کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5116577 1485216 2017 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Integrating multisensor satellite data merging and image reconstruction in support of machine learning for better water quality management
ترجمه فارسی عنوان
ادغام داده های ماهواره ای چندرسانه ای و ادغام تصویر برای حمایت از یادگیری ماشین برای مدیریت کیفیت بهتر آب
کلمات کلیدی
کیفیت آب، سنجش از دور، فراگیری ماشین، فعال کردن فناوری، مدیریت آبخیزداری،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی انرژی انرژی های تجدید پذیر، توسعه پایدار و محیط زیست
چکیده انگلیسی
Monitoring water quality changes in lakes, reservoirs, estuaries, and coastal waters is critical in response to the needs for sustainable development. This study develops a remote sensing-based multiscale modeling system by integrating multi-sensor satellite data merging and image reconstruction algorithms in support of feature extraction with machine learning leading to automate continuous water quality monitoring in environmentally sensitive regions. This new Earth observation platform, termed “cross-mission data merging and image reconstruction with machine learning” (CDMIM), is capable of merging multiple satellite imageries to provide daily water quality monitoring through a series of image processing, enhancement, reconstruction, and data mining/machine learning techniques. Two existing key algorithms, including Spectral Information Adaptation and Synthesis Scheme (SIASS) and SMart Information Reconstruction (SMIR), are highlighted to support feature extraction and content-based mapping. Whereas SIASS can support various data merging efforts to merge images collected from cross-mission satellite sensors, SMIR can overcome data gaps by reconstructing the information of value-missing pixels due to impacts such as cloud obstruction. Practical implementation of CDMIM was assessed by predicting the water quality over seasons in terms of the concentrations of nutrients and chlorophyll-a, as well as water clarity in Lake Nicaragua, providing synergistic efforts to better monitor the aquatic environment and offer insightful lake watershed management strategies.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Environmental Management - Volume 201, 1 October 2017, Pages 227-240
نویسندگان
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