Predicting Wheat Grain and Biomass Yield Using Canopy Reflectance of Booting Stage

Field experiment was conducted in a sandy loam soil of Indian Agricultural Research Institute, New Delhi during the year 2011–13 to see the effect of irrigation, mulch and nitrogen on canopy spectral reflectance indices and their use in predicting the grain and biomass yield of wheat. The canopy reflectances were measured using a hand held ASD FieldSpec Spectroradiometer at booting stage of wheat. Four spectral reflectance indices (SRIs) viz. RNDVI (Red Normalized Difference Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), SR (Simple Ratio) and WI (Water Index) were computed using the spectral reflectance data. Out of these four indices, RNDVI, GNDVI and SR were significantly and positively related with the grain and biomass yield of wheat whereas WI was significantly and negatively related with the grain and biomass yield of wheat. Calibration with the second year data showed that among the SRIs, WI could account for respectively, 85 % and 86 % variation in grain and biomass yield of wheat with least RMSE (395 kg ha^?1 (15 %) for grain yield and 1609 kg ha^?1 (20 %) for biomass yield) and highest d index (0.95 for grain yield and 0.91 for biomass yield). Therefore it can be concluded that WI measured at booting stage can be successfully used for prediction of grain and biomass yield of wheat.     

A spectral gradient difference based approach for land cover change detection

Change detection with remotely sensed imagery plays an important role in land cover mapping, process analysis and dynamic information services. Euclidean distance, correlation and other mathematic metrics between spectral curves have been used to calculate change magnitude in most change detection methods. However, many pseudo changes would also be detected because of inter-class spectral variance, which remains a significant challenge for operational remote sensing applications. In general, different land cover types have their own spectral curves characterized by typical spectral values and shapes. These spectral values are widely used for designing change detection algorithms. However, the shape of spectral curves has not yet been fully considered. This paper proposes to use spectral gradient difference (SGD) to quantitatively describe the spectral shapes and the differences in shape between two spectra. Change magnitude calculated in the new spectral gradient space is used to detect the change/no-change areas. Then, a chain model is employed to represent the SGD pattern both qualitatively and quantitatively. Finally, the land cover change types are determined by pattern matching with the knowledgebase of reference SGD patterns. The effectiveness of this SGD-based change detection approach was verified by a simulation experiment and a case study of Landsat data. The results indicated that the SGD-based approach was superior to the traditional methods.     

Analysis of sorghum yield dependence on crop growth characteristics using noaa-avhrr data

Growth profiles of 1987-88 rabi sorghum crop cultivated in spatially extensive sites in six tehsils of Solapur and Ahmadnagar districts in Maharashtra have been generated using multidate NOAA AVHRR data based on crop growth equation suggested by Badhwar (1980). The sensitive parameters for sorghum yield modelling have been identified. The correlation of final grain yield with growth parameters shows that yield relationship is stronger when logarithmic senescence rate and timeintegrated logarithmic senescence rate are considered as the parameters instead of its value on any day during 30 days senescence period after attaining maximum vegetative cover.     

Study of fractional vegetation cover using high spectral resolution data

A field experiment was conducted to study the effect of vegetation cover on soil spectra and relationship of spectral indices with vegetation cover. Multi-date spectral measurements were carried out on twelve wheat fields. Five sets of measurements were taken during the growth period of wheat crop. Field reflectance data were collected in the range 350 to 1800 nm using ASD spectroradiometer. Analysis of data was done to select narrow spectral bands for estimation of ground cover. The ratio of reflectance from vegetation covered soil and reflectance from bare soil indicated that spectral reflectance at 670 and 710 nm are the most sensitive bands. Two bands in visible (670 and 560 nm), three bands in near infrared (710, 870 and 1100 nm) and three bands in middle infrared (1480, 1700 and 1800 nm) were found highly correlated with fractional cover. Vegetation indices developed using narrow band spectral data have been found to be better than those developed using broad- band data for estimation of ground cover.     

Evaluation of MIVIS Hyperspectral Data for Mapping Soil Degradation in an Upland Ecosystem of Southern Italy

Abstract

This paper describes the first stage of an experiment aiming to evaluate the potential and limitations of MIVIS data for mapping the degradational state of soils in a sub‐scene of a southern Apennines study area (Italy). After radiometric rectification of the image data and the collection of a field/laboratory spectral library, linear spectral mixture modelling (SMA) was used to decompose image spectra into fractions of spectrally distinct mixing components. Spectral endmember selection was based upon a principal component analysis (PCA) applied to a set of soil spectra, collected from the spectral library. The resulting abundance estimates (fractions) trough SMA were then analysed to identify soil conditions and to obtain an improved measure of dry and green vegetation cover. A map of soil conditions and dry‐green vegetation abundance, based upon MIVIS data was then derived from normalised fractions of soil‐vegetation endmembers obtained from SMA.     

Land cover classification and economic assessment of citrus groves using remote sensing

The citrus industry has the second largest impact on Florida's economy, following tourism. Estimation of citrus area coverage and annual forecasts of Florida's citrus production are currently dependent on labor-intensive interpretation of aerial photographs. Remotely sensed data from satellites has been widely applied in agricultural yield estimation and cropland management. Satellite data can potentially be obtained throughout the year, making it especially suitable for the detection of land cover change in agriculture and horticulture, plant health status, soil and moisture conditions, and effects of crop management practices. In this study, we analyzed land cover of citrus crops in Florida using Landsat Enhanced Thematic Mapper Plus (ETM+) imagery from the University of Maryland Global Land Cover Facility (GLCF). We hypothesized that an interdisciplinary approach combining citrus production (economic) data with citrus land cover area per county would yield a correlation between observable spectral reflectance throughout the year, and the fiscal impact of citrus on local economies. While the data from official sources based on aerial photography were positively correlated, there were serious discrepancies between agriculture census data and satellite-derived cropland area using medium-resolution satellite imagery. If these discrepancies can be resolved by using imagery of higher spatial resolution, a stronger correlation would be observed for citrus production based on satellite data. This would allow us to predict the economic impact of citrus from satellite-derived spectral data analysis to determine final crop harvests.     

运用多尺度图像纹理进行城市扩展变化检测

 应用遥感数据检测城市扩展变化时，单纯基于光谱信息的变化检测法很难取得理想效果。本文将多尺度的纹理与光谱信息结合应用于变化检测，并评价其在检测城市扩展变化中的性能。变化检测采用分类后比较法。研究表明，如果纹理尺度与数据组合合适，与单纯基于光谱信息的检测结果相比，纹理特征与光谱特征结合的变化检测精度显著提高，而多尺度纹理辅助变化检测得到的检测精度最高。研究还发现，纹理辅助变化检测在某些地物类别的边缘会产生假变化信息。     

SZ-3 CMODIS 数据的质量及应用分析

中等分辨率成像光谱仪(CMODIS)具有34个光谱段,包含了从可见光、近红外、短波红外和热红外波段的波谱信息.本文结合2002年5月在北京地区开展的地面同步观测实验,对CMODIS数据质量进行了初步分析与评价,并对其在土地利用覆盖和水体污染分布监测等方面的应用效果进行了研究与分析.研究结果说明,CMODIS 数据质量达到了应用要求,可正确地反映不同地物类型的波谱响应.     

卫星动态观测数据揭示中国地表色调分布及时序变化特征

本文对2001年—2010年的MODIS光谱反射率数据进行时空聚类,得到2001年—2010年8天合成的色调信息,并初步分析了中国地表色调的时空分布格局及变化趋势,得出以下主要结论:(1)中国地表主色调主要由代表植被的绿色、裸土的褐色、裸土与植被混合的黄色、水体的蓝色以及冰雪的白色这5种颜色组成。分布在西北地区的褐色在一年中4个季节都为主色调。NDVI值较低的绿色光谱簇在春秋冬3个季节都为主色调,分布在南方的热带、亚热带针叶林以及灌木区。NDVI值较高的绿色光谱簇只在夏季一个季节为主色调,分布在长江中下游、华南、西南以及东北部分地区。永久白色主要分布在西藏、青海、四川等多年积雪地区。(2)黄淮海平原农业区色调在一年中呈现褐色—绿色—褐色—绿色—褐色的变化,与第一轮生长—间歇—第二轮生长物候历一致。长江中下游以及华南水稻播种和插秧时节有独特的光谱簇,呈现出植被与水体混合的特征。从华南地区、长江中下游地区到河北、陕西、甘肃、宁夏再到黑龙江,从南到北呈现出作物播种时间推迟、收割时间提早、生长期变短的现象。(3)甘肃北部、四川北部、山西北部、河北北部是年际主色调在绿色和黄色之间变化最为频繁的区域。这些年际主色调变化频繁区域也是中国绿度变化最显著的区域,也是在LUCC分类中易产生错误的地区。     

Predicting grain protein content of winter wheat using remote sensing data based on nitrogen status and water stress

Advanced site-specific knowledge of grain protein content of winter wheat from remote sensing data would provide opportunities to manage grain harvest differently, and to maximize output by adjusting input in fields. In this study, remote sensing data were utilized to predict grain protein content. Firstly, the leaf nitrogen content at winter wheat anthesis stage was proved to be significantly correlated with grain protein content (R² = 0.36), and spectral indices significantly correlated to leaf nitrogen content at anthesis stage were potential indicators for grain protein content. The vegetation index, VI_green, derived from the canopy spectral reflectance at green and red bands, was significantly correlated to the leaf nitrogen content at anthesis stage, and also highly significantly correlated to the final grain protein content (R² = 0.46). Secondly, the external conditions, such as irrigation, fertilization and temperature, had important influence on grain quality. Water stress at grain filling stage can increase grain protein content, and leaf water content is closely related to irrigation levels, therefore, the spectral indices correlated to leaf water content can be potential indicators for grain protein content. The spectral reflectance of TM channel 5 derived from canopy spectra or image data at grain filling stage was all significantly correlated to grain protein content (R² = 0.31 and 0.37, respectively). Finally, not only this study proved the feasibility of using remote sensing data to predict grain protein content, but it also provided a tentative prediction of the grain protein content in Beijing area using the reflectance image of TM channel 5.     

Forest cover classification using Landsat ETM+ data and time series MODIS NDVI data

Forest cover plays a key role in climate change by influencing the carbon stocks, the hydrological cycle and the energy balance. Forest cover information can be determined from fine-resolution data, such as Landsat Enhanced Thematic Mapper Plus (ETM+). However, forest cover classification with fine-resolution data usually uses only one temporal data because successive data acquirement is difficult. It may achieve mis-classification result without involving vegetation growth information, because different vegetation types may have the similar spectral features in the fine-resolution data. To overcome these issues, a forest cover classification method using Landsat ETM+ data appending with time series Moderate-resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data was proposed. The objective was to investigate the potential of temporal features extracted from coarse-resolution time series vegetation index data on improving the forest cover classification accuracy using fine-resolution remote sensing data. This method firstly fused Landsat ETM+ NDVI and MODIS NDVI data to obtain time series fine-resolution NDVI data, and then the temporal features were extracted from the fused NDVI data. Finally, temporal features combined with Landsat ETM+ spectral data was used to improve forest cover classification accuracy using supervised classifier. The study in North China region confirmed that time series NDVI features had significant effects on improving forest cover classification accuracy of fine resolution remote sensing data. The NDVI features extracted from time series fused NDVI data could improve the overall classification accuracy approximately 5% from 88.99% to 93.88% compared to only using single Landsat ETM+ data.     

地表覆盖遥感产品更新完善的研究动向

近年来,多尺度地表覆盖遥感产品的不断涌现,为环境变化研究、地球系统模拟、地理国(世)情监测和可持续发展规划等提供了重要科学数据。为更好地满足广大用户日益增长的应用需求,应对地表覆盖遥感产品进行持续更新完善,保持其时效性、增强时序性、丰富多样性。针对大面积地表覆盖遥感产品更新完善所面临的主要问题,介绍和评述了国内外有关研究动向,包括影像与众源信息相结合的更新、数据类型细化与完善、地表覆盖真实性验证,并作了简要展望。     

光谱与纹理信息复合的土地利用/覆盖变化动态监测研究

及时、准确地动态监测地球表面特性对于掌握人类和自然现象之间的关系和相互作用是非常重要的,并为决策的制定奠定基础。传统卫星遥感的土地利用/覆盖变化动态监测方法基本上可分为光谱直接比较法和分类比较法两大类,但两类方法多以光谱信息为基础来提取土地利用变化信息,而忽略纹理等空间信息。本研究中,基于变化向量分析方法,将光谱与纹理两种信息复合计算变化强度,并采用支持向量机法提取变化/非变化信息,通过监督分类确定变化区域内的土地利用/覆盖类型的转移方向,完成土地利用/覆盖动态监测。最后,利用两期TM数据,对海淀区1997—2004年进行土地利用/覆盖变化动态监测,以验证该方法。该方法较分类后比较法在一定程度上减少误差积累,降低了错误类型转化,提取的变化信息总精度达到93.1%,Kappa为0.862,比利用光谱信息双窗口变步长的变化向量分析方法提取出土地利用/覆盖变化信息的精度有一定的提高(总体精度为90.2%,Kappa为0.804)。纹理信息与光谱信息复合,能够更大拉开变化/非变化信息之间的距离,有利于动态变化信息的提取,是该方法能够有效提取变化信息的关键所在。     

Crop cover and crop phenological information from red and infrared spectral responses

Possibility of utilizing the red and infrared spectral information for assessing status of vegetation cover and consequential crop phenological information are discussed. The experiment was conducted in a potential agricultural area around Mandya town of Karnataka State and airborne spectral information was obtained through modular multispectral scanner from a height of 1000 meters above the ground level. The spectral information of red (0.66–0.70 urn) and infrared (0.77–0.86 urn) bands was extracted with the aid of an interactive computer system : the multispectral data analysis system. Based on the spectral information, the data was analysed and interpreted with the support of ground information. Crop fields without vegetation were observed to have infrared/red ratio in the range of 0.70 to 0.97 and also it was possible to distinguish wet and dry paddy field. Crop fields covered with vegetation exhibited higher infrared/red ratio depending on the nature of crop growth. For instance, rice crop exhibited spectral ratio of 0.78 at the time of planting, 3.52 at the time of maximum vegetation growth and 2.04 during the maturation phase. In case of sugarcane crop, the increase and decrease in spectral ratio were gradual because of its longer duration. From infrared and red band information it was possible to distinguish crop species based on rate of change of vegetation cover which corresponded with the change in spectral ratios. The temporal information expressed in two dimensional space for red and infrared band also enabled clearly to distinguish between rice and sugarcane.     

Data structure characterization of multispectral data using principal component and principal factor analysis

Abstract

Both principal component analysis (PCA) and principal factor analysis (PFA) were used to analyze an experimental multispectral data structure in terms of common and unique variance. Only the common variance of the multispectral data was associated with the principal factor, while higher‐order principal components were associated with both common and unique variance. The unique variance was found to represent small spectral variations within each cover type as well as noise vectors, and was most abundant in the lower‐order principal components. The lower‐order principal components can be useful in research designed to discriminate minor physical variations within features, and to highlight localized change when using multitemporal‐multispectral data. Conversely, PFA of the multispectral data provided an insight into a great potential for discriminating basic land‐cover types by excluding the unique variance which was related to the noise and minor spectral variations.     

基于变化向量分析（CVA）的土地利用／覆盖变化动态监测（Ⅱ）—变化类型的确定方法

以光谱直接比较为基础的变化向量分析法是一种非常有效的土地利用／覆盖变化动态监测方法，在双窗口变步长阈值搜寻方法确定变化和非变化像元的基础上，提出了参考图像分类并结合变化向量方向余弦最小距离分类的变化类型确定方法，同时应用该方法在北京市海淀区进行了实验研究，得到了较为理想的结果。变化类型的判断精度达到70％以上，显示了新方法的优越性和技术可行性。     

基于变化向量分析（CVA）的土地利用／覆盖变化动态监测（Ⅱ）—变化类型的确定方法

以光谱直接比较为基础的变化向量分析法是一种非常有效的土地利用／覆盖变化动态监测方法，在双窗口变步长阈值搜寻方法确定变化和非变化像元的基础上，提出了参考图像分类并结合变化向量方向余弦最小距离分类的变化类型确定方法，同时应用该方法在北京市海淀区进行了实验研究，得到了较为理想的结果。变化类型的判断精度达到70％以上，显示了新方法的优越性和技术可行性。     

Drivers of land cover and land use changes in St. Louis metropolitan area over the past 40 years characterized by remote sensing and census population data

In this study, we explored the spatial and temporal patterns of land cover and land use (LCLU) and population change dynamics in the St. Louis Metropolitan Statistical Area. The goal of this paper was to quantify the drivers of LCLU using long-term Landsat data from 1972 to 2010. First, we produced LCLU maps by using Landsat images from 1972, 1982, 1990, 2000, and 2010. Next, tract level population data of 1970, 1980, 1990, 2000, and 2010 were converted to 1-km square grid cells. Then, the LCLU maps were integrated with basic grid cell data to represent the proportion of each land cover category within a grid cell area. Finally, the proportional land cover maps and population census data were combined to investigate the relationship between land cover and population change based on grid cells using Pearson's correlation coefficient, ordinary least square (OLS), and local level geographically weighted regression (GWR). Land cover changes in terms of the percentage of area affected and rates of change were compared with population census data with a focus on the analysis of the spatial-temporal dynamics of urban growth patterns. The correlation coefficients of land cover categories and population changes were calculated for two decadal intervals between 1970 and 2010. Our results showed a causal relationship between LCLU changes and population dynamics over the last 40 years. Urban sprawl was positively correlated with population change. However, the relationship was not linear over space and time. Spatial heterogeneity and variations in the relationship demonstrate that urban sprawl was positively correlated with population changes in suburban area and negatively correlated in urban core and inner suburban area of the St. Louis Metropolitan Statistical Area. These results suggest that the imagery reflects processes of urban growth, inner-city decline, population migration, and social spatial inequality. The implications provide guidance for sustainable urban planning and development. We also demonstrate that grid cells allow robust synthesis of remote sensing and socioeconomic data to advance our knowledge of urban growth dynamics from both spatial and temporal scales and its association with population change.     

基于TM图像的农业区域植被覆盖变化检测

以交叉相关光谱匹配(cross correlogram spectral matching,CCSM)为基础构建土地覆盖变化强度指标,利用华北农业植被覆盖区2期不同时相的TM图像计算该地区土地覆盖变化强度图像.认为变化强度图像任意二阶邻域中像素的变化强度服从隐马尔可夫模型,用马尔可夫随机场-最大后验估计(maxium a psteriori estimationof markov random field,MRF - MAP)的方法从变化强度图像中提取植被变化区域.实验证明:该方法能够有效识别各种外源噪声造成的农业植被覆盖区域同物异谱的现象,可准确提取植被变化区域;但对于水体区域存在误判现象.     

Motivation,development and validation of a new spectral greenness index: A spectral dimension related to foliage projective cover

A method is presented for the development of a regional Landsat-5 Thematic Mapper (TM) and Landsat-7 Enhanced Thematic Mapper plus (ETM+) spectral greenness index, coherent with a six-dimensional index set, based on a single ETM+ spectral image of a reference landscape. The first three indices of the set are determined by a polar transformation of the first three principal components of the reference image and relate to scene brightness, percent foliage projective cover (FPC) and water related features. The remaining three principal components, of diminishing significance with respect to the reference image, complete the set.The reference landscape, a 2200 km² area containing a mix of cattle pasture, native woodland and forest, is located near Injune in South East Queensland, Australia. The indices developed from the reference image were tested using TM spectral images from 19 regionally dispersed areas in Queensland, representative of dissimilar landscapes containing woody vegetation ranging from tall closed forest to low open woodland. Examples of image transformations and two-dimensional feature space plots are used to demonstrate image interpretations related to the first three indices. Coherent, sensible, interpretations of landscape features in images composed of the first three indices can be made in terms of brightness (red), foliage cover (green) and water (blue). A limited comparison is made with similar existing indices. The proposed greenness index was found to be very strongly related to FPC and insensitive to smoke. A novel Bayesian, bounded space, modelling method, was used to validate the greenness index as a good predictor of FPC. Airborne LiDAR (Light Detection and Ranging) estimates of FPC along transects of the 19 sites provided the training and validation data. Other spectral indices from the set were found to be useful as model covariates that could improve FPC predictions. They act to adjust the greenness/FPC relationship to suit different spectral backgrounds. The inclusion of an external meteorological covariate showed that further improvements to regional-scale predictions of FPC could be gained over those based on spectral indices alone.