共查询到20条相似文献，搜索用时 945 毫秒
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豫北盐碱化土壤的遥感影像特征及成因机理浅析 总被引：1，自引：0，他引：1
遥感资料的分析表明,豫北土壤盐渍化受半干旱气候、圈闭地形和浅埋藏地下水等因素制约。黄河侧渗为本区提供了丰富的地下水和盐份。地表及埋藏古河道与决口扇造就了该区复杂的微地貌与水文地质结构,并决定了盐渍化程度和类型。本区盐渍化的宏观地质背景是难以改变的,但微观地质环境可以改造。盐渍化土壤的主要治理目标应是经常保持水盐动态平衡,不断采取措施淡化地下水盐份,使土壤含盐浓度从结晶浓度向低盐浓度转化。那种期望一次治理可永远根绝盐渍化的想法是不现实的。 相似文献
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柴达木盆地土壤盐渍化程度快速动态监测 总被引：1，自引：0，他引：1
针对柴达木盆地土壤盐渍化的时空变化问题,该文采用了反演土壤盐度指数的方法快速评估了该地区的盐渍化程度变化及其空间分布,并且选择2015年利用土壤采样方法获取标本验证SI在研究区的适用性,集成RS技术和GIS空间分析的优势,综合利用空间分析和时序分析技术对柴达木盆地2000-2015年的盐渍化程度和分布面积进行了时空变化分析.研究结果表明了 SI值在研究区的适用性,同时发现2000-2015年,柴达木盆地土壤盐渍化的面积和程度整体上均有明显降低趋势,尤其在重度盐渍化区域更为明显,但中、低度积盐面积,程度均有所增加.研究可以为柴达木盆地土壤盐渍化程度的快速评估提供参考. 相似文献
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盐渍化土壤光谱特征分析与建模 总被引：2，自引：0，他引：2
为建立土壤盐渍化遥感监测模型,选取宁夏回族自治区平罗县典型土壤盐渍化发生区域作为研究区,以野外原位光谱测量数据和实验室内测得的土壤含盐量与p H值数据为基础,进行高光谱数据处理,分析不同盐渍化程度土壤的光谱特征;对实测土壤光谱反射率进行倒数、对数、均方根及其一阶微分等光谱变换,计算高光谱指数;与土壤样本含盐量进行相关性分析,筛选盐渍化土壤的光谱特征波段,利用多元线性回归分析建立土壤盐渍化监测模型。研究结果表明:以倒数一阶微分变换后的940 nm和1 094 nm波段作为特征波段构建的土壤盐渍化遥感监测模型最优。 相似文献
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银川平原土壤盐渍化与中低产田遥感应用研究 总被引：10，自引：0，他引：10
土壤盐渍化是影响农业生产与生态环境的一个重要因素。长期以来,宁夏银川平原的次生土壤盐渍化问题十分突出,形成了许多盐渍型中低产田,影响和制约了区域农业的可持续发展。主要采用遥感解译方法提取了银川平原土壤盐渍化的分类与分布的现状信息。在此基础上,依据中低产田的分类标准,将相关的耕地类型、土壤肥力、作物产量、灌排水指标等因子转化成数字化专题图层,采用多源信息复合分类的方法,通过“综合分析,主导因子判定”实现了GIS辅助下的中低产田分类,为遥感和地理信息系统技术在土壤盐渍化和中低产田调查研究中的应用,探索出了一条可行的途径。 相似文献
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基于GRNN的ALI多光谱遥感数据土壤盐分反演研究 总被引：1，自引：0，他引：1
受环境变化和人类活动的双重影响,土壤盐渍化已经成为土壤退化的重要形式.及时展开土壤盐渍化研究对改善现状和预防其进一步发展具有重要意义.本文以黄河三角洲一处典型区域为研究对象,在野外光谱测量和实验室理化分析的基础上,采用广义回归神经网络(GRNN)方法建立了土壤盐分反演模型,模型的决定系数为0.855,均方根误差为0.119 9·kg-1.将GRNN模型应用到ALI反射率图像上得到土壤盐分反演分布图.结合野外调查结果发现,GRNN方法得到的土壤盐分值的空间分布结果与实际情况一致. 相似文献
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基于3S技术的干旱区绿洲土壤盐渍化动态监测 总被引：1，自引：0，他引：1
选择生态环境脆弱的艾比湖地区绿洲为研究对象,采用1990年、2001年、2010年3期TM和ETM+影像为数据源进行土壤盐渍化分类,对研究区20 a土壤盐渍化动态变化进行了统计与分析。研究结果表明,非盐渍化土壤分布的面积最为广泛,但动态变化最为缓慢;轻度盐渍地经历了先增加后减少的过程,中度盐渍地在这20 a里一直呈现出下降的趋势;重度盐渍地在1990年分布面积最多,为366.35 km2;极重度盐渍地分布面积最少,但面积变化最为剧烈。 相似文献
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土地盐渍化是影响区域生态环境质量和农业生产安全的重要因素,掌握其区域分布规律对盐渍化的预防和治理具有重要意义。基于ALOS影像和实地调查、土壤样品分析数据建立内蒙古杭锦后旗农用地盐渍化等级划分标准和遥感解译标志,分析了不同地物在不同波段的光谱特征,获得杭锦后旗土地盐渍化等级分类图。结果表明,杭锦后旗农用地盐渍化严重,中度以上盐渍化农用地占土地总面积的15.76%,占农用地总面积的25.68%;重度以上盐渍化农用地占土地总面积的3.28%,占农用地总面积的5.33%。研究区微度和轻度盐渍化农用地分布最广,中度盐渍化农用地分布比较分散,重度盐渍化农用地和盐土则主要沿灌渠和海子边缘分布,由西北向东南重度以上盐渍化土地比例有增加的趋势。 相似文献
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Throughout Australia, there is concern that land use change is mobilizing salt stored in the landscape, causing salinity in soil and water resources. Salt in the landscape becomes a salinity risk only if it is mobilized by groundwater movement. A combination of modelled groundwater behaviour under various land uses with three-dimensional salt-load maps developed from airborne electromagnetic survey (AEM) provides a practical tool to assess potential salt movement.AEM survey of the country around St. George, SE Queensland, revealed a potential salinity threat: significant salt stores in the uplands adjacent to flood plains which support important irrigation developments and which drain to the Darling River system. A conceptual model of the regional hydrogeology was built upon three-dimensional AEM data, an investigation-drilling program, and direct field measurement of hydraulic conductivities. This information was incorporated in a Flowtube groundwater model and groundwater responses to five different land management options were tested over a 100-year period. Surface water storage on relatively permeable soils and continuous irrigated cotton both resulted in water tables reaching the soil surface; rain-fed wheat and pasture both resulted in a raised watertable, but both established a new equilibrium without the water table reaching the ground surface. 相似文献
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土壤含盐量反演的研究 总被引：1，自引：0，他引：1
运用Hyperion数据,以黑龙江省大庆市某一实验区为例,开展对土壤含盐量定量提取的研究,通过对图像预处理、特征提取、建立BP神经网络模型（Back Propagation Network）等研究工作,探讨反演土壤含盐量的方法。研究结果表明：神经网络模型具有极强的线性和非线性拟合能力,模拟遥感影像特征与土壤盐分之间比较复杂的关系上有很大优势。研究结果不但为利用Hyperion数据反演土壤含盐量提供理论依据,而且还为其它地表参数的反演提供参考。 相似文献
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V. K. Verma R. K. Setia P. K. Sharma Harpinder Singh 《Journal of the Indian Society of Remote Sensing》2008,36(3):273-281
Secondary salinisation is the most harmful and extended phenomenon of the unfavourable effects of irrigation on the soil and
environment. An attempt was made to study the impact of poor quality ground water on soils in terms of secondary salinisation
and availability of soil nutrients in Faridkot district of Punjab of northern India. Based on physiographic analysis of IRS
1C LISS-III data and semi-detailed soil survey, the soil map was finalized on a 1:50,000 scale and digitized using Arc Info
GIS. Georeferenced surface soil samples (0–0.15 m) from 231 sites were collected and analyzed for available phosphorus (P)
and potassium (K). Interpolation by kriging produced digital spatial maps of available P and K. Ground water quality map was
generated in GIS domain on the basis of EC (electrical conductivity) and RSC (residual sodium carbonate) of ground water samples
collected from 374 georeferenced tube wells. Integration of soil and ground water quality maps enabled generating a map showing
degree (high, moderate and low) and type (salinity, sodicity and both) of vulnerability to secondary salinization. Fine-textured
soils have been found to be highly sensitive to secondary salinisation, whereas medium-textured soils as moderately sensitive
to secondary salinisation. The resultant map was integrated with available P and K maps to show the combined influence of
soil texture and ground water quality on available soil nutrients. The results show that available P and K in the soils of
different physiographic units were found in the order of Ap1 < Ap2 < Ap3. The soils of all physiographic units had sizeable
area having high content of P (>22.5 kg / ha) and medium available K (135–335 kg ha−1) in most of the test sites when irrigated with saline, sodic or poor quality water. 相似文献
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The presence of salt in the soil profile negatively affects the growth and development of vegetation. As a result, the spectral reflectance of vegetation canopies varies for different salinity levels. This research was conducted to (1) investigate the capability of satellite-based hyperspectral vegetation indices (VIs) for estimating soil salinity in agricultural fields, (2) evaluate the performance of 21 existing VIs and (3) develop new VIs based on a combination of wavelengths sensitive for multiple stresses and find the best one for estimating soil salinity. For this purpose a Hyperion image of September 2, 2010, and data on soil salinity at 108 locations in sugarcane (Saccharum officina L.) fields were used. Results show that soil salinity could well be estimated by some of these VIs. Indices related to chlorophyll absorption bands or based on a combination of chlorophyll and water absorption bands had the highest correlation with soil salinity. In contrast, indices that are only based on water absorption bands had low to medium correlations, while indices that use only visible bands did not perform well. From the investigated indices the optimized soil-adjusted vegetation index (OSAVI) had the strongest relationship (R2 = 0.69) with soil salinity for the training data, but it did not perform well in the validation phase. The validation procedure showed that the new salinity and water stress indices (SWSI) implemented in this study (SWSI-1, SWSI-2, SWSI-3) and the Vogelmann red edge index yielded the best results for estimating soil salinity for independent fields with root mean square errors of 1.14, 1.15, 1.17 and 1.15 dS/m, respectively. Our results show that soil salinity could be estimated by satellite-based hyperspectral VIs, but validation of obtained models for independent data is essential for selecting the best model. 相似文献
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This study evaluates the feasibility of hyperspectral and multispectral satellite imagery for categorical and quantitative mapping of salinity stress in sugarcane fields located in the southwest of Iran. For this purpose a Hyperion image acquired on September 2, 2010 and a Landsat7 ETM+ image acquired on September 7, 2010 were used as hyperspectral and multispectral satellite imagery. Field data including soil salinity in the sugarcane root zone was collected at 191 locations in 25 fields during September 2010. In the first section of the paper, based on the yield potential of sugarcane as influenced by different soil salinity levels provided by FAO, soil salinity was classified into three classes, low salinity (1.7–3.4 dS/m), moderate salinity (3.5–5.9 dS/m) and high salinity (6–9.5) by applying different classification methods including Support Vector Machine (SVM), Spectral Angle Mapper (SAM), Minimum Distance (MD) and Maximum Likelihood (ML) on Hyperion and Landsat images. In the second part of the paper the performance of nine vegetation indices (eight indices from literature and a new developed index in this study) extracted from Hyperion and Landsat data was evaluated for quantitative mapping of salinity stress. The experimental results indicated that for categorical classification of salinity stress, Landsat data resulted in a higher overall accuracy (OA) and Kappa coefficient (KC) than Hyperion, of which the MD classifier using all bands or PCA (1–5) as an input performed best with an overall accuracy and kappa coefficient of 84.84% and 0.77 respectively. Vice versa for the quantitative estimation of salinity stress, Hyperion outperformed Landsat. In this case, the salinity and water stress index (SWSI) has the best prediction of salinity stress with an R2 of 0.68 and RMSE of 1.15 dS/m for Hyperion followed by Landsat data with an R2 and RMSE of 0.56 and 1.75 dS/m respectively. It was concluded that categorical mapping of salinity stress is the best option for monitoring agricultural fields and for this purpose Landsat data are most suitable. 相似文献
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Assessing NDVI Spatial Pattern as Related to Irrigation and Soil Salinity Management in Al-Hassa Oasis,Saudi Arabia 总被引：5，自引：0，他引：5
Yousef Y. Aldakheel 《Journal of the Indian Society of Remote Sensing》2011,39(2):171-180
Sustainability of irrigated agriculture in arid and semi arid lands depends, mainly on the level of soil salinity and the
quality of irrigation water. Remotely sensed data can provide information about the extent of vegetated irrigated areas. Al-Hassa
oasis, Saudi Arabia is probably the largest oasis in the world depends mostly on tapped ground water to irrigate mainly date
palm groves for its economic survival. This study tried to investigate the extent of soil salinity and the quality of irrigation
water and the relationship with vegetation growth, employing NDVI derived from Landsat satellite imagery. 相似文献
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Soil salinization is a worldwide environmental problem with severe economic and social consequences. In this paper, estimating the soil salinity of Pingluo County, China by a partial least squares regression (PLSR) predictive model was carried out using QuickBird data and soil reflectance spectra. At first, a relationship between the sensitive bands of soil salinity acquired from measured reflectance spectra and the spectral coverage of seven commonly used optical sensors was analyzed. Secondly, the potentiality of QuickBird data in estimating soil salinity by analyzing the correlations between the measured reflectance spectra and reflectance spectra derived from QuickBird data and analyzing the contributions of each band of QuickBird data to soil salinity estimation Finally, a PLSR predictive model of soil salinity was developed using reflectance spectra from QuickBird data and eight spectral indices derived from QuickBird data. The results indicated that the sensitive bands covered several bands of each optical sensor and these sensors can be used for soil salinity estimation. The result of estimation model showed that an accurate prediction of soil salinity can be made based on the PLSR method (R2 = 0.992, RMSE = 0.195). The PLSR model's performance was better than that of the stepwise multiple regression (SMR) method. The results also indicated that using spectral indices such as intensity within spectral bands (Int1, Int2), soil salinity indices (SI1, SI2, SI3), the brightness index (BI), the normalized difference vegetation index (NDVI) and the ratio vegetation index (RVI) as independent model variables can help to increase the accuracy of soil salinity mapping. The NDVI and RVI can help to reduce the influences of vegetation cover and soil moisture on prediction accuracy. The method developed in this paper can be applied in other arid and semi-arid areas, such as western China. 相似文献
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V. K. Verma Anil Sood P. K. Sharma Charanjit Singh M. L. Manchanda 《Journal of the Indian Society of Remote Sensing》1996,24(1):11-23
The arid tract of Punjab experiences various problems like thick sand cover (sand dunes) in large area, poor retention of water and nutrients in coarse textured soils, soil salinity and/or alkalinity, water logging and poor ground water quality. In the present study multidate remotely sensed data both in the form of aerial photographs and satellite imagery on 1:50,000 scale were interpreted visually to map physiography and soils. The ground water samples from tubewells distributed all over the area were collected and analysed to prepare ground water quality map. The soil and ground water quality maps were integrated to produce a resource constraint map of the area showing physical, chemical and hydrological constraints. The study revealed that alluvial plain suffers from hydrological constraints due to marginal to.poor ground water in 86% of the total area. The sand dunes show both physical and hydrological constraints due to coarse textured (sandy) soils and brackish ground water. The basins having soil salinity and brackish ground water cover 0.10% of the area. Keeping in view the type of constraint, locale specific measures like levelling and stabilisation of sand dunes, reclamation of salt affected and water logged areas followed by plantation of tree species which act as biopumps are suggested. The conjuctive use of surface (canal) and ground water is essential to prevent secondary salinization and sodification. The study demonstrates the potential usefulness of remote sensing technology in mapping natural resources and assess the nature, magnitude and spatial distribution of resource constraints. 相似文献
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B. M. Bogachkin Yu. V. Nechayev Ye. A. Rogazhin B. N. Khovanskiy P. N. Yazev 《地理信息系统科学与遥感》2013,50(2):171-184
Soil salinity is one of the most important problems affecting Egyptian soils. It is caused by: (1) a rising water table, or (2) the misuse of the irrigation water. Two Landsat images acquired in 1987 and 1999 were used to detect and monitor soil salinity over the Siwa Oasis, Western Desert, Egypt. DN values of these images were converted to percent reflectance. Inspection of Landsat images revealed that saline soils had an overall higher spectral reflectance in all spectral bands except the two MIR bands. The reflectance curves of saline soils show a strong relationship between the existence of salts in the soil and the difference between bands 4 and 5. A salinity index (SI) was calculated for both images. The majority of pixels in the 1987 image have salinity index values ranging between 0 and 0.2, whereas the values in the 1999 image histogram ranged between 0 and 0.4. These values indicate that soil salinity has increased twofold during the 12 years spanning the imagery. These values show a strong correlation with vegetation index images, in which the 1999 vegetation index image reveals the appearance of surface water lakes formed due to a rising water table. This study presents a model for the identification of soil salinity using remote sensing measurements in conjunction with piezometer readings taken during the time of image acquisition. 相似文献