=Paper=
{{Paper
|id=Vol-2665/paper18
|storemode=property
|title=Analysis of the accuracy of determining the vegetation edges according to the Landsat remote sensing data over the territory of the Sverdlovsk Region
|pdfUrl=https://ceur-ws.org/Vol-2665/paper18.pdf
|volume=Vol-2665
|authors=Nina Vinogradova,Andrey Sosnovsky,Stepan Yegorov
}}
==Analysis of the accuracy of determining the vegetation edges according to the Landsat remote sensing data over the territory of the Sverdlovsk Region ==
https://ceur-ws.org/Vol-2665/paper18.pdf
Analysis of the Accuracy of Determining the
Vegetation Edges according to the Landsat Remote
Sensing Data over the Territory of the Sverdlovsk
Region
Nina Vinogradova Andrey Sosnovsky Stepan Yegorov
Institute of Radio Electronics and Institute of Radio Electronics and Institute of Radio Electronics and
Information Technology Information Technology Information Technology
Ural Federal University Ural Federal University Ural Federal University
Ekaterinburg, Russia Ekaterinburg, Russia Ekaterinburg, Russia
n.s.vinogradova@urfu.ru sav83@e1.ru mr.stepone@mail.ru
Abstract—The work is devoted to the study of the most predominance of wooded ridges and ridges, the other half -
commonly used vegetation indices in relation to the territory of in the adjacent territories of the West Siberian Plain [2].
the Sverdlovsk region according to Landsat-7 images. For the
image fragments, vegetation maps were constructed using Thus, when analyzing vegetation, it must be taken into
various indices. The accuracy was evaluated of the vegetation account that sown and meadow areas make up no more than
map according to digital topographic maps based on the 15% of the territory while most of the vegetation indices are
criteria of false alarm errors and missing errors, as well as the adapted specifically for such types of vegetation.
total number. The indices having the smallest errors are found. Accordingly, it is necessary to establish which of the existing
Recommendations on the use of indices of vegetative regions vegetation indices are optimal specifically for the designated
covered by coniferous and mixed forests are given. region.
Keywords— remote sensing data, vegetation indices, Landsat- II. OVERVIEW OF EXISTING VEGETATION INDICES
7, accuracy estimation There are several commonly used vegetation indices. The
I. INTRODUCTION calculation of most of them is based on spectral brightness
coefficients for the two most stable regions of the spectral
Monitoring agricultural and forest land is one of the most reflection curve of vascular plants - red and infrared bands.
important spheres for using Earth remote sensing data. The most common of them is NDVI [3]. NDVI values range
Assessment of vegetation cover is usually carried out using from -1 to 1. Along with the NDVI index under conditions
vegetation indices, which can be used for regional mapping when vegetation occupies less than 30% of the image, the
and analysis of various types of landscapes, studying the RVI index [4] is often used, which range takes values [0, ∞].
dynamics of plant communities. The results of the There are also perpendicular vegetation indices (PVI, WDVI,
calculation of vegetation indices are maps of forest and DVI [5,6,7]), transformed ones (TVI [8]) and other [8], a list
agricultural land productivity, maps of landscape types, of indices used in the work, as well as formulas for their
vegetation and natural zones, and it is also possible to use calculation, are given in the table 1.
them to obtain numerical data for use in estimating and
forecasting yields and productivity, biological diversity, and TABLE I. THE MAIN VEGETATION INDICES USED IN THE VEGETATION
damage from various natural and man-made disasters, ANALYSIS
accidents, etc. One of the most popular satellites used to Vegetation Vegetati
solve the indicated problems is the Landsat spacecraft system Formula Formula
index on index
[1]. Despite the fact that there is a huge springboard for N IR R E D
research in this sphere, most of them relate to the territories NDVI DVI N IR R E D
N IR R E D
of the satellite owner, that is, accordingly, in the United N IR
States, numerous vegetation indices adapted in the first RVI PVI sin N IR c o s R E D
RED
category for subtropical and tropical climatic zones. N IR
IPVI WDVI N IR R E D
The territory of the Russian Federation is located mainly N IR R E D
in the subarctic and temperate climatic zones, naturally, the N IR R E D 1 N IR R E D
TVI SAVI (1 L )
problem arises of analyzing vegetation indices as applied to N IR R E D 2 N IR R E D L
the designated territory. The features of the territory of the
Sverdlovsk region, which must be taken into account when
monitoring plant cover, are: a relatively short growing season Vegetation maps on the territory of the Sverdlovsk
(up to 130 days per year) with an average duration of snow Region (with a total area of 4300 sq. km), located within the
cover of 170 days per year with the accumulation of large boundaries of the city of Nizhny Tagil and Gornouralsky city
masses of snow; continental climate; the location is mainly in district, are based on the Landsat-7 system data. [9]. The
the taiga zone (83% of the territory is covered by forest terrain is low-mountainous, the predominant type of
vegetation, 40% of which are pine forests). From the point of vegetation is coniferous and mixed forests. Date: July 15,
view of the terrain, approximately half of the region’s 2016. The image fragment is shown in Fig. 1. Ranges for
territory is located on the eastern slope of the Ural Mountains various vegetation indices were limited according to
(the Middle and partially the Northern Urals) with a previously indicated publications [10,11,12].
Copyright © 2020 for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)
Image Processing and Earth Remote Sensing
calculated according to the “tasseled cap” graph (Fig. 2).
The angle of the line of soils is 9.7°. It should also be noted
that for the SAVI vegetation index, according to the articles,
there is uncertainty in the indication of the parameter L,
therefore, the need arose to consider it. The results are
presented in Fig. 3 and Fig.4. It was found that the right
parameter value for the selected area should be equal to
0.43.
Fig. 1. The image fragment obtained by Landsat-7 system (true colors).
To construct the vegetation indices according to table 1,
two Landsat-7 channels were used: № 3 (wavelength is 0.63–
0.68 μm) and № 4 (wavelength is 0.84–0.88 μm). The
calculations were carried out using MATLAB 2018,b.
III. RESULTS
To assess the accuracy of the obtained vegetation maps,
digital topographic maps of scale 1: 200 000 were used,
taking into account the fact that half of the territory is located
on a hilly area, the average error in the contours of areal
objects is equal to 30 m. In general, three types of errors
were calculated: δ1 is the relative missing error (in relation to
the standard) when according to the standard there should be Fig. 4. Vegetation maps a) SAVI; b) NDVI; c) DVI; d) PVI.
vegetation, but there is nothing on the vegetation map, and
δ2 is the relative false error when vegetation is present on the IV. CONCLUSION
vegetation map, and on the standard it is absent, as well as The most widely used vegetation indices were studied in
the total error δ1 + δ2. The results are shown in table 2. relation to the territory of the Sverdlovsk region In the course
of the work. It has been found that the most suitable index
for the studied fragment of the territory of the Sverdlovsk
region is IPVI the total classification error of 19,2 %.
Vegetation maps based on indices NDVI (20,2 %), RVI
(20.2 %) and SAVI (20,3 %) show slightly worse results. In
general, the errors turned out to be quite large, which is due
to the systematic deforestation and generally high dynamics
of the terrain. In the future, it is planned to carry out a more
detailed study of the range boundaries for the space systems
of Landsat-7, Landsat-8 and Sentinel-2, corresponding to
different types of vegetation, to identify the best vegetation
indices in order to adapt them to the specifics of the
Sverdlovsk region territory.
ACKNOWLEDGMENT
Fig. 2. Red and near-infrared scatter chart («the tasseled cap»). The line of The work was supported by the RFFI grant No.19-29-
soils is shown as dotted line
09022\19.
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