Material for publication

Abstract

Experimental applications of data from multispectral and other advanced sensors have demonstrated that remote sensing can make a valuable contribution to the monitoring and management of Canada's land resources. More frequent coverage and additional spectral bands on satellites planned for the mid-1980s and beyond will increase the opportunities for regular use of remotely sensed data. To effectively utilize these data in resource management, the remote sensing input must be matched with the resource management systems existing at that time. Thus, it is essential to anticipate the needs of resource management systems of the late 1980s and 1990s, to determine the appropriate role for remotely sensed data and to develop and implement a plan which will yield the remote sensing systems and methodologies necessary to meet the operational resource management requirements

Previous studies of resource information requirements indicate that there will be a need for geocoded remotely sensed data, improved image analysis techniques and better information integration concepts for future resource management systems. To develop a plan for meeting the anticipated requirements, the flow from the recording of the remotely sensed data to the end use of the derived information is considered first. The timeliness and accuracy requirements of different users, the diverse data types and forms for individual applications, the analysis methods/decision models needed and the implications of these factors for the configuration(s) of remote sensing input into the future resource management systems are examined. From this analysis, areas requiring further work (research, development, demonstration, transfer) are identified, and a plan of action is suggested.     

A multistage database of field measurements and synoptic remotely sensed data to support model validation and testing in Earth observation

This paper presents a novel database of ground and remotely sensed data from the United Kingdom, which is uniquely suited to scaling-up multispectral measurements from a single plot to the scale of satellite sensor observations. Multiple aircraft and satellite sensors were involved, and most of the data were acquired on a single day in June 2006, providing a synoptic view which, at its largest extent, covered most of southern England and Wales. Three airborne imaging spectrometers were involved (Specim AISA Eagle, Itres CASI-2 and -3) and three satellite sensors (UK-DMC, PROBA/CHRIS, and SPOT HRG), complemented with airborne LiDAR, multispectral survey cameras, and ground measurements (land cover, LAI, reflectance factors, and atmospheric measurements). In this paper the NCAVEO Field Campaign (NFC) database is described and an example of its use to produce a high spatial resolution leaf area index map for the validation of medium-resolution products (MODIS, VEGETATION, and MERIS) is presented.     

Correlation between potato yield and MODIS‐derived vegetation indices

Monitoring of crop growth and forecasting its yield well before harvest is very important for crop and food management. Remote sensing images are capable of identifying crop health, as well as predicting its yield. Vegetation indices (VIs), such as the normalized difference vegetation index (NDVI), leaf area index (LAI) and fraction of photosynthetically active radiation (fPAR) calculated from remotely sensed data have been widely used to monitor crop growth and to predict crop yield. This study used 8 day TERRA MODIS reflectance data of 500 m resolution for the years 2005 to 2006 to estimate the yield of potato in the Munshiganj area of Bangladesh. The satellite data has been validated using ground truth data from fields of 50 farmers. Regression models are developed between VIs and field level potato yield for six administrative units of Munshiganj District. The yield prediction equations have high coefficients of correlation (R ²) and are 0.84, 0.72 and 0.80 for the NDVI, LAI and fPAR, respectively. These equations were validated by using data from 2006 to 2007 seasons and found that an average error of estimation is about 15% for the study region. It can be concluded that VIs derived from remote sensing can be an effective tool for early estimation of potato yield.     

卫星遥感数字图像的地面辐射改正研究

讨论了地形、大气以及地理位置对卫片像元土尤辐射三分量(直射、散射、邻坡反射)的影响和这种影响所引起的卫星遥感数据变化。研究了地面光辐射、地形、大气和遥感数据之间的定量关来，并以此为基础，研究了卫星遥感数字图像地面辐射改正的原理和方法。经地面辐射改正后的卫星遥感数据能满足遥感应用基础研究和地面辐射状况研完的需要。     

赤潮遥感进展与算法研究

回顾了国内外赤潮遥感研究概况,介绍赤潮遥感的生物物理化学依据,并总结利用卫星光学遥感数据反演赤潮信息的算法研究成果。     

Scale influences on the remote estimation of evapotranspiration using multiple satellite sensors

There is considerable interest in using remote sensing to characterize the hydrologic behavior of the land surface on a routine basis. Information on moisture fluxes between the surface and lower atmosphere reveals linkages and land-atmosphere feedback mechanisms, aiding our understanding of energy and water balance cycles. Techniques that combine information on land and atmospheric properties with remotely sensed variables would allow improved prediction for a number of hydrological variables. Over the last few decades, there has been a focus on better determining evapotranspiration and its spatial variability, but for many regions routine prediction is not generally available at a spatial resolution appropriate to the underlying surface heterogeneity. Over agricultural regions, this is particularly critical, since the spatial extent of typical field scales is not regularly resolved within the pixel resolution of satellite sensors. Understanding the role of landscape heterogeneity and its influence on the scaling behavior of surface fluxes as observed by satellite sensors with different spatial resolutions is a critical research need. To attend this task, data from Landsat-ETM (60 m), ASTER (90 m), and MODIS (1020 m) satellite platforms are employed to independently estimate evapotranspiration. The range of the satellite sensor resolutions allows analyses that span scales from (point-scale) in-situ tower measurements to the MODIS kilometer-scale. Evapotranspiration estimates derived at these multiple resolutions were assessed against eddy covariance flux measurements collected during the 2002 Soil Moisture Atmospheric Coupling Experiment (SMACEX) over the Walnut Creek watershed in Iowa. Together, these data allow a comprehensive scale intercomparison of remotely sensed predictions, which include intercomparisons of the evapotranspiration products from the various sensors as well as a statistical analysis for the retrievals at the watershed scale. A high degree of consistency was observed between the retrievals from the higher-resolution satellite platforms (Landsat-ETM and ASTER). The MODIS-based estimates, while unable to discriminate the influence of land surface heterogeneity at the field scale, effectively reproduced the watershed average response, illustrating the utility of this sensor for regional-scale evapotranspiration estimation.     

Lineament mapping in a tropical environment using Landsat imagery

Remote sensing has proved to be a useful tool in lineament identification and mapping. This study demonstrates the use of multispectral Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM?+) satellite data obtained over two acquisition dates in 1990 and 2002 for lineament interpretation in a Malaysian tropical environment. A digital elevation model (DEM) was generated to improve the interpretation. We found that most of the major orientations in the field station could be successfully detected from the remotely sensed imagery. The results from the study show that the remote sensing technique is capable of extracting lineament trends in an inaccessible tropical forest.     

The application of remote sensing techniques to sugarcane (Saccharum spp. hybrid) production: a review of the literature

Remote sensing techniques provide timely, up‐to‐date and relatively accurate information for the management of sugarcane crop. This article reviews the literature on the application of remote sensing to sugarcane agriculture and highlights the challenges and opportunities pertinent to the success of this application. The aim of the review was to provide accurate and fundamental information relating the spectral properties of sugarcane to its agronomic, health and nutritional status characteristics that would be of importance to cane farmers and farm managers. The applications of the remote sensing techniques in sugarcane agriculture have been undertaken with particular emphasis on sugarcane classification and areal extent mapping, thermal age group identification, varietal discrimination, yield prediction and crop health and nutritional status monitoring. It can be concluded that by selecting appropriate spatial and spectral resolution as well as suitable processing techniques for extracting sugarcane spectral information, remotely sensed data should find use in sugarcane agriculture in all areas of application with satisfactory results.     

A first attempt at assimilating microwave-derived SST to improve the predictive capability of a coupled,high-resolution Eta-POM forecasting system

This study implements the assimilation of sea surface temperature (SST) data acquired by passive microwave remote sensing to a high-resolution, primitive-equation ocean model. The aim was to improve a forecasting tool capable of predicting the surface ocean processes linked to the air–sea interactions at sub-mesoscale level using one-way coupled, atmosphere–ocean modelling. An assimilation scheme based on a Newtonian relaxation scheme was fine-tuned to improve the forecasting skill of the ocean model. The ocean model was driven by predicted, synchronous air–sea fluxes derived by an overlying atmosphere model, remotely sensed SST and lateral boundary conditions derived from its previous run. The estimation of the model forecasting error was based on statistical and spatial comparison with remotely sensed observations. The optimal nudging coefficient was found to be 5 × 10^?4 for 12 hours, giving a mean bias of ?0.07°C. Forecast validation was done against calibrated AVHRR scenes using a new approach to calibrate region-specific scenes based on the split-window technique. This work demonstrates the benefit of using passive microwave remote sensing to improve high-resolution ocean forecasting systems. It also shows the high complementarity of infrared and passive microwave satellite sensors to provide information on the surface thermodynamics of the Ionian Sea.     

An improved SVM method P‐SVM for classification of remotely sensed data

A support vector machine (SVM) is a mathematical tool which is based on the structural risk minimization principle. It tries to find a hyperplane in high dimensional feature space to solve some linearly inseparable problems. SVM has been applied within the remote sensing community to multispectral and hyperspectral imagery analysis. However, the standard SVM faces some technical disadvantages. For instance, the solution of an SVM learning problem is scale sensitive, and the process is time‐consuming. A novel Potential SVM (P‐SVM) algorithm is proposed to overcome the shortcomings of standard SVM and it has shown some improvements. In this letter, the P‐SVM algorithm is introduced into multispectral and high‐spatial resolution remotely sensed data classification, and it is applied to ASTER imagery and ADS40 imagery respectively. Experimental results indicate that the P‐SVM is competitive with the standard SVM algorithm in terms of accuracy of classification of remotely sensed data, and the time needed is less.     

TM遥感与地块内冬小麦产量变异

卫星遥感可以为农作物的准确管理提供必要,及时并具有空间连续性的信息,但高成本一直是限制该项技术在农业上深入发展的主要障碍,利用价格相对较为低廉的TM卫星影像作为信息源来评价其对估测小区域内作物产量空间变异并为规划管理单元提供必要信息的可行性做了初步的研究,结果表明,利用TM图像所获得的植被指数能较好地反映小麦各生育时期的基本特点,两种植被指数（NDVI及RVI）都表现出一定程序的空间,而且都以小麦抽穗后期的变异程度为最大,而且,小麦生长发育的三个重要时期（分蘖期,抽穗期及拔节期）的两种植被指数之间具有极显著相关关系,两个试验地块小麦11月8日的归一化植被指数都与产量表现出了良好的相关关系,另外,两种植被指数在表现作物千粒重和亩穗数等产量指标信息方面,也有一定的效果。     

IHS和小波变换结合多源遥感影像融合质量对小波分解层数的响应

随着遥感技术的快速发展以及遥感数据的广泛应用,影像的融合处理已成为多源遥感影像信息聚合、获取高质量空间影像的有效途径。基于SPOT全色和多光谱、TM多光谱遥感数据,运用IHS和小波变换相结合的融合方法,进行了不同来源影像融合、融合图像质量对小波分解层数的响应以及这种响应对研究区域面积的敏感性分析。结果表明,多源影像之间的IHS和小波变换相结合的融合方法明显地改善了影像的质量;融合图像质量与原始影像空间分辨率相关,如经1层小波变换融合,TM,SPOT融合图像熵值的增幅分别为2095%,019%。小波融合图像质量对小波分解的层数的敏感性较强,在小波分解层数为2,3或4时,都能获得高质量的融合图像;小波分解层数等于或大于5时融合图像质量下降,7是大幅下降的临界层数。融合图像质量对小波分解层数的响应特性对面积大小变化是敏感的,特别是小面积图像,为此,实际应用中需特别注意最佳分解层数问题。     

Estimating fragmentation effects on simulated forest net primary productivity derived from satellite imagery

Conversion of native forests to agriculture and urban land leads to fragmentation of forested landscapes with significant consequences for habitat conservation and forest productivity. When quantifying land-cover patterns from airborne or spaceborne sensors, the interconnectedness of fragmented landscapes may vary depending on the spatial resolution of the sensor and the extent at which the landscape is being observed. This scale dependence can significantly affect calculation of remote sensing vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and its subsequent use to predict biophysical parameters such as the fraction of photosynthetically active radiation intercepted by forest canopies (fPAR). This means that simulated above-ground net primary productivity (NPP_A) using canopy radiation interception models such as 3-PG (Physiological Principles for Predicting Growth), coupled with remote sensing observations, can yield different results in fragmented landscapes depending on the spatial resolution of the remotely sensed data.We compared the amount of forest fragmentation in 1?km SPOT-4 VEGETATION pixels using a simultaneously acquired 20?m SPOT-4 multispectral (XS) image. We then predicted NPP_A for New Zealand native forest ecosystems using the 3-PG model with satellite-derived estimates of the fPAR obtained from the SPOT-4 VEGETATION sensor, using NDVI values with and without correction for fragmentation. We examined three methods to correct for sub-pixel fragmentation effects on NPP_A. These included: (1) a simple conversion between the broad 1?km scale NDVI values and the XS NDVI values; (2) utilization of contextural information from XS NDVI pixels to derive a single coefficient to adjust the 1?km NDVI values; and (3) calculation of the degree of fragmentation within each VEGETATION 1?km pixel and reduce NDVI by an empirically derived amount based on the proportional areal coverage of forest in each pixel. Our results indicate that predicted NPP_A derived from uncorrected 1?km VEGETATION pixels was significantly higher than estimates using adjusted NDVI values; all three methods reduced the predicted NPP_A. In areas of the landscape with a large degree of forest fragmentation (such as forest boundaries) predictions of NPP_A indicate that the fragmentation effect has implications for spatially extensive estimates of carbon uptake by forests.     

基于遥感数据和作物生长模型的小麦变量施肥研究进展

就国内外基于遥感数据和作物生长模型在变量施肥技术的研究应用作了阐述, 提出了快速、无损农业测试技术将是精准变量农业和数字农业今后的发展方向, 对作物生长模型以及精准变量施肥技术的研究进展作了较系统的调查研究, 阐述了将遥感数据与作物生长模型进行数据同化, 实现以高产、优质、环保为目的农业生产的可行性。并结合我国国情提出了发展精准农业变量施肥技术所面临的困难和出路。     

Remote sensing with small satellites

Abstract

Some design issues relevant to the adaptation of small satellites for remote sensing applications are presented. Particular emphasis is placed on those areas which will necessitate the advancement of current small satellite technology. The aim of such a programme would be to provide a cost-effective but reliable way of obtaining remotely sensed data from a range of novel sensors, thus complementing the efforts of major missions towards the 21st century.     

A conceptual framework to define the spatial resolution requirements for agricultural monitoring using remote sensing

Satellite remote sensing is an invaluable tool to monitor agricultural resources. However, spatial patterns in agricultural landscapes vary significantly across the Earth resulting in different imagery requirements depending on what part of the globe is observed. Furthermore, there is an increasing diversity of Earth observation instruments providing imagery with various configurations of spatial, temporal, spectral and angular resolutions. In terms of spatial resolution, the choice of imagery should be conditioned by knowing the appropriate spatial frequency at which the landscape must be sampled with the imaging instrument in order to provide the required information from the targeted fields. This paper presents a conceptual framework to define quantitatively such requirements for both crop area estimation and crop growth monitoring based on user-defined constraints. The methodological development is based on simulating how agricultural landscapes, and more specifically the fields covered by a crop of interest, are seen by instruments with increasingly coarser resolving power. The results are provided not only in terms of acceptable pixel size but also of pixel purity which is the degree of homogeneity with respect to the target crop. This trade-off between size and purity can be adjusted according to the end-user's requirements. The method is implemented over various agricultural landscapes with contrasting spatial patterns, demonstrating its operational applicability. This diagnostic approach can be used: (i) to guide users in choosing the most appropriate imagery for their application, (ii) to evaluate the adequacy of existing remote sensing systems for monitoring agriculture in different regions of the world and (iii) to provide guidelines for space agencies to design future instruments dedicated to agriculture monitoring.     

Optimum pixel size for hyperspectral studies of ecosystem function in southern California chaparral and grassland

Hyperspectral remotely sensed data are useful for studying ecosystem processes and patterns. However, spatial characterization of such remotely sensed images is needed to optimize sampling procedures and address scaling issues. We have investigated spatial scaling in ground-based and airborne hyperspectral data for canopy- to watershed-level ecosystem studies of southern California chaparral and grassland vegetation. Three optical reflectance indices, namely, Normalized Difference Vegetation Index (NDVI), Water Band Index (WBI) and Photochemical Reflectance Index (PRI) were used as indicators of biomass, plant water content and photosynthetic activity, respectively. Two geostatistical procedures, the semivariogram and local variance, were used for the spatial scaling analysis of these indices. The results indicate that a pixel size of 6 m or less would be optimal for studying functional properties of southern California grassland and chaparral ecosystems using hyperspectral remote sensing. These results provide a guide for selecting the spatial resolution of future airborne and satellite-based hyperspectral sensors.     

Local spatial autocorrelation characteristics of remotely sensed imagery assessed with the Getis statistic

To enable data collection by remote sensing instruments the Earth's continuously varying surface is regularized into a grid of consistently sized and shaped pixels. Remotely sensed data, as a result, is often highly spatially autocorrelated. The characterization and quantification of spatial autocorrelation can provide a valuable source of information for both theoretical and applied studies in remote sensing. Consequently, various techniques have been developed to assess the spatial dependence characteristics of remotely sensed imagery. Typically such techniques yield summary measures which enable the identification of distinctive regions of spatial dependency within the image. In contrast, local indicators of spatial association (LISA) measures, focus upon variations within the regions of spatial dependence. This letter provides an introduction to one such LISA measure, the Getis statistic, and indicates how it may be used in remote sensing research and applications as a complement to existing approaches. The Getis statistic provides a measure of spatial dependence for each pixel while also indicating the relative magnitudes of the digital numbers in the neighbourhood of the pixel.     

基于CNN和农作物光谱纹理特征进行作物分布制图

以卷积神经网络（Convolutional Neural Network, CNN）为代表的深度学习技术,在农作物遥感分类制图领域具有广阔的应用前景。以多时相Landsat 8 多光谱遥感影像为数据源,搭建CNN模型对农作物进行光谱特征提取与分类,并与支撑向量机（SVM）常规分类方法进行对比。进一步引入影像纹理信息,利用CNN对农作物光谱和纹理特征进行提取,优化作物分布提取结果。实验表明：① 基于光谱特征的农作物分布提取,验证结果对比显示,CNN对应各类别精度、总体精度均优于SVM,其中二者总体精度分别为95.14%和91.77%;② 引入影像纹理信息后,基于光谱和纹理特征的CNN农作物分类总体精度提高至96.43%,Kappa系数0.952,且分类结果的空间分布更为合理,可有效区分花生、道路等精细地物,说明纹理特征可用于识别不同作物。基于光谱和纹理信息的CNN特征提取,可面向种植结构复杂区域实现农作物精准分类与分布制图。     

斑块状植被遥感检测研究进展

斑块状植被是世界上干旱—半干旱区常见的景观类型,对于它们的形成、结构和演替研究能够提高人们对干旱—半干旱地区生态系统动态及其重要的生态水文过程的理解,具有重要的理论研究意义和应用价值.传统的基于地面调查和长期定位观测的方法观测范围有限,已无法满足目前区域斑块状植被分布及其空间格局特征研究的需要.利用遥感技术快速重复获取...