Floresta e Ambiente
Floresta e Ambiente
Original Article Forest Management

Anthropogenic Disturbances Affect the Relationship Between Spectral Indices and the Biometric Variables of Brazilian Savannas

Eduarda Martiniano de Oliveira Silveira; Fausto Weimar Acerbi Júnior; Sérgio Teixeira Silva; José Márcio de Mello

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ABSTRACT: According to previous studies involving biometric variables modeling using remote sensing (RS), data did not consider the effects of anthropogenic disturbance as relevant factor. The main objective of this study was to model aboveground biomass (AGB) and total wood volume (TWV) of Brazilian Savanna biome using vegetation indices (VI) from LANDSAT 5 TM. Multiple linear regression (MLR) and random forest (RF) algorithm were applied across 641 field plots of cerrado sensu stricto of the state of Minas Gerais, Brazil, comparing two models: non-stratified, and stratified according to plot’s anthropization degree. AGB and TWV obtained from non-anthropized plots presented linear relation with VIs (R2 = 0.82 and 0.74, respectively) and, on the other hand, presented nonlinear relation when plots were affected by anthropogenic disturbances or were not stratified. This finding helps improving estimates by stratifying plots into their anthropization degree, mainly in the Brazilian Savanna biome undergoing anthropogenic disturbances.


remote sensing, stratification, modeling, wood volume, aboveground biomass


Aguiar TJA, Monteiro MSL. Modelo agrícola e desenvolvimento sustentável: a ocupação do Cerrado Piauiense. Ambiente & Sociedade 2005; 8(2): 1-17. http://dx.doi.org/10.1590/S1414-753X2005000200009.

Alvarenga LHV, Mello JM, Guedes ICL, Scolforo JRS. Performance of stratification in a Brazilian Savanna fragment by using geostatistical interpolator. Cerne 2012; 18(4): 675-681. http://dx.doi.org/10.1590/S0104-77602012000400018.

Arantes AE, Ferreira LG, Coe MT. The seasonal carbon and water balances of the Cerrado environment of Brazil: past, present, and future influences of land cover and land use. ISPRS Journal of Photogrammetry and Remote Sensing 2016; 117: 66-78. http://dx.doi.org/10.1016/j.isprsjprs.2016.02.008.

Aslan A, Rahman AF, Warren MW, Robeson SM. Mapping spatial distribution and biomass of Coastal wetland vegetation in Indonesian Papua by combining active and passive remotely sensed data. Remote Sensing of Environment 2016; 183: 65-81. http://dx.doi.org/10.1016/j.rse.2016.04.026.

Berner LT, Law BE. Plant traits, productivity, biomass and soil properties from forest sites in the Pacific Northwest, 1999-2014. Scientific Data 2016; 3(1): 160002. http://dx.doi.org/10.1038/sdata.2016.2. PMid:26784559.

Breiman L. Random forests. Machine Learning 2001; 45(1): 5-32. http://dx.doi.org/10.1023/A:1010933404324.

Bueno IT, Acerbi-Júnior FW, Silveira EMO, Mello JM, Carvalho LMT, Gomide LR et al. Object-based change detection in the Cerrado biome using landsat time series. Remote Sensing 2019; 11(5): 1-14. http://dx.doi.org/10.3390/rs11050570.

Cabral OMR, Rocha HR, Gash JH, Freitas HC, Ligo MAV. Water and energy fluxes from a woodland savanna (cerrado) in southeast Brazil. Journal of Hydrology: Regional Studies 2015; 4: 22-40.

DeVries B, Pratihast AK, Verbesselt J, Kooistra L, Herold M. Characterizing forest change using community-based monitoring data and Landsat time series. PLoS One 2016; 11(3): 1-25. http://dx.doi.org/10.1371/journal.pone.0147121. PMid:27018852.

Frolking S, Palace MW, Clark DB, Chambers JQ, Shugart HH, Hurtt GC. Forest disturbance and recovery: a general review in the context of space borne remote sensing of impacts on aboveground biomass and canopy structure. Journal of Geophysical Research 2009; 114(3): 1-27. http://dx.doi.org/10.1029/2008JG000911.

Garrigues S, Allard D, Baret F, Weiss M. Quantifying spatial heterogeneity at the landscape scale using variogram models. Remote Sensing of Environment 2006; 103(1): 81-96. http://dx.doi.org/10.1016/j.rse.2006.03.013.

Garroutte EL, Hansen AJ, Lawrence RL. Using NDVI and EVI to map spatiotemporal variation in the biomass and quality of forage for migratory elk in the Greater Yellowstone Ecosystem. Remote Sensing 2016; 8(5): 1-25. http://dx.doi.org/10.3390/rs8050404.

Gleason CJ, Im J. Forest biomass estimation from airborne LiDAR data using machine learning approaches. Remote Sensing of Environment 2012; 125: 80-91. http://dx.doi.org/10.1016/j.rse.2012.07.006.

González-Sanpedro MC, Le Toan T, Moreno J, Kergoat L, Rubio E. Seasonal variations of leaf area index of agricultural fields retrieved from Landsat data. Remote Sensing of Environment 2008; 112(3): 810-824. http://dx.doi.org/10.1016/j.rse.2007.06.018.

Hermosilla T, Wulder MA, White JC, Coops NC, Hobart GW. Disturbance-informed annual land cover classification maps of Canada's forested ecosystems for a 29-year landsat time series. Canadian Journal of Remote Sensing 2018; 44(1): 1-21. http://dx.doi.org/10.1080/07038992.2018.1437719.

Hoekstra JM, Boucher TM, Ricketts TH, Roberts C. Confronting a biome crisis: global disparities of habitat loss and protection. Ecology Letters 2005; 8(1): 23-29. http://dx.doi.org/10.1111/j.1461-0248.2004.00686.x.

Huete AR. A Soil-Adjusted Vegetation Index (SAVI). Remote Sensing of Environment 1988; 25(3): 295-309. http://dx.doi.org/10.1016/0034-4257(88)90106-X.

Lu D, Chen Q, Wang G, Liu L, Li G, Moran E. A survey of remote sensing-based aboveground biomass Estimation methods in forest ecosystems. International Journal of Digital Earth 2016; 9(1): 63-105. http://dx.doi.org/10.1080/17538947.2014.990526.

Morais VA, Mello JM, Gomide LR, Scolforo JR, Araújo EJG, Rufini AL. Influence of diameter measuring height on the adjustment of volume and biomass equations of Cerrado in Minas Gerais. Ciência e Agrotecnologia 1995; 2014(38): 230-239.

Nakaji T, Ide R, Takagi K, Kosugi Y, Ohkubo S, Nasahara KN et al. Utility of spectral vegetation indices for estimation of light conversion efficiency in coniferous forests in Japan. Agricultural and Forest Meteorology 2008; 148(5): 776-787. http://dx.doi.org/10.1016/j.agrformet.2007.11.006.

Pawar GV, Singh L, Jhariya MK, Sahu KP. Effect of anthropogenic disturbances on biomass and carbon storage potential of a dry tropical forest in India. Journal of Applied and Natural Science 2014; 6(2): 383-392. http://dx.doi.org/10.31018/jans.v6i2.432.

Prabhakara K, Hively WD, McCarty GW. Evaluating the relationship between biomass, percent groundcover and remote sensing indices across six winter cover crop fields in Maryland, United States. International Journal of Applied Earth Observation and Geoinformation 2015; 39: 88-102. http://dx.doi.org/10.1016/j.jag.2015.03.002.

R Core Team. A language and environment for statistical computing, version 1.0.136. Viena; 2014.

Reis AA, Mello JM, Acerbi-Júnior FW, Carvalho LMT. Estratificação em Cerrado sensu stricto a partir de imagens de sensoriamento remoto e técnicas geoestatísticas. Scientia Forestalis 2015; 43(106): 377-386.

Reis AA, Carvalho MC, Mello JM, Gomide LR, Ferraz AC Fo, Acerbi-Júnior FW. Spatial prediction of basal area and volume in Eucalyptus stands using Landsat TM data: an assessment of prediction methods. New Zealand Journal of Forestry Science Scientia Forestalis 2018; 48(1): 1-17. http://dx.doi.org/10.1186/s40490-017-0108-0.

Ribeiro A, Ferraz AC Fo. Estudo da metodologia proposta para classificação dos diferentes estágios de regeneração no Cerrado. Pesquisa Florestal Brasileira 2013; 33(73): 91-98. http://dx.doi.org/10.4336/2013.pfb.33.73.390.

Rouse JW, Hass RH, Schell JA, Deering DW. Monitoring vegetation systems in the Great plains with ERTS. In: Proceedings of the 3rd Earth Resources Technology Satellite (ERTS) Symposium; 1973; Washington. Washington: NASA; 1973. p. 309-317.

Rufini AL, Scolforo JRS, Oliveira AD, Mello JM. Volume equations for the Savannah (Cerrado), in Minas Gerais state. Cerne 2010; 16(1): 1-11. http://dx.doi.org/10.1590/S0104-77602010000100001.

Sano EE, Rosa R, Brito JLS, Ferreira LG. Land Cover Mapping of the tropical savanna region in Brazil. Environmental Monitoring and Assessment 2010; 166(1-4): 113-124. http://dx.doi.org/10.1007/s10661-009-0988-4. PMid:19504057.

Schwieder M, Leitão PJ, Bustamante MMC, Ferreira LG, Rabe A, Hostert P. Mapping Brazilian Savanna vegetation gradients with Landsat time series. International Journal of Applied Earth Observation and Geoinformation 2016; 52: 361-370. http://dx.doi.org/10.1016/j.jag.2016.06.019.

Scolforo HF, Scolforo JRS, Mello CR, Mello JM, Ferraz AC Fo. Spatial distribution of aboveground carbon stock of the arboreal vegetation in Brazilian biomes of Savanna, Atlantic Forest and Semi-Arid Woodland. PLoS One 2015; 10(6): 1-20. http://dx.doi.org/10.1371/journal.pone.0128781. PMid:26066508.

Scolforo HF, Scolforo JRS, Mello JM, Mello CR, Morais CA. Spatial interpolators for improving the mapping of carbon stock of the arboreal vegetation in Brazilian biomes of Atlantic Forest and Savanna. Forest Ecology and Management 2016; 376: 24-35. http://dx.doi.org/10.1016/j.foreco.2016.05.047.

Scolforo JR, Melo JM, Oliveira AD, Pereira RM, Souza FN, Guedes ICLV. Volumetria, peso de matériaseca e carbono. In: Scolforo JRS, Mello JM, Oliveira AD, editors. Inventário florestal de Minas Gerais: Cerrado: florística, estrutura, diversidade, similaridade, distribuição diamétrica e de altura, Volumetria, tendências de crescimento e áreas aptas para manejo florestal. Lavras: UFLA; 2008. p. 361-438.

Silva JF, Fariñas MR, Felfili JM, Klink CA. Spatial heterogeneity, land use and conservation in the Cerrado region of Brazil. Journal of Biogeography 2006; 33(3): 536-548. http://dx.doi.org/10.1111/j.1365-2699.2005.01422.x.

Silveira EMO, Bueno IT, Acerbi-Junior FW, Mello JM, Scolforo JRS, Wulder MA. Using spatial features to reduce the impact of seasonality for detecting tropical forest changes from Landsat time series. Remote Sensing 2018a; 10(6): 1-21. http://dx.doi.org/10.3390/rs10060808.

Silveira EMO, Espírito-Santo FD, Acerbi-Júnior FW, Galvão LS, Withey KD, Blackburn GA et al. Reducing the effects of vegetation phenology on change detection in tropical seasonal biomes. GIScience & Remote Sensing 2018b; 00: 1-19.

Silveira EMO, Mello JM, Acerbi-Júnior FW, Carvalho LMT. Object-based land-cover change detection applied to Brazilian seasonal Savannahs using geostatistical features. International Journal of Remote Sensing 2018c; 39(8): 2597-2619. http://dx.doi.org/10.1080/01431161.2018.1430397.

Silveira EMO, Menezes MD, Acerbi-Júnior FW, Castro MNST, Mello JM. Assessment of geostatistical features for object-based image classification of contrasted landscape vegetation cover. Journal of Applied Remote Sensing 2017; 11(3): 036004. http://dx.doi.org/10.1117/1.JRS.11.036004.

Silveira EMO, Terra MCNS, Acerbi-Júnior FW, Scolforo JRS. Estimating aboveground biomass loss from deforestation in the savanna and semi-arid biomes of brazil between 2007 and 2017. In: Silveira EMO, Terra MCNS, Acerbi-Júnior FW, Scolforo JRS, editors. Tropical forests in transition: the role of deforestation and impacts from community composition to regional climate change. London: Intechopen; 2019a. p. 1-17.

Silveira EMO, Espírito-Santo FD, Wulder MA, Acerbi-Júnior FW, Carvalho MC, Mello CR et al. Pre-stratified modelling plus residuals kriging reduces the uncertainty of aboveground biomass estimation and spatial distribution in heterogeneous savannas and forest environments. Forest Ecology and Management 2019b; 445: 96-109. http://dx.doi.org/10.1016/j.foreco.2019.05.016.

Veenendaal EM, Torello-Raventos M, Feldpausch TR, Domingues TF, Gerard F, Schrodt F et al. Structural, physiognomic and above-ground biomass variation in savanna–forest transition zones on three continents – how different are co-occurring savanna and forest formations? Biogeosciences 2015; 12(10): 2927-2951. http://dx.doi.org/10.5194/bg-12-2927-2015.

Verbesselt J, Hyndman R, Newnham G, Culvenor D. Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment 2010; 114(1): 106-115. http://dx.doi.org/10.1016/j.rse.2009.08.014.

Vieilledent G, Gardi O, Grinand C, Burren C, Andriamanjato M, Camara C et al. Bioclimatic envelope models predict a decrease in tropical forest carbon stocks with climate change in Madagascar. Journal of Ecology 2016; 104(3): 703-715. http://dx.doi.org/10.1111/1365-2745.12548.

Wang L, Zhou X, Zhu X, Dong Z, Guo W. Estimation of biomass in wheat using random forest regression algorithm and remote sensing data. The Crop Journal 2016; 4(3): 212-219. http://dx.doi.org/10.1016/j.cj.2016.01.008.

White JC, Wulder MA, Hermosilla T, Coops NC, Hobart GW. A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series. Remote Sensing of Environment 2017; 194: 303-321. http://dx.doi.org/10.1016/j.rse.2017.03.035.

Wulder MA, White JC, Fournier RA, Luther JE, Magnussen S. Spatially explicit large area biomass estimation: three approaches using forest inventory and remotely sensed imagery in a GIS. Sensors 2008; 8(1): 529-560. http://dx.doi.org/10.3390/s8010529. PMid:27879721.

Yang S, Feng Q, Liang T, Liu B, Zhang W, Xie H. Modeling grassland above-ground biomass based on artificial neural network and remote sensing in the three-river Headwaters region. Remote Sensing of Environment 2018; 204: 204. http://dx.doi.org/10.1016/j.rse.2017.10.011.

Zhao F, Xu B, Yang X, Jin Y, Li J, Xia L et al. Remote sensing estimates of grassland aboveground biomass based on MODIS net primary productivity (NPP): A case study in the Xilingol grassland of northern China. Remote Sensing 2014; 6(6): 5368-5386. http://dx.doi.org/10.3390/rs6065368.

Zhu X, Liu D. Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series. ISPRS Journal of Photogrammetry and Remote Sensing 2015; 102: 222-231. http://dx.doi.org/10.1016/j.isprsjprs.2014.08.014.

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