کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
4464585 1313835 2016 6 صفحه PDF ندارد دانلود رایگان
عنوان انگلیسی مقاله
Automatic classification of Google Earth images for a larger scale monitoring of bush encroachment in South Africa
ترجمه فارسی عنوان
طبقه بندی خودکار از تصاویر گوگل ارث برای نظارت در مقیاس بزرگتر تجاوز بوش در آفریقای جنوبی
کلمات کلیدی
تجاوز بوش؛ نظارت بر محیط زیست؛ گوگل ارث؛ جنگل های تصادفی؛ مراتع؛ آفریقای جنوبی
Bush encroachment; Environmental monitoring; Google Earth; Random Forests; Rangelands; South Africa
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات کامپیوتر در علوم زمین
چکیده انگلیسی


• We present a method to automatically classify woody vegetation in Google Earth images.
• Woody vegetation was successfully delineated from RGB channels using Random Forests.
• The classified images can be used as training sites for a larger scale monitoring.

Bush encroachment of savannas and grasslands is a common form of land degradation in the rangelands of South Africa. To assess the carrying capacity of the land and to understand underlaying processes of bush encroachment, continuous monitoring of this phenomenon is needed. The aim of this study is to provide training sites for satellite-based monitoring of bush encroachment in South Africa on a medium spatial resolution satellite sensor (e.g. MODIS or Landsat) scale. Since field surveys are time consuming and of limited spatial extent, the satellite based creation of training sites using Google Earth images is intended. Training pixels for woody vegetation and non-woody land cover were manually digitized from 50 sample Google Earth images. A Random Forests model was trained to delineate woody from non-woody pixels. The results indicate a high performance of the model (AUC = 0.97). The model was applied to a further 500 Google Earth images with a spatial extent of 250 m × 250 m. The classified images form the database of training sites which can be used for larger scale monitoring of bush encroachment in South Africa.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Applied Earth Observation and Geoinformation - Volume 50, August 2016, Pages 89–94
نویسندگان
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