Monitoring deforestation in Iran, Jangal-Abr Forest using multi-temporal satellite images and spectral mixture analysis method
详细信息 查看全文
- 作者：Neamat Karimi ; Saeed Golian ; Danesh Karimi
- 关键词：Deforestation ; Remote sensing ; Landsat ; Spectral mixture analysis ; Jangal ; Abr
- 刊名：Arabian Journal of Geosciences
- 出版年：2016
- 出版时间：March 2016
- 年：2016
- 卷：9
- 期：3
- 全文大小：1,653 KB
- 参考文献：Abbasi A, Mohammadzadeh S (2001) Investigation of world experiences local participation of forest resources management and utilization of successful experience in Iran. In: National proceedings of north forests management and sustainable development conference. Iranian Rangelands and Forests Organization, Tehran
Achard F, Eva HD, Stibig H-J, Mayaux P, Gallego J, Richards T, Malingreau J-P (2002) Determination of deforestation rates of the world’s humid tropical forests Science 297:999–1002 doi:10.1126/science.1070656
Achard F, DeFries R, Herold M, Mollicone D, Pandey D, De Souza JC (2008) Guidance on monitoring of gross changes in forest area, Chapter 3 in GOFC-GOLD Reducing greenhouse gas emissions from deforestation and degradation in developing countries: a sourcebook of methods and procedures for monitoring, measuring and reporting. GOFC-GOLD Report version COP13-2. GOFC-GOLD Project Office. Natural Resources Canada, Alberta, Canada
Adams JB, Sabol DE, Kapos V, Almeida Filho R, Roberts DA, Smith MO, Gillespie AR (1995) Classification of multispectral images based on fractions of endmembers: application to land-cover change in the Brazilian Amazon. Remote Sens Environ 52:137–154CrossRef
Amirnejad H, Khalilian S, Assareh MH, Ahmadian M (2006) Estimating the existence value of north forests of Iran by using a contingent valuation method Ecological Economics 58:665–675 doi:10.1016/j.ecolecon.2005.08.015
Anderson LO, Shimabukuro YE, Defries RS, Morton D (2005) Assessment of deforestation in near real time over the Brazilian Amazon using multi temporal fraction images derived from Terra MODIS. IEEE Geosci Remote Sens Lett 3:315–318CrossRef
Asner GP, Keller M, Pereira JR, Zweede JC, Silva JNM (2004) Canopy damage and recovery after selective logging in Amazonia: field and satellite studies. Ecological Applications 14:280–298. doi:10.1890/01-6019 CrossRef
Avtar R, Sawada H (2013) Use of DEM data to monitor height changes due to deforestation. Arab J Geosci 6:4859–4871. doi:10.1007/s12517-012-0768-2 CrossRef
Bernstein LS, Adler-Golden SM, Sundberg RL, Ratkowski AJ (2006) Improved reflectance retrieval from hyper- and multispectral imagery without prior scene or sensor information., pp 63622–63628
Brandt JS, Townsend PA (2006) Land cover conversion, regeneration and degra-dation in the high elevation Bolivian Andes. Landsc Ecol 21:607–623CrossRef
Bryant RG (1996) Validated linear mixture modelling of Landsat TM data for mapping evaporite minerals on a playa surface: methods and applications. Int J Remote Sens 17:315–330. doi:10.1080/01431169608949008 CrossRef
Collado AD, Chuvieco E, Camarasa A (2002) Satellite remote sensing analysis to monitor desertification processes in the crop-rangeland boundary of Argentina Journal of Arid Environments 52:121–133 doi:10.1006/jare.2001.0980
Cracknell AP (1998) Review article Synergy in remote sensing-what’s in a pixel? Int J Remote Sens 19:2025–2047. doi:10.1080/014311698214848 CrossRef
Dawelbait M, Morari F (2012) Monitoring desertification in a Savannah region in Sudan using Landsat images and spectral mixture analysis Journal of Arid Environments 80:45–55 doi:10.1016/j.jaridenv.2011.12.011
Downton MW (1995) Measuring tropical deforestation: development of the methods. Environmental Conservation 22:229–240. doi:10.1017/S0376892900010638 CrossRef
Elmore AJ, Mustard JF, Manning SJ, Lobell DB (2000) Quantifying vegetation change in semiarid environments: precision and accuracy of spectral mixture analysis and the normalized difference vegetation index. Remote Sens Environ 73:87–102CrossRef
Esmaeili A, Nasrnia F (2014) Deforestation and the Environmental Kuznets Curve in Iran Small-scale Forestry 13:397–406 doi:10.1007/s11842-014-9261-y
Etteieb S, Louhaichi M, Kalaitzidis C, Gitas I (2013) Mediterranean forest mapping using hyper-spectral satellite imagery. Arab J Geosci 6:5017–5032. doi:10.1007/s12517-012-0748-6 CrossRef
FAO (1990) Forest resources assessment 1990: tropical countries. Paper presented at the FAO Forestry. Rome, Italy
FAO (2003) State of the world’s forests. Italy, Rome
Fisher P (1997) The pixel: a snare and a delusion. Int J Remote Sens 18:679–685. doi:10.1080/014311697219015 CrossRef
Gebrehiwot S, Bewket W, Gärdenäs A, Bishop K (2014) Forest cover change over four decades in the Blue Nile Basin Ethiopia: comparison of three watersheds. Reg Environ Change 14:253–266. doi:10.1007/s10113-013-0483-x CrossRef
Guang-jun W, Mei-chen F, Qiu-ping X, Zeng W Monitoring grassland desertification around the built-up area of the city based on multi-temporal remotely sensed images. In: Management and Service Science, 2009. MASS’09. International Conference on, 20–22 Sept. 2009 2009. pp 1–4. doi:10.1109/ICMSS.2009.5303133
Hashemi S, Fallah Chai M (2013) Investigation of NDVI in relation to the growth phases of beech leaves in forest. Arab J Geosci 6:3341–3347CrossRef
Hashemi S, Fallah Chai M, Bayat S (2013) An analysis of vegetation indices in relation to tree species diversity using by satellite data in the northern forests of Iran. Arab J Geosci 6:3363–3369. doi:10.1007/s12517-012-0576-8 CrossRef
Jacobson CR (2014) The effects of endmember selection on modelling impervious surfaces using spectral mixture analysis: a case study in Sydney Australia. Int J Remote Sens 35:715–737. doi:10.1080/01431161.2013.871594 CrossRef
Kang S, Choi W (2014) Forest cover changes in North Korea since the 1980s. Reg Environ Change 14:347–354. doi:10.1007/s10113-013-0497-4 CrossRef
Kavian A, Azmoodeh A, Solaimani K (2014) Deforestation effects on soil properties, runoff and erosion in northern Iran Arab J Geosci 7:1941–1950 doi:10.1007/s12517-013-0853-1
Kelarestaghi A, Jafarian Jeloudar Z (2011) Land use/cover change and driving force analyses in parts of northern Iran using RS and GIS techniques. Arab J Geosci 4:401–411. doi:10.1007/s12517-009-0078-5 CrossRef
Khiry MA, Csaplovics E (2007). Appropriate methods for monitoring and mapping land cover changes in semi-arid areas in North Kordofan (Sudan) by using satellite imagery and spectral mixture analysis. In: Analysis of Multi-temporal Remote Sensing Images, 2007. MultiTemp 2007. International Workshop on the, 18–20. pp 1–4. doi:10.1109/MULTITEMP.2007.4293065
Lunetta RS (1999) Applications, project formulation, and analytical approach. In: Lunetta RS, Elvidge CD (eds) Remote sensing change detection: environmental monitoring methods and applications. Taylor & Francis Press, London, pp 1–19
Mas J-F (2005) Assessing protected area effectiveness using surrounding (buffer) areas environmentally similar to the target area. Environ Monit Assess 105:69–80. doi:10.1007/s10661-005-3156-5 CrossRef
Moridnejad A, Karimi N, Ariya PA (2015) Newly desertified regions in Iraq and its surrounding areas: significant novel sources of global dust particles Journal of Arid Environments 116:1–10 doi:10.1016/j.jaridenv.2015.01.008
Munyati C (2000) Wetland change detection on the Kafue Flats Zambia, by classification of a multitemporal remote sensing image dataset. Int J Remote Sens 21:1787–1806. doi:10.1080/014311600209742 CrossRef
Munyati C, Kabanda T (2009) Using multitemporal Landsat TM imagery to establish land use pressure induced trends in forest and woodland cover in sections of the Soutpansberg Mountains of Venda region Limpopo Province, South Africa. Reg Environ Change 9:41–56. doi:10.1007/s10113-008-0066-4 CrossRef
Phua M-H, Tsuyuki S, Furuya N, Lee JS (2008) Detecting deforestation with a spectral change detection approach using multitemporal Landsat data: a case study of Kinabalu Park, Sabah, Malaysia Journal of Environmental Management 88:784–795 doi:10.1016/j.jenvman.2007.04.011
Rosenberg N (1992) A Commentary on: Deforestation, climate change and sustainable nutrition security: a case study of India. In: Myers N (ed) Tropical Forests and Climate. Springer Netherlands, pp 211–213. doi:10.1007/978-94-017-3608-4_21
Shimabukuro YE, Smith A (1991) The least-squares mixing models to generate fraction images derived from remote sensing multispectral data Geoscience and Remote Sensing. IEEE Trans 29:16–20
Small C (2001) Estimation of urban vegetation abundance by spectral mixture analysis. Int J Remote Sens 22:1305–1334. doi:10.1080/01431160151144369 CrossRef
Small C (2004) The Landsat ETM+ spectral mixing space Remote Sensing of Environment 93:1–17 doi:10.1016/j.rse.2004.06.007
Smith MO, Johnson PE, Adams JB (1985) Quantitative determination of mineral types and abundances from reflectance spectra using principal components analysis. Journal of Geophysical Research Solid Earth 90:C797–C804. doi:10.1029/JB090iS02p0C797 CrossRef
Somers B, Verbesselt J, Ampe EM, Sims N, Verstraeten WW, Coppin P (2010) Spectral mixture analysis to monitor defoliation in mixed-aged Eucalyptus globulus Labill plantations in southern Australia using Landsat 5-TM and EO-1 Hyperion data International Journal of Applied Earth Observation and Geoinformation 12:270–277 doi:10.1016/j.jag.2010.03.005
Somers B, Asner GP, Tits L, Coppin P (2011) Endmember variability in spectral mixture analysis: a review Remote Sensing of Environment 115:1603–1616 doi:10.1016/j.rse.2011.03.003
Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation Remote Sensing of Environment 8:127–150 doi:10.1016/0034-4257(79)90013-0
Tucker CJ, Townshend JRG (2000) Strategies for monitoring tropical deforestation using satellite data. Int J Remote Sens 21:1461–1471. doi:10.1080/014311600210263 CrossRef
Veraverbeke S, Somers B, Gitas I, Katagis T, Polychronaki A, Goossens R (2012) Spectral mixture analysis to assess post-fire vegetation regeneration using Landsat Thematic Mapper imagery: accounting for soil brightness variation International Journal of Applied Earth Observation and Geoinformation 14:1–11 doi:10.1016/j.jag.2011.08.004
Vitousek PM (1994) Beyond global warming: ecology and global change Ecology 75:1862–1876 doi:10.2307/1941591
Wertz-Kanounnikoff S (2008) Monitoring forest emissions: a review of methods. Center for International Forestry Research (CIFOR). Bogor, Indonesia
Wessman CA, Bateson CA, Benning TL (1997) Detecting fire and grazing patterns in tallgrass prairie using spectral mixture analysis. Ecol Appl 7:493–511. doi:10.1890/1051-0761(1997)007[0493:DFAGPI]2.0.CO;2 CrossRef - 作者单位：Neamat Karimi (1)
Saeed Golian (2)
Danesh Karimi (3)
1. Department of Water Resources Research, Water Research Institute, Tehran, Iran
2. Civil Engineering Department, Shahrood University of Technology, Shahrood, Iran
3. Department of Remote Sensing, Satellite Driven Knowledge (SDK) Institute, Tehran, Iran - 刊物类别：Earth and Environmental Science
- 出版者：Springer Berlin Heidelberg
- ISSN：1866-7538
文摘
Satellite remote sensing assessment provides a systematic and object means to quantify the rate and form of forest changes. The main objective of this study was to quantify the forest change and forest degradation in one of the oldest forests of Iran named Jangal-Abr Forest at the north of Shahrood city. Based on Landsat-TM and OLI images acquired from 1987 to 2013, four vegetation and soil fraction images were produced and compared to map the land-cover changes occurred over 26 years. Spectral mixture analysis (SMA) method was applied to estimate fraction images of the selected components over the study area. This technique examines the direction of physical changes between multi-temporal images. Then, change vector analysis (CVA) approach was used for detecting forest changes. Results revealed that during the last 26 years, 147.5 km2 of forests were degraded mostly with medium severity. Part of the deforestation related to the northern and eastern regions of the study area in the vicinity of residential areas in contrast, forest regrowth area during the study period was estimated to be 62 km2 typically classified with low and medium severity in the eastern parts of the study area. CVA demonstrates that deforestation in the Jangal-Abr area has significantly accelerated from 4.6 km2/year in period 1987–2000 to about 9.6 km2/year in period 2010–2013. The rate of regrowth has been decreased gradually from 3.3 km2/year in period 1 (1987–2000) to about 0.76 km2/year in period 3 (1987–2000). By comparison the results of SMA method with Normalized Difference Vegetation Index (NDVI) changes method, it was found that except for some negligible differences, the results of both methods was very closed in terms of both spatially and the area of degradation and regrowth regions.