Mapping pasture management in the Brazilian Amazon from dense Landsat time series

Remote Sensing of Environment

Volumn 205, Issue , 2018, Pages 453-468

  Jakimow, Benjamin ^a   Griffiths, Patrick ^a   van der Linden, Sebastian ^a   Hostert, Patrick ^a  

^a HUMBOLDT UNIVERSITY   (Germany)

Author keywords

fire; land use intensification; Landsat time series; Random Forests; tillage; Tropical pastures

Indexed keywords

AGRICULTURAL MACHINERY; DECISION TREES; DEFORESTATION; LAND USE; MAPPING; TIME SERIES;

BRAZILIAN AMAZON; CATTLE RANCHING; DENSE-TIME; LAND USE INTENSIFICATION; LANDSAT; LANDSAT TIME SERIES; MANAGEMENT PRACTISES; RANDOM FORESTS; TIMES SERIES; TROPICAL PASTURE;

LANDSAT; TILLAGE;

CATTLE; DEFORESTATION; LAND MANAGEMENT; LAND USE CHANGE; LANDSAT; MANAGEMENT PRACTICE; PASTURE; RANCHING; REGROWTH; SPATIAL RESOLUTION; SURFACE REFLECTANCE; TEMPORAL VARIATION; TILLAGE; TIME SERIES ANALYSIS;

AMAZON RIVER; BRAZIL; NOVO PROGRESSO; PARA [BRAZIL];

BOS;

EID: 85034634859     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2017.10.009     Document Type: Article

References (91)

1. Almeida, C.A.D., Coutinho, A.C., Esquerdo, J.C.D.M., Adami, M., Venturieri, A., Diniz, C.G., Dessay, N., Durieux, L., Gomes, A.R., High spatial resolution land use and land cover mapping of the Brazilian Legal Amazon in 2008 using Landsat-5/TM and MODIS data. Acta Amazon. 46 (2016), 291–302.
2. Alves Filho, J.P., Ribeiro, H., Saúde ambiental no campo: o caso dos projetos de desenvolvimento sustentável em assentamentos rurais do Estado de São Paulo. Saúde e Sociedade 23 (2014), 448–466.
3. Aragão, L.E.O.C., Shimabukuro, Y.E., The incidence of fire in Amazonian forests with implications for REDD. Science 328 (2010), 1275–1278.
4. Arvor, D., Meirelles, M., Dubreuil, V., Begue, A., Shimabukuro, Y.E., Analyzing the agricultural transition in Mato Grosso, Brazil, using satellite-derived indices. Appl. Geogr. 32 (2012), 702–713.
5. Barbosa, F.A., Filho, B.S.S., Merry, F.D., Azevedo, H.D.O., Costa, W.L.S., Coe, M.T., Batista, E.L.D.S., Maciel, T.G., Sheepers, L.C., Oliveira, A.R.D., Rodrigues, H.O., Cenários para a pecuária de corte amazônica. 2015, Centro de Sensoriamento Remoto da Universidade Federal de Minas Gerais, Belo Horizonte.
6. Barlow, J., Parry, L., Gardner, T.A., Ferreira, J., Aragão, L.E.O.C., Carmenta, R., Berenguer, E., Vieira, I.C.G., Souza, C., Cochrane, M.A., The critical importance of considering fire in REDD + programs. Biol. Conserv. 154 (2012), 1–8.
7. Barretto, A.G.O.P., Berndes, G., Sparovek, G., Wirsenius, S., Agricultural intensification in Brazil and its effects on land-use patterns: an analysis of the 1975–2006 period. Glob. Chang. Biol. 19 (2013), 1804–1815.
8. Bastarrika, A., Chuvieco, E., Martín, M.P., Mapping burned areas from Landsat TM/ETM + data with a two-phase algorithm: balancing omission and commission errors. Remote Sens. Environ. 115 (2011), 1003–1012.
9. Bastarrika, A., Alvarado, M., Artano, K., Martinez, M., Mesanza, A., Torre, L., Ramo, R., Chuvieco, E., BAMS: a tool for supervised burned area mapping using Landsat data. Remote Sens. 6 (2014), 12360–12380.
10. Bowman, M.S., Amacher, G.S., Merry, F.D., Fire use and prevention by traditional households in the Brazilian Amazon. Ecol. Econ. 67 (2008), 117–130.
11. Boy, J., Strey, S., Schönenberg, R., Strey, R., Weber-Santos, O., Nendel, C., Klingler, M., Schumann, C., Hartberger, K., Guggenberger, G., Seeing the forest not for the carbon: why concentrating on land-use-induced carbon stock changes of soils in Brazil can be climate-unfriendly. Reg. Environ. Chang., 2016, 1–13.
12. Breiman, L., Random forests. Mach. Learn. 45 (2001), 5–32.
13. Bricklemyer, R.S., Lawrence, R.L., Miller, P.R., Battogtokh, N., Predicting tillage practices and agricultural soil disturbance in north central Montana with Landsat imagery. Agric. Ecosyst. Environ. 114 (2006), 210–216.
14. Bustamante, M.C., Nobre, C., Smeraldi, R., Aguiar, A.D., Barioni, L., Ferreira, L., Longo, K., May, P., Pinto, A., Ometto, J.H.B., Estimating greenhouse gas emissions from cattle raising in Brazil. Clim. Chang. 115 (2012), 559–577.
15. Bustamante, M.M.C., Roitman, I., Aide, T.M., Alencar, A., Anderson, L.O., Aragão, L., Asner, G.P., Barlow, J., Berenguer, E., Chambers, J., Costa, M.H., Fanin, T., Ferreira, L.G., Ferreira, J., Keller, M., Magnusson, W.E., Morales-Barquero, L., Morton, D., Ometto, J.P.H.B., Palace, M., Peres, C.A., Silvério, D., Trumbore, S., Vieira, I.C.G., Toward an integrated monitoring framework to assess the effects of tropical forest degradation and recovery on carbon stocks and biodiversity. Glob. Chang. Biol. 22 (2016), 92–109.
16. Cano-Crespo, A., Oliveira, P.J.C., Boit, A., Cardoso, M., Thonicke, K., Forest edge burning in the Brazilian Amazon promoted by escaping fires from managed pastures. J. Geophys. Res. Biogeosci. 120 (2015), 2095–2107.
17. Cao, X., Chen, J., Matsushita, B., Imura, H., Wang, L., An automatic method for burn scar mapping using support vector machines. Int. J. Remote Sens. 30 (2009), 577–594.
18. Carmenta, R., Vermeylen, S., Parry, L., Barlow, J., Shifting cultivation and fire policy: insights from the Brazilian Amazon. Hum. Ecol. 41 (2013), 603–614.
19. Carreiras, J.M.B., Pereira, J.M.C., Campagnolo, M.L., Shimabukuro, Y.E., Assessing the extent of agriculture/pasture and secondary succession forest in the Brazilian Legal Amazon using SPOT VEGETATION data. Remote Sens. Environ. 101 (2006), 283–298.
20. Cerri, C.E.P., Easter, M., Paustian, K., Killian, K., Coleman, K., Bernoux, M., Falloon, P., Powlson, D.S., Batjes, N.H., Milne, E., Cerri, C.C., Predicted soil organic carbon stocks and changes in the Brazilian Amazon between 2000 and 2030. Agric. Ecosyst. Environ. 122 (2007), 58–72.
21. Cohen, W.B., Yang, Z., Kennedy, R., Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync — tools for calibration and validation. Remote Sens. Environ. 114 (2010), 2911–2924.
22. Cohn, A.S., Mosnier, A., Havlík, P., Valin, H., Herrero, M., Schmid, E., O'Hare, M., Obersteiner, M., Cattle ranching intensification in Brazil can reduce global greenhouse gas emissions by sparing land from deforestation. Proc. Natl. Acad. Sci. 111 (2014), 7236–7241.
23. Crist, E.P., A TM tasseled cap equivalent transformation for reflectance factor data. Remote Sens. Environ. 17 (1985), 301–306.
24. Davidson, E.A., Asner, G.P., Stone, T.A., Neill, C., Figueiredo, R.O., Objective indicators of pasture degradation from spectral mixture analysis of Landsat imagery. J. Geophys. Res. Biogeosci., 113, 2008, G00B03.
25. Davidson, E.A., de Araujo, A.C., Artaxo, P., Balch, J.K., Brown, I.F., C. Bustamante, M.M., Coe, M.T., De Fries, R.S., Keller, M., Longo, M., Munger, J.W., Schroeder, W., Soares-Filho, B.S., Souza, C.M., Wofsy, S.C., The Amazon basin in transition. Nature 481 (2012), 321–328.
26. DeVries, B., Decuyper, M., Verbesselt, J., Zeileis, A., Herold, M., Joseph, S., Tracking disturbance-regrowth dynamics in tropical forests using structural change detection and Landsat time series. Remote Sens. Environ. 169 (2015), 320–334.
27. DeVries, B., Verbesselt, J., Kooistra, L., Herold, M., Robust monitoring of small-scale forest disturbances in a tropical montane forest using Landsat time series. Remote Sens. Environ. 161 (2015), 107–121.
28. Dutrieux, L.P., Jakovac, C.C., Latifah, S.H., Kooistra, L., Reconstructing land use history from Landsat time-series: case study of a swidden agriculture system in Brazil. Int. J. Appl. Earth Obs. Geoinf. 47 (2016), 112–124.
29. Erb, K.-H., Haberl, H., Jepsen, M.R., Kuemmerle, T., Lindner, M., Müller, D., Verburg, P.H., Reenberg, A., A conceptual framework for analysing and measuring land-use intensity. Curr. Opin. Environ. Sustain. 5 (2013), 464–470.
30. de Espindola, G.M., de Aguiar, A.P.D., Pebesma, E., Câmara, G., Fonseca, L., Agricultural land use dynamics in the Brazilian Amazon based on remote sensing and census data. Appl. Geogr. 32 (2012), 240–252.
31. Fearnside, P.M., Brazil's Cuiabá-Santarém (BR-163) highway: the environmental cost of paving a soybean corridor through the Amazon. Environ. Manag. 39 (2007), 601–614.
32. García, M.J.L., Caselles, V., Mapping burns and natural reforestation using thematic mapper data. Geochem. Int. 6 (1991), 31–37.
33. Gerard, F., Plummer, S., Wadsworth, R., Sanfeliu, A.F., Iliffe, L., Balzter, H., Wyatt, B., Forest fire scar detection in the boreal forest with multitemporal SPOT-VEGETATION data. IEEE Trans. Geosci. Remote Sens. 41 (2003), 2575–2585.
34. Gil, J., Siebold, M., Berger, T., Adoption and development of integrated crop–livestock–forestry systems in Mato Grosso, Brazil. Agric. Ecosyst. Environ. 199 (2015), 394–406.
35. Godar, J., Tizado, E.J., Pokorny, B., Who is responsible for deforestation in the Amazon? A spatially explicit analysis along the Transamazon Highway in Brazil. For. Ecol. Manag. 267 (2012), 58–73.
36. Gollnow, F., Lakes, T., Policy change, land use, and agriculture: the case of soy production and cattle ranching in Brazil, 2001–2012. Appl. Geogr. 55 (2014), 203–211.
37. Grogan, K., Pflugmacher, D., Hostert, P., Kennedy, R., Fensholt, R., Cross-border forest disturbance and the role of natural rubber in mainland Southeast Asia using annual Landsat time series. Remote Sens. Environ. 169 (2015), 438–453.
38. Hohnwald, S., Rischkowsky, B., King, J., Camarão, A., Rodrigues Filho, J., Zeppenfeld, T., Intensive cattle browsing did not prevent fallow recuperation on smallholder grass-capoeira pastures in the NE-Amazon. Agrofor. Syst., 2015, 1–16.
39. Huete, A.R., A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 25 (1988), 295–309.
40. Huete, A.R., Didan, K., Shimabukuro, Y.E., Ratana, P., Saleska, S.R., Hutyra, L.R., Yang, W., Nemani, R.R., Myneni, R., Amazon rainforests green-up with sunlight in dry season. Geophys. Res. Lett., 33, 2006 n/a-n/a.
41. INPE (2015). Projeto PRODES - Monitoramento da Floresta Amazonica Brasileira por Satelite In.
42. Jiang, Z., Huete, A.R., Didan, K., Miura, T., Development of a two-band enhanced vegetation index without a blue band. Remote Sens. Environ. 112 (2008), 3833–3845.
43. Ju, J., Roy, D.P., Vermote, E., Masek, J., Kovalskyy, V., Continental-scale validation of MODIS-based and LEDAPS Landsat ETM + atmospheric correction methods. Remote Sens. Environ. 122 (2012), 175–184.
44. King, M.D., Platnick, S., Menzel, W.P., Ackerman, S.A., Hubanks, P.A., Spatial and temporal distribution of clouds observed by MODIS onboard the Terra and Aqua satellites. IEEE Trans. Geosci. Remote Sens. 51 (2013), 3826–3852.
45. Lambin, E.F., Gibbs, H.K., Ferreira, L., Grau, R., Mayaux, P., Meyfroidt, P., Morton, D.C., Rudel, T.K., Gasparri, I., Munger, J., Estimating the world's potentially available cropland using a bottom-up approach. Glob. Environ. Chang. 23 (2013), 892–901.
46. Lapola, D.M., Martinelli, L.A., Peres, C.A., Ometto, J.P.H.B., Ferreira, M.E., Nobre, C.A., Aguiar, A.P.D., Bustamante, M.M.C., Cardoso, M.F., Costa, M.H., Joly, C.A., Leite, C.C., Moutinho, P., Sampaio, G., Strassburg, B.B.N., Vieira, I.C.G., Pervasive transition of the Brazilian land-use system. Nat. Clim. Chang. 4 (2014), 27–35.
47. Latawiec, A.E., Strassburg, B.B.N., Valentim, J.F., Ramos, F., Alves-Pinto, H.N., Intensification of cattle ranching production systems: socioeconomic and environmental synergies and risks in Brazil. Animal 8 (2014), 1255–1263.
48. Lima, A., Silva, T.S.F., Aragão, L.E.O.E.C.D., Feitas, R.M.D., Adami, M., Formaggio, A.R., Shimabukuro, Y.E., Land use and land cover changes determine the spatial relationship between fire and deforestation in the Brazilian Amazon. Appl. Geogr. 34 (2012), 239–246.
49. Malhi, Y., Roberts, J.T., Betts, R.A., Killeen, T.J., Li, W., Nobre, C.A., Climate change, deforestation, and the fate of the Amazon. Science 319 (2008), 169–172.
50. Martín, M.P., Gómez, I., Chuvieco, E., Burnt Area Index (BAIM) for burned area discrimination at regional scale using MODIS data. For. Ecol. Manag., 234(Supplement), 2006, S221.
51. Masek, J.G., Vermote, E.F., Saleous, N.E., Wolfe, R., Hall, F.G., Huemmrich, K.F., Feng, G., Kutler, J., Teng-Kui, L., A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geosci. Remote Sens. Lett. 3 (2006), 68–72.
52. Masek, J.G., Huang, C., Wolfe, R., Cohen, W., Hall, F., Kutler, J., Nelson, P., North American forest disturbance mapped from a decadal Landsat record. Remote Sens. Environ. 112 (2008), 2914–2926.
53. Maus, V., Câmara, G., Cartaxo, R., Sanchez, A., Ramos, F.M., Queiroz, G.R.D., A time-weighted dynamic time warping method for land-use and land-cover mapping. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 1–11.
54. Mausel, P., Wu, Y., Li, Y., Moran, E.F., Brondizio, E.S., Spectral identification of successional stages following deforestation in the Amazon. Geocarto Int. 8 (1993), 61–71.
55. Mohler, R.L., Goodin, D.G., Temporal burn scar evolution in tallgrass prairie based on field spectroscopy. Int. J. Remote Sens. 34 (2013), 7199–7217.
56. Moran, E.F., Packer, A., Brondizio, E., Tucker, J., Restoration of vegetation cover in the eastern Amazon. Ecol. Econ. 18 (1996), 41–54.
57. Müller, H., Griffiths, P., Hostert, P., Long-term deforestation dynamics in the Brazilian Amazon—uncovering historic frontier development along the Cuiabá–Santarém highway. Int. J. Appl. Earth Obs. Geoinf. 44 (2016), 61–69.
58. Norton, J., Glenn, N., Germino, M., Weber, K., Seefeldt, S., Relative suitability of indices derived from Landsat ETM + and SPOT 5 for detecting fire severity in sagebrush steppe. Int. J. Appl. Earth Obs. Geoinf. 11 (2009), 360–367.
59. Numata, I., Roberts, D.A., Chadwick, O.A., Schimel, J., Sampaio, F.R., Leonidas, F.C., Soares, J.V., Characterization of pasture biophysical properties and the impact of grazing intensity using remotely sensed data. Remote Sens. Environ. 109 (2007), 314–327.
60. Okujeni, A., van der Linden, S., Tits, L., Somers, B., Hostert, P., Support vector regression and synthetically mixed training data for quantifying urban land cover. Remote Sens. Environ. 137 (2013), 184–197.
61. Olofsson, P., Foody, G.M., Herold, M., Stehman, S.V., Woodcock, C.E., Wulder, M.A., Good practices for estimating area and assessing accuracy of land change. Remote Sens. Environ. 148 (2014), 42–57.
62. Pacheco, P., Actor and frontier types in the Brazilian Amazon: assessing interactions and outcomes associated with frontier expansion. Geoforum 43 (2012), 864–874.
63. Pinty, B., Verstraete, M.M., GEMI: a non-linear index to monitor global vegetation from satellites. Vegetatio 101 (1992), 15–20.
64. Pivello, V.R., The use of fire in the Cerrado and Amazonian rainforests of Brazil: past and present. The Journal of the Association for Fire Ecology 7 (2011), 24–39.
65. Polikar, R., Bootstrap - inspired techniques in computation intelligence. IEEE Signal Process. Mag. 24 (2007), 59–72.
66. Powell, R.L., Matzke, N., de Souza Jr, C., Clark, M., Numata, I., Hess, L.L., Roberts, D.A., Sources of error in accuracy assessment of thematic land-cover maps in the Brazilian Amazon. Remote Sens. Environ. 90 (2004), 221–234.
67. Rouse, J.W., Haas, R.H., Deering, D.W., Schell, J.A., Harlan, J.C., Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation. 1974, Texas A&M University, College Station, Texas, 77843.
68. Roy, D.P., Wulder, M.A., Loveland, T.R., W., C.E., Allen, R.G., Anderson, M.C., Helder, D., Irons, J.R., Johnson, D.M., Kennedy, R., Scambos, T.A., Schaaf, C.B., Schott, J.R., Sheng, Y., Vermote, E.F., Belward, A.S., Bindschadler, R., Cohen, W.B., Gao, F., Hipple, J.D., Hostert, P., Huntington, J., Justice, C.O., Kilic, A., Kovalskyy, V., Lee, Z.P., Lymburner, L., Masek, J.G., McCorkel, J., Shuai, Y., Trezza, R., Vogelmann, J., Wynne, R.H., Zhu, Z., Landsat-8: science and product vision for terrestrial global change research. Remote Sens. Environ. 145 (2014), 154–172.
69. Rufin, P., Müller, H., Pflugmacher, D., Hostert, P., Land use intensity trajectories on Amazonian pastures derived from Landsat time series. Int. J. Appl. Earth Obs. Geoinf. 41 (2015), 1–10.
70. Sánchez-Azofeifa, G.A., Castro-Esau, K.L., Kurz, W.A., Joyce, A., Monitoring carbon stocks in the tropics and the remote sensing operational limitations: from local to regional projects. Ecol. Appl. 19 (2009), 480–494.
71. Schultz, B., Immitzer, M., Formaggio, A., Sanches, I., Luiz, A., Atzberger, C., Self-guided segmentation and classification of multi-temporal Landsat 8 images for crop type mapping in Southeastern Brazil. Remote Sens., 7, 2015, 14482.
72. Shimabukuro, Y.E., Santos, J.R., Formaggio, A.R., Duarte, V., Rudorff, B.F.T., The Brazilian Amazon monitoring program. Global Forest Monitoring from Earth Observation, 2012, 167–184 CRC Press.
73. Shimabukuro, Y.E., Beuchle, R., Grecchi, R.C., Achard, F., Assessment of forest degradation in Brazilian Amazon due to selective logging and fires using time series of fraction images derived from Landsat ETM + images. IEEE Geosci. Remote Sens. Lett. 5 (2014), 773–782.
74. Soler, L., Verburg, P., Alves, D., Evolution of land use in the Brazilian Amazon: from frontier expansion to market chain dynamics. Land 3 (2014), 981–1014.
75. Souza, Carlos Jr., Siqueira, J., Sales, M., Fonseca, A., Ribeiro, J., Numata, I., Cochrane, M., Barber, C., Roberts, D., Barlow, J., Ten-year Landsat classification of deforestation and forest degradation in the Brazilian Amazon. Remote Sens. 5 (2013), 5493–5513.
76. Stabile, M., Azevedo, A., Nepstad, D., Brazil's ‘Low Carbon Agriculture Program’: Barriers to Implementation. 2012, Amazon Environmental Research Institute (IPAM), Brasilia.
77. Strassburg, B.B.N., Latawiec, A.E., Barioni, L.G., Nobre, C.A., da Silva, V.P., Valentim, J.F., Vianna, M., Assad, E.D., When enough should be enough: improving the use of current agricultural lands could meet production demands and spare natural habitats in Brazil. Glob. Environ. Chang. 28 (2014), 84–97.
78. Trigg, S., Flasse, S., An evaluation of different bi-spectral spaces for discriminating burned shrub-savannah. Int. J. Remote Sens. 22 (2001), 2641–2647.
79. Uhl, C., Buschbacher, R., A disturbing synergism between cattle-ranch burning practices and selective tree harvesting in the eastern Amazon. Biotropica 17 (1985), 165–168.
80. Uhl, C., Buschbacher, R., Serrao, E.A.S., Abandoned pastures in Eastern Amazonia. I. Patterns of plant succession. J. Ecol. 76 (1988), 663–681.
81. Undersander, D., Albert, B., Cosgrove, D., Johnson, D., Peterson, P., Pastures for Profit: A Guide to Rotational Grazing. 2014, Board of Regents of the University of Wisconsin System.
82. USGS, Landsat 4-7 Climate Data Record (CDR) Surface Reflectance, Version 6.6. 2016, U.S. Geological Survey, Department of the Interior.
83. USGS, Provisional Landsat 8 Surface Reflectance Code (LaSRC) Product, Version 3.1. 2016, U.S. Geological Survey, Department of the Interior.
84. Van Deventer, A., Ward, A., Gowda, P., Lyon, J., Using thematic mapper data to identify contrasting soil plains and tillage practices. Photogramm. Eng. Remote. Sens. 63 (1997), 87–93.
85. Watts, J.D., Lawrence, R.L., Miller, P.R., Montagne, C., Monitoring of cropland practices for carbon sequestration purposes in north central Montana by Landsat remote sensing. Remote Sens. Environ. 113 (2009), 1843–1852.
86. Wulder, M.A., Masek, J.G., Cohen, W.B., Loveland, T.R., Woodcock, C.E., Opening the archive: how free data has enabled the science and monitoring promise of Landsat. Remote Sens. Environ. 122 (2012), 2–10.
87. Wulder, M.A., Hilker, T., White, J.C., Coops, N.C., Masek, J.G., Pflugmacher, D., Crevier, Y., Virtual constellations for global terrestrial monitoring. Remote Sens. Environ. 170 (2015), 62–76.
88. Zheng, B., Campbell, J.B., de Beurs, K.M., Remote sensing of crop residue cover using multi-temporal Landsat imagery. Remote Sens. Environ. 117 (2012), 177–183.
89. Zheng, B., Campbell, J.B., Serbin, G., Galbraith, J.M., Remote sensing of crop residue and tillage practices: present capabilities and future prospects. Soil Tillage Res. 138 (2014), 26–34.
90. Zhu, Z., Woodcock, C.E., Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sens. Environ. 118 (2012), 83–94.
91. Zhu, Z., Woodcock, C.E., Continuous change detection and classification of land cover using all available Landsat data. Remote Sens. Environ. 144 (2014), 152–171.