1.Pennington, R. T., Lehmann, C. E. R. & Rowland, L. M. Tropical savannas and dry forests. Curr. Biol. 28, R541–R545 (2018).CAS
PubMed
Google Scholar
2.Piao, S. et al. Interannual variation of terrestrial carbon cycle: Issues and perspectives. Glob. Change Biol. 26, 300–318 (2019).ADS
Google Scholar
3.Fan, L. et al. Satellite-observed pantropical carbon dynamics. Nat. Plants. 5, 944–951 (2019).CAS
PubMed
Google Scholar
4.Humphrey, V. et al. Sensitivity of atmospheric CO2 growth rate to observed changes in terrestrial water storage. Nature 560, 628–631 (2018).ADS
CAS
PubMed
Google Scholar
5.Moro, M. F., Nic Lughadha, E., de Araújo, F. S. & Martins, F. R. A phytogeographical metaanalysis of the semiarid caatinga domain in Brazil. Bot. Rev. 82, 91–148 (2016).6.Terra, M., et al. Water availability drives gradients of tree diversity, structure and functional traits in the Atlantic–Cerrado–Caatinga transition, Brazil. J. Plant Ecol. 11, 803–814 (2018).7.Leite, M. B., Xavier, R. O., Oliveira, P. T. S., Silva, F. K. G. & Silva Matos, D. M. Groundwater depth as a constraint on the woody cover in a Neotropical Savanna. Plant Soil. 426, 1–15 (2018).8.Silvertown, J., Araya, Y. & Gowing, D. Hydrological niches in terrestrial plant communities: a review. J. Ecol. 103, 93–108 (2014).
Google Scholar
9.Poulter, B. et al. Plant functional type classification for earth system models: results from the European Space Agency’s Land Cover Climate Change Initiative. Geosci. Model Dev. 8, 2315–2328 (2015).ADS
Google Scholar
10.Congalton, R. G., Gu, J., Yadav, K., Thenkabail, P. & Ozdogan, M. Global land cover mapping: A review and uncertainty analysis. Remote Sens. 6(12), 12070–12093 (2014).ADS
Google Scholar
11.Phiri, D. & Morgenroth, J. Developments in Landsat land cover classification methods: A review. Remote Sens. 9(9), 967 (2017).
Google Scholar
12.Joshi, N. et al. A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sens. 8(1), 70 (2016).ADS
Google Scholar
13.Xiao, J. et al. Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years. Remote Sens. Environ. 233, 111383 (2019).ADS
Google Scholar
14.Françoso, R. D. et al. Delimiting floristic biogeographic districts in the Cerrado and assessing their conservation status. Biodivers. Conserv. 29, 1477–1500 (2019).
Google Scholar
15.Eiten, G. Delimitation of the cerrado concept. Plant Ecol. 36, 169–178 (1978).
Google Scholar
16.Ribeiro, J.F. & Walter, B.M.T. As principais fitofisionomias do bioma Cerrado in Cerrado: ecologia e flora (ed. Sano, S.M., Almeida, S.P. & Ribeiro, J.F.) 151–212 (EMBRAPA, 2008).17.Oliveria, P.S. & Marquis, R.J. The Cerrados of Brazil: ecology and natural history of a neotropical savanna. (Columbia University Press, 2002).18.Buchhorn, M. et al. Copernicus Global Land Cover Layers—Collection 2. Remote Sens. 12, 1044 (2020).ADS
Google Scholar
19.European Space Agency. Data. https://climate.esa.int/en/projects/land-cover/data/ (2021).20.Pellegrini, A. F. A. Nutrient limitation in tropical savannas across multiple scales and mechanisms. Ecology 97, 313–324 (2016).PubMed
Google Scholar
21.Vourlitis, G. L. et al. Variations in stand structure and diversity along a soil fertility gradient in a Brazilian savanna (Cerrado) in Southern Mato Grosso. Soil Sci. Soc. Am. J. 77, 1370–1379 (2013).ADS
CAS
Google Scholar
22.Abrahão, A. et al. Soil types select for plants with matching nutrient-acquisition and use traits in hyperdiverse and severely nutrient-impoverished campos rupestres and cerrado in Central Brazil. J. Ecol. 107, 1302–1316 (2018).
Google Scholar
23.de Assis, A. C. C., Coelho, R. M., da Silva Pinheiro, E. & Durigan, G. Water availability determines physiognomic gradient in an area of low-fertility soils under Cerrado vegetation. Plant Ecol. 212, 1135–1147 (2011).24.Oliveira, P. T. S. et al. Groundwater recharge decrease with increased vegetation density in the Brazilian cerrado. Ecohydrology. 10, e1759 (2016).25.de Oliveira Xavier, R., Leite, M. B., Dexter, K. & da Silva Matos, D. M. Differential effects of soil waterlogging on herbaceous and woody plant communities in a Neotropical savanna. Oecologia. 190, 471–483 (2019).26.Zappi, D. C., Moro, M. F., Meagher, T. R. & Nic Lughadha, E. Plant biodiversity drivers in Brazilian campos rupestres: insights from phylogenetic structure. Front. Plant Sci. 8, (2017).27.Neri, A. V., Schaefer, C. E. G. R., Souza, A. L., Ferreira-Junior, W. G. & Meira-Neto, J. A. A. Pedology and plant physiognomies in the cerrado, Brazil. An. Acad. Bras. Ciênc. 85, 87–102 (2013).CAS
PubMed
Google Scholar
28.Simon, M. F. & Pennington, T. Evidence for Adaptation to Fire Regimes in the Tropical Savannas of the Brazilian Cerrado. Int. J. Plant Sci. 173, 711–723 (2012).
Google Scholar
29.de Castro, E. A. & Kauffman, J. B. Ecosystem structure in the Brazilian Cerrado: a vegetation gradient of aboveground biomass, root mass and consumption by fire. J. Trop. Ecol. 14, 263–283 (1998).
Google Scholar
30.da Silva, D. M. & Batalha, M. A. Soil–vegetation relationships in cerrados under different fire frequencies. Plant Soil 311, 87–96 (2008).CAS
Google Scholar
31.Durigan, G. Zero-fire: Not possible nor desirable in the Cerrado of Brazil. Flora. 268, 151612 (2020).32.Lloyd, J. & Veenendaal, E. M. Are fire mediated feedbacks burning out of control? (2016)33.Bueno, M. L. et al. The environmental triangle of the Cerrado Domain: Ecological factors driving shifts in tree species composition between forests and savannas. J. Ecol. 106, 2109–2120 (2018).
Google Scholar
34.Alencar, A. et al. Mapping Three Decades of Changes in the Brazilian Savanna Native Vegetation Using Landsat Data Processed in the Google Earth Engine Platform. Remote Sens. 12, 924 (2020).ADS
Google Scholar
35.INPE. Projeto TerraClass Cerrado Mapeamento do Uso e Cobertura Vegetal do Cerrado. http://www.inpe.br/cra/projetos_pesquisas/dados_terraclass.php (2019)36.Sano, E. E., Rosa, R., Brito, J. L. S. & Ferreira, L. G. Land cover mapping of the tropical savanna region in Brazil. Environ. Monit. Assess. 166, 113–124 (2009).PubMed
Google Scholar
37.Sano, E. E. et al. Cerrado ecoregions: A spatial framework to assess and prioritize Brazilian savanna environmental diversity for conservation. J. Environ. Manag. 232, 818–828 (2019).
Google Scholar
38.Monteiro, L. M. et al. Evaluating the impact of future actions in minimizing vegetation loss from land conversion in the Brazilian Cerrado under climate change. Biodivers. Conserv. 29, 1701–1722 (2018).
Google Scholar
39.Silva, J. F., Farinas, M. R., Felfili, J. M. & Klink, C. A. Spatial heterogeneity, land use and conservation in the cerrado region of Brazil. J. Biogeogr. 33, 536–548 (2006).
Google Scholar
40.Strassburg, B. B. N. et al. Moment of truth for the Cerrado hotspot. Nat. Ecol. Evol. 1, 0099 (2017).
Google Scholar
41.Soares-Filho, B. et al. Cracking Brazil’s Forest Code. Science 344, 363–364 (2014).ADS
CAS
PubMed
Google Scholar
42.Gomes, L., Miranda, H. S. & Bustamante, M. M. C. How can we advance the knowledge on the behavior and effects of fire in the Cerrado biome? For. Ecol. Manag. 417, 281–290 (2018).43.Hartley, A. J., MacBean, N., Georgievski, G. & Bontemps, S. Uncertainty in plant functional type distributions and its impact on land surface models. Remote Sens. Environ. 203, 71–89 (2017).ADS
Google Scholar
44.Cava, M. G. B., Pilon, N. A. L., Ribeiro, M. C. & Durigan, G. Abandoned pastures cannot spontaneously recover the attributes of old-growth savannas. J. Appl. Ecol. 55, 1164–1172 (2017).
Google Scholar
45.Brancalion, P. H. S. et al. Governance innovations from a multi-stakeholder coalition to implement large-scale Forest Restoration in Brazil. World Dev. Perspect. 3, 15–17 (2016).
Google Scholar
46.Seddon, N. et al. Understanding the value and limits of nature-based solutions to climate change and other global challenges. Phil. Trans. R. Soc. B. 375, 20190120 (2020).PubMed
PubMed Central
Google Scholar
47.MMA. Plano de Manejo Parque Nacional Chapada dos Veadeiros. Ministro de Estado do Meio Ambiente. Brasília. (2009).48.Hunke, P., Roller, R., Zeilhofer, P., Schröder, B. & Mueller, E. N. Soil changes under different land-uses in the Cerrado of Mato Grosso, Brazil. Geoderma Reg. 4, 31–43 (2015).
Google Scholar
49.Sampaio, A.B. et. al. Guia de restauração do Cerrado: volume 1: semeadura direta. Embrapa Cerrados-Livro técnico (INFOTECA-E, 2015).50.Schmidt, I. B. et al. Tailoring restoration interventions to the grassland-savanna-forest complex in central Brazil. Restor. Ecol. 27, 942–948 (2019).
Google Scholar
51.Schmidt, I. B. et al. Community-based native seed production for restoration in Brazil: the role of science and policy. Plant Biol. J. 21, 389–397 (2018).
Google Scholar
52.Strassburg, B. B. N. et al. Author Correction: Strategic approaches to restoring ecosystems can triple conservation gains and halve costs. Nat. Ecol. Evol. 4, 765–765 (2020).PubMed
Google Scholar
53.Assis, G. B., Pilon, N. A. L., Siqueira, M. F. & Durigan, G. Effectiveness and costs of invasive species control using different techniques to restore cerrado grasslands. Restor. Ecol. 29, (2020).54.Torello-Raventos, M. et al. On the delineation of tropical vegetation types with an emphasis on forest/savanna transitions. Plant Ecol. Divers. 6, 101–137 (2013).
Google Scholar
55.da Silva, D. P., Amaral, A. G., Bijos, N. R. & Munhoz, C. B. R. Is the herb-shrub composition of veredas (Brazilian palm swamps) distinguishable?. Acta Bot. Bras. 32, 47–54 (2017).
Google Scholar
56.Munhoz, C. B. R. & Felfili, J. M. Florística do estrato herbáceo-subarbustivo de um campo limpo úmido em Brasília, Brasil. Biota. Neotrop. 7, 205–215 (2007).
Google Scholar
57.Franco, A. C. et al. Leaf functional traits of Neotropical savanna trees in relation to seasonal water deficit. Trees 19, 326–335 (2004).
Google Scholar
58.Oliveras, I. & Malhi, Y. Many shades of green: the dynamic tropical forest–savannah transition zones. Phil. Trans. R. Soc. B. 371, 20150308 (2016).PubMed
PubMed Central
Google Scholar
59.Cianciaruso, MV. & Batalha, MA. A year in a Cerrado wet grassland: a non-seasonal island in a seasonal savanna environment. Braz. J. Biol. 68, 495–501 (2008).60.MapBiomas. MapBiomas v5.0. https://mapbiomas.org (2021).61.Souza, C. M. Jr. et al. Reconstructing three decades of land use and land cover changes in Brazilian biomes with landsat archive and earth engine. Remote Sens. 12, 2735 (2020).ADS
Google Scholar
62.Gorelick, N. et al. Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).ADS
Google Scholar
63.Crouzeilles, R. et al. There is hope for achieving ambitious Atlantic Forest restoration commitments. Perspect. Ecol. Conserv. 17, 80–83 (2019).
Google Scholar
64.Smith, C. C. et al. Secondary forests offset less than 10% of deforestation-mediated carbon emissions in the Brazilian Amazon. Glob. Change Biol. 26, 7006–7020 (2020).ADS
Google Scholar
65.Rosan, T. M. et al. Extensive 21st-Century Woody Encroachment in South America’s Savanna. Geophys. Res. Lett. 46, 6594–6603 (2019).ADS
Google Scholar
66.Schwieder, M. et al. Mapping Brazilian savanna vegetation gradients with Landsat time series. Int. J. Appl. Earth Obs. Geoinf. 52, 361–370 (2016).ADS
Google Scholar
67.Ribeiro, F. F. et al. Geographic Object-Based Image Analysis Framework for Mapping Vegetation Physiognomic Types at Fine Scales in Neotropical Savannas. Remote Sens. 12, 1721 (2020).ADS
Google Scholar
68.Jacon, A. D., Galvão, L. S., dos Santos, J. R. & Sano, E. E. Seasonal characterization and discrimination of savannah physiognomies in Brazil using hyperspectral metrics from Hyperion/EO-1. Int. J. Remote Sens. 38, 4494–4516 (2017).
Google Scholar
69.Neves, A. K. et al. Hierarchical mapping of Brazilian Savanna (Cerrado) physiognomies based on deep learning. J. App. Remote Sens. 15, 044504–1–044504–23 (2021).70.de Souza Mendes, F., Baron, D., Gerold, G., Liesenberg, V. & Erasmi, S. Optical and SAR remote sensing synergism for mapping vegetation types in the endangered cerrado/amazon ecotone of nova mutum—mato grosso. Remote Sens. 11, 1161 (2019).ADS
Google Scholar
71.Sano, E. E., Ferreira, L. G., Asner, G. P. & Steinke, E. T. Spatial and temporal probabilities of obtaining cloud-free Landsat images over the Brazilian tropical savanna. Int. J. Remote Sens. 28, 2739–2752 (2007).
Google Scholar
72.Flores-Anderson, A.I., Herndon, K.E., Thapa, R.B. & Cherrington, E. The SAR handbook: comprehensive methodologies for forest monitoring and biomass estimation. (SERVIR, 2019).73.Bendini, H. N. et al. Detailed agricultural land classification in the Brazilian cerrado based on phenological information from dense satellite image time series. Int. J. Appl. Earth. Obs. Geoinf. 82, 101872 (2019).74.Bendini, H. N. et al. Combining environmental and Landsat analysis ready data for vegetation mapping: a case study in the Brazilian savanna biome. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. XLIII-B3–2020, 953–960 (2020).75.ECMWF. Climate reanalysis. https://climate.copernicus.eu/climate-reanalysis (2021).76.UNESCO. MINOR MODIFICATIONS PROPOSAL TO THE BOUNDARIES of Cerrado Protected Areas World Heritage: Chapada dos Veadeiros and Emas National Parks. (UNESCO Brasília, 2019)77.ICUN. Advisory mission to Cerrado Protected Areas World Heritage Property (Chapada Dos Veadeiros component) (Brazil). (International Union for Conservation of Nature, 2016)78.EMBRAPA. Sistema brasileiro de classificação dos solos. (EMBRAPA, 2006)79.IBGE. Mapa de Solos do Brasil do IBGE escala 1:250.000 https://www.ibge.gov.br/geociencias/downloads-geociencias.html (IBGE, 2020).80.Rodrigues, J. A. et al. How well do global burned area products represent fire patterns in the Brazilian Savannas biome? An accuracy assessment of the MCD64 collections. Int. J. Appl. Earth. Obs. Geoinf. 78, 318–331 (2019).ADS
Google Scholar
81.NASA, MCD64A1 v6. https://lpdaac.usgs.gov/products/mcd64a1v006/ (2021)82.GEE. Earth Engine Data Catalog. https://developers.google.com/earth-engine/datasets (2021)83.Vreugdenhil, M. et al. Sensitivity of sentinel-1 backscatter to vegetation dynamics: an Austrian case study. Remote Sens. 10, 1396 (2018).ADS
Google Scholar
84.Harfenmeister, K., Spengler, D. & Weltzien, C. Analyzing temporal and spatial characteristics of crop parameters using sentinel-1 backscatter data. Remote Sens. 11, 1569 (2019).ADS
Google Scholar
85.European Space Agency. Level-2A Algorithm Overview https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm (2021)86.GEE. Sentinel-2 MSI: MultiSpectral Instrument, Level-2A. https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR (2021).87.GEE. USGS Landsat 8 Level 2, Collection 2, Tier 1. https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_L2 (2021)88.Xue, J. & Su, B. Significant remote sensing vegetation indices: a review of developments and applications. J. Sens. 2017, 1353691 (2017).
Google Scholar
89.Parente, L. & Ferreira, L. Assessing the spatial and occupation dynamics of the Brazilian pasturelands based on the automated classification of MODIS images from 2000 to 2016. Remote Sens. 10, 606 (2018).ADS
Google Scholar
90.Hill, M. J., Zhou, Q., Sun, Q., Schaaf, C. B. & Palace, M. Relationships between vegetation indices, fractional cover retrievals and the structure and composition of Brazilian Cerrado natural vegetation. Int. J. Remote Sens. 38, 874–905 (2017).
Google Scholar
91.Nomura, K. & Mitchard, E. More than meets the eye: using sentinel-2 to map small plantations in complex forest landscapes. Remote Sens. 10, 1693 (2018).ADS
Google Scholar
92.Hagen-Zanker, A. A computational framework for generalized moving windows and its application to landscape pattern analysis. Int. J. Appl. Earth. Obs. Geoinf. 44, 205–216 (2016).ADS
Google Scholar
93.Wantzen, K. M. et al. Soil carbon stocks in stream-valley-ecosystems in the Brazilian Cerrado agroscape. Agric. Ecosyst. Environ. 151, 70–79 (2012).CAS
Google Scholar
94.ESA. Copernicus DEM: Global and European Digital Elevation Model (COP-DEM). https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198 (2021)95.Breiman, L. Mach. Learn. 45, 5–32 (2001).96.Chen, D. & Wei, H. The effect of spatial autocorrelation and class proportion on the accuracy measures from different sampling designs. ISPRS J. Photogramm. Remote Sens. 64, 140–150 (2009).ADS
Google Scholar
97.Olofsson, P., Foody, G. M., Stehman, S. V. & Woodcock, C. E. Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation. Remote Sens. Environ. 129, 122–131 (2013).ADS
Google Scholar
98.Foody, G. M. Status of land cover classification accuracy assessment. Remote Sens. Environ. 80, 185–201 (2002).ADS
Google Scholar
99.Jank, L., Barrios, S. C., do Valle, C. B., Simeão, R. M. & Alves, G. F. The value of improved pastures to Brazilian beef production. Crop Pasture Sci. 65, 1132 (2014).100.Oliveira, J. et al. Choosing pasture maps: An assessment of pasture land classification definitions and a case study of Brazil. Int. J. Appl. Earth. Obs. Geoinf. 93, 102205 (2020).101.Pereira, O., Ferreira, L., Pinto, F. & Baumgarten, L. Assessing pasture degradation in the Brazilian cerrado based on the analysis of MODIS NDVI time-series. Remote Sens. 10, 1761 (2018).ADS
Google Scholar
102.Meirelles, M.L., Ferreira, E.A.B. and Franco, A.C. Dinâmica sazonal do carbono em campo úmido do cerrado. Embrapa Cerrados-Documentos (INFOTECA-E, 2006).103.França, A. M. S., Paiva, R. J. O., Sano, E. E. & Carvalho, A. M. Estimates for carbon stocks in soil under humid grassland areas in the federal district of Brazil. OJE 04, 777–787 (2014).
Google Scholar
104.Silveira, F. A. O. et al. Ecology and evolution of plant diversity in the endangered campo rupestre: a neglected conservation priority. Plant Soil. 403, 129–152 (2015).
Google Scholar
105.Pereira, E. G., Siqueira-Silva, A. I., de Souza, A. E., Melo, N. M. J. & Souza, J. P. Distinct ecophysiological strategies of widespread and endemic species from the megadiverse campo rupestre. Flora 238, 79–86 (2018).
Google Scholar
106.Moreira, S. N., Pott, V. J., Pott, A., da Silva, R. H. & Júnior, G. A. D. Flora and vegetation structure of Vereda in southwestern Cerrado. Oecol. Aust. 23, 776–798 (2019).
Google Scholar
107.Pinto, J. R. R., Lenza, E. & Pinto, A. de S. Composição florística e estrutura da vegetação arbustivo-arbórea em um cerrado rupestre, Cocalzinho de Goiás, Goiás. Rev. Bras. Bot. 32, (2009).108.Gomes, L., Lenza, E., Maracahipes, L., Marimon, B. S. & Oliveira, E. A. de. Comparações florísticas e estruturais entre duas comunidades lenhosas de cerrado típico e cerrado rupestre, Mato Grosso, Brasil. Acta Bot. Bras. 25, 865–875 (2011).109.Gomes, D.L. Classificação fitofisionômica do cerrado no Parque Nacional da Chapada dos Veadeiros, GO, com a aplicação de uma análise combinatória com filtros adaptativos em imagens TM Landsat. (Dissertação de Mestrado, Brasília, 2008).110.Neyret, M. et al. Examining variation in the leaf mass per area of dominant species across two contrasting tropical gradients in light of community assembly. Ecol. Evol. 6, 5674–5689 (2016).PubMed
PubMed Central
Google Scholar
111.Abreu, R. C. R. et al. The biodiversity cost of carbon sequestration in tropical savanna. Sci. Adv. 3, e1701284 (2017).112.Morais, V. A. et al. Carbon and biomass stocks in a fragment of cerradão in Minas Gerais state, Brazil. Cerne 19, 237–245 (2013).
Google Scholar
113.Bispo, P. da C. et al. Woody aboveground biomass mapping of the Brazilian savanna with a multi-sensor and machine learning approach. Remote Sens. 12, 2685 (2020).114.Taberelli, M. & Gascon, C. Lessons from fragmentation research: improving management and policy guidelines for biodiversity conservation. Conserv. Biol. 19, 734–739 (2005).
Google Scholar
115.Holder, D. N. H., Dockary, M. & Barber, J. The red edge of plant leaf reflectance. Int. J. Remote Sens. 4, 273–288 (1983).
Google Scholar
116.Li, J. & Roy, D. A global analysis of sentinel-2A, sentinel-2B and landsat-8 data revisit intervals and implications for terrestrial monitoring. Remote Sens. 9, 902 (2017).ADS
Google Scholar
117.Hunter, F. D. L., Mitchard, E. T. A., Tyrrell, P. & Russell, S. Inter-seasonal time series imagery enhances classification accuracy of grazing resource and land degradation maps in a savanna ecosystem. Remote Sens. 12, 198 (2020).ADS
Google Scholar
118.Ramos, D. M., Diniz, P., Ooi, M. K. J., Borghetti, F. & Valls, J. F. M. Avoiding the dry season: dispersal time and syndrome mediate seed dormancy in grasses in Neotropical savanna and wet grasslands. J. Veg. Sci. 28, 798–807 (2017).
Google Scholar
119.de Camargo, M. G. G., de Carvalho, G. H., Alberton, B. de C., Reys, P. & Morellato, L. P. C. Leafing patterns and leaf exchange strategies of a cerrado woody community. Biotropica. 50, 442–454 (2018).120.Rüetschi, M., Schaepman, M. & Small, D. Using multitemporal sentinel-1 C-band backscatter to monitor phenology and classify deciduous and coniferous forests in Northern Switzerland. Remote Sens. 10, 55 (2017).ADS
Google Scholar
121.Sano, E. E., Ferreira, L. G. & Huete, A. R. Synthetic aperture radar (L band) and optical vegetation indices for discriminating the Brazilian savanna physiognomies: a comparative analysis. Earth Interact. 9, 1–15 (2005).
Google Scholar
122.Joshi, N. et al. A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sens. 8, 70 (2016).ADS
Google Scholar
123.Nicolau, A. P., Flores-Anderson, A., Griffin, R., Herndon, K. & Meyer, F. J. Assessing SAR C-band data to effectively distinguish modified land uses in a heavily disturbed Amazon forest. Int. J. Appl. Earth. Obs. Geoinf. 94, 102214 (2021).124.Notarnicola, C. & Posa, F. Inferring vegetation water content from C- and L-band SAR images. IEEE Trans. Geosci. Remote Sens. 45, 3165–3171 (2007).ADS
Google Scholar
125.El Hajj, M., Baghdadi, N., Bazzi, H. & Zribi, M. Penetration analysis of SAR signals in the C and L bands for wheat, maize, and grasslands. Remote Sens. 11, 31 (2018).ADS
Google Scholar
126.JAXA. Global PALSAR-2/PALSAR/JERS-1 Mosaic and Forest/Non-Forest map. https://www.eorc.jaxa.jp/ALOS/en/palsar_fnf/fnf_index.htm (JAXA, 2021).127.Zimbres, B. et al. Mapping the stock and spatial distribution of aboveground woody biomass in the native vegetation of the Brazilian Cerrado biome. For. Ecol. Manag. 499, 119615 (2021).128.Ryan, C. M. et al. Quantifying small-scale deforestation and forest degradation in African woodlands using radar imagery. Glob. Change Biol. 18, 243–257 (2011).ADS
Google Scholar
129.Yu, Y. & Saatchi, S. Sensitivity of L-Band SAR backscatter to aboveground biomass of global forests. Remote Sens. 8, 522 (2016).ADS
Google Scholar
130.Pilon, N. A. L. et al. The diversity of post-fire regeneration strategies in the cerrado ground layer. J. Ecol. 109, 154–166 (2020).
Google Scholar
131.Schmidt, I. B. & Eloy, L. Fire regime in the Brazilian Savanna: Recent changes, policy and management. Flora. 268, 151613 (2020).132.Boschetti, L. et al. Global validation of the collection 6 MODIS burned area product. Remote Sens. Environ. 235, 111490 (2019).133.Humber, M. L., Boschetti, L., Giglio, L. & Justice, C. O. Spatial and temporal intercomparison of four global burned area products. Int. J. Digit. Earth. 12, 460–484 (2018).PubMed
PubMed Central
Google Scholar
134.Arruda, V. L. S., Piontekowski, V. J., Alencar, A., Pereira, R. S. & Matricardi, E. A. T. An alternative approach for mapping burn scars using Landsat imagery, Google Earth Engine, and Deep Learning in the Brazilian Savanna. Remote Sens. Appl. Soc. Environ. 22, 100472 (2021).135.Santos, F. L. M. et al. Assessing VIIRS capabilities to improve burned area mapping over the Brazilian Cerrado. Int. J. Remote Sens. 41, 8300–8327 (2020).
Google Scholar
136.Marques, E. Q. et al. Redefining the Cerrado-Amazonia transition: implications for conservation. Biodivers. Conserv. 29, 1501–1517 (2019).
Google Scholar
137.Marimon, B. S. et al. Disequilibrium and hyperdynamic tree turnover at the forest–cerrado transition zone in southern Amazonia. Plant Ecol. Divers. 7, 281–292 (2013).
Google Scholar
138.Mellor, A., Boukir, S., Haywood, A. & Jones, S. Exploring issues of training data imbalance and mislabelling on random forest performance for large area land cover classification using the ensemble margin. ISPRS J. Photogramm. Remote Sens. 105, 155–168 (2015).ADS
Google Scholar
139.DRYFLOR. Plant diversity patterns in neotropical dry forests and their conservation implications. Science. 353, 1383–1387 (2016). More