High accuracy Land Use Land Cover (LULC) maps for detecting agricultural drought effects in rainfed agro-ecosystems in central Mexico

• Autorzy: Sierra-Soler A. , Adamowski J. , Qi Z. , Saadat H. , Pingale S.

Identyfikatory

DOI

10.1515/jwld-2015-0014

Warianty tytułu

PL

Dokładne mapy użytkowania i pokrycia terenu (LULC) w śledzeniu suszy rolniczej w zasilanych opadowo agro-ekosystemach środkowego Meksyku

• Języki publikacji: EN

Abstrakty

EN

Satellite remote sensing provides a synoptic view of the land and a spatial context for measuring drought impacts, which have proved to be a valuable source of spatially continuous data with improved information for monitoring vegetation dynamics. Many studies have focused on detecting drought effects over large areas, given the wide availability of low-resolution images. In this study, however, the objective was to focus on a smaller area (1085 km²) using Landsat ETM+ images (multispectral resolution of 30 m and 15 m panchromatic), and to process very accurate Land Use Land Cover (LULC) classification to determine with great precision the effects of drought in specific classes. The study area was the Tortugas-Tepezata sub watershed (Moctezuma River), located in the state of Hidalgo in central Mexico. The LULC classification was processed using a new method based on available ancillary information plus analysis of three single date satellite images. The newly developed LULC methodology developed produced overall accuracies ranging from 87.88% to 92.42%. Spectral indices for vegetation and soil/vegetation moisture were used to detect anomalies in vegetation development caused by drought; furthermore, the area of water bodies was measured and compared to detect changes in water availability for irrigated crops. The proposed methodology has the potential to be used as a tool to identify, in detail, the effects of drought in rainfed agricultural lands in developing regions, and it can also be used as a mechanism to prevent and provide relief in the event of droughts.

PL

Teledetekcja zapewnia synoptyczny ogląd Ziemi i kontekst przestrzenny do pomiarów efektów susz, co okazało się cennym źródłem ciągłych danych dla monitorowania dynamiki roślinności. Wiele badań koncentrowało się na śledzeniu skutków suszy na rozległych obszarach ze względu na łatwą dostępność obrazów o małej rozdzielczości. Celem przedstawionej pracy była jednak analiza mniejszego obszaru (1085 km²) z użyciem zdjęć Landsat ETM+ (wielospektralna rozdzielczość 30 m, panchromatyczna – 15 m) oraz przeprowadzenie dokładnej klasyfikacji użytkowania i pokrycia powierzchni terenu (ang. Land Use Land Cover – LULC) z zamiarem określenia z dużą dokładnością skutków suszy w poszczególnych klasach. Terenem badań była Tortugas-Tepezata zlewnia II rzędu rzeki Moctezuma, zlokalizowana w stanie Hidalgo w środkowym Meksyku. Klasyfikację LULC przeprowadzono z użyciem nowej metody bazującej na dostępnych dodatkowych informacjach i analizie trzech zdjęć satelitarnych wykonanych w różnym czasie. Opracowana na nowo metodyka LULC zapewniła dokładność w granicach od 87,88 do 92,42%. Spektralne wskaźniki dla roślinności i wilgotności gleby oraz roślin wykorzystano do wykrycia anomalii w rozwoju roślinności spowodowanych suszą. Ponadto zmierzono i porównano powierzchnię zbiorników wodnych w celu sprawdzenia zmian w dostępności wody do nawadniania upraw. Proponowana metodyka może służyć jako narzędzie szczegółowej identyfikacji skutków suszy w zasilanych opadowo obszarach rolniczych oraz jako mechanizm zapobiegania i łagodzenia skutków suszy.

Słowa kluczowe

EN

drought; LULC maps; remote sensing; spectral indices

PL

mapy LULC; susza; teledetekcja; wskaźniki spektralne

• Wydawca: Wydawnictwo ITP
• Czasopismo: Journal of Water and Land Development
• Rocznik: 2015
• Tom: no. 26
• Strony: 19--35
• Opis fizyczny: Bibliogr. 72 poz., rys., tab.

Twórcy

autor

Sierra-Soler A.

• McGill University, Faculty of Agricultural and Environmental Sciences, Department of Bioresource Engineering, Quebec, Canada, H9X 3V9

autor

Adamowski J.

• McGill University, Faculty of Agricultural and Environmental Sciences, Department of Bioresource Engineering, Quebec, Canada, H9X 3V9

autor

Qi Z.

• McGill University, Faculty of Agricultural and Environmental Sciences, Department of Bioresource Engineering, Quebec, Canada, H9X 3V9

autor

Saadat H.

• McGill University, Faculty of Agricultural and Environmental Sciences, Department of Bioresource Engineering, Quebec, Canada, H9X 3V9

autor

Pingale S.

• Indian Institute of Technology Roorkee, Department of Water Resource Development and Management, Roorkee 247 667 (UA), India

Bibliografia

• ADAMOWSKI J., ADAMOWSKI K., BOUGADIS J. 2010. Influence of trend on short duration design storms. Water Resources Management. Vol. 24. Iss. 3 p. 401–413.
• ADAMOWSKI J., CHAN H., PRASHER S., SHARDA V.N. 2012a. Comparison of multivariate adaptive regression splines with coupled wavelet transform artificial neural networks for runoff forecasting in Himalayan microwatersheds with limited data. Journal of Hydroinformatics. Vol. 14. Iss. 3 p. 731–744.
• ADAMOWSKI J., PROKOPH A. 2013. Assessing the impacts of the urban heat island effect on streamflow patterns in Ottawa, Canada. Journal of Hydrology. Vol. 496 p. 225–237.
• ADAMOWSKI K., PROKOPH A., ADAMOWSKI J. 2009. Development of a new method of wavelet aided trend detection and estimation. Hydrological Processes. Vol. 23 p. 2686–2696.
• ADAMOWSKI K., PROKOPH A., ADAMOWSKI J. 2012b. Influence of the 11 year solar cycle on annual streamflow maxima in Canada. Journal of Hydrology. Vol. 442–443 p. 55–62.
• APPENDINI K., LIVERMAN D. 1994. Agricultural policy, climate change and food security in Mexico. Food Policy. Vol. 19. Iss. 2 p. 149–164.
• ARAGHI A., ADAMOWSKI J., NALLEY D., MALARD J. 2015. Using wavelet transforms to estimate surface temperature trends and dominant periodicities in Iran based on gridded reanalysis data. Journal of Atmospheric Research. Vol. 11 p. 52–72.
• BELAYNEH A., ADAMOWSKI J., KHALIL B., OZGA-ZIELINSKI B. 2014. Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet-support vector regression models. Journal of Hydrology. Vol. 508 p. 418–429.
• BOT A., BENITES J. 2005. The importance of soil organic matter: Key to drought-resistant soil and sustained food production. FAO. Web. 16 Oct 2013.
• BUTLER C., ADAMOWSKI J. 2015. Empowering marginalized communities in water resources management: Addressing inequitable practices in Participatory Model Building. Journal of Environmental Management. Vol. 153 p. 153–162.
• CAMPISI S., ADAMOWSKI J., ORON G. 2012. Forecasting urban water demand via wavelet- denoising and neural network models. Case study: city of Syracuse, Italy. Water Resources Management. Vol. 26 p. 3539–3558.
• COHEN Y., SHOSHANY M. 2002. A national knowledge-based crop recognition in Mediterranean environment. International. Journal of Applied Earth Observation and Geoinformation. Vol. 4. Iss. 1 p. 75–87.
• CONANP 2003. Programa de Manejo Reserva de La Biosfera Barranca de Metztitlán. Mexico DF.
• CRIST E., CICONE R. 1984. A physically-based transformation of Thematic Mapper data – The TM Tasseled Cap. Geoscience and Remote Sensing, IEEE Transactions. Vol GE-22. Iss. 3 p. 256–263.
• DANESHMAND F., KARIMI A., NIKOO M., BAZARGAN-LARI M., ADAMOWSKI J. 2014. Mitigating socio-economicenvironmental impacts during drought periods by optimizing the conjunctive management of water resources. Water Resources Management. Vol. 28. Iss. 6 p. 1517–1529.
• DE ASIS A., OMASA K. 2007. Estimation of vegetation parameter for modeling soil erosion using linear Spectral Mixture Analysis of Landsat ETM data. ISPRS Journal of Photogrammetry and Remote Sensing. Vol. 62. Iss. 4 p. 309–324.
• DIOUF A., LAMBIN E. 2001. Monitoring land-cover changes in semi-arid regions: remote sensing data and field observations in the Ferlo, Senegal. Journal of Arid Environments. Vol. 48. Iss. 2 p. 129–148.
• EAKIN H. 2000. Smallholder maize production and climatic risk: A case study from Mexico. Climatic Change. Vol. 45. Iss. 1 p. 19–36.
• FADHIL A. 2011. Drought mapping using Geoinformation technology for some sites in the Iraqi Kurdistan region. International Journal of Digital Earth. Vol. 4. Iss. 3 p. 239–257.
• FOCARDI S., LOISELLE S.A., MAZZUOLI S., BRACCHINI L., DATTILO A.M., ROSSI C. 2008. Satellite-based indices in the analysis of land cover for municipalities in the province of Siena, Italy. Journal of Environmental Management. Vol. 86. Iss. 2 p. 383–389.
• GAO B. 1996. NDWI A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment. Vol. 58. Iss. 3 p. 257–266.
• GOWARD S., COMPTON J., DYE D. 1985. North American vegetation patterns observed with the NOAA-7 advanced very high resolution radiometer. Vegetatio. Vol. 64. Iss. 1 p. 3–14.
• GU Y., BROWN J. F., VERDIN J. P., WARDLOW B. 2007. A five-year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States. Geophysical Research Letters. Vol. 34. Iss. 6.
• GU Y., HUNT E., WARDLOW B., BASARA J.B., BROWN J.F., VERDIN J.P. 2008. Evaluation of MODIS NDVI and NDWI for vegetation drought monitoring using Oklahoma Mesonet soil moisture data. Geophysical Research Letters. Vol. 35. Iss. 22.
• HAIDARY A., AMIRI B.J., ADAMOWSKI J., FOHRER N., NAKANE K. 2013. Assessing the impacts of four land use types on the water quality of wetlands in Japan. Water Resources Management. Vol. 27. Iss. 7 p. 2217–2229.
• HALBE J., PAHL-WOSTL C., SENDZIMIR J., ADAMOWSKI J. 2013. Towards adaptive and integrated management paradigms to meet the challenges of water governance. Water Science and Technology: Water Supply. Vol. 67. Iss. 11 p. 2651–2660.
• HEWITT K. 1997. Regions of risk: A geographical introduction to disasters. London. Longman. ISBN 0582210054 pp. 389.
• INAM A., ADAMOWSKI J., HALBE J., PRASHER S. 2015. Using causal loop diagrams for the initialization of stakeholder engagement in soil salinity management in agricultural watersheds in developing countries: A case study in the Rechna Doab watershed, Pakistan. Journal of Environmental Management. Vol. 152 p. 251–267.
• JIN S., SADER S. 2005. Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances. Remote Sensing of Environment. Vol. 94. Iss. 3 p. 364–372.
• JOHAN R. 2003. Resilience building and water demand management for drought mitigation. Physics and Chemistry of the Earth. Parts A/B/C. Vol. 28. Iss. 20 p. 869–877.
• JOSHI P.K., ROY P.S., SINGH S., AGRAWAL S., YADAV D. 2006. Vegetation cover mapping in India using multitemporal IRS Wide Field Sensor (WiFS) data. Remote Sensing of Environment. Vol. 103 p. 190–202.
• KOGAN F. 1990. Remote sensing of weather impacts on vegetation in non-homogeneous areas. International Journal of Remote Sensing. Vol. 11. Iss. 8 p. 1405–1419.
• KOGAN F. 1995a. Application of vegetation index and brightness temperature for drought detection. Advances in Space Research. Vol. 15. Iss. 11 p. 91–100.
• KOGAN F. 1995b. Droughts of the late 1980s in the United States as derived from NOAA Polar-Orbiting Satellite data. Bulletin of the American Meteorological Society. Vol. 76. Iss. 5 p. 655–668.
• KOLINJIVADI V., ADAMOWSKI J., KOSOY N. 2014a. Juggling multiple dimensions in a complex socioecosystem: The issue of targeting in payments for ecosystem services. GeoForum. Vol. 58 p. 1–13.
• KOLINJIVADI V., ADAMOWSKI J., KOSOY N. 2014b. Recasting payments for ecosystem services (PES) in water resource management: A novel institutional approach. Ecosystem Services. Vol. 10 p. 144–154.
• LABRECQUE S., FOURNIER R.A., LUTHER J. E., PIERCEY D. 2006. A comparison of four methods to map biomass from Landsat-TM and inventory data in western Newfoundland. Forest Ecology and Management. Vol. 226. Iss. 1 p. 129–144.
• LENNEY M.P., WOODCOCK C.E., COLLINS J.B., HAMDI H. 1996. The status of agricultural lands in Egypt: the use of multitemporal NDVI features derived from Landsat TM. Remote Sensing of Environment. Vol. 56. Iss. 1 p. 8–20.
• LIVERMAN D. 1990. Drought impacts in Mexico: Climate, agriculture, technology, and land tenure in Sonora and Puebla. Annals of the Association of American Geographers. Vol. 80. Iss. 1 p. 49–72.
• MAHMOUD S., ADAMOWSKI J., ALAZBA A., EL-GINDY A. 2015. Rainwater harvesting for the management of agricultural droughts in arid and semi-arid regions. Paddy and Water Environment. In press.
• MASELLI F., CONESE C., PETKOV L., RESTI R. 1992. Inclusion of prior probabilities derived from a nonparametric process into the maximum-likelihood classifier. Photogrammetric Engineering and Remote Sensing. Vol. 58. No. 2 p. 201–207.
• MEDEMA W., LIGHT S., ADAMOWSKI J. 2014. Integrating adaptive learning into water resources management. Journal of Environmental Engineering and Management. Vol. 13. Iss. 7 p. 1801–1816.
• MEDEMA W., WALS A., ADAMOWSKI J. 2014. Virtual learning platforms for sustainable land and water governance: An innovative mechanism for facilitating multiloop social learning. Wageningen University Journal of Life Sciences. Vol. 69 p. 23–38.
• MISHRA A., SINGH V. 2010. A review of drought concepts. Journal of Hydrology. Vol. 391. Iss. 1 p. 202–216.
• NALLEY D., ADAMOWSKI J., KHALIL B. 2012. Using discrete wavelet transforms to analyze trends in streamflow and precipitation in Quebec and Ontario (1954–2008). Journal of Hydrology. Vol. 475 p. 204–228.
• NALLEY D., ADAMOWSKI J., KHALIL B., OZGA-ZIELINSKI B. 2013. Trend detection in surface air temperature in Ontario and Quebec, Canada during 1967–2006 using the discrete wavelet transform. Journal of Atmospheric Research. Vol. 132–133 p. 375–398.
• NAMDAR M., ADAMOWSKI J., SAADAT H., SHARIFI F., KHIRI A. 2014. A new approach to developing LULC maps for semi-arid regions using independent component analysis and expert classification. International Journal of Remote Sensing. Vol. 35. Iss. 24 p. 8057–8073.
• NIAZI A., PRASHER S., ADAMOWSKI J. 2014. A system dynamics model to conserve arid region water resources through aquifer storage and recovery in conjunction with a dam. Journal of Water. Vol. 6. Iss. 12 p. 2300–2321.
• OBASI G.O.P. 1994. WMO's role in the international decade for natural disaster reduction. Bulletin of the American Meteorological Society. Vol. 75. Iss. 9 p. 1655–1661.
• OETTER D.R., COHEN W.B., BERTERRETCHE M., MAIERSPERGER T.K., KENNEDY R.E. 2001. Land cover mapping in an agricultural setting using multiseasonal thematic mapper data. Remote Sensing of Environment. Vol. 76. Iss. 2 p. 139–155.
• PANU U., SHARMA T. 2002. Challenges in drought research: Some perspectives and future directions. Hydrological Sciences Journal. Vol. 47 p. S19–S30.
• PETERS A.J., WALTER-SHEA E.A., JI L., VINA A., HAYES M., SVOBODA M.D. 2002. Drought monitoring with NDVIbased standardized vegetation index. Photogrammetric Engineering and Remote Sensing. Vol. 68. No. 1 p. 71–75.
• PINGALE S., KHARE D., JAT M., ADAMOWSKI J. 2014. Spatial and temporal trends of mean and extreme rainfall and temperature for the 33 urban centres of the arid and semi-arid state of Rajasthan, India. Journal of Atmospheric Research. Vol. 138 p. 73–90.
• QUARMBY J., FORSTER C. 1995. An examination of the structure of UASB granules. Water Research. Vol. 29. Iss. 11 p. 2449–2454.
• RAHIMZADEH BAJGIRAN P., DARVISHSEFAT A.A., KHALILI A., MAKHDOUM M.F. 2008. Using AVHRR-based vegetation indices for drought monitoring in the Northwest of Iran. Journal of Arid Environments. Vol. 72. Iss. 6 p. 1086–1096.
• RAHIMZADEH-BAJGIRAN P., OMASA K., SHIMIZU Y. 2012. Comparative evaluation of the Vegetation Dryness Index (VDI), the Temperature Vegetation Dryness Index (TVDI) and the Improved TVDI (iTVDI) for water stress detection in semi-arid regions of Iran. ISPRS Journal of Photogrammetry and Remote Sensing. Vol. 68 p. 1–12.
• RASMUSSEN M. 1992. Assessment of millet yields and production in Northern Burkina Faso using integrated NDVI from the AVHRR. International Journal of Remote Sensing. Vol. 13. Iss. 18 p. 3431–3442.
• REDMOND K. 2002. The depiction of drought: A commentary. Bulletin of the American Meteorological Society. Vol. 83. Iss. 8 p. 1143–1147.
• RHEE J., IM J., CARBONE G. 2010. Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data. Remote Sensing of Environment. Vol. 114. Iss. 12 p. 2875–2887.
• RUEFENACHT B., VANDERZANDEN D., MORRISON M. 2002. New technique for segmenting images. Salt Lake City. USDA Forest Service Remote Sensing Applications Center.
• SAADAT H., ADAMOWSKI J., BONNELL R., SHARIFI F., NAMDAR M., ALE-EBRAHIM S. 2011. Land use and land cover classification over a large area in Iran based on single date analysis of satellite imagery. Journal of Photogrammetry and Remote Sensing. Vol. 66 p. 608–619.
• SAADAT H., ADAMOWSKI J., TAYEFI V., NAMDAR M., SHARIFI F., ALE-EBRAHIM S. 2014. A new approach for regional scale interrill and rill erosion intensity mapping using brightness index assessments from medium resolution satellite images. Catena. Vol. 113 p. 306–313.
• SIERRA-SOLER A., ADAMOWSKI J., MALARD J., SAADAT H., QI Z. 2015. Assessing agricultural drought at a regional scale using LULC classification, SPI, and vegetation indices: Case study in a rainfed agro-ecosystem in Central Mexico. Journal of Geomatics, Natural Hazards and Risk. In press.
• SIVANPILLAI R., SRINIVASAN R., SMITH C. T., MESSINA M.G., WU X.B. 2007. Estimating regional forest cover in east Texas using Advanced Very High Resolution Radiometer (AVHRR) data. International Journal of Applied Earth Observation and Geoinformation. Vol. 9. Iss. 1 p. 41–49.
• STRAITH D., ADAMOWSKI J., REILLY K. 2014. Exploring the attributes, strategies and contextual knowledge of champions of change in the Canadian water sector. Canadian Water Resources Journal. Vol. 393 p. 255–269.
• TIWARI M., ADAMOWSKI J. 2013. Urban water demand forecasting and uncertainty assessment using ensemble wavelet-bootstrap-neural network models. Water Resources Research. Vol. 49. Iss. 10 p. 6486–6507.
• UNGANAI L., KOGAN F. 1998. Drought monitoring and corn yield estimation in Southern Africa from AVHRR data. Remote Sensing of Environment. Vol. 63. Iss. 3 p. 219–232.
• VANDERPOST C., RINGROSE S., MATHESON W., ARNTZEN J. 2011. Satellite based long-term assessment of rangeland condition in semi-arid areas: An example from Botswana. Journal of Arid Environments. 75. P. 383–389.
• WARDLOW B., EGBERT S., KASTENS J. 2007. Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains. Remote Sensing of Environment. Vol. 108. Iss. 3 p. 290–310.
• WILHELMI O., WILHITE D. 2002. Assessing vulnerability to agricultural drought: A Nebraska case study. Natural Hazards. Vol. 25. Iss. 1 p. 37–58.
• WILHITE D., SVOBODA M., HAYES M. 2007. Understanding the complex impacts of drought: A key to enhancing drought mitigation and preparedness. Water Resources Management. Vol. 21. Iss. 5 p. 763–774.
• YUREKLI K., KURUNC K. 2006. Simulating agricultural drought periods based on daily rainfall and crop water consumption. Journal of Arid Environments. Vol. 67. Iss. 4 p. 629–640.