Remote sensing semi-automatic measurements approach for monitoring bioenergetic crops of Miscanthus spp.

eng Artykuł w języku angielskim DOI: 10.14313/PAR_234/77

wyślij Katarzyna Kubiak*, Karol Rotchimmel*, Krzysztof Stereńczak**, Marta Damszel***, Zbigniew Sierota**** * Remote Sensing Department, Institute of Aviation ** Laboratory of Geomatics, Forest Research Institute, Sękocin Stary *** Department of Entomology, Phytopathology and Molecular Diagnostics, University of Warmia and Mazury in Olsztyn **** Department of Forestry and Forests Ecology, University of Warmia and Mazury in Olsztyn

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Abstract

The paper presents the review of the potential application of remote sensing techniques at ground, air, and satellite levels in monitoring, yield assessment for bioenergy crops and the evaluation of natural grass communities of Miscanthus spp. According to the Directive 2009/28, the EC countries are obliged to increase the participation of energy production from renewable energy sources by 20% by 2020. This objective can be achieved in part by using biomass from high energy plantations. Monitoring of Miscanthus growth, one of the most prospective crop species, is important because of its use as bioenergy crop, to evaluate quality and quantity, and for environmental reasons. As Miscanthus is a non-native species in Europe, its uncontrolled spread may threaten the diversity of native species. Contrary to the traditional field-based observations of Miscanthus communities, the remote sensing provide suitable data enable the acquisition of precise data about biomass state and habitat quality. Such methods are highly efficient tools for precise quantitative assessment in agriculture and for the monitoring of natural Miscanthus communities. 

Keywords

databases, Miscanthus spp., monitoring, remote sensing methods

Półautomatyczne pomiary metodą teledetekcji do monitorowania bioenergetycznych upraw Miscanthus spp.

Streszczenie

W pracy przedstawiono przegląd najnowszej literatury dotyczącej możliwości zastosowania technik teledetekcyjnych naziemnych, lotniczych i satelitarnych do monitorowania, prognozowania plonu oraz oceny zbiorowisk naturalnych bioenergetycznych traw należących do Miscanthus spp. Zgodnie z dyrektywą 2009/28 kraje należące do Unii Europejskiej zobowiązane są do 2020 r. do zwiększenia udziału produkcji energii z odnawialnych źródeł o 20%. Cel ten może zostać częściowo osiągnięty przez wykorzystywanie biomasy na cele energetyczne. Monitorowanie wzrostu bioenergetycznej trawy – miskanta, jednego z najbardziej perspektywicznych gatunków roślin uprawnych, jest istotne nie tylko ze względu na jego przeznaczenie jako uprawy bioenergetycznej, ale także ze względów środowiskowych. Ponieważ miskant jest gatunkiem obcym w Europie, jego niekontrolowane rozprzestrzenienie się może zagrozić różnorodności gatunków rodzimych. W przeciwieństwie do tradycyjnych metod obserwacji zbiorowisk miskanta, metody teledetekcyjne dostarczają dokładnych danych o stanie biomasy i jakości zbiorowisk. Metody te są wysoce wydajnymi narzędziami do precyzyjnej oceny ilościowej i jakościowej upraw oraz monitorowania naturalnych zbiorowisk roślinności, m.in. miskanta. 

Słowa kluczowe

bazy danych, metody teledetakcyjne, Miscanthus spp., monitoring

Bibliografia

  1. Ahamed T., Tian L., Jiang Y.S., Zhao B., Liu H., Ting K.Ch., Tower remote-sensing system for monitoring energy crops; image acquisition and geometric corrections. “Biosystems Engineering”, Vol. 112, No. 2, 2012, 93–107,  DOI: 10.1016/j.biosystemseng.2012.03.003.
  2. Aparicio N., Villegas D., Casadesus J., Araus J.L., Royo C., Spectral vegetation indices as non-destructive tools for determining durum wheat yield, “Agronomy Journal Abstract – AGROCLIMATOLOGY”, Vol. 92, No. 1, 2000, 83–91,  DOI: 10.2134/agronj2000.92183x.
  3. Berni J., Zarco-Tejada P., Suarez L., Fereres E., Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. “IEEE Transactions on Geoscience and Remote Sensing”, Vol. 47, No. 3, 2009, 722–738, DOI: 10.1109/TGRS.2008.2010457.
  4. Berni J., Zarco-Tejada P., Suarez L., Fereres E., Remote sensing of vegetation from UAV platforms using lightweight multispectral and thermal imaging sensors. “ISPRS Archives”, Vol. 38, No. 6, 2014.
  5. Collura S., Azambre B., Finqueneisel G., Zimny T., Weber J.V., Miscanthus × Giganteus straw and pellets as sustainable fuels. “Environmental Chemistry Letters”, Vol. 4, No. 2, 2006, 75–78, DOI: 10.1007/s10311-006-0036-3.
  6. Ehlert D., Heisin M., Sources of angle-dependent errors in terrestrial laser scanner-based crop stand measurement. “Computers and Electronics in Agriculture”, Vol. 93(C), 2013, 10–16, DOI: 10.1016/j.compag.2013.01.002.
  7. Ishihama F., Watabe Y., Oguma H., Validation of a high-resolution, remotely operated aerial remote-sensing system for the identification of herbaceous plant species. “Applied Vegetation Science”, Vol. 15, No. 3, 2012, 383–389, DOI: 10.1111/j.1654-109X.2012.01184.x.
  8. Jensen A., Han Y. Chen Y.Q., Using aerial images to calibrate the inertial sensors of a low-cost multispectral autonomous remote sensing platform (AggieAir), [in:] IEEE International Geoscience and Remote Sensing Symposium, 2, 2009, 555–558, DOI: 10.1109/IGARSS.2009.5417547.
  9. Jeżowski S., Yield traits of six clones of Miscanthus in the first 3 years following planting in Poland. “Industrial Crops and Products”, Vol. 27, No. 1, 2008, 65–68, DOI: 10.1016/j.indcrop.2007.07.013.
  10. Jørgensen U., Genotypic variation in dry matter accumulation and content of N, K and Cl in Miscanthus in Denmark. “Biomass and Bioenergy”, Vol. 12, No. 3, 1997, 155–169, DOI: 10.1016/S0961-9534(97)00002-0.
  11. Jørgensen U., Mortensen J., Ohlsson C., Light interception and dry matter conversion effciency of Miscanthus genotypes estimated from spectral reflectance measurements. “New Phytologist”, Vol. 157, No. 2, 2002, 263–270,  DOI: 10.1046/j.1469-8137.2003.00661.x.
  12. Kaneko K., Nohara S., Review of Effective Vegetation Mapping Using the UAV (Unmanned Aerial Vehicle) Method. “Journal of Geographic Information System”, Vol. 6, No. 6, 2014, 733–742, DOI: 10.4236/jgis.2014.66060.
  13. Li L., Tian L., Ahamed T., Preharvest monitoring of biomass production, [in:] Shastri Y., Hansen A., Rodríguez L., Ting K.C. (Eds), Engineering and Science of Biomass Feedstock Production and Provision, Springer, New York, US, 2014, 61–83.
  14. Liaghat S., Balasundram S.K., A Review: the role of remote sensing in precision agriculture. “American Journal of Agricultural and Biological Sciences”, Vol. 5, No. 1, 2010, 50–55, DOI: 10.3844/ajabssp.2010.50.55.
  15. Lopatina A., Rapid assessment of energy biomass resources using aerial photographs from unmanned aerial vehicles, Master’s thesis in Forestry and Environmental Engineering, Finnish-Russian Cross-Border University (CBU), Faculty of Science and Forestry, University of Eastern Finland, 2013.
  16. Lu S., Funakoshi S., Shimizu Y., Ishii Y., de Asis A.M., Ajima M., Washitani I., Omasa K., Estimation of plant abundance and distribution of Miscanthus sacchariflorus and Phragmites australis using matched filtering of hyperspectral image. “Eco-Engineering”, Vol. 18, No. 2, 2006, 65–70, DOI: 10.14877/agrmet2.2006sp.0.70.0.
  17. Majewska-Sawka A., Miscanthus. A clean energy/ Miskant olbrzymi. Czysta energia, 11, 2009, 34–35 (in Polish).
  18. Motohka T., Nasahara K.N., Oguma H., Tsuchida S., Applicability of green-red vegetation index for remote sensing of vegetation phenology. “Remote Sensing”, Vol. 2, No. 10, 2010, 2369–2387, DOI: 10.3390/rs2102369.
  19. Nordberg M.L., Evertson J., Vegetation index differencing and linear regression for change detection in a Swedish mountain range using Landsat TM and ETM+ imagery. “Land Degradation and Development”, Vol. 16, No. 2, 2003, 139–149. DOI: 10.1002/ldr.660.
  20. Oguma H., Usami M., Shimazaki H., Ishihama F., A high-resolution remote sensing by radio control helicopter and apply to species discrimination of individual level of wetland herbaceous plant. 57th Annual Confe- rence of Ecological Society of Japan, Tokyo, 15–20 March 2010, Abstract.
  21. Oki K., Funakoshi Y., Inamura M., Study on estimation of the specific land cover ratio in a pixel using hyper-spectral data. Estimation of the vegetation cover ratio. “Journal of the Remote Sensing Society of Japan”, Vol. 20, No. 3, 2000, 241–257, DOI: 10.11440/rssj1981.20.241.
  22. Qin Z., Zhuang Q., Chen M., Impacts of land use change due to biofuel crops on carbon balance, bioenergy production, and agricultural yield, in the conterminous United States. “GCB Bioenergy”, Vol. 4, No. 3, 2012, 277–288, DOI: 10.1111/j.1757-1707.2011.01129.x.
  23. Quarmby N.A., Milnes M., Hindle T.L., Silleos N., The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction. “International Journal Remote Sensing”, Vol. 14, No. 2, 1993, 199–210. DOI: 10.1080/01431169308904332.
  24. Richter G.M., Agostini F., Barker A., Costomiris D., Qi A., Assessing on-farm productivity of Miscanthus crops by combining soil mapping, yield modelling and remote sensing. “Biomass and Bioenergy”, Vol. 85, 2016, 252–261, DOI: 10.1016/j.biombioe.2015.12.024.
  25. Smith L.L., Barney J.N., The relative risk of invasion: evaluation of Miscanthus ×giganteus seed establishment. “Invasive Plant Science & Management”, Vol. 7, 2014, 93–106, DOI: 10.1614/IPSM-D-13-00051.1.
  26. Sritarapipat T., Rakwatin P., Kasetkasem T., Automatic rice crop height measurement using a field server and digital image processing. “Sensors”, Vol. 14, No. 1, 2014, 900–926, DOI: 10.3390/s140100900.
  27. Strecha C., Fletcher A., Lechner A., Erskine P., Fua P., Developing species specific vegetation maps using multi-spectral hyperspatial imagery from unmanned aerial vehicle. “ISPRS Annals”, 2012, 1–3,  DOI: 10.5194/isprsannals-I-3-311-2012.00061-5.
  28. VanLoocke A., Bernacchi C.J., Twine T.E., The impacts of Miscanthus ×giganteus production on the Midwest US hydrologic cycle. “GCB Bioenergy”, Vol. 2, No. 4, 2010, 180–191, DOI: 10.1111/j.1757-1707.2010.01053.x.
  29. Vargas L.A., Andersen M.N., Jensen C.R., Jørgensen U., Estimation of leaf area index, light interception and biomass accumulation of Miscanthus sinensis ‘Goliath’ from radiation measurements. “Biomass and Bioenergy”, Vol. 22, No. 1, 2002, 1–14, DOI: 10.1016/S0961-9534(01)00058-7.
  30. Wang C., Guo L., Li Y., Wang Z., Systematic comparison of C3 and C4 plants based on metabolic network analysis. “BMC Systems Biology”, Vol. 6(Suppl 2), 2012, S9,  DOI: 10.1186/1752-0509-6-S2-S9.
  31. Williams A.P., Hunt J.E.R., Estimation of leafy spurge cover from hyperspectral imagery using mixture tuned matched filtering, “Remote Sensing of Environment”, Vol. 82, No. 2–3, 2002, 446–456, DOI: 10.1016/S0034-4257(02)00061-5.
  32. Zhang L., Grift T.E., A LIDAR-based crop height measurement system for Miscanthus giganteus. “Computers and Electronics in Agriculture”, Vol. 85, 2012, 70–76,  DOI: 10.1016/j.compag.2012.04.001.
  33. Zub H.W., Arnoult S., Brancourt-Hulmel M., Key traits for biomass production identified in different Miscanthus species at two harvest dates. “Biomass and Bioenergy”, Vol. 35, No. 1, 2011, 637–651, DOI: 10.1016/j.biombioe.2010.10.020. 34. Everitt J.H., Anderson G.L., Escobar D.E., Davis M.R., Spencer N.R., Andrascik R.J., Use of Remote Sensing for Detecting and Mapping Leafy Spruge (Euphoribia estula), “Weed Techn”, Vol. 9, No. 3, 1995, 599–609.