Forest Community Mapping Using Hyperspectral (CHRIS/PROBA) and Sentinel-2 Multispectral Images

• Autorzy: Ewa Głowienka , Nicole Zembol
• Języki publikacji: EN

Abstrakty

EN

The possibility to use hyperspectral images (CHRIS/PROBA) and multispec-tral images (Sentinel-2) in the classification of forest communities is assessed in this article. The pre-processing of CHRIS/PROBA image included: noise re-duction, radiometric correction, atmospheric correction, geometric correction. Due to MNF transformation the number of the hyperspectral image channels was reduced (to 10 channels) and smiling errors were removed. Sentinel-2 im-age (level 2A) did not require pre-processing. Three tree genera occurring in the study area were selected for the classification: pine (Pinus), alder (Alnus) and birch (Betula). Image classification was carried out with three methods: SAM (Spectral Angle Mapper), MTMF (Mixture Tuned Matched Filtering), SVM (Support Vector Machine). For the CHRIS/PROBA image, the algo-rithm SVM turned out to be the best. Its overall accuracy (OA) was 72%. The poorest result (OA = 52%) was for the MTMF classifier. In the classification of Sentinel-2 multispectral image the best result was for the MTMF method: OA = 82%, kappa coefficient 0.7. For other methods, the overall accuracy ex-ceeded 65%. Among the classified genera, the highest producer's accuracy was obtained for pine (PA = 96%), and the broad-leaf genera: alder and birch had PA ranging from 42% to 85%.(original abstract)

Słowa kluczowe

EN

Image processing; Image analysis; Machine learning

PL

Przetwarzanie obrazu; Analiza obrazu; Uczenie maszynowe

• Czasopismo: Geomatics and Environmental Engineering
• Rocznik: 2022
• Numer: nr 16/4
• Strony: 103--118

Twórcy

autor

Ewa Głowienka

• AGH University of Science and Technology Kraków, Poland

autor

Nicole Zembol

• SATIM, Krakow, Poland

Bibliografia

• Astola H., Häme T., Sirro L., Molinier M., Kilpi J.: Comparison of Sentinel-2 and Landsat 8 imagery for forest variable prediction in boreal region. Remote Sensing of Environment, vol. 223, 2019, pp. 257-273. https://doi.org/10.1016/j.rse.2019.01.019.
• Banskota A., Kayastha N., Falkowski M., Wulder M., Froese R., White J.: Forest monitoring using Landsat time series data: A review. Canadian Journal of Remote Sensing, vol. 40, no. 5, 2014, pp. 362-384. https://doi.org/10.1080/07038992.2014.987376.
• Barnsley M., Settle J., Cutter M., Lobb D., Teston F.: The PROBA/CHRIS mission: A low-cost smallsat for hyperspectral multiangle observations of the Earth surface and atmosphere. IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 7, 2004, pp. 1512-1520. https://doi.org/10.1109/TGRS.2004.827260.
• Bartkowiak P., Osińska-Skotak K.: Analiza możliwości wykorzystania obrazów hiperspektralnych HySpex do inwentaryzacji drzewostanów leśnych Puszczy Białowieskiej. Teledetekcja Środowiska, t. 55, 2016, pp. 24-44.
• Boardman J.: Leveraging the High Dimensionality of AVIRIS Data for Improved Sub-Pixel Target Unmixing and Rejection of False Positives: Mixture Tuned Match Filtering. [in:] Summaries of the Seventh JPL Airobrne Earth Science Workshop, vol. 1, 1998, pp. 53.
• Chrysafis I., Mallinis G., Siachalou S., Patias P.: Assessing the relationships between growing stock volume and sentinel-2 imagery in a mediterranean forest ecosystem. Remote Sensing Letters, vol. 8, 2017, pp. 508-517. https://doi.org/10.1080/2150704X.2017.1295479.
• Close O., Petit S., Beaumont B., Hallot E.: Evaluating the Potentiality of Sentinel-2 for Change Detection Analysis Associated to LULUCF in Wallonia, Belgium. Land, vol. 10, 2021, 55. https://doi.org/10.3390/land10010055.
• Dadon A., Mandelmilch M., Ben-Dor E., Sheffer E.: Sequential PCA-based Classification of Mediterranean Forest Plants using Airborne Hyperspectral Remote Sensing. Remote Sensing, vol. 11, 2019, 2800. https://doi.org/10.3390/rs11232800.
• Dalponte M., Ørka H.O., Gobakken T., Gianelle D., Næsset E.: Tree Species Classification in Boreal Forests with Hyperspectral Data. IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 5, 2013, pp. 2632-2645. https://doi.org/10.1109/TGRS.2012.2216272.
• Duca R., Del Frate F.: Hyperspectral and multiangle CHRIS-PROBA images for the generation of land cover maps. IEEE Transactions on Geoscience and Remote Sensing, vol. 46, no. 10, 2008, pp. 2857-2866. https://doi.org/10.1109/TGRS.2008.2000741.
• Encyklopedia drzew. http://encyklopediadrzew.pl [access: 30.07.2021].
• ESA. https://earth.esa.int/eogateway/missions [access: 10.04.2021].
• Głowienka-Mikrut E.: Analiza porównawcza metod przetwarzania danych hiperspektralnych o zróżnicowanej dokładności. AGH, Kraków 2014 [Ph.D. thesis].
• Goodenough D., Bhogal A., Dyk A., Hollinger A., Mah Z., Niemann O., Pearlman J. et al.: Monitoring forests with Hyperion and ALI. [in:] IEEE International Geoscience and Remote Sensing Symposium, vol. 2, IEEE, Piscataway 2002, pp. 882-885. https://doi.org/10.1109/IGARSS.2002.1025717.
• Gómez-Chova L., Alonso L., Guanter L., Camps-Valls G., Calpe J., Moreno J.: Correction of systematic spatial noise in push-broom hyperspectral sensors: application to CHRIS/PROBA images. Applied Optics, vol. 47, no. 28, 2008, pp. F46-F60. https://doi.org/10.1364/AO.47.000F46.
• Green A., Berman M., Switzer P., Craig M.: A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Transactions on Geoscience and Remote Sensing, vol. 26, no. 1, 1988, pp. 65-74. https://doi.org/10.1109/36.3001.
• GUGIK. http://geoportal.gov.pl [access: 15.05.2021].
• Guanter L., Alonso L., Moreno J.: A method for the surface reflectance retrieval from PROBA/CHRIS data over land: Application to ESA SPARC campaigns. IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 12, 2005, pp. 2908-2917. https://doi.org/10.1109/TGRS.2005.857915.
• Gupta N., Milton E.J.: Quality Assessment of CHRIS/PROBA Image and Recommendation for Land Cover Classification. [in:] Proceedings of the Remote Sensing and Photogrammetry Society Annual Conference 2009, pp. 118-126.
• Hejmanowska B., Kramarczyk P., Głowienka E., Mikrut S.: Reliable Crops Classification Using Limited Number of Sentinel-2 and Sentinel-1 Images. Remote Sensing, vol. 13, no. 16, 2021, 3176. https://doi.org/10.3390/rs13163176.
• Hycza T., Stereńczak K., Bałazy R.: Potential use of hyperspectral data to classify forest tree species. New Zeland Journal of Forestry Science, vol. 48, 2018, pp. 1-13. https://doi.org/10.1186/s40490-018-0123-9.
• Immitzer M., Vuolo F., Atzberger C.: First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe. Remote Sensing, vol. 8(3), 2016, 166. https://doi.org/10.3390/rs8030166.
• Janssen F., van der Wel F.: Accuracy assessment of satellite derived land cover data: A review. Photogrammetric Engineering and Remote Sensing, vol. 60, 1994, pp. 419-426.
• Kayitakire F., Defourny P.: Forest type discrimination using multi-angle hyperspectral data. [in:] Proceedings 2nd CHRIS/PROBA Workshop, ESA SP, Frascati, Italy, 2004, 2004, pp. 72-84. http://hdl.handle.net/2078.1/76194.
• Knauer U., Von Rekowski C., Stecklina M., Krokotsch T., Pham Minh T., Hauffe V., Kilias D. et al.: Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers. Remote Sensing, vol. 11, 2019, 2788. https://doi.org/10.3390/rs11232788.
• Kokaly R., Despain D., Clark R., Livo K.: Mapping vegetation in Yellowstone National Park using spectral feature analysis of AVIRIS data. Remote Sensing of Environment, vol. 84, no. 3, 2003, pp. 437-456. https://doi.org/10.1016/S0034-4257(02)00133-5.
• Kruse F., Lefkoff A., Dietz J.: Expert System-Based Mineral Mapping in northern Death Valley, California/Nevada using the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Remote Sensing of Environment, special issue on AVIRIS, vol. 44, 1993, pp. 309-336. https://doi.org/10.1016/0034-4257(93)90024-R.
• L3HARRIS. https://www.l3harrisgeospatial.com/docs [access: 11.04.2021].
• Lary D., Alavi A., Gandomi A., Walker A.: Machine learning in geosciences and remote sensing. Geoscience Frontiers, vol. 7, no. 1, 2016, pp. 3-10. https://doi.org/10.1016/j.gsf.2015.07.003.
• Laurin G., Puletti N., Hawthorne W., Liesenberg V., Corona P., Papale D., Chen Q., Valentini R.: Discrimination of tropical forest types, dominant species, and mapping of functional guilds by hyperspectral and simulated multispectral Sentinel-2 data. Remote Sensing of Environment, vol. 176, 2016, pp. 163-176. https://doi.org/10.1016/j.rse.2016.01.017.
• Leckie D., Tinis S., Nelson T., Burnett C., Gougeon F., Cloney E., Paradine D.: Issues in species classification of trees in old growth conifer stands. Canadian Journal of Remote Sensing, vol. 31, 2005, pp. 175-190. https://doi.org/10.5589/m05-004.
• Markiet V., Mõttus M.: Estimation of boreal forest floor reflectance from airborne hyperspectral data of coniferous forests. Remote Sensing of Environment, vol. 249, 2020, 112018. https://doi.org/10.1016/j.rse.2020.112018.
• Modzelewska A., Kamińska A., Fassnacht F., Stereńczak K.: Multitemporal hyperspectral tree species classification in the Białowieża Forest World Heritage site, Forestry. An International Journal of Forest Research, vol. 94, no. 3, 2021, pp. 464-476. https://doi.org/10.1093/forestry/cpaa048.
• Nezami S., Khoramshahi E., Nevalainen O., Pölönen I., Honkavaara E.: Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks. Remote Sensing, vol. 12, 2020, 1070. https://doi.org/10.3390/rs12071070.
• Phiri D., Simwanda M., Salekin S., Nyirenda V.R., Murayama Y., Ranagalage M.: Sentinel-2 Data for Land Cover/Use Mapping: A Review. Remote Sensing, vol. 12, no. 14, 2020, 2291. https://doi.org/10.3390/rs12142291.
• Raczko E., Zagajewski B.: Tree Species Classification of the UNESCO Man and the Biosphere Karkonoski National Park (Poland) Using Artificial Neural Networks and APEX Hyperspectral Images. Remote Sensing, vol. 10, 2018, 1111. https://doi.org/10.3390/rs10071111.
• Sabat-Tomala A., Raczko E., Zagajewski B.: Comparison of Support Vector Machine and Random Forest Algorithms for Invasive and Expansive Species Classification Using Airborne Hyperspectral Data. Remote Sensing, vol. 12, 2020, 516. https://doi.org/10.3390/rs12030516.
• Shoot C., Andersen H., Moskal L., Babcock C., Cook B., Morton D.: Classifying Forest Type in the National Forest Inventory Context with Airborne Hyperspectral and Lidar Data. Remote Sensing, vol. 13, 2021, 1863. https://doi.org/10.3390/rs13101863.
• Skoupý O., Zejdová L., Hanuš J.: The use of hyperspectral remote sensing for mapping the age composition of forest stands. Journal of Forest Science, vol. 58, 2012, pp. 287-297. https://doi.org/10.17221/86/2011-JFS.
• Stagakis S., Vanikiotis T., Sykiotia O.: Estimating forest species abundance through linear unmixing of CHRIS/PROBA imagery. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 119, 2016, pp. 79-89. https://doi.org/10.1016/j.isprsjprs.2016.05.013.
• Stoffels J., Mader S., Hill J., Werner W., Ontrup G.: Satellite-based stand-wise forest cover type mapping using a spatially adaptive classification approach. European Journal of Forest Research, vol. 131, 2012, pp. 1071-1089. https://doi.org/10.1007/s10342-011-0577-2.
• Trier Ø., Salberg A., Kermit M., Rudjord Ø., Gobakken T., Næsset E., Aarsten D.: Tree species classification in Norway from airborne hyperspectral and airborne laser scanning data. European Journal of Remote Sensing, vol. 51, 2018, pp. 336-351. https://doi.org/10.1080/22797254.2018.1434424.
• Verrelst J., Schaepman M.E., Koetz B., Kneubühler M.: Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data. Remote Sensing of Environment, vol. 112, no. 5, 2008, pp. 2341-2353. https://doi.org/10.1016/j.rse.2007.11.001.
• Wan L., Lin Y., Zhang H., Wang F., Liu M., Lin H.: GF-5 Hyperspectral Data for Species Mapping of Mangrove in Mai Po, Hong Kong. Remote Sensing, vol. 12, 2020, 656. https://doi.org/10.3390/rs12040656.

Identyfikatory

DOI

10.7494/geom.2022.16.4.103