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2016 | T. 55 | 15--26
Tytuł artykułu

Zmienność wskaźników NDVI oraz NDMI na przykładzie analizy uprawy kukurydzy w Etiopii

Treść / Zawartość
Warianty tytułu
EN
Variability of NDVI and NDMI indicators on the example of maize cultivation in Etiopia
Języki publikacji
PL
Abstrakty
PL
Celem pracy jest analiza zmian kondycji uprawy kukurydzy na przestrzeni trzech lat, z zastosowaniem danych satelitarnych Landsat-8. Wykorzystano tu obrazy prezentujące rozkład przestrzenny dwóch wskaźników teledetekcyjnych: NDVI oraz NDMI. Pierwszy pozwala badać wielkość biomasy a tym samym potencjalny plon upraw. Drugi natomiast wrażliwy jest na zawartość wody w strukturach komórkowych roślin, co pozwala na detekcję stresu wodnego. Przebadano zróżnicowanie przestrzenne i zmienność tych dwóch wskaźników od 2014 do 2016 roku. Obliczono średnią i odchylenie standardowe dla uprawy oraz wydzielonych w niej 4 stref. Przeanalizowano również zmienność wskaźników na podstawie opracowanych map uprawy. Analiza przedstawiona w pracy jest konkretnym przykładem zastosowania średniorozdzielczych scen Landsat do monitoringu upraw, a tym samym tzw. precyzyjnego rolnictwa, gotowym do zaaplikowania na platformie COMOZ tworzonej w Instytucie Lotnictwa. Statystyki wskazują na konkretne daty kiedy kondycja upraw była najlepsza a kiedy najgorsza. Mapowanie zjawiska pozwala na śledzenie trendów w czasie i przestrzeni. Dodatkowo interpretacja wizualna i pośrednie cechy interpretacyjne obrazu mogą wskazywać na prowadzone na miejscu zabiegi hydrotechniczne.
EN
The aim of the study was to analyze changes in the condition of maize over three years, using satellite data Landsat-8. Spatial distribution and statistics of two remote sensing indices: NDVI and NDMI were shown. First of mentioned allows biomass estimation and thus potential crop yield. The second one is sensitive to water content of the plant cell structure, which allows for the detection of water stress. In the study spatial heterogeneity and variability of these two indicators from 2014 until 2016 were presented. The mean and standard deviation for 4 separated region of interests were calculated. Spatial variability of indices based on developed crops maps were also analyzed The analysis presented in this work is a concrete example of the application of medium-resolution Landsat scenes for monitoring crops and thus the so-called precision farming, ready to implement in COMOZ platform developed in Institute of Aviation. Statistics indicate a specific date when the condition of the crop was the best and when the worst. Mapping the phenomenon allows to track trends over time and space. In addition, the visual interpretation of indirect features of the image can indicate on the agricultural treatments characterization.
Słowa kluczowe
Wydawca

Rocznik
Tom
Strony
15--26
Opis fizyczny
Bibliogr. 40 poz., rys., tab.
Twórcy
  • Zakład Teledetekcji, Centrum Technologii Kosmicznych, Instytut Lotnictwa, al. Krakowska 110/114, 02-256 Warszawa, sylwianasilowska@gmail.com
autor
  • Zakład Teledetekcji, Centrum Technologii Kosmicznych, Instytut Lotnictwa, al. Krakowska 110/114, 02-256 Warszawa, katarzyna.kubiak@ilot.edu.pl
Bibliografia
  • Abera, T., Semu, E., Debele, T., Wegar,y D. and Kim, H., 2015, “Sensor Based In-Season Nitrogen Prediction for Quality Protein Maize Varieties on Farmers’ Field around Bako-Tibe, Western Ethiopia”, American-Eurasian J. Agric. Environ. Sci., 15 (8), pp. 1556-1567.
  • Ahmed, A., Akhtar, A., Khalid, B. and Shamim, A., 2013, “Correlation of meteorological parameters and remotely sensed normalized difference vegetation index (NDVI) with cotton leaf curl virus (CLCV) in Multan”, 6th Vacuum and Surface Sciences Conference of Asia and Australia (VASSCAA-6), Journal of Physics: Conference Series 439: 012044.
  • Ashourloo, D., Mobasheri, M.R. and Huete, A., 2014, “Developing Two Spectral Disease Indices for Detection of Wheat Leaf Rust (Pucciniatriticina)”, Remote Sens., 20 (6), pp. 4723-4740.
  • Bagheri, N., Ahmadi, H., Alavipanah, S.K. and Omid, M., 2013, “Multispectral remote sensing for site-specific nitrogen fertilizer management”, Pesq. Agropec. Bras., 48(10), pp.1394-1401.
  • Bolton, D. and Mark, A., 2013, “Friedl Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics”, Agric. For. Meteorol., 173, pp. 74-84.
  • Bongiovanni, R. and Lowenberg-DeBoer, J., 2004, “Precision agriculture and sustainability”, Prec, Agric., 5, pp. 359-387.
  • Chew, W.C., Hashim, M., Lau, A.M.S, Battay, A.E. and Kang, S.C., 2014, “Early detection of plant disease using close range sensing system for input into digital earth environment”, 8th International Symposium of the Digital Earth (ISDE8), IOP Conf. Series: Earth and Environmental Science 18: 012143.
  • CIMMYT (International Maize and Wheat Improvement Center), 2004, “Second Semi-Annual Progress Report for the QPM Development Project for the Horn and East Africa”, July 1-December 31.
  • Dragomir, L.O., Petrosani, P.D.E.C.H. and Oncia, S., 2012, “Using satellite images Landsat TM for calculating normalizeddifference indexes for the landscape of the Parang Mountains”, RevCAD, 13.
  • Ethiopia, 2016, Report: Humanitarian Requirements Document.
  • FAO-GIEWS 2016 (Food and Agriculture Organization - Global Information and Early Warning System) Country Briefs, http://www.fao.org/giews/countrybrief/country. jsp?lang=en&code=ETH
  • Getachew S., 2012, “Growing Vulnerability; Population Pressure, food insecurity and Environmental Degradation, Central Rift Valley, Ethiopia”, J. Biodivers. Environ. Sci., 2, pp.33-41.
  • Girolimetto, D. and Venturini, V., 2013, “Water Stress Estimation from NDVI-Ts Plot and the Wet Environment Evapotranspiration”, Adv. Remote Sens., 2, pp. 283-291.
  • Govaerts, B. and Verhulst, N., 2010, “The normalized difference vegetation index (NDVI) GreenSeeker TM handheld sensor: Toward the integrated evaluation of crop management, Part A: Concepts and case studies”, CIMMYT, Mexico, D.F.
  • Hatfield, J.L. and Prueger, J.H., 2010, “Value of Using Different Vegetative Indices to Quantify Agricultural Crop Characteristics at Different Growth Stages under Varying Management Practices”, Remote Sens., 2, pp. 562-578.
  • Henik, J. J., 2012, “Utilizing NDVI and remote sensing data to identify spatial variability in plant stress as influenced bymanagement”, Graduate Theses and Dissertations, paper 12341.
  • Herbei, M.V. and Sala, F., 2015, “Use Landsat Image To Evaluate Vegetation Stage In Sunflower Crops”, AgroLife Scien. J., 4 (1), pp. 79-86.
  • Hunt, E. R. Jr., Daughtry, C.S.T. , Eitel, J.U.H. and Long, D.S., 2011,” A visible band index for remote sensing leaf chlorophyll content at the canopy scale”, Agron. J., 103 (4), pp.1090-1099.
  • Hunt, Jr. E.R., Chen, T., Riaño, D., Ustin, S.L., Wang, L., Hao, X., Qu, J.J. and Daughtry, C.S.T., 2013, “Determining leaf dry matter content using the Normalized Dry Matter Index and its possible application for estimating fuel moisture content” [abstract], HyspIRI 2013 Symposium, May 29-30, NASA GSFC, Greenbelt, MD.
  • Kidane, H., Yirga, H., Teklay, Y., Teklay, Z. and Juhar, J., 2016, “Pre-extension Popularization of Improved Maize Varieties: In Raya-Azebo Woreda South of Tigray, Ethiopia”, JEDS, 7 (9), pp. 5-19.
  • Leblois, A. and Quirion, P., 2013, “Agricultural insurances based on meteorological indices: Realizations, methods and research challenges”, Meteor. Appl., 20(1), pp. 1-9.
  • Li, P. and Feng, Z., 2016, “Extent and Area of Swidden in Montane Mainland Southeast Asia: Estimation by Multi-Step Thresholds with Landsat-8 OLI Data”, Remote sens., 8 (1), pp. 1-17.
  • Lopresti, M.F., Di Bella, C.M. and Degioanni, A.J., 2015, “Relationship between MODIS-NDVI data and wheat yield: A case study in Northern Buenos Aires province, Argentina”, Inf. Proc. Agric., 2(2), pp. 73-84.
  • Panda, S.S., Ames, D.P. and Panigrahi, S., 2010, “Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques”, Remote Sens., 2, pp. 673-696.
  • Pinter, P.J., Hatfield, J.L., Schepers, J.S., Barnes, E.M., Moran, S.M., Daughtry, C.S.T. and Upchurch, D.R., 2003, “Remote sensing for crop management. Photogrammetric” Eng. & Remote Sensing, 69, pp. 647-664.
  • Ponzoni, F.J., Borges, da Silva, C., Benfica dos Santos, S., Montanher, C.O. and Batista dos Santos, T., 2014, “Local Illumination Influence on Vegetation Indices and Plant Area Index (PAI) Relationships”, Remote Sens., 6, pp. 6266-6282.
  • Prasad, A. Chai, L., Singh, R., and Kafatos, M., 2006, “Crop yield estimation model for Iowa using remote sensing and surface parameters”, Int. J. Appl. Earth Obs. Geoinf., 8(1), pp. 26-33.
  • Rapaport, T., Hochberg, U., Rachmilevitch S. and Karnieli A., 2014, “The Effect of Differential Growth Rates across Plants on Spectral Predictions of Physiological Parameters”, PLoS One, 9(2), e88930.
  • Rouse, J.W., Jr., Haas, R.H., Schell, J.A. and Deering, D.W., 1974,”Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation”, E74-10676, NASA-CR-139243, PR-7, Texas A&M Univ, United States.
  • Stratoulias, D., Balzter, H., Zlinszky A. and Tóth V.R., 2015, “Assessment of ecophysiology of lake shore reed vegetation based on chlorophyll fluorescence, field spectroscopy and hyperspectral airborne imagery”, Remote Sens. Environ., 157, pp. 72-84.
  • Sultana, S.R., Ali, A., Ahmad, A.,Mubeen, M.,Zia-Ul-Haq, M., Ahmad, S.,Ercisli, S. and Jaafar, H.Z.E.,2014, “Normalized Difference Vegetation Index as a Tool for Wheat Yield Estimation: A Case Study from Faisalabad, Pakistan”, Sci. World J., 725326.
  • Tadesse, A., Kim, H.K. and Debela, A., 2015, “Calibration Of Nitrogen Fertilizer For Quality Protein Maize (Zea Mays L.) Based On In-Season Estimated Yield Using A Handheld Ndvisensor In The Central Rift Valley Of Ethiopia”, Asia Pac. J. Energy Environ., 2(1), pp.25-32.
  • Tefera, A., 2016, “Grain and Feed Annual Ethiopia. Worst Drought in Decades Pushes Grain Production Down 4.5 MMT”, ET1608, USDA-FAS.
  • Turvey, C.G. and Mclaurin, M.K., 2012, “Applicability of the Normalized Difference Vegetation Index (NDVI) in Index-Based Crop Insurance Design”, Am. Meteorol. Soc., 4, pp. 271-284.
  • USGS/NASA Landsat, Earth Resources Observations and Science (EROS) Center Science Processing Architecture (ESPA) on Demand Interface, https://espa.cr.usgs.gov/ordering/new/.
  • Viña, A., Gitelson, A.A., Nguy-Robertson, A.L. and Peng, Y., 2011, “Comparison of different vegetation indices for the remote assessment of green leaf area index of crops”, Remote Sens. Environ., 115, pp. 3468-3478.
  • Wang, L., Hunt, Jr E.R., Qu, J.J., Hao, X. and Daughtry, C.S.T., 2011, “Towards estimation of canopy foliar biomass with spectral reflectance measurements”, Remote Sens. Environ., 115 (3), pp. 836-840.
  • Wang, L., Qu, J.J., Hao, X. and Hunt, Jr E.R., 2011, “Estimating dry matter content from spectral reflectance for green leaves of different species”, Int. J. Remote Sen., 32, pp. 7097-7109.
  • Yang, Z., Willis, P. and Mueller, R., 2008, “Impact Of Band-Ratio Enhanced Awifs Image To Crop Classifi Cation Accuracy”, National Agricultural Statistics Service. Pecora 17 - The Future of Land Imaging...Going Operational, Denver, Colorado.
  • Zinna, A.W. and Suryabhagavan, K.V., 2016, “Remote Sensing and GIS Based Spectro-Agrometeorological Maize Yield Forecast Model for South Tigray Zone, Ethiopia”, J. Geogr. Inf. System, 8, pp. 282-292.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-406ba91b-3100-414d-9b08-c0f84e929c2f
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