Assessment of forest cover and carbon stock changes in sub-tropical pine forest of Azad Jammu & Kashmir (AJK), Pakistan using multi-temporal Landsat satellite data and field inventory


Autoři: Iftikhar Ahmad Khan aff001;  Mobushir Riaz Khan aff002;  Muhammad Hasan Ali Baig aff002;  Zaker Hussain aff001;  Nasir Hameed aff003;  Junaid Aziz Khan aff004
Působiště autorů: Department of Forest, Azad Jammu & Kashmir, Pakistan aff001;  Institute of Geo-information & Earth Observation (IGEO), PMAS-Arid Agriculture University Rawalpindi, Pakistan aff002;  Land Use Planning Section, Planning & Development Department, Azad Jammu & Kashmir, Pakistan aff003;  Institute of Geographic Information System, National University of Science & Technology- Islamabad, Pakistan aff004
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: 10.1371/journal.pone.0226341

Souhrn

This study aimed at estimating temporal (1989–2018) change in forest cover, carbon stock and trend in corresponding CO2 emissions/sequestration of a sub-tropical pine forest (STPF) in AJK, Pakistan. Our field inventory estimation shows an average above ground biomass (AAGB) accumulation of 0.145 Kt/ha with average carbon stock (ACS) value of 0.072 Kt/ha. Landsat TM, ETM+ and OLI images of 1989, 1993, 1999, 2005, 2010, 2015 and 2018 were used to extract vegetation fractions through Linear Spectral Mixture Analysis (LSMA) and forest area was calculated for respective years. Based on the forest area and estimated ACS value, the biomass carbon stock with corresponding CO2 emissions/sequestration was worked out for each time and change in forest carbon stock was determined for different time periods from 1989 to 2018. Our analysis shows net increase of 561 ha in forest cover and 40.39 Kt of ACS along with increase in corresponding CO2 sequestrations of 147.83 Kt over the study period. The results based on combination of remote sensing and field inventory provide valuable information and scientific basis to plan and ensure sustainable forest management (SFM) through reforestation, protection and conservation to enhance and maintain adequate forest cover and reduce CO2 emissions.

Klíčová slova:

Carbon dioxide – Carbon sequestration – Forests – Pines – Temperate forests – Trees – Wood – Composite images


Zdroje

1. Keenan RJ, Reams GA, Achard F, de Freitas JV, Grainger A, Lindquist E. Dynamics of global forest area: Results from the FAO Global Forest Resources Assessment 2015. For Ecol Manage. 2015;352:9–20. doi: 10.1016/j.foreco.2015.06.014

2. Kumar KK, Nagai M, Witayangkurn A, Kritiyutanant K, Nakamura S. Above Ground Biomass Assessment from Combined Optical and SAR Remote Sensing Data in Surat Thani Province, Thailand. Journal of Geographic Information System. 2016;8:506. doi: 10.4236/jgis.2016.84042

3. Houghton R, Hall F, Goetz SJ. Importance of biomass in the global carbon cycle. J Geophys Res Biogeosciences. 2009;114. doi: 10.1029/2009JG000935

4. Gibbs HK, Brown S, Niles JO, Foley JA. Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environ Res Lett. 2007;2:045023. doi: 10.1088/1748-9326/2/4/045023

5. Stern N, Stern NH. The economics of climate change: the Stern review: cambridge University press; 2007.

6. FAO. Global forest resource assessment 2005: progress towards Sustainable forest management. FAO forestry Paper 147 Rome: FAO; 2006. 2005

7. Dibaba A, Soromessa T, Workineh B. Carbon stock of the various carbon pools in Gerba-Dima moist Afromontane forest, South-western Ethiopia. Carbon balance and management. 2019;14:1. doi: 10.1186/s13021-019-0116-x 30712188

8. Satish K, Saranya K, Reddy CS, Krishna PH, Jha C, Rao PP. Geospatial assessment and monitoring of historical forest cover changes (1920–2012) in Nilgiri Biosphere Reserve, Western Ghats, India. Environ Monit Assess. 2014;186:8125–8140. doi: 10.1007/s10661-014-3991-3 25117494

9. Ningthoujam R, Tansey K, Balzter H, Morrison K, Johnson S, Gerard F, et al. Mapping forest cover and forest cover change with airborne S-band radar. Remote Sens. 2016;8:577. doi: 10.3390/rs8070577

10. Potapov PV, Turubanova SA, Hansen MC, Adusei B, Broich M, Altstatt A, et al. Quantifying forest cover loss in Democratic Republic of the Congo, 2000–2010, with Landsat ETM+ data. Remote Sens Environ. 2012;122:106–116. doi: 10.1016/j.rse.2011.08.027

11. Furby S. Land cover change: specification for remote sensing analysis. 2002

12. Maliqi E, Penev P. Monitoring of vegetation change by using RS and GIS techniques in Mitrovica, Kosovo. Journal of Cartography and Geographic Information Systems. 2018;1:1–13. doi: 10.23977/jcgis.2018.11001

13. Huang C, Song K, Kim S, Townshend JR, Davis P, Masek JG, et al. Use of a dark object concept and support vector machines to automate forest cover change analysis. Remote Sens Environ. 2008;112:970–985. doi: 10.1016/j.rse.2007.07.023

14. Souza CM Jr, Siqueira JV, Sales MH, Fonseca AV, Ribeiro JG, Numata I, et al. Ten-year Landsat classification of deforestation and forest degradation in the Brazilian Amazon. Remote Sens. 2013;5:5493–5513. doi: 10.3390/rs5115493

15. Souza C, Firestone L, Silva LM, Roberts D. Mapping forest degradation in the Eastern Amazon from SPOT 4 through spectral mixture models. Remote Sens Environ. 2003;87:494–506. doi: 10.1016/j.rse.2002.08.002

16. Renó VF, Novo EM, Suemitsu C, Renno CD, Silva TS. Assessment of deforestation in the Lower Amazon floodplain using historical Landsat MSS/TM imagery. Remote Sens Environ. 2011;115:3446–3456. doi: 10.1016/j.rse.2011.08.008

17. Phua M-H, Tsuyuki S, Furuya N, Lee JS. Detecting deforestation with a spectral change detection approach using multitemporal Landsat data: A case study of Kinabalu Park, Sabah, Malaysia. Journal of Environmental Management. 2008;88:784–795. doi: 10.1016/j.jenvman.2007.04.011 17629393

18. Liu T, Yang X. Mapping vegetation in an urban area with stratified classification and multiple endmember spectral mixture analysis. Remote Sens Environ. 2013;133:251–264. doi: 10.1016/j.rse.2013.02.020

19. Karimi N, Golian S, Karimi D. Monitoring deforestation in Iran, Jangal-Abr Forest using multi-temporal satellite images and spectral mixture analysis method. Arab J Geosci. 2016;9:214. doi: 10.1007/s12517-015-2250-4

20. Deng Y, Wu C. Development of a class-based multiple endmember spectral mixture analysis (C-MESMA) approach for analyzing urban environments. Remote Sens. 2016;8:349. doi: 10.3390/rs8040349

21. Thenkabail PS, Biradar CM, Noojipady P, Dheeravath V, Li Y, Velpuri M, et al. Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium. Int J Remote Sens. 2009;30:3679–3733. doi: 10.1080/01431160802698919

22. Gong P, Wang J, Yu L, Zhao Y, Zhao Y, Liang L, et al. Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data. Int J Remote Sens. 2013;34:2607–2654. doi: 10.1080/01431161.2012.748992

23. Townshend JR, Masek JG, Huang C, Vermote EF, Gao F, Channan S, et al. Global characterization and monitoring of forest cover using Landsat data: opportunities and challenges. Int J Digit Earth. 2012;5:373–397. doi: 10.1080/17538947.2012.713190

24. Jia K, Li Q, Tian Y, Wu B, Zhang F, Meng J. Crop classification using multi-configuration SAR data in the North China Plain. Int J Remote Sens. 2012;33:170–183. doi: 10.1080/01431161.2011.587844

25. Nordberg ML, Evertson J. Vegetation index differencing and linear regression for change detection in a Swedish mountain range using Landsat TM® and ETM+® imagery. Land Degradation & Development. 2005;16:139–149. doi: 10.1002/ldr.660

26. Langley SK, Cheshire HM, Humes KS. A comparison of single date and multitemporal satellite image classifications in a semi-arid grassland. J Arid Environ. 2001;49:401–411. doi: 10.1006/jare.2000.0771

27. Tomppo E, Gschwantner T, Lawrence M, McRoberts RE, Gabler K, Schadauer K, et al. National forest inventories. Pathways for Common Reporting European Science Foundation. 2010:541–553

28. Xie Y, Sha Z, Yu M. Remote sensing imagery in vegetation mapping: a review. J Plant Ecol. 2008;1:9–23. doi: 10.1093/jpe/rtm005

29. Siddiqui K. Asia-Pacific Forestry Sector Outlook Study. Country Report—Pakistan. Working paper no: APFSOS/WP/11 Food and Agriculture Organization of the United Nations. 1997

30. Pearson T, Walker S, Brown S. Sourcebook for land use, land-use change and forestry projects. Biocarbon Fund and Winrock International. 2005.

31. Malhi Y, Baker TR, Phillips OL, Almeida S, Alvarez E, Arroyo L, et al. The above‐ground coarse wood productivity of 104 Neotropical forest plots. Glob Chang Biol. 2004;10:563–591. doi: 10.1111/j.1529-8817.2003.00778.x

32. Basuki T, Van Laake P, Skidmore A, Hussin Y. Allometric equations for estimating the above-ground biomass in tropical lowland Dipterocarp forests. For Ecol Manage. 2009;257:1684–1694. doi: 10.1016/j.foreco.2009.01.027

33. Quintano C, Fernández-Manso A, Shimabukuro YE, Pereira G. Spectral unmixing. Int J Remote Sens. 2012;33:5307–5340. doi: 10.1080/01431161.2012.661095

34. Smith MO, Ustin SL, Adams JB, Gillespie AR. Vegetation in deserts: I. A regional measure of abundance from multispectral images. Remote Sens Environ. 1990;31:1–26. doi: 10.1016/0034-4257(90)90074-V

35. Lu D, Moran E, Batistella M. Linear mixture model applied to Amazonian vegetation classification. Remote Sens Environ. 2003;87:456–469. doi: 10.1016/j.rse.2002.06.001

36. Adams JB, Sabol DE, Kapos V, Almeida Filho R, Roberts DA, Smith MO, et al. Classification of multispectral images based on fractions of endmembers: Application to land-cover change in the Brazilian Amazon. Remote Sens Environ. 1995;52:137–154. doi: 10.1016/0034-4257(94)00098-8

37. Lunetta RS, and Christopher Elvidge. Remote Sensing Change Detection: Environmental Monitoring Methods and Applications: Chelsea, Mich: Ann Arbor Press; 1998.

38. Collado AD, Chuvieco E, Camarasa A. Satellite remote sensing analysis to monitor desertification processes in the crop-rangeland boundary of Argentina. J Arid Environ. 2002;52:121–133. doi: 10.1006/jare.2001.0980

39. Salih AA, Ganawa E-T, Elmahl AA. Spectral mixture analysis (SMA) and change vector analysis (CVA) methods for monitoring and mapping land degradation/desertification in arid and semiarid areas (Sudan), using Landsat imagery. Egypt J Remote Sens Sp Sci. 2017;20:S21–S29. doi: 10.1016/j.ejrs.2016.12.008

40. Foody GM. Ground reference data error and the mis-estimation of the area of land cover change as a function of its abundance. Remote Sens Lett. 2013;4:783–792. doi: 10.1080/2150704X.2013.798708

41. Foody GM. Classification accuracy comparison: hypothesis tests and the use of confidence intervals in evaluations of difference, equivalence and non-inferiority. Remote Sens Environ. 2009;113:1658–1663. doi: 10.1016/j.rse.2009.03.014

42. Congalton RG, Green K. Assessing the accuracy of remotely sensed data: principles and practices: CRC press; 2008.

43. McRoberts RE, Næsset E, Gobakken T. Comparing the stock-change and gain–loss approaches for estimating forest carbon emissions for the aboveground biomass pool. Canadian Journal of Forest Research. 2018;48:1535–1542. doi: 10.1139/cjfr-2018-0295

44. Watson C. Forest carbon accounting: overview and principles. For carbon Account Overv Princ. 2009

45. Munawar S, Khokhar MF, Atif S. Reducing emissions from deforestation and forest degradation implementation in northern Pakistan. Int Biodeterior Biodegradation. 2015;102:316–323. doi: 10.1016/j.ibiod.2015.02.027

46. Shaheen H, Khan RWA, Hussain K, Ullah TS, Nasir M, Mehmood A. Carbon stocks assessment in subtropical forest types of Kashmir Himalayas. Pak J Bot. 2016;48:2351–2357

47. Nizami SM. The inventory of the carbon stocks in sub tropical forests of Pakistan for reporting under Kyoto Protocol. Journal of Forestry Research. 2012;23:377–384. doi: 10.1007/s11676-012-0273-1

48. Jina B, Sah P, Bhatt M, Rawat Y. Estimating carbon sequestration rates and total carbon stockpile in degraded and non-degraded sites of Oak and Pine forest of Kumaun Central Himalaya. Ecoprint: An International Journal of Ecology. 2009;15:75–81. doi: 10.3126/eco.v15i0.1946

49. van Noordwijk M, Cerri C, Woomer PL, Nugroho K, Bernoux M. Soil carbon dynamics in the humid tropical forest zone. Geoderma. 1997;79:187–225. doi: 10.1016/S0016-7061(97)00042-6

50. Harmon ME, Hua C. Coarse woody debris dynamics in two old-growth ecosystems. BioScience. 1991;41:604–610. doi: 10.2307/1311697

51. Sharma CM, Baduni NP, Gairola S, Ghildiyal SK, Suyal S. Tree diversity and carbon stocks of some major forest types of Garhwal Himalaya, India. For Ecol Manage. 2010;260:2170–2179. doi: 10.1016/j.foreco.2010.09.014

52. Geist HJ, Lambin EF. Proximate causes and underlying driving forces of tropical deforestation: Tropical forests are disappearing as the result of many pressures, both local and regional, acting in various combinations in different geographical locations. BioScience. 2002;52:143–150. doi: 10.1641/0006-3568(2002)052[0143:PCAUDF]2.0.CO;2

53. Turner BL, Meyer WB. Global land-use and land-cover change: an overview. Changes in land use and land cover: a global perspective. 1994;4

54. Sloan S, Sayer JA. Forest Resources Assessment of 2015 shows positive global trends but forest loss and degradation persist in poor tropical countries. For Ecol Manage. 2015;352:134–145. doi: 10.1016/j.foreco.2015.06.013

55. MacDicken KG, Sola P, Hall JE, Sabogal C, Tadoum M, de Wasseige C. Global progress toward sustainable forest management. For Ecol Manage. 2015;352:47–56. doi: 10.1016/j.foreco.2015.02.005

56. Federici S, Tubiello FN, Salvatore M, Jacobs H, Schmidhuber J. New estimates of CO2 forest emissions and removals: 1990–2015. For Ecol Manage. 2015;352:89–98. doi: 10.1016/j.foreco.2015.04.022


Článek vyšel v časopise

PLOS One


2020 Číslo 1