J. For. Sci., 2021, 67(2):71-79 | DOI: 10.17221/56/2020-JFS
Satellite image processing of the Buxus hyrcana Pojark dieback in the Northern Forests of IranOriginal Paper
- 1 Department of Computer, Faculty of Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran
- 2 Research Institute of Plant Protection, Agricultural Research, Education and Extension Organization, Tehran, Iran
Pests and diseases can cause a variety of reactions in plants. In recent years, the boxwood dieback has become one of the essential concerns of practitioners and natural resources managers in Iran. To control the boxwood dieback spread, the early detection and disease distribution maps are required. The boxwood dieback causes a range of changes in colour, shape and leaf size with respect to photosynthesis and transpiration. Through remote sensing techniques, e.g. satellite image processing data, the variation of thermal and visual characteristics of the plant could be used to measure and illustrate the symptoms of the disease. In this study, five common vegetation indices like difference vegetation index (DVI), normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), simple ratio (SR), and plant health index (PHI) were extracted and calculated from Landsat 8 satellite image data from six regions in the Gilan province, located in the northern part of Iran out of 150 maps over the time period 2014‒2018. It turned out that among the aforementioned indices, based upon the results of the models, SR and NDVI indices were more useful for the disease spread, respectively. Our disease progression model fitting criteria showed that this technique could probably be used to assess the extent of the affected areas and also the disease progression in the investigated regions in future.
Keywords: image processing; vegetation indices; Landsat 8; forest; remote sensing
Published: February 8, 2021 Show citation
References
- Asadi H., Esmailzadeh O., Hosseini S.M., Asri Y., Zare H. (2016): Application of cocktail method in vegetation classification. Journal of Taxonomy and Biosystematics, 8: 21-38.
- Baloloy A.B., Blanco A.C., Candido C.G., Argamosa R J.L, Dumalag J.B.L.C., Dimapilis L.L.C., Paringit E.C. (2018): Estimation of mangrove forest aboveground biomass using multispectral bands, vegetation indices and biophysical variables derived from optical satellite imageries: rapideye, planetscope and sentinel-2. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 4: 29-36.
Go to original source...
- Bella S. (2013): The box tree moth Cydalima perspectalis (Walker, 1859) continues to spread in southern Europe: new records for Italy (Lepidoptera Pyraloidea Crambidae). Redia, 96: 51-55.
- Boobak H. (1994). Natural Forests and Woody Plants of Iran Forests and Rangelands. Teheran, Research Institute of Forests and Rangelands.
- Esmaeilzadeh O., Asadi H., Ahmadi A. (2012): Phytosociology of Khybus Protected Area. Journal of Wood and Forest Science and Technology, 19: 1-20.
- Esmaili R., Soosani J., Shataee J.S., Naghavi H., Poorshakori F. (2016): Spatial distribution of buxus blight and its relation with some environmental factors (Case study: Khiboos Anjilsi protected area). Journal of Wood and Forest Science and Technology, 23: 147-167.
- Esmaili R., Jouibary S.S., Soosani J., Naghavi H. (2020): Mapping of understory infested boxwood trees using high resolution imagery. Remote Sensing Applications: Society and Environment, 18: 100289.
Go to original source...
- Garcia P., Perez E. (2016): Mapping of soil sealing by vegetation indexes and built-up index: A case study in Madrid (Spain). Geoderma, 268: 100-107.
Go to original source...
- GISGeography (2019): What is NDVI (Normalized Difference Vegetation Index). Available at: https://gisgeography.com/ndvi-normalized-difference-vegetation-index/
- Hazel G.G. (2001). Object-level change detection in spectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 39: 553-561.
Go to original source...
- Hussain M., Chen D., Cheng A., Wei H., Stanley D. (2013): Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 80: 91-106.
Go to original source...
- Ito S., Nakayama R., Buckley G.P. (2004): Effects of previous land-use on plant species diversity in semi-natural and plantation forests in a warm-temperate region in Southeastern Kyushu, Japan. Forest Ecology and Management, 196: 213-225.
Go to original source...
- Kazeminia A. (2018): Application of remote sensing and GIS in the investigating vegetation coverage. Geospatial Engineering Journal, 9: 75-85.
- Kerr J.T., Ostrovsky M. (2003): From space to species: ecological applications for remote sensing. Trends in Ecology & Evolution, 18: 299-305.
Go to original source...
- Knipling E.B. (1970): Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sensing of Environment, 1: 155-159.
Go to original source...
- Lantschner M.V., Corley J. C. (2015): Spatial pattern of attacks of the invasive woodwasp Sirex noctilio, at landscape and stand scales. PLoS ONE, 10: e0127099.
Go to original source...
Go to PubMed...
- Liu D., Kelly M., Gong P., Guo Q. (2007): Characterizing spatial-temporal tree mortality patterns associated with a new forest disease. Forest Ecology and Management, 253: 220-231.
Go to original source...
- Maglione P., Parente C., Vallario A. (2013): Using WorldView-2 satellite imagery to support geoscience studies on Phlegraean area. American Journal of Geoscience, 3: 1-12.
Go to original source...
- Melillos G., Hadjimitsis D.G. (2020): Using simple ratio (SR) vegetation index to detect deep man-made infrastructures in Cyprus. In: Proc. 15th Conf. Detection and Sensing of Mines, Explosive Objects, and Obscured Targets, April 27-May 8, 2020: 114180E.
Go to original source...
- Menge D., Makobe M., Shomari S. (2013): Effect of environmental conditions on the growth of Cryptosporiopsis spp. causing leaf and nut blight on cashew (Anacardium occidentale Linn.). Journal of Yeast and Fungal Research, 4: 12-20.
- Mohajer M.M. (2006): Forestry and Silviculture. Teheran, University of Tehran Press.
- Naji T.A: (2018): Study of vegetation cover distribution using DVI, PVI, WDVI indices with 2D-space plot. Journal of Physics Conference Series, 1003: 012083.
Go to original source...
- Nepstad D.C., Tohver I.M., Ray D., Moutinho P., Cardinot G. (2007): Mortality of large trees and lianas following experimental drought in an Amazon forest. Ecology, 88: 2259-2269.
Go to original source...
Go to PubMed...
- Ray S., Das G., Singh J.P., Panigrahy S. (2006): Evaluation of hyperspectral indices for LAI estimation and discrimination of potato crop under different irrigation treatments. International Journal of Remote Sensing, 27: 5373-5387.
Go to original source...
- Ray S.S., Jain N., Arora R.K., Chavan S., Panigrahy S. (2011): Utility of hyperspectral data for potato late blight disease detection. Journal of the Indian Society of Remote Sensing, 39: 161.
Go to original source...
- Robinson N.P., Allred B.W., Jones M.O., Moreno A., Kumball J.S., Naugle D.E., Erickson T.A., Richardson A.D. (2017): A dynamic Landsat derived normalized difference vegetation index (NDVI) product for the conterminous United States. Remote Sensing, 9: 863.
Go to original source...
- Sabety H. (1994): Iran's Forests, Trees and Shrubs. Yazd, Yazd University Press.
- Strachinis I., Kazilas C., Karamaouna F., Papanikolaou N.E., Partsinevelos G.K., Milonas P.G. (2015): First record of Cydalima perspectalis (Walker, 1859) (Lepidoptera: Crambidae) in Greece. Hellenic Plant Protection Journal, 8: 66-72.
Go to original source...
- Vani V., Mandla V.R. (2017): Comparative study of NDVI and SAVI vegetation indices in Anantapur district semiarid areas. International Journal of Civil Engineering & Technology, 8: 287-300.
- Whiteside T.G., Boggs G.S., Maier S.W. (2011): Comparing object-based and pixel-based classifications for mapping savannas. International Journal of Applied Earth Observation and Geoinformation, 13: 884-893.
Go to original source...
- Zhang J., Huang Y., Pu R., González-Moreno P., Yuan L., Wu K., Huang W. (2019): Monitoring plant diseases and pests through remote sensing technology: A review. Computers and Electronics in Agriculture, 165: 104943.
Go to original source...
This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY NC 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.