کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4458881 1621249 2014 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Post-classification approaches to estimating change in forest area using remotely sensed auxiliary data
ترجمه فارسی عنوان
روش های پس از طبقه بندی برای برآورد تغییر در منطقه جنگل با استفاده از داده های کمکی سنجیده از راه دور
کلمات کلیدی
برآوردگر رگرسیون با کمک مدل، برآوردگر مبتنی بر مدل، بوت استرپ
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات کامپیوتر در علوم زمین
چکیده انگلیسی


• The study area was in Minnesota, USA.
• Model-assisted and model-based approaches to estimating forest cover change were used.
• For model-based approaches, assesses statistical significance of pixel-level change.
• Estimates of net deforestation were not significantly different from zero.
• Estimates of deforestation and afforestation were significantly different from zero.

Multiple remote sensing-based approaches to estimating gross afforestation, gross deforestation, and net deforestation are possible. However, many of these approaches have severe data requirements in the form of long time series of remotely sensed data and/or large numbers of observations of land cover change to train classifiers and assess the accuracy of classifications. In particular, when rates of change are small and equal probability sampling is used, observations of change may be scarce. For these situations, post-classification approaches may be the only viable alternative. The study focused on model-assisted and model-based approaches to inference for post-classification estimation of gross afforestation, gross deforestation, and net deforestation using Landsat imagery as auxiliary data. Emphasis was placed on estimation of variances to support construction of statistical confidence intervals for estimates. Both analytical and bootstrap approaches to variance estimation were used. For a study area in Minnesota, USA, estimates of net deforestation were not statistically significantly different from zero.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Remote Sensing of Environment - Volume 151, August 2014, Pages 149–156
نویسندگان
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