METHODOLOGICAL PROPOSAL FOR THE IDENTIFICATION OF MARGINAL LANDS WITH REMOTE SENSING-DERIVED PRODUCTS AND ANCILLARY DATA

Jesús Torralba, L. Ruiz, C. Georgiadis, P. Patias, Rodrigo Gómez-Conejo, N. Verde, Maria Tassapoulou, Fernando Bezares Sanfelip, Ewa Grommy, S. Aleksandrowicz, Elke Krätzschmar, M. Krupiński, J. P. Carbonell-Rivera
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引用次数: 2

Abstract

The concept of marginal land (ML) is dynamic and depends on various factors related to the environment, climate, scale,culture, and economic sector. The current methods for identifying ML are diverse, they employ multiple parameters andvariables derived from land use and land cover, and mostly reflect specific management purposes. A methodologicalapproach for the identification of marginal lands using remote sensing and ancillary data products and validated on samplesfrom four European countries (i.e., Germany, Spain, Greece, and Poland) is presented in this paper. The methodologyproposed combines land use and land cover data sets as excluding indicators (forest, croplands, protected areas,impervious areas, land-use change, water bodies, and permanent snow areas) and environmental constraints informationas marginality indicators: (i) physical soil properties, in terms of slope gradient, erosion, soil depth, soil texture, percentageof coarse soil texture fragments, etc.; (ii) climatic factors e.g. aridity index; (iii) chemical soil properties, including soil pH,cation exchange capacity, contaminants, and toxicity, among others. This provides a common vision of marginality thatintegrates a multidisciplinary approach. To determine the ML, we first analyzed the excluding indicators used to delimit theareas with defined land use. Then, thresholds were determined for each marginality indicator through which the landproductivity progressively decreases. Finally, the marginality indicator layers were combined in Google Earth Engine. Theresult was categorized into 3 levels of productivity of ML: high productivity, low productivity, and potentially unsuitable land.The results obtained indicate that the percentage of marginal land per country is 11.64% in Germany, 19.96% in Spain,18.76% in Greece, and 7.18% in Poland. The overall accuracies obtained per country were 60.61% for Germany, 88.87%for Spain, 71.52% for Greece, and 90.97% for Poland.
利用遥感衍生产品和辅助数据识别边缘土地的方法建议
边际土地(ML)的概念是动态的,取决于与环境、气候、规模、文化和经济部门有关的各种因素。目前识别机器学习的方法多种多样,它们使用了来自土地利用和土地覆盖的多个参数和变量,并且大多反映了特定的管理目的。本文提出了一种利用遥感和辅助数据产品识别边缘土地的方法学方法,并在四个欧洲国家(即德国、西班牙、希腊和波兰)的样本上进行了验证。该方法将土地利用和土地覆盖数据集作为排除指标(森林、农田、保护区、不透水区、土地利用变化、水体和永久雪区)和环境约束信息作为边缘指标:(i)土壤物理性质,包括坡度、侵蚀、土壤深度、土壤质地、粗糙土壤质地碎片百分比等;(ii)气候因素,例如干旱指数;(iii)土壤化学性质,包括土壤pH值、阳离子交换能力、污染物和毒性等。这提供了一种整合多学科方法的关于边缘的共同愿景。为了确定ML,我们首先分析了用于划定土地使用区域的排除指标。然后,对土地生产力逐渐下降的各个边际指标确定阈值。最后,在谷歌Earth Engine中合并边际指标层。结果将ML的生产力分为3个级别:高生产力,低生产力和潜在不适宜的土地。结果表明,各国边际土地占比分别为德国11.64%、西班牙19.96%、希腊18.76%和波兰7.18%。每个国家获得的总体准确率为德国60.61%,西班牙88.87%,希腊71.52%,波兰90.97%。
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