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Revista mexicana de fitopatología
On-line version ISSN 2007-8080Print version ISSN 0185-3309
Rev. mex. fitopatol vol.38 n.1 Texcoco Jan. 2020 Epub Nov 27, 2020
https://doi.org/10.18781/r.mex.fit.1907-3
Phytopathological notes
Monitoring severity of Lophodermium sp. in pine forest with satellite images Sentinel 2
1 Ingeniería Forestal Comunitaria1, Posgrado en Manejo Sustentable de Recursos Naturales. Universidad Intercultural del Estado de Puebla. Calle principal a Lipuntahuaca S/N. 73475, Lipuntahuaca, Huehuetla, Puebla, México.
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.
Key words: Needle; NDVI; MSI; SAVI; Pinus sp
En el presente estudio se estimó la severidad causada por el tizón de los pinos (Lophodermium sp.) en rodales de pino mediante imágenes provenientes del satélite Sentinel 2 y evaluaciones de campo. Para tal efecto, se utilizaron tres índices: a) Índice de Vegetación de Diferencia Normalizada (NDVI), b) Índice de Estrés de Humedad (MSI) y el Índice de Vegetación Ajustado al Suelo (SAVI); obtenidos de la combinación de bandas satelitales adquiridas mensualmente durante febrero a julio del 2017. Los valores obtenidos por los índices se correlacionaron con la severidad del tizón de los pinos, estimada en 24 sitios de muestreo. Los datos obtenidos mediante el MSI tuvieron una alta correlación positiva con los valores observados de la severidad (0.70783, p<0.0001), mientras que los valores estimados mediante el NDVI y el SAVI tuvieron una moderada (0.53316, p<0.0001) y baja correlación positiva (0.24844, p=0.0062), respectivamente. Los resultados mostraron que el uso de imágenes satelitales Sentinel 2 y el MSI tienen potencial para ser utilizados como una herramienta en el monitoreo de Lophodermium sp. en bosque de pino.
Palabras clave: Tizón; NDVI; MSI; SAVI; Pinus sp
Lophodermium sp. is an endophytic fungus belonging to the Rhytismataceae family (Rhytismatales, Ascomycota). It grows intracellularly between the epidermis and hypodermis of pine needles and produces chlorosis, which then forms apothecium-type small fruitful bodies beneath the epidermal tissue of the needles. The fruitful bodies have a longitudinal opening where filiform and hyaline ascospores with no septa are stored (Ortiz-García et al., 2003; Cibrián et al., 2007). This pathogen causes defoliation and reduces the productivity of infected trees because it inhibits photosynthesis, which causes losses both in the greenhouse and under field conditions. In Asia, the United States, Sweden and India, detection and research have focused on mitigating the impact of the pathogen in forests (Stenström and Ihrmark, 2005; Ahanger et al., 2017; Neimane et al., 2018).
In Mexico, Lophodermium sp. has caused damage in forests by affecting several susceptible pine species such as Pinus oaxacana, P. patula, P. montezumae, P. teocote, P. pseudostrobus and P. leiophylla (CONAFOR, 2018). In 2015, the disease affected forests in the states of Tlaxcala and Hidalgo, and at least 3,000 hectares sown to pines in the Sierra Norte de Puebla (Claudio et al., 2012; Reséndiz et al., 2015; Pérez et al., 2016). It is estimated that, due to climate change, the pathogen could find favorable conditions for a wider distribution in Mexico (Pérez et al., 2016). For this reason, research must be conducted to monitor and predict the disease in order to minimize the loss of forest production and ecosystem services (Sacristán, 2006; Sturrock et al., 2011; Millar and Stephenson, 2015; Seidl et al., 2017). In view of this challenge, the use of satellite images is an alternative for monitoring and evaluating the epidemiology of Lophodermium, since their viability is based on the biophysical and biochemical changes that occur in the plant cover during the epidemic development of the disease, as well as on the alterations in the pigments that absorb the light, the internal structure of the leaf and the moisture content at the cellular level, which are reflected in the spectral response (Chuvieco, 1996; Peña and Altmann, 2009; Rullan-Silva et al., 2013; Alizadeh et al., 2017). These changes can be identified by combining the satellite bands from the Sentinel-2 satellite and using vegetation indexes such as the Normalized Difference Vegetation Index (NDVI) and the Moisture Stress Index, mainly with bands ranging from near-infrared (NIR) and mid-infrared, or short-wave infrared (SWIR) (James et al., 2013; Houborg et al., 2015; Rullan-Silva et al., 2013; Yu et al., 2018).
Cano et al. (2005) used multispectral images from the IKONOS sensor and vegetation indexes to study the mass decay process in Quercus suber affected by Phytophthora cinnamomi in southern Spain. Similarly, Navarro-Cerrillo et al. (2007) used images from the ASTER satellite and NDVI to map defoliation caused by mass decay of Pinus sylvestris and P. nigra in Sierra de los Filabres in Spain. Yu et al. (2018) identified infection spots and, using NDVI, Normalized Difference Moisture Index (NDMI) and MSI, estimated the severity of the damage caused by Tomicus yunnanensis and Tomicus minor in Pinus yunnanensis, while Sangüesa-Barrera et al. (2014) studied the level of severity caused by Thaumetopoea pityocampa in pine forests using vegetation indexes including MSI, NDVI and SAVI. Therefore, the objective of this study was to evaluate the use of satellite images from Sentinel 2 to estimate the level of severity caused by needle cast of pine (Lophodermium sp.) using vegetation indexes.
The study was conducted in the ejido lands of Xonocuautla, Tlatlauquitepec, located in the northeast area of the state of Puebla, Mexico, coordinates 19.731326 and -97.5494835, at 2,530 masl, from February to July 2017, in 303 ha of forest where pine species grow (Pinus patula, P. montezumae) that are susceptible to Lophodermium sp. (CONAFOR, 2018). Samples were taken in 24 sites that were georeferenced and randomly distributed. Each site corresponded to circular plots with a radius of 4.9 m and an area of 75.4 m2, where the disease incidence was evaluated by estimating the ratio of infected trees. The disease severity was estimated by determining the percentage of canopy in each tree inside the circumference; this was done by using a 0-5 severity scale, where 0=no symptoms observed; 1=canopy chlorosis >0-5%; 2=canopy chlorosis >5-25%; 3= moderate canopy chlorosis and necrosis >25-50%, 4=severe canopy chlorosis and necrosis >50-75%; and 5=necrotic canopy >75-100% (Campbell and Neher, 1994; Cayuela et al., 2014). A total of 221 trees were evaluated. The identity of Lophodermium sp. was corroborated through observations under a compound and dissecting microscope (Ortiz-García et al., 2003; Herrera and Ulloa, 2013; Koukol et al., 2015). The evaluations were conducted on February 23, March 18, April 16, May 14, June 3, June 23, and July 13, 2017; on those same days, satellite images were obtained. Additionally, from the National Phytosanitary Epidemiological Reference Laboratory, data on the temperature, relative humidity and dew point (minimum, average and maximum) in the municipality of Tlatlauquitepec during the study period were consulted (LANREF-DGSV, 2019).
At the same time, five images from the Sentinel 2 satellite were downloaded from the official Sentinel platform (https://scihub.copernicus.eu/dhus/#/home); the images were selected because they were cloudless and coincided with the dates on which the disease was evaluated in the field. The images corresponded to the following dates: February 23, May 14, June 3, June 23 and July 13, 2017. These images were used because of the potential of their characteristics, especially the infrared bands (NIR) that make it possible to differentiate the reflectance between healthy and infected biomass in forest ecosystems (ESA, 2015; Chemura et al., 2017; Zarco-Tejada et al., 2019).
Each satellite image was processed to convert the digital numbers (DN) to top-of-atmosphere (TOA) reflectance, for which this value was divided by a 10,000-scale factor (Gascon et al., 2016). Each image was later submitted to an atmospheric correction process using the ATCOR module, which is based on the MODTRAN model (Moderate Resolution Atmospheric Transmission) that models the prevailing atmospheric conditions at the time the platform passes over a given area (Peña and Altmann, 2009). These processes were carried out using Geomatica 2017 and ArcGis 10.2 software. The NDVI, MSI and SAVI indexes were estimated using the corrected multispectral data (Rouse et al., 1974; Rock et al., 1986; Huete, 1988). The results of each estimated index were re-scaled and indexes at the 0 to 1 levels were obtained, where, in the case of NDVI and SAVI, 1 corresponded to 100% severity, and in the case of MSI, 1 corresponded to absence of the disease (Sangüesa et al., 2014). The vegetation indexes were estimated by processing satellite images using ArcGis 10.2 software. The climatic variables were analyzed to determine their correlation with the incidence and severity. The values of disease severity caused by Lophodermium sp. that were obtained in the field and the values obtained from the indexes by processing the satellite images were subjected to an analysis of correlation using the R statistical software (Cano et al., 2005).
The incidence of Lophodermium sp. was moderate-to-high (64-98%) with low-to-moderate severity (< 33%). In February, the average severity reached 7.1%, while in March the severity increased and reached a value of 13.8%. However, in April and May, the severity decreased (11.9% and 10.6%, respectively), which could be due to the fall of infected needles. During the first days of June, the severity increased (13.0%) and continued to grow during the following evaluation (25.7%). Finally, in July, the severity reached a value of 32.8% (Figure 1A). During this period, the relative humidity was high (61.0-99.6%) with average temperatures between 15.7 and 24.5 °C (Figure 1B).
These conditions are favorable for the development of Lophodermium since it has been reported that high relative humidity with temperatures between 14 and 22 °C are optimum for the pathogen to develop, although it can survive at minimum temperatures of -2 to 1 °C, and maximum temperatures of 25 to 35 °C (Ahanger et al., 2016; Polmanis et al., 2017).
Disease severity was moderately correlated with climatic variables, mainly with relative humidity (0.50938, p=0.2429). However, the incidence had a positive correlation with the average relative humidity (0.8611, p=0.0128) and with the average dew point (0.8415, p=0.0175). These results are in agreement with those reported by Ahanger et al. (2016) and Polmanis et al. (2017), who indicated that the combination of optimum temperatures with high relative humidity influences Lophodermium sp. expression and spread.
The correlation analysis using the data estimated by MSI and the observed values indicated a correlation of 0.70783, p<0.0001; the estimation of severity using this index is shown in Figure 2. The MSI has been used to evaluate defoliation in pine forests because of its sensitivity to detecting changes in water content in vegetation, a fact that is closely related to the weakness or vulnerability of pine trees to pest and disease attacks (Townsend et al., 2012; Sangüesa et al., 2014; Rullán et al., 2015).
Figure 1. Incidence and severity of the damage caused by Lophodermium sp. in pine forests in Xonocuatla, Tlatlauquitepec, and their relationship with climatic variables. A) Development of needle cast of pine. B) Climatic variables. Each point represents the mean.
On the other hand, data estimated using NDVI and the data observed had a correlation of 0.53316, p<0.0001; the values estimated using NDVI are shown in Figure 3. James et al. (2013) and Zarco-Tejada et al. (2018) demonstrated that the NDVI obtained using images from the Sentinel 2 satellite had adequate capacity to evaluate the chlorophyll content. MSI and NDVI are indexes that are highly related with the chlorophyll and water content in trees and have been used to evaluate forest mass decay caused by foliar pests and diseases (Peña and Altmann, 2009; Olsson et al., 2016).
These results are in agreement with those reported by Yu et al. (2018), where MSI was more precise when estimating Tomicus sp. severity in Pinus yunnanensis compared to NDVI and the Normalized Difference Moisture Index (NDMI).
Figure 2. Severity of damage caused by Lophodermium sp. in Xonocuautla, Tlatlauquitepec, estimated using the Moisture Stress Index (MSI) obtained using images from the Sentinel 2 satellite in 2017: A) February 23; B) May 14; C) June 03; D) June 23; E) July 13. The white areas on each map indicate cropping regions.
The estimated severity using SAVI had a low correlation with the observed severity (0.24844, p=0.0062), which could be due an overestimation of that variable, along with the effect soil has on the reflectance (Figure 4). Sangüesa et al. (2014) used SAVI to study defoliation in pine trees caused by Thaumetopoea pityocampa, but the index had limitations when distinguishing changes in plant cover.
Figure 3. Severity of damage caused by Lophodermium sp. in Xonocuautla, Tlatlauquitepec, estimated using the Normalized Difference Vegetation Index (NDVI) obtained using images from the Sentinel 2 satellite in 2017: A) February 23; B) May 14; C) June 03; D) June 23; E) July 13. The white areas on each map indicate cropping regions.
The analysis allowed detecting differences in the sensitivity of each index to evaluate changes in plant canopy health. MSI had a positive and higher correlation compared to NDVI and SAVI. Therefore, the results obtained suggest that satellite images from Sentinel 2 and MSI can be used as a tool to monitor the severity of needle cast of pine. These results provide information about the space-time behavior of Lophodermium sp. in pine forests in Mexico, which, once they are validated through further studies, can be used in forest management programs.
Figure 4. Severity of damage caused by Lophodermium sp. in Xonocuautla, Tlatlauquitepec, estimated using the Soil Adjusted Vegetation Index (SAVI) obtained using images from the Sentinel 2 satellite in 2017: A) February 23; B) May 14; C) June 03; D) June 23; E) July 13. The white areas on each map indicate the cropping regions.
Literatura Citada
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 [ Links ]
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.aspx?target=ijor:skuastjr&volume=19&issue=1&article=007 [ Links ]
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 [ Links ]
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 [ Links ]
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 [ Links ]
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 [ Links ]
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/s11119-016-9495-0 [ Links ]
Chuvieco, E. 1996. Fundamentos de Teledetección espacial. Editorial Rialp. Madrid. 568 p. [ Links ]
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. [ Links ]
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 22:5-12. http://www.actauniversitaria.ugto.mx/index.php/acta/article/view/397 [ Links ]
CONAFOR. 2018. Diagnóstico fitosanitario del estado de Puebla. Comisión Nacional Forestal. Gerencia Estatal Puebla. México. 25 p. http://sivicoff.cnf.gob.mx/frmProgramasdetrabajoanuales.aspx [ Links ]
ESA. 2015. Sentinel-2 User handbook. Standar document. European Space Agency. Issue 1, Review 2. 64 p. [ Links ]
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 [ Links ]
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. [ Links ]
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 [ Links ]
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 [ Links ]
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 [ Links ]
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 [ Links ]
LANREF-DGSV. 2019. Datos climáticos. Laboratorio Nacional de Referencia Fitosanitaria-Dirección General de Sanidad Vegetal. http://www.royacafe.lanref.org.mx (Consulta, octubre 2019). [ Links ]
Millar, CI. and Stephenson, NL. 2015. Temperate forest health in an era of emerging megadisturbance. Science 349:823-826. https://doi.org/10.1126/science.aaa9933 [ Links ]
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 [ Links ]
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 [ Links ]
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 [ Links ]
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 [ Links ]
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 [ Links ]
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 [ Links ]
Polmanis, K., Gaitnieks, T., Beļevičam, 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. [ Links ]
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 [ Links ]
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 [ Links ]
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 [ Links ]
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 [ Links ]
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 [ Links ]
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 [ Links ]
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 [ Links ]
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 [ Links ]
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 [ Links ]
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 [ Links ]
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 [ Links ]
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 [ Links ]
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 223:320-335. https://doi.org/10.1016/j.rse.2019.01.031 [ Links ]
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 [ Links ]
Received: July 31, 2019; Accepted: November 15, 2019
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