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مقایسۀ سه روش سنجش از دوری، واحدهای فیزیوگرافی و ژئومورفولوژیکی جهت تهیۀ نقشۀ پوشش گیاهی | ||
نشریه علمی - پژوهشی مرتع و آبخیزداری | ||
مقاله 10، دوره 70، شماره 3، آذر 1396، صفحه 661-68 اصل مقاله (990.54 K) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22059/jrwm.2017.22825. | ||
نویسندگان | ||
شهربانو رحمانی* 1؛ عطاالله ابراهیمی2؛ علیرضا داودیان3 | ||
1کارشناس ارشد مرتعداری دانشگاه شهرکرد | ||
2دکترای مرتعداری،عضو هیئت علمی گروه مرتع و آبخیز دانشگاه شهرکرد | ||
3دکترای زمین شناسی، عضو هیئت علمی گروه مرتع و آبخیز دانشگاه شهرکرد | ||
چکیده | ||
در این تحقیق سه روش سنجش از دوری، فیزیوگرافیکی و ژئومورفولوژیکی برای تهیۀ نقشۀ پوشش گیاهی مورد بررسی قرار گرفت. در روش سنجش از دور، علاوه بر تصاویرسنجندۀ IRS-LISSIII از نقشۀ مدل رقومی ارتفاعی زمین[1] و شاخص گیاهی تفاضلی نرمال شده[2] بهعنوان دادههای کمکی استفاده و برای طبقهبندی آنها از روش نظارتشدۀ حداکثر احتمال استفاده شد. بررسی دقت نقشههای تولیدی، نشان داد زمانی که تنها از دادههای سنجش از دور استفاده شود، میزان دقت و ضریب کاپای حاصل به ترتیب 82% و 43/79% و دقت و ضریب کاپای به همراه دادههای کمکی به ترتیب 93% و 63/90% میباشد. در روش فیزیوگرافی، پس از تعیین مهمترین عوامل فیزیوگرافیکی شامل شیب، جهت شیب، ارتفاع از سطح دریا، میانگین سالانۀ بارش، درجۀ حرارت و میزان تابش خورشیدی بهعنوان عوامل تعیینکنندۀ پوشش گیاهی و رابطۀ این عوامل با پوشش گیاهی مورد آزمون قرار گرفت. بدین منظور، با استفاده از مدل رگرسیون لجستیک چندجملهای نقشۀ پوشش گیاهی با دقت 08/47% پیشبینی شد. در روش ژئومورفولوژی نیز نقشههای سنگشناسی، شکل پستی و بلندی و رخسارههای ژئومورفولوژی تعیین و جهت پیشبینی نقشۀ پوشش گیاهی از روش شبکۀ عصبی مصنوعی استفاده گردید. این روش دقتی برابر با 1/39% را نشان داد. تفاوت فاحشی که در دقت تصاویر حاصل از دو روش فیزیوگرافیکی و ژئومرفولوژیکی با روش سنجش از دور مشاهده میگردد، بیانگر این است که روش سنجش از دوری دقت قابل توجه بیشتری برای پیشبینی پوشش گیاهی در مقایسه با دو روش دیگر حتی در صورت استفاده نکردن از لایههای کمکی دارد. [1] DEM [2] NDVI | ||
کلیدواژهها | ||
نقشۀ پوشش گیاهی؛ تیپبندی گیاهی مراتع؛ سنجش از دور؛ ژئومورفولوژی؛ فیزیوگرافی؛ سبزکوه | ||
عنوان مقاله [English] | ||
Comparison of three methods of vegetation/land cover mapping, including remote sensing, Physiographic and Geomorphologic | ||
نویسندگان [English] | ||
shahrebanoo rahmani1؛ Ataollah Ebrahimi2؛ alireza davoudian3 | ||
1u | ||
2u | ||
3u | ||
چکیده [English] | ||
In this study, three methods were evaluated for vegetation mapping. For remote sensing method, in addition to IRS data of LISSIII, Ddigital Elevation Model (DEM) and Normalized Difference Vegetation Index (NDVI) were used for classification of 14 classes of land covers mostly vegetation types using a maximum likelihood algorithm. After comparing of produced vegetation maps, overall accuracy and Kappa index were 82% and 79.43% respectively when only the IRS were used. Whereas, the overall accuracy and Kappa index were increased to 93% and 90.63% respectively, when ancillary data of DEM and NDVI were added. Slope, slope direction, elevation above sea level, annual precipitation, temperature, and sun radiation were selected as the main physiographic after a broad literature review. Then the relationship between of these six factors with vegetation types was evaluated. so a multivariate logistic regression was used to draw vegetation map of the study area based on the sixth independent variables. The result showed a predicted vegetation map of 47.08% accuracy.Finally, in the morphological method, relationship between three maps of lithology, undulating form of geomorphology and faces with vegetation/land cover were determined using a neural network synthetic approach and predict vegetation map was drawn as the output. The accuracy of resulted map was 39.1%. Comparison of accuracy of vegetation mapping by three methods of RS, physiographic and geomorphological methods revealed that RS method of vegetation/land cover mapping is significantly promising due to a meaningfully higher accuracy even without using ancillary data such as DEM and NDVI in this method. | ||
کلیدواژهها [English] | ||
Vegetation mapping, Rangelands vegetation mapping, remote sensing, geomorphology, Physiographic, Sabzkouh | ||
مراجع | ||
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