Yield estimation of summer crops grown in a mixed cropping area,a spectral growth curve profile approach

Estimation of crop production in advance of the harvest has been an intensively researched field in agriculture. Spectral parameters derived from the spectral growth profile being indicator of growth and development characteristics of the crop have a direct utility in crop-yield modeling. The present study is undertaken in a mixed cropping area of Karveer taluka, Kolhapur district, Maharashtra, to assess feasibility of multi-date moderately coarse WiFS data in developing spectral growth curves following Badhwar model (1980) for summer groundnut and paddy. The analysis highlighted potential of moderately coarse resolution WiFS data in discriminating the crops grown in fragmented conditions, provided detailed and adequate ground truth is used. The regression models using spectral parameters explained 94 % variation in paddy yield. However, model using ground information as peak LAI in addition to spectral variables, could explain 91 % variation in groundnut yield; thus for prediction of low-yielding and poorly managed crop a convergent model is essential. Vegetative growth rate during the pre-heading phase and total growing season absorbed photosynthetically active radiation (APAR) indicated by the area under the curve are the main predictors.     

Estimation of growth stages of wheat from spectral data

Growth stages of wheat subjected to different fertillizer treatments and sown on different dates have been estimated using spectral data. Greenness profiles were generated for different wheat plots. The profile parameters have been used to calculate growth stages at various times in the growing season. The model proposed by Badhwar (1981) has been used for this purpose. Results show that the model is capable of predicting growth stages accurately. There is a high correlation (r=0.97) between the observed and the predicted growth stages of wheat grown under various treatments.     

Shoreline and coral reef ecosystem changes in gulf of Mannar, Southeast coast of India

Changes in shoreline, coral reef and seafloor have been mapped using remote sensing satellite data of IRS LISS-III (1998), IRS LISS-II (1988), Survey of India Topographic sheet (1969), Naval Hydrographic Chart (NHO) 1975 and bathymetry data (1999) with ARC-INFO and ARC-VIEW GIS. The analysis of multi-date shoreline maps showed that 4.34 and 23.49 km2 of the mainland coast and 4.14 and 3.31 km² areas of island coast have been eroded and accreted, respectively, in the Gulf of Mannar. The analysis of multi-date coral reef maps showed that 25.52 km² of reef area and 2.16 km² of reef vegetation in Gulf of Mannar have been lost over a period of ten years. The analysis of multi-date bathymetry data indicates that the depth of seafloor has decreased along the coast and around the islands in the study area. The average reduction of depth in seafloor has been estimated as 0.51m over a period of twenty four years. The increased suspended sediment concentration due to coastal and island erosion, and raised reef due to emerging of coast by tectonic movement are responsible for coral reef degradation in the Gulf of Mannar. Validation by ground truth has confirmed these results.     

利用GPS/MET与CHAMP掩星资料反演地球中性大气参数的比较

采用Abel积分反演算法 ,在干大气模式下 ,分别针对GPS/MET与CHAMP特定掩星事件的Level2数据进行处理 ,得到了相应的折射指数与大气参数廓线。分别将反演结果与UCAR和GFZ公布的结果进行比较 ,证明了反演算法的正确性与通用性。分析了两个温度反演结果都存在系统误差和CHAMP温度反演结果误差较大的原因 ,并提出了改进建议     

Camera calibration from profile of revolution

This paper focuses on the problem of calibrating a pinhole camera from images of profile of a revolution. In this paper, the symmetry of images of profiles of revolution has been extensively exploited and a practical and accurate technique of camera calibration from profiles alone has been developed. Compared with traditional techniques for camera calibration, for instance, it may involve taking images of some precisely machined calibration pattern (such as a calibration grid), or edge detection for determining vanish points which are often far from images center or even do not physically exist or calculation of fundamental matrix and Kruppa equations which can be numerically unstable, the method presented here uses just profiles of revolution, which are commonly found in daily life (e. g. bowls and vases), to make the process easier as a result of the reduced cost and increased accessibility of the calibration objects. This paper firstly analyzes the relationship between the symmetry property of profile of revolution and the intrinsic parameters of a camera, and then shows how to use images of profile of revolution to provide enough information for determining intrinsic parameters. During the process, high-accurate profile extraction algorithm has also been used. Finally, results from real data are presented, demonstrating the efficiency and accuracy of the proposed methods.     

Camera calibration from profile of revolution

CLCNUMBERP246.1Introduction CameraCalibrationistheprocessdetermining theintrinsicparameters(i.e.focallengthand principalpoint)ofacamera.Itisanessential stepforbothmotionestimationand3Drecon struction,whicharetwoimportanttasksincom putervision.Itisalsoanim…     

Multiple forecasts of kharif rice in orissa state-four year experience of fasal pilot study

Considering the requirement of multiple pre-harvest crop forecasts, the concept of Forecasting Agricultural output using Space, Agrometeorology and Land based observations (FASAL) has been formulated. Development of procedure and demonstration of this technique for four in-season forecasts for kharif rice has been carried out as a pilot study in Orissa State since 1998. As the availability of cloud-free optical remote sensing data during kharif season is very poor for Orissa state, multi-date RADARSAT SCANSAR data were used for acreage estimation of kharif rice. Meteorological models have been developed for early assessment of acreage and prediction of yield at mid and late crop growth season. Four in-season forecasts were made during four kharif seasons (1998-2001); the first forecast of zone level rice acreage at the beginning of kharif crop season using meteorological models, second forecast of district level acreage at mid growth season using two-date RADARSAT SCANSAR data and yield using meteorological models, third forecast at late growth season of district level acreage using three-date RADARSAT SCANSAR data and yield using meteorological models and revised forecast incorporating field observations at maturity. The results of multiple forecasts have shown rice acreage estimation and yield prediction with deviation up to 14 and 11 per cent respectively. This study has demonstrated the potential of FASAL concept to provide inseason multiple forecasts using data of remote sensing, meteorology and land based observations.     

Spatio-temporal analysis of urban growth in selected small,medium and large Indian cities

This study attempts to ascertain the spatial and temporal variations in the evolution of Indian cities using multi-date remote sensing data. A two-stage object-based nearest neighbour classification approach with hierarchical segmentation was used to extract built-up area in selected small, medium and large cities, whereas object-based temporal inversion was applied for change analysis. The temporal trend of net population density, degree of scattered development and compactness of urban core in each city was determined using the built-up area. The study observed a declining trend in growth rate of built-up area in small and medium sized-cities, in contrast to large cities. However, the net population density in cities of all types is decreasing as urban growth has outpaced the corresponding population growth. Furthermore, small and medium cities indicated greater tendency for scattered development in comparison to large cities, whereas the core urban areas of the later appeared relatively less compact.     

Evaluation of data fusion and image segmentation in earth observation based rapid mapping workflows

This paper is an exploratory study, which aimed to discover the synergies of data fusion and image segmentation in the context of EO-based rapid mapping workflows. Our approach pillared on the geographic object-based image analysis (GEOBIA) focusing on multiscale, internally-displaced persons’ (IDP) camp information extraction from very high spatial resolution (VHSR) images. We applied twelve pansharpening algorithms to two subsets of a GeoEye-1 image scene that was taken over a former war-induced ephemeral settlement in Sri Lanka. A multidimensional assessment was employed to benchmark pansharpening algorithms with respect to their spectral and spatial fidelity. The multiresolution segmentation (MRS) algorithm of the eCognition Developer software served as the key algorithm in the segmentation process. The first study site was used for comparing segmentation results produced from the twelve fused products at a series of scale, shape, and compactness settings of the MRS algorithm. The segmentation quality and optimum parameter settings of the MRS algorithm were estimated by using empirical discrepancy measures. Non-parametric statistical tests were used to compare the quality of image object candidates, which were derived from the twelve pansharpened products. A wall-to-wall classification was performed based on a support vector machine (SVM) classifier to classify image objects candidates of the fused images. The second site simulated a more realistic crisis information extraction scenario where the domain expertise is crucial in segmentation and classification. We compared segmentation and classification results of the original images (non-fused) and twelve fused images to understand the efficacy of data fusion. We have shown that the GEOBIA has the ability to create meaningful image objects during the segmentation process by compensating the fused image’s spectral distortions with the high-frequency information content that has been injected during fusion. Our findings further questioned the necessity of the data fusion step in rapid mapping context. Bypassing time-intensive data fusion helps to actuate EO-based rapid mapping workflows. We, however, emphasize the fact that data fusion is not limited to VHSR image data but expands over many different combinations of multi-date, multi-sensor EO-data. Thus, further research is needed to understand the synergies of data fusion and image segmentation with respect to multi-date, multi-sensor fusion scenarios and extrapolate our findings to other remote sensing application domains beyond EO-based crisis information retrieval.     

Estimation and validation of snow surface temperature using modis data for snow-avalanche studies in NW-Himalaya

Snow avalanche studies require different snow-meteorological parameters for avalanche forecasting. Snow surface temperature is one of the major parameters, which is responsible for the evolution of snow pack characteristics. In the present paper, the snow surface temperature was estimated using TERRA satellite based — Moderate resolution imaging spectroradiometer (MODIS) sensor for NW-Himalayas. Ground data observed by automatic weather stations (AWS) was used to calibrate the brightness temperature obtained by MODIS thermal bands data into the actual snow surface temperature data through regression analysis. A split window technique has been implemented for the estimation of snow surface temperature. The multi-date satellite derived snow surface temperature was validated with ground data of winter 2004–05 and 2005–06 collected at various observation stations located in different ranges of NW-Himalaya. Good correlations were observed for Upper Himalaya (0.98, 0.98), Middle Himalaya (0.92, 0.96) and Lower Himalaya (0.88, 0.82) for 2004–05 and 2005–06 winter respectively. Further, estimated snow surface temperature was also verified with snow-cover information collected by manned observatories and area delineated by thematic maps of snow surface temperature was validated with the different snow climatic zones of NW-Himalaya.     

Evaluation of approaches for AWiFS multi-date registration

AWiFS onboard IRS-P6 belongs to the category of high-repetivity sensors based on large swath, but with ground trace based on narrow-swath sensor (LISS-III). This is useful for cloud removal as well as vegetation phenology studies. Such multi-date analysis has a prerequisite of accurate multi-date registration. This study investigates the accuracy of multi-date registration over a mixed plain and hilly terrain in northern India (29–31°N latitude and 77.5–79.5°E longitude; 200–4000 m.a.s.l.). Simple polynomial rectification, multi-date registration using ortho-correction technique on standard product (level-2) and radiometric product (level-1) as a function of number of ground control points (GCPs) and external Digital Elevation Model (DEM) were investigated. The results indicated that ortho-rectification on level-1 product provided better accuracy in comparison to simple rectification and ortho-rectification on level-2 product.     

Wheat growth profile: satellite monitoring and crop yield modelling

The most important advantage of the low resolution National Oceanic and Atmospheric Administration’s Advanced Very High Resolution Radiometer (NOAA AVHRR) data is its high temporal frequency and high radiometric sensitivity which helps in vegetation detection in the visible and near-infrared spectral regions. In areas where most of the crop cultivation is in large contiguous areas, and if the AVHRR data are selected for time period such that the crop of interest is well discriminated from other crops, these data can be used for monitoring vegetative growth and condition very effectively. The present study deals with the application of AVHRR data for the monitoring of the wheat crop in its seventeen main growing districts of the Rajasthan state. The fourteen date AVHRR data covering the entire growth period have been used to generate the normalized difference vegetation index (NDV1) growth profile for the crop by masking the non-crop pixels following the two-date NDVI change method. The growth profile parameters and other derived parameters, such as post-anthesis senescence rate and areas under the entire growth profile or under selected growth periods have been related to the district average wheat yield through statistical regression models. Various methods adopted for wheat pixels masking have been critically evaluated. It is found that the wheat yield can be predicted well by the area under the profile in different growth periods.     

Profile Development for the Spatial Data Transfer Standard

The Spatial Data Transfer Standard (SDTS), or Federal Information Processing Standard (FIPS) 173, is designed to support all types of spatial data. Implementing all of the standard's options at one time is impractical. Therefore, implementation of the SDTS is being accomplished through the use of profiles. Profiles are clearly defined, limited subsets of the SDTS created for use with a specific type or model of data and designed with as few options as possible. When a profile is proposed, specific choices are made for encoding possibilities that were not addressed, left optional, or left with numerous choices within the SDTS. Profile development is coordinated by the U.S. Geological SUIVey's SDTS Task Force. When completed, profiles are submitted to the National Institute of Standards and Technology (NIST) for approval as official amendments to the SDTS. The first profile, the Topological Vector Profile (TVP), has been completed. A Raster Profile has been tested and is being finalized for submission to the NIST. Other vector profiles, such as those for network and nontopological data, are also being considered as future implementation options for the SDTS.     

Normalisation of multi-date digital remote sensing data using scene statistics

Some of the studies involving digital temporal data require normalisation. Many of the normalisation methods need information on prevailing atmospheric conditions, sensor-related details etc., whereas some other methods use only the information which are within the data set. A study was conducted in Kudremukh Iron Ore miningsite, Karnataka using multi-date data from Landsat MSS of 1976 and 1985. A normalisation method using only the scene statistics of the images is suggested which brings the multi-date data at par. This method is compared with some of the other methods which use only the information content of the data set. Comparison of these methods was made by scattergrams, distance measure etc. By generating scattergrams for various combinations of non-normalised and normalised images, different methods were compared. Based on ground information, some changed and unchanged classes were noted on the various normalised and original images. Using the distance measure for classes in different images, the normalisation procedures were compared. The proposed method turned out to be the best among the methods compared.     

单站地基GPS天顶延迟反演大气剖面研究

基于一定的大气折射率剖面模型和搜索方法,可以由GPS天顶延迟反演大气折射率剖面。利用上海气象站的探空数据和上海IGS站的GPS数据,对基于单站地基GPS天顶延迟的大气折射率剖面反演方法进行了验证。结果表明,根据目前处理得到的天顶延迟,反演剖面与实际剖面吻合较好。     

利用剖面测线磁场数据反演三度磁性体参数

常规的格网状加密探测方式很难适用于航空手段对水下动态磁性体进行快速定位与识别,为此,尝试采用磁性体周围少量的几条测线对其进行探测,并利用改进的最小二乘法反演三度磁性体空间位置及磁性参数。根据观测值与磁矩的线性关系以及磁性体中心位置的非线性关系,对磁性体位置与磁性参数分步求解。理论模型推导过程中,顾及了地磁背景场影响和多磁性体情况,给出了相应的数据处理方法。通过实测数据验证表明,提出的方法是收敛的,能达到很高的磁性体几何及磁性参数解算精度。     

Mapping and monitoring of Rann (tidal) ingress in Banni Plains,Kachchh, Gujarat using multi-temporal satellite data

The Rann ingress into the ‘Banni Plains’ of Kachchh district, Gujarat which is famous for the sprawling grasslands supporting significant cattle population is taking place at an alarming rate. This has resulted in the desertion of many villages due to intrusion of Rann waters. In the present investigation multi-date satellite data has been analysed to map and monitor the Rann ingress during the period 1975 to 1989. Detailed analysis has brought out that the inundation is quite rapid affecting about 244 sq. km area during 1960–1989 period. Mapping of the creeks has also been done to understand the possible mechanism of ‘Rann ingress’.     

Derivation of indices using remote sensing data to evaluate cropping systems

This paper presents the work done in Bathinda District of Punjab state of India for evaluating the cropping system efficiency using multi-date, multi-year and multi-sensor satellite based remote sensing data along with various spatial and non-spatial collateral data. Three efficiency indices, such as Multiple Cropping Index (MCI), Area Diversity Index (DI), Cultivated Land Utilization Index (CLUI), have been worked out to characterize the cropping systems. The salient findings point out that, the MCI has, increased remarkably. A further increase is possible by only taking a third crop. The ADI has increased in kharif (rainy) season, due to introduction of rice in the cotton belt, however in rabi (winter) season the ADI has reduced nearly to one, showing it to be a mono-cropped situation. The CLUI is low (> 0.5) in many blocks, showing there is a great scope to improve it. Since in summer the land is remaining unutilized, a summer crop can very well be taken up to improve it.     

Estimation of Wheat/Rice Residue Burning Areas in Major Districts of Haryana,India, Using Remote Sensing Data

Haryana has emerged as an important state for Rice & Wheat production in India contributing significantly in the central pool. Mechanized combine harvesting technologies, which have become common in Rice Wheat System (RWS) in India, leave behind large quantities of straw in the field for open burning of residue. Besides causing pollution, the burning kills the useful micro flora of the soil causing soil degradation. There is no field survey (Girdawari) data available with the Government for the areas where stubble burning is taking place. The present paper describes the methodology and results of wheat and rice residue burning areas for three districts of Haryana namely Kaithal, Kurukshetra and Karnal for the year 2010 using complete enumeration approach of multi-date IRS-P6 AWiFS and LISS-III data. In season ground truth was collected using hand held GPS and used to identify area of burnt wheat/rice residues, associated crops and land features. After geo-referencing the satellite images, district images were masked-out and multi-date image data stacks were created. Normalized Difference Vegetation Index (NDVI) of each date was generated and used at the time of classification along with other spectral bands. The non-agricultural classes in the image included: forest, wasteland, water bodies, urban/settlement and permanent vegetation etc. The vector of these non-agriculture classes were extracted from the land use, imported and mask was generated. During the classification non-agriculture area was excluded by using mask of these classes. From this the agricultural area could be separated out. The area was estimated by computing pixels under the classified image mask. In season multi-date AWiFS data along with available single-date LISS-III data between third week of April to last week of May are found to be useful for estimation of wheat residue burning areas estimation. The data between second week of October to last week of November is useful for estimation of rice residue burning areas estimation at district level.     

Evaluation of potential usefulness of irs-1c wifs simulated data for dryland rabi sorghum crop discrimination

Two band simulad WiFS data for five dates correspfonding to rabi sorghun growing season of 1993-94 has been generated for Aurangabad district of Maharashtra. Ground truth data has been used for supervised classificatioa of one date raw image and five date NDVI of simulated WiFS data and the results were compared with those derived from single date IRS LISS I data. Analysis of classification accuracies indicate that single date WIFS data gives slightly lower accuracy of 79 per cent against 81 per cent obtained for single date LISS I data. Overall accuracy for 5-date WiFS data is 96 per cent which shows that classification performance of five date WiFS NDVI data is far superior to the single date data of the IRS-IC WiFS as well as the IRS LISS I. The study thus shows the importance of temporal domain of data acquisition in sorghum crop discrimination, Growth profile for sorghum and other crop classes were generated from multidate WiFS derived NDVI data. Differences in growth profiles of sorghum vigour classes as well as amongst different crop types and forests corroborate the premise of better discrimination of crop types and their vigour on multidate remotely sensed data.