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研究生: 馬莉亞
Maria Leticia Cardozo Torres
論文名稱: 應用Google Earth Engine與影像分類技術於巴拉圭查科地區進行森林砍伐評估
Deforestation Assessment in the Paraguayan Chaco using Google Earth Engine
指導教授: 姜壽浩
Shou-Hao Chiang
口試委員:
學位類別: 碩士
Master
系所名稱: 工學院 - 國際永續發展碩士在職專班
International Environment Sustainable Development Program
論文出版年: 2019
畢業學年度: 107
語文別: 英文
論文頁數: 77
中文關鍵詞: 森林砍伐Google Earth Engine巴拉圭查克地區Landsat隨機森林分類器
外文關鍵詞: Deforestation, Google Earth Engine, Paraguayan Chaco, Landsat, Random Forest Classifier
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  • 森林作為生物多樣性的資源庫,能為野生動物提供棲息地,有利固碳,並緩解氣候溫室效應。近年來,人類活動是造成森林砍筏的主要因素。本研究的目的是通過雲端的Google Earth Engine(GEE)平台,進行衛星影像整合及分析,對南美洲巴拉圭查科的森林砍伐問題進行評估。本研究直接透過GEE平台上收集、整合2013年和2017年二年期查科地區的多幅Landsat衛星影像,利用隨機森林分類器(Random Forest Classifier)進行監督式影像分類,將研究區不同類型之土地覆蓋類別進行區分,用以後了解此二年期間森林砍伐之空間分布並評估森林覆蓋率變化的狀況。
    利用GEE平台獲得的分類結果經檢核點驗證後得到:2013年分類總體正確度(overall accuracy)為0.86,2017年為0.87。結果表明利用GEE平台不僅可進行快速、有效影像整合,亦可產出可靠的影像分類圖資。
    研究結果顯示2013年森林覆蓋面積為172,861.85平方公里,2017年為163,875.54平方公里;也就是說此四年內的森林覆蓋面積損失達到了8,986.31平方公里。另外檢視查克地區的森林砍伐狀況,發現砍伐的地區去有明顯幾何矩形特徵,反映了大規模的植生移除以及重型機械的使用。透過文獻分析已知查科地區森林砍伐的主要驅動力是牛牧場,而本研究發現森林砍伐的持續發生與巴拉圭肉類出口量的持續增加情形相吻合,本研究並預期此森林砍伐的問題很可能會持續發生,乃是基於巴拉圭政府當前之政策目標為在2020年前將持續增加其牛肉的產量。從森林砍伐的分布位置發現,森來砍伐事實上在國家森林保護區內蔓延,並威脅者當地原住民社區的土地和和文化,顯示了巴拉圭國家法律的禁制在這些地區實屬有限。


    Forests act as a reservoir of biodiversity, shelter to wildlife, carbon sinks, and mitigates climate change. Despite their importance, they have been threatened by human activities, which is the main driver of deforestation. The objectives of this study are the assessment and analysis of deforestation in the Paraguayan Chaco, South America, by means of satellite image classification and analysis through the cloud-based Google Earth Engine (GEE) platform.
    Landsat images from the years of 2013 and 2017 were classified by a pixel-based supervised Random Forest Classifier. The classification results from different land-cover in the study area were then use to assess the deforestation, which expose a forest cover loss, in 2013 the forest cover was 172,862 km2 and 163,875.5 km2 in 2017; this revealed a cover loss of 8,986.3 km2 in 4 years. Furthermore, the classification results obtained in GEE platform were validated with validation points, the classification overall accuracy obtained was 0.86 for 2013 and 0.87 for 2017. The results indicate that GEE perform a rapid image processing and it is an effective reliable platform for deforestation assessment within the Chaco area.
    The results shown that the deforestation process leaves geometry rectangular features in the land surface and they are easily visualized, this suggests that there is a large scale clearing and heavy machinery is use. The main driver of deforestation in the Chaco is cattle ranching, and the results of this study indicate that the deforestation in continue advancing. During the years of study, Paraguay increase the amount of meat exportation, situating the country within the top 10 major countries worldwide exporter of meat, and this coincide with the increase of deforestation in the Chaco, this problem is likely to continue, because Paraguay set a country goal of reaching more cattle heads by 2020.
    Moreover, the results revealed that the deforestation process was spread within and in the buffer zones of the national forest protected areas, this suggest that there is lack of compliance with law were it stated that the protected areas are forbidden to use for economic purposes. Indigenous communities were also affected by the deforestation in the study period, threatening their home land and heritage.

    1. Introduction 1 2. Literature Review 5 2.1 Deforestation 5 2.1.1 Deforestation in the Chaco 14 2.2 Google Earth Engine 10 2.2.1 Google Earth Engine studies 12 3. Study area 16 3.1 Location 16 3.2 Biodiversity 17 3.3 Protected areas 20 4. Data Acquisition 22 4.1 Satellite imagery 22 4.2 GIS data 26 5. Methods 29 5.1 General Flowchart 29 5.2 Image classification 30 5.3 Accuracy assessment 35 5.4 Deforestation assessment 37 6. Results and Discussions 39 6.1 Image classification and accuracy assessment 39 6.2 Deforestation Assessment 44 6.3 Deforestation, protected areas and indigenous communities 50 6.3.1 Protected areas 50 6.3.2 Indigenous communities 56 7. Conclusions 58 References 59

    Aide, T. M., Clark, M. L., Grau, H. R., López-Carr, D., Levy, M. A., Redo, D., Muñiz, M., 2012. Deforestation and Reforestation of Latin America and the Caribbean (2001-2010). Biotropica, 45(2), pp. 262–271
    Assunção, J., Gandour, C., Rocha, R., 2015. Deforestation slowdown in the Brazilian Amazon: prices or policies? Environment and Development Economics, 20(06), pp. 697–722
    Baumann, M., Gasparri, I., Piquer-Rodríguez, M., Gavier Pizarro, G., Griffiths, P., Hostert, P., Kuemmerle, T., 2016. Carbon emissions from agricultural expansion and intensification in the Chaco. Global Change Biology, 23(5), pp. 1902–1916
    Blackie R, Baldauf C, Gautier D, Gumbo D, Kassa H, Parthasarathy N, Paumgarten F, Sola P, Pulla S, Waeber P and Sunderland T., 2014. Tropical dry forests: The state of global knowledge and recommendations for future research. Discussion Paper. Bogor, Indonesia: CIFOR.
    Blanc, L., Gond, V., Ho Tong Minh, D., 2016. Remote Sensing and Measuring Deforestation. Land Surface Remote Sensing, pp. 27–53
    Bonan, G., 2008. Forests and Climate Change: Forcing’s, Feedbacks, and the Climate Benefits of Forests
    Caldas, M. M., Goodin, D., Sherwood, S., Campos Krauer, J. M., Wisely, S. M., 2013. Land-cover change in the Paraguayan Chaco: 2000–2011. Journal of Land Use Science, 10(1), pp. 1–18
    Campos-Krauer, J., Wisely, S., 2010. Deforestation and cattle ranching drive rapid range expansion of capybara in the Gran Chaco ecosystem. Global Change Biology, 17(1), pp. 206–218
    Chakraborty, A., Sachdeva, K., Joshi, P. K.,2016. Mapping long-term land use and land cover change in the central Himalayan region using a tree-based ensemble classification approach. Applied Geography, 74, pp. 136–150
    Estatuto de las comunidades indígenas. Ley 904/81. Retrieved from: http://www.tierraviva.org.py/wp-content/uploads/2013/11/PDF.pdf
    Fehlenberg, V., Baumann, M., Gasparri, N. I., Piquer-Rodriguez, M., Gavier-Pizarro, G., & Kuemmerle, T., 2017. The role of soybean production as an underlying driver of deforestation in the South American Chaco. Global Environmental Change, 45, pp. 24–34
    Foley, J. A., 2005. Global Consequences of Land Use. Science, 309(5734), pp. 570–574
    Food and Agriculture Organization of the United Nations, 1995. Forest Resource Assessment 1990. Global Synthesis. FAO, Rome.
    Garrett, R. D., Rueda, X., Lambin, E., 2013. Globalization’s unexpected impact on soybean production in South America: linkages between preferences for non-genetically modified crops, eco-certifications, and land use. Environmental Research Letters, 8(4), pp. 44-55
    Global Forest Watch, 2019. Global Forest Watch: Chaco deforestation. Retrieved from: https://data.globalforestwatch.org/datasets/gran-chaco-deforestation
    Gillespie, T. W., Lipkin, B., Sullivan, L., Benowitz, D. R., Pau, S., & Keppel, G., 2012. The rarest and least protected forests in biodiversity hotspots. Biodiversity and Conservation, 21(14), pp. 3597–3611
    Google Earth Engine, 2019. Earth Engine code editor. Retrieved from: https://developers.google.com/earth-engine/playground
    Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R., 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, pp. 18–27
    Graesser, J., Aide, T. M., Grau, H. R., & Ramankutty, N., 2015. Cropland/pastureland dynamics and the slowdown of deforestation in Latin America. Environmental Research Letters, 10(3)
    Gullison, R. E., Frumhoff, P. C., Canadell, J. G., Field, C. B., Nepstad, D. C., Hayhoe, K., Nobre, C., 2007. ENVIRONMENT: Tropical Forests and Climate Policy. Science, 316(5827), pp. 985–986
    Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Townshend, J. R. G., 2013. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science, 342(6160), pp. 850–853
    Houghton, R., 2005. Tropical deforestation as a source of greenhouse gas emissions. Tropical Deforestation and Climate Change, pp.13-21. Retrieved from: https://www.edf.org/sites/default/files/4930_TropicalDeforestation_and_ClimateChange.pdf
    Houspanossian, J., Giménez, R., Jobbágy, E., & Nosetto, M., 2017. Surface albedo raise in the South American Chaco: Combined effects of deforestation and agricultural changes. Agricultural and Forest Meteorology, 232, pp. 118–127
    Huang, C., Kim, S., Song, K., Townshend, J. R. G., Davis, P., Altstatt, A., Musinsky, J., 2009. Assessment of Paraguay’s forest cover change using Landsat observations. Global and Planetary Change, 67(1-2), pp. 1–12
    Huang, C., Kim, S., Song, K., Townshend, J. R. G., Davis, P., Altstatt, A., Musinsky, J., 2009. Assessment of Paraguay’s forest cover change using Landsat observations. Global and Planetary Change, 67(1-2), pp. 1–12
    Hussain, M., Chen, D., Cheng, A., Wei, H., Stanley, D., 2013. Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 80, pp. 91–106
    Illegal Deforestation Monitor, 2019. Nearly quarter of the deforestation potentially illegal, says Paraguay enforcement agency. Retrieved from: http://www.bad-ag.info/nearly-a-quarter-of-chaco-deforestation-potentially-illegal-says-paraguay-enforcement-agency/
    Janzen, 1988. Tropical forest: the most endangered major tropical forest. Retrieved from: https://www.ncbi.nlm.nih.gov/books/NBK219281/
    Karsenty, A., Blanco, C., Dufour, T., 2003. Forest and climate change. Retrieved from: http://www.fao.org/tempref/docrep/fao/011/ac836e/ac836e00.pdf
    Kastner, T., Rivas, M. J. I., Koch, W., Nonhebel, S., 2012. Global changes in diets and the consequences for land requirements for food. Proceedings of the National Academy of Sciences, 109(18), pp. 6868–6872
    Kavzoglu, T., 2017. Object-Oriented Random Forest for High Resolution Land Cover Mapping Using Quickbird-2 Imagery. Handbook of Neural Computation, pp. 607–619
    Koehrsen, 2017. Random Forest simply explanation. Retrieved from: https://medium.com/@williamkoehrsen/random-forest-simple-explanation-377895a60d2d
    Kumar, L., & Mutanga, O. (2018). Google Earth Engine Applications since Inception: Usage, Trends, and Potential. Remote Sensing, 10(10), pp. 1509
    Lawson, 2014. Forests trends reports. Retrieved from: https://www.forest-trends.org/wp-content/uploads/2014/09/doc_4718.pdf
    Ley de Áreas Silvestres Protegidas. Ley 352/94. Retrieved from: http://archivo.seam.gov.py/sites/default/files/ley_352_0.pdf
    Lindberg, K., Furze, M., Staff, R., Black, R., 1997. Ecotourism and other services derived from forests in the ASIA-PACIFIC region. Retrieved from: http://www.fao.org/3/a-w7714e.pdf
    MADES – DGPCB, 2016. Quinto Informe al Convenio de Diversidad Biológica. Proyecto: Asistencia a las Partes que reúnen las condiciones para la elaboración del sexto informe nacional sobre la Diversidad Biológica (6NR). GEF. PNUD. Asunción. Paraguay. 341 p.
    MADES – DGPCB, 2019. Sexto Informe al Convenio de Diversidad Biológica. Proyecto: Asistencia a las Partes que reúnen las condiciones para la elaboración del sexto informe nacional sobre la Diversidad Biológica (6NR). GEF. PNUD. Asunción. Paraguay. 341 p.
    Miles, L., Newton, A. C., DeFries, R. S., Ravilious, C., May, I., Blyth, S., Gordon, J., 2006. A global overview of the conservation status of tropical dry forests. Journal of Biogeography, 33(3), pp. 491–505
    Millennium Ecosystem Assessment, 2005. Ecosystems and Human Well-being: Synthesis. Island Press, Washington, DC. Retrieved from: https://www.millenniumassessment.org/documents/document.356.aspx.pdf
    Ministerio de Agricultora Y Ganaderia, 2014. Sintesis estadísticas: producción agropecuaria 2013/2014. Retrieved from: http://www.mag.gov.py/Censo/SINTESIS%202014-texto%20completo.pdf
    Ministerio de Agricultora Y Ganaderia, 2018. Sintesis estadísticas: producción agropecuaria 2017/2018. Retrieved from: http://www.mag.gov.py/Censo/SINTESIS%20Estadisticas%202017_2018%20_pdf%20NOV.pdf
    Mongabay, 2012. Investigation reveals illegal cattle ranching in Paraguay vanishing’s Chaco. Retrieved from: https://news.mongabay.com/2018/12/investigation-reveals-illegal-cattle-ranching-in-paraguays-vanishing-chaco/
    Murphy, P., Lugo, A., 1986. Ecology of tropical dry forest Annual Review of Ecologic and Systematic, 17, pp. 67–88
    Mutanga, O., & Kumar, L., 2019. Google Earth Engine Applications. Remote Sensing, 11(5), pp. 591
    Nolte, C., le Polain de Waroux, Y., Munger, J., Reis, T. N. P., Lambin, E. F., 2017. Conditions influencing the adoption of effective anti-deforestation policies in South America’s commodity frontiers. Global Environmental Change, 43, pp. 1–14
    Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., D'Amico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R., 2001. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11), pp. 933-938.
    Pan, Y., Birdsey, R. A., Fang, J., Houghton, R., Kauppi, P. E., Kurz, W. A., Hayes, D., 2011. A Large and Persistent Carbon Sink in the World’s Forests. Science, 333(6045), pp. 988–993
    Portillo, C., Sanchez, G., Calvo, J., Quesada, M., Espírito, M., 2014. The role of tropical dry forests for biodiversity, carbon and water conservation in the neotropics: lessons learned and opportunities for its sustainable management. Regional Environmental Change
    Portillo-Quintero, C. A., Sánchez-Azofeifa, G. A., 2010. Extent and conservation of tropical dry forests in the Americas. Biological Conservation, 143(1), pp. 144–155
    Singh, A., 1989. Review Article Digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing, 10(6), pp. 989–1003
    Survival, 2016. Paraguay: Government defies order to protect uncontacted tribe. Retrieved from: https://www.survivalinternational.org/news/11401
    Teluguntla, P., Thenkabail, P. S., Oliphant, A., Xiong, J., Gumma, M. K., Congalton, R. G., … Huete, A., 2018. A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform. ISPRS Journal of Photogrammetry and Remote Sensing, 144, pp. 325–340
    Tsai, Y., Stow, D., Chen, H., Lewison, R., An, L., Shi, L., 2018. Mapping Vegetation and Land Use Types in Fanjingshan National Nature Reserve Using Google Earth Engine. Remote Sensing, 10(6), pp. 927
    U.S. Geological Survey, 2019. Landsat 8 Handbook. U.S. Geological Survey, South Dakota
    U.S. Meat Export Federation. Paraguay becoming rising star in beef exports. Retrieved from: https://www.usmef.org/paraguay-becoming-rising-star-in-beef-exports/
    UNEP-WCMC, 2019. Protected Area Profile for Paraguay from the World Database of Protected Areas. Retrieved from: https://www.protectedplanet.net/country/PY
    UNFCC 2011. Land use, land-use change and forestry. Retrieved from: https://unfccc.int/sites/default/files/11cp7.pdf
    Urban, J., Čermák, J. Ceulemans, R., 2015. Above and below ground biome, surface and volume and stored water in a mature scots pine stand. Europena Journal of Forest Research,134, pp. 61 - 74
    Vallejos, M., Volante, J. N., Mosciaro, M. J., Vale, L. M., Bustamante, M. L., Paruelo, J. M., 2015. Transformation dynamics of the natural cover in the Dry Chaco ecoregion: A plot level geo-database from 1976 to 2012. Journal of Arid Environments, 123, pp. 3–11
    Veit, P., Sarsfield, R., 2017. Land Rights, Beef Commodity Chains, and Deforestation Dynamics in the Paraguayan Chaco. Washington, DC.USAID Tenure and Global Climate Change Program.
    Wildlife Conservation Society (2019). Wildlife Chaco, Paraguay. Retrieved from: https://paraguay.wcs.org/en-us/Wildlife.aspx
    Wright, S., 2010. The future of tropical forests. Annals of the New York Academy of Sciences, 1195(1), pp. 1–27
    Yiu, Tony. 2019. Understanding Random Forest. Retrieved from: https://towardsdatascience.com/understanding-random-forest-58381e0602d2

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