A REVIEW OF THE USE OF REMOTE SENSING FOR MONITORING AND QUANTIFYING CARBON SEQUESTRATION IN MARGINAL LANDS

J. P. Carbonell-Rivera, J. Estornell, L. Ruiz, Alfonso Abad, B. Felten, Jesús Torralba
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引用次数: 2

Abstract

In recent years, Remote Sensing (RS) and its derived products have been used as a key tool for the detection, monitoring,management and future use of Marginal Lands (ML). Currently, there is no single, universally accepted definition of theterm and there is a wide variety of synonyms. In this paper, we conduct a compilation of synonyms and meanings thatencompass the term, as well as propose a definition. To reach this objective, an overview of the state of the art of ML isdone, visualising trends by science maps, based on bibliographic data of established research journals, found in GoogleScholar, Web of Science (WoS) and Scopus search engines. The bibliographic review carried out shows that the study ofML has traditionally been carried out with an ad hoc basis focused on the objective to be achieved, this aspect and otherknowledge gaps are discussed to analyse the global study of ML. Due to the broad spectrum of uses in which ML havebeen studied, the work has been focused on RS for monitoring and characterizing ML, focusing on two different aspects:(i) satellite monitoring of marginal lands; and (ii) determining carbon sequestration potential of marginal lands using remotesensing.
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利用遥感监测和量化边缘土地碳固存的综述
近年来,遥感及其衍生产品已成为边缘土地(ML)检测、监测、管理和未来利用的重要工具。目前,这个词还没有一个统一的、被普遍接受的定义,同义词的种类繁多。在本文中,我们对包含该术语的同义词和词义进行了汇编,并提出了一个定义。为了实现这一目标,我们对机器学习的现状进行了概述,通过科学地图将趋势可视化,这些趋势是基于在GoogleScholar、Web of science (WoS)和Scopus搜索引擎中找到的已建立的研究期刊的书目数据。所进行的文献综述表明,机器学习的研究传统上是在一个特别的基础上进行的,重点是要实现的目标,这方面和其他知识缺口进行了讨论,以分析机器学习的全球研究。由于研究机器学习的广泛用途,工作重点是RS监测和表征机器学习,重点是两个不同的方面:(i)边缘土地的卫星监测;(二)利用遥感技术确定边际土地的固碳潜力。
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