面向青藏高原地区耕地资源质量评估的空间抽样方法对比与应用
F323.21; 充分认识青藏高原地区耕地资源质量的空间格局与时间变化特征,是探索区域性耕地资源保护与可持续利用路径的重要基础,对维护青藏高原地区的粮食安全和社会稳定具有重要意义.设计空间抽样方案,利用较少的样点尽可能全面地表征区域耕地质量的总体特征,对于开展耕地资源质量长期观测与机理研究具有重要意义.该研究以青海省互助土族自治县为研究区开展基于耕地质量指标数据驱动的空间抽样实证研究,从信息熵、Kullback-Leibler散度、相似度距离、样本对总体耕地质量空间异质性的表征能力、样本空间分布均匀性等视角,对比分析随机抽样法(random sampling method,RSM),空间覆盖随...
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| Published in | 农业工程学报 Vol. 39; no. 21; pp. 246 - 257 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
北京师范大学地表过程与资源生态国家重点实验室,北京,100875
01.11.2023
北京师范大学地理科学学部,北京 100875%北京师范大学地理科学学部,北京 100875 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1002-6819 |
| DOI | 10.11975/j.issn.1002-6819.202306156 |
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| Abstract | F323.21; 充分认识青藏高原地区耕地资源质量的空间格局与时间变化特征,是探索区域性耕地资源保护与可持续利用路径的重要基础,对维护青藏高原地区的粮食安全和社会稳定具有重要意义.设计空间抽样方案,利用较少的样点尽可能全面地表征区域耕地质量的总体特征,对于开展耕地资源质量长期观测与机理研究具有重要意义.该研究以青海省互助土族自治县为研究区开展基于耕地质量指标数据驱动的空间抽样实证研究,从信息熵、Kullback-Leibler散度、相似度距离、样本对总体耕地质量空间异质性的表征能力、样本空间分布均匀性等视角,对比分析随机抽样法(random sampling method,RSM),空间覆盖随机抽样法(spatial coverage sampling and random sampling,SPCOSA),条件拉丁超立方体法(conditioned latin hypercube sampling,CLHS),加入平面坐标的条件拉丁超立方体法(CLHS with x andy coordinates as covariates,XY_CLHS),空间覆盖随机抽样与条件拉丁超立方体抽样的混合抽样法(spatial coverage sampling and random sampling-conditioned latin hypercube sampling,SPCOSA_CLHS)5 种空间抽样方法在青藏高原县级区域耕地质量指标调查观测点位布局应用中的优劣特征与适用性,并探索了研究区适宜的观测点位数量.结果表明:SPCOSA_CLHS可以以较低空间约束的方式将SPCOSA指示的空间异质性特征集成到CLHS模型中,在表达总体的耕地质量指标属性特征和空间异质性特征方面更具优势;当抽样数量缩减到40～50时,抽样结果对总体耕地质量指标属性信息量的表征能力与抽样数量为100～200时近似;且SPCOSA CLHS方法在表达耕地质量指标空间异质性、设计样点分布的空间均匀性、模拟总体耕地质量特征的准确性方面具有明显优势.该研究可以为青藏高原地区耕地资源质量调查监测工作提供方法支持,对理解该地区耕地资源质量变化过程、探索耕地可持续利用路径具有重要作用. |
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| AbstractList | F323.21; 充分认识青藏高原地区耕地资源质量的空间格局与时间变化特征,是探索区域性耕地资源保护与可持续利用路径的重要基础,对维护青藏高原地区的粮食安全和社会稳定具有重要意义.设计空间抽样方案,利用较少的样点尽可能全面地表征区域耕地质量的总体特征,对于开展耕地资源质量长期观测与机理研究具有重要意义.该研究以青海省互助土族自治县为研究区开展基于耕地质量指标数据驱动的空间抽样实证研究,从信息熵、Kullback-Leibler散度、相似度距离、样本对总体耕地质量空间异质性的表征能力、样本空间分布均匀性等视角,对比分析随机抽样法(random sampling method,RSM),空间覆盖随机抽样法(spatial coverage sampling and random sampling,SPCOSA),条件拉丁超立方体法(conditioned latin hypercube sampling,CLHS),加入平面坐标的条件拉丁超立方体法(CLHS with x andy coordinates as covariates,XY_CLHS),空间覆盖随机抽样与条件拉丁超立方体抽样的混合抽样法(spatial coverage sampling and random sampling-conditioned latin hypercube sampling,SPCOSA_CLHS)5 种空间抽样方法在青藏高原县级区域耕地质量指标调查观测点位布局应用中的优劣特征与适用性,并探索了研究区适宜的观测点位数量.结果表明:SPCOSA_CLHS可以以较低空间约束的方式将SPCOSA指示的空间异质性特征集成到CLHS模型中,在表达总体的耕地质量指标属性特征和空间异质性特征方面更具优势;当抽样数量缩减到40～50时,抽样结果对总体耕地质量指标属性信息量的表征能力与抽样数量为100～200时近似;且SPCOSA CLHS方法在表达耕地质量指标空间异质性、设计样点分布的空间均匀性、模拟总体耕地质量特征的准确性方面具有明显优势.该研究可以为青藏高原地区耕地资源质量调查监测工作提供方法支持,对理解该地区耕地资源质量变化过程、探索耕地可持续利用路径具有重要作用. |
| Abstract_FL | Spatial patterns and temporal variation of arable land resources can greatly contribute to regional protection and sustainable utilization,particularly for food security and social stability in the Qinghai-Tibet Plateau.A spatial sampling with fewer points is required to comprehensively characterize the overall features of regional arable land.Long-term observation is also of great significance in evaluating the quality of arable land resources.In this study,a data-driven spatial sampling was presented to determine the indicators of arable land quality in the Tu Autonomous Country of Huzhu,Qinghai Province,China.The grid units of the sample population were set as 1 km of arable land,with a total of 790 points.21 indicators were extracted from three dimensions of topographic features,soil properties,and tillage technical conditions.An indicator system was then constructed for the sampling of arable land at the point scale,such as the slope,soil bulk density,organ carbon content,and agriculture mechanization level.The performance of spatial sampling was quantified by the natural quality,technical level,and arable land productivity index,which were collected from the pilot project of the Ministry of Natural Resources of China.Local spatial heterogeneity was represented to simulate the accuracy of the overall quality of arable land.Multiple indicators were also calculated from five dimensions of topographic features,soil properties,tillage technical conditions,environmental conditions and biological characteristics,in order to evaluate the quality and productivity of arable land.RSM(random sampling),SPCOSA(spatial coverage sampling and random sampling),CLHS(conditioned Latin hypercube sampling),XY_CLHS(CLHS with x and y coordinates as covariates),and SPCOSA_CLHS(spatial coverage sampling and random sampling-conditioned Latin hypercube sampling)were compared from the perspectives of information entropy,Kullback-Leibler divergence,similarity distance,the spatial heterogeneity and distribution uniformity of samples in the overall quality of arable land.A survey of observation points was carried out on the indicators of arable land quality in the county-level areas.Entropy-based tests were also performed on each sampling using the nine groups with the multigroup sample sizes:10,20,30,40,50,75,100,150,and 200.A suitable number of points were explored in the study area.The results show that the SPCOSA_CLHS model integrated the SPCOSA into the CLHS model,and then represented the spatial heterogeneity with the lower spatial constraints.A better performance was achieved to express the overall index attribute and spatial heterogeneity of arable land quality.When the number of sampling points was between 100 and 200,five sampling models shared similar applicability for the attribute in the overall quality indicators of arable land.Once the sample size dropped below 100,there were the greatest differences among sampling models,where the SPCOSA and RSM offset most.A better performance of SPCOSA_CLHS was also obtained in the information entropy,KL divergence,and similarity distance,in terms of convergence and stability.When the number of sample points was 40-50,a similar spatial sampling was observed as the sample size of 100-200.Therefore,SPCOSA_CLHS can be expected to describe the spatial heterogeneity of arable land quality indicators,the spatial uniformity of sample point distribution,and the simulated accuracy of the overall quality.This finding can provide strong support to the survey and monitoring of arable land quality in the Qinghai-Tibet Plateau.In turn,the evolution of arable land quality can also be used to explore the sustainable use pathways of arable land. |
| Author | 蒋嘉益 宋长青 高培超 任书义 叶思菁 |
| AuthorAffiliation | 北京师范大学地表过程与资源生态国家重点实验室,北京,100875;北京师范大学地理科学学部,北京 100875%北京师范大学地理科学学部,北京 100875 |
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| Author_FL | SONG Changqing REN Shuyi YE Sijing GAO Peichao JIANG Jiayi |
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| DocumentTitle_FL | Comparison and application of the spatial sampling methods for assessing the quality of arable land resources in Qinghai-Tibet Plateau |
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| Keywords | 耕地质量 GIS land use 青藏高原 空间抽样 resources arable land quality 土地利用 Qinghai-Tibet Plateau 资源 spatial sampling |
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| Publisher | 北京师范大学地表过程与资源生态国家重点实验室,北京,100875 北京师范大学地理科学学部,北京 100875%北京师范大学地理科学学部,北京 100875 |
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| Title | 面向青藏高原地区耕地资源质量评估的空间抽样方法对比与应用 |
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