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2020 | 164 | 02 |

Tytuł artykułu

Program Copernicus źródłem informacji o dominującym typie drzewostanu w Polsce – ocena dokładności krajowej warstwy wysokorozdzielczej

Treść / Zawartość

Warianty tytułu

EN
Copernicus Program as a source of information on the dominant leaf type in Poland – assessment of the accuracy of the national high resolution layer

Języki publikacji

PL

Abstrakty

EN
Information on the spatial distribution and variability of forests is important in monitoring of forest resources, biodiversity assessment, threat prevention, estimation of carbon content and forest management. The Pan−European High Resolution Layers (HRLs) produced as part of the European Earth Monitoring Programme – Copernicus provide detailed information on the land cover characteristics in Europe. The HRLs are produced using satellite imagery based on an interactive rule−based classification. There are the following HRL themes: imperviousness, forest, water and wetness and grasslands. The HRLs are available for the reference year 2012 and 2015, at the spatial resolution of 20 m. The forest related HRL consists of tree cover density, dominant tree type and forest type products. In this study, we performed a) the qualitative and quantitative analysis of the accuracy of the dominant leaf type (DLT) layer for the 2015 year at the national scale, and b) detailed analysis of the data quality at the forest stand level over the selected forest districts. The DLT layer was compared with the national orthophotos. The detailed analysis was carried out using Sentinel−2 images and forest inventory data obtained from the Forest Data Bank over the selected forest districts. The accuracy analysis of the national DLT layer revealed the high omission error equal to 18.8%, and lower commission error of 5.4%. The omission error is mostly related to the omitted orchards and young forest plantations, which are included in the DLT layer. The commission error of the broadleaved forest is related mostly to the small patches of coniferous forest that was misclassified as broadleaved. In general, commission errors were identified more frequently in broadleaved forest than in the coniferous forest. In many locations the patches of coniferous forest were misclassified as broadleaved forest. In general, the area of the broadleaved forest is overestimated.

Wydawca

-

Czasopismo

Rocznik

Tom

164

Numer

02

Opis fizyczny

s.151-160,rys.,bibliogr.

Twórcy

autor
  • Centrum Teledetekcji, Instytut Geodezji i Kartografii, ul.Modzelewskiego 27, 02-679 Warszawa
  • Centrum Teledetekcji, Instytut Geodezji i Kartografii, ul.Modzelewskiego 27, 02-679 Warszawa
autor
  • Centrum Teledetekcji, Instytut Geodezji i Kartografii, ul.Modzelewskiego 27, 02-679 Warszawa

Bibliografia

  • Barbati A., Marchetti M., Chirici G., Corona P. 2014. European forest types and forest Europe SFM indicators: tools for monitoring progress on forest biodiversity conservation. Forest Ecology and Management 321: 145-157.
  • Bengtsson J., Nilson S., Franc A., Menozzi P. 2000. Biodiversity, disturbances, ecosystem function and management of European forests. Forest Ecology and Management 132: 39-50.
  • Fassnacht F. E., Latifi H., Stereńczak K., Modzelewska A., Lefsky M., Waser L. T., Straub C., Ghosh A. 2016. Review of studies on tree species classification from remotely sensed data. Remote Sensing of Environment 186: 64-87.
  • Forest Resources Assessment. 2012. FAO Working Paper 180.
  • Hofmann H., Wickham H., Kafadar K. 2017. Letter-Value Plots: Boxplots for Large Data. Journal of Computational and Graphical Statistics 26 (3).
  • Hościło A., Lewandowska A. 2019. Mapping Forest Type and Tree Species on a Regional Scale Using Multi-Temporal Sentinel-2 Data. Remote Sensing 11 (8): 929.
  • Hościło A., Mirończuk A. 2016. Europejski Program Obserwacji Ziemi Copernicus źródłem danych do ocen oddziaływania na środowisko. W: Nowak M. [red]. GIS i dane przestrzenne w ocenach oddziaływania na środowisko – podręcznik dobrych praktyk. Wydawnictwo Naukowe UAM 2016. 211-221.
  • Instrukcja wykonywania wielkoobszarowej inwentaryzacji stanu lasu. 2014. Instytut Badawczy Leśnictwa, Sękocin Stary.
  • Langanke T. 2018. Guidelines for verification of High Resolution Layers produced by the CLMS (Copernicus Land Monitoring Service) as part of the 2015 reference year production. Version 1.4. European Environment Agency.
  • Laurin G. V., Puletti N., Hawthorne W, Liesenberg V., Corona P. Papale D., Chen Q., Valentini R. 2016. Discrimination of tropical forest types, dominant species, and mapping of functional guilds by hyperspectral and simulated multi-spectral Sentinel-2 data. Remote Sensing of Environment 176: 163-176.
  • Naudts K., Chen Y., McGrath M. J., Ryder J., Valade A., Otto J., Luyssaert S. 2016. Europe’s forest management did not mitigate climate warming. Science 351: 597-600.
  • Puletti N., Chianucci F., Castaldi C. 2017. Use of Sentinel-2 for forest classification in Mediterranean environments. Annals of Silvicultural Research 42: 32-38.
  • Report on the Determination of Poland’s Assigned Amount under the Kyoto Protocol to the United Nations Framework Convention on Climate Change. 2006. Republic of Poland.
  • Sentinel-2 User Handbook. 2015. ESA Standard Document 1 (2).
  • Vihervaara P., Auvinen A. P., Mononen L., Törmä M., Ahlroth P., Anttila S., Böttcher K., Forsius M., Heino J., Heliölä J. 2017. How essential biodiversity variables and remote sensing can help national biodiversity monitoring. Global Ecology and Conservation 10: 43-59.
  • Waser L. T., Fischer C., Wang Z., Ginler C. 2015. Wall-to-wall forest mapping based on digital surface models from image-based point clouds and a NFI forest definition. Forests 6: 4510-4528.
  • Wessel M., Brandmeire M., Tiede D. 2018. Evaluation of Different Machine Learning Algorithms for Scalable Classification of Tree Types and Tree Species Based on Sentinel-2 Data. Remote Sensing 10 (9) 1419: 1-21.
  • Wieloobszarowa Inwentaryzacja Stanu Lasu w Polsce, wyniki za okres 2013-2017. 2018. BULiGL, Sękocin Stary.

Typ dokumentu

Bibliografia

Identyfikatory

Identyfikator YADDA

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