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黄晶晶1, 于银霞2, 于东升1, 潘 月1, 陆晓松1, 徐志超1
判断历史土壤类型图件的制图精度,是正确利用这些珍贵数据资料的前提条件。利用收集整理的、标记为福建省1:25万土壤图的全国第二次土壤普查数据,分析了土类、亚类、土属不同土壤分类层次在栅格数据下15个景观指数的粒度效应,以粒度30 m×30 m对应的景观指数为基准数据,不同粒度对应的景观指数与基准数据比较,设定相对变异百分数|VIV|< 1% 时所对应的最大粒度为土壤矢量图栅格化的最佳表征粒度,并以此推断土壤类型图的比例尺,定量化评估其制图精度。结果表明,景观指数具有明显的粒度效应,土类、亚类、土属水平的最佳表征粒度分别为4.00 km×4.00 km、3.45 km×3.45 km和1.90 km×1.90 km,对应实际土壤图的比例尺分别为1:180万、1:160万、1:85万。为判断历史土壤类型图的制图精度提供了新的有效途径和方法,对于珍贵历史数据资料的正确判断和利用具有重要价值。
关键词:  景观指数  土壤类型图  制图精度  土壤分类层次
基金项目:国家自然科学基金项目(41571206)、国家重点研发计划专项 (2016YFD0200301)和国家科技基础性工作专项(2015FY110700-S2)
Quantitative Evaluation of Mapping Precision of Historical Soil Maps with Landscape Indices
HUANG Jingjing1, YU Yinxia2, YU Dongsheng1, PAN Yue1, LU Xiaosong1, XU Zhichao1
1.State Key Laboratory of Soil and Sustainable Agriculture,Institute of Soil Science,Chinese Academy of Sciences;2.Yangzhou Polytechnic Institute
【Objective】It is a precondition for making proper use of these precious data like soil maps to judge mapping precision of historical soil type maps.【Method】A map marked as 1: 250 000 scale Fujian Province soil map plotted on the basis of the soil survey data of the Second National Soil Survey, was collected and digitized into a vector soil map, which was rasterized by taxonomical hierarchy. From each level of the hierarchy, 50 raster grains, varying in interval from 30 m to 8 000 m, were selected for calculation of 15 landscape indices of each grain in three taxonomical levels, i.e. great group, subgroup and family, separately, using Fragstats 4.2, a software for landscape pattern analysis, and further for analysis of responses of the landscape indices to variation of the raster grains. Five landscape indices, i.e. TA, PR, PRD, SHDI and SHEI, were selected as indices to quantitatively evaluate soil mapping precision. The landscape indices corresponding to grains 30 m×30 m in size were set as benchmarks. Comparison was made of landscape indices corresponding to grains of various sizes with the benchmarks. Given that when the absolute values of relative variability (VIV, %) of all the selected landscape index are less than 1%, the corresponding maximum grain size is deemed as the optimal characterization grain size for data conversion from parent vector soil map to raster soil map. Then based on the functional relationship, y= -0.80×10-6x2 + 0.0 228x + 0.0 211(R2 = 0.9994,P< 0.05), between optimal characterization grain size and soil mapping scale, soil mapping scales could be deduced and mapping precision of the map quantitatively evaluated. 【Result】Results show that landscape index is obviously grain-size dependent, and based on the trend of landscape indices varying with rising grain size, grain-size dependence of landscape indices could be divided into four types, i.e. Type I: landscape indices increasing with rising grain size, such as PAFRAC and AREA_SD; Type II: landscape indices declining with rising grain size, such as TE, LSI, COHESION and AI; Type III: landscape indices varying irregularly with rising grain size, such as LPI, DIVISION, MESH and SPLIT; and Type Ⅳ: landscape indices remaining almost unchanged or fluctuating later on with rising grain size, such as TA, PR, PRD, SHDI and SHEI. The optimal characterization grain size at the soil great group level, subgroup level and family level is 4.00 km×4.00 km, 3.45 km×3.45 km and 1.90 km×1.90 km, respectively, and their corresponding soil map scale, 1: 1 800 000, 1: 1 600 000 and 1: 850 000, respectively, which all differ significantly from the marked map scale 【Conclusion】This study provides a novel and effective way and method to judge mapping precision of historical soil maps, which is of great value to correctly judge and utilize precious historical soil data.
Key words:  Landscape index  Soil type map  Mapping precision  Soil taxonomical level