Large-area rice yield forecasting using satellite imageries

Ability to make large-area yield prediction before harvest is important in many aspects of agricultural decision-making. In this study, canopy reflectance band ratios (NIR/RED, NIR/GRN) of paddy rice (Oryza sativa L.) at booting stage, from field measurements conducted from 1999 to 2005, were correlated with the corresponding yield data to derive regression-type yield prediction models for the first and second season crop, respectively. These yield models were then validated with ground truth measurements conducted in 2007 and 2008 at eight sites, of different soil properties, climatic conditions, and various treatments in cultivars planted and N application rates, using surface reflectance retrieved from atmospherically corrected SPOT imageries. These validation tests indicated that root mean square error of predicting grain yields per unit area by the proposed models were less than 0.7 T ha⁻¹ for both cropping seasons. Since village is the basic unit for national rice yield census statistics in Taiwan, the yield models were further used to forecast average regional yields for 14 selected villages and compared with officially reported data. Results indicate that the average yield per unit area at village scale can be forecasted with a root mean square error of 1.1 T ha⁻¹ provided no damaging weather occurred during the final month before actual harvest. The methodology can be applied to other optical sensors with similar spectral bands in the visible/near-infrared and to different geographical regions provided that the relation between yield and spectral index is established.     

地表反射率地形校正物理模型与效果评价方法研究进展

地形校正是准确获取地形复杂区遥感反射率的重要步骤,对提高山区地表遥感参数定量化反演精度,扩大遥感产品应用广度具有重要意义。从20世纪80年代开始,国内外学者开始对准确获取山区地表遥感反射率进行研究,建立了多种地形校正模型来减少或消除遥感图像中地形效应影响,减少同种地表类型的反射率差异,并将地形校正模型分为经验模型和物理模型。根据构建物理模型时是否考虑地表非朗伯体特性,将物理模型分为朗伯体假设模型和非朗伯体假设模型,本文分别从朗伯体假设模型和非朗伯体假设校正模型展开叙述。从两类模型构建的理论基础,模型特点,局限性等几方面进行分析和讨论,描述了两类模型的发展历程,系统阐述了朗伯体假设模型和非朗伯体假设模型的适用性和不足,剖析了目前地形校正模型存在的问题与挑战。同时,本文也比较了应用于地形校正的效果评价方法,并展望了地形校正方法和地形校正评价方法的未来主要发展方向。     

多源遥感影像水体指数的交互对比

在对地观测系统中,Landsat和EO-1数据比较适合于监测城市水体变化。但是这两种传感器数据的水体指数之间的关系不清楚,针对这一问题。该文利用两对同日过空的Landsat-7ETM+与EO-1ALI影像来查明二者水体指数之间的定量关系,通过回归统计分析来求出两种传感器水体指数之间的转换方程。结果表明,Landsat-7ETM+与EO-1ALI影像分别基于DN数据、IACM反射率数据计算的MNDWI水体指数间,均存在较好的线性相关关系,决定系数均在0.75以上;基于EO-1ALI的MNDWI指数影像信息更为丰富,因为其动态范围大于Landsat-7ETM+。     

Cloud classification in NOAA AVHRR imageries using spectral and textural features

Clouds contribute significantly to the formation of many of the natural hazards. Hence cloud mapping and its classification becomes a major component of the various physical models which are used for forecasting natural hazards. The problem of cloud data classification from NOAA AVHRR (advance very high resolution radiometer) satellite imagery using image transformation techniques is considered in this paper. The singular value decomposition (SVD) scheme is used to extract the salient spectral and textural features attributed to satellite snow and cloud data in visible and IR channels. The goals of this paper are to discriminate between clear sky and clouds in an 8 × 8 pixel array of 1.1 km AVHRR data. If clouds are present then classify them as low, medium or high range. This scheme can effectively segregate clouds and non-cloud features in the visible and IR bands of the imagery. It can also classify clouds as low, medium or high range with a success rate of 70–90%. Computer-based snow and cloud discrimination and automatic cloud classification system will help the forecaster in various climatological applications, viz., energy balance estimation, precipitation forecasting, landslide forecasting, weather forecasting and avalanche forecasting etc.     

Impact assessment of industrial effluent of arid soils by using satellite imageries

Visual interpretation ofIRS-LISS-II (1:50,000 Scale) FCC, band 2, 3 and 4 was carried out for the Industrial effluence assessment on soils (21900 ha) along the Bandi river course in Pali district of arid Rajasthan. Very severe (4378 ha), severe (3427 ha), moderate (5856 ha) and slight (1388 ha) categories of anthropogenic salt affected soils with the varied image characteristics in conjunction with ground truth Have been identified and delineated from satellite imagery. The morphological and physico-chemical characteristics revealed that very severe category soil has thick salt crust dominantly in light gray color at surface, manifestation of strong sub-angular to angular blocky structure, extra-ordinary hardness, high ECe, SAR and ESP but low pHs. Severe category soils have light brownish gray colour at surface, manifestation of strong angular to columnar structure, high Ece, pHs, SAR and ESP but slightly lower than very severe category.Moderate category soils have light brownish gray colour at surface, moderate to strong manifestation of prismatic structure, low ECe but high pHs, moderate to high SAR and ESP.Slight category soils have pale brown colour and hard crust at surface, moderate hardness with the tendency to form weak prismatic structure in subsoil, low ECe (<4dSm^-) but moderate pHs. The cations and anaions in saturation extract was in the order of Na⁺>Ca⁺⁺ > Mg⁺⁺ > K⁺ and Cl^- > SO₄ ^-- > HCO₃ ^-- > CO₃ ^--, respectively and salinity/sodicity was Sodium-Chloride-Sulfate type. Dynamics of salt accumulation, available nutrient status and amelioration measures required for their reclamation and improvement has been discussed in this paper.     

Determining oil slick thickness using hyperspectral remote sensing in the Bohai Sea of China

Abstract

Determining oil slick thickness plays an important role in assessing oil spill volume and its environmental impacts on the ocean. In this study, we used a Hyperion image of an oil spill accident area and seawater and fresh crude oil samples collected in the Bohai Sea of China. A well-controlled laboratory experiment was designed to simulate spectral responses to different oil slick thicknesses. Spectral resampling and normalization methods were used to reduce the differences in spectral reflectances between the experimental background seawater sample and real background seawater. Fitting the analysis with laboratory experimental data results showed a linear relationship between normalized oil slick reflectance and normalized oil slick thickness [20th band (R ²=0.92938, n=49, p<0.01), 26th band (R ²=0.93806, n=49, p<0.01), 29th band (R ²=0.93288, n=49, p<0.01)]. By using these statistical models, we successfully determined the normalized oil slick thickness with the Hyperion image. Our results indicate that hyperspectral remote sensing technology is an effective method to monitor oil spills on water. The spectral ranges of visible green and red light were the optimal bands for estimating oil slick thickness in case 2 water. The high, stabilized spectral reflectance of background seawater will be helpful in oil slick thickness inversion.     

Long term shoreline oscillation and changes of Cauvery delta coastline inferred from satellite imageries

Coastal zone is highly volatile ecosystem which is always in adjustments. Loss of shore line will cause severe impact on human life and as well as their properties. Remote sensing is a reliable technique to study the historical shoreline changes. Therefore in this paper long term shoreline oscillations of Cauvery delta shorelines at Poompuhar, Tharangambadi and Nagapattinam were studied using satellite imageries and the same was physically observed at the above three locations with the help of reference pillars and compared mutually. It was observed that the shoreline at Poompuhar is under accretion at the rate of 1.79m/ year and other shoreline stretches at Tharangambadi and Nagapattinam were under erosion at 0.4888m/ year and 0.4985m/ year respectively. It was also observed that the remote sensing study qualitatively matches with the physical observation for all the three coastal stretches of the study area.     

Pixel-based and object-based classifications using high- and medium-spatial-resolution imageries in the urban and suburban landscapes

With the increasing availability of high-spatial-resolution remote sensing imageries and with the observed limitations of pixel-based techniques, the development and testing of geographic object-based image analysis (GEOBIA) techniques for image classification have become one of the main research areas in geospatial science. This paper examines and compares the classification performance of a pixel-based method and an object-based method as applied to high- (QuickBird satellite image) and medium- (Landsat TM image) spatial-resolution imageries in the context of urban and suburban landscapes. For the pixel-based classification, the maximum-likelihood supervised classification approach was employed. And for the object-based classification, the pixel-based classified maps were integrated with a set of image segments produced using various calibrations. The results show evidence that the object-based method can produce classifications that are more accurate for both high- and medium-spatial- resolution imageries in the context of urban and suburban landscapes.     

Tree species classification using UAS-based digital aerial photogrammetry point clouds and multispectral imageries in subtropical natural forests

Tree species composition of forest stand is an important indicator of forest inventory attributes for assessing ecosystem health, understanding successional processes, and digitally displaying forest biodiversity. In this study, we acquired high spatial resolution multispectral and RGB imagery over a subtropical natural forest in southwest China using a fixed-wing UAV system. Digital aerial photogrammetric (DAP) technique was used to generate multi-spectral and RGB derived point clouds, upon which individual tree crown (ITC) delineation algorithms and a machine learning classifier were used to identify dominant tree species. To do so, the structure-from-motion method was used to generate RGB imagery-based DAP point clouds. Then, three ITC delineation algorithms (i.e., point cloud segmentation (PCS), image-based multiresolution segmentation (IMRS), and advanced multiresolution segmentation (AMRS)) were used and assessed for ITC detection. Finally, tree-level metrics (i.e., multispectral, texture and point cloud metrics) were used as metrics in the random forest classifier used to classify eight dominant tree species. Results indicated that the accuracy of the AMRS ITC segmentation was highest (F₁-score = 82.5 %), followed by the segmentation using PCS (F₁-score = 79.6 %), the IMRS exhibited the lowest accuracy (F₁-score = 78.6 %); forest types classification (coniferous and deciduous) had a higher accuracy than the classification of all eight tree species, and the combination of spectral, texture and structural metrics had the highest classification accuracy (overall accuracy = 80.20 %). In the classification of both eight tree species and two forest types, the classification accuracies were lowest when only using spectral metrics, indicated that the texture metrics and point cloud structural metrics had a positive impact on the classification (the overall accuracy and kappa accuracy increased by 1.49–4.46 % and 2.86–6.84 %, respectively).     

Study on the urban heat island effects and its relationship with surface biophysical characteristics using MODIS imageries

This study assesses surface urban heat island (UHI) and its associated surface physical characteristics using remote sensing approaches. TERRA/MODIS images acquired in 2005 in three different seasons were selected to generate land surface tem-perature and surface characteristics for the Changsha-Zhuzhou-Xiangtan metropolitan area in China. The intensity of urban heat is-land effects and its seasonal variations were examined. The result showed that UHI effects were significant both in the summer and the spri...     

高斯混合模型自动阈值法遥感冰川信息提取

利用阈值法进行遥感地物提取效率高、准确率高,但是在阈值的选取方面,传统的手动选取阈值,自动化低,需反复试操作,且易受主观因素影响。文中通过期望最大算法对局部冰川区域归一化雪覆盖指数建立高斯混合模型,去除区域内的混合像元类,再利用高斯混合模型模拟纯净化后的冰川类、非冰川类的NDSI分布情况,根据改进后的高斯混合模型分布情况,自动计算出区域内的冰川提取阈值。本文对不同海拔的3个区域进行算法实验,然后将新疆哈密的哈尔里克山冰川提取边界与冰川编目数据进行对比验证。研究结果表明,该方法自动计算的冰川提取阈值结果可靠、精度高,在差异较大区域仍较稳定,有一定的应用价值。     

Chronology and backtracking of oil slick trajectory to source in offshore environments using ultraspectral to multispectral remotely sensed data

Offshore natural seepage confirms the occurrence of an active petroleum system with thermal maturation and migration, regardless its economic viability for petroleum production. Ocean dynamics, however, impose a challenge for correlation between oil seeps detected on the water surface and its source at the ocean floor. This hinders the potential use of seeps in petroleum exploration. The present study aims to estimate oil exposure time on the water surface via remote sensing in order to help locating ocean floor seepage sources. Spectral reflectance properties of a variety of fresh crude oils, oil films on water and oil–water emulsions were determined. Their spectral identity was used to estimate the duration of exposure of oil–water emulsions based on their temporal spectral responses. Laboratory models efficiently predicted oil status using ultraspectral (>2000 bands), hyperspectral (>300 bands), and multispectral (<10 bands) sensors covering near infrared and shortwave infrared wavelengths. An oil seepage recorded by the ASTER sensor on the Brazilian coast was used to test the designed predictive model. Results indicate that the model can successfully forecast the timeframe of crude oil exposure in the ocean (i.e., the relative “age” of the seepage). The limited spectral resolution of the ASTER sensor, though, implies less accurate estimates compared to higher resolution sensors. The spectral libraries and the method proposed here can be reproduced for other oceanic areas in order to approximate the duration of exposure of noticeable natural oil seepages. This type of information is optimal for seepage tracing and, therefore, for oceanic petroleum exploration and environmental monitoring.     

地面成像光谱数据的田间杂草识别

地面成像光谱数据兼具高光谱分辨率与高空间分辨率,在田间杂草识别中具有很好的应用前景。目前基于机器视觉的杂草识别方法以形状特征为主,当作物杂草形态相似时识别的困难和利用高光谱特征以像元为单元识别时效率较低,不利于实时自动化除草,因此,本文提出一种综合面向对象与高光谱特征匹配的杂草识别方法,在对作物杂草对象样本的形状特征和光谱曲线提取分析的基础上,建立基于形状特征规则与光谱角匹配的植物对象识别决策树,用于识别实验田中的作物杂草对象。实验结果表明,当场景中某些不同种类植物对象的形态相似时,基于形状特征规则与光谱角匹配的杂草识别方法可借助高光谱特征精细区分植物对象的种类,且在形状特征规则约束下使用高光谱特征匹配法识别植物对象,可克服"同物异谱"和"同谱异物"现象带来的不确定性,该方法识别精度可优于仅使用光谱角匹配法的情况,并优于使用颜色和形状分析技术的情况。     

FY-4A AGRI陆地气溶胶光学厚度反演

风云四号A星（FY-4A）是中国第二代静止气象卫星的首颗星,多通道扫描成像辐射计AGRI (Advanced Geosynchronous Radiation Imager)是搭载在FY-4A上的主要光学载荷之一。AGRI具有高频率观测特点（每天观测205次）,在大气气溶胶的遥感高频监测方面具有良好应用潜力,但目前官方还未发布相应的气溶胶数据集。本文旨在针对AGRI数据的特点开发基于地表反射率比值库的反演算法以生产高精度的AGRI气溶胶数据集。本文首先基于再分析数据对去云后的AGRI L1级数据进行气体吸收订正;然后利用背景气溶胶光学厚度AOD(Aerosol Optical Depth)对一个月内的“次暗像元”进行大气校正以获取AGRI 0.65μm和0.83μm通道的地表反射率,进而获取这两个通道的地表反射率的比值,完成每个月的地表反射率比值库的构建;最后便可以基于已构建的地表反射率比值库实现地气解耦,完成气溶胶的遥感反演。该算法已被应用于2019年5—10月京津冀地区的气溶胶反演,AGRI AOD反演结果与美国国家航天局发布的MODIS (Moderate-resolution ...     

一种基于多源遥感影像的亚端元光谱提取方法

提出一种利用多源影像来提取低分辨影像亚端元光谱的方法,该方法利用高分辨分类得到的组分与低分辨的光谱之间的线性混合关系,建立了反演出低分辨影像的亚端元光谱的线性模型,并提出一种快速鲁棒反演亚端元光谱的提取方法。利用IKONOS和ETM数据验证了本文提出方法,实验结果表明了该方法的有效性。     

多模型耦合下的植被冠层可燃物含水率遥感反演

植被冠层可燃物含水率FMC(Fuel Moisture Content)是评估野火风险及估算火灾蔓延速率的重要指标。以中国西部6个典型研究区为例,基于辐射传输模型,利用Landsat 5 TM及Landsat 8 OLI遥感数据,开展草原、森林冠层FMC定量反演研究。为克服基于物理模型的病态反演问题、FMC自身的弱敏感性问题及西南森林多具复杂的双层冠层结构问题,研究中考虑了模型参数之间的相关特征,使用多波段遥感数据及耦合辐射传输模型等方法。反演结果显示,总体植被冠层FMC反演精度R~2为0.64,RMSE为44.86%,其中草地冠层FMC的反演精度(R~2=0.64,RMSE=47.57%)略低于森林冠层FMC的反演精度(R~2=0.71,RMSE=30.82%)。为进一步论证该反演结果对野火风险评估的有效性,研究中选取并分析了2011年3月2日于云南大理白族自治州剑川县金华镇金和村森林火灾爆发前、爆发时及灾后该区域植被冠层FMC的变化特征。结果显示,火灾爆发时该地区植被冠层FMC明显低于火灾发生前后(约一月时间)植被冠层FMC,证明了本文FMC反演结果对野火风险评估的有效性。     

Automatic main road extraction from high resolution satellite imageries by means of particle swarm optimization applied to a fuzzy-based mean calculation approach

Manual extraction of road network by human operator is an expensive and time-consuming procedure. Alternatively, automation of the extraction process would be a great advancement. For this purpose, an automatic method is proposed to extract roads from high resolution satellite images. In this study, using few samples from road surface, a particle swarm optimization is applied to a fuzzy-based mean calculation system to obtain road mean values in each band of high resolution satellite colour images. Then, the images are segmented using the calculated mean values from the fuzzy system. Optimizing the fuzzy cost function by particle swarm optimization enables the fuzzy approach to be the best mean value of road with sub-grey level precision. Initially, this method was applied to simulated images where the calculated mean values are consistent with the hypothetic mean values. Application of the method to IKONOS satellite images has shown a prospective outcome for automatic road extraction. Mathematical morphology is subsequently used to extract an initial main road centreline from the segmented image. Then, small redundant segments are automatically removed. The quality of the extracted road centreline indicates the effectiveness of the proposed approach.     

NPPVIIRS数据反演气溶胶光学厚度

利用NPP卫星的VIIRS传感器数据,基于暗像元法反演陆地气溶胶光学厚度AOD。首先,根据红外波段的归一化植被指数NDVI来对暗像元进行识别;然后,利用6S软件进行辐射传输计算构建查找表;最后,根据VIIRS数据从查找表插值得到AOD,并对其进行海拔校正。选取华北地区作为反演实验区,获得了2013年9月1日的气溶胶分布。利用AERONET北京站太阳光度计地基观测结果对反演结果对比验证,发现二者具有显著的相关性,相关系数达到0.7920。将2013年9月1日的MODIS AOD产品与本研究反演的AOD进行比对,发现二者分布趋势一致,相关系数为0.7059,相关性显著。反演结果表明,本文算法反演陆地AOD效果较好,为大气颗粒物环境监测提供了良好方法手段和数据源。     

Efficient spoofing identification using baseline vector information of multiple receivers

In a spoofing environment, a Global Navigation Satellite System (GNSS) receiver must employ anti-spoofing techniques for obtaining a normal navigation solution from the GNSS signal. We propose a new method for identifying spoofing signals using the norm of the difference of baseline vectors (NDB) obtained from multiple receivers. The main focuses of this research are to reduce the initial time required to identify the spoofing signal and to mitigate the physical constraints on multiple antennas placement. First, the multi-correlators of each receiver track both GNSS and spoofing signals simultaneously and classify them into two signal groups. Then, the baseline vectors are generated from the double-differenced carrier phase measurements of the classified signal groups, and the NDB is calculated. If the target positions of the spoofing signal groups are almost the same, the NDB has a fairly small value when the base position of the selected baseline vectors is calculated from one of the GNSS groups and the rover positions of the baseline vectors are calculated from each spoofing group of the multiple receivers. Using the NDB, a hypothesis is established, and a hypothesis test is conducted for identifying the spoofing signal. The performance of the proposed test statistics is analyzed with respect to the distance between the GPS antennas and the tuning parameter. Our experimental results show that the proposed method effectively performs spoofing identification with a short baseline. Additionally, the method exhibits a very low probability of fault detection and fast response time. This means that the immediate anti-spoofing can work properly in spoofing environments.     

重力异常对地下异常体边界的识别算法

针对常见的地下异常体边界处理算法存在着识别出的边界发散、不准确的问题,该文通过两组理论模型进行了多种边界识别方法的试算,以比较分析其各自的效果。结果表明:常见方法中,水平梯度、HTD法和Theta图法均能一定程度上反映出边界信息,但效果一般;而与其相比,该文在此基础上构建的3种基于更高一阶导数的边界识别滤波器能更有效、准确地提取边界信息;并且在不同深度异常体的组合模型中,改进二阶倾斜角ITA_2算子依旧能在有效均衡不同强度异常的同时,非常清晰地识别出不同深度异常体的边界,有利于实际工作中重力异常的解释。