Dragon 4-Satellite Based Analysis of Diseases on Permanent and Row Crops in Italy and China

doi:10.11947/j.JGGS.2020.0410

Abstract

Abstract:

The AMEOS (Assimilating Multi-source Earth Observation Satellite data for crop pests and diseases monitoring and forecasting) project aims to bring together cutting edge research to provide pest and disease monitoring and forecast information, integrating multi-source information (Earth Observation, meteorological, entomological and plant pathological, etc.) to support decision making in the sustainable management of insect pests and diseases in agriculture. The main objective of the project, that is, improving crop diseases and pests monitoring and forecasting, will be achieved by utilizing EO data, developing new algorithms, and combining new and existing data from multi-source EO sensors to produce high spatial and temporal land surface information. The project foresees the assessment of the possibility of using available satellite images datasets to assess the evolution of diseases on permanent (olive groves, vineyards), or row crops (wheat) in Italy and China. The paper describes the results of the research activity which focused on: ① improving the classification of the agricultural areas devoted to winter wheat and olive trees, starting from what has been made available from the Corine Land Cover initiative; ② developing an approach suitable to be automated for estimating trees by using Sentinel 2 images; ③ developing a new index, REDSI (consisting of Red, Re₁, and Re₃ bands), for detecting and monitoring yellow rust infection of winter wheat at the canopy and regional scale. The research activity covers the: Province of Lecce, that is the Italian area strongly affected, since 2015, by the Xylella fastidiosa disease which causes a rapid decline in olive plantations. Province of Anyang, Neihuang county, which was affected by the yellow rust disease in the spring 2017.

Key words: disease; reflectance; index; morphology; classification

Giovanni LANEVE, Roberto LUCIANI, Pablo MARZIALETTI, Stefano PIGNATTI, Wenjiang HUANG, Yue SHI, Yingying DONG, Huichun YE. Dragon 4-Satellite Based Analysis of Diseases on Permanent and Row Crops in Italy and China[J]. Journal of Geodesy and Geoinformation Science, 2020, 3(4): 98-109.

Figures/Tables 16

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Tab.4

Summary of spectral vegetation indexes used for detecting yellow rust (Sentinel 2 case)"

SVIs	Definition	Formula
NDVI	Normalized difference vegetation index	(R_INR-R_R)/(R_NIR+R_R)
EVI	Enhanced vegetation index	2.5(R_NIR-R_R)/(R_NIR+6R_R-0.5R_B+1)
RGR	Ration of red and green	R_R/R_G
VARI_green	Visible atmospherically resistant index	(R_G-R_R)/(R_G+R_R)
NDV $I R e 1$	Normalized difference vegetation index red-edge1	(R_NIR- $R R e 1$ )/(R_NIR*+ $R R e 1$ )
NREDI1	Normalized red-edge1 index	( $R R e 2$ - $R R e 1$ )/( $R R e 2$ + $R R e 1$ )
NREDI2	Normalized red-edge2 index	( $R R e 3$ - $R R e 1$ )/( $R R e 3$ + $R R e 1$ )
NREDI3	Normalized red-edge3 index	( $R R e 3$ - $R R e 2$ )/( $R R e 3$ - $R R e 2$ )
PSRI	Plant senescence reflectance index	(R_R-R_G)/ $R R e 1$
REDSI	Red edge disease stress index	**(705-665)·( $R R e 3$ -R₄)-(783-665)·( $R R e 1$ -R₄))/2·R₄

Tab.4

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References 31

[1]	ELBEAINO T, YASEEN T, VALENTINI F, et al. Identification of three potential insect vectors of Xylella fastidiosa in southern Italy[J]. Phytopathologia Mediterranea, 2014,53(1):328-332.
[2]	EDDINE B M I, VALERIO M, FRANCO V, et al. Seasonal Fluctuations of Sap-Feeding Insect Species Infected by Xylella fastidiosa in Apulian Olive Groves of Southern Italy[J]. Journal of Economic Entomology, 2016(4):1512-1518. doi: 10.1093/jee/tow123 pmid: 27401111
[3]	JANSE J D, OBRADOVIC A, XYLELLA. Fastidiosa: its biology, diagnosis, control and risks[J]. Journal of Plant Pathology, 2010,92(S1):35-48.
[4]	DORIGO W, ZURITA-MILLA R, DE WIT A J, et al. A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling[J]. International Journal of Applied Earth Observation & Geoinformation, 2007,9(2):165-193.
[5]	XIE Q, HUANG W, DASH J, et al. Evaluating the potential of vegetation indices for winter wheat LAI estimation under different fertilization and water conditions[J]. Advances in Space Research, 2015,56(11):2365-2373.
[6]	CHRISTOU P, TWYMAN R M. The potential of genetically enhanced plants to address food insecurity[J]. Nutrition Research Reviews, 2004. DOI: 10.1079/NRR200373. doi: 10.1017/S0954422420000074 pmid: 32151298
[7]	STRANGE R N, SCOTT P R. Plant disease: a threat to global food security.[J]. Annual Review of Phytopathology, 2005,43(1):83-116.
[8]	HUANG W, LUO J, ZHANG J, et al. Crop Disease and Pest Monitoring by Remote Sensing[M]. London: Intech,Editor Boris Escalante, 2012. DOI: 10.5772/35204.
[9]	RAIKES C, BURPEE L L. Use of Multispectral Radiometry for Assessment of Rhizoctonia Blight in Creeping Bentgrass[J]. Phytopathology, 1998,88(5):446-9. doi: 10.1094/PHYTO.1998.88.5.446 pmid: 18944925
[10]	MARTINELLI F, SCALENGHE R, DAVINO S, et al. Advanced methods of plant disease detection. A review[J]. Agronomy for Sustainable Development, 2015,35(1):1-25.
[11]	MAHLEIN A K, STEINER U, CHRISTIAN HILLNHÜTTER, et al. Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases[J]. Plant Methods, 2012,8(1):3. doi: 10.1186/1746-4811-8-3 pmid: 22273513
[12]	LÓPEZ-LÓPEZ M, CALDERÓN R, GONZÁLEZ-DUGO V, et al. Early detection and quantification of almond red leaf blotch using high-resolution hyperspectral and thermal imagery[J]. Remote Sensors, 2016,8(4):276.
[13]	MAHLEIN A K, KUSKA M T, THOMAS S, et al. Plant disease detection by hyperspectral imaging: from the lab to the field[J]. Advances in Animal Biosciences: Precision Agriculture (ECPA), 2017,8(2):238-243.
[14]	WANG X, ZHANG M, ZHU J, et al. Spectral prediction of Phytophthora infestans infection on tomatoes using artificial neural network (ANN)[J]. International Journal of Remote Sensing, 2008,29(6):1693-1706. doi: 10.1080/01431160701281007
[15]	DELALIEUX S, AARDT J V, KEULEMANS W, et al. Detection of biotic stress (Venturia inaequalis) in apple trees using hyperspectral data: Non-parametric statistical approaches and physiological implications[J]. European Journal of Agronomy, 2007,27(1):130-143. doi: 10.1016/j.eja.2007.02.005
[16]	BECK P S A, ZARCO-TEJADA P, STROBL P, et al. The feasibility of detecting trees affected by the Pine Wood Nematode using remote sensing[J]. JRC Technical Report, 2015. DOI: 10.2788/711975.
[17]	MAHLEIN A K. Plant disease detection by imaging sensors-parallels and specific demands for precision agriculture and plant phenotyping[J]. Plant Disease, 2016,100(2):241-251. doi: 10.1094/PDIS-03-15-0340-FE pmid: 30694129
[18]	AA.VV. Stimulating Scientific Exchange in Earth Observation [C]//Science and Technology. Wuhan: DRAGON 4 Cooperation, 2018: 86-89.
[19]	TUCKER C J. Red and photographic infrared linear combinations for monitoring vegetation[J]. Remote Sensing of Environment, 1979,8(2):127-150. doi: 10.1016/0034-4257(79)90013-0
[20]	GENESIS T, YENGOH D D, LENNART O, et al. The use of the Normalized Difference Vegetation Index (NDVI) to assess land degradation at multiple scales: a review of the current status, future trends, and practical considerations[R]. Lund: Lund University Center for Sustainability Studies (LUCSUS), and The Scientific and Technical Advisory Panel of the Global Environment Facility (STAP/GEF), 2014.
[21]	DIAZ-PACHECO J, GUTIÉRREZ J. Exploring the limitations of CORINE Land Cover for monitoring urban land-use dynamics in metropolitan areas[J]. Journal of Land Use Science, 2014,9(3):243-259. doi: 10.1080/1747423X.2012.761736
[22]	FERANEC J, HAZEU G, CHRISTENSEN S, et al. Corine land cover change detection in Europe (case studies of the Netherland and Slovakia)[J]. Land Use Policy, 2007,24(1):234-247. doi: 10.1016/j.landusepol.2006.02.002
[23]	HEIJMANS H J A M, RONSE C. The algebraic basis of mathematical morphology I. Dilations and Erosions[J]. Computer Vision, Graphics, and Image Processing, 1990,50(3):245-295. doi: 10.1016/0734-189X(90)90148-O
[24]	SOLOMON C, BRECKON T. Fundamentals of Digital Image Processing: a practical approach with examples in MATLAB[J]. Wiley-Blackwell, 2011. DOI: 10.1002/9780470689776.
[25]	BRADLEY B A, MUSTARD J F. Comparison of phenology trends by land cover class: a case study in the Great Basin, USA[J]. Global Change Biology, 2008. DOI: 10.1111/j.1365-2486.2007.01479.x. doi: 10.1111/gcb.15499 pmid: 33347685
[26]	AGUILERA F, FORNACIARI M, RUIZ-VALENZUELA L, et al. Phenological models to predict the main flowering phases of olive (Olea europaea L.) along a latitudinal and longitudinal gradient across the Mediterranean region[J]. Int. J. Biometeorol, 2015. DOI: 10.1007/s00484-014-0876-7. doi: 10.1007/s00484-020-02041-5 pmid: 33219418
[27]	GITELSON A, ZUR Y, CHIVKUNOVA O, et al. Assessing Carotenoid Content in Plant Leaves with Reflectance Spectroscopy[J]. Photochemistry and Photobiology, 2002. DOI: 10.1562/0031-8655(2002)0750272ACCIPL2.0.CO2. doi: 10.1111/php.13361 pmid: 33274440
[28]	ZHENG Q, HUANG W, CUI X, et al. New spectral index for detecting wheat yellow rust using sentinel-2 multispectral imagery[J], Sensors, 2018,18(3):868-886. doi: 10.3390/s18030868
[29]	SHI Y, HUANG W, GONZÁLEZ-MORENO P, et al. Based Rust Spectral Feature Set (WRSFs): A novel spectral feature set based on continuous Wavelet transformation for tracking progressive host-pathogen interaction of yellow rust on wheat[J]. Remote Sensing, 2018,10(4):525-543. doi: 10.3390/rs10040525
[30]	SHI Y, HUANG W, LUO J, et al. Detection and discrimination of pests and diseases in winter wheat based on spectral indices and kernel discriminant analysis[J]. Computers & Electronics in Agriculture, 2018. DOI: 10.1016/j.compag.2017.07.019. doi: 10.1016/j.compag.2013.08.003 pmid: 26937062
[31]	FERNAND MEYER. Topographic distance and watershed lines[J]. Signal Processing, 1994,38(1):113-125. doi: 10.1016/0165-1684(94)90060-4

2015	2016 (cont)	2017	2017 (cont)	2017 (cont)
2015/07/15	2016/05/30	2017/01/02	2017/06/14	2017/08/18
2015/07/25	2016/06/26	2017/01/25	2017/06/21	2017/08/23
2015/08/01	2016/06/29	2017/02/11	2017/06/24	2017/08/25
2015/08/14 (0 e 1)	2016/07/06	2017/02/14	2017/07/01	2017/08/28
2015/08/24	2016/07/09	2017/03/03	2017/07/04	2017/08/30
2015/08/31	2016/07/19	2017/03/06	2017/07/06	2017/09/02
2015/09/13	2016/07/29	2017/03/16	2017/07/09	2017/09/12
2015/10/23	2016/08/15	2017/03/23	2017/07/11	2017/10/17
2015/12/09	2016/08/25	2017/04/12	2017/07/14	2017/10/27
2015/12/22	2016/08/28	2017/04/15	2017/07/19	2017/10/29
2016	2016/10/14	2017/05/02	2017/07/21	2017/12/06
2016/01/01	2016/11/13	2017/05/05	2017/07/24	2017/12/08
2016/04/20	2016/11/23	2017/05/22	2017/07/29	2017/12/23
2016/04/30	2016/12/03	2017/06/01	2017/07/31	2017/12/31
2016/05/07	2016/12/16	2017/06/04	2017/08/05	—
2016/05/27	2016/12/26	—	2017/08/08	—

	VHR sensor	HR sensor
Main sensor characteristics	2×HR (High Resolu- tion Cameras)	4×WFV(Wide field of view Cameras)
Spatial resolution	Pan: 2m, MS: 8m	MS: 16m
Spectral resolution	Pan: 0.45~0.90 B1: 0.45~0.52μm B2: 0.52~0.59μm B3: 0.63~0.69μm B4: 0.77~0.89μm	B1: 0.45~0.52μm B2: 0.52~0.59μm B3: 0.63~0.69μm B4: 0.77~0.89μm
Swath width	69km with 2 cameras	830km with 4 cameras mosaic
Data quantization	10bit	10bit
Revisit capability	4 days at equator (roll needed)	4 days (no roll needed)

Classification	Olive groves total extension/ha	Difference /ha	Difference /(%)
Corine (CLC)	78982	—	—
Dragon(Corine polygon)	56642	-22340	-28
Dragon (new areas)	103801	+24819	+23