| Issue № 3 |
Conference proceedings May 22, 2020 |
pdf-version |
Identification of invariant states of agricultural landscapes based on hierarchical factor analysis of remote sensing information
| Krenke Alexander Nikolaevich | PhD, Institute of Geography, Russian Academy of Sciences, Staromonetny ereulok, 29, Moscow, 119017, krenke-igras@yandex.ru |
|
Keywords: remote sensing agroecological conditions invariant factor analysis |
Summary: The proposed work shows the possibilities of analyzing the time series of multispectral satellite images of medium resolution to identify invariant spatial structures under conditions of strong anthropogenic impact. The procedure for selecting spatial invariants is shown on the example of multi-year LANDSAT data series. The procedure is based on applying the method of hierarchical factor analysis of the multidimensional space of initial variables and segmentation of the resulting factor space into discrete states. Since each multispectral image is a cross-section of the state of the landscape cover and its ability to convert solar energy, each cross-section must have its own set of spatial invariants that describe most of the information about the work of the surface. From the invariant describing individual cross-sections, it is possible to construct new invariants that describe the stationary states of the time series as a whole. Using an iterative K-means procedure, the spatial differentiation of these states can be distinguished. The number of such states is determined by the principle of maximum entropy. The stability of the obtained discrete states is investigated using discriminant analysis, when the invariant states obtained from one time series serve as a training sample for another time series of images for the same territory. In this article, this approach is used to study the agricultural landscapes of the Samara region. We demonstrate high repeatability of discrete spatial invariants in the space of integral factors for time series separated in time by 10 years. Independently conducted field studies suggest that the selected stationary states can be correlated with soil types. This type of analysis makes it possible to establish the genetic properties of a territory, using remote methods, even under strong anthropogenic influence. © Petrozavodsk State University |
Received on: 22 July 2020 Published on: 08 October 2020 | |
References
Jorgensen S. E., Svirezhev Yu. M. Towards a Thermodynamic Theory for Ecological Systems. Oxford: Elsever, 2004. 366 p.
Kozlov D. N. Puzachenko M. Yu. Fedyaeva M. V. Puzachenko Yu. G. Display of spatial variation of landscape cover properties based on remote sensing information and digital elevation model, Izvestiya Rossiyskoy akademii nauk. Ser. geogr. 2008. No. 4. P. 112–124.
Krenke A. N. Puzachenko M. Yu. Puzachenko Yu. G. Corкection of the content of thematic maps based on remote sensing data, Izvestiya Rossiyskoy akademii nauk. Ser. geogr. 2011. No. 4. P. 86–96.
Nawar S., Buddenbaum H., Hill J. Digital Mapping of Soil Properties Using Multivariate Statistical Analysis and ASTER Data in an Arid Region, Remote Sensing. 2015. Vol. 7 (2). P. 1181–1205.
Puzachenko M. Yu. Chernen'kova T. V. Determination of factors of spatial variation of vegetation cover using remote sensing data, digital elevation models and field data on the example of the central part of the Murmansk region, Sovremennye problemy distancionnogo zondirovaniya Zemli iz kosmosa. 2016. T. 13. No. 5. P. 167–191.
Puzachenko M. Yu. Puzachenko Yu. G. Kozlov D. N. Fedyaeva M. V. Mapping the thickness of the organogenic and humus horizons of forest soils and bogs in the southern taiga landscape (southwest of the Valdai hills) based on a three-dimensional relief model and remote sensing information (LANDSAT 7), Issledovanie Zemli iz kosmosa. 2006. No. 4. P. 70–79.
Puzachenko Yu. G. Baybar A. S. Varlagin A. V. The thermal field of the southern taiga landscape of the Russian Plain, Izvestiya Rossiyskoy akademii nauk. Ser. geogr. 2019a. No. 2. P. 51–68.
Puzachenko Yu. G. Horoshev A. V. Aleschenko G. M. Analysis of organization of landscape on the basis of satellite images, Issledovanie Zemli iz kosmosa. 2003. No. 3. P. 63–71.
Puzachenko Yu. G. Krenke A. N. Puzachenko M. Yu. Estimation of thermodynamic parameters of landscape cover from multispectral measurements of reflected solar radiation (landsat) based on non-extensive statistical mechanics, Doklady Akademii nauk. 2019b. T. 487. No. 3. P. 310–316.
Stasyuk N. V., Tseits M. A., Konyushkova M. V., and Marechek M. S. Verification of predicted dynamics of soil degradation using satelite imagery, Moscow University Soil Science Bulletin. 2017. Vol. 72 (4). P. 161–164.
Yufeng Ge, Thomasson J., Ruixiu Sui. Remote Sensing of Soil Properties in Precision Agriculture: A Review, Frontiers of Earth Science. 2011. Vol. 5. P. 229–238.
© 2011 - 2024
Petrozavodsk State University
technical support - RCNIT
The editor-in-chief - Andrey Korosov
ecopri@petrsu.ru