Application of Land Surface temperature from Landsat series to monitor and analyze forest ecosystems: A bibliometric analysis
Abstract
Aim of study: Land surface temperature (LST) is an essential variable to monitor and characterize forest ecosystems. This variable has been consistently captured for almost four decades by the Landsat program. The current study aimed at identifying trends, knowledge gaps and opportunity areas in the use of Landsat derived LST for the monitoring and analysis of forest ecosystems.
Materials and methods: A bibliometric analysis of scientific articles indexed in Scopus in the period 1995-2020 was conducted.
Main results: Annual increase rate in the number of publications on the topic analyzed was 22.58%. The journal with more publications on the topic was Proceedings of SPIE, followed by Remote Sensing. The authors with the highest productivity on this topic were C. Quintano, I. Vorovencii, O. E. Yakubailik and M. A. Zoran. Regarding productivity by country, 38 countries with publications on this topic were identified, with the highest productivity located in China, USA and India. This group of countries also represented the most solid network of cooperation between countries. Forest ecosystems more frequently analyzed were temperate forests, followed by tropical forests. The analysis of keywords highlighted topics such as remote sensing, NDVI, MODIS and evapotranspiration. The analysis of thematic evolution indicated that areas of research and interpretation of LST data has evolved in parallel with remote sensing areas.
Research highlights: Landsat LST analysis is an evolving topic with potential to contribute to improve ecosystem knowledge and to support diverse challenges in forest resources decision-making.
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References
Ahmad P, Asif JA, Alam MK, Slots J, 2020. A bibliometric analysis of Periodontology 2000. Periodontol 82: 286-297. https://doi.org/10.1111/prd.12328
Amalyahya A, 2015. Vegetation cover density and land surface temperature interrelationship using satellite data, case study of Wadi Bisha, South KSA. ARS 4: 248-262. https://doi.org/10.4236/ars.2015.43020
Ara S, Islam MA, Showkat S, 2016. Effect of land-use intensity on surface temperature: A study on Chittagong city corporation area. 5th Int Conf on Informatics, Electronics and Vision (ICIEV), pp: 72-77. https://doi.org/10.1109/ICIEV.2016.7759971
Arekhi M, Yesil A, Ozkan UY, Sanli FB, 2018. Detecting treeline dynamics in response to climate warming using forest stand maps and Landsat data in a temperate forest. For Ecosyst 5: 23. https://doi.org/10.1186/s40663-018-0141-3
Aria M, Cuccurullo C, 2017. Bibliometrix: An R-tool for comprehensive science mapping analysis. J Informetr 11: 959-975. https://doi.org/10.1016/j.joi.2017.08.007
Arif N, Khasanah AN, Jaya R, Gozan M, Hendrawan B, 2019. The effect of land surface temperature and land use on energy system development in Gorontalo City. J Phys Conf Ser 1179: 012103. https://doi.org/10.1088/1742-6596/1179/1/012103
Arvidson T, Barsi J, Jhabvala M, Reuter D, 2013. Landsat and thermal infrared imaging. In: Thermal infrared remote sensing: Sensors, methods, applications; Kuenzer C & Dech S (eds). Springer Netherlands, Dordrecht, pp: 177-196. https://doi.org/10.1007/978-94-007-6639-6_9
Ayele D, Zewotir T, Mwambi H, 2014. Multiple correspondence analysis as a tool for analysis of large health surveys in African settings. Afr Health Sci 14(4): 1036-1045. https://doi.org/10.4314/ahs.v14i4.35
Banskota A, Kayastha N, Falkowski MJ, Wulder MA, Froese RE, White JC, 2014. Forest monitoring using Landsat Time Series Data: A review. Can J Remote Sens 40: 362-384. https://doi.org/10.1080/07038992.2014.987376
Barros Santiago D, Correia Filho WLF, de Oliveira-Júnior JF, Silva-Junior CA, 2019. Mathematical modeling and use of orbital products in the environmental degradation of the Araripe Forest in the Brazilian Northeast. Model Earth Syst Environ 5: 1429-1441. https://doi.org/10.1007/s40808-019-00614-x
Bendib A, Dridi H, Kalla MI, 2017. Contribution of Landsat 8 data for the estimation of land surface temperature in Batna city, Eastern Algeria. Geocarto Int 32: 503-513. https://doi.org/10.1080/10106049.2016.1156167
Boike J, Grau T, Heim B, Günther F, Langer M, Muster S, et al., 2016. Satellite-derived changes in the permafrost landscape of central Yakutia, 2000-2011: Wetting, drying, and fires. Glob Planet Change 139: 116-127. https://doi.org/10.1016/j.gloplacha.2016.01.001
Bonilla CA, Merigó JM, Torres-Abad C, 2015. Economics in Latin America: A bibliometric analysis. Scientometrics 105: 1239-1252. https://doi.org/10.1007/s11192-015-1747-7
Del Mundo VKM, Tiburan Jr CL, 2019. Analyzing the effects of land cover change on surface temperature in Mount Makiling Forest Reserve (MMFR) and its neighboring municipalities using Landsat data. ISPRS Archives XLII-4/W19: 165-172. https://doi.org/10.5194/isprs-archives-XLII-4-W19-165-2019
Duan P, Wang Y, Yin P, 2020. Remote sensing applications in monitoring of protected areas: A bibliometric analysis. Remote Sens 12(5): 772. https://doi.org/10.3390/rs12050772
FAO, 2020. Global forest resources assessment 2020: Main report. Rome. https://www.fao.org/documents/card/en/c/ca8753en/ [20 Sept 2021].
Fu P, Weng Q, 2016. A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery. Remote Sens Environ 175: 205-214. https://doi.org/10.1016/j.rse.2015.12.040
Gallardo-Salazar JL, Pompa-García M, Aguirre-Salado CA, López-Serrano PM, Meléndez-Soto A, 2020. Drones: Tecnología con futuro promisorio en la gestión forestal. Rev Mex Cienc Forest 11 (61). https://doi.org/10.29298/rmcf.v11i61.794
GCOS, 2016. Essential climate variables. Global Climate Observing System. https://gcos.wmo.int/en/essential-climate-variables [20 Sept 2021].
Ghazaryan G, Dubovyk O, Graw V, Schellberg J, 2018. Vegetation monitoring with satellite time series: an integrated approach for user-oriented knowledge extraction. Proc of SPIE 10783, Remote Sens Agr Ecosyst Hydrol XX, October 10. https://doi.org/10.1117/12.2325762
Gillespie TW, Willis KS, Ostermann-Kelm S, 2014. Spaceborne remote sensing of the world's protected areas. Prog Phys Geogr: Earth Environ 39: 388-404. https://doi.org/10.1177/0309133314561648
Gosteva A, Matuzko A, Yakubailik O, 2019a. Detection of changes in urban environment based on infrared satellite data. IOP Conf Ser: Mater Sci Eng 537: 062051. https://doi.org/10.1088/1757-899X/537/6/062051
Gosteva A, Matuzko A, Yakubailik O, 2019b. Identification of changes in urban environment on the basis of the satellite data of the infrared range (on the example of Krasnoyarsk). InterCarto InterGIS 25: 90-100. https://doi.org/10.35595/2414-9179-2019-2-25-90-100
Guan X, Shen H, Wang Y, Chu D, Li X, Yue L, et al., 2021. Fusing MODIS and AVHRR products to generate a global 1-km continuous NDVI time series covering four decades. Earth Syst Sci Data. https://doi.org/10.5194/essd-2021-156
Herrero HV, Southworth J, Bunting E, Kohlhaas RR, Chil B, 2019. Integrating surface-based temperature and vegetation abundance estimates into land cover classifications for conservation efforts in savanna landscapes. Sensors 19(16): 3456. https://doi.org/10.3390/s19163456
Hou CH, Li FP, He BJ, Gu H, Song W, 2020. Study on surface thermal environment differentiation effect in mining intensive area through developing remote sensing assessment model. J Infrared Millim Terahertz Waves 39: 635-649.
Hua AK, Ping OW, 2018. The influence of land-use/land-cover changes on land surface temperature: A case study of Kuala Lumpur metropolitan city. Eur J Remote Sens 51: 1049-1069. https://doi.org/10.1080/22797254.2018.1542976
Huang H, Xie W, Sun H, 2015. Simulating 3D urban surface temperature distribution using ENVI-MET model: Case study on a forest park. Proc IEEE Int Geosci Remote Sens Symp (IGARSS). pp: 1642-1645. https://doi.org/10.1109/IGARSS.2015.7326100
Huang S, Tang L, Hupy JP, Wang Y, Shao G, 2021. A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. J For Res 32: 1-6. https://doi.org/10.1007/s11676-020-01155-1
Jana C, Mandal D, Shrimali SS, Alam NM, Kumar R, Sena DR, Kaushal R, 2020. Assessment of urban growth effects on green space and surface temperature in Doon Valley, Uttarakhand, India. Environ Monit Assess 192: 257. https://doi.org/10.1007/s10661-020-8184-7
Joshi A, 2017. Comparison between Scopus & ISI Web of science. J Glob Values 7: 2016.
Kayet N, Pathak K, Chakrabarty A, Sahoo S, 2016. Spatial impact of land use/land cover change on surface temperature distribution in Saranda Forest, Jharkhand. Model Earth Syst Environ 2: 127. https://doi.org/10.1007/s40808-016-0159-x
Li ZL, Tang BH, Wu H, Ren H, Yan G, Wan Z, et al., 2013. Satellite-derived land surface temperature: Current status and perspectives. Remote Sens Environ 131: 14-37. https://doi.org/10.1016/j.rse.2012.12.008
Lotka AJ, 1926. The frequency distribution of scientific productivity. J Wash Acad Sci 16: 317-323
Mora L, Bolici R, Deakin M, 2017. The first two decades of smart-city research: A bibliometric analysis. J Urban Technol 24(1): 3-27. https://doi.org/10.1080/10630732.2017.1285123
Moral-Muñoz JA, Herrera-Viedma E, Santisteban-Espejo A, Cobo MJ, 2020. Software tools for conducting bibliometric analysis in science: An up-to-date review. Profesional de la Información 29(1). https://doi.org/10.3145/epi.2020.ene.03
Neinavaz E, Darvishzadeh R, Skidmore AK, Abdullah H, 2019. Integration of Landsat-8 thermal and visible-short wave infrared data for improving prediction accuracy of forest leaf area index. Remote Sens 11(4): 390. https://doi.org/10.3390/rs11040390
Pan L, Block, D, 2011. English as a "global language" in China: An investigation into learners' and teachers' language beliefs. System 39(3): 391-402. https://doi.org/10.1016/j.system.2011.07.011
Peters EB, Wythers KR, Zhang S, Bradford JB, Reich PB, 2013. Potential climate change impacts on temperate forest ecosystem processes. Can J For Res 43(10). https://doi.org/10.1139/cjfr-2013-0013
Quintano C, Fernández-Manso A, Calvo L, Marcos R, Valbuena L, 2015. Land surface temperature as potential indicator of burn severity in forest Mediterranean ecosystems. Int J Appl Earth Obs Geoinf 36: 1-12. https://doi.org/10.1016/j.jag.2014.10.015
Quintano C, Fernández-Manso A, Roberts DA, 2017. Burn severity mapping from Landsat MESMA fraction images and land surface temperature. Remote Sens Environ 190: 83-95. https://doi.org/10.1016/j.rse.2016.12.009
Quispe-Reymundo BJ, Révolo-Acevedo RH, 2020. Surface temperature and states of the vegetation of the forest of Polylepis spp, district of San Marcos de Rocchac, Huancavelica - Peru. Enfoque UTE 11: 69-86. https://doi.org/10.29019/enfoqueute.v11n3.592
Ramanan SS, George AK, Chavan SB, Kumar S, Jayasubha S, 2020. Progress and future research trends on Santalum album: A bibliometric and science mapping approach. Ind Crops Prod 58: 112972. https://doi.org/10.1016/j.indcrop.2020.112972
Ramdhani NF, Sulistyawati E, Sutrisno, 2019. Land surface temperature analysis of post-mining area using Landsat 8 imagery. Proc of SPIE 11372, 6th Int Symp on LAPAN-IPB Satellite 113720P, Dec 24. https://doi.org/10.1117/12.2541811
Rasul A, Balzter H, Smith C, Remedios J, Adam B, Sobrino JA, et al., 2017. A review on remote sensing of urban heat and cool islands. Land 6(2): 38. https://doi.org/10.3390/land6020038
RStudio Team, 2020. RStudio: Integrated Development for R. RStudio, PBC, Boston, MA, USA. http://www.rstudio.com/ [20 Sept 2021].
Sabajo CR, le Maire G, June T, Meijide A, Roupsard O, Knohl A, 2017. Expansion of oil palm and other cash crops causes an increase of the land surface temperature in the Jambi province in Indonesia. Biogeosciences 14(20): 4619-4635. https://doi.org/10.5194/bg-14-4619-2017
Sahana M, Ahmed R, Sajjad H, 2016. Analyzing land surface temperature distribution in response to land use/land cover change using split window algorithm and spectral radiance model in Sundarban Biosphere Reserve, India. Model Earth Syst Environ 2: 81. https://doi.org/10.1007/s40808-016-0135-5
Sánchez JM, Bisquert M, Rubio E, Caselles V, 2015. Impact of land cover change induced by a fire event on the surface energy fluxes derived from remote sensing. Remote Sens 7(11): 14899-14915. https://doi.org/10.3390/rs71114899
Savastru DM, Zoran MA, Savastru RS, 2020. Assessing climate impacts on urban forest changes using time series satellite data. Proc SPIE, 8th Int Conf on Remote Sens Geoinform Environ (RSCy2020); 115241V. https://doi.org/10.1117/12.2570453
Shen W, He J, Huang C, Li M, 2020. Quantifying the actual impacts of forest cover change on surface temperature in Guangdong, China. Remote Sens 12(15): 2354. https://doi.org/10.3390/rs12152354
Siyal AA, Siyal AG, Mahar RB, 2017. Spatial and temporal dynamics of Pai forest vegetation in Pakistan assessed by RS and GIS. J For Res 28: 593-603. https://doi.org/10.1007/s11676-016-0327-x
Sprintsin M, Chen JM, Czurylowicz P, 2011. Combining land surface temperature and shortwave infrared reflectance for early detection of mountain pine beetle infestations in western Canada. J Appl Remote Sens 5: 1-14. https://doi.org/10.1117/1.3662866
Tan J, Yu D, Li Q, Tan X, Zhou W, 2020. Spatial relationship between land-use/land-cover change and land surface temperature in the Dongting Lake area, China. Sci Rep 10: 9245. https://doi.org/10.1038/s41598-020-66168-6
UN, 1992. United Nations framework convention on climate change. https://unfccc.int/files/essential_background/background_publications_htmlpdf/application/pdf/conveng.pdf [10 Nov 2021].
USGS, 2020. Landsat-Earth observation satellites. https://pubs.er.usgs.gov/publication/fs20153081 [15 Oct 2021].
van Leeuwen TT, Frank AJ, Jin Y, Smyth P, Goulden M, van der Werf GR, Randerson JT, 2011. Optimal use of land surface temperature data to detect changes in tropical forest cover. J Geophys Res Biogeosci 116. https://doi.org/10.1029/2010JG001488
Vlassova L, Pérez-Cabello F, 2016. Effects of post-fire wood management strategies on vegetation recovery and land surface temperature (LST) estimated from Landsat images. Int J Appl Earth Obs Geoinf 44: 171-183. https://doi.org/10.1016/j.jag.2015.08.011
Vorovencii I, 2015. Assessing and monitoring the risk of desertification in Dobrogea, Romania, using Landsat data and decision tree classifier. Environ Monit Assess 187: 204. https://doi.org/10.1007/s10661-015-4428-3
Wan Mohd Jaafar WS, Abdul Maulud KN, Muhmad Kamarulzaman AM, Raihan A, Md Sah S, Ahmad A, et al., 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 L, Zhang S, 2015. Analysis on the relationship between the pattern of green spaces and land surface temperature based on normalized difference vegetation index: A case study in Changchun city, China. Fresenius Environ Bull 24: 2444-2451.
Wang F, Qin Z, Song C, Tu L, Karnieli A, Zhao S, 2015. An improved mono-window algorithm for land surface temperature retrieval from Landsat 8 thermal infrared sensor data. Remote Sens 7(4): 4268-4289. https://doi.org/10.3390/rs70404268
Wang J, Qian YG, Han LJ, Zhou WQ, 2016. Relationship between land surface temperature and land cover types based on GWR model: A case of Beijing-Tianjin-Tangshan urban agglomeration, China. Chin J Appl Ecol 27: 2128-2136.
Wang Q, Yu X, Shu Q, 2017. Forest burned scars area extraction using time series remote sensing data. J Nat Disaster Sci 26: 1-10.
Wang X, Chen S, Wang J, 2020. An adaptive snow identification algorithm in the forests of Northeast China. IEEE J Sel Top Appl Earth Obs Remote Sens 13: 5211-5222. https://doi.org/10.1109/JSTARS.2020.3020168
Xiao H, Weng Q, 2007. The impact of land use and land cover changes on land surface temperature in a karst area of China. J Environ Manage 85: 245-257. https://doi.org/10.1016/j.jenvman.2006.07.016
Xie H, Zhang Y, Wu Z, Lv T, 2020a. A bibliometric analysis on land degradation: Current status, development, and future directions. Land 9(1): 28. https://doi.org/10.3390/land9010028
Xie H, Zhang Y, Zeng X, He Y, 2020b. Sustainable land use and management research: A scientometric review. Landsc Ecol 35: 2381-2411. https://doi.org/10.1007/s10980-020-01002-y
Xu L, Niu R, Li J, Dong Y, 2011. Soil moisture status estimation over Three Gorges area with Landsat TM data based on temperature vegetation dryness index. Proc SPIE 8006, MIPPR 2011: Remote Sensing Image Processing, GIS and Other Applications, 80061F, Nov 23. https://doi.org/10.1117/12.902433
Yang Y, Anderson MC, Gao F, Hain CR, Semmens KA, Kustas WP, et al., 2017. Daily Landsat-scale evapotranspiration estimation over a forested landscape in North Carolina, USA, using multi-satellite data fusion. Hydrol Earth Syst Sci Discuss 21: 1017-1037. https://doi.org/10.5194/hess-21-1017-2017
Yin G, Mariethoz G, McCabe MF, 2017. Gap-filling of Landsat 7 imagery using the direct sampling method. Remote Sens 9(1): 12. https://doi.org/10.3390/rs9010012
Yin J, Wu X-x, Shen M, Zhang X, Zhu C, Xiang H, et al., 2019. Impact of urban greenspace spatial pattern on land surface temperature: A case study in Beijing metropolitan area, China. Landsc Ecol 34: 2949-2961. https://doi.org/10.1007/s10980-019-00932-6
Zhu J, Ju W, Ren Y, 2010. Effects of land cover types and forest age on evapotranspiration detected by remote sensing in Xiamen City, China. Proc 18th Int Conf on Geoinformatics, June 18-20. pp: 1-5. https://doi.org/10.1109/GEOINFORMATICS.2010.5567518
Zhu X, Wang X, Yan D, Liu Z, Zhou Y, 2019. Analysis of remotely-sensed ecological indexes' influence on urban thermal environment dynamic using an integrated ecological index: a case study of Xi'an, China. Int J Remote Sens 40: 3421-3447. https://doi.org/10.1080/01431161.2018.1547448
Zhuang Y, Liu X, Nguyen T, He Q, Hong S, 2013. Global remote sensing research trends during 1991-2010: A bibliometric analysis. Scientometrics 96: 203-219. https://doi.org/10.1007/s11192-012-0918-z
Zuo S, Dai S, Song X, Xu C, Liao Y, Chang W, et al., 2018. Determining the mechanisms that influence the surface temperature of urban forest canopies by combining remote sensing methods, ground observations, and spatial statistical models. Remote Sens 10(11): 1814. https://doi.org/10.3390/rs10111814
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