Generation of fraction images from AVHRR data using linear mixing model

The coarse resolution satellite data have been widely used for regional and global studies as they provide high temporal frequency. The information contained in the coarse resolution pixels are mostly mixture of several components. The extraction of information contained in a pixel find its role in Geosphere-Biosphere context. The present study address the utility of constrained least square model applied to coarse spatial resolution data from NOAA-AVHRR for generating fraction images of vegetation, soil and water/shade. The red and near-infrared channels have been used to run the constrained least square model to generate fraction images. The derived fraction images are related to normalised difference vegetation index (NDVI) for model validation. The results suggest that vegetation fraction components are strongly correlated with NDVI values (r²=0.98). The soil fractions (r²=?0.84) and water/shade fractions (4²=?0.97) are negatively correlated with NDVI. The relationship between the fraction images and NDVI show the potential of the model in deriving sub-pixel component information using coarse resolution data.     

NOAA卫星图像云点检测的三维统计图集合判别

在应用NOAA-AVHRR图像对地球资源环境进行监测的研究中,云点检测是一项基本工作。本文以实例分析云的图像特征,选取特征指标建立像元分布三维统计图,直接在统计图上观察云点集群分布阈值,并以此作为云点集合判别的条件,以实现自动检测。此法灵敏度高,稳定性好,且直观方便。     

高分二号异轨立体数据的森林高度提取

森林植被碳储量的空间分布格局及其动态变化是陆地生态系统碳收支核算的基础。作为森林地上生物量的重要指示因子,森林高度的精确估算是提高森林植被碳储量估算精度的关键。现有研究已证明,由专业星载摄影测量系统获取的立体观测数据可用于森林高度提取,但光学遥感数据最大的问题是受云雨等天气因素的影响严重。区域森林地上生物量产品的生产需要充分挖掘潜在数据源。国产高分二号卫星(GF-2)虽然不是为获取立体观测数据而设计的专业星载摄影测量系统,但其获取的图像空间分辨率可达0.8 m,且具备±35°的的侧摆能力,在重复观测区域可构成异轨立体观测。本文以分别获取于2015年6月20日和2016年7月19的GF-2数据作为立体像对,其标称轨道侧摆角分别为0.00118°和20.4984°,以激光雷达数据获取的林下地形(DEM)和森林高度(CHM)为参考,对利用GF-2立体观测数据进行森林高度提取进行了研究。通过对立体处理得到的摄影测量点云的栅格化得到DSM,以激光雷达数据提供的DEM作为林下地形,得到了GF-2的CHM。结果表明GF-2提取的CHM与激光雷达CHM空间分布格局较为一致,两者之间存在明显的相关性,像素对像素的线性相关性(R2)达到0.51,均方根误差(RMSE)为3.6 m。研究结果表明,在林下地形已知的情况下,GF-2立体观测数据可用于森林高度估算。     

ERS-2 SAR and IRS-1C LISS III data fusion: A PCA approach to improve remote sensing based geological interpretation

Fusion of optical and synthetic aperture radar data has been attempted in the present study for mapping of various lithologic units over a part of the Singhbhum Shear Zone (SSZ) and its surroundings. ERS-2 SAR data over the study area has been enhanced using Fast Fourier Transformation (FFT) based filtering approach, and also using Frost filtering technique. Both the enhanced SAR imagery have been then separately fused with histogram equalized IRS-1C LISS III image using Principal Component Analysis (PCA) technique. Later, Feature-oriented Principal Components Selection (FPCS) technique has been applied to generate False Color Composite (FCC) images, from which corresponding geological maps have been prepared. Finally, GIS techniques have been successfully used for change detection analysis in the lithological interpretation between the published geological map and the fusion based geological maps. In general, there is good agreement between these maps over a large portion of the study area. Based on the change detection studies, few areas could be identified which need attention for further detailed ground-based geological studies.     

国产高分辨率遥感卫星影像自动云检测

云检测一直是卫星影像处理的难题,特别是混有地物光谱特性的薄云长期成为影像产品生产的阻碍。本文所介绍的国产高分辨率遥感卫星影像自动云检测方法能够有效克服这一难题。首先采用改进的颜色转换模型,将影像由RGB转换至HIS颜色空间,利用影像强度信息与饱和度信息生成基底图,并使用影像近红外与色调信息对其进行优化,生成修正图。然后利用直方图均衡化与双边滤波结合带限定条件的Otsu阈值分割提取纹理信息,并对修正图进行误差剔除生成云种子图。最后以HIS颜色空间的强度信息为向导,结合云种子图进行云精确提取。与不同自动、人工交互式云检测方法相比,总体精度提高了10%左右,并且能够较好地提升云检测效率。     

农田蒸散双层模型及其在干旱遥感监测中的应用

该文建立了在部分植被条件下估算农田蒸散的双层模型，利用此模型和ＮＯＡＡ－ＡＶＨＲＲ数据对黄淮海平原春季旱情进行了评估，并做出了干旱及蒸散分布图。对双层模型和单层模型的估算结果进行了比较，结果表明在部分植被条件下双层模型估算的精度要高。     

Cloud height determination using satellite stereoscopy from Along Track Scanning Radiometer onboard ERS-1 satellite: A simulation study

The first ERS-1 satellite launched in May 1991 promises to make a substantial contribution towards the cloud stereoscopy. One of its payload, Along Track Scanning Radiometer (hereafter ATSR) has IR split window channels and scans the earth conically leading to earth views at two view angles (0° and 55°) in two curved swaths registered with respect to each other in image frame. This enables us to construct stereoscopic image of a cloud thereby determining its height geometrically. The present paper describes the utility of ATSR onboard ERS-1 in retrieving cloud height by synthetic stereo imaging over NOAA-AVHRR visible and thermal band observations.     

中国小比例尺卫星影象地图的设计与制作

利用数字化的NOAA AVHRR数据进行小比例尺、准同步宏观制图是一种低成本和快速的制图方法。笔者应用本文方法制作了第一幅小比例尺的、完整的中国假彩色卫星影象地图。它包含了全部的南中国海,这是使用其他的遥感数据(如陆地卫星或SPOT卫星)难以做到的。该图采用了热红外、近红外和可见光的红波段所构成的假彩色合成方案,提供了丰富的色彩和影象信息。本图制作时实施的数据处理内容包括:地图投影变换、影象反差增强和锐化、彩色平衡调整、去云处理和海陆分离处理、利用植被指数原理的影象时相修正和数字镶嵌操作等。根据影象地图的载负量和视觉效果,图上精心选取并设计了地理要素、地图符号和注记。该图是应用遥感新技术开发的地图新产品,被十七届国际摄影测量与遥感会议选为展示成果,并被该会评为获奖成果。     

Vegetational response of rainfall in Rajasthan using AVHRR imagery

The purpose of this paper is to demonstrate the suitability of NOAA-AVHRR imagery for monitoring vegetal cover. Of course, climate is recognized as the most important control on natural vegetation. However, spatial and seasonal changes in land cover are dependent on several factors including the crop species, soil hydrology, grazing conditions, harvest timing and above all on the frequency and amount of rainfall. The analysis is based on six maximum value composite (MVC) NDVI images with a spatial resolution of approximately 5.5 km. An attempt has also been made to examine the serial relationship between the NDVI and the rainfall occurrences at different stations. ‘Thar Desert’ is clearly visible in the images of summer months. A transitional zone is situated between the arid and semi-arid areas along the western fringe of the Aravalli Range. The elimination of time lag in vegetational response of rainfall indicates expected positive relationship between vegetal cover and rainfall.     

Mapping Wildfire Burn Severity in Southern California Forests and Shrublands Using Enhanced Thematic Mapper Imagery

Abstract

Wildfire is a major disturbance agent in Mediterranean Type Ecosystems (MTEs). Providing reliable, quantitative information on the area of burns and the level of damage caused is therefore important both for guiding resource management and global change monitoring. Previous studies have successfully mapped burn severity using remote sensing, but reliable accuracy has yet to be gained using standard methods over different vegetation types. The objective of this research was to classify burn severity across several vegetation types using Landsat ETM imagery in two areas affected by wildfire in southern California in June 1999. Spectral mixture analysis (SMA) using four reference endmembers (vegetation, soil, shade, non‐photosynthetic vegetation) and a single (charcoal‐ash) image endmember were used to enhance imagery prior to burn severity classification using decision trees. SMA provided a robust technique for enhancing fire‐affected areas due to its ability to extract sub‐pixel information and minimize the effects of topography on single date satellite data. Overall kappa classification accuracy results were high (0.71 and 0.85, respectively) for the burned areas, using five canopy consumption classes. Individual severity class accuracies ranged from 0.5 to 0.94.     

Forest cover trends from time series Landsat data for the Australian continent

In perennial and natural vegetation systems, monitoring changes in vegetation over time is of fundamental interest for identifying and quantifying impacts of management and natural processes. Subtle changes in vegetation cover can be identified by calculating the trends of a vegetation density index over time. In this paper, we apply such an index-trends approach, which has been developed and applied to time series Landsat imagery in rangeland and woodland environments, to continental-scale monitoring of disturbances within forested regions of Australia. This paper describes the operational methods used for the generation of National Forest Trend (NFT) information, which is a time-series summary providing visual indication of within-forest vegetation changes (disturbance and recovery) over time at 25 m resolution. This result is based on a national archive of calibrated Landsat TM/ETM+ data from 1989 to 2006 produced for Australia's National Carbon Accounting System (NCAS). The NCAS was designed in 1999 initially to provide consistent fine-scale classifications for monitoring forest cover extent and changes (i.e. land use change) over the Australian continent using time series Landsat imagery. NFT information identifies more subtle changes within forested areas and provides a capacity to identify processes affecting forests which are of primary interest to ecologists and land managers. The NFT product relies on the identification of an appropriate Landsat-based vegetation cover index (defined as a linear combination of spectral image bands) that is sensitive to changes in forest density. The time series of index values at a location, derived from calibrated imagery, represents a consistent surrogate to track density changes. To produce the trends summary information, statistical summaries of the index response over time (such as slope and quadratic curvature) are calculated. These calculated index responses of woody vegetation cover are then displayed as maps where the different colours indicate the approximate timing, direction (decline or increase), magnitude and spatial extent of the changes in vegetation cover. These trend images provide a self-contained and easily interpretable summary of vegetation change at scales that are relevant for natural resource management (NRM) and environmental reporting.     

应用遥感图像分析植物群落的分布图式

本文探讨了依据遥感图像,从整体上自动地(或半自动地)研究了植物群落的方法。针对植物群落的复杂性,本文研究了下述三个方面的内容:(1)划分植物群落,并从图像上分析群落的分异规律;(2)将植物群落序列结构化;(3)确定植物群落的分布图式。上述三个方面研究成果已应用于西藏班戈地区的植物群落分析中,事实证明,这是一种新颖且有意义的植物学研究方法     

Remotely-sensed assessment of the impact of century-old biochar on chicory crop growth using high-resolution UAV-based imagery

In recent years, special attention has been given to the long-term effects of biochar on the performance of agro-ecosystems owing to its potential for improving soil fertility, harvested crop yields, and aboveground biomass production. The present experiment was set up to identify the effects on soil-plant systems of biochar produced more than 150 years ago in charcoal mound kiln sites in Wallonia (Belgium). Although the impacts of biochar on soil-plant systems are being increasingly discussed, a detailed monitoring of the crop dynamics throughout the growing season has not yet been well addressed. At present there is considerable interest in applying remote sensing for crop growth monitoring in order to improve sustainable agricultural practices. However, studies using high-resolution remote sensing data to focus on century-old biochar effects are not yet available. For the first time, the impacts of century-old biochar on crop growth were investigated at canopy level using high-resolution airborne remote sensing data over a cultivated field. High-resolution RGB, multispectral and thermal sensors mounted on unmanned aerial vehicles (UAVs) were used to generate high frequency remote sensing information on the crop dynamics. UAVs were flown over 11 century-old charcoal-enriched soil patches and the adjacent reference soils of a chicory field. We retrieved crucial crop parameters such as canopy cover, vegetation indices and crop water stress from the UAV imageries. In addition, our study also provides in-situ measurements of soil properties and crop traits. Both UAV-based RGB imagery and in-situ measurements demonstrated that the presence of century-old biochar significantly improved chicory canopy cover, with greater leaf lengths in biochar patches. Weighted difference vegetation index imagery showed a negative influence of biochar presence on plant greenness at the end of the growing season. Chicory crop stress was significantly increased by biochar presence, whereas the harvested crop yield was not affected. The main significant variations observed between reference and century-old biochar patches using in situ measurements of crop traits concerned leaf length. Hence, the output from the present study will be of great interest to help developing climate-smart agriculture practices allowing for adaptation and mitigation to climate.     

Generation of thermal inertia image over a part of Gujarat: A new tool for geological mapping

Thermal inertia is a volume property and can be used to detect the subsurface features in an alluvium-covered area. A thermal inertia image has been generated over a part of Gujarat using consecutive day and night NOAA-AVHRR data. Gujarat contains two important rift basins in the western margin of India, namely, Cambay and Kutch basins. Major land covers exist in this region are alluvium, continental sediments, various rocks of Mesozoic and Cenozoic origin followed by Deccan Volcanics, tertiary and quaternary deposits. Validation of thermal inertia parameters with existing values obtained from literature indicates the efficacy of the developed technique. The study indicates that thermal inertia image can be used as a new method for geologic mapping/basin delineation where exposure is less or contrast is negligible between litho-units using traditional photography/sensing techniques.     

单体建筑物点云数据的CSF净化处理

从场景整体点云数据中提取单体建筑物的点云是建筑物单体三维建模的基础。然而,现有点云提取方法在提取建筑物点云数据时往往包含部分植被、地面等非建筑数据点,不利于建筑物对象建模。针对该问题,本文提出使用CSF方法对初步提取的建筑物点云数据进行净化处理。该方法首先将场景点云数据投影生成点云图像,根据图像特征初步提取单体建筑物点云数据;然后对获得的单体建筑物点云数据采用CSF方法进行净化处理,可以获得较为纯净的单体建筑物点云数据。本文以南京师范大学仙林校区部分区域为研究对象对该方法进行了验证。结果表明,该方法可以较好地对建筑物点云数据进行净化,得到较为纯净的单体建筑物点云数据,为基于点云数据的建筑物单体模型构建打下了良好的基础。     

Landcover Classification in the Taieri River Catchment,New Zealand: A Focus on the Riparian Zone

Abstract

Riparian vegetation has a fundamental influence on the biological, chemical and physical nature of rivers. The quantification of riparian landcover is now recognised as being essential to the holistic study of the ecosystem characteristics of rivers. Medium resolution satellite imagery is now commonly used as an efficient and cost effective method for mapping vegetation cover; however such data often lack the resolution to provide accurate information about vegetation cover within riparian corridors. To assess this, we measure the accuracy of SPOT multispectral satellite imagery for classification of riparian vegetation along the Taieri River in New Zealand. In this paper, we discuss different sampling strategies for the classification of riparian zones. We conclude that SPOT multispectral imagery requires considerable interpretative analysis before being adequate to produce sufficiently detailed maps of riparian vegetation required for use in stream ecological research.     

Woody vegetation and land cover changes in the Sahel of Mali (1967–2011)

In the past 50 years, the Sahel has experienced significant tree- and land cover changes accelerated by human expansion and prolonged droughts during the 1970s and 1980s. This study uses remote sensing techniques, supplemented by ground-truth data to compare pre-drought woody vegetation and land cover with the situation in 2011. High resolution panchromatic Corona imagery of 1967 and multi-spectral RapidEye imagery of 2011 form the basis of this regional scaled study, which is focused on the Dogon Plateau and the Seno Plain in the Sahel zone of Mali. Object-based feature extraction and classifications are used to analyze the datasets and map land cover and woody vegetation changes over 44 years. Interviews add information about changes in species compositions. Results show a significant increase of cultivated land, a reduction of dense natural vegetation as well as an increase of trees on farmer's fields. Mean woody cover decreased in the plains (−4%) but is stable on the plateau (+1%) although stark spatial discrepancies exist. Species decline and encroachment of degraded land are observed. However, the direction of change is not always negative and a variety of spatial variations are shown. Although the impact of climate is obvious, we demonstrate that anthropogenic activities have been the main drivers of change.     

Classifying historical remotely sensed imagery using a tempo-spatial feature evolution (T-SFE) model

Large and growing archives of orbital imagery of the earth’s surface collected over the past 40 years provide an important resource for documenting past and current land cover and environmental changes. However uses of these data are limited by the lack of coincident ground information with which either to establish discrete land cover classes or to assess the accuracy of their identification. Herein is proposed an easy-to-use model, the Tempo-Spatial Feature Evolution (T-SFE) model, designed to improve land cover classification using historical remotely sensed data and ground cover maps obtained at later times. This model intersects (1) a map of spectral classes (S-classes) of an initial time derived from the standard unsupervised ISODATA classifier with (2) a reference map of ground cover types (G-types) of a subsequent time to generate (3) a target map of overlaid patches of S-classes and G-types. This model employs the rules of Count Majority Evaluation, and Subtotal Area Evaluation that are formulated on the basis of spatial feature evolution over time to quantify spatial evolutions between the S-classes and G-types on the target map. This model then applies these quantities to assign G-types to S-classes to classify the historical images. The model is illustrated with the classification of grassland vegetation types for a basin in Inner Mongolia using 1985 Landsat TM data and 2004 vegetation map. The classification accuracy was assessed through two tests: a small set of ground sampling data in 1985, and an extracted vegetation map from the national vegetation cover data (NVCD) over the study area in 1988. Our results show that a 1985 image classification was achieved using this method with an overall accuracy of 80.6%. However, the classification accuracy depends on a proper calibration of several parameters used in the model.     

应用气象卫星NOAA-AVHRR图像进行植被研究的多时相途径

本文讨论了应用NOAA-AVHRR数字图像研究植被季相变化和进行植被分类的多时相方法。对生长季节的各时相植被指数VI图像进行图像分割,可揭示植被绿锋的进退和季相变化及其在空间上的表现。对多时相的植被指数VI图像进行主成分变换,据第一主成分的直方图各特征峰的形状和位置进行图像分割,可获得植被景观图,这一结果与现行的植被分布图相当一致。     

Integrating stream gage data and Landsat imagery to complete time-series of surface water extents in Central Valley,California

Accurate monitoring of surface water location and extent is critical for the management of diverse water resource phenomena. The multi-decadal archive of Landsat satellite imagery is punctuated by missing data due to cloud cover during acquisition times, hindering the assembly of a continuous time series of inundation dynamics. This study investigated whether streamflow volume measurements could be integrated with satellite data to fill gaps in monthly surface water chronologies for the Central Valley region of California, USA, from 1984 to 2015. We aggregated measurements of maximum monthly water extent within each of the study area’s 50 8-digit hydrologic unit code (HUC) watersheds from two Landsat-derived datasets: the European Commission’s Joint Research Centre (JRC) Monthly Water History and the U.S. Geological Survey Dynamic Surface Water Extent (DSWE). We calculated Spearman rank correlation coefficients between water extent values in each HUC and streamflow discharge data. Linear regression fits of the water extent/streamflow data pairs with the highest correlations served as the basis for interpolation of missing imagery surface water values on a HUC-wise basis. Results show strong (ρ > 0.7) maximum correlations in 11 (22.4%) and 25 (51.0%) HUCs for the DSWE and JRC time series, respectively, when comparisons were restricted to imagery and gages co-located in each HUC. Strong maximum correlations occurred in 39 (79.6%; DSWE) and 42 (85.7%; JRC) HUCs when imagery was paired with discharge data from any study area gage, providing a solid basis for reconstruction of water extent values. We generated continuous time series of 30+ years in 35 HUCs, demonstrating that this technique can provide quantitative estimates of historical surface water extents and elucidate flooding or drought events over the period of data collection. Results of a non-parametric trend analysis of the long-term time series on an annual, seasonal, and monthly basis varied among HUCs, though most trends indicate an increase in surface water over the past 30 years.