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Open AccessArticle

Modeling of Forest Communities’ Spatial Structure at the Regional Level through Remote Sensing and Field Sampling: Constraints and Solutions

1
Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Leninsky Ave. 33, 119071 Moscow, Russia
2
Institute of Geography, Russian Academy of Sciences, Staromonetniy Pereulok 29, 119017 Moscow, Russia
*
Author to whom correspondence should be addressed.
Forests 2020, 11(10), 1088; https://doi.org/10.3390/f11101088
Received: 20 August 2020 / Revised: 2 October 2020 / Accepted: 9 October 2020 / Published: 13 October 2020
(This article belongs to the Special Issue Modeling of Species Distribution and Biodiversity in Forests)
This study tests modern approaches to spatial modeling of forest communities at the regional level based on a supervised classification. The study is conducted by the example of mapping the composition of forest communities in a large urbanized region (the Moscow Region, area 4.69 million hectares). A database of 1684 field descriptions is used as sample plots. As environmental variables, Landsat spectral reflectances, vegetation indices (5 images), digital elevation model and morphometric parameters of the relief, 54 layers in total, are used. Additionally, the Palsar-2 radar dataset is included. The main mapped units are formations and groups of associations identified on the basis of the ecological-phytocoenotic classification. Formations and groups of associations are similar in semantics and principles of allocation to units of forest typology. It is shown that the maximum entropy method has a wide range of applications, in particular, for mapping the typological diversity of forest cover. The method is used in combination with geographically structured spatial jack-knifing, spatial rarefication of occurrence data and independent testing of model feature classes and regularization parameters. Spatial rarefication is a critical technique when points are not evenly distributed in space. The resulting model of the spatial structure of forest cover is based on the integration of the best models of each thematic class of different types of forest cover into a single cartographic layer. It is shown that under conditions of uneven and sparse distribution of points, it is possible to provide an average point matching level of 0.45 for formations and 0.29 for association groups. Herewith, the spatial structure and the ratio of the formation’s composition correspond to the official data of the forest inventory. An attempt is made to identify and evaluate the distribution of more detailed syntaxonomic units: association groups. The necessary requirements for improving the quality of the forest cover model of the study area for 2 hierarchical typological units of forest cover are formulated. These include the additional sampling in order to equalize their spatial density, as well as to achieve equality of samples based on stratification according to the resulting map. View Full-Text
Keywords: spatial modeling; forest formation; association group; ecological-phytocoenotic classification; MaxEnt; SDMtoolbox; spatial modeling; Moscow Region; Landsat spatial modeling; forest formation; association group; ecological-phytocoenotic classification; MaxEnt; SDMtoolbox; spatial modeling; Moscow Region; Landsat
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Kotlov, I.; Chernenkova, T. Modeling of Forest Communities’ Spatial Structure at the Regional Level through Remote Sensing and Field Sampling: Constraints and Solutions. Forests 2020, 11, 1088.

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