An Accuracy Analysis Comparison of Supervised Classification Methods for Mapping Land Cover Using Sentinel 2 Images in the Al-Hawizeh Marsh Area, Southern Iraq

• Autorzy: Pietro Belba , Spartak Kucaj , Jorgaq Thanas
• Języki publikacji: EN

Abstrakty

EN

The availability of Sentinel satellites for providing open data with optical and SAR imagery leads to better opportunities related to Earth surface mapping and monitoring. Recently, optical fusion with radar data has shown improvement in classification quality and the accuracy of information acquired. In this setting, the main objective of this research is to monitor the environmental impact of an open-pit mine on water, vegetation, and non-vegetation areas by exploring the single and combined use of Sentinel-1 and Sentinel-2 data. The data utilized in this paper were collected from the European Space Agency Copernicus program. After selecting the Selenica region, we explored the products in the Sentinel Application Platform. According to our data, Sentinel-2 misses the small water ponds but successfully identifies the river and open-pit areas. It mistakenly identifies urban structures and cloud areas as non-vegetated and does not identify non-vegetated areas which correspond to mining operation areas. Sentinel-1 identifies very small water ponds and delivers additional information in the cloudy areas, but misses a part of the river. Alongside the strong contribution in identifying the vegetation, it also roughly identifies the non-vegetation areas of mining operations. (original abstract)

Słowa kluczowe

EN

Water container; Satellite systems; Monitoring of environmental pollution

PL

Zbiorniki wodne; Systemy satelitarne; Monitoring zanieczyszczeń środowiska

• Czasopismo: Geomatics and Environmental Engineering
• Rocznik: 2022
• Numer: nr 16/3
• Strony: 5--25

Twórcy

autor

Pietro Belba

• Polytechnic University of Tirana, Albania

autor

Spartak Kucaj

• Polytechnic University of Tirana, Albania

autor

Jorgaq Thanas

• Polytechnic University of Tirana, Albania

Bibliografia

• Tempfli K., Kerle N., Huurneman G.C., Janssen L.L.F. (eds.): Principles of Remote Sensing: An Introductory Textbook. International Institute for Geo-Information Science and Earth Observation (ITC), Enschede, The Netherlands 2009. http://www.itc.nl/library/papers_2009/general/PrinciplesRemoteSensing.pdf [access: 14.12.2021].
• Pohl C., Van Genderen J.L.: Review article Multisensor image fusion in remote sensing: Concepts, methods and applications. International Journal of Remote Sensing, vol. 19(5), 1998, pp. 823-854. https://doi.org/10.1080/014311698215748.
• Ghosh S.M., Behera M.D.: Aboveground biomass estimation using multi-sensor data synergy and machine learning algorithms in a dense tropical forest. Applied Geography, vol. 96, 2018, pp. 29-40. https://doi.org/10.1016/ j.apgeog. 2018.05.011.
• Deus D.: Integration of ALOS PALSAR and Landsat Data for Land Cover and Forest Mapping in Northern Tanzania. Land, vol. 5(4), 2016, 43. https://doi.org/ 10.3390/land5040043.
• Akar Ö., Güngör O.: Integrating multiple texture methods and NDVI to the Random Forest classification algorithm to detect tea and hazelnut plantation areas in northeast Turkey. International Journal of Remote Sensing, vol. 36(2), 2015, pp. 442-464. https://doi.org/10.1080/01431161.2014.995276.
• Sarker M.L.R., Nichol J., Iz H.B., Ahmad B.B., Rahman A.A.: Forest Biomass Estimation Using Texture Measurements of High-Resolution Dual-Polarization C-Band SAR Data. IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 6, 2013, pp. 3371-3384. https://doi.org/10.1109/TGRS.2012.2219872.
• Ma M., Wang X., Veroustraete F., Dong L.: Change in area of Ebinur Lake during the 1998-2005 period. International Journal of Remote Sensing, vol. 28(4), 2007, pp. 5523-5533. https://doi.org/10.1080/01431160601009698.
• Jain S.K., Singh R.D., Jain M.K., Lohani A.K.: Delineation of flood-prone areas using remote sensing techniques. Water Resources Management, vol. 19(4), 2005, pp. 333-347. https://doi.org/10.1007/s11269-005-3281-5.
• Xu H.Q.: Modification of Normalised Difference Water Index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, vol. 27(14), 2006, pp. 3025-3033. https://doi.org/ 10.1080/01431160600589179.
• Dold B.: Evolution of acid mine drainage formation in sulphidic mine tailings. Minerals, vol. 4(3), 2014, pp. 621-641. https://doi.org/10.3390/min4030621.
• Pettorelli N., Vik J.O., Mysterud A., Gaillard J.-M., Tucker C.J., Stenseth N.C.: Using the satellite-derived Normalized Difference Vegetation Index (NDVI) to assess ecological effects of environmental change. Trends in Ecology & Evolution, vol. 20(9), 2005, pp. 503-510. https://doi.org/10.1016/j.tree.2005.05.011.
• Alcaraz-Segura D., Liras E., Tabik S., Paruelo J., Cabello J.: Evaluating the consistency of the 1982-1999 NDVI trends in the Iberian Peninsula across four time-series derived from the AVHRR sensor: LTDR, GIMMS, FASIR, and PALII. Sensors, vol. 10(2), 2010, pp. 1291-1314. https://doi.org/10.3390/s100201291.
• Bao N., Wu L., Liu S., Li N.: Scale parameter optimization through high-resolution imagery to support mine rehabilitated vegetation classification. Ecological Engineering, vol. 97, 2016, pp. 130-137. https://doi.org/10.1016/J.ECOLENG.2016.06.117.
• Cai X.H., He B.H., Li X.G., Wang L.: An Investigation of Plants and Vegetation in Some Mine Slag Disposal Yards. Journal of Southwest University (Natural Science), vol. 32(7), 2010, pp. 101-106.
• Wanat N., Joussein E., Soubrand M., Lenain J.-F.: Arsenic (As), antimony (Sb), and lead (Pb) availability from Au-mine Technosols: A case study of transfer to natural vegetation cover in temperate climates. Environmental Geochemistry and Health, vol. 36(4), 2014, pp. 783-795. https://doi.org/10.1007/s10653-014-9596-5.
• Erinjery J.J., Singh M., Kent R.: Mapping and assessment of vegetation types in the tropical rainforests of the Western Ghats using multispectral Sentinel-2 and SAR Sentinel-1 satellite imagery. Remote Sensing of Environment, vol. 216, 2018, pp. 345-354. https://doi.org/10.1016/J.RSE.2018.07.006.
• Whyte A., Ferentinos K.P., Petropoulos G.P.: A new synergistic approach for monitoring wetlands using Sentinels-1 and 2 data with object-based machine learning algorithms. Environmental Modelling & Software, vol. 104, 2018, pp. 40-54. https://doi.org/10.1016/j.envsoft.2018.01.023.
• Clerici N., Valbuena Calderón C.A., Posada J.M.: Fusion of sentinel-1a and sentinel-2A data for land cover mapping: A case study in the lower Magdalena region, Colombia. Journal of Maps, vol. 13(2), 2017, pp. 718-726. https://doi.org/10.1080/17445647.2017.1372316.
• Haas J., Ban Y.: Sentinel-1A SAR and sentinel-2A MSI data fusion for urban ecosystem service mapping. Remote Sensing Applications: Society and Environment, vol. 8, 2017, pp. 41-53. https://doi.org/10.1016/j.rsase.2017.07.006.
• Corbane Ch., Faure J.-F., Baghdadi N., Villeneuve N., Petit M.: Rapid urban mapping using SAR/optical imagery synergy. Sensors, vol. 8(11), 2008, pp. 7125-7143. https://doi.org/10.3390/s8117125.
• Gamba P., Dell'Acqua F., Dasarathy B.V.: Urban remote sensing using multiple data sets: Past, present, and future. Information Fusion, vol. 6(4), 2005, pp. 319-326. https://doi.org/10.1016/j.inffus.2005.02.007.
• Madasa A., Orimoloye I.R., Ololade O.O.: Application of geospatial indices for mapping land cover/use change detection in a mining area. Journal of African Earth Sciences, vol. 175, 2021, 104108. https://doi.org/10.1016/ j.jafrearsci. 2021.104108.
• Ghasemian Sorboni N., Pahlavani P., Bigdeli B.: Vegetation mapping of sentinel-1 and sentinel-2 satellite images using convolutional neural network and random forest with the aid of dual polarized and optical vegetation indexes. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-4/W18, 2019, pp. 435-440. https://doi.org/ 10.5194/isprs-archives-XLII-4-W18-435-2019.
• Ghimire P., Lei D., Juan N.: Effect of image fusion on vegetation index quality - A comparative study from Gaofen-1, Gaofen-2, Gaofen-4, Landsat-8 OLI and MODIS imagery. Remote Sensing, vol. 12(10), 2020, 1550. https://doi.org/10.3390/rs12101550.
• Kotaridis I., Lazaridou M.: Delineation of Open-Pit Mining Boundaries on Multispectral Imagery. [in:] Hammond A., Keleher P. (eds.), Remote Sensing, IntechOpen, London 2020. https://doi.org/ 10.5772/intechopen.94120.
• Frantz D., Schug F., Okujeni A., Navacchi C., Wagner W., van der Linden S., Hostert P.: National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series. Remote Sensing of Environment, vol. 252, 2021, 112128. https://doi.org/10.1016/j.rse.2020.112128.
• European Space Agency (ESA): S2 MPC Level 2A Data, Quality report. https://sentinel.esa.int/documents/247904/685211/Sentinel-2-L2A-Data-Quality-Report [access: 14.12.2021].
• Stendardi L., Karlsen S.R., Niedrist G., Gerdol R., Zebisch M., Rossi M., Notarnicola C.: Exploiting time series of Sentinel-1 and Sentinel-2 imagery to detect meadow phenology in mountain regions. Remote Sensing, vol. 11(5), 2019, 524. https://doi.org/10.3390/rs11050542.
• Selenizza® Natural Additive Asphaltite. https://www.selenizza.eu/selenizza.php [access: 14.12.2021].
• European Space Agency (ESA): Sentinel-2 User Handbook. https://Sentinels.copernicus.eu/web/Sentinel/user-guides/document-library/-/asset_publisher/xlslt4309D5h/content/Sentinel-2-user-handbook [access: 14.12.2021].
• European Space Agency (ESA): Copernicus Open Access Hub. https://scihub.copernicus.eu/ [access: 14.12.2021].
• European Space Agency (ESA): SNAP-Science Toolbox Exploitation Platform. http://step.esa.int/main/toolboxes/snap/ [access: 14.12.2021].
• European Space Agency (ESA): Level 1 Preprocessing. https://sentinel.esa.int/ja/web/sentinel/level-1-pre-processing-algorithms [access: 14.12.2021].
• Rouse J.W., Haas R.H., Schell J.A., Deering D.W.: Monitoring vegetation systems in the Great Plains with ERTS. [in:] Third Earth Resources Technology Satellite-1 Symp., December 10-15 1974, Greenbelt, MD, paper A20, NASA, Washington 1974, pp. 301-317.
• Neigh C.S.R., Tucker C.J., Townshend J.R.G.: North American vegetation dynamics observed with multi-resolution satellite data. Remote Sensing of Environment, vol. 112(4), 2008, pp. 1749-1772. https://doi.org/10.1016/ j.rse.2007.08.018.
• Dong Y., Milne A.K., Forster B.C.: A review of SAR speckle filters: texture restoration and preservation. [in:] IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120), vol. 2, IEEE, 2000, pp. 633-635. https://doi.org/10.1109/IGARSS.2000.861654.
• Touzi R.: A review of speckle filtering in the context of estimation theory. IEEE Transactions on Geoscience and Remote Sensing, vol. 40(11), 2002, pp. 2392-2404. https://doi.org/10.1109/TGRS.2002.803727.
• Lee J.S., Jurkevich L., Dewaele P., Wambacq P., Oosterlinck A.: Speckle filtering of synthetic aperture radar images: A review. Remote Sensing Reviews, vol. 8(4), 1994, pp. 313-340. https://doi.org/10.1080/02757259409532206.
• Fan X., Liu Y., Wu G., Zhao X.: Compositing the Minimum NDVI for Daily Water Surface Mapping. Remote Sensing, vol. 12(4), 2020, 700. https://doi.org/ 10.3390/rs12040700.
• Chen Q., Zhang Y., Ekroos A., Hallikainen M.: The role of remote sensing technology in the EU water framework directive (WFD). Environmental Science & Policy, vol. 7, 2004, pp. 267-276. https://doi.org/10.1016/j.envsci.2004.05.002.
• Nicia P., Bejger R., Sterzyńska M., Zadrożny P., Parzych P., Bieda A., Kwartnik-Pruc A.: Recovery in soil cover and vegetation structure after ancient landslide in mountain fens under Caltho-Alnetum community and response of soil microarthropods (Hexapoda: Collembola) to natural restoration process. Journal of Soils and Sediments, vol. 20, 2020, pp. 714-722. https://doi.org/10.1007/s11368-019-02434-z.
• Manjusree P., Prasanna Kumar L., Bhatt C.M., Rao G.S., Bhanumurthy V.: Optimization of threshold ranges for rapid flood inundation mapping by evaluating backscatter profiles of high incidence angle SAR images. International Journal of Disaster Risk Science, vol. 3, 2012, pp. 113-122. https://doi.org/10.1007/s13753-012-0011-5.
• Ovakoglou G., Cherif I., Alexandridis T.K., Pantazi X.-E., Tamouridou A.-A., Moshou D., Tseni X., Raptis I., Kalaitzopoulou S., Mourelatos S.: Automatic detection of surface-water bodies from Sentinel-1 images for effective mosquito larvae control. Journal of Applied Remote Sensing, vol. 15(1), 2021, 014507. https://doi.org/10.1117/1.JRS.15.014507.
• Dabrowska-Zielinska K., Musial J., Malinska A., Budzynska M., Gurdak R., Kiryla W., Bartold M., Grzybowski P.: Soil Moisture in the Biebrza Wetlands Retrieved from Sentinel-1 Imagery. Remote Sensing, vol. 10(12), 2018, 1979. https://doi.org/10.3390/rs10121979.
• Kasischke E.S., Melack J.M., Dobson M.C.: The use of imaging radars for ecological applications - A review. Remote Sensing of Environment, vol. 59(2), 1997, pp. 141-156. https://doi.org/10.1016/S0034-4257(96)00148-4.
• Bourgeau-Chavez L.L., Kasischke E.S., Brunzell S.M., Mudd J.P., Smith K.B., Frick A.L.: Analysis of space-borne SAR data for wetland mapping in Virginia riparian ecosystems. International Journal of Remote Sensing, vol. 22(18), 2001, pp. 3665-3687. https://doi.org/10.1080/01431160010029174.

Identyfikatory

DOI

10.7494/geom.2022.16.3.5