Monitoring severity of Lophodermium sp. in pine forest with satellite images Sentinel 2

José Antonio Molina-Serrano, Marja Liza Fajardo-Franco, Martin Aguilar-Tlatelpa, Arturo Castañeda-Mendoza

Abstract


In this paper, we evaluated satellite images from Sentinel 2 to estimate the severity of needle cast in pine and field evaluations. Three indexes were used: a) Normalized Difference Vegetation Index (NDVI), b) Moisture Stress Index (MSI), and c) Soil-adjusted Vegetation Index (SAVI). These indexes were obtained from the combination of satellite bands acquired monthly during February to July 2017. The values obtained by the indexes were correlated with the severity of needle cast of pine, estimated in 24 sampling sites. The values obtained from MSI correlated positively with the observed values of severity (0.70783, p<0.0001), the values obtained from NDVI had a moderate positive correlation with severity (0.53316, p<0.0001). Nevertheless, the data obtained from SAVI had a low positive correlation with severity (0.24844, p=0.0062). The results showed that the use of satellite images from Sentinel 2 and MSI can be used like a tool for monitoring the severity of Lophodermium sp. in pine forest.


Keywords


Needle; NDVI; MSI; SAVI; Pinus sp

Full Text:

PDF

References


Ahanger FA, Dar GH, Beig MA, Sofi TA, and Ganie SA. 2016. Effect of weather parameters on Blue pine (Pinus wallichiana J.) needle blight and ascospore release of Lophodermium pinastri in India. International Journal of Agricultural Technology 12:1099-1112. http://www.ijat-aatsea. com/past_v12_n6.html

Ahanger FA, Hassan DG, Being MA, Sofi TA, Shah MD and Ganaie SA. 2017. In vitro evaluation of fungicides against Lophodermium pinastri causing needle blight disease of blue pine in Kashmir (India). SKUAST Journal of Research 19:66-71. http://www.indianjournals.com/ijor.asp x?target=ijor:skuastjr&volume=19&issue=1&article=007

Alizadeh M, Moharrami M and Rasouli AA. 2017. Geographic Information System (GIS) as a Tool in the Epidemiological Assessment of Wetwood Disease on elm Trees in Tabriz City, Iran. Cercetari Agronomice in Moldova 50:91-100. https://doi.org/10.1515/cerce-2017-0018

Campbell CL and Neher DA. 1994. Estimating Disease Severity and Incidence. Pp. 117-147. In: Campbell CL y Benson DM (eds.). Epidemiology and Management of Root Diseases. Springer, Berlin, Heidelberg. 344 p. https://doi. org/10.1007/978-3-642-85063-9_5

Cano F, Navarro RM, García A, y Sánchez De La Orden M. 2005. Evaluación de la defoliación mediante imágenes IKONOS en masas de Quercus suber L. en el sur de España. Investigaciones Agrarias: Sistemas y Recursos Forestales 14:242-252. https://recyt.fecyt.es/index.php/IA/article/ view/2280/1687

Cayuela L, Hernández R, Hódar JA, Sánchez G and Zamora R. 2014. Tree damage and population density relationships for the pine processionary moth: Prospects for ecological research and pest management. Forest Ecology and Management 328:319-325. https://doi.org/10.1016/j.foreco.2014.05.051

Chemura A, Mutanga O and Dube T. 2017. Separability of coffee leaf rust infection levels with machine learning methods at Sentinel-2 MSI spectral resolutions. Precision Agriculture 18:859-881. https://doi.org/10.1007/s11119016-9495-0

Chuvieco E. 1996. Fundamentos de Teledetección espacial. Editorial Rialp. Madrid. 568 p.

Cibrián TD, García DS, Alvarado RD, Colomo GI, Palacios HG, Meléndez HF y Sámano MJ. 2007. En: Cibrián TD, Alvarado RD, García DS. 2007. Enfermedades Forestales en México. 1ra. Ed. Universidad Autónoma de Chapingo. Estado de México, México. 585 p.

Claudio GL, Góngora RF, Toledo GS, Jaime GR y García QE. 2012. Evaluación de daños por patógenos fúngicos en Pinus y Quercus del Área de Protección de Flora y Fauna “La Primavera” Jalisco, México. Acta Universitaria 22:512. http://www.actauniversitaria.ugto.mx/index.php/acta/ article/view/397

CONAFOR. 2018. Diagnóstico fitosanitario del estado de Puebla. Comisión Nacional Forestal. Gerencia Estatal Puebla. México. 25 p. http://sivicoff.cnf.gob.mx/frmProgramasdetrabajoanuales.aspx

ESA. 2015. Sentinel-2 User handbook. Standar document. European Space Agency. Issue 1, Review 2. 64 p.

Gascon F, Thépaut O, Jung M, Francesconi B, Louis J, Lonjou V, Lafrance B, Massera S, Gaudel-Vacaresse A, Languille F, Alhammoud B, Viallefont F, Pflug B, Bieniarz J, Clerc S, Pessiot L, Trémas T, Cadau E, De Bonis R, Isola C, Martimort P and Fernandez V. 2016. Copernicus Sentinel-2 Calibration and Products Validation Status. Remote Sensing 9:584. https://doi.org/10.3390/rs9060584

Herrera T y Ulloa M. 2013. El reino de los hongos. Micología básica aplicada. Segunda Reimpresión. UNAM. Fondo de Cultura Económica. México, D.F. 551 p.

Houborg R, Fisher JB and Skidmore AK. 2015. Advances in remote sensing of vegetation function and traits. International Journal of Applied Earth Observation and Geoinformation 43:1-6. http://dx.doi.org/10.1016/j.jag.2015.06.001

Huete A. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 25:295-309. https://doi. org/10.1016/0034-4257(88)90106-X

James FW, Dash J, Watmough G and James ME. 2013. Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation. Journal of Photogrammetry and Remote Sensing 82: 83-92. https:// doi.org/10.1016/j.isprsjprs.2013.04.007

Koukol O, Pusz W and Minter D. 2015. A new species of Lophodermium on needles of mountain pine (Pinus mugo) from the Giant Mountains in Poland. Mycological Progress 14:1-13. https://doi.org/10.1007/s11557-015-1038-y

LANREF–DGSV. 2019. Datos climáticos. Laboratorio Nacional de Referencia Fitosanitaria-Dirección General de Sanidad Vegetal. www.royacafe.lanref.org.mx (Consulta, octubre 2019).

Millar CI and Stephenson NL. 2015. Temperate forest health in an era of emerging megadisturbance. Science 349:823826. https://doi.org/10.1126/science.aaa9933

Navarro-Cerrillo RM, Varo MA, Lanjeri S, y Hernández-Clemente R. 2007. Cartografía de defoliación en los pinares de pino silvestre (Pinus sylvestris L.) y pino salgareño (Pinus nigra Arnold.) en la Sierra de los Filabres. Ecosistemas 16: 163-171. https://www.revistaecosistemas.net/ index.php/ecosistemas/article/download/464/445

Neimane U, Polmnis K, Zaluma A, Klavina D, Gaitnieks T and Jansons A. 2018. Damage caused by Lophodermium needle cast in open-pollinated and control-crossed progeny trials of Scots pine (Pinus sylvestris L.). The Forestry Chronicle 94:155-161. https://doi.org/10.5558/tfc2018-024

Olsson PO, Lindström J and Eklundh L. 2016. Near real-time monitoring of insect induced defoliation in subalpine birch forests with MODIS derived NDVI. Remote Sensing of Environment 181:42-53. https://doi.org/10.1016/j. rse.2016.03.040

Ortiz-García S, Gernandt DS, Stone JK, Johnston PR, Chapela IR, Salas LR and Alvarez BE. 2003. Phylogenetics of Lophodermium from pine. Mycologia 95:846-859. https:// doi.org/10.1080/15572536.2004.11833044

Peña MA y Altmann SH. 2009. Reconocimiento del efecto de Cinara cupressi (Hemiptera: Aphididae) en el estado sanitario de Austrocedrus chilensis mediante imágenes multiespectrales. Bosque 30:151-158. http://dx.doi. org/10.4067/S0717-92002009000300005

Pérez MR, Romero SM, González HA, Pérez SE y Arriola PJ. 2016. Distribución Potencial de Lophodermium sp. en Bosques de Coníferas de Puebla y Estados Próximos, con Escenarios de Cambio Climático. Revista Mexicana de Ciencias Forestales 7:81-97. https://doi.org/10.29298/ rmcf.v7i36.61

Polmanis K, Gaitnieks T, Be?evi?am V, Rungis D and Baumane A. 2017. Occurrence of Lophodermium spp. in young scots pine stands in Latvia. Forestry and Wood Processing 1:1-20.

Reséndiz MJ, Guzmán DL, Muñoz VA, De Pascual PC y Olvera CL. 2015. Enfermedades foliares del arbolado en el Parque Cultural y Recreativo Tezozómoc, Azcapotzalco, D.F. Revista Mexicana de Ciencias Forestales 6:106-123. https://doi.org/10.29298/rmcf.v6i30.211

Rock BN, Vogelmann, JE, Williams DL, Vogehnann AF and Hoshizaki T. 1986. Remote detection of forest damage. Ecology from Space 36:439-445. https://doi. org/10.2307/1310339

Rouse JW, Haas RH, Schell JA and Deering DW. 1974. Monitoring vegetation systems in the Great Plains with ERTS. Pp. 309-317. In: Freden SC, Mercanti EP, Becker MA. (eds.). Third Earth Resources Technology Satellite-1 Symposium, Washington DC. 1008 p. https://ntrs.nasa.gov/ search.jsp?R=19740022614

Rullan-Silva CD, Olthoff AE, Delgado de la Mata JA and Parajes-Alonso JA. 2013. Remote monitoring of forest insect defoliation. A review. Forest Systems 22:377-391. https:// doi.org/10.5424/fs/2013223-04417

Rullán-Silva C, Olthoff AE, Pando V, Pajares JA and Delgado JA. 2015. Remote monitoring of defoliation by the beech leaf-mining weevil Rhynchaenus fagi in northern Spain. Forest Ecology and Management 347:200-208. https:// doi.org/10.1016/j.foreco.2015.03.005

Sacristán RF. 2006. La Teledetección satelital y los sistemas de protección Ambiental. Universidad Complutense de Madrid. Aqua TIC 24:13-41. https://doi. org/10.22518/16578953.701

Sangüesa BG, Camarero JJ, García MA, Hernández R and De la Riva J. 2014. Remote-sensing and tree-ring based characterization of forest defoliation and growth loss due to the Mediterranean pine processionary moth. Forest Ecology and Management 320:171-181. https://doi. org/10.1016/j.foreco.2014.03.008

Seidl R, Thom D, Kautz M, Martín BD, Peltoniemi M, Vacchiano G, Wild J, Ascoli D, Petr M, Honkaniemi J, Lexer M, Trotsiuk V, Mairota P, Svoboda M, Fabrika M, A. Nagel T and Reyer C. 2017. Forest disturbances under climate change. Nature Climate Change 7:395-402. https:// doi.org/10.1038/nclimate3303

Stenström BE and Ihrmark K. 2005. Identification of Lophodermium seditiosum and L. pinastri in Swedish forest nurseries using species-specific PCR primers from the ribosomal ITS region. Forest Pathology 35:163-172. https://doi. org/10.1111/j.1439-0329.2005.00398.x

Sturrock RN, Frankel SJ, Brown AV, Hennon PE, Kliejunas JT, Lewis KJ, Worrall JJ and Woods AJ. 2011. Climate change and forest diseases. Plant Pathology 60:133-149. https:// doi.org/10.1111/j.1365-3059.2010.02406.x

Townsend PA, Singh A, Foster JR, Rehberg NJ, Kingdon CC, Eshleman KN and Seagle SW. 2012. A general Landsat model to predict canopy defoliation in broadleaf deciduous forests. Remote Sensing of Environment 119:255-265. https://doi.org/10.1016/j.rse.2011.12.023

Yu L, Huang J, Zong S, Huang H, and Luo Y. 2018. Detecting shoot beetle damage on yunnan pine using landsat time-series data. Forests 9:2-14. https://doi.org/10.3390/f9010039

Zarco-Tejada PJ, Hornero A, Becka PSA, Kattenbornd T, Kempeneersa P and Hernández-Clemente R. 2019. Chlorophyll content estimation in an open-canopy conifer forest with Sentinel-2A and hyperspectral imagery in the context of forest decline. Remote Sensing of Environment 223:320335. https://doi.org/10.1016/j.rse.2019.01.031

Zarco-Tejada PJ, Hornero A, Hernández-Clemente R and Beck PS. 2018. Understanding the temporal dimension of the red-edge spectral region for forest decline detection using high-resolution hyperspectral and Sentinel-2a imagery. ISPRS Journal of Photogrammetry and Remote Sensing 137:134-148. https://doi.org/10.1016/j.isprsjprs.2018.01.017




DOI: http://dx.doi.org/10.18781/R.MEX.FIT.1907-3

Refbacks

  • There are currently no refbacks.