197 УДК 630*182.58 [630*421+528.7](470.1/.6) Александр
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630*182.58 [630*421+528.7](470.1/.6) Ⱥɥɟɤɫɚɧɞɪ Ɇɢɯɚɣɥɨɜɢɱ Ʉɪɵɥɨɜ, , amkrylov@gmail.com, ȿɤɚɬɟɪɢɧɚ Ƚɟɧɧɚɞɶɟɜɧɚ Ɇɚɥɚɯɨɜɚ, , katyarlz@yandex.ru, ɎȻɍ «Ɋɨɫɥɟɫɨɡɚɳɢɬɚ», ɇɚɞɟɠɞɚ Ⱥɥɟɤɫɟɟɜɧɚ ȼɥɚɞɢɦɢɪɨɜɚ, nadiopt@yandex.ru, 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. ȼɜɟɞɟɧɢɟ. . , . , , [1 ( . 7–8), 2]. , - . – - - , 8–10 / , . . . 20–24 / , . , 2009–2010 – 35–40 [3]. - / . . , . « ». , - . ( ), - . 197 , , : ( ) - . , . – . : – – – ; ( , ); . . 2009–2011 . - . . Ɇɚɬɟɪɢɚɥɵ ɢ ɦɟɬɨɞɵ. LANDSAT TM/ETM+ ( – 185 ). – 30 / LANDSAT . , Landsat, (10 / ) PRISM (2,5 SPOT 5 (2,5–10 ). ), Rapid Eye (6 ) , - ALOS Avnir Geo-Eye (0,4–1,6 ), - Landsat . Landsat – . « », - . [4]. . ( . 1) SWVI ( . ) Landsat, - , , SWVI. SWVI 198 Landsat . - 5-4-3. - - . Ɋɢɫ. 1. , . – . , , , , , . . , (10–50 ) 2010 . (100–300 ) . . . - . 199 . : – SWVI : NDVI - ( ); – – (SVM); . . - Landsat ERDAS , ERDAS, [5]. , , - , - . 100 - . – SWVI NDVI. NDVI . - Landsat : NDVI = (NIR-RED)/(NIR+RED), NIR – Landsat TM ( Landsat TM ( , SWVI ( : 4); RED – - 4); SWIR – SWVI - , - 3). NDVI, ). SWVI [6] SWVI = (NIR-SWIR)/(NIR+SWIR), NIR – Landsat TM ( . , Landsat TM ( 5). . SWVI , 200 NDVI. – - , . , ( , - ). . , . STATISTICA QGIS RasterCalc , . , . : ENVI [7], ɦɟɬɨɞɚ ɨɩɨɪɧɵɯ ɜɟɤɬɨɪɨɜ – R [8]. , . - . , , . SVM , . , . , , . , . - . Ɇɟɬɨɞ ɞɟɪɟɜɚ ɪɟɲɟɧɢɣ . – . – « , » « , - , » . , – . , . . ., . , - ( ») « . - (360 (256 ). ), . 201 . Ɋɟɡɭɥɶɬɚɬɵ ɢ ɨɛɫɭɠɞɟɧɢɟ. 2009–2010 20 . »( . 2). . . 226 . . , « . Ɋɢɫ. 2. , 2009–2010 . Landsat . , – , - Landsat . 2009 . . . - . 202 , , - . . - . – Landsat. , , . . , Landsat, c , 72 % ( ) . – . - Landsat . . - , - . – . , , , , . 9 %. , , , , . . , , (10 % , ). - . . , SWVI , NDVI. - , . - . 203 Ɋɟɡɭɥɶɬɚɬɵ ɚɜɬɨɦɚɬɢɡɢɪɨɜɚɧɧɨɝɨ ɜɵɹɜɥɟɧɢɹ ɜɟɬɪɨɜɚɥɨɜ ɪɚɡɥɢɱɧɵɦɢ ɚɥɝɨɪɢɬɦɚɦɢ , - , - . - , , . , % , . % 237 19 19 0 0 5 26 237 19 19 0 0 4 21 237 18 18 1 5 5 26 , - . . . 21 26 %. , . . , , - , . . : - . ȼɵɜɨɞɵ. , , . . . ( Landsat TM/ETM+. GeoEye ) Rapid Eye, SPOT5, WorldView-2, . . : Landsat TM/ETM+, . 204 50 – . - . , , . , , SVM , SWVI - . – - . . , , , . , , - . » , ( « , « »). 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
