Landsat phenological metrics and their relation to aboveground carbon in the Brazilian Savanna

Scholes RJ, Smart K. 4.09—carbon storage in terrestrial ecosystems A2. In: Pielke RA, editor. Climate Vulnerability. Oxford: Academic Press; 2013. p. 93–108.

Sabine CL, Heimann M, Artaxo P, Bakker DC, Chen C-TA, Field CB, et al. Current status and past trends of the global carbon cycle. Scope Sci Comm Probl Environ Int Counc Sci Unions. 2004;62:17–44.

Mitchard ETA, Saatchi SS, Baccini A, Asner GP, Goetz SJ, Harris NL, et al. Uncertainty in the spatial distribution of tropical forest biomass: a comparison of pan-tropical maps. Carbon Balance Manag. 2013;8(1):10.

Bonan GB. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science. 2008;320(5882):1444–9.

Rodríguez-Veiga P, Wheeler J, Louis V, Tansey K, Balzter H. Quantifying forest biomass carbon stocks from space. Curr For Rep. 2017;3(1):1–18.

Biermann F, Kanie N, Kim RE. Global governance by goal-setting: the novel approach of the UN Sustainable Development Goals. Curr Opin Environ Sustain. 2017;26–27:26–31.

Goetz S, Baccini A, Laporte N, Johns T, Walker W, Kellndorfer J, et al. Mapping and monitoring carbon stocks with satellite observations: a comparison of methods. Carbon Balance Manag. 2009;4(1):1–7.

Houghton R, Goetz S. New satellites offer a better approach for determining sources and sinks of carbon. EOS Trans Am Geophys Union. 2008;43:417–8.

Saatchi SS, Halligan K, Despain DG, Crabtree RL. Estimation of forest fuel load from radar remote sensing. IEEE Trans Geosci Remote Sens. 2007;45(6):1726–40.

Mitchard ETA, Saatchi SS, Woodhouse IH, Nangendo G, Ribeiro NS, Williams M, et al. Using satellite radar backscatter to predict above-ground woody biomass: a consistent relationship across four different African landscapes. Geophys Res Lett. 2009. https://doi.org/10.1029/2009GL040692 .

Asner GP, Mascaro J, Muller-Landau HC, Vieilledent G, Vaudry R, Rasamoelina M, et al. A universal airborne LiDAR approach for tropical forest carbon mapping. Oecologia. 2012;168(4):1147–60.

Avitabile V, Baccini A, Friedl MA, Schmullius C. Capabilities and limitations of Landsat and land cover data for aboveground woody biomass estimation of Uganda. Remote Sens Environ. 2012;117:366–80.

Clark ML, Roberts DA, Ewel JJ, Clark DB. Estimation of tropical rain forest aboveground biomass with small-footprint lidar and hyperspectral sensors. Remote Sens Environ. 2011;115(11):2931–42.

Grace J, Mitchard E, Gloor E. Perturbations in the carbon budget of the tropics. Global Change Biol. 2014;20(10):3238–55.

Lehmann CER, Archibald SA, Hoffmann WA, Bond WJ. Deciphering the distribution of the Savanna biome. New Phytol. 2011;191(1):197–209.

de Miranda SdC, Bustamante M, Palace M, Hagen S, Keller M, Ferreira LG. Regional variations in biomass distribution in Brazilian Savanna Woodland. Biotropica. 2014;46(2):125–38.

González-Roglich M, Swenson JJ. Tree cover and carbon mapping of Argentine Savannas: scaling from field to region. Remote Sens Environ. 2016;172:139–47.

Stephanie AS, Avery SC, Leah KV, Jack FM, Bernardo FR, Joel R, et al. Recent cropping frequency, expansion, and abandonment in Mato Grosso, Brazil had selective land characteristics. Environ Res Lett. 2014;9(6):064010.

Ratter JA, Ribeiro JF, Bridgewater S. The Brazilian Cerrado Vegetation and Threats to its Biodiversity. Ann Bot. 1997;80(3):223–30.

Oliveira-Filho AT, Ratter JA. Vegetation Physiognomies and Woody Flora of the Cerrado Biome. In: Oliveira PEM, Marquis RJ, editors. The Cerrados of Brazil - Ecology and Natural History of a Neotropical Savanna. New York: Columbia University Press; 2002.

Beerling DJ, Osborne CP. The origin of the Savanna biome. Global Change Biol. 2006;12(11):2023–31.

Francoso RD, Haidar RF, Machado RB. Tree species of South America central Savanna: endemism, marginal areas and the relationship with other biomes. Acta Bot Bras. 2016;30(1):78–86.

Myers N, Mittermeier RA, Mittermeier CG, da Fonseca GAB, Kent J. Biodiversity hotspots for conservation priorities. Nature. 2000;403(6772):853–8.

Mittermeier RA, Turner WR, Larsen FW, Brooks TM, Gascon C. Global Biodiversity Conservation: the Critical Role of Hotspots. In: Zachos FE, Habel JC, editors. Biodiversity Hotspots: Distribution and Protection of Conservation Priority Areas. Berlin: Springer; 2011. p. 3–22.

Ferreira ME, Ferreira LG, Miziara F, Soares-Filho BS. Modeling landscape dynamics in the central Brazilian Savanna biome: future scenarios and perspectives for conservation. J Land Use Sci. 2012;8(4):403–21.

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 J Photogramm Remote Sens. 2016;117:66–78.

Richardson AD, Keenan TF, Migliavacca M, Ryu Y, Sonnentag O, Toomey M. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric For Meteorol. 2013;169:156–73.

Sano EE, Rosa R, Brito JL, Ferreira LG. Land cover mapping of the tropical Savanna region in Brazil. Environ Monit Assess. 2010;166(1–4):113–24.

Ribeiro SC, Fehrmann L, Soares CPB, Jacovine LAG, Kleinn C, Gaspar RD. Above- and belowground biomass in a Brazilian Cerrado. For Ecol Manag. 2011;262(3):491–9.

Sano EE, Ferreira LG, Asner GP, Steinke ET. Spatial and temporal probabilities of obtaining cloud-free Landsat images over the Brazilian tropical Savanna. Int J Remote Sens. 2007;28(12):2739–52.

Franca H, Setzer AW. AVHRR temporal analysis of a Savanna site in Brazil. Int J Remote Sens. 1998;19(16):3127–40.

Ferreira LG, Huete AR. Assessing the seasonal dynamics of the Brazilian Cerrado vegetation through the use of spectral vegetation indices. Int J Remote Sens. 2004;25(10):1837–60.

Ratana P, Huete AR, Ferreira L. Analysis of cerrado physiognomies and conversion in the MODIS seasonal-temporal domain. Earth Interact. 2005;9:1–22.

Zhang X, Ni-meister W. Remote Sensing of Forest Biomass. In: Hanes JM, editor. Biophysical Applications of Satellite Remote Sensing. Springer Remote Sensing/Photogrammetry. Berlin: Springer; 2014. p. 99–125.

Melaas EK, Friedl MA, Zhu Z. Detecting interannual variation in deciduous broadleaf forest phenology using Landsat TM/ETM plus data. Remote Sens Environ. 2013;132:176–85.

Baccini A, Friedl MA, Woodcock CE, Zhu Z. Scaling field data to calibrate and validate moderate spatial resolution remote sensing models. Photogramm Eng Rem S. 2007;73(8):945–54.

Karlson M, Ostwald M, Reese H, Sanou J, Tankoano B, Mattsson E. Mapping tree canopy cover and aboveground biomass in Sudano-Sahelian woodlands using Landsat 8 and random forest. Remote Sens. 2015;7(8):10017–41.

Zhang X, Wang J, Gao F, Liu Y, Schaaf C, Friedl M, et al. Exploration of scaling effects on coarse resolution land surface phenology. Remote Sens Environ. 2017;190:318–30.

Pasquarella VJ, Holden CE, Kaufman L, Woodcock CE. From imagery to ecology: leveraging time series of all available Landsat observations to map and monitor ecosystem state and dynamics. Remote Sens Ecol Conserv. 2016;2(3):152–70.

Fisher JI, Mustard JF, Vadeboncoeur MA. Green leaf phenology at Landsat resolution: scaling from the field to the satellite. Remote Sens Environ. 2006;100(2):265–79.

Melaas EK, Sulla-Menashe D, Gray JM, Black TA, Morin TH, Richardson AD, et al. Multisite analysis of land surface phenology in North American temperate and boreal deciduous forests from Landsat. Remote Sens Environ. 2016;186:452–64.

Zhu Z, Woodcock CE, Holden C, Yang Z. Generating synthetic Landsat images based on all available Landsat data: predicting Landsat surface reflectance at any given time. Remote Sens Environ. 2015;162:67–83.

Schwieder M, Leitão PJ, da Cunha Bustamante MM, Ferreira LG, Rabe A, Hostert P. Mapping Brazilian Savanna vegetation gradients with Landsat time series. Int J Appl Earth Observ Geoinform. 2016;52:361–70.

Hill MJ, Román MO, Schaaf CB. Biogeography and dynamics of global tropical and subtropical Savannas. Ecosystem Function in Savannas. Boca Raton: CRC Press; 2010. p. 3–37.

Alvares CA, Stape JL, Sentelhas PC, Goncalves JLD, Sparovek G. Koppen’s climate classification map for Brazil. Meteorol Z. 2013;22(6):711–28.

Ribeiro JF, Walter BMT. As principais fitofisionomias do bioma Cerrado. In: Sano SM, de Almeida SP, Ribeiro JF, editors. Cerrado: Ecologia e Flora. 2. Brasília - DF. Brazil: Embrapa Cerrados; 2008. p. 151–212.

Ottmar RD, Vihnanek RE, Miranda HS, Sata MN, Andrade SM. Stereo photo series for quantifying cerrado fuels in Central Brazil. Northwest Research Station, vol. I. Portland: USDA-FS; 2001.

Teixeira AMC, Pinto JRR, Amaral AG, Munhoz CBR. Angiosperm species of “Cerrado” sensu stricto in Terra Ronca State Park, Brazil: floristics, phytogeography and conservation. Braz J Bot. 2016;40(1):225–34.

Pinto JRR, Sano EE, Reino CM, Pinto CAS. Parques Nacionais do Cerrado e os tipos de formações vegetacionais preservados. Nat Conservacao. 2009;7(2):57–71.

Magnusson WE, Lima AP, Luizão R, Luizão F, Costa FRC, Castilho CVd, et al. RAPELD: a modification of the Gentry method for biodiversity surveys in long-term ecological research sites. Biota Neotrop. 2005;5(2):19–24.

Rezende AV, Vale AT, Sanquetta CR, Figueiredo Filho A, Felfili JM. Comparison of mathematical models to volume, biomass and carbon stock estimation of the woody vegetation of a cerrado sensu stricto in Brasília, DF. Sci For. 2006;71:65–76.

Leitão PJ, Schwieder M, Pötzschner F, Pinto JRR, Teixeira A, Pedroni F, et al. From sample to pixel: multi-scale remote sensing data for upscaling aboveground carbon data in heterogeneous landscapes. Ecosphere. 2018 (In review).

Peng Y, Gitelson AA. Remote estimation of gross primary productivity in soybean and maize based on total crop chlorophyll content. Remote Sens Environ. 2012;117:440–8.

USGS. Data available from the U.S. geological survey. Reston: USGS; 2017.

Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ. 2002;83(1–2):195–213.

Jönsson P, Eklundh L. TIMESAT—a program for analyzing time-series of satellite sensor data. Comput Geosci-UK. 2004;30(8):833–45.

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. Int J Digit Earth. 2016;9(1):63–105.

Breiman L, Friedman J, Olshen R, Stone C. Classification and regression trees. Belmont: Wadsworth and Brooks; 1984. p. 358.

Breiman L. Random Forests. Mach Learn. 2001;45(1):5–32.

Liaw A, Wiener M. Classifiaction and Regression by randomForest. R News. 2002;2(3):18–22.

Greenwell MB. pdp: an R Package for constructing partial dependence plots. R J. 2017;9(1):421–36.

R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2018. https://www.R-project.org/ . Accessed 10 May 2018.

Chave J, Condit R, Aguilar S, Hernandez A, Lao S, Perez R. Error propagation and scaling for tropical forest biomass estimates. Philos Trans R Soc B. 2004;359(1443):409–20.

Zolkos SG, Goetz SJ, Dubayah R. A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing. Remote Sens Environ. 2013;128:289–98.

Drusch M, Del Bello U, Carlier S, Colin O, Fernandez V, Gascon F, et al. Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sens Environ. 2012;120:25–36.

Lumbierres M, Méndez P, Bustamante J, Soriguer R, Santamaría L. Modeling biomass production in seasonal wetlands using MODIS NDVI land surface phenology. Remote Sens. 2017;9(4):392.

Diouf A, Brandt M, Verger A, Jarroudi M, Djaby B, Fensholt R, et al. Fodder biomass monitoring in Sahelian rangelands using phenological metrics from FAPAR time series. Remote Sens. 2015;7(7):9122.

Schucknecht A, Meroni M, Kayitakire F, Boureima A. Phenology-based biomass estimation to support rangeland management in semi-arid environments. Remote Sens. 2017;9(5):463.

Lenza E, Klink CA. Comportamento fenológico de espécies lenhosas em um cerrado sentido restrito de Brasília, DF. Braz J Bot. 2006;29:627–38.

Pirani FR, Sanchez M, Pedroni F. Fenologia de uma comunidade arbórea em cerrado sentido restrito, Barra do Garças, MT, Brasil. Acta Bot Bras. 2009;23(4):1096–109.

Gessner U, Knauer K, Kuenzer C, Dech S. Land surface phenology in a West African Savanna: impact of land use, land cover and fire. In: Kuenzer C, Dech S, Wagner W, editors. Remote sensing time series: revealing land surface dynamics. Cham: Springer International Publishing; 2015. p. 203–23.

Englund O, Sparovek G, Berndes G, Freitas F, Ometto JP, Oliveira PVDCE, et al. A new high-resolution nationwide aboveground carbon map for Brazil. Geo Geogr Environ. 2017;4(2):e00045.

Zarin DJ, Harris NL, Baccini A, Aksenov D, Hansen MC, Azevedo-Ramos C, et al. Can carbon emissions from tropical deforestation drop by 50% in 5 years? Global Change Biol. 2016;22(4):1336–47.

Vourlitis G, da Rocha H. Flux Dynamics in the Cerrado and Cerrado-forest transition of Brazil. Ecosystem Function in Savannas. Boca Raton: CRC Press; 2010. p. 97–116.