Definition of factors of spatial variation in vegetation using RSD, DEM and field data by example of the central part of Murmansk Region

Puzachenko, M. Yu.; Chernen’kova, T. V.

doi:10.21046/2070-7401-2016-13-5-167-191

Cited by 6 publications

(6 citation statements)

References 7 publications

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“…A wide range of statistical models exist to model the spatial structure of natural features and phenomena: Resource Selection Function, Generalized Linear Models, Artificial Neural Networks, Maximum Entropy and Classification and Regression Trees [21]. Formerly for the Moscow Region, it was shown that Linear Discriminant Analysis demonstrates satisfactory results for modeling the spatial structure of forests based on field data and Landsat 8 spectral reflectance supplemented with digital elevation model and their derivative parameters [22]. However, linear functions of multispectral data demonstrate limited capabilities, which is demonstrated in our study.…”

Section: Discussionmentioning

confidence: 99%

“…A variety of modeling approaches are available currently [21]. The Linear Discriminant Analysis was tested by the authors and it was demonstrated that non-linear features of environmental variables Forests 2020, 11, of 20 might potentially improve the robustness of the model [22]. In the current study, the SDMtoolbox (Spatial Distribution Modeling toolbox 2.4, Durham, NC/Manhattan, NY/Carbondale, IL, USA) is chosen as a modeling tool.…”

Section: Introductionmentioning

confidence: 99%

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

Kotlov

Chernen’kova

2020

Forests

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Kotlov

Chernen’kova

2020

Forests

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“…Plants 2023, 12, 3698 2 of 20 the presence of oak (Quercus robur), maple (Acer platanoides) and herb species typical for nemoral deciduous forests of a temperate zone and hemiboreal forests of the Siberian type. In accordance with floristic classification, these ecotone forests are included into class Carpino-Fagetea sylvaticae Jakucs ex Passarge 1968, order Carpinetalia betuli P.Fukarek 1968, alliance Aconito lycoctoni-Tilion cordatae Solomeshch et Grigoriev in Willner et al 2016, suballiance Tilio cordatae-Pinenion sylvestris Shirokikh et al 2021 [4].…”

Section: Introductionmentioning

confidence: 99%

“…Changes in forest health under the influence of global climate change have been recorded worldwide [7][8][9][10][11][12][13][14][15][16][17]. In many temperate regions of Europe, growing season temperature has increased by more than 1 • C over the last four decades, resulting in a higher atmospheric evaporative demand [18,19].…”

Section: Introductionmentioning

confidence: 99%

“…In the SU over the past 100 years, the average annual temperature has increased by 1.4-1.5 • C [42]. The Southern Ural region is currently experiencing changes in the distribution boundaries of tree species, as well as plant community boundaries [12,43,44], including a shift of the upper boundaries of the mountain forests and a reduction in the distribution of mountain tundra communities [45], as well as a shift in the border ranges of broad-leaved tree species [46].…”

Section: Introductionmentioning

confidence: 99%

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Analysis of the Potential Range of Mountain Pine-Broadleaf Ecotone Forests and Its Changes under Moderate and Strong Climate Change in the 21st Century

Fedorov,

Zhigunova,

Shirokikh

et al. 2023

Plants

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Climatic changes have a significant impact on the composition and distribution of forests, especially on ecotone ones. In the Southern Ural, pine-broadleaf ecotone forests were widespread during the early Holocene time, but now have persisted as relic plant communities. This study aimed to analyze the current potential range and to model changes in habitat suitability of relic pine-broadleaf ecotone forests of the suballiance Tilio-Pinenion under scenarios of moderate (RCP4.5) and strong (RCP8.5) climate change. For modelling, we used MaxEnt software with the predictors being climate variables from CHELSA Bioclim, the global digital soil mapping system SoilGrids and the digital elevation model. In the Southern and Middle Urals, climate change is expected to increase the areas with suitable habitat conditions of these forests by the middle of the 21st century and decrease them in the second half of the century. By the middle of the 21st century, the eastern range boundary of these forests will shift eastward due to the penetration of broad-leaved tree species into coniferous forests of the Southern Ural. In the second half of the century, on the contrary, it is expected that climate aridization will again shift the potential range border of these forests to the west due to their gradual replacement by hemiboreal coniferous forests. The relationship between the floristic composition of pine-broadleaf forests and habitat suitability was identified. In low and medium habitat suitability, pine-broadleaf forests contain more nemoral species characteristic of deciduous forests of the temperate zone, and can be replaced by broadleaf forests after thinning and removal of pine. In the Volga Upland, suitable habitats are occupied by pine-broadleaf forests of the vicariant suballiance Querco robori-Tilienion cordatae. Projected climatic changes will have a significant impact on these ecotone forests, which remained completely unaltered for a long time.

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Using a Multifunctional Approach for Cartographic Modeling of Organic Carbon Content in Natural and Arable Soils of the Central Caucasus

Tembotov

2023

Cosmic Res

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Definition of factors of spatial variation in vegetation using RSD, DEM and field data by example of the central part of Murmansk Region

Cited by 6 publications

References 7 publications

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

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

Analysis of the Potential Range of Mountain Pine-Broadleaf Ecotone Forests and Its Changes under Moderate and Strong Climate Change in the 21st Century

Using a Multifunctional Approach for Cartographic Modeling of Organic Carbon Content in Natural and Arable Soils of the Central Caucasus

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