Web Gis Support of Environmental-Geographical Assessment of Land Resources in the New Moscow Territory

Lurie, Irina K.; Прохорова, Е. А.; Semin, Vladimir; Sakirkina, M. A.

doi:10.24057/2414-9179-2016-2-22-216-226

Cited by 1 publication

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“…At the beginning of the 21st century, the direction of influence changed to post-industrial. The growth of the population along with the construction of housing stock and transport infrastructure started in connection with the development of the financial sector of the economy [36,37]. In addition, deterioration in the volume of forestry activities aimed at maintaining the sustainability of forest plantations was recorded.…”

Section: Study Areamentioning

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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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: Study Areamentioning

confidence: 99%