Impact of Climate Change and Variability on Spatiotemporal Variation of Forest Cover; World Heritage Sinharaja Rainforest, Sri Lanka
Additional Files
Deprecated: json_decode(): Passing null to parameter #1 ($json) of type string is deprecated in /home/journal33/public_html/plugins/generic/citations/CitationsPlugin.inc.php on line 49
Rainforests are continuously threatened by various anthropogenic activities. In addition, the ever-changing climate severely impacts the world’s rainforest cover. The consequences of these are paid back to human at a higher cost. Nevertheless, little or no significant attention was broadly given to this critical environmental issue. The World Heritage Sinharaja Rainforest in Sri Lanka is originating news on its forest cover due to human activities and changing climates. The scientific analysis is yet to be presented on the related issues. Therefore, this paper presents a comprehensive study on the possible impact on the Sinharaja Rainforest due to changing climate. Landsat images with measured rainfall data for 30 years were assessed and the relationships are presented. Results showcased that the built-up areas have drastically been increased over the last decade in the vicinity and the declared forest area. The authorities found the issues are serious and a sensitive task to negotiate in conserving the forest. The rainfall around the forest area has not shown significant trends over the years. Therefore, the health of forest cover was not severely impacted. Nevertheless, six cleared-up areas were found inside the Singaraja Rainforest under no human interactions. This can be due to a possible influence from the changing climate. This was justified by the temporal variation of Land Surface Temperature (LST) assessments over these six cleared-up areas. Therefore, the World Heritage rainforest is threatened due to human activities and under the changing climate change. Hence, the conservation of the Sinharaja Rainforest would be challenging in the future.
Anderson, M. C., Allen, R. G., Morse, A., & Kustas, W. P. (2012). Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources. Remote Sensing of Environment, 122, 50-65. https://doi.org/10.1016/j.rse.2011.08.025
Athick, A. M. A., Shankar, K., & Naqvi, H. R. (2019). Data on time series analysis of land surface temperature variation in response to vegetation indices in twelve Wereda of Ethiopia using mono window, split window algorithm and spectral radiance model. Data in brief, 27, 104773. https://doi.org/10.1016/j.dib.2019.104773
Baker, J. R. (1937). The Sinharaja Rain-Forest, Ceylon. The Geographical Journal, 89(6), 539–551. https://doi.org/10.2307/1787913
Bebi, P., Seidl, R., Motta, R., Fuhr, M., Firm, D., Krumm, F., Conedera, M., Ginzler, C., Wohlgemuth, T. and Kulakowski, D. (2017). Changes of forest cover and disturbance regimes in the mountain forests of the Alps. Forest ecology and management, 388, 43–56. https://doi.org/10.1016/j.foreco.2016.10.028
Benchimol, M., & Peres, C. A. (2015). Widespread forest vertebrate extinctions induced by a mega hydroelectric dam in lowland Amazonia. PloS one, 10(7), e0129818. https://doi.org/10.1371/journal.pone.0129818
Birhane, E., Ashfare, H., Fenta, A. A., Hishe, H., Gebremedhin, M. A., G. Wahed, H., & Solomon, N. (2019). Land use land cover changes along topographic gradients in Hugumburda national forest priority area, Northern Ethiopia. Remote Sensing Applications: Society and Environment, 13, 61–68. https://doi.org/10.1016/j.rsase.2018.10.017
Chatterjee, R. S., Singh, N., Thapa, S., Sharma, D., & Kumar, D. (2017). Retrieval of land surface temperature (LST) from landsat TM6 and TIRS data by single channel radiative transfer algorithm using satellite and ground-based inputs. International journal of applied earth observation and geoinformation, 58, 264-277. https://doi.org/10.1016/j.jag.2017.02.017
Chen, H., Chandrasekar, V., Cifelli, R., & Xie, P. (2019). A machine learning system for precipitation estimation using satellite and ground radar network observations. IEEE Transactions on Geoscience and Remote Sensing, 58(2), 982-994. https://doi.org/10.1109/TGRS.2019.2942280
Choi, S., Lee, W. K., Kwak, D. A., Lee, S., Son, Y., Lim, J. H., & Saborowski, J. (2011). Predicting forest cover changes in future climate using hydrological and thermal indices in South Korea. Climate Research, 49(3), 229-245. https://doi.org/10.3354/cr01026
Congedo, L. (2016). Semi-automatic classification plugin documentation. Release, 4(0.1), 29. https://doi.org/10.21105/joss.03172
Cristóbal, J., Jiménez-Muñoz, J. C., Prakash, A., Mattar, C., Skoković, D., & Sobrino, J. A. (2018). An improved single-channel method to retrieve land surface temperature from the Landsat-8 thermal band. Remote Sensing, 10(3), 431. https://doi.org/10.3390/rs10030431
Dash, P., Göttsche, F. M., Olesen, F. S., & Fischer, H. (2001). Retrieval of land surface temperature and emissivity from satellite data: physics, theoretical limitations and current methods. Journal of the Indian Society of Remote Sensing, 29(1), 23-30. https://doi.org/10.1007/BF02989910
De Silva, R. P., Dayawansa, N. D. K., & Ratnasiri, M. D. (2007). A comparison of methods used in estimating missing rainfall data. The Journal of Agricultural Sciences, 3(2), 101-108. https://doi.org/10.4038/jas.v3i2.8107
Fokeng, R. M., Forje, W. G., Meli, V. M., & Bodzemo, B. N. (2020). Multi-temporal forest cover change detection in the Metchie-Ngoum protection forest reserve, West Region of Cameroon. The Egyptian Journal of Remote Sensing and Space Science, 23(1), 113-124. https://doi.org/10.1016/j.ejrs.2018.12.002
García-Santos, V., Cuxart, J., Martínez-Villagrasa, D., Jiménez, M. A., & Simó, G. (2018). Comparison of three methods for estimating land surface temperature from landsat 8-tirs sensor data. Remote Sensing, 10(9), 1450. https://doi.org/10.3390/rs10091450
Gibbs, H. K., Ruesch, A. S., Achard, F., Clayton, M. K., Holmgren, P., Ramankutty, N., & Foley, J. A. (2010). Tropical forests were the primary sources of new agricultural land in the 1980s and 1990s. Proceedings of the National Academy of Sciences, 107(38), 16732–16737. https://doi.org/10.1073/pnas.0910275107
Gunathilake, N., De Mel, T., De Mel, W. C. P., Sheriff, M. H. R., & Dharmadasa, K. (1987). The value of the use of renal function indices in distinguishing prerenal failure from established acute oliguric renal failure.
Gunatilleke, S., Gunatilleke, I., Sheppard, D., Sax, J., Forster, M., Hoffmann, T., Fernando, V., Synge, H., Edisvik, H., De Zoysa, N., Fernando, R. & Forest Dept. Staff. (1987). Sinharaja Forest Reserve (Sri Lanka), World Heritage Nomination - IUCN Summary, 405, 67-71.
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., ... & Townshend, J. (2013). High-resolution global maps of 21st-century forest cover change. Science, 342(6160), 850-853. https://doi.org/10.1126/science.1244693
Härkönen, S., Neumann, M., Mues, V., Berninger, F., Bronisz, K., Cardellini, G., ... & Mäkelä, A. (2019). A climate-sensitive forest model for assessing impacts of forest management in Europe. Environmental Modelling & Software, 115, 128-143. https://doi.org/10.1016/j.envsoft.2019.02.009
Haylock, M. R., Hofstra, N., Klein Tank, A. M. G., Klok, E. J., Jones, P. D., & New, M. (2008). A European daily high‐resolution gridded data set of surface temperature and precipitation for 1950–2006. Journal of Geophysical Research: Atmospheres, 113(D20). https://doi.org/10.1029/2008JD010201
Heartsill-Scalley, T., & Aide, T. M. (2003). Riparian vegetation and stream condition in a tropical agriculture–secondary forest mosaic. Ecological Applications, 13(1), 225-234. https://doi.org/10.1890/1051-761(2003)013[0225:RVASCI]2.0.CO;2
Hirsch, R. M., & Slack, J. R. (1984). A nonparametric trend test for seasonal data with serial dependence. Water Resources Research, 20(6), 727-732. https://doi.org/10.1029/WR020i006p00727
Huang, C., Kim, S., Song, K., Townshend, J. R., Davis, P., Altstatt, A., ... & Musinsky, J. (2009). Assessment of Paraguay's forest cover change using Landsat observations. Global and Planetary Change, 67(1-2), 1-12. https://doi.org/10.1016/j.gloplacha.2008.12.009
Ji, W., Wang, Y., Zhuang, D., Song, D., Shen, X., Wang, W., & Li, G. (2014). Spatial and temporal distribution of expressway and its relationships to land cover and population: A case study of Beijing, China. Transportation Research Part D: Transport and Environment, 32, 86-96. https://doi.org/10.1016/j.trd.2014.07.010
Jin, X., & Davis, C. H. (2007). Vehicle detection from high-resolution satellite imagery using morphological shared-weight neural networks. Image and Vision Computing, 25(9), 1422-1431. https://doi.org/10.1016/j.imavis.2006.12.011
Karmeshu, N. (2012). Trend detection in annual temperature & precipitation using the Mann Kendall test–a case study to assess climate change on select states in the northeastern United States [Master Thesis]. University of Pennsylvania.
Karnieli, A., Agam, N., Pinker, R. T., Anderson, M., Imhoff, M. L., Gutman, G. G., ... & Goldberg, A. (2010). Use of NDVI and land surface temperature for drought assessment: Merits and limitations. Journal of climate, 23(3), 618-633. https://doi.org/10.1175/2009JCLI2900.1
Khan, S. H., He, X., Porikli, F., & Bennamoun, M. (2017). Forest change detection in incomplete satellite images with deep neural networks. IEEE Transactions on Geoscience and Remote Sensing, 55(9), 5407-5423. https://doi.org/10.1109/TGRS.2017.2707528
Khaniya, B., Jayanayaka, I., Jayasanka, P., & Rathnayake, U. (2019). Rainfall trend analysis in Uma Oya basin, Sri Lanka, and future water scarcity problems in perspective of climate variability. Advances in Meteorology, 2019. https://doi.org/10.1155/2019/3636158
Kim, S. R., Lee, W. K., Kwak, D. A., Biging, G. S., Gong, P., Lee, J. H., & Cho, H. K. (2011). Forest cover classification by optimal segmentation of high resolution satellite imagery. Sensors, 11(2), 1943-1958. https://doi.org/10.3390/s110201943
Kislov, D. E., Korznikov, K. A., Altman, J., Vozmishcheva, A. S., & Krestov, P. V. (2021). Extending deep learning approaches for forest disturbance segmentation on very high‐resolution satellite images. Remote Sensing in Ecology and Conservation, 7(3), 355-368. https://doi.org/10.1002/rse2.194
Labbi, A., & Mokhnache, A. (2015). Derivation of split-window algorithm to retrieve land surface temperature from MSG-1 thermal infrared data. European Journal of Remote Sensing, 48(1), 719-742. https://doi.org/10.5721/EuJRS20154840
Leach, M. and Fairhead, J. (2000). Challenging neo-Malthusian deforestation analyses in West Africa’s dynamic forest landscapes. Population and Development Review, 26, 17–43. https://doi.org/10.1111/j.1728-4457.2000.00017.x
Leimgruber, P., Kelly, D. S., Steininger, M. K., Brunner, J., Müller, T., & Songer, M. (2005). Forest cover change patterns in Myanmar (Burma) 1990–2000. Environmental Conservation, 32(4), 356-364. https://doi.org/10.1017/S0376892905002493
Lindström, S., Mattsson, E., & Nissanka, S. P. (2012). Forest cover change in Sri Lanka: The role of small scale farmers. Applied Geography, 34, 680-692. https://doi.org/10.1016/j.apgeog.2012.04.011
Mann, H. B. (1945). Nonparametric tests against trend. Econometrica: Journal of the econometric society, 13(3) 245-259. https://doi.org/10.2307/1907187
Mao, D., Wang, Z., Luo, L., & Ren, C. (2012). Integrating AVHRR and MODIS data to monitor NDVI changes and their relationships with climatic parameters in Northeast China. International Journal of Applied Earth Observation and Geoinformation, 18, 528-536. https://doi.org/10.1016/j.jag.2011.10.007
Margono, B. A., Potapov, P. V., Turubanova, S., Stolle, F., & Hansen, M. C. (2014). Primary forest cover loss in Indonesia over 2000–2012. Nature climate change, 4(8), 730-735. https://doi.org/10.1038/nclimate2277
Matricardi, E. A., Skole, D. L., Pedlowski, M. A., Chomentowski, W., & Fernandes, L. C. (2010). Assessment of tropical forest degradation by selective logging and fire using Landsat imagery. Remote Sensing of Environment, 114(5), 1117-1129. https://doi.org/10.1016/j.rse.2010.01.001
Mujabar, S., & Rao, V. (2018). Estimation and analysis of land surface temperature of Jubail Industrial City, Saudi Arabia, by using remote sensing and GIS technologies. Arabian Journal of Geosciences, 11(23), 1-13. https://doi.org/10.1007/s12517-018-4109-y
Perera, K., & Tsuchiya, K. (2009). Experiment for mapping land cover and it’s change in southeastern Sri Lanka utilizing 250 m resolution MODIS imageries. Advances in Space Research, 43(9), 1349-1355. https://doi.org/10.1016/j.asr.2008.12.016
Peterson, C. J. (2000). Catastrophic wind damage to North American forests and the potential impact of climate change. Science of the Total Environment, 262(3), 287-311. https://doi.org/10.1016/S0048-9697(00)00529-5
Qin, Z., Karnieli, A., & Berliner, P. (2001). A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. International journal of remote sensing, 22(18), 3719-3746. https://doi.org/10.1080/01431160010006971
Ranagalage, M., Gunarathna, M. H. J. P., Surasinghe, T. D., Dissanayake, D., Simwanda, M., Murayama, Y., ... & Sathurusinghe, A. (2020). Multi-decadal forest-cover dynamics in the tropical realm: Past trends and policy insights for forest conservation in dry zone of Sri Lanka. Forests, 11(8), 836. https://doi.org/10.3390/f11080836
Rathnayake, C. W., Jones, S., & Soto-Berelov, M. (2020). Mapping land cover change over a 25-year period (1993–2018) in Sri Lanka using Landsat time-series. Land, 9(1), 27. https://doi.org/10.3390/land9010027
Rathnayake, U. (2019). Comparison of statistical methods to graphical methods in rainfall trend analysis: case studies from tropical catchments. Advances in Meteorology, 2019. https://doi.org/10.1155/2019/8603586
Rongali, G., Keshari, A. K., Gosain, A. K., & Khosa, R. (2018). Split-window algorithm for retrieval of land surface temperature using Landsat 8 thermal infrared data. Journal of Geovisualization and Spatial Analysis, 2(2), 1-19. https://doi.org/10.1007/s41651-018-0021-y
Sahin, S., & Cigizoglu, H. K. (2010). Homogeneity analysis of Turkish meteorological data set. Hydrological Processes: An International Journal, 24(8), 981-992. https://doi.org/10.1002/hyp.7534
Sandholt, I., Rasmussen, K., & Andersen, J. (2002). A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of environment, 79(2-3), 213-224. https://doi.org/10.1016/S0034-4257(01)00274-7
Sekertekin, A., & Bonafoni, S. (2020). Land surface temperature retrieval from Landsat 5, 7, and 8 over rural areas: Assessment of different retrieval algorithms and emissivity models and toolbox implementation. Remote Sensing, 12(2), 294. https://doi.org/10.3390/rs12020294
Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall's tau. Journal of the American statistical association, 63(324), 1379-1389. https://doi.org/10.1080/01621459.1968.10480934
Sirisena, D., & Suriyagoda, L. D. (2018). Toward sustainable phosphorus management in Sri Lankan rice and vegetable-based cropping systems: A review. Agriculture and Natural Resources, 52(1), 9-15. https://doi.org/10.1016/j.anres.2018.03.004
Sobrino, J. A., Jiménez-Muñoz, J. C., Sòria, G., Romaguera, M., Guanter, L., Moreno, J., Plaza, A. & Martínez, P. (2008). Land surface emissivity retrieval from different VNIR and TIR sensors. IEEE transactions on geoscience and remote sensing, 46(2), 316-327. https://doi.org/10.1109/TGRS.2007.904834
Song, W., Mu, X., Ruan, G., Gao, Z., Li, L., & Yan, G. (2017). Estimating fractional vegetation cover and the vegetation index of bare soil and highly dense vegetation with a physically based method. International journal of applied earth observation and geoinformation, 58, 168-176. https://doi.org/10.1016/j.jag.2017.01.015
Stibig, H. J., & Malingreau, J. P. (2003). Forest cover of insular Southeast Asia mapped from recent satellite images of coarse spatial resolution. AMBIO: A Journal of the Human Environment, 32(7), 469-475. https://doi.org/10.1579/0044-7447-32.7.469
Sun, L., Liu, X., Yang, Y., Chen, T., Wang, Q., & Zhou, X. (2018). A cloud shadow detection method combined with cloud height iteration and spectral analysis for Landsat 8 OLI data. ISPRS Journal of Photogrammetry and Remote Sensing, 138, 193-207. https://doi.org/10.1016/j.isprsjprs.2018.02.016
Sy, S., & Quesada, B. (2020). Anthropogenic land cover change impact on climate extremes during the 21st century. Environmental Research Letters, 15(3), 034002. https://doi.org/10.1088/1748-9326/ab702c
Sylvain, J. D., Drolet, G., & Brown, N. (2019). Mapping dead forest cover using a deep convolutional neural network and digital aerial photography. ISPRS Journal of Photogrammetry and Remote Sensing, 156, 14-26. https://doi.org/10.1016/j.isprsjprs.2019.07.010
Temudo, M. P., & Silva, J. M. (2012). Agriculture and forest cover changes in post-war Mozambique. Journal of Land Use Science, 7(4), 425-442. https://doi.org/10.1080/1747423X.2011.595834
van Leeuwen, T. T., Frank, A. J., Jin, Y., Smyth, P., Goulden, M. L., van der Werf, G. R., & Randerson, J. T. (2011). Optimal use of land surface temperature data to detect changes in tropical forest cover. Journal of Geophysical Research: Biogeosciences, 116(G2). https://doi.org/10.1029/2010JG001488
Viana, C. M., Oliveira, S., Oliveira, S. C., & Rocha, J. (2019). Land use/land cover change detection and urban sprawl analysis. In Spatial modeling in GIS and R for earth and environmental sciences. In Pourghasemi, H. R. & Gokceoglu, C. (Eds.), Spatial Modeling in GIS and R for Earth and Environmental Sciences (pp. 621-651). Elsevier. https://doi.org/10.1016/B978-0-12-815226-3.00029-6
Walthert, L., Pannatier, E. G., & Meier, E. S. (2013). Shortage of nutrients and excess of toxic elements in soils limit the distribution of soil-sensitive tree species in temperate forests. Forest ecology and management, 297, 94-107. https://doi.org/10.1016/j.foreco.2013.02.008
Wan Mohd Jaafar, W. S., Abdul Maulud, K. N., Muhmad Kamarulzaman, A. M., Raihan, A., Md Sah, S., Ahmad, A., Saad, S.N.M., Ahmad Tarmizi Mohd Azmi, A.T.M., Syukri, N.K.A.J. & Razzaq Khan, W. (2020). The influence of deforestation on land surface temperature—a case study of Perak and Kedah, Malaysia. Forests, 11(6), 670. https://doi.org/10.3390/f11060670
Wang, F., Fan, W., Lin, X., Liu, J. and Ye, X. (2020). Does population mobility contribute to urbanization convergence? Empirical evidence from three major urban agglomerations in China. Sustainability. 12(2), 458. https://doi.org/10.3390/su12020458
Wang, M., Zhang, Z., He, G., Wang, G., Long, T., & Peng, Y. (2016). An enhanced single‐channel algorithm for retrieving land surface temperature from Landsat series data. Journal of Geophysical Research: Atmospheres, 121(19), 11-712. https://doi.org/10.1002/2016JD025270
Yu, X., Guo, X., & Wu, Z. (2014). Land surface temperature retrieval from Landsat 8 TIRS—Comparison between radiative transfer equation-based method, split window algorithm and single channel method. Remote sensing, 6(10), 9829-9852. https://doi.org/10.3390/rs6109829
Zhang, R., Zhao, X., Zhang, C., & Li, J. (2020). Impact of rapid and intensive land use/land cover change on soil properties in arid regions: a case study of Lanzhou new area, China. Sustainability, 12(21), 9226. https://doi.org/10.3390/su12219226
Zhu, Z., & Waller, E. (2003). Global forest cover mapping for the United Nations Food and Agriculture Organization forest resources assessment 2000 program. Forest science, 49(3), 369-380. https://doi.org/10.1093/forestscience/49.3.369
Copyright (c) 2022 Forest and Society
This work is licensed under a Creative Commons Attribution 4.0 International License.
This is an open access journal which means that all contents is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles in this journal without asking prior permission from the publisher or the author. This is in accordance with the BOAI definition of open access.
Submission of an article implies that the work described has not been published previously (except in the form of an abstract or as part of a published lecture or academic thesis), that it is not under consideration for publication elsewhere, that its publication is approved by all authors and tacitly or explicitly by the responsible authorities where the work was carried out, and that, if accepted, will not be published elsewhere in the same form, in English or in any other language, without the written consent of the Publisher. An article based on a section from a completed graduate dissertation may be published in Forest and Society, but only if this is allowed by author's(s') university rules. The Editors reserve the right to edit or otherwise alter all contributions, but authors will receive proofs for approval before publication.
Forest and Society operates a CC-BY 4.0 © license for journal papers. Copyright remains with the author, but Forest and Society is licensed to publish the paper, and the author agrees to make the article available with the CC-BY 4.0 license. Reproduction as another journal article in whole or in part would be plagiarism. Forest and Society reserves all rights except those granted in this copyright notice
