مدل سازی تغییر پوشش سطحی زمین برای حوضه رودخانه نسبتا کم آب در برزیل
ترجمه نشده

مدل سازی تغییر پوشش سطحی زمین برای حوضه رودخانه نسبتا کم آب در برزیل

عنوان فارسی مقاله: مدل سازی تغییر پوشش سطحی زمین بر اساس یک شبکه عصبی مصنوعی برای حوضه رودخانه نسبتا کم آب در شمال شرقی برزیل
عنوان انگلیسی مقاله: Modeling land cover change based on an artificial neural network for a semiarid river basin in northeastern Brazil
مجله/کنفرانس: بوم شناسی و حفاظت جهانی - Global Ecology And Conservation
رشته های تحصیلی مرتبط: کامپیوتر، جغرافیا، محیط زیست
گرایش های تحصیلی مرتبط: مخاطرات آب و هوایی، مهندسی طراحی محیط زیست، تغییرات آب و هوایی
کلمات کلیدی فارسی: تغییر پوشش سطحی زمین، مدل سازی پویا، شبکه عصبی مصنوعی
کلمات کلیدی انگلیسی: Land cover change، Dynamic modeling، Artificial neural network
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: DOAJ - Scopus - Master Journals List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.gecco.2019.e00811
دانشگاه: UFPB - Federal University of Paraíba, Center for Technology, Graduate Program in Civil and Environmental Engineering, Brazil
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2020
ایمپکت فاکتور: 3/000 در سال 2019
شاخص H_index: 20 در سال 2020
شاخص SJR: 1/264 در سال 2019
شناسه ISSN: 2351-9894
شاخص Quartile (چارک): Q1 در سال 2019
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 13
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: دارد
کد محصول: E14339
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست مطالب (انگلیسی)

Abstract


1- Introduction


2- Materials and methods


3- Results and discussion


4- Conclusions


References

بخشی از مقاله (انگلیسی)

Abstract


Accelerated changes in land cover cause changes in environmental dynamics and may cause land degradation. The goals of the present paper were to analyze changes in land cover and to estimate a future scenario for 2035 using an artificial neural network for the Taperoá River basin, located in northeastern Brazil. The classification of land cover was carried out for years t1 (1990), t2 (1999) and t3 (2002), with the latter being used to validate the land cover prediction to obtain an estimate for year t4 (2035). The land cover classes identified in the basin were (a) water bodies, (b) tree-shrub vegetation, (c) shrub vegetation, (d) herbaceous-shrub vegetation, and (e) herbaceous vegetation. The results of the classifications and of the land cover prediction were analyzed using the kappa coefficient, total operating characteristic (TOC), and area under the curve (AUC). The dynamic modeling of the land cover was based on a multilayer perceptron (MLP) neural network, which presented very good results, with an accuracy = 89.69% after 10,000 iterations, kappa = 0.61 and AUC = 0.67. The results of the land cover change analysis showed a decrease in the tree-shrub class and an increase in the shrub vegetation class between the years analyzed. The scenario predicted for 2035 showed an increase in the herbaceous-shrub vegetation class and a decrease in the area occupied by tree-shrub vegetation.


Introduction


Changes in land cover generated by anthropogenic actions are responsible for changing landscape characteristics and modifying the dynamics of natural processes in river basins (Mendoza et al., 2011) and are of great interest to researchers due to their impact on the local and global environment (Abuelaish and Olmedo, 2016). The main causes of land cover changes vary according to the nature and extent of the area but include deforestation, changing to pasture, agricultural intensification, and overexploitation (Bezak et al., 2015). Spatial changes in land cover over time also affect the future provision and localization of ecosystem services in the landscape and the fragmentation of green areas (Hoyer and Chang, 2014; Bai et al., 2019). Changes in land cover pose threats to landscapes, for example, by removing characteristic features of the landscape by scale enlargement or deterioration due to lack of management (Schulp et al., 2019). The risk of these problems is even higher in the semiarid region of Brazil due to the lack of studies regarding the phenological behavior of the vegetation and the recurrence of droughts and rainfall variability, together with the geology of the region and the saline soil types (Silva et al., 2018a). The characteristics of the Caatinga biome make it difficult to produce forecasting maps of the vegetation cover due to the prolonged dry season in the region, which influences the spectral response of the vegetation and, consequently, the estimates of land cover changes (Santos et al., 2017).

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