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自然资源学报 2016年第11期31卷 1949-1957页

作者：朱大运熊康宁肖华蓝家程贵州师范大学喀斯特研究院/国家喀斯特石漠化防治工程技术研究中心贵阳550001

植被指数是运用多源遥感影像提取石漠化过程中的主要参考指标之一。为了在石漠化提取中选择最优植被指数,论文以GF-1和landsat-oli为源数据,运用欧氏距离对多种植被指数在石漠化提取过程中的可分性和类型识别能力进行了定量的对比评价... 详细信息

植被指数是运用多源遥感影像提取石漠化过程中的主要参考指标之一。为了在石漠化提取中选择最优植被指数,论文以GF-1和landsat-oli为源数据,运用欧氏距离对多种植被指数在石漠化提取过程中的可分性和类型识别能力进行了定量的对比评价。结果表明:landsatoli在石漠化与非石漠化、不同等级石漠化信息提取的可分性上略优于GF-1,共有71个参数的欧氏距离大于等于阈值1.56;通过植被指数光谱特征,可以对非岩溶区与石漠化地区进行较好的区分,其类型间欧氏距离普遍高于阈值;然而由于相邻等级石漠化之间植被覆盖率存在渐近式过渡关系,在遥感影像上光谱反射率接近,比间隔等级石漠化更加难于区分。在石漠化类型识别能力方面,波段差和比方法优于单一光谱指数。对于GF-1和landsat-oli而言,石漠化信息提取中推荐使用的最优植被指数均为NDVI,其次为GRNDVI。

关键词：最优植被指数石漠化欧氏距离 GF-1 landsat-oli

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Comparative Study among Different Semi-Empirical Models for Soil Salinity Prediction in an Arid Environment Using oli landsat-8 Data

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Advances in Remote Sensing 2017年第1期6卷 23-39页

作者： A. El-Battay A. Bannari N. A. Hameid A. A. Abahussain Department of Geoinformatics College of Graduate Studies Arabian Gulf University Manama Kingdom of Bahrain Natural Resources and Environment Department College of Graduate Studies Arabian Gulf University Manama Kingdom of Bahrain

Salt-affected soils, caused by natural or human activities, are a common environmental hazard in semi-arid and arid landscapes. Excess salts in soils affect plant growth and production, soil and water quality and, therefore, increase soil erosion and land degradation. This research investigates the performance of five different semi-empirical predictive models for soil salinity spatial distribution mapping in arid environment using oli sensor image data. This is the first attempt to test remote sensing based semi-empirical salinity predictive models in this area: the Kingdom of Bahrain. To achieve our objectives, oli data were standardized from the atmosphere interferences, the sensor radiometric drift, and the topographic and geometric distortions. Then, the five semi-empirical predictive models based on the Normalized Difference Salinity Index (NDSI), the Salinity Index-ASTER (SI-ASTER), the Salinity Index-1 (SI-1), the Soil Salinity and Sodicity Index-1 and Index-2 (SSSI-1 and SSSI-2), developed for slight and moderate salinity in agricultural land, were implemented and applied to oli image data. For validation purposes, a fieldwork was organized and different important spots-locations representing different salinity levels were visited, photographed, and localized using an accurate GPS (σ ≤ ±30 cm). Based on this a priori knowledge of the soil salinity, six validation sites were selected to reflect non-saline, low, moderate, high and extreme salinity classes, descriptive statistics extracted from polygons and/or transects over these sites were used. The obtained results showed that the models based on NDSI, SI-1 and SI-ASTER all failed to detect salinity bounds for both extreme salinity (Sabkhah) and non-saline conditions. In Fact, NDSI and SI-ASTER gave respectively only 35% dS/m and 25% dS/m in extreme salinity validation site, while SI-1 and SI-ASTER indicated 38% dS/m and 39% dS/m in non-saline validation site. Therefore, these three models were deemed inadequate for the study site. However, both SSSI-1 a

关键词： Soil Salinity Spectral Indices Semi-Empirical Models Arid Land landsat-oli

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Salt-Affected Soil Mapping in an Arid Environment Using Semi-Empirical Model and landsat-oli Data

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Advances in Remote Sensing 2017年第4期6卷 260-291页

作者： Abderrazak Bannari Ali El-Battay Nadir Hameid Fadia Tashtoush Department of Geoinformatics College of Graduate Studies Arabian Gulf University Manama Kingdom of Bahrain Department of Natural Resources and Environment Arabian Gulf University Manama Kingdom of Bahrain

The aim of this research is to map the salt-affected soil in an arid environment using an advanced semi-empirical predictive model, Operational Land Imager (oli) data, a digital elevation model (DEM), field soil sampling, and laboratory and statistical analyses. To achieve our objectives, the oli data were atmospherically corrected, radiometric sensor drift was calibrated, and distortions of topography and geometry were corrected using a DEM. Then, the soil salinity map was derived using a semi-empirical predictive model based on the Soil Salinity and Sodicity Index-2 (SSSI-2). The vegetation cover map was extracted from the Transformed Difference Vegetation Index (TDVI). In addition, accurate DEM of 5-m pixels was used to derive topographic attributes (elevation and slope). Visual comparisons and statistical validation of the semi-empirical model using ground truth were undertaken in order to test its capability in an arid environment for moderate and strong salinity mapping. To accomplish this step, fieldwork was organized and 120 soil samples were collected with various degrees of salinity, including non-saline soil samples. Each one was automatically labeled using a digital camera and an accurate global positioning system (GPS) survey (σ ≤ ± 30 cm) connected in real time to the geographic information system (GIS) database. Subsequently, in the laboratory, the major exchangeable cations (Ca2+, Mg2+, Na+, K+, Cl- and SO42-), pH and the electrical conductivity (EC-Lab) were extracted from a saturated soil paste, as well as the sodium adsorption ratio (SAR) being calculated. The EC-Lab, which is generally accepted as the most effective method for soil salinity quantification was used for statistical analysis and validation purposes. The obtained results demonstrated a very good conformity between the derived soil salinity map from oli data and the ground truth, highlighting six major salinity classes: Extreme, very high, high, moderate, low and non-saline. The laboratory chemical analyses corroborate these result

关键词： Soil Salinity Remote Sensing landsat-oli GIS Semi-Empirical Model Salinity Spectral Indices Topographic Attributes Arid Environment

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基于landsat-oli影像数据的向海湿地水体叶绿素a反演模型研究

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现代农村科技 2019年第6期 60-62页

作者：马兰刘吉平吉林师范大学旅游与地理科学学院

本文以向海国家级保护区湿地水体为研究对象,以“3S”技术为支撑,基于实测水质数据与landsat-oli遥感影像数据,分析了波段的遥感反射率与叶绿素a浓度之间的相关性,采用回归分析的方法,建立并筛选出了最优反演模型。结果表明:landsat-OL... 详细信息

本文以向海国家级保护区湿地水体为研究对象,以“3S”技术为支撑,基于实测水质数据与landsat-oli遥感影像数据,分析了波段的遥感反射率与叶绿素a浓度之间的相关性,采用回归分析的方法,建立并筛选出了最优反演模型。结果表明:landsat-oli数据的第4波段与向海湿地水体叶绿素a浓度的相关性最好,其构建的线性模型为反演叶绿素a浓度的最优模型。

关键词：沼泽湿地叶绿素a 遥感反演 landsat-oli

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