Monitoring and Prediction of Salinity Soil in Oasis Based on High-Resolution Remote Sensing Data
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【Author in Chinese】 朱冠华；
【Author's Information】 北京交通大学， 摄影测量与遥感， 2018， 硕士
【Abstract in Chinese】 土壤盐化是土壤退化的主要形式之一。我国绿洲大多处于新疆干旱半干旱环境下,土壤盐化灾害尤为严重。盐化灾害对于绿洲区脆弱的生态环境的危害不容小觑。利用遥感手段掌握盐化土壤分布情况,对目前多以第一产业作为基础的绿洲经济十分必要。目前研究多采用中低分辨率遥感数据进行盐化土壤提取,存在定位精度不高、提取精度欠佳等问题。本文利用QuickBird及SPOT高分辨率遥感影像,结合盐化指数和植被覆盖度,采用随机森林分类器的方法,有效提取了新疆建设兵团农七师一二五团区域内的盐斑土壤及其他土地利用信息,并结合土地利用分类结果基于FLUS模型对2025年盐化土壤情况进行预测分析。本文的主要研究内容及结论如下:(1)本文使用QuickBird及SPOT遥感影像通过盐化特征指数计算及采样点分析,结合Pearson相关性分析,筛选出较为有利的盐化指数,结合试验区结果最终得到适用于研究区的盐化指数SI1,可有效提取研究区盐斑土壤信息。(2)基于随机森林分类器提取土地利用信息达到预期效果,总体精度达到0.9,实现研究区内不同程度的盐化土壤精确识别;通过2005年、2015年两期数据的盐化土壤特征对比发现,研究区内盐化土壤主要呈现退缩趋势,重度盐化土壤尤其明显;不同程度的盐化土壤之间转化关系较复杂,且其分布呈现一定规律性。(3)基于FLUS模型对2025年盐化土壤进行灾害预警,Kappa系数达到0.87,总体精度明显优于Markov-CA模型,具有良好可靠性;通过2005年及2015年土地利用信息及2025年预测结果分析发现,团场总体土地利用开发强度逐渐降低,但重度盐化土壤的变化速率仍高于其他土地利用类型;针对团场范围内盐化土壤变化情况不同的情况,经选取的三个典型变化区域分析后,灾害发生情况主要包含两方面:一是自然河流及水工建筑用地带动的水盐运动可能造成土壤含盐程度降低后发生次生盐渍化;二是土壤质地原因形成的顽固盐化土壤灾害,该区域应及早修筑水工建筑,做好防渗工作,加大技术投入,逐步减轻土壤含盐量。
【Abstract】 Soil salinization is one of the major forms of soil degradation.Most oasis in China located in arid and semi-arid areas in Xinjiang,where soil salinization is particularly serious.The harm of salinization to the fragile ecological environment in the Oasis region should not be underestimated.The use of remote sensing to understand the distribution of salinized soil is essential for the current oasis economy based on the primary industry.At present,most of the researches use low and middle resolution remote sensing data to extract salinized soil,which has such problems as low positioning accuracy and poor extraction accuracy.In this paper,QuickBird and SPOT high resolution remote sensing images,salinity index and vegetation coverage,combined with random forest classifier was used to extract the salinity soil and other land use in 125 Regime of Bingtuan.Then based on the FLUS model and land use classification results,the salinized soil conditions in 2025 were predicted and analyzed.The main contents and conclusions of this paper are as follows:1)In this paper,QuickBird and SPOT remote sensing images are used to calculate characteristic of salinity index and analyze sampling points,and the Pearson correlation analysis are used to select the more favorable salinity index.In combination with the results of the test area,the salinization index SI1,which is most suitable for the study area,soil information of salt spots in the study area can be extracted effectively.(2)Based on the random forest classifier to extract land use information,the overall accuracy reached 0.9,realizing the accurate identification of the salinized soil in the study area.By comparing the salinized soil characteristics of the two periods in 2005 and 2015,it proves that the salinized soil in the study area shows the tendency of withdrawal,and the severe salinized soil was particularly obvious.The transformation between different degrees of salinized soil is more complex.The distribution of salinized soil in the study area has a certain regularity.(3)By using the FLUS model,a disaster warning for salinized soil is carried out in 2025.The Kappa coefficient reached 0.87,and the overall accuracy is significantly higher than that of Markov-CA model.Compared with the results of land use in 2005 and 2015 and forecast result of 2025,it is found that the overall intensity of land use development of study area gradually decreased,but the change rate of severe salinized soil was still higher than that of other land use types.According to the situation of different salinized soils in the group field,the occurrence of disasters in the selected areas,mainly includes two aspects.First,the water salt movement caused by natural river and hydraulic construction land may cause secondary salinization after the decrease of soil salinity.The second is the stubborn salted soil disaster caused by the soil texture.This area should be constructed as early as possible hydraulic construction,increase technical input,and gradually reduce the soil salt content.
- 【Contributor】 北京交通大学 【Year of Internet Publish】201812
- 【CLC code】S156.4;X87