Remotely-Sensed Ecosystem Health Assessment (RSEHA) model for assessing the changes of ecosystem health of Lake Urmia Basin

IF 1.8 Q3 REMOTE SENSING

International Journal of Image and Data Fusion Pub Date : 2021-05-25 DOI:10.1080/19479832.2021.1924880

N. Abbaszadeh Tehrani, H. Z. Mohd Shafri, S. Salehi, J. Chanussot, M. Janalipour

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引用次数: 25

Abstract

ABSTRACT The widespread, severe negative impacts of human activities on Earth’s ecosystems over the past few decades have highlighted the importance of continuous and up-to-date monitoring of ecosystems health. On the other hand, it has been proven that the use of remote sensing technology in environmental studies can lead to accurate and reliable results with spending less cost and time. This research attempts to use remote sensing indicators and the framework of Vigour, Organization, Resilience, and Services (VORS) to assess ecosystem health by introducing Remotely Sensed Ecosystem Health Assessment (RSEHA) Model. By applying 10 spatiotemporal indices, ecosystem health has been assessed in Lake Urmia Basin (LUB) during the years 2001–2014. The results showed that the health status of LUB in its different parts varied from ‘very strong’ to ‘very poor’. The health status around LUB has changed from ‘poor’ to ‘very poor’, while it has improved, especially in cultivated lands. The health of the lake has been sacrificed in favour of the development of agricultural areas in the basin. Based on validation results, the RSEHA model can determine the ecosystem conditions at pixel level at any time at reasonable cost and accuracy.

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用于评估乌尔米亚湖流域生态系统健康变化的遥感生态系统健康评估（RSEHA）模型

在过去的几十年里，人类活动对地球生态系统产生了广泛而严重的负面影响，这凸显了对生态系统健康进行持续和最新监测的重要性。另一方面，事实证明，在环境研究中使用遥感技术可以以较少的成本和时间获得准确可靠的结果。本研究试图通过引入遥感生态系统健康评估模型(RSEHA)，利用遥感指标和活力、组织、弹性和服务(VORS)框架对生态系统健康进行评估。采用10个时空指数，对2001-2014年乌尔米亚湖流域生态系统健康状况进行了评价。结果表明，不同部位的LUB健康状况从“非常强”到“非常差”不等。LUB周围的健康状况已从"差"变为"极差"，同时有所改善，特别是在耕地方面。为了盆地农业地区的发展，湖泊的健康被牺牲了。基于验证结果，RSEHA模型能够以合理的成本和精度随时确定像元水平的生态系统状况。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Image and Data Fusion REMOTE SENSING-

CiteScore

5.00

自引率

0.00%

发文量

期刊介绍： International Journal of Image and Data Fusion provides a single source of information for all aspects of image and data fusion methodologies, developments, techniques and applications. Image and data fusion techniques are important for combining the many sources of satellite, airborne and ground based imaging systems, and integrating these with other related data sets for enhanced information extraction and decision making. Image and data fusion aims at the integration of multi-sensor, multi-temporal, multi-resolution and multi-platform image data, together with geospatial data, GIS, in-situ, and other statistical data sets for improved information extraction, as well as to increase the reliability of the information. This leads to more accurate information that provides for robust operational performance, i.e. increased confidence, reduced ambiguity and improved classification enabling evidence based management. The journal welcomes original research papers, review papers, shorter letters, technical articles, book reviews and conference reports in all areas of image and data fusion including, but not limited to, the following aspects and topics: • Automatic registration/geometric aspects of fusing images with different spatial, spectral, temporal resolutions; phase information; or acquired in different modes • Pixel, feature and decision level fusion algorithms and methodologies • Data Assimilation: fusing data with models • Multi-source classification and information extraction • Integration of satellite, airborne and terrestrial sensor systems • Fusing temporal data sets for change detection studies (e.g. for Land Cover/Land Use Change studies) • Image and data mining from multi-platform, multi-source, multi-scale, multi-temporal data sets (e.g. geometric information, topological information, statistical information, etc.).