241
Views
0
CrossRef citations to date
0
Altmetric
Original Article

Multi-Decadal Spatial and Temporal Forest Cover Change Analysis of Nkandla Natural Reserve, South Africa

, ORCID Icon &

References

  • Addae, B., & Oppelt, N. (2019). Land-Use/land-cover change analysis and urban growth modelling in the greater Accra Metropolitan Area (GAMA), Ghana. Urban Science, 3(26), 1. https://doi.org/10.3390/urbansci3010026
  • Agrawal, A., Cashore, B., Hardin, R., Shepherd, G., Benson, C., & Miller, D. (2013). Economic contributions of forests. Background Paper, 1.
  • Aguejdad, R., Houet, T., & Hubert-Moy, L., J. E. M., & Assessment. (2017). Spatial validation of land use change models using multiple assessment techniques: A case study of transition potential models. Environmental Modeling and Assessment, 22(6), 591–606. https://doi.org/10.1007/s10666-017-9564-4
  • Ahammad, R., Stacey, N., & Sunderland, T. C. (2019). Use and perceived importance of forest ecosystem services in rural livelihoods of Chittagong Hill Tracts, Bangladesh. Ecosystem Services, 35, 87–98. https://doi.org/10.1016/j.ecoser.2018.11.009
  • Arroyo-Rodriguez, V., Fahrig, L., Tabarelli, M., Watling, J. I., Tischendorf, L., Benchimol, M., Cazetta, E., Faria, D., Leal, I. R., Melo, F. P. L., Morante-Filho, J. C., Santos, B. A., Arasa-Gisbert, R., Arce-Pena, N., Cervantes-Lopez, M. J., Cudney-Valenzuela, S., Galan-Acedo, C., San-Jose, M., Vieira, I. C. G., Slik, J. W. F., … Tscharntke, T. (2020). Designing optimal human-modified landscapes for forest biodiversity conservation. Ecology Letters, 23(9), 1404–1420. https://doi.org/10.1111/ele.13535
  • Barlow, J., Lennox, G. D., Ferreira, J., Berenguer, E., Lees, A. C., Mac Nally, R., Thomson, J. R., de Barros Ferraz, S. F., Louzada, J., Oliveira, V. H. F., Parry, L., Ribeiro de Castro Solar, R., Vieira, I. C. G., Aragão, L. E. O. C., Begotti, R. A., Braga, R. F., Cardoso, T. M., Jr, R. C. D. O., Souza Jr, C. M., Moura, N. G., & Gardner, T. A. (2016). Anthropogenic disturbance in tropical forests can double biodiversity loss from deforestation. Nature, 535(7610), 144–147. https://doi.org/10.1038/nature18326
  • Boedhihartono, A. (2017). Can Community Forests Be Compatible With Biodiversity Conservation in Indonesia? Land, 6(1), 1. https://doi.org/10.3390/land6010021
  • Boivin, N. L., Zeder, M. A., Fuller, D. Q., Crowther, A., Larson, G., Erlandson, J. M., Denham, T., & Petraglia, M. D. (2016). Ecological consequences of human niche construction: Examining long-term anthropogenic shaping of global species distributions. Proceedings of the National Academy of Sciences, 113(23), 6388–6396. https://doi.org/10.1073/pnas.1525200113
  • Boucher, D., Elias, P., Lininger, K., May-Tobin, C., Roquemore, S., & Saxon, E. (2011). The root of the problem: What’s driving tropical deforestation today? The Root of the Problem: What’s Driving Tropical Deforestation Today? 1–13.
  • Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., & Lopez, A. (2020). A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 408, 189–215. https://doi.org/10.1016/j.neucom.2019.10.118
  • Chambers, J. C., Roundy, B. A., Blank, R. R., Meyer, S. E., & Whittaker, A. (2007). What makes Great Basin sagebrush ecosystems invasible by Bromus tectorum? Ecological Monographs, 77(1), 117–145. https://doi.org/10.1890/05-1991
  • Chaudhary, S., Chettri, N., Uddin, K., Khatri, T. B., Dhakal, M., Bajracharya, B., & Ning, W. J. E. C. (2016). Implications of land cover change on ecosystems services and people’s dependency: A case study from the Koshi Tappu Wildlife Reserve, Nepal. Ecological Complexity, 28, 200–211. https://doi.org/10.1016/j.ecocom.2016.04.002
  • Chen, C.-F., Son, N.-T., Chang, N.-B., Chen, C.-R., Chang, L.-Y., Valdez, M., Centeno, G., Thompson, C., & Aceituno, J. (2013). Multi-decadal mangrove forest change detection and prediction in Honduras, Central America, with landsat imagery and a markov chain model. Remote Sensing, 5(12), 6408–6426. https://doi.org/10.3390/rs5126408
  • Da Ponte, E., Roch, M., Leinenkugel, P., Dech, S., & Kuenzer, C. (2017). Paraguay’s Atlantic Forest cover loss–Satellite-based change detection and fragmentation analysis between 2003 and 2013. Applied Geography, 79, 37–49. https://doi.org/10.1016/j.apgeog.2016.12.005
  • DAFF. (2015). State of the forests report 2010-2012 report, Department Agriculture, Forestry and Fisheries, Republic of South Africa. Unpublished, 88.
  • Daume, S., Albert, M., & von Gadow, K. (2014). Forest monitoring and social media – Complementary data sources for ecosystem surveillance? Forest Ecology and Management, 316, 9–20. https://doi.org/10.1016/j.foreco.2013.09.004
  • Davies, K. W., Boyd, C. S., Bates, J. D., & Hulet, A. (2015). Dormant season grazing may decrease wildfire probability by increasing fuel moisture and reducing fuel amount and continuity. International Journal of Wildland Fire, 24(6), 849–856. https://doi.org/10.1071/WF14209
  • Davies, K. W., Boyd, C. S., Beck, J. L., Bates, J. D., Svejcar, T. J., & Gregg, M. A. (2011). Saving the sagebrush sea: An ecosystem conservation plan for big sagebrush plant communities. Biological Conservation, 144(11), 2573–2584. https://doi.org/10.1016/j.biocon.2011.07.016
  • Deb, S., Debnath, M. K., Chakraborty, S., Weindorf, D. C., Kumar, D., Deb, D., & Choudhury, A. (2018). Anthropogenic impacts on forest land use and land cover change: Modelling future possibilities in the Himalayan Terai. Anthropocene, 21, 32–41. https://doi.org/10.1016/j.ancene.2018.01.001
  • Dube, T., & Mutanga, O. (2015). Evaluating the utility of the medium-spatial resolution Landsat 8 multispectral sensor in quantifying aboveground biomass in uMgeni catchment, South Africa. ISPRS Journal of Photogrammetry and Remote Sensing, 101, 36–46. https://doi.org/10.1016/j.isprsjprs.2014.11.001
  • Eastman, J. R. (2015). TerrSet tutorial. J Clark Labs Clark University.
  • Ezemvelo KZN Wildlife. (2015). Nkandla Forest Complex: Protected Area Management Plan. Unpublished, Version 1.0, 173
  • Fassnacht, F. E., Latifi, H., Stereńczak, K., Modzelewska, A., Lefsky, M., Waser, L. T., Straub, C., & Ghosh, A. (2016). Review of studies on tree species classification from remotely sensed data. Remote Sensing of Environment, 186, 64–87. https://doi.org/10.1016/j.rse.2016.08.013
  • 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 Space Science, 23(1), 113–124. https://doi.org/10.1016/j.ejrs.2018.12.002
  • Fontes, C. G., Chambers, J. Q., & Higuchi, N. (2018). Revealing the causes and temporal distribution of tree mortality in Central Amazonia. Forest Ecology and Management, 424, 177–183. https://doi.org/10.1016/j.foreco.2018.05.002
  • Fragal, E. H., Silva, T. S. F., & Novo, E. M. L. D. M. (2016). Reconstructing historical forest cover change in the Lower Amazon floodplains using the LandTrendr algorithm. Acta Amazonica, 46(1), 13–24. https://doi.org/10.1590/1809-4392201500835
  • Ghosh, A., Fassnacht, F. E., Joshi, P.K., Koch, B., (2014). A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales. International Journal of Applied Earth Observation and Geoinformation, 26, 49–63. http://dx.doi.org/10.1016/j.jag.2013.05.017
  • Gilroy, J. J., Woodcock, P., Edwards, F. A., Wheeler, C., Baptiste, B. L., Uribe, C. A. M., Haugaasen, T., & Edwards, D. P. (2014). Cheap carbon and biodiversity co-benefits from forest regeneration in a hotspot of endemism. Nature Climate Change, 4(6), 503. https://doi.org/10.1038/nclimate2200
  • Gómez, C., White, J. C., & Wulder, M. A. (2016). Optical remotely sensed time series data for land cover classification: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 116, 55–72. https://doi.org/10.1016/j.isprsjprs.2016.03.008
  • Gyamfi-Ampadu, E., Gebreslasie, M., & Mendoza-Ponce, A. (2020). Mapping natural forest cover using satellite imagery of Nkandla forest reserve, KwaZulu-Natal, South Africa. Remote Sensing Applications: Society and Environment, 18, 1-14. https://doi.org/10.1016/j.rsase.2020.100302
  • Hansen, M. C., & Loveland, T. R. (2012). A review of large area monitoring of land cover change using Landsat data. Remote Sensing of Environment, 122, 66–74. https://doi.org/10.1016/j.rse.2011.08.024
  • He, Z., Liu, J., Su, S., Zheng, S., Xu, D., Wu, Z., Hong, W., & Wang, J. L.-M. (2015). Effects of forest gaps on soil properties in Castanopsis kawakamii nature forest. PLoS One, 10(10), e0141203. https://doi.org/10.1371/journal.pone.0141203
  • He, Z., Wang, L., Jiang, L., Wang, Z., Liu, J., Xu, D., & Hong, W. (2019). Effect of microenvironment on species distribution patterns in the regeneration layer of forest gaps and non-gaps in a subtropical natural forest, China. Forests, 10(2), 90. https://doi.org/10.3390/f10020090
  • Kamusoko, C., Aniya, M., Adi, B., & Manjoro, M. (2009). Rural sustainability under threat in Zimbabwe–simulation of future land use/cover changes in the Bindura district based on the Markov-cellular automata model. Applied Geography, 29(3), 435–447. https://doi.org/10.1016/j.apgeog.2008.10.002
  • Khatami, R., Mountrakis, G., & Stehman, S. V. (2016). A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research. Remote Sensing of Environment, 177, 89–100. https://doi.org/10.1016/j.rse.2016.02.028
  • Kumar, M., Singh, M. P., Singh, H., Dhakate, P. M., & Ravindranath, N. H. (2019). Forest working plan for the sustainable management of forest and biodiversity in India. Journal of Sustainable Forestry, 39(1), 1–22. https://doi.org/10.1080/10549811.2019.1632212
  • Leal, L. C., Andersen, A. N., & Leal, I. R. (2014). Anthropogenic disturbance reduces seed-dispersal services for myrmecochorous plants in the Brazilian Caatinga. Oecologia, 174(1), 173–181. https://doi.org/10.1007/s00442-013-2740-6
  • Lohbeck, M., Poorter, L., Martínez-Ramos, M., & Bongers, F. (2015). Biomass is the main driver of changes in ecosystem process rates during tropical forest succession. Ecology, 96(5), 1242–1252. https://doi.org/10.1890/14-0472.1
  • Lu, D., Li, G., & Moran, E. (2014). Current situation and needs of change detection techniques. International Journal of Image and Data Fusion, 5(1), 13–38. https://doi.org/10.1080/19479832.2013.868372
  • MacLean, D. A. (2016). Impacts of insect outbreaks on tree mortality, productivity, and stand development. The Canadian Entomologist, 148(S1), S138–S159. https://doi.org/10.4039/tce.2015.24
  • Maithani, S. (2015). Neural networks-based simulation of land cover scenarios in Doon valley, India. Geocarto International, 30(2), 163–185. https://doi.org/10.1080/10106049.2014.927535
  • Malhi, Y., Baker, T. R., Phillips, O. L., Almeida, S., Alvarez, E., Arroyo, L., Chave, J., Czimczik, C. I., Fiore, A. D., Higuchi, N., Killeen, T. J., Laurance, S. G., Laurance, W. F., Lewis, S. L., Montoya, L. M. M., Monteagudo, A., Neill, D. A., Vargas, P. N., Patiño, S., Pitman, N. C. A., & Lloyd, J. (2004). The above‐ground coarse wood productivity of 104 Neotropical forest plots. Global Change Biology, 10(5), 563–591. https://doi.org/10.1111/j.1529-8817.2003.00778.x
  • Martorell, C., & Peters, E. M. (2005). The measurement of chronic disturbance and its effects on the threatened cactus Mammillaria pectinifera. Biological Conservation, 124(2), 199–207. https://doi.org/10.1016/j.biocon.2005.01.025
  • McGeoch, M. A., Dopolo, M., Novellie, P., Hendriks, H., Freitag, S., Ferreira, S., Grant, R., Kruger, J., Bezuidenhout, H., Randall, R. M., Vermeulen, W., Kraaij, T., Russell, I. A., Knight, M. H., Holness, S., & Oosthuizen, A. (2011). A strategic framework for biodiversity monitoring in South African National Parks: Essay. Koedoe: African Protected Area Conservation and Science, 53(2), 1–10. https://doi.org/10.4102/koedoe.v53i2.991
  • McRoberts, R. E., & Westfall, J. A. (2016). Propagating uncertainty through individual tree volume model predictions to large-area volume estimates. Annals of Forest Science, 73(3), 625–633. https://doi.org/10.1007/s13595-015-0473-x
  • Mihai, B., Săvulescu, I., Rujoiu-Mare, M., & Nistor, C. (2017). Recent forest cover changes (2002–2015) in the Southern Carpathians: A case study of the Iezer Mountains, Romania. Science of the Total Environment, 599, 2166–2174. https://doi.org/10.1016/j.scitotenv.2017.04.226
  • Millard, K., & Richardson, M. (2015). On the importance of training data sample selection in random forest image classification: A case study in peatland ecosystem mapping. Remote Sensing, 7(7), 8489–8515. https://doi.org/10.3390/rs70708489
  • Mishra, V. N., & Rai, P. K. (2016). A remote sensing aided multi-layer perceptron-Markov chain analysis for land use and land cover change prediction in Patna district (Bihar), India. Arabian Journal of Geosciences, 9, 4. https://doi.org/10.1007/s12517-015-2138-3
  • Muscolo, A., Bagnato, S., Sidari, M., & Mercurio, R. (2014). A review of the roles of forest canopy gaps. Journal of Forestry Research, 25(4), 725–736. https://doi.org/10.1007/s11676-014-0521-7
  • Neumann, M., Mues, V., Moreno, A., Hasenauer, H., & Seidl, R. (2017). Climate variability drives recent tree mortality in Europe. Global Change Biology, 23(11), 4788–4797. https://doi.org/10.1111/gcb.13724
  • Ngwira, S., & Watanabe, T. (2019). An analysis of the causes of deforestation in Malawi: A case of Mwazisi. Land, 8(3), 48. https://doi.org/10.3390/land8030048
  • Pal, S., & Ziaul, S. (2017). Detection of land use and land cover change and land surface temperature in English Bazar urban centre. The Egyptian Journal of Remote Sensing and Space Science, 20(1), 125–145. https://doi.org/10.1016/j.ejrs.2016.11.003
  • Peres, C. A., Barlow, J., & Laurance, W. F. (2006). Detecting anthropogenic disturbance in tropical forests. Trends in Ecology & Evolution, 21(5), 227–229. https://doi.org/10.1016/j.tree.2006.03.007
  • Phiri, D., & Morgenroth, J. (2017). Developments in Landsat land cover classification methods: A review. Remote Sensing, 9(9), 967. https://doi.org/10.3390/rs9090967
  • Pontius, J. R. G. (2002). Statistical methods to partition effects of quantity and location during comparison of categorical maps at multiple resolutions. Photogrammetric Engineering and Remote Sensing, 68(10), 1041–1050.
  • Pontius, J. R. G., & Schneider, L. C. (2001). Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agriculture, Ecosystems & Environment, 85(1–3), 239–248. https://doi.org/10.1016/S0167-8809(01)00187-6
  • Ranagalage, M., Wang, R., Gunarathna, M. H. J. P., Dissanayake, D. M. S. L. B., Murayama, Y., & Simwanda, M. (2019). Spatial forecasting of the landscape in rapidly urbanizing hill stations of South Asia: A case study of Nuwara Eliya, Sri Lanka (1996–2037). Remote Sensing, 11, 15. https://doi.org/10.3390/rs11151743
  • Rawat, J. S., & Kumar, M. (2015). Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science, 18(1), 77–84. https://doi.org/10.1016/j.ejrs.2015.02.002
  • Reddy, C. S., Pasha, S. V., Satish, K., Saranya, K., Jha, C., & Murthy, Y. K. (2018). Quantifying nationwide land cover and historical changes in forests of Nepal (1930–2014): Implications on forest fragmentation. Biodiversity and Conservation, 27(1), 91–107.  https://doi.org/10.1007/s10531-017-1423-8
  • Ribeiro, E. A. M., Arroyo‐Rodríguez, V., Santos, B. A., Tabarelli, M., & Leal, I. R. (2015). Chronic anthropogenic disturbance drives the biological impoverishment of the Brazilian Caatinga vegetation. Journal of Applied Ecology, 52(3), 611–620. https://doi.org/10.1111/1365-2664.12420
  • Rimal, B., Zhang, L., Keshtkar, H., Haack, B. N., Rijal, S., & Zhang, P. (2018). Land use/land cover dynamics and modeling of urban land expansion by the integration of cellular automata and markov chain. ISPRS International Journal of Geo-Information, 7(4), 154. https://doi.org/10.3390/ijgi7040148
  • Rousta, I., Sarif, M. O., Gupta, R. D., Olafsson, H., Ranagalage, M., Murayama, Y., Zhang, H., & Mushore, T. D. (2018). Spatiotemporal analysis of land use/land cover and its effects on surface urban heat island using Landsat data: A case study of Metropolitan City Tehran (1988–2018). Sustainability, 10(12), 4433. https://doi.org/10.3390/su10124433
  • Roy, D. P., Wulder, M. A., Loveland, T. R., W., C. E., Allen, R. G., Anderson, M. C., Helder, D., Irons, J. R., Johnson, D. M., Kennedy, R., Scambos, T. A., Schaaf, C. B., Schott, J. R., Sheng, Y., Vermote, E. F., Belward, A. S., Bindschadler, R., Cohen, W. B., Gao, F., Hipple, J. D., … Zhu, Z. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, 145, 154–172. https://doi.org/10.1016/j.rse.2014.02.001
  • Sang, L., Zhang, C., Yang, J., Zhu, D., & Yun, W. (2011). Simulation of land use spatial pattern of towns and villages based on CA–Markov model. Mathematical and Computer Modelling, 54(3–4), 938–943. https://doi.org/10.1016/j.mcm.2010.11.019
  • Sharma, R., Rimal, B., Baral, H., Nehren, U., Paudyal, K., Sharma, S., Rijal, S., Ranpal, S., Acharya, R. P., Alenazy, A. A., & Kandel, P. (2019). Impact of land cover change on ecosystem services in a tropical forested landscape. Resources, 8(1), 18. https://doi.org/10.3390/resources8010018
  • Snyder, R. L., Spano, D., Duce, P., Baldocchi, D., Xu, L., & Paw U, K. T. (2006). A fuel dryness index for grassland fire-danger assessment. Agricultural and Forest Meteorology, 139(1–2), 1–11. https://doi.org/10.1016/j.agrformet.2006.05.006
  • Team, R. D. C. (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing. h ttps.w ww.R-project.org
  • Tewkesbury, A. P., Comber, A. J., Tate, N. J., Lamb, A., & Fisher, P. F. (2015). A critical synthesis of remotely sensed optical image change detection techniques. Remote Sensing of Environment, 160, 1–14. https://doi.org/10.1016/j.rse.2015.01.006
  • Thanh Noi, P., & Kappas, M. J. S. (2018). Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using Sentinel-2 imagery. Sensors, 18(1), 18. https://doi.org/10.3390/s18010018
  • Tugume, P., Kakudidi, E. K., Buyinza, M., Namaalwa, J., Kamatenesi, M., Mucunguzi, P., & Kalema, J. (2016). Ethnobotanical survey of medicinal plant species used by communities around Mabira Central Forest Reserve, Uganda. Journal of Ethnobiology Ethnomedicine, 12(1), 1–28. https://doi.org/10.1186/s13002-015-0077-4
  • Vázquez-Quintero, G., Solís-Moreno, R., Pompa-García, M., Villarreal-Guerrero, F., Pinedo-Alvarez, C., & Pinedo-Alvarez, A. (2016). Detection and projection of forest changes by using the markov chain model and cellular automata. Sustainability, 8 (3), https://doi.org/10.3390/su8030236
  • Vittek, M., Brink, A., Donnay, F., Simonetti, D., & Desclée, B. (2014). Land cover change monitoring using landsat MSS/TM satellite image data over West Africa between 1975 and 1990. Remote Sensing, 6(1), 658–676. https://doi.org/10.3390/rs6010658
  • Voight, C., Hernandez-Aguilar, K., Garcia, C., & Gutierrez, S. (2019). Predictive modeling of future forest cover change patterns in Southern Belize. Remote Sensing, 11 (7). https://doi.org/10.3390/rs11070823
  • Wang, M., Wan, Y., Ye, Z., & Lai, X. (2017). Remote sensing image classification based on the optimal support vector machine and modified binary coded ant colony optimization algorithm. Information Sciences, 402, 50–68. https://doi.org/10.1016/j.ins.2017.03.027
  • Wang, S., Zheng, X., & Zang, X. (2012). Accuracy assessments of land use change simulation based on Markov-cellular automata model. Procedia Environmental Sciences, 13, 1238–1245. https://doi.org/10.1016/j.proenv.2012.01.117
  • Wessels, K., van den Bergh, F., Roy, D., Salmon, B., Steenkamp, K., MacAlister, B., Swanepoel, D., & Jewitt, D. (2016). rapid land cover map updates using change detection and robust random forest classifiers. Remote Sensing, 8(11), 11. https://doi.org/10.3390/rs8110888
  • Wu, H., Li, Z., Clarke, K. C., Shi, W., Fang, L., Lin, A., & Zhou, J. (2019). Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change. International Journal of Geographical Information Science, 33(5), 1040–1061. https://doi.org/10.1080/13658816.2019.1568441
  • Yang, X., Zheng, X.-Q., & Lv, L.-N. (2012). A spatiotemporal model of land use change based on ant colony optimization, Markov chain and cellular automata. Ecological Modelling, 233, 11–19. https://doi.org/10.1016/j.ecolmodel.2012.03.011
  • Zhang, L., Xiao, J., Zhou, Y., Zheng, Y., Li, J., & Xiao, H. (2016). Drought events and their effects on vegetation productivity in China. Ecosphere, 7(12), e01591. https://doi.org/10.1002/ecs2.1591
  • Zhen, Z., Quackenbush, L. J., Stehman, S. V., & Zhang, L. (2013). Impact of training and validation sample selection on classification accuracy and accuracy assessment when using reference polygons in object-based classification. International Journal of Remote Sensing, 34(19), 6914–6930. https://doi.org/10.1080/01431161.2013.810822
  • Zhu, X., Liu, W., Jiang, X. J., Wang, P., & Li, W. (2018). Effects of land‐use changes on runoff and sediment yield: Implications for soil conservation and forest management in Xishuangbanna, Southwest China. Land Degradation and Development, 29(9), 2962–2974. https://doi.org/10.1002/ldr.3068

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.