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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, Vol. 15, No. 5, pp. 96-109

Mapping of forest site index classes in Primorskiy Krai based on satellite images and terrain characteristics

E.N. Sochilova 1 , N.V. Surkov 1, 2 , D.V. Ershov 1, 3 , V.A. Egorov 3 , S.S. Bartalev 3 , S.A. Bartalev 3 
1 Center for Forest Ecology and Productivity RAS, Moscow, Russia
2 Lomonosov Moscow State University , Moscow, Russia
3 Space Research Institute RAS, Moscow, Russia
Accepted: 23.08.2018
DOI: 10.21046/2070-7401-2018-15-5-96-109
The paper presents the results of the study of the possibility of forest site index mapping using their forest spectral reflectance and parameters of digital elevation model. Primorskiy kray selected as a test region. It is characterised by high diversity of tree species due to local climate and terrain specifics. The sixteen different features were generated. The spectral characteristics of the forests from the red near and middle infrared channels of cloud free winter and summer composite images of the Proba-V instrument were extracted. The parameters of the relief (height, slope and aspect) and relief-derived indices (solar radiation, deep-to-water index, relief curvatures) were used to describe the forest growing conditions. We used also ground data of forest stand parameters for classification training and validation of dominated forest spices and site indices maps. The quality of sampling data was controlled via the relationship between the average age and height of a stand for each specie. We selected 4465 etalons for such tree species as spruce, fir, cedar, larch, birches white and yellow, oak, aspen, birch stone, linden, ash and mountain pine (pinus pumila). Random Forest algorithm used for classification data, which allows to preliminary estimate the training sample and the informative features. We used all sixteen features for recognition of dominated forest species. Total accuracy of dominated species classification was 89.0%. The contribution of each feature in site index classification was determined using the confusion matrix of the recognised classes. As a result, twelve informative features for the site indices classification were selected. The classification accuracy of forest site indices was 84.8%.
The paper presents the results of the study of the possibility of forest sites index mapping using their forest spectral reflectance and parameters of digital elevation model. Primorskiy Krai was selected as a test region. It is characterised by high diversity of tree species due to local climate and terrain specifics. Sixteen different features were generated. The spectral characteristics of the forests from the red near and middle infrared channels of cloud free winter and summer composite images of the Proba-V instrument were extracted. The parameters of the relief (height, slope and aspect) and relief-derived indices (solar radiation, deep-to-water index, relief curvatures) were used to describe the forest growing conditions. We used also ground data of forest stand parameters for classification training and validation of dominated forest spices and site indices maps. The quality of sampling data was controlled via the relationship between the average age and height of a stand for each specie. We selected 4465 etalons for such tree species as spruce, fir, cedar, larch, birches white and yellow, oak, aspen, birch stone, linden, ash and mountain pine (pinus pumila). Random Forest algorithm was used for classification data, which allows to preliminary estimate the training sample and the informative features. We used all sixteen features for recognition of dominated forest species. Total accuracy of dominated species classification was 89.0 %. The contribution of each feature in site index classification was determined using the confusion matrix of the recognised classes. As a result, twelve informative features for the site indices classification were selected. The classification accuracy of forest site indices was 84.8 %. Recognition mistakes occur mostly between adjacent classes. Thus, combination of the spectral characteristics of forests and parameters of digital elevation model can be used with a satisfactory accuracy for classification of forest site indices in mountainous regions. The obtained experience and results is useful for hard-to-reach forest regions of Siberia and the Far East. However, this requires a large sample of ground or forest inventory data to provide a statistical basis for the preparation and training of the classifier.

Keywords: tree species, site index, remote sensing data, Proba-V, digital elevation model, classification, Random Forest
Full text

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