Methods of verification of soils prediction maps: a case study from Chernivtsi region, Ukraine

IF 0.5 Q3 GEOGRAPHY Geographia Cassoviensis Pub Date : 2019-01-01 DOI:10.33542/gc2019-2-04
V. Cherlinka, Y. Dmytruk, D. Barabas
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引用次数: 4

Abstract

Knowing the spatial distribution of individual soil taxonomic units is a key factor in man-aging efficient land use not only for agriculture but also for forestry. The use of a comprehensive soil surveys held in past decades and based on fieldwork created the basis for the initial spatial representation of the soil fund structure. However, the spatial distribution of the soil cover was the result of fieldwork and the experience of the person who drew this map. Often this led to some errors in determining the types of soils and their boundaries. To date, there is a growing need for precise methods of land taxation, based on correct information on soil cover. In countries with a large area, such as Ukraine, field surveys still do not cover the whole territory, often the density of the allocation of soil pits was too low, which in some cases led to an incorrect demarcation of soil boundaries. Since such a problem is very urgent for Ukraine, the search and identification of probable problem soil maps by constructing their predicted versions, their comprehensive analysis and cross-validation is an important task. The conducted investigations revealed that morphometric parameters of the relief and their derivatives obtaining from the analyses of DEM are a reliable basis for the predictive modelling of the spatial distribution of soil cover with sufficiently high accuracy, and the methodology based on 11 types of prognostic algorithms would have a significant prospect in solving scientific and production problems. Very important in this process is the selection of predictors derived from the DEM, as well as the structure and distribution of the training dataset, based on which the model will be built later. An equally important part is the control of results, including on the basis of cross-validation of the models used, as well as a comparison of the results with field studies. The article presents the results of 11 simulations, evaluates the quality of predictive algorithms and the models obtained. As a consequence, several possible ways to check the cartographic and simulation results of the spatial distribution of soil taxonomic units were described, as well as their comparison with those actually existing in nature. The most reliable method of the 11 presented is a direct study of the soil in the field and comparing them with the soil map. It is recommended to use it in case of suspicion of poorly executed maps, although financially it is very expensive. More preferred is a set of modelling methods that is based on the data already collected. With reliable sources, they provide an opportunity to predict the soil in places where the survey was not conducted at all. Verification of the quality of the tested models was carried out on a fragment of the Ukrainian region within the boundaries of the Chernivtsi region, confined to the Prut-Dniester and Prut-Siret interfluves.
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土壤预测图的验证方法:以乌克兰切尔诺夫茨地区为例
了解单个土壤分类单位的空间分布是农业和林业有效利用土地的关键因素。利用过去几十年进行的综合土壤调查,并以实地工作为基础,为土壤基金结构的初步空间表示奠定了基础。然而,土壤覆盖的空间分布是田野调查和绘制这张地图的人的经验的结果。这常常导致在确定土壤类型及其边界时出现一些错误。迄今为止,越来越需要根据关于土壤覆盖的正确资料制定精确的土地征税方法。在面积较大的国家,如乌克兰,实地调查仍然没有覆盖整个领土,往往分配土坑的密度太低,这在某些情况下导致了土壤边界的不正确划分。由于这一问题对乌克兰来说非常紧迫,通过构建其预测版本,对可能的问题土壤图进行搜索和识别,对其进行综合分析和交叉验证是一项重要任务。研究表明,通过DEM分析获得的地形形态参数及其衍生物是土壤覆盖空间分布预测建模的可靠基础,具有较高的精度,基于11种预测算法的预测方法在解决科学和生产问题方面具有重要的应用前景。在这个过程中,非常重要的是从DEM中得到的预测因子的选择,以及训练数据集的结构和分布,将在此基础上建立模型。一个同样重要的部分是结果的控制,包括在所使用的模型的交叉验证的基础上,以及结果与实地研究的比较。本文给出了11次仿真的结果,评价了预测算法和模型的质量。在此基础上,提出了几种检验土壤分类单元空间分布的制图和模拟结果的可能方法,并与自然界中实际存在的土壤分类单元进行了比较。提出的11种方法中最可靠的方法是直接对田间土壤进行研究,并将其与土壤图进行比较。建议在怀疑地图执行不佳的情况下使用它,尽管它在财务上非常昂贵。更可取的是一套基于已收集数据的建模方法。有了可靠的来源,他们就有机会预测那些根本没有进行调查的地方的土壤。对测试模型的质量进行了核查,地点是切尔诺夫茨地区边界内的乌克兰地区的一小块区域,仅限于普鲁特-德涅斯特和普鲁特-锡雷特河段。
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来源期刊
CiteScore
0.80
自引率
0.00%
发文量
4
期刊介绍: Geographia Cassoviensis is a biannual peer-reviewed journal published by the Pavol Jozef Šafárik University in Košice since 2007. It is available both in print and open-access electronic version. The journal publishes original research articles from Geography and other closely-related research fields. Since 2016 the journal is indexed in SCOPUS and ERIH PLUS - European Reference Index for Humanities and Social Sciences, and since 2017 also in Emerging Sources Citation Index by Clarivate Analytics.
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