Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, Vol. 16, No. 4, pp. 102-110
Forest change detection based on sub-pixel estimation of crown cover density using bitemporal satellite data
T.S. Khovratovich
1 , S.А. Bartalev
1 , A.V. Kashnitskii
1 1 Space Research Institute RAS, Moscow, Russia
Accepted: 17.05.2019
DOI: 10.21046/2070-7401-2019-16-4-102-110
A method for forest change detection based on optical remote sensing data is proposed. The method provides the simplicity of the obtained result interpretation, the applicability in different areas and seasons due to its adaptability to the phenological changes. The method is based on the use of bitemporal satellite images and sub-pixel estimation of the cover density of tree canopy, performed by using linear spectral mixture analysis for estimation of forest area and treeless areas proportion in each pixel. The paper describes the main steps of the change detection method and the influence of input settings of the algorithm on the results. It is concluded that the use of an automatic procedure for determining the reference spectral reflectance values of a forest and treeless areas gives more stable results than their assessment by experts’ settings. Stratification of the territory by forest cover levels increases the area of the detected changes by 10–13 %. According to the experiments, the accuracy of change detection using summer satellite data is the highest, but with possible presence of gaps up to 35–55 % of the area. Using the satellite data of winter time period, the method ensures maximum completeness of detecting forest changes with a sufficiently low commission errors.
Keywords: remote sensing, forest cover, projective cover, closure, detection of changes, cutting, spectral mixture separation, sub-pixel analysis
Full textReferences:
- Bartalev S. A., Kuryatnikova T. S., Stibig H. J., Metody ispol’zovaniya vremennykh serii sputnikovykh izobrazhenii vysokogo prostranstvennogo razresheniya dlya otsenki masshtabov i dinamiki vyrubok taezhnykh lesov (Methods for the analysis of time-series of high-resolution satellite images for the assessment of logging in the taiga), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2005, Issue 2, Vol. 2, pp. 217–227.
- Bartalev S. A., Khovratovich T. S., Elsakov V. V., Ispol’zovanie sputnikovykh izobrazhenii dlya otsenki poter’ ugleroda lesnymi ekosistemami v rezul’tate vyrubok (Estimation of carbon losses in a forest ecosystem caused by logging with the use of satellite data), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2009, Issue 6, Vol. 2, pp. 343–351.
- Bartalev S. A., Ershov D. V., Loupian E. A., Tolpin V. A., Vozmozhnosti ispol’zovaniya sputnikovogo servisa VEGA dlya resheniya razlichnykh zadach monitoringa nazemnykh ekosistem (Possibilities of satellite service VEGA using for different tasks of land ecosystems monitoring), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2012, Vol. 9, No. 1, pp. 49–56.
- Loupian E. A., Proshin A. A., Burtsev M. A., Balashov I. V., Bartalev S. A., Efremov V. Yu., Kashnitskiy A. V., Mazurov A. A., Matveev A. M., Sudneva O. A., Sychugov I. G., Tolpin V. A., Uvarov I. A., Tsentr kollektivnogo pol’zovaniya sistemami arkhivatsii, obrabotki i analiza sputnikovykh dannykh IKI RAN dlya resheniya zadach izucheniya i monitoringa okruzhayushchei sredy (IKI center for collective use of satellite data archiving, processing and analysis systems aimed at solving the problems of environmental study and monitoring), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2015, Vol. 12, No. 5, pp. 263–284.
- Milne A. K., Change direction analysis using Landsat imagery: a review of methodology, Proc. IGARSS’88, 1988, pp. 541–544.
- Shimabukuro Y. E., Smith J. A., The least-squares mixing models to generate fraction images derived from remote sensing multi spectral data, IEEE Transactions on Geoscience and Remote Sensing, 1991, Vol. 29, Issue 1, pp. 16–20.
- Zhe Z., Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications, ISPRS J. Photogrammetry and Remote Sensing, 2017, Vol. 130, pp. 370–384.