Określanie lesistości Polesia Ukraińskiego na podstawie wyników klasyfikacji sezonowych obrazów kompozytowych Landsat 8 OLI

• Autorzy: Lakyda P. , Myroniuk V. , Bilous A. , Boiko S.

Warianty tytułu

EN

Estimation of forest cover in Ukrainian Polissia using classification of seasonal composite Landsat 8 OLI images

• Języki publikacji: PL

Abstrakty

EN

Training dataset for modelling of forest cover was created after classification of multispectral satellite imagery IKONOS−2 with spatial resolution 3.2 m (acquisition date – 12.08.2011). As a result, we created binary forest cover map with 2 categories: ‘forest’ and ‘not−forest’. That allowed us to compute the tree canopy cover for each pixel of Landsat 8 OLI, using vector grid with cell size of 30×30 m. Classification model was developed using training dataset that included 17,000 observations, 10,000 of them represented results of IKONOS−2 classification. Aiming to avoid errors of agricultural lands inclusion into forest mask because of lack of data, additionally we collected about 7000 random observations with canopy cover 0% that had been evenly distributed within unforested area. Random Forest (RF) model we developed allowed us to create continuous map of forests within study area that represents in each pixel value of tree canopy closeness (0−100%). To convert it into a discrete map, we recoded all values less than 30% as ‘no data’ and values from 30 to 100% as 1. Forest mask for two selected administrative districts of Chernihiv region (NE Ukraine) was created after screening map from small pixel groups that covered area less than 0.5 ha. Obtained results were compared with Global Forest Change (GFC) map and proved that GFC data can be used for forest mapping with tree canopy closeness threshold 40%. On considerable areas of abandoned agricultural lands in the analysed regions of Ukraine, forest stands are formed by Scots pine, silver birch, black alder and aspen. Existence of such forests substantially increases (on 6−8%) the forested area of Gorodnya and Snovsk districts of Chernihiv region – comparing to official forest inventory data. However, such stands are not protected and have high risks to be severed by wildfires, illegal cuttings with aim to renew the agricultural production, by diseases, insects and other natural disturbances.

Słowa kluczowe

PL

lesnictwo; Ukraina; Polesie; lesistosc; teledetekcja; zdjecia satelitarne; satelita Landsat 8 OLI

• Wydawca: -
• Czasopismo: Sylwan
• Rocznik: 2019
• Tom: 163
• Numer: 09
• Opis fizyczny: s.754-764,tab.,rys.,bibliogr.

Twórcy

autor

Lakyda P.

• Katedra Taksacji i Urządzania Lasu, Narodowy Uniwersytet Nauk Przyrodniczych i Środowiskowych, Heroiv Oborony 15, 03041 Kijów, Ukraina

autor

Myroniuk V.

• Katedra Taksacji i Urządzania Lasu, Narodowy Uniwersytet Nauk Przyrodniczych i Środowiskowych, Heroiv Oborony 15, 03041 Kijów, Ukraina

autor

Bilous A.

• Katedra Taksacji i Urządzania Lasu, Narodowy Uniwersytet Nauk Przyrodniczych i Środowiskowych, Heroiv Oborony 15, 03041 Kijów, Ukraina

autor

Boiko S.

• Ośrodek Kultury Leśnej, ul.Działyńskich 2, 63-322 Gołuchów

Bibliografia

• Berberoglu S., Donmez C., Ozkan C., Sunar F. 2008. Percent tree cover mapping from Elista MERIS and MODIS data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 37 B8: 1115-1119.
• Bey A., Diaz A. S. P., Maniatis D., Marchi G., Mollicone D., Ricci S., Miceli G. 2016. Collect Earth: Land Use and Land Cover Assessment through Augmented Visual Interpretation. Remote Sensing 8 (10). DOI: https:// doi.org/10.3390/rs8100807.
• Bilous A., Myroniuk V., Holiaka D. 2017. Mapping growing stock volume and forest live biomass: a case study of the Polissya region of Ukraine. International Journal of Digital Earth 1.
• Breiman L. 2001. Random Forest. Machine Learning 45 (1): 5-32.
• Chavez P. S. 1988. An Improved Dark-Object Subtraction Technique for Atmospheric Scattering Correction of Multispectral Data. Remote Sensing of the Environment 24: 459-479.
• Cohen W. B. Goward S. 2004. Landsat’s role in ecological applications of Remote Sensing. Bioscience 54: 535-545.
• Coulston J. W., Moisen G. G., Wilson B. T., Finco M. V., Cohen W. B., Brewer C. K. 2012. Modeling Percent tree canopy cover: a pilot study. Photogrammetric Engineering & Remote Sensing 78 (7): 715-727.
• Hansen M. C., DeFries R. S., Townshend J. R. G., Carroll M., Dimiceli C., Sohlberg R. A. 2003. Global percent tree cover spatial resolution of 500 meters: first results of the MODIS Vegetation Continuous Fields algorithm. Earth Interactions 7: 1-15.
• Hansen M. C., Egorov A., Potapov P. V. 2014. Monitoring conterminous Unite States (CONUS) land cover change with Web-Enabled Landsat Data (WELD). Remote Sensing of Environment 140: 466-484.
• Hansen M. C., Egorov A., Roy D. P., Potapov P., Ju J. , Turubanova S., Kommareddy I., Loveland T. R. 2011. Continuous fields of land cover for the conterminous United States using Landsat data: first results from the Web-Enabled Landsat Data (WELD) project. Remote Sensing Letters 2: (4) 279-288. DOI: https://doi.org/ 10.1080/01431161.2010.519002.
• Hansen M. C., Potapov P. V., Moore R. 2013. High-resolution global maps of 21st century forest cover change. Science 342: 850-853.
• Hill R. A., Wilson A. K., George M., Hinsley S. A. 2010. Mapping tree species in temperate deciduous woodland using time-series multi-spectral data. Applied Vegetation Science 13 (1): 86-99.
• Hłotka D. W. 2013. Heodani Global Forest Change dla utocznennia lisystosti subbasejniw Riczky Desna. Naukowi praci UkrNDHMI 265: 34-39.
• Holben B. N. 1986. Characteristics of maximum-value composite images from temporal AVHRR data. International Journal of Remote Sensing 7 (11): 1417-1434.
• Lakyda P. I., Myroniuk W. W., Hilitucha D. W. 2014. Analiz ta interpretacja karty wysokoho prostorowoho rozriznennia lisowych ekosystem Polissia Ukrajiny. Zbałansowane pryrodokorystuwannia 4: 5-9.
• Loveland T. R. 2011. Continuous fields of land cover for the conterminous United States using Landsat data: first results from the Web-Enabled Landsat Data (WELD) project. Remote Sensing Letters 2 (4): 279-288.
• Łesiw M. J., Szczepaszczenko D. H., Szwydenko A. Z., Buń R. A. 2012. Pobudowa karty lisiw Ukrajiny za danymy hłobalnych cyfrowych kart zemelnoho pokrywu. Naukowyj Wisnyk NŁTU Ukrainy 22 (9): 24-30.
• Mironiuk W. W., Gieorgiian M. I. 2017. Zastosuwannia stratyfikowanoji wybirky dla rehionalnoji ocinky płoszczi lisiw Ukrajiny za danymy hłobalnych kart lisowoho pokrywu. Zbałansowane pryrodokorystuwannia 1: 69-74.
• Mykłusz S. I., Czaskowśkyj O. H., Hawryluk S. A. 2013. Deszyfruwannia riznopłanowych kosmicznych znimkiw dla ociniuwannia hrup porid. Naukowi Praci Lisiwnyczoji akademiji nauk Ukrajiny: zbirnyk naukowych prać 11: 144-150.
• Olofsson P., Foody G. M., Herold M., Stehman S. V., Woodcock C. E., Wulder M. A. 2014. Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment 148: 42-57. DOI: https://doi.org/10.1016/j.rse.2014.02.015.
• Popkow M., Kożuszko J., Sawuszczik N. 2009. Lesorazwiedienije w Ukrainie: fakty i iłłuzii.
• Roy D. P., Ju J., Kline K., Scaramuzza P. L., Kovalskyy V., Hansen M., Loveland T. R., Vermote E., Zhang C. 2010. Web-Enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States. Remote Sensing of Environment 114: 35-49.
• Sannier C., McRoberts R. E., Fichet L.-V. 2016. Suitability of Global Forest Change data to report forest cover estimates at national level in Gabon. Remote Sensing of Environment 173: 326-338. https://doi.org/10.1016/j.rse. 2015.10.032.
• Song X.-P., Huang Ch., Sexton L. O., Channan S., Townshend J. R. 2014. Annual detection of forest cover loss using time series satellite measurements of percent tree cover. Remote Sensing 6 (9): 8878-8903.
• Ustawa z dnia 21 grudnia 2010 Nr 2818-VI Strategia Państwowej Polityki Środowiskowej Ukrainy. 2010.
• Woodcock C. E., Allen R., Anderson M. 2008. Free access to Landsat imagery. Science 320: 10-11.
• Zald H. S. J., Wulder M. A., White J. C. 2016. Integrating Landsat pixel composites and change metrics with lidar plots to predictively map forest structure and aboveground biomass in Saskatchewan, Canada. Remote Sensing of Environment 176: 188-201.

Identyfikatory

DOI

10.26202/sylwan.2018158