. 2003. A remote sensing-based primary production model for grassland biomes. Ecol Model 169: 131-155. https://doi.org/10.1016/S0304-3800(03)00267-9
  32. Secretaria de Ambiente y Desarrollo Sustentable de la Nacion. 2007. First National Inventory of Native Forests, IBRD Native Forests and Protected Areas Project 4085-AR, Argentina. https://www.argentina.gob.ar/sites/default/files/primer_inventario_nacional_-_informe_nacional_1.pdf. Accessed 7 Mar 2023. (in Spanish)
  33. Talamo A, Caziani SM. 2003. Variation in woody vegetation among sites with different disturbance histories in the Argentine Chaco. For Ecol Manag 184: 79-92. https://doi.org/10.1016/S0378-1127(03)00150-6
  34. Talamo A, De Casenave JL, Caziani SM. 2012. Components of woody plant diversity in semi-arid Chaco forests with heterogeneous land use and disturbance histories. J Arid Environ 85: 79-85. https://doi.org/10.1016/j.jaridenv.2012.05.008
  35. Tiedemann JL, Moreira A. 2023. Above-ground net primary productivity and rain use efficiency of Chaco Semi-arid Forest in Copo National Park, Santiago del Estero, Argentina. Rev Fac Agron 122: 120.
  36. Tiedemann JL. 2011. Spatial and temporal dynamics of the Normalized Difference Vegetation Index in Santiago del Estero. PhD thesis. National University of Cordoba, Cordoba, Argentina. (in Spanish)
  37. Zhang W, Brandt M, Tong X, Tian Q, Fensholt R. 2018. Impacts of the seasonal distribution of rainfall on vegetation productivity across the Sahel. Biogeosciences 15: 319-330.  https://doi.org/10.5194/bg-15-319-2018