Assessment of soil fertility degradation affected by mining disturbance and land use in a coalfield via machine learning

The degradation of soil fertility in mining areas poses great risks to agricultural production and the ecological environment, and has increasingly become a worldwide concern. In this study, soil assessments were conducted to evaluate the spatial and temporal variations of soil fertility indicators...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Ziguan Wang, Guangcai Wang, Tingyu Ren, Haibo Wang, Qingyu Xu, Guanghui Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Soil fertility degradation DBSCAN Random forest Land use type Coalfield Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/4be01b49613843e68fba923f79c2dd54
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:4be01b49613843e68fba923f79c2dd54
record_format	dspace
spelling	oai:doaj.org-article:4be01b49613843e68fba923f79c2dd542021-12-01T04:49:21ZAssessment of soil fertility degradation affected by mining disturbance and land use in a coalfield via machine learning1470-160X10.1016/j.ecolind.2021.107608https://doaj.org/article/4be01b49613843e68fba923f79c2dd542021-06-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21002739https://doaj.org/toc/1470-160XThe degradation of soil fertility in mining areas poses great risks to agricultural production and the ecological environment, and has increasingly become a worldwide concern. In this study, soil assessments were conducted to evaluate the spatial and temporal variations of soil fertility indicators and characteristics of soil fertility degradation under different types of land use and mining disturbances in a coalfield on the Loess Plateau of China, where soil fertility degradation caused by mining activities has become a serious environmental issue. Soil samples (depth: 0–20 cm) were collected twice from the same 50 sampling points in 2017 and 2019. The sampling points covered three land use types (cropland, shrubland, and grassland) and three years of mining disturbance (2011, 2013, and 2016). Soil organic matter (SOM), total nitrogen (TN), soil-available phosphorus, soil-available potassium, and the fine soil particles in topsoils were measured for each sample. The spatial distributions of the properties and degradation of soil fertility were analysed using kriging interpolation, and the degree to which fertility degraded was analysed via density-based spatial clustering of applications with noise (DBSCAN) and validated using SoftMax and random forest algorithms. The study revealed that the intensity of the degradation of soil fertility could be classified into three clusters (i.e. severely degraded, moderately degraded, and slightly degraded), as indicated by the DBSCAN results, and based on the variation in soil fertility indicators. Validation using random forest and SoftMax suggested that the accuracy of clustering was over 95%. Land use types and coal mining years significantly affected the degree of degradation, and total nitrogen and soil organic matter had the most noticeable impacts on the classification of soil fertility degradation.Ziguan WangGuangcai WangTingyu RenHaibo WangQingyu XuGuanghui ZhangElsevierarticleSoil fertility degradationDBSCANRandom forestLand use typeCoalfieldEcologyQH540-549.5ENEcological Indicators, Vol 125, Iss , Pp 107608- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Soil fertility degradation DBSCAN Random forest Land use type Coalfield Ecology QH540-549.5
spellingShingle	Soil fertility degradation DBSCAN Random forest Land use type Coalfield Ecology QH540-549.5 Ziguan Wang Guangcai Wang Tingyu Ren Haibo Wang Qingyu Xu Guanghui Zhang Assessment of soil fertility degradation affected by mining disturbance and land use in a coalfield via machine learning
description	The degradation of soil fertility in mining areas poses great risks to agricultural production and the ecological environment, and has increasingly become a worldwide concern. In this study, soil assessments were conducted to evaluate the spatial and temporal variations of soil fertility indicators and characteristics of soil fertility degradation under different types of land use and mining disturbances in a coalfield on the Loess Plateau of China, where soil fertility degradation caused by mining activities has become a serious environmental issue. Soil samples (depth: 0–20 cm) were collected twice from the same 50 sampling points in 2017 and 2019. The sampling points covered three land use types (cropland, shrubland, and grassland) and three years of mining disturbance (2011, 2013, and 2016). Soil organic matter (SOM), total nitrogen (TN), soil-available phosphorus, soil-available potassium, and the fine soil particles in topsoils were measured for each sample. The spatial distributions of the properties and degradation of soil fertility were analysed using kriging interpolation, and the degree to which fertility degraded was analysed via density-based spatial clustering of applications with noise (DBSCAN) and validated using SoftMax and random forest algorithms. The study revealed that the intensity of the degradation of soil fertility could be classified into three clusters (i.e. severely degraded, moderately degraded, and slightly degraded), as indicated by the DBSCAN results, and based on the variation in soil fertility indicators. Validation using random forest and SoftMax suggested that the accuracy of clustering was over 95%. Land use types and coal mining years significantly affected the degree of degradation, and total nitrogen and soil organic matter had the most noticeable impacts on the classification of soil fertility degradation.
format	article
author	Ziguan Wang Guangcai Wang Tingyu Ren Haibo Wang Qingyu Xu Guanghui Zhang
author_facet	Ziguan Wang Guangcai Wang Tingyu Ren Haibo Wang Qingyu Xu Guanghui Zhang
author_sort	Ziguan Wang
title	Assessment of soil fertility degradation affected by mining disturbance and land use in a coalfield via machine learning
title_short	Assessment of soil fertility degradation affected by mining disturbance and land use in a coalfield via machine learning
title_full	Assessment of soil fertility degradation affected by mining disturbance and land use in a coalfield via machine learning
title_fullStr	Assessment of soil fertility degradation affected by mining disturbance and land use in a coalfield via machine learning
title_full_unstemmed	Assessment of soil fertility degradation affected by mining disturbance and land use in a coalfield via machine learning
title_sort	assessment of soil fertility degradation affected by mining disturbance and land use in a coalfield via machine learning
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/4be01b49613843e68fba923f79c2dd54
work_keys_str_mv	AT ziguanwang assessmentofsoilfertilitydegradationaffectedbyminingdisturbanceandlanduseinacoalfieldviamachinelearning AT guangcaiwang assessmentofsoilfertilitydegradationaffectedbyminingdisturbanceandlanduseinacoalfieldviamachinelearning AT tingyuren assessmentofsoilfertilitydegradationaffectedbyminingdisturbanceandlanduseinacoalfieldviamachinelearning AT haibowang assessmentofsoilfertilitydegradationaffectedbyminingdisturbanceandlanduseinacoalfieldviamachinelearning AT qingyuxu assessmentofsoilfertilitydegradationaffectedbyminingdisturbanceandlanduseinacoalfieldviamachinelearning AT guanghuizhang assessmentofsoilfertilitydegradationaffectedbyminingdisturbanceandlanduseinacoalfieldviamachinelearning
_version_	1718405721356238848

Assessment of soil fertility degradation affected by mining disturbance and land use in a coalfield via machine learning

Ejemplares similares