Identification of hazelnut fields using spectral and Gabor textural features

Land cover identification and monitoring agricultural resources using remote sensing imagery are of great significance for agricultural management and subsidies. Particularly, permanent crops are important in terms of economy (mainly rural development) and environmental protection. Permanent crops (including nut orchards) are extracted with very high resolution remote sensing imagery using visual interpretation or automated systems based on mainly textural features which reflect the regular plantation pattern of their orchards, since the spectral values of the nut orchards are usually close to the spectral values of other woody vegetation due to various reasons such as spectral mixing, slope, and shade. However, when the nut orchards are planted irregularly and densely at fields with high slope, textural delineation of these orchards from other woody vegetation becomes less relevant, posing a challenge for accurate automatic detection of these orchards. This study aims to overcome this challenge using a classification system based on multi-scale textural features together with spectral values. For this purpose, Black Sea region of Turkey, the region with the biggest hazelnut production in the world and the region which suffers most from this issue, is selected and two Quickbird archive images (June 2005 and September 2008) of the region are acquired. To differentiate hazel orchards from other woodlands, in addition to the pansharpened multispectral (4-band) bands of 2005 and 2008 imagery, multi-scale Gabor features are calculated from the panchromatic band of 2008 imagery at four scales and six orientations. One supervised classification method (maximum likelihood classifier, MLC) and one unsupervised method (self-organizing map, SOM) are used for classification based on spectral values, Gabor features and their combination. Both MLC and SOM achieve the highest performance (overall classification accuracies of 95% and 92%, and Kappa values of 0.93 and 0.88, respectively) when multi temporal spectral values and Gabor features are merged. High F_β values (a combined measure of producer and user accuracy) for detection of hazel orchards (0.97 for MLC and 0.94 for SOM) indicate the high quality of the classification results. When the classification is based on multi spectral values of 2008 imagery and Gabor features, similar F_β values (0.95 for MLC and 0.93 for SOM) are obtained, favoring the use of one imagery for cost/benefit efficiency. One main outcome is that despite its unsupervised nature, SOM achieves a classification performance very close to the performance of MLC, for detection of hazel orchards.     

An approach to estimate the evapotranspiration using NOAA/AVHRR DATA

Evapotranspiration (ET) is continued process wherein moisture from soil and vegetated surface is transferred to the atmosphere. Changes in evapotranspiration are likely to have large impacts on terrestrial vegetation. Evapotranspiration is a seasonally varying property at a given place; changes in it reflect the status of soil moisture and terrestrial vegetation. Through water balance, ET can include major shifts in vegetative patterns and or its condition leading to climate change. Therefore, in this paper, it is attempted to estimate the evapotranspiration over various land cover using National Oceanic and Atmospheric Administration (NOAA)/ Advanced Very High Resolution Radiometer (AVHRR) data at coarse spatial resolution of 1.1 km. For this purpose, a semi-empirical model has been proposed to estimate the ET. Regression analysis has been carried out to develop an empirical relation between individual land cover surface temperature and ET, which will be helpful to know the effect of each land cover surface temperature on ET. In which, it is observed that surface temperature over grassland is more effective on ET in comparison to other land cover in March 1999 on the Mupfure, Zimbabwe catchment area. This type of estimation will be helpful for climate modeler, climatologists, ecosystem modeler and regional planner.     

Land surface temperature retrieval and its validation using NOAA AVHRR thermal data

The retrieval of land (soil-vegetation complex) surface temperature (LST) was carried out over semi-arid mixed agriculture landscape of Gujarat using thermal bands (channel 4 and 5) and ground emissivity from atmospherically corrected NDVI of NOAA AVHRR LAC images. The atmospheric correction of Visible and NIR band reflectance was done using SMAC model. The LST computed from split-window method and subsequently corrected with fractional vegetation cover were then compared with near synchronous ground observations of soil and air temperatures made during 13–17 January and April, 1997 at five Land Surface Processes Experiment (LASPEX) sites of Anand, Sanand, Derol, Arnej and Khandha covering 100 km x 100 km. The fractional vegetation cover corrected LST at noon hrs. varied from 301.6 – 311.9K in January and from 315.8 – 325.6K in April. The LST_corr were found to lie in the mid way between AT and ST during January. But in April, LST were found to be more close to ST which may be due to relatively poor vegetation growth as indicated by lower NDVI values in April indicating more contribution to LST from exposed soil surface.     

应用气象卫星识别薄云覆盖下的水体

本文根据地物光谱特性和卫星的信号接收原理，提出了一种利用气象卫星识别水体的简单而有效的方法，使得薄云覆盖下的水体和云影中的水体得到较好的识别效果。在洪水监测中使用该方法，可以充分利用气象卫星数据获取丰富的洪水动态信息．并以１９９１年江淮洪涝灾害为背景，对试验结果进行了分析．     

Object-based crop classification using multi-temporal SPOT-5 imagery and textural features with a Random Forest classifier

In this study, an object-based image analysis (OBIA) approach was developed to classify field crops using multi-temporal SPOT-5 images with a random forest (RF) classifier. A wide range of features, including the spectral reflectance, vegetation indices (VIs), textural features based on the grey-level co-occurrence matrix (GLCM) and textural features based on geostatistical semivariogram (GST) were extracted for classification, and their performance was evaluated with the RF variable importance measures. Results showed that the best segmentation quality was achieved using the SPOT image acquired in September, with a scale parameter of 40. The spectral reflectance and the GST had a stronger contribution to crop classification than the VIs and GLCM textures. A subset of 60 features was selected using the RF-based feature selection (FS) method, and in this subset, the near-infrared reflectance and the image acquired in August (jointing and heading stages) were found to be the best for crop classification.     

Automatic searching of control point in NOAA AVHRR image

This paper discusses the approaches for automatical searching of control points in the NOAA AVHRR image on the basis of data rearrangement in the form of latitude and longitude grid. The vegetation index transformation and multi-level matching strategies have been proven effective and successful as the experiments show while the control point database is established.     

Automatic searching of control point in NOAA AVHRR image

This paper discusses the approaches for automatical searching of control points in the NOAA AVHRR image on the basis of data rearrangement in the form of latitude and longitude grid. The vegetation index transformation and multi-level matching strategies have been proven effective and successful as the experiments show while the control point database is established.     

基于NOAA/AVHRR数据的北极反照率反演及其变化研究

极地海冰反照率直接影响极区的热收支,反照率的变化对地气系统热量收支平衡及气候变化等的研究具有重要意义。本文采用由美国国家海洋与大气管理局NOAA (National Oceanic and Atmospheric Administration)发射的NOAA卫星携带的先进的甚高分辨率辐射仪AVHRR (Advanced Very High Resolution Radiometer) Level-1B (L1B) 数据,经宽带反射率转换、各向异性校正、大气订正、云检测等处理,得到4 km宽带晴空地表反照率产品。将AVHRR反照率与北冰洋地表热收支SHEBA (Surface Heat Budget of the Arctic Ocean)实验数据进行印证,印证结果显示在冰雪冻结期二者平均偏差为-0.07,标准偏差为0.05。本文处理了2008年—2010年的AVHRR数据,结合第4次北极科学考察现场观测数据研究了北极冰面月平均反照率的变化,从降雪和冰脊两个方面分析了反照率的变化,结果显示反照率在冰雪融化过程中变化约为0.3,变化较大且较为迅速,表面粗糙的多年冰海域和较为平滑的一年冰海域的反照率在雪融化时期变化约为0.2且变化相对缓慢。研究结果表明,由冰雪融化引起的反照率变化较为快速且幅度较大,是引起北极反照率变化的主导因素。     

TM classification using local spectral variability

A classification method which takes into account not only spectral but also spatial features for LANDSAT‐4 and 5 Thematic Mapper (TM) data is proposed. In accordance with improvement of Instantaneous Field of View (IFOV), spatial information such as textural, contextual, etc. is also increased so that some treatments of such information is highly required. One of the simplest spatial features is local spectral variability such as standard deviation, variability constant, variance, etc. in small cells such as 2x2,3x3 pixels. Such information can be used together with conventional spectral features in an unified way, for the traditional classifier such as a pixel‐wise Maximum Likelihood Decision Rule (MLDR). From the experiments, there was a substantial improvement in overall classification accuracy for TM forestry data. The probability of correct classification (PCC) for the new clearcut and the alpine meadow classes increased by 7% to 97% correct. The confusion between alpine meadow and new clearcut was reduced from 9% to 3%.     

M变换在NOAA/AVHRR数据变化检测中的应用

将基于典型相关分析的正交变换的基础理论用于NOAA/AVHRR数据的多元变化检测 ,对具体实施步骤进行了深入的探讨 ,并对部分中间结果进行了分析。实验表明 ,该方法应用于NOAA/AVHRR数据的多元变化检测具有明显的优势 ,克服了传统方法存在的缺陷 ,具有良好的应用前景。     

Monitoring the regional and temporal variability of winter snowfall in the arid Andes using digital NOAA/AVHRR data

Abstract

This paper deals with the spatial distribution and the temporal variability of snowfall in the most arid part of the Andes (18°‐ 28°S) during southern hemisphere winter (May‐September). As the official precipitation data is of poor quality, analyses were carried out by means of digital image processing techniques, using NOAA/AVHRR satellite‐data. Through analysis of 24 different snowfall events from six winters, a previously unknown spatial and temporal precipitation pattern in this remote and unexplored area was revealed. Snowfall is most abundant in the southernmost part of the research area and on the western side of the Andes, indicating the Pacific origin of the snowfall.

Nevertheless, the typical snowfall pattern is modified during different periods of the winter. Three typical time periods could be defined and distinguished from one another. Each of these three periods is characterized by typical weather conditions (cold fronts and “cut‐offs “) leading to a distinct snowfall pattern.

As this study is part of a broader paleoclimatic project, the results will serve as a basis for paleoclimatic reconstruction of past climate. Only by knowing the modern circulation and precipitation patterns is it possible to interpret paleoclimatic signals and archives found in the study area (e.g. paleosol, moraines) correctly.     

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).     

NOAA/AVHRR sea surface temperature accuracy in the East/Japan Sea

Sea surface temperature (SST) retrieved from Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration (NOAA) polar orbiting environmental satellites were validated in the East/Japan Sea (EJS) using surface drifter measurements as ground truths from 2005 to 2010. Overall, the root-mean-square (rms) errors of multichannel SSTs (MCSSTs) and non-linear SSTs (NLSSTs) using global SST coefficients were approximately 0.85°C and 0.80°C, respectively. An analysis of the SST errors (satellite – drifter) revealed a dependence on the amount of atmospheric moisture. In addition, satellite-derived SSTs tended to be related to wind speeds, particularly during the night. The SST errors also demonstrated diurnal variations with relatively higher rms from 0.80°C to 1.00°C during the night than the day, with a small rms of about 0.50°C. Bias also exhibited reasonable diurnal differences, showing small biases during the daytime. Although a satellite zenith angle has been considered in the global SST coefficients, its effect on the SST errors still remained in case of the EJS. Given the diverse use of SST data, the continuous validation and understanding of the characteristic errors of satellite SSTs should be conducted based on extensive in-situ temperature measurements in the global ocean as well as local seas.     

用NOAA气象卫星AVHRR的定量资料计算冬小麦种植面积的两种方法

本文提出了用NOAA气象卫星AVHRR的定量资料计算冬小麦种植面积的方法,即绿度指数法和单通道法。绿度指数法是根据冬小麦在特定生育期内绿度值基本上保持为常数的特点,用几块巳知样地的种植面积,推算整体种植面积;单通道法是只使用AVHRR第二通道的反照率来计算冬小麦种植面积。这两种方法的优点在于不需考虑大气削弱的订正处理问题,从而使计算得以简化。     

基于NOAA/AVHRR的中亚地区荒漠化动态变化监测

本文通过对影像特征的荒漠化解译,监测荒漠化土地面积和荒漠化程度,分析探讨1989年至2009年中亚地区荒漠化的变化趋势。结果表明本文所采用的监测方法,具有一定可靠性和实用性,能够对中亚地区进行快速地荒漠化的宏观实时监测。     

联合光谱和纹理特征的支持向量机滩涂分类方法研究

滩涂作为海岸带的重要组成部分,是重要的土地资源。针对遥感影像滩涂分类的提取,文中提出一种联合光谱和纹理特征支持向量机(SVM)滩涂分类的方法。首先介绍纹理特征影像获取方法,通过灰度共生矩阵分析得到滩涂纹理特征影像;然后将光谱影像与纹理影像叠加形成一幅多维特征影像,用SVM分类算法中的OAR分类器进行分类实验,对分类结果进行实验分析。实验结果表明,该算法对提高海岸带地理信息获取能力,提升海洋遥感测绘信息化保障水平有积极意义。     

结合Sentinel-2光谱与纹理信息的冬小麦作物茬覆盖度估算

作物茬覆盖度的估算对于探究农业耕作方式对周围环境的影响具有十分重要的意义。目前,基于多光谱影像的作物茬指数是作物茬覆盖度估算的常用方法。然而,在作物茬高覆盖区域,指数法容易出现“饱和”现象。已有研究结果表明结合影像的光谱与纹理信息有助于改善指数法的“饱和”问题。Sentinel-2作为一颗多光谱卫星,空间分辨率可达10 m,与Landsat OLI相比,具有更丰富的纹理信息。因此,探究Sentinel-2光谱与纹理信息相结合在作物茬覆盖度估算上的潜力具有重要意义。本文以山东省禹城市为研究区,分析了Sentinel-2各波段反射率、归一化差值指数以及不同窗口大小下灰度共生矩阵统计量等遥感因子与野外实测作物茬覆盖度的相关性,并利用最优子集法对遥感因子进行筛选,构建作物茬覆盖度的最优估算模型。同时,使用留一法交叉验证对模型进行评价。结果表明在单因子分析中,归一化差异耕作指数NDTI（Normalized Difference Residue Index）与作物茬覆盖度的相关性最好,相关系数达0.735。使用NDTI、5×5窗口下Sentinel-2 8A波段的相关性统计量以及12波段的方差统计量构建的多元方程是作物茬覆盖度估算的最优模型,相关系数为0.869,均方根误差为11%。与仅使用光谱信息的最优模型相比,相关系数提高了0.094,均方根误差下降了3.5%。可见,结合Sentinel-2的纹理信息有助于提高作物茬覆盖度的估算精度。     

Determination of the bulk temperature from NOAA/AVHRR satellite data in a midlatitude lake

The spatial and temporal variability of the bulk temperature gives important insights into biological and hydrodynamic processes. However, standard algorithms for satellite data only provide information of the surface temperature. The comparison of current and new split-window coefficients applied on NOAA-14/AVHRR brightness temperatures of Lake Constance showed that a regional adaption was most promising. To derive the bulk temperature information, a priori progression from a weather station was included into the AVHRR analysis. Among the weather is data, the mean temperature of the three preceding days and the day of the year were the most relevant additional information. By a multiple regression approach the bulk temperature in the upper 4 m of Lake Constance could be determined with an accuracy of ±1.20 °C. The training of a neural network improved the determination of the bulk temperature to ±1.04 °C.An extended field campaign demonstrated that the algorithm is also applicable to other sensors with the same spectral band settings (in this case NOAA-16/AVHRR) with an acceptable error and that it is equally accurate over the entire lake.     

基于光谱和空域信息的城区变化检测方法研究

目前的变化检测研究主要集中在利用中、小比例尺遥感图像进行自然环境等方面的变化检测,如草场的季节性变化、灾害检测、植被分布变化、土地使用规划等。然而随着城市的快速发展,为了满足城区管理规划中对道路、房屋等变化细节进行分析的需求,并由于高分辨率卫星诸如Ikonos,Quickbird等的出现,我们有必要并且有可能开发出一套实用、有效的、可靠的城区自动变化检测系统。针对城市区域的复杂性及其高分辨率卫星影像的配准误差问题,本文提出一种通过模糊逻辑结合光谱特征和空域特征的城区变化检测方法,以期望减小图像整体及局部配准误差对变化检测精度的影响。