摩洛哥布雷格- chaouia地区地表温度和地表能量通量的遥感时空制图

A. Farah, A. Algouti, Abdellah Algouti, Mohammed Ifkirne, A. Rafik
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引用次数: 1

摘要

近几十年来,布尔格雷朝亚地区受到城市发展和农业用地减少的影响,这改变了该地区的环境变量,使其容易受到气候变化的影响。通过对该地区1987 - 2015年智能空间数据的挖掘,提出了对该地区土地利用及部分环境参数(植被覆盖、反照率、地表温度)的时空监测。遥感产品基于1987年、2000年和2015年旱季获得的Landsat 5 TM、Landsat 7 ETM+和Landsat 8 Oli/TIRS影像进行计算。结果表明,NDVI植被指数从1987年的~ 0.86下降到~ 0。从2000年的0.56增加到2015年的0.54),地表反照率增加(从1987年的0.51增加到2000年的0.52)。温度(1987年为67°C, 2000年为54°C, 2015年为40°C),为了了解城市化对环境参数变化的影响,研究人员将建成区的演变作为一个决定性因素。然而,1987年的面积为3.27%,2000年为7.45%,2015年为28.18%。的确,新技术(地理信息系统和遥感)的贡献对于更好地管理和监测城市扩张对环境状况的影响是必不可少的。结果表明,智能空间数据在大尺度上评价城市环境演变的贡献和可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Remote Sensing for Spatio-temporal Mapping of Land surface temperature and Surface Energy Fluxes in the Bouregreg-Chaouia Region of Morocco
In recent decades, the Bouregreg Chaouia region has been subject to urban growth and a reduction in agricultural land in this region, which has changed its environmental variables and made it vulnerable to climate change. This work raises the spatiotemporal monitoring of land use and certain environmental parameters (vegetation cover, albedo, surface temperature from 1987 to 2015 by exploring intelligent spatial data in the region. The remote sensing products were computed from Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 Oli/TIRS images obtained during the dry seasons 1987, 2000 and 2015. The results showed a reduction in NDVI vegetation index (∼0.86 in 1987 to ∼0. 56 in 2000 to ∼ 0.54 in 2015) and with an increase in surface albedo (0.51 in 1987 to 0.52 in 2000 to 0. 69 in 2015), temperature (∼67°C in 1987 to 54°C in 2000 to 40°C in 2015) and to understand the impact of urbanization on the variation of environmental parameters, the evolution of the built-up area has been followed as a determining factor. However, it recorded 3.27% surface area in 1987 to 7.45% in 2000 to 28.18% in 2015. Indeed, the contribution of new technologies (GIS and remote sensing) is essential for better management and monitoring of the impact of urban expansion on the state of the environment. The results obtained remain so promising and highlight the contribution and feasibility of intelligent spatial data to assess the evolution of the urban environment on a large scale.
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