CA-Markov与LCM模型的黄河三角洲湿地变化模拟比较

doi:10.12082/dqxxkx.2019.180660

摘要/Abstract

摘要：

为了了解黄河三角洲湿地景观类型演变最优模拟模型以及景观的变化趋势,本文采用1996、2006、2016年3期黄河三角洲分类影像,分别利用CA-Markov、LCM、2种模型叠加开展变化模拟。研究发现：① 在相同驱动力因子影响下,空间模拟上LCM比CA-Markov好,数量模拟上,CA-Markov比LCM更贴合,对于变化较大研究区,综合2种模型优势来模拟该湿地变化最佳;② 对于较强的人为、自然灾害干扰,会对模拟精度有影响。在LCM模型中,驱动力相同情况下,生成适宜性图像的转移子模型数量越多,模拟精度越高。对于CA-Markov模型,比例误差系数适宜的设置对数量模拟的精度也有提升;③ 在保持2006-2016年的变化趋势下,综合2种模型模拟的2026年自然湿地面积1252.69 km ²,人工湿地面积1265.00 km ²,非湿地面积924.51 km ²。从2026年黄河三角洲模拟的结果可看出,自然、非湿地的面积减少,人工湿地大量的增加并不断向浅海区域扩张。通过对黄河三角洲湿地变化进行预测分析,可为湿地资源的合理有效利用与管理等提供科学依据。

关键词: 湿地, 模拟, 黄河三角洲, CA-Markov模型, LCM模型

Abstract:

The aim of this paper is to select the optimization model of the region and understand the future quantity and spatial variation trend of the wetland landscape types in the Yellow River Delta. We used the classified maps of the three periods of 1996, 2006 and 2016, of which the 1996 and 2006 maps were modeled for predicting 2016; we then compared the classified and simulated maps of 2016 to assess the model performances. The best model were used to take the classified 2006 and 2016 maps to simulate the landscape of the Yellow River Delta in 2026. We found that: ① For the simulation of the landscape types of the Yellow River Delta, under the influence of the same driving force factors, the LCM (Land Change Modeler) model performed better than the CA-Markov model in terms of spatial error, while CA-Markov was more suitable for the actual wetland change trend modeling than the LCM model in terms of numerical error. For the areas of larger landscape changes, the advantages of the two models should be combined to best simulate the change trend of wetlands. ② The interference of some human factors and the impact of natural disasters on the landscape types cannot be considered the model, it would cause some interference to simulation accuracy. For the LCM model, the number of transition sub-models had an effect on the simulation results with the same driving force factor, the more transition sub-models were used to generate suitable images, the higher the simulation accuracy. For CA-Markov model, the setting of proportional error coefficient was suitable for improving the accuracy of numerical simulation. ③ Assuming the continuation of the landscape dynamics trend during 2006-2016, and by simulation via combining the two simulation methods up to year 2026, the simulated natural wetlands area was 1252.69 km ², the human-made wetlands area was 1265.00 km ², and the non-wetlands area was 924.51 km ². The simulated results suggest that natural wetlands and non-wetlands area will likely reduce, and human-made wetlands area will increase and expand to even shallow sea areas. Our findings can provide a scientific basis for the rational layout planning of the regional development space and the rational and effective utilization and management of wetland resources.

Key words: wetland, simulation, Yellow River Delta, CA-Markov model, Land Change Modeler

陈柯欣, 丛丕福, 卢伟志, 曲丽梅. CA-Markov与LCM模型的黄河三角洲湿地变化模拟比较[J]. 地球信息科学学报, 2019, 21(12): 1903-1910.DOI:10.12082/dqxxkx.2019.180660

CHEN Kexin, CONG Pifu, LU Weizhi, QU Limei. Comparison of the CA-Markov and LCM Models in Simulating Wetland Change in the Yellow River Delta[J]. Journal of Geo-information Science, 2019, 21(12): 1903-1910.DOI:10.12082/dqxxkx.2019.180660

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过程	错误比例	数量误差
CA-Markov过程	0	0.15
CA-Markov过程	0.15	0.11

过程	转移子模型数量	数量误差	空间误差	整体精度验证/%
LCM第一组	1	0.15	0.20	44.48
LCM第二组	6	0.15	0.15	52.07