197 УДК 630*182.58 [630*421+528.7](470.1/.6) Александр

630*182.58 [630*421+528.7](470.1/.6)
Ⱥɥɟɤɫɚɧɞɪ Ɇɢɯɚɣɥɨɜɢɱ Ʉɪɵɥɨɜ,
, [email protected],
ȿɤɚɬɟɪɢɧɚ Ƚɟɧɧɚɞɶɟɜɧɚ Ɇɚɥɚɯɨɜɚ,
, [email protected],
ɎȻɍ «Ɋɨɫɥɟɫɨɡɚɳɢɬɚ»,
ɇɚɞɟɠɞɚ Ⱥɥɟɤɫɟɟɜɧɚ ȼɥɚɞɢɦɢɪɨɜɚ,
[email protected], ɇɉ «ɉɪɨɡɪɚɱɧɵɣ ɦɢɪ»
,
ȼɕəȼɅȿɇɂȿ ɂ ɈɐȿɇɄȺ ɉɅɈɓȺȾȿɃ
ɄȺɌȺɋɌɊɈɎɂɑȿɋɄɂɏ ȼȿɌɊɈȼȺɅɈȼ 2009–2010 ɝɝ.
ɉɈ ȾȺɇɇɕɆ ɄɈɋɆɂɑȿɋɄɈɃ ɋɔȿɆɄɂ
ȼɟɬɪɨɜɚɥ, ɛɭɪɟɥɨɦ, ɞɚɧɧɵɟ ɞɢɫɬɚɧɰɢɨɧɧɨɝɨ ɡɨɧɞɢɪɨɜɚɧɢɹ, ɜɢɡɭɚɥɶɧɨɟ
ɢ ɚɜɬɨɦɚɬɢɡɢɪɨɜɚɧɧɨɟ ɞɟɲɢɮɪɢɪɨɜɚɧɢɟ.
Windfall, windbreak, remote sensing, visual and automatic interpretation.
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Ɋɟɡɭɥɶɬɚɬɵ ɚɜɬɨɦɚɬɢɡɢɪɨɜɚɧɧɨɝɨ ɜɵɹɜɥɟɧɢɹ ɜɟɬɪɨɜɚɥɨɜ ɪɚɡɥɢɱɧɵɦɢ
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Ȼɢɛɥɢɨɝɪɚɮɢɱɟɫɤɢɣ ɫɩɢɫɨɤ
1. ɍɥɚɧɨɜɚ, ɇ.Ƚ.
[
. .
. – .:
2. ɍɥɚɧɨɜɚ, ɇ.Ƚ.
] :
. … -
[
. .
III
,
.
3.
. .
-
.
/
, 2006. – 434 c.
, . .
. . II. –
,
.
]/ . .
//
:
,
[
, 2007 – . 245–249.
] /
.
.
. – . 3. – .:
, 2004. – . 135–136.
4. Ʌɚɛɭɬɢɧɚ, ɂ.Ⱥ.
[
]:
. / . .
. – .:
, 2004. – . 60–79, 160–167.
5. Ȼɟɥɨɜɚ, ȿ.ɂ.
Landsat-TM/ETM+
[
]/ . .
, . .
//
:
,
,
: .
. . – .:
,
2011. – . 8, № 1. – . 73–82.
6. Gao, B. NDWI – A normalized difference water index for remote sensing of vegetation liquid water from space [Text] / B. Gao // Remote Sensing of Environment. – 1996. –
no. 58. – P. 257–266.
. .
205
. .
7. ȼɚɩɧɢɤ, ȼ.ɇ.
. – .:
8. [
[
, 1979. – 448 c.
]. –
]/
: http://gis-lab.info/qa/classify-trees-r.html
ȼɜɟɞɟɧɢɟ.
.
,
.
,
,
.
2009–2010
-
.
.
-
,
«
».
,
.
(
),
.
Ɇɚɬɟɪɢɚɥɵ ɢ ɦɟɬɨɞɵ.
LANDSAT TM/ETM+ (
– 185 ).
LANDSAT
.
ALOS Avnir (10 /
Eye (6 ) Geo-Eye (0,4–1,6 ), SPOT 5 (2,5–10 ).
– 30
/
,
,
)
PRISM (2,5
Landsat,
), Rapid
-
Landsat
.
:
: NDVI
SWVI
(
(SVM),
),
.
-
.
Ɋɟɡɭɥɶɬɚɬɵ.
2009–2010
.
.
226
.
20
.
.
-
.
ȼɵɜɨɞɵ.
.
(
Eye, SPOT5, WorldView-2, GeoEye
206
.
Landsat TM/ETM+.
Rapid
50 )
. .
***
Introduction. In 2009–2010 the forests of the European part of Russia were damaged by
several catastrophic windfalls. Such a catastrophic damage made the field research insufficient for identification of all the windfalls and estimation of the areas of damage. Thus, studies of application of remote sensing data were conducted.
Materials and methods. To identify and assess the windfalls we used the following algorithm: visual interpretation of Landsat data and manual outlining the damaged area and automatic identification and mapping of the windfall by analysis of vegetation indices NDVI
and SWVI; calculation of the difference between the rates before and after the windfall. According to the results of visual analysis the threshold values were selected and the valuesabove the threshold were considered to be windfalls. Also some methods of supervised
classification were tested, namely the discriminant analysis, the support vector method, and
the method of decision tree.
Results. The use of vegetation indices proved to be quite good to map the windfalls
within only the one Landsat scene, but there are some difficulties in defining the thresholds
when the windfall is located at many scenes. Comparison of supervised classification methods
showed that their accuracy is almost identical, but to clarify the boundaries of windfalls and to
assess the damage to trees we need to use the images of higher resolution.
ɋonclusions. The satellite imagery can be considered as a reliable and cheap information
source about the forests damage by the windfalls. The accuracy of this information is linearly
dependent on the image resolution. Large windfall sites (of a width exceeding 50 meters) are
reliably detected in Landsat TM / ETM + images For the detection of smaller windfall areas it
is better to use Rapid Eye, SPOT5, WorldView-2, GeoEye data. Our results have been successfully used in the eliminating the consequences of the windfalls and in the creation of automated forest pathology monitoring systems (VEGA, «Lesopatolog» and AIS «Forest
Health»).
207