Litter stock in different areas in the process of ecological restoration
DOI:
https://doi.org/10.5965/223811712342024763Keywords:
recovery of degraded areas, forest ecology, forest species, NDVI, RPAAbstract
The objective of this study was to quantify and compare the accumulated litter in areas undergoing forest restoration that underwent different management methods. The different types of management methods for restoration were: natural regeneration after clear-cutting of eucalyptus plantations; planting of native species after clear-cutting of eucalyptus plantations; planting of native species after 50% clear-cutting of eucalyptus plantations; and secondary forest (reference). Ten permanent plots of 25 x 25 m were systematically allocated in each area, totaling 40 plots (2.5 ha). Subsequently, the absolute density of tree individuals (ind. ha-1) was measured. Litter (0.25 x 0.25 x 0.05 m) was collected in the center of each plot. Soil density was calculated for each plot at depths of 0-5, 5-10 and 10-20. The normalized difference vegetation index (NDVI) was estimated from spectral images obtained by a remotely piloted aircraft (RPA). Dense 3D point clouds were also generated from digital aerial photogrammetry and canopy openness values were estimated for each plot. Then, the Pearson correlation matrix was calculated between the accumulated litter values and the environmental variables canopy openness, soil density and absolute tree density. The accumulated litter showed a significant positive correlation with NDVI. The different ecological restoration systems, whether intercropped with eucalyptus or only with native forest species, were not efficient in promoting litter accumulation similar to a secondary forest. Eucalyptus did not compete with native forest species and provided a greater amount of accumulated litter. NDVI was the parameter that most correlated with accumulated litter.
Downloads
References
AERTS R & CHAPIN FS. 2000. The mineral nutrition of wild plants revisited: a re-evaluation of processes and patterns. In: Advances in ecological research. Cambridge: Academic Press. p.1-67.
AGISOFT. 2023. Agisoft Metashape User Manual: Professional Edition, version 2.0. LLC Agisoft: St. Petersburg, Russia.
AMAZONAS NT et al. 2018. High diversity mixed plantations of Eucalyptus and native trees: An interface between production and restoration for the tropics. Forest Ecology and Management 417: 247-256.
BERTOLINI IC et al. 2019. Propriedades físicas de solo em Floresta Ombrófila Mista sob processo de restauração passiva. Scientia Forestalis 47: 696-707.
CORREIA GGS et al. 2016. Estoque de serapilheira em floresta em restauração e em Floresta Atlântica de tabuleiro no Sudeste Brasileiro. Revista Árvore 40: 13-20.
DINIZ AR et al. 2015. Biomassa, estoques de carbono e de nutrientes em estádios sucessionais da Floresta Atlântica, RJ. Revista Brasileira de Ciências Agrarias 10: 443-451.
FERNANDES MRM et al. 2017. Ecologia da Paisagem de uma Bacia Hidrográfica dos Tabuleiros Costeiros do Brasil. Floresta e Ambiente 24: 1-9.
FONTES JAC. 2010. Caracterização geoambiental da sub-bacia do rio Fundo. Dissertação de Mestrado (Meio Ambiente e Desenvolvimento, São Cristovão: UFS. 120p.
GAMARRA R et al. 2016. Uso do NDVI na análise da estrutura da vegetação e efetividade da proteção de unidade de conservação no cerrado. Revista Ra'e Ga Espaço Geográfico em Análise 37: 307-332.
JOSÉ MB. 2023. Viabilidade econômica e ecológica dos modelos sucessionais de restauração florestal nos serviços ambientais na mata atlântica brasileira. Dissertação de Mestrado (Meio Ambiente e Desenvolvimento), São Cristovão: UFS. 77p.
MARTINS SV. 2012. Ecologia de florestas tropicais do Brasil. 2.ed. Viçosa: Editora UFV. 371p.
O’CONNELL AM & SANKARAN KV. 1997. Organic matter accretion, decomposition and mineralisation. In: NAMBIAR EKS & BROWN AG. (Ed.) Management of soil, nutrients and water in tropical plantations forests. Canberra: ACIAR Australia/CSIRO. p 443-480.
PONZONI FJ & SHIMABUKURO YE. 2007. Sensoriamento Remoto no Estudo da Vegetação. São José dos Campos-: INPE-Editora Parêntese. 280p.
R Core Team. 2024. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Áustria. <https://www.R-project.org/>.
RODRIGUES RR et al. 2009. On the restoration of high diversity forests: 30 years of experience in the Brazilian Atlantic Forest. Biological conservation 142: 1242-1251.
SILVA CA et al. 2019. ForestGapR: An R Package for Airborne Laser Scanning-derived Tropical Forest Gaps Analysis. Methods Ecology and Evolution. 10: 1347-1356.
SMITH GM & MILTON EJ. 1999. The use of the empirical line method to calibrate remotely sensed data to reflectance. International Journal of Remote Sensing 20: 2653–2662.
POGGIANI F et al. 1998. Indicadores de sustentabilidade das plantações florestais. Série Técnica IPEF 12: 33-44.
VALADÃO MBX et al. 2019. Litterfall, litter layer and leaf decomposition in Eucalyptus stands on Cerrado soils. Scientia Forestalis 122: 256-264.
VITOUSEK PM & SANFORD RL. 1986. Nutrient cycling in moist tropical forest. Annual Review of Ecology and Systematics 17: 137-167.
ZAHAWI RA et al. 2014. Hidden costs of passive restoration. Restoration Ecology 22: 284-287.
ZANINNI AM et al. 2021. The effect of ecological restoration methods on carbon stocks in the Brazilian Atlantic Forest. Forest Ecology and Management 481: 118-138.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Authors & Revista de Ciências Agroveterinárias
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors publishing in this journal are in agreement with the following terms:
a) Authors maintain the copyrights and concede to the journal the copyright for the first publication, according to Creative Commons Attribution Licence.
b) Authors have the authority to assume additional contracts with the content of the manuscript.
c) Authors may supply and distribute the manuscript published by this journal.