Articles | Volume 16, issue 1
https://doi.org/10.5194/essd-16-321-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-321-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Annual maps of forest cover in the Brazilian Amazon from analyses of PALSAR and MODIS images
Yuanwei Qin
School of Biological Sciences, University of Oklahoma, Norman, OK 73019, USA
Xiangming Xiao
CORRESPONDING AUTHOR
School of Biological Sciences, University of Oklahoma, Norman, OK 73019, USA
Department of Geography, National University of Singapore, 1 Arts Link, Kent Ridge, 117570 Singapore
Ralph Dubayah
Department of Geographical Sciences, University of Maryland, College Park, MD 20740, USA
Russell Doughty
College of Atmospheric and Geographic Sciences, University of Oklahoma, Norman, OK 73019, USA
Diyou Liu
College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Fang Liu
School of Biological Sciences, University of Oklahoma, Norman, OK 73019, USA
Yosio Shimabukuro
Brazilian National Institute for Space Research, INPE, São José dos Campos, SP 12227, Brazil
Egidio Arai
Brazilian National Institute for Space Research, INPE, São José dos Campos, SP 12227, Brazil
Xinxin Wang
Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Institute of Biodiversity Science and Institute of Eco-Chongming, School of Life Sciences, Fudan University, Shanghai 200438, China
Berrien Moore III
College of Atmospheric and Geographic Sciences, University of Oklahoma, Norman, OK 73019, USA
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Cited articles
Achard, F., Beuchle, R., Mayaux, P., Stibig, H. J., Bodart, C., Brink, A., Carboni, S., Desclee, B., Donnay, F., Eva, H. D., Lupi, A., Rasi, R., Seliger, R., and Simonetti, D.: Determination of tropical deforestation rates and related carbon losses from 1990 to 2010, Global Change Biol., 20, 2540–2554, https://doi.org/10.1111/gcb.12605, 2014.
Almeida, C. T., Oliveira-Júnior, J. F., Delgado, R. C., Cubo, P., and Ramos, M. C.: Spatiotemporal rainfall and temperature trends throughout the Brazilian Legal Amazon, 1973–2013, Int. J. Climatol., 37, 2013–2026, https://doi.org/10.1002/joc.4831, 2017.
Aragão, L. E. O. C., Anderson, L. O., Fonseca, M. G., Rosan, T. M., Vedovato, L. B., Wagner, F. H., Silva, C. V. J., Silva Junior, C. H. L., Arai, E., Aguiar, A. P., Barlow, J., Berenguer, E., Deeter, M. N., Domingues, L. G., Gatti, L., Gloor, M., Malhi, Y., Marengo, J. A., Miller, J. B., Phillips, O. L., and Saatchi, S.: 21st Century drought-related fires counteract the decline of Amazon deforestation carbon emissions, Nat. Commun., 9, 536, https://doi.org/10.1038/s41467-017-02771-y, 2018.
Baccini, A., Goetz, S. J., Walker, W. S., Laporte, N. T., Sun, M., Sulla-Menashe, D., Hackler, J., Beck, P. S. A., Dubayah, R., Friedl, M. A., Samanta, S., and Houghton, R. A.: Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps, Nat. Clim. Change, 2, 182–185, https://doi.org/10.1038/Nclimate1354, 2012.
Chen, B. Q., Xiao, X. M., Ye, H. C., Ma, J., Doughty, R., Li, X. P., Zhao, B., Wu, Z. X., Sun, R., Dong, J. W., Qin, Y. W., and Xie, G. S.: Mapping Forest and Their Spatial-Temporal Changes From 2007 to 2015 in Tropical Hainan Island by Integrating ALOS/ALOS-2 L-Band SAR and Landsat Optical Images, Ieee J.-Stars, 11, 852–867, https://doi.org/10.1109/jstars.2018.2795595, 2018.
Dubayah, R., Blair, J. B., Goetz, S., Fatoyinbo, L., Hansen, M., Healey, S., Hofton, M., Hurtt, G., Kellner, J., Luthcke, S., Armston, J., Tang, H., Duncanson, L., Hancock, S., Jantz, P., Marselis, S., Patterson, P. L., Qi, W., and Silva, C.: The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth's forests and topography, Sci. Remote Sens., 1, 100002, https://doi.org/10.1016/j.srs.2020.100002, 2020.
Espírito-Santo, F. D. B., Gloor, M., Keller, M., Malhi, Y., Saatchi, S., Nelson, B., Junior, R. C. O., Pereira, C., Lloyd, J., Frolking, S., Palace, M., Shimabukuro, Y. E., Duarte, V., Mendoza, A. M., López-González, G., Baker, T. R., Feldpausch, T. R., Brienen, R. J. W., Asner, G. P., Boyd, D. S., and Phillips, O. L.: Size and frequency of natural forest disturbances and the Amazon forest carbon balance, Nat. Commun., 5, 3434, https://doi.org/10.1038/ncomms4434, 2014.
Fan, L., Wigneron, J. P., Ciais, P., Chave, J., Brandt, M., Fensholt, R., Saatchi, S. S., Bastos, A., Al-Yaari, A., Hufkens, K., Qin, Y., Xiao, X., Chen, C., Myneni, R. B., Fernandez-Moran, R., Mialon, A., Rodriguez-Fernandez, N. J., Kerr, Y., Tian, F., and Penuelas, J.: Satellite-observed pantropical carbon dynamics, Nat. Plants, 5, 944–951, https://doi.org/10.1038/s41477-019-0478-9, 2019.
Fanin, T. and van der Werf, G. R.: Relationships between burned area, forest cover loss, and land cover change in the Brazilian Amazon based on satellite data, Biogeosciences, 12, 6033–6043, https://doi.org/10.5194/bg-12-6033-2015, 2015.
FAO: Expert Meeting on Harmonizing Forest-Related Definitions for Use by Various Stakeholders, FAO, https://www.fao.org/3/cb7646en/cb7646en.pdf (last access: 23 October 2023), 2002.
FAO: Global Forest Resources Assessment 2020: Main report, Rome, https://doi.org/10.4060/ca9825en, 2020.
Fearnside, P. M.: Deforestation in Brazilian Amazonia: History, rates, and consequences, Conserv. Biol., 19, 680–688, https://doi.org/10.1111/j.1523-1739.2005.00697.x, 2005.
FRA: Global Forest Resources Assessment: Terms and Definitions, edited by: Pekkarinen, A., https://www.fao.org/3/I8661EN/i8661en.pdf (last access: 23 October 2023), 2020.
Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., and Huang, X. M.: MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets, Remote Sens. Environ., 114, 168–182, https://doi.org/10.1016/j.rse.2009.08.016, 2010.
Fritz, S., McCallum, I., Schill, C., Perger, C., See, L., Schepaschenko, D., van der Velde, M., Kraxner, F., and Obersteiner, M.: Geo-Wiki: An online platform for improving global land cover, Environ. Modell. Softw., 31, 110–123, https://doi.org/10.1016/j.envsoft.2011.11.015, 2012.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R.: Google Earth Engine: Planetary-scale geospatial analysis for everyone, Remote Sens. Environ., 202, 18–27, https://doi.org/10.1016/j.rse.2017.06.031, 2017.
Hansen, M. C., DeFries, R. S., Townshend, J. R. G., Sohlberg, R., Dimiceli, C., and Carroll, M.: Towards an operational MODIS continuous field of percent tree cover algorithm: examples using AVHRR and MODIS data, Remote Sens. Environ., 83, 303–319, https://doi.org/10.1016/S0034-4257(02)00079-2, 2002.
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., and Townshend, J. R.: High-resolution global maps of 21st-century forest cover change, Science, 342, 850–853, https://doi.org/10.1126/science.1244693, 2013 (data available at: http://earthenginepartners.appspot.com/science-2013-global-forest, last access: 23 October 2023).
Hudak, A. T., Lefsky, M. A., Cohen, W. B., and Berterretche, M.: Integration of lidar and Landsat ETM+ data for estimating and mapping forest canopy height, Remote Sens. Environ., 82, 397–416, https://doi.org/10.1016/S0034-4257(02)00056-1, 2002.
INPE: PRODES Legal Amazon Deforestation Monitoring System, http://www.obt.inpe.br/OBT/assuntos/programas/amazonia/prodes (last access: 23 October 2023), 2023.
Jenkins, C. N., Pimm, S. L., and Joppa, L. N.: Global patterns of terrestrial vertebrate diversity and conservation, P. Natl. Acad. Sci. USA, 110, E2602–E2610, https://doi.org/10.1073/pnas.1302251110, 2013.
Leitold, V., Morton, D. C., Longo, M., dos-Santos, M. N., Keller, M., and Scaranello, M.: El Niño drought increased canopy turnover in Amazon forests, New Phytol., 219, 959–971, https://doi.org/10.1111/nph.15110, 2018.
Li, G., Lu, D., Moran, E., Calvi, M. F., Dutra, L. V., and Batistella, M.: Examining deforestation and agropasture dynamics along the Brazilian TransAmazon Highway using multitemporal Landsat imagery, Gisci Remote Sens,, 56, 161–183, https://doi.org/10.1080/15481603.2018.1497438, 2019.
Lovejoy, T. E. and Nobre, C.: Amazon tipping point: Last chance for action, Sci, Adv,, 5, eaba2949, https://doi.org/10.1126/sciadv.aba2949, 2019.
Markus, T., Neumann, T., Martino, A., Abdalati, W., Brunt, K., Csatho, B., Farrell, S., Fricker, H., Gardner, A., Harding, D., Jasinski, M., Kwok, R., Magruder, L., Lubin, D., Luthcke, S., Morison, J., Nelson, R., Neuenschwander, A., Palm, S., Popescu, S., Shum, C. K., Schutz, B. E., Smith, B., Yang, Y., and Zwally, J.: The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2): Science requirements, concept, and implementation, Remote Sens. Environ., 190, 260–273, https://doi.org/10.1016/j.rse.2016.12.029, 2017.
Matricardi, E. A. T., Skole, D. L., Costa, O. B., Pedlowski, M. A., Samek, J. H., and Miguel, E. P.: Long-term forest degradation surpasses deforestation in the Brazilian Amazon, Science, 369, 1378–1382, https://doi.org/10.1126/science.abb3021, 2020.
Mitchard, E. T. A.: The tropical forest carbon cycle and climate change, Nature, 559, 527–534, https://doi.org/10.1038/s41586-018-0300-2, 2018.
Nepstad, D., McGrath, D., Stickler, C., Alencar, A., Azevedo, A., Swette, B., Bezerra, T., DiGiano, M., Shimada, J., da Motta, R. S., Armijo, E., Castello, L., Brando, P., Hansen, M. C., McGrath-Horn, M., Carvalho, O., and Hess, L.: Slowing Amazon deforestation through public policy and interventions in beef and soy supply chains, Science, 344, 1118–1123, https://doi.org/10.1126/science.1248525, 2014.
Ochoa-Quintero, J. M., Gardner, T. A., Rosa, I., Ferraz, S. F. D., and Sutherland, W. J.: Thresholds of species loss in Amazonian deforestation frontier landscapes, Conserv. Biol., 29, 440–451, https://doi.org/10.1111/cobi.12446, 2015.
Olofsson, P., Stehman, S. V., Woodcock, C. E., Sulla-Menashe, D., Sibley, A. M., Newell, J. D., Friedl, M. A., and Herold, M.: A global land-cover validation data set, part I: fundamental design principles, Int. J. Remote Sens., 33, 5768–5788, https://doi.org/10.1080/01431161.2012.674230, 2012.
Qin, Y. and Xiao, X.: Codes for forest and evergreen forest mapping in the Brazilian Amazon, figshare [code], https://doi.org/10.6084/m9.figshare.21445626.v1, 2022a.
Qin, Y. and Xiao, X.: Annual PALSAR/MODIS forest and evergreen forest maps in the Brazilian Amazon, figshare [data set], https://doi.org/10.6084/m9.figshare.21445590.v1, 2022b.
Qin, Y., Xiao, X., Dong, J., Zhang, G., Roy, P. S., Joshi, P. K., Gilani, H., Murthy, M. S., Jin, C., Wang, J., Zhang, Y., Chen, B., Menarguez, M. A., Biradar, C. M., Bajgain, R., Li, X., Dai, S., Hou, Y., Xin, F., and Moore, B., 3rd: Mapping forests in monsoon Asia with ALOS PALSAR 50-m mosaic images and MODIS imagery in 2010, Sci. Rep., 6, 20880, https://doi.org/10.1038/srep20880, 2016.
Qin, Y. W., Xiao, X. M., Dong, J. W., Zhang, G. L., Shimada, M., Liu, J. Y., Li, C. G., Kou, W. L., and Moore, B.: Forest cover maps of China in 2010 from multiple approaches and data sources: PALSAR, Landsat, MODIS, FRA, and NFI, Isprs J. Photogramm. Remote, 109, 1–16, https://doi.org/10.1016/j.isprsjprs.2015.08.010, 2015.
Qin, Y. W., Xiao, X. M., Dong, J. W., Zhang, G. L., Roy, P. S., Joshi, P. K., Gilani, H., Murthy, M. S. R., Jin, C., Wang, J., Zhang, Y., Chen, B. Q., Menarguez, M. A., Biradar, C. M., Bajgain, R., Li, X. P., Dai, S. Q., Hou, Y., Xin, F. F., and Moore, B.: Mapping forests in monsoon Asia with ALOS PALSAR 50-m mosaic images and MODIS imagery in 2010, Sci. Rep., 6, 20880, https://doi.org/10.1038/srep20880, 2016.
Qin, Y. W., Xiao, X. M., Dong, J. W., Zhou, Y. T., Wang, J., Doughty, R. B., Chen, Y., Zou, Z. H., and Moore, B.: Annual dynamics of forest areas in South America during 2007–2010 at 50 m spatial resolution, Remote Sens. Environ., 201, 73–87, https://doi.org/10.1016/j.rse.2017.09.005, 2017.
Qin, Y. W., Xiao, X. M., Dong, J. W., Zhang, Y., Wu, X. C., Shimabukuro, Y., Arai, E., Biradar, C., Wang, J., Zou, Z. H., Liu, F., Shi, Z., Doughty, R., and Moore, B.: Improved estimates of forest cover and loss in the Brazilian Amazon in 2000–2017, Nat. Sustainabil., 2, 764–772, https://doi.org/10.1038/s41893-019-0336-9, 2019.
Qin, Y. W., Xiao, X. M., Wigneron, J. P., Ciais, P., Brandt, M., Fan, L., Li, X. J., Crowell, S., Wu, X. C., Doughty, R., Zhang, Y., Liu, F., Sitch, S., and Moore, B.: Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon, Nat. Clim. Change, 11, 442–448, https://doi.org/10.1038/s41558-021-01026-5, 2021.
Reiche, J., Lucas, R., Mitchell, A. L., Verbesselt, J., Hoekman, D. H., Haarpaintner, J., Kellndorfer, J. M., Rosenqvist, A., Lehmann, E. A., Woodcock, C. E., Seifert, F. M., and Herold, M.: Combining satellite data for better tropical forest monitoring, Nat. Clim. Change, 6, 120–122, https://doi.org/10.1038/nclimate2919, 2016.
Saatchi, S. S., Harris, N. L., Brown, S., Lefsky, M., Mitchard, E. T. A., Salas, W., Zutta, B. R., Buermann, W., Lewis, S. L., Hagen, S., Petrova, S., White, L., Silman, M., and Morel, A.: Benchmark map of forest carbon stocks in tropical regions across three continents, P. Natl. Acad. Sci. USA, 108, 9899–9904, https://doi.org/10.1073/pnas.1019576108, 2011.
Sexton, J. O., Noojipady, P., Song, X.-P., Feng, M., Song, D.-X., Kim, D.-H., Anand, A., Huang, C., Channan, S., Pimm, S. L., and Townshend, J. R.: Conservation policy and the measurement of forests, Nat. Clim. Change, 6, 192–196, https://doi.org/10.1038/nclimate2816, 2015.
Shimada, M., Isoguchi, O., Tadono, T., and Isono, K.: PALSAR Radiometric and Geometric Calibration, IEEE T. Geosci. Remote, 47, 3915–3932, https://doi.org/10.1109/Tgrs.2009.2023909, 2009.
Shimada, M., Itoh, T., Motooka, T., Watanabe, M., Shiraishi, T., Thapa, R., and Lucas, R.: New global forest/non-forest maps from ALOS PALSAR data (2007–2010), Remote Sens. Environ., 155, 13–31, https://doi.org/10.1016/j.rse.2014.04.014, 2014.
Skole, D. and Tucker, C.: Tropical Deforestation and Habitat Fragmentation in the Amazon – Satellite Data from 1978 to 1988, Science, 260, 1905–1910, https://doi.org/10.1126/science.260.5116.1905, 1993.
Sonter, L. J., Herrera, D., Barrett, D. J., Galford, G. L., Moran, C. J., and Soares-Filho, B. S.: Mining drives extensive deforestation in the Brazilian Amazon, Nat. Commun., 8, 1013, https://doi.org/10.1038/s41467-017-00557-w, 2017.
Souza, C. M., Z. Shimbo, J., Rosa, M. R., Parente, L. L., A. Alencar, A., Rudorff, B. F. T., Hasenack, H., Matsumoto, M., G. Ferreira, L., Souza-Filho, P. W. M., de Oliveira, S. W., Rocha, W. F., Fonseca, A. V., Marques, C. B., Diniz, C. G., Costa, D., Monteiro, D., Rosa, E. R., Vélez-Martin, E., Weber, E. J., Lenti, F. E. B., Paternost, F. F., Pareyn, F. G. C., Siqueira, J. V., Viera, J. L., Neto, L. C. F., Saraiva, M. M., Sales, M. H., Salgado, M. P. G., Vasconcelos, R., Galano, S., Mesquita, V. V., and Azevedo, T.: Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine, Remote Sens.-Basel, 12, 2735, https://doi.org/10.3390/rs12172735, 2020.
Stehman, S. V., Olofsson, P., Woodcock, C. E., Herold, M., and Friedl, M. A.: A global land-cover validation data set, II: augmenting a stratified sampling design to estimate accuracy by region and land-cover class, Int. J. Remote Sens., 33, 6975–6993, https://doi.org/10.1080/01431161.2012.695092, 2012.
Tang, H., Armston, J., Hancock, S., Marselis, S., Goetz, S., and Dubayah, R.: Characterizing global forest canopy cover distribution using spaceborne lidar, Remote Sens. Environ., 231, 111262, https://doi.org/10.1016/j.rse.2019.111262, 2019a.
Tang, H., Song, X.-P., Zhao, F. A., Strahler, A. H., Schaaf, C. L., Goetz, S., Huang, C., Hansen, M. C., and Dubayah, R.: Definition and measurement of tree cover: A comparative analysis of field-, lidar- and landsat-based tree cover estimations in the Sierra national forests, USA, Agr. Forest Meteorol., 268, 258–268, https://doi.org/10.1016/j.agrformet.2019.01.024, 2019b.
Thapa, R. B., Itoh, T., Shimada, M., Watanabe, M., Takeshi, M., and Shiraishi, T.: Evaluation of ALOS PALSAR sensitivity for characterizing natural forest cover in wider tropical areas, Remote Sens. Environ., 155, 32–41, https://doi.org/10.1016/j.rse.2013.04.025, 2014.
Tyukavina, A., Hansen, M. C., Potapov, P. V., Stehman, S. V., Smith-Rodriguez, K., Okpa, C., and Aguilar, R.: Types and rates of forest disturbance in Brazilian Legal Amazon, 2000–2013, Sci. Adv., 3, e1601047, https://doi.org/10.1126/sciadv.1601047, 2017.
Woodcock, C. E., Allen, R., Anderson, M., Belward, A., Bindschadler, R., Cohen, W., Gao, F., Goward, S. N., Helder, D., Helmer, E., Nemani, R., Oreopoulos, L., Schott, J., Thenkabail, P. S., Vermote, E. F., Vogelmann, J., Wulder, M. A., Wynne, R., and Team, L. S.: Free access to Landsat imagery, Science, 320, 1011–1011, 2008.
Xiao, X., Boles, S., Frolking, S., Salas, W., Moore, B., Li, C., He, L., and Zhao, R.: Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using VEGETATION sensor data, Int. J. Remote Sens., 23, 3009–3022, https://doi.org/10.1080/01431160110107734, 2002.
Xiao, X., Dorovskoy, P., Biradar, C., and Bridge, E.: A library of georeferenced photos from the field, Eos, Transactions American Geophysical Union, 92, 453–454, https://doi.org/10.1029/2011eo490002, 2011.
Xiao, X. M., Biradar, C. M., Czarnecki, C., Alabi, T., and Keller, M.: A Simple Algorithm for Large-Scale Mapping of Evergreen Forests in Tropical America, Africa and Asia, Remote Sens.-Basel, 1, 355–374, https://doi.org/10.3390/Rs1030355, 2009.
Short summary
Forest definition has two major biophysical parameters, i.e., canopy height and canopy coverage. However, few studies have assessed forest cover maps in terms of these two parameters at a large scale. Here, we assessed the annual forest cover maps in the Brazilian Amazon using 1.1 million footprints of canopy height and canopy coverage. Over 93 % of our forest cover maps are consistent with the FAO forest definition, showing the high accuracy of these forest cover maps in the Brazilian Amazon.
Forest definition has two major biophysical parameters, i.e., canopy height and canopy coverage....
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