利用多時期Landsat衛星影像進行森林砍伐之評估 -以尼加拉瓜波沙瓦生態保護區為例

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武樂(Noel Ivan Ulloa) 查詢紙本館藏

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遙測科技碩士學位學程

論文名稱

利用多時期Landsat衛星影像進行森林砍伐之評估 -以尼加拉瓜波沙瓦生態保護區為例
(Multi-Temporal Analysis of Landsat Images for Deforestation Assessment In The Bosawas Biosphere Reserve, Nicaragua)

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摘要(中)

熱帶森林是生物多樣性最高的陸地生態系統。儘管熱帶森林對人類的生存至關重要，卻經常處於威脅之中，主要是因為高強度的土地利用。本研究的目的是通過多時期的遙測影像監測技術來分析並評估中美洲尼加拉瓜波沙瓦生態保護區中核心區域的森林砍伐問題及其過程。
本研究使用了2011年，2015年和2016年的Landsat影像進行分析，並應用物件式影像分析方法(Object-based Image Analysis, OBIA)進行影像分類，用以偵測森林的覆蓋範圍，並進行森林砍伐情形之評估。結果顯示森林覆蓋面積從2011年的6206.77平方公里降至2015年的5825.14平方公里。此外本研究利用地理資訊系統(Geographic Information System, GIS)整合相關的環境圖層作為解釋因子，並應用最大熵模型(MaxEnt)以評估研究區森林砍伐之潛勢，其中包含包括完全砍伐區(砍伐後土壤裸露)與砍伐植被區(砍伐後有植生覆蓋)。本研究判釋了2015-2016年間影像中的砍伐之區塊，並使用ROC曲線(receiver operating characteristic curve)下面積，AUC(area under ROC curve)來驗證模型。模式驗證成果中，針對完全砍伐區可得AUC為0.71，植被區AUC為0.88；這些結果表明，最大熵模型可應用於評估波沙瓦核心區域內可能受到森林砍伐之區域。
　　　　模式分析結果發現，距離裸露地的遠近以及距離其他類型的植生地的遠近分別對於完全砍伐區以及砍伐植被區也最佳的解釋力，並顯示了森林砍伐過程中的不同階段：完全砍伐區為較早的階段，空間分布上有集中的情形亦有分散的狀況，而這樣的分布主要與土地的可購買與否有相關；而砍伐植被區則屬與下一個階段，其分布狀況則隨時間漸漸擴張。不論是針對完全砍伐區或者砍伐植被區的評估模式中，本研究發現距離民居社區的遠近以及距離道路、河川的遠近則對模式的解釋力無顯著的貢獻：這說明了交通、可及性等因子對於本研究區的森林砍伐問題並無明顯關聯。本研究建議，除了利用環境因子來評估雨林砍伐的問題之外，可以進一步分析砍伐區域內的區塊特性，例如：種類、形態、演進等過程等，以期對森林砍伐議題能有更深入、更全面的了解。

摘要(英)

Tropical forests represent the terrestrial ecosystem with the highest biodiversity. Despite the importance of tropical forest to human species survival, these are frequently at risk, mainly because of the dynamic use of the land. The objective of this study is to assess and analyze the deforestation process in the nucleus zone of the Bosawas Biosphere Reserve, in Nicaragua, Central America, by means of remote sensing image classification and interpretation.
Landsat images from 2011, 2015 and 2016 were classified using an object-based approach. The resulting land-cover classification maps were then utilized to conduct a deforestation assessment, which revealed a loss in forest cover, going from 6206.77 km2 in 2011, to 5825.14 km2 in 2015. Moreover, with applying GIS (Geographic Information System), this study incorporated environmental factors into the Maximum Entropy model (MaxEnt) to assess areas most susceptible to deforestation, including clear-cut and vegetated areas. Deforestation patches from 2015-2016 were used to validate the models. The Area Under the Curve of Receiver Operating Curve (AUC) for the clear-cut patch model was 0.71, and 0.88 for the vegetated patch model. The results indicate that MaxEnt is a reliable method to identify the areas more likely to be affected by deforestation within the Bosawas nucleus zone.
Results show that distance to soil and distance to other types of vegetation are the most important variables for the clear-cut and vegetated patch models, respectively. This indicates that the two types of patches have different deforestation dynamics because they represent different stages of the deforestation process. Clear-cut patches belong to the early stages of deforestation, they are clustered and diffuse, and their appearance can be related to the availability of purchasable lands in the area. On the other side, vegetated patches represent more advanced phases of the deforestation process, and they tend to expand through the years. In both models, however, variables including distance to communities, roads and rivers do not show significant contribution to deforestation, meaning that accessibility is not a vital factor in the deforestation process of Bosawas. In addition to analyze the relationship between deforestation and environmental factors, to study the characteristics of the patches (e.g. morphology, type, evolution) is also suggested an important approach to broaden the understanding of the complex issue of deforestation in tropical rainforest.

關鍵字(中)

★ 森林砍伐
★ 波沙瓦生態保護區
★ 衛星影像

關鍵字(英)

論文目次

Abstract …………………………………………………………………………………………………………………………………………………..ii
Acknowledgement ………………………………………………………………………………………………………………………………….iv
1. Introduction 1
2. Literature Review 6
2.1 Deforestation studies 6
2.2 Deforestation Patches 8
2.3 Object Based Image Analysis 10
2.4 Methods for assessing deforestation 12
2.4.1 Land Change Modeler 13
2.4.2 GEOMOD 14
2.4.3 Dinamica 15
2.5 History of Bosawas 17
3. Study Area 24
3.1 Location 24
3.2 Biodiversity 25
3.3 Indigenous Population 26
4. Data Acquisition 28
4.1 Satellite Imagery 28
4.2 GIS Data 32
5. Methods 36
5.1 General Flowchart 36
5.2 SLC-off image gap filling 37
5.3 Image Classification 40
5.3 Accuracy Assessment 45
5.4 Deforestation Assessment 46
5.5 Maximum Entropy Model 47
5.5.1 Using MaxEnt for susceptibility analysis 50
5.6 Validation 51
6. Results and Discussions 53
6.1 Land cover classification and accuracy assessment 53
6.2 Deforestation Assessment 58
6.3 Susceptibility Analysis 59
6.3.1 Clear- cut patch model 59
6.3.2 Vegetated patch model 64
6.4 Validation of the models 68
7. Conclusions 73

參考文獻

Aerts, R., Honnay, O., 2011. Forest restoration, biodiversity and ecosystem functioning. BMC Ecology, 9(10).
Aguilar-Amuchastegui, N., Riveros, J., and Forrest, J., 2014. Identifying areas of deforestation risk for REDD+ using a species modeling tool. Carbon Balance and Management, 11(29).
Armenteras, D., Rodríguez, N., and Retana, J., 2013. Landscape Dynamics in Northwestern Amazonia: An Assessment of Pastures, Fire and Illicit Crops as Drivers of Tropical Deforestation. PLoS ONE, 8(1), e54310.
Arnold, J.D., Brewer, S.C., and Dennison, P.E., 2014. Modeling climate-fire connections within the Great Basin and Upper Colorado River Basin, western United States. Fire Ecology, 10(2). doi: 10.4996/fireecology.1002064
Blaschke, T., 2009. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), pp. 2-16.
Bonan, G., 2008. Forests and Climate Change: Forcings, Feedbacks, and the Climate Benefits of Forests. Science, 320, pp. 1444-1449.
Camara, G., Dos santos, M., Sobral, M., and Modesto, R., 2008. Remote-sensing image mining: detecting agents of land-use change in tropical forest areas. International Journal of Remote Sensing, 29(16), pp. 4803–4822
Campagnolo, M.L., and Cerdeira, K.O., 2007. Contextual classification of remotely sensed images with integer linear programming. Proceedings of CompIMAGE-Computational Modeling of Objects Represented in Images: Fundamentals, Methods, and Applications, edited by Taylor and Francis, Bristol, pp. 123-128.
Cancelan visita de comisión interinstitucional a Bosawas, El Nuevo Diario. Retrieved from http://www.elnuevodiario.com.ni/nacionales/67509-cancelan-visita-comision-interinstitucional-bosawa/
Carrere, M., 2017. Cattle ranching devours Nicaragua’s Bosawas biosphere reserve. Retrieved from: https://news.mongabay.com/2017/03/cattle-ranching-devours-nicaraguas-bosawas-biosphere-reserve/
Chilar, J., 2000. Land cover mapping of large areas from satellites: status and research priorities. International Journal of Remote sensing, 21, pp. 1093–1114.
Clark Labs, 2015. Geospetial Software for Monitoring and Modeling the Earth System. Retrieved from http://www.clarklabs.org
Staver, A. C., W. de Jong, and D. Kaimowitz. 2007. Nicaragua’s Frontier: the Bosawas Biosphere Reserve. In De Jong, W., D. Donovan, and A.K. Ichi (eds). Extreme conflicts and tropical forests (pp. 57-74). Springer: Berlin.
Dudley, N. and Stolton, S., 2003. Running Pure: The importance of forest protected area to drinking water. Washington, D.C.: World Bank.
Elith, J., Phillips S.J., Hastie, T., Dudík, M., Chee, Y.E., and Yates, C.J., 2011. A statistical explanation of MaxEnt for ecologists. Diversity and Distribution, 17, pp. 43–47.
Environmental Systems Research Institute, 2016. What is image classification?. Retrieved from http://desktop.arcgis.com/en/arcmap/latest/extensions/spatial-analyst/image-classification/what-is-image-classification-.htm
Foley, J. et al., 2005. Global consequences of land use. Science, 309, pp. 570-574.
Food and Agriculture Organization of the United Nations, 1995. Forest Resource Assessment 1990. Global Synthesis. FAO, Rome.
Food and Agriculture Organization of the United Nations, 2015. Global Forest Resources Assessment 2015. Available online at: http://www.fao.org/3/ai4793e.pdf
Fourcade, Y., Engler, J.O., Rödder, D. and Secondi, J., 2014. Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias. PLoS ONE, 9(5), p. e97122.
Gergel, S., 2007. New directions in landscape pattern analysis and linkages with remote sensing. In Wulder, A. and Franklin S. (Eds.) Understanding Forest Disturbances and Spatial Pattern: Remote Sensing and GIS Approaches. Taylor & Francis Group, Boca Raton, Florida, pp 173-208.
Glaser, A., 2013. High-Yield Biostatistics, Epidemiology, and Public Health. Lippincott Williams & Wilkins.
Hojas-Gascon, L., Cerutti, P., Eva, H., Nasi, R., and Marius, C., 2015. Monitoring deforestation and forest degradation in the context of REDD+: Lessons from Tanzania. Center for International Forestry Research, Indonesia.
Intergovernmental Panel on Climate Change, 2007. Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA., 851 pp.
Iqbal, M., and Khan, I., 2014. Spatiotemporal land use land cover change analysis and erosion risk mapping of Azad Jammu and Kashmir, Pakistan. The Egyptian Journal of Remote Sensing and Space Science, 17(2), pp. 209–229.
Kaimowitz, D., Faune, A., and Mendoza, M. 2003. Your biosphere is my backyard—the story of Bosawas in Nicaragua. Policy Matters, 12, pp. 6-15.
Lambin, E., Geist, H. and Lepers, E., 2003. Dynamics of land-use and land-cover change in tropical regions. Annual Review of Environment and Resources, 28, pp. 205-241.
Liu, S., and Du, P., 2010. Object-oriented change detection from multi-temporal remotely sensed images. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVIII-4/C7L
Lee, S., Ryu, J., Kim, L., 2007. Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin, Korea. Landslides, 4(4), pp. 327-338.
Lindberg, K., Furze, B., Staff, M., and Black, R., 1997. Ecotourism and other services derived from forest in the Asia-Pacific region: outlook to 2010. Asia-Pacific Forestry Sector Outlook Study Working Paper Series (FAO)
López, M., 2012. Análisis de las causas de la deforestación y avance de la Frontera Agrícola en las zonas de Amortiguamiento y Zona Núcleo de la Reserva de Biósfera. Managua: GIZ.
Luyssaert, S., Inglima, I., Jung, M. et al., 2007. The CO2-balance of boreal, temperate and tropical forests derived from a global database. Global Change Biology, 13, pp. 2509-2537.
Mayfield, H., Smith, C., Gallagher, M. and Hockings, M., 2016. Use of freely available datasets and machine learning methods in predicting deforestation. Environmental modelling and software, 87, pp. 17-18.
McGarigal, K. and Marks, B., 1995. FRAGSTATS: Spatial Pattern Analysis Program for Quantifying Landscape Structure. General Technical Report PNW-GTR-351, USDA Forest Service, Pacific Northwest Research Station, Portland, OR.
McRoberts, R.E., and Taomppo, E.O., 2007. Remote sensing support for national forest inventories. Remote Sensing of Environment, 110, pp. 412-419.
Menon, S., Pontius Jr., R., Rose, J., Kahn, M. and Bawa, K., 2001. Identifying conservation priority areas in the tropics: a land-use change modeling approach. Conservation Biology 15(2), pp. 501-512
Mertens, B., and Lambin, E., 1997. Spatial modelling of deforestation in southern Cameroon. Applied Geography, 17 (2), pp. 143-162.
Miles, L., and Kapos, V., 2008. Reducing greenhouse gas emissions from deforestation and forest degradation: Global Land-Use implications. Science, 320, pp. 1454-1455.
Myers, N., Mittermeier, R., Mittermeier, C., da Fonseca, G. and Kent, J., 2000. Biodiversity hot spots for conservation priorities. Nature, 403, pp. 853-858.
Nemmour, H. and Chibani, Y., 2006. Multiple support vector machines for land cover change detection: An application for mapping urban extensions, Photogrammetry & Remote Sensing. 61, pp.125-133.
Paegelow, M., Camacho,O., 2008. Modelling Environmental Dynamics. Advances in geomatic solutions. Springer, series: Environmental Science and Engineering, 390 p.
Palmer, B., 2011. Community forest monitoring in REDD+: the ‘M’ in MRV?. Environmental Science & Policy, 14 (2), pp. 181-187.
Phillips, S., and Schapire, R., 2005. Maximum entropy modeling of species geographic distribution. Ecological modelling, 190, pp. 231-259.
Pontius, R., Cornell, J., and Hall, C., 2001. Modeling the spatial pattern of land-use change with Geomod2: application and validation for Costa Rica. Agriculture, Ecosystems & Environment, 85, pp. 191-203.
Rajan, D., 2010. Understanding the drivers affecting land use change in Ecuador: an application of the Land Change Modeler software (master’s thesis). Retrieved from: https://www.era.lib.ed.ac.uk/handle/1842/3740
Servicio Militar, matar para no morir (2017, March 26), La Prensa. Retrieved from http://www.laprensa.com.ni
Society of American Foresters, 1983. Terminology of Forest Science Technology, Practice and Products. Bethesda, Maryland. 370 pp.
Sabine, C.L., Heimann, M., Artaxo, P., Bakker, D., Chen, C.-T.A., Field, C.B., Gruber, N., Le Quéré, C., Prinn, R.G., Richey, J.E., Lankao, P.R., Sathaye, J. and Valentini, R., 2004. Current status and past trends of the global carbon cycle. In Field, C.B. and Raupach, M.R. (eds) The Global Carbon Cycle: Integrating Humans, Climate, and the Natural World. Scope 62, Island Press, Washington, DC, pp. 17–44.
Sheram, K., 1993. The Environmental Data Book. The World Bank, Washington DC.
Smith, J. 2003. Land-Cover Assessment of Conservation and Buffer Zones in the BOSAWAS Natural Resource Reserve of Nicaragua. Environmental Management, 31 (2), pp. 252–262
Soares-Filho, B.S., Pennachin, C.L. and G. Cerqueira, 2002. DINAMICA – a stochastic cellular automata model designed to simulate the landscape dynamics in an Amazonian colonization frontier. Ecological Modelling, 154 (3), pp. 217–235.
Stocks, A., 1996. The Bosawas Natural Reserve and the Mayangna of Nicaragua. In: Traditional Peoples and Biodiversity Conservation in Large Tropical Landscapes, edited by The Nature Conservancy, Washington, pp. 1-31.
Trimble, 2014. eCognition Developer 9.0 Reference Book, Version 1.2.0. München, Germany: Trimble.
Tucker, C., and Townshend, R., 2000. Strategies for monitoring tropical deforestation using satellite data. International Journal of Remote sensing, 21 (6), 1461-1471.
U.S. Geological Survey, 2014. Landsat 8 Handbook. U.S. Geological Survey, South Dakota.
Weih, R., and Riggan, N., 2010. Object-based classification vs. Pixel-based classification: comparative importance of multi-resolution imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVIII-4/C7
Willhauck, G., Schneider, T., De Kok, R., and Ammer, U., 2000. Comparison of object-oriented classification techniques and standard image analysis for the use of change detection betweeen SPOT multispectral satellite images and aerial photos. Paper presented at of XIX ISPRS Congress, Amsterdam, 16-22 July.
World Bank: Forest area. Retrieved August 5, 2016, from http://data.worldbank.org/indicator/AG.LND.FRST.K2?end=2015&start=1990&view=chart
Whiteside, T. and Ahmad, W. (2005). A comparison of object-oriented and pixel-based classification methods for mapping land cover in northern Australia. Paper presented at SSC2005 Spatial intelligence, innovation and praxis: The national biennial Conference of the Spatial Sciences Institute, Melbourne, September.
US Department of Agriculture, 2012. The Landscape Toolbox. Retrieved from http://wiki.landscapetoolbox.org/doku.php/remote_sensing_methods:object-based_classification20

指導教授

姜壽浩(Chiang Shou Hao)

審核日期

2017-7-7

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