Abstract. The authors offer methods for mapping nature, in particular, vegetation and relief maps using remote sensing data. These thematic maps are most often used by administrators of different levels for environmental and territorial management. In the Russian Federation administrative territories occupied large areas. The algorithm for constructing visual models using remote sensing data for large administrative areas differs from the algorithms for working with small territories. Automated mapping method includes the analysis of the territory by indicators of topography and dominant vegetation, the selection of satellite images, processing, composing mosaics, composites, classification of plant objects, post-processing. The authors offer to use a specific data source, because the quality of the materials is sufficient for working with large areas. Classifications – the most complicated section. At the moment, scientists have not proposed an unambiguous solution to the choice of algorithm. However, the authors of this study experimentally came to the most convenient algorithm, which we characterize as the main one precisely for the purposes of managing natural resources of large administrative structures (regions with legally fixed boundaries). Examples of the thematic maps fragments and results of intermediate versions of visual models built by automated methods are given. The potential use of methods by municipal employees, rather than narrow specialists, was taken into account. In this regard, the value of the study is an exclusively applied nature and can be used in the administrative structures of the executive authorities.
[1]
J. A. Schell,et al.
Monitoring vegetation systems in the great plains with ERTS
,
1973
.
[2]
M. Jessell,et al.
Automated regolith landform mapping using airborne geophysics and remote sensing data, Burkina Faso, West Africa
,
2018
.
[3]
G. Singh,et al.
Feature Extraction using Normalized Difference Vegetation Index (NDVI): A Case Study of Jabalpur City
,
2012
.
[4]
Justin Morgenroth,et al.
Developments in Landsat Land Cover Classification Methods: A Review
,
2017,
Remote. Sens..
[5]
Robert A. Schowengerdt,et al.
Remote sensing, models, and methods for image processing
,
1997
.
[6]
P. Kennelly.
Terrain maps displaying hill-shading with curvature
,
2008
.
[7]
Quinten Vanhellemont,et al.
ATMOSPHERIC CORRECTION OF LANDSAT-8 IMAGERY USING SEADAS
,
2014
.