<Emphasis Type="Italic">C</Emphasis>/<Emphasis Type="Italic">N</Emphasis><Subscript>0</Subscript> estimators for high-sensitivity snapshot GNSS receivers

We address the problem of estimating the carrier-to-noise ratio (C/N₀) in weak signal conditions. There are several environments, such as forested areas, indoor buildings and urban canyons, where high-sensitivity global navigation satellite system (HS-GNSS) receivers are expected to work under these reception conditions. The acquisition of weak signals from the satellites requires the use of post-detection integration (PDI) techniques to accumulate enough energy to detect them. However, due to the attenuation suffered by these signals, estimating their C/N₀ becomes a challenge. Measurements of C/N₀ are important in many applications of HS-GNSS receivers such as the determination of a detection threshold or the mitigation of near-far problems. For this reason, different techniques have been proposed in the literature to estimate the C/N₀, but they only work properly in the high C/N₀ region where the coherent integration is enough to acquire the satellites. We derive four C/N₀ estimators that are specially designed for HS-GNSS snapshot receivers and only use the output of a PDI technique to perform the estimation. We consider four PDI techniques, namely non-coherent PDI, non-quadratic non-coherent PDI, differential PDI and truncated generalized PDI and we obtain the corresponding C/N₀ estimator for each of them. Our performance analysis shows a significant advantage of the proposed estimators with respect to other C/N₀ estimators available in the literature in terms of estimation accuracy and computational resources.     

Synergistic use of multi-temporal Radarsat SAR and AWiFS data for <Emphasis Type="Italic">Rabi</Emphasis> rice identification

A national level project on kharif rice identification and acreage estimation is being carried out successfully for several states in the country. A similar methodology based on the temporal profile for identification and delineation of various land cover classes has been followed for the Rabi rice acreage estimation. To define rabi rice, rabi season in India starts from November — February to March — June. Though the main growing season is predominantly winter but the uncertainty of getting cloud free data during the season has resulted in the use of microwave data. A feasibility study was taken up for early forecasting of the rabi rice area using microwave data. Hierarchical decision rule classification technique was used for the identification of the different land cover classes. Land preparation, puddling and transplantation were the reasons for the specific backscatter of rice growing areas. The increase or decrease in the SAR backscatter due to progress in the crop phenology or due to delayed sowing respectively forms the basis for identifying the rice areas. In addition the potential of optical data of a later date has been utilized in the form of various indices from bands including MIR to distinctly separate the late sown areas and also the puddled areas from other areas. This study emphasizes the synergistic use of SAR and optical data for delineating the rabi rice areas which is of immense use in giving an early forecast.     

Monitoring of <Emphasis Type="Italic">Aphis gossypii</Emphasis> Using Greenseeker and SPAD Meter

Cotton aphid (Aphis gossypii) is considered as one of the most important agriculture pest for the cotton production. However, it is generally labor-intensive and time-consuming to obtain some information of Cotton aphid with conventional methods through direct measurement by sampling in the field. This study explores the potential of using a new method to obtain information of the Cotton aphid rapidly. In our study, the cotton canopy spectral indices (NDVI, VI_2, REDrefc, NIRrefc) and chlorophyll concentration, obtained from hand-held high spectrometer GreenSeeker and chlorophyll meter SPAD-502 and Cotton aphid amount derived from the artificial field-based survey were used to uncover the relationship between Cotton aphid amount and canopy spectral index and SPAD value of the cotton in city of Shihezi, China. The results showed that NDVI and NIRrefc were negatively related to Cotton aphid amount. VI_2 content had a significant and positive relationship with its amount. The non-linear three cubic models with alate Aphid amount as independent variables have been established between VI_2 value and alatae Aphid amount, which could explain 92.37 % of VI_2 value variance. SPAD values were also significantly and negatively correlated to the Aphid amount. The non-linear logarithm model with wingless Aphid amount as independent variables was the best for uncovering the relationship between SPAD value and wingless Aphid amount, which could explain 85.48 % of SPAD value variance. The results demonstrate the establishment of the function model provides a theoretical basis and techniques for indirect and rapid monitoring and management of Cotton aphid.     

基于改进的模糊c-均值聚类方法遥感影像分类研究(英文)

Classification is always the key point in the field of remote sensing. Fuzzy c-Means is a traditional clustering algorithm that has been widely used in fuzzy clustering. However, this algorithm usually has some weaknesses, such as the problems of falling into a local minimum, and it needs much time to accomplish the classification for a large number of data. In order to overcome these shortcomings and increase the classification accuracy, Gustafson-Kessel （GK） and Gath-Geva （GG） algorithms are proposed to improve the traditional FCM algorithm which adopts Euclidean distance norm in this paper. The experimental result shows that these two methods are able to detect clusters of varying shapes, sizes and densities which FCM cannot do. Moreover, they can improve the classification accuracy of remote sensing images.     

A note on the SG(<Emphasis Type="Italic">m</Emphasis>) test

López et al. (Reg Sci Urban Econ 40(2–3):106–115, 2010) introduce a nonparametric test of spatial dependence, called SG(m). The test is claimed to be consistent and asymptotically Chi-square distributed. Elsinger (Reg Sci Urban Econ 43(5):838–840, 2013) raises doubts about the two properties. Using a particular counterexample, he shows that the asymptotic distribution of the SG(m) test may be far from the Chi-square family; the property of consistency is also questioned. In this note, the authors want to clarify the properties of the SG(m) test. We argue that the cause of the conflict is in the specification of the symbolization map. The discrepancies can be solved by adjusting some of the definitions made in the original paper. Moreover, we introduce a permutational bootstrapped version of the SG(m) test, which is powerful and robust to the underlying statistical assumptions. This bootstrapped version may be very useful in an applied context.     

Study of the optimum time span for distinguishing <Emphasis Type="Italic">Avena ludoviciana</Emphasis> from wheat crop based on their spectral reflectance characteristics

The study to establish the optimum time span for distinguishing Avena ludoviciana from wheat crop based on their spectral signatures was carried out at Student’s Research Farm, Department of Agronomy during 2006–07 and 2007–08. The experimental sites during both the seasons were sandy loam in texture, with normal soil reaction and electrical conductivity, low in organic carbon and available nitrogen and medium in available phosphorus and potassium. The experiment was laid out in randomized block design with four replications and consisting of twelve treatments comprising 0, 10, 15, 25, 50, 75, 100, 125, 150, 200, 250 plants m⁻² and a pure Avena ludoviciana plot (T_max). The results revealed that in all the treatments irrespective of wheat and weeds, the red reflectance (%) value decreased from 34 to 95 DAS (days after sowing) in 2006–07 and 45 DAS to 100 DAS during 2007–08, and thereafter a sharp increase was observed in all the treatments. This trend might be due to increased chlorophyll index after 34 DAS as red reflectance was reduced by chlorophyll absorption. Among all the treatments, T_max (Pure Avena ludoviciana plot) had the highest red reflectance and T₀ (Pure wheat plot) had a lowest value of red reflectance during both the years. The highest value of IR reflectance was obtained at 95 DAS (2006–07) and 70 DAS (2007–08) in all the treatments. IR reflectance of wheat crop ranged between 24.61 and 61.21 per cent during 2006–07 and 27.33 and 67.3 per cent during 2007–08. However, IR reflectance values declined after 95 DAS and 70 DAS up to harvesting during 2006–07 and 2007–08. This lower reflectance may have been due to the onset of senescence. The highest RR and NDVI values were recorded under pure wheat treatment and minimum under pure weed plots. This may be due to dark green colour and better vigor of the wheat as compared to Avena ludoviciana. It was observed that by using RR and NDVI, pure wheat can be distinguished from pure populations of Avena ludoviciana after 34 DAS and different levels of weed populations can be discriminated amongst themselves from 68 DAS up to 107 DAS during both the years of investigation.     

<Emphasis Type="Italic">3D</Emphasis> Modelling of Urban Area Using Synthetic Aperture Radar (SAR)

Synthetic aperture radar (SAR) is a newly-developed remote sensing technology that works in all weather and independent of daylight. Recent satellite designs such as TerraSAR-x, which have resolutions of a couple of meters and sub-meters, have provided appropriate data for modelling and monitoring of urban areas. Image classification and height information extraction is possible considering the nature of SAR data. In this paper, a proper classification method for high-resolution SAR images has been used in urban areas. This classifier is based on statistical models. First, statistical models that are well adapted to urban SAR images are selected. Initial labelling is performed using the maximum likelihood method. A method based on Markov random fields is applied to improve the results by considering neighbourhood information. Meanwhile, topographic information is extracted using the phase difference obtained from SAR interferometry. After classification and height extraction, the homogeneous regions consisting of locations with similar objects are determined. The homogeneous region adjacency graph are generated using vectors containing classification information, extracted objects, height of pixels forming each region, and information on the neighbouring areas. Height and classification information are then merged by assigning height conditions based on the nature of objects and optimizing an energy function. The results obtained, including buildings, streets, and corner reflectors, are easily recognizable. The overall accuracy is improved from 57% in the initial classification to 95% in the employed procedure. Moreover, the accuracy of height estimation is about 2.74 m, which is acceptable for height estimations of buildings with more than one floor.     

Remote Sensing-Based Yield Forecasting for Sugarcane (<Emphasis Type="Italic">Saccharum officinarum</Emphasis> L.) Crop in India

Early yield assessment at local, regional and national scales is a major requirement for various users such as agriculture planners, policy makers, crop insurance companies and researchers. This current study explored a remote sensing-based approach of predicting sugarcane yield, at district level, using Vegetation Condition Index (VCI), under the FASAL programme of the Ministry of Agriculture & Farmers’ Welfare. 13-years’ historical database (2003–2015) of NDVI was used to derive the VCI. NDVI products (MOD-13A2) of MODIS instrument on board Terra satellite at 16-day interval from first fortnight of June to second fortnight of October (peak growing period) were used to calculate the VCI. Stepwise regression technique was used to develop empirical models between VCI and historical yield of sugarcane over 52 major sugarcane-growing districts in five states of India. For all the districts, the empirical models were found to be statistically significant. A large number of statistical parameters were computed to evaluate the performance of VCI-based models in predicting district-level sugarcane yield. Though there was variation in model performance in different states, overall, the study showed the usefulness of VCI, which can be used as an input for operational sugarcane yield forecasting.     

Wheat Monitoring by Using Kernel Based Possibilistic <Emphasis Type="Italic">c</Emphasis>-Means Classifier: Bi-sensor Temporal Multi-spectral Data

The existence of mixed pixels in the satellite images has always been an area of concern. Adding to the challenge is an occurrence of non-linearity between the classes, which is generally overlooked. The study makes an attempt to solve the two frequently occurring problems by kernel based fuzzy approach. This research work deals with Possibilistic c-Means (PCM) classifier with local, global, spectral angle and hyper tangent kernels for wheat crop (Triticum aestivum) identification in Haridwar, Uttarakhand, India. The multi-temporal vegetation index data of Formosat-2 have been used which covers the whole phenology of wheat crop. The additional sensor Landsat-8 OLI imagery has been filled the crucial gap of Formosat-2 temporal datasets. Nine types of proposed kernels based PCM classifier have been applied on three temporal datasets (four, five and six date combinations) to classify two classes early sown and late sown wheat crop. These test results have been concluded that at optimized weighted constant KMOD and polynomial kernel was found effective to separate wheat crop. The five and six date combination were sufficient to discriminate early sown and late sown wheat crop.     

Integrating a fuzzy <Emphasis Type="Italic">k</Emphasis>-means classification and a Bayesian approach for spatial prediction of landslide hazard

A robust method for spatial prediction of landslide hazard in roaded and roadless areas of forest is described. The method is based on assigning digital terrain attributes into continuous landform classes. The continuous landform classification is achieved by applying a fuzzy k-means approach to a watershed scale area before the classification is extrapolated to a broader region. The extrapolated fuzzy landform classes and datasets of road-related and non road-related landslides are then combined in a geographic information system (GIS) for the exploration of predictive correlations and model development. In particular, a Bayesian probabilistic modeling approach is illustrated using a case study of the Clearwater National Forest (CNF) in central Idaho, which experienced significant and widespread landslide events in recent years. The computed landslide hazard potential is presented on probabilistic maps for roaded and roadless areas. The maps can be used as a decision support tool in forest planning involving the maintenance, obliteration or development of new forest roads in steep mountainous terrain.     

Some issues in contextual fuzzy <Emphasis Type="Italic">c</Emphasis>-means classification of remotely sensed data for land cover mapping

Earlier for the hard classification techniques contextual information was used to improve classification accuracy. While modelling the spatial contextual information for hard classifiers using Markov Random Field it has been found that Metropolis algorithm is easier to program and it performs better in comparison to the Gibbs sampler. In the present study it has been found that incase of soft contextual classification Metropolis algorithm fails to sample from a random field efficiently and from the analysis it was found that Metropolis algorithm is not suitable for soft contextual classification due to the high dimensionality of the soft outputs.     

Estimation of parameters in a split functional model of geodetic observations (<Emphasis Type="Italic">M</Emphasis><Subscript>split</Subscript> estimation)

The paper presents a method of estimating parameters in two competitive functional models. The models considered here are concerned with the same observation set and are based on the assumption that an observation may result from a realization of either of two different random variables. These variables differ from one another at least in the main characteristic (for example, outliers can be realizations of one variable). A quantity that describes the opportunity of identifying a single observation with one random variable is assumed to be known. That quantity, called the elementary split potential, is strictly referred to the amount of information that an observation can provide about two competitive assumptions concerning the observation distribution. Parameter assessments that maximize the global elementary split potential (concerning all observations), are called M _split estimators. A generalization of M _split estimation presented in the paper refers to the theoretical foundation of M-estimation. An erratum to this article can be found at     

Edge Enhancement for Remote Sensing Image Using Norm Algorithm in L2 Vector Space

The Householder transformation-norm structure function in L2 vector space of linear algebra is introduced, and the edge enhancement for remote sensing images is realized. The experiment result is compared with traditional Laplacian and Sobel edge enhancements and it shows that the effect of the new method is better than that of the traditional algorithms.     

Designing a protected area network for conservation planning in <Emphasis Type="Italic">Jhum</Emphasis> landscapes of Garo Hills,Meghalaya

We studied vegetation and land cover characteristics within the existing array of protected areas (PAs) in South Garo Hills of Meghalaya, northeast India and introduce the concept of protected area network (PAN) and methods to determine linkages of forests among existing PAs. We describe and analyse potential elements of a PAN, including PAs, reserved forests, surrounding buffers as zones of influence, and connecting forest corridors, which collectively can provide old-forest habitat for wildlife species linked across a landscape dominated by jhum (shifting cultivation) agriculture. ANOVA and Chisquare analyses of patch characteristics and forest tree diversity suggested the presence of equally species-rich and diverse old forest cover (tropical evergreen, semi-evergreen and deciduous forest types) in portions of unprotected private and community owned land, which could be designated as additions to, and network linkages among, existing PAs. Such additions and linkages would help provide for conservation of elephants and existing native forest biodiversity and would constitute a PAN in the region. Most (80%) of the total forest cover of the region belongs to private or community owned land. Therefore, such additions could be formally recognized under the aegis of the 2003 amendments of the Wildlife (Protection) Act 1972, which include provisions to designate selected forest patches within private lands as Community Reserves.     

Geospatial modeling of Brown oak (<Emphasis Type="Italic">Quercus semecarpifolia</Emphasis>) habitats in the Kumaun Himalaya under climate change scenario

The study explores the use of multiple criteria decision techniques in predicting spatial niche of Brown oak (also known as Kharsu oak, Quercus semecarpifolia Sm.) formation in midaltitude (2,400–3,500 meter amsl) Kumaun Himalaya. Predictive models using various climatic and topographical factors influencing Brown oak’s growth and survival were developed to define its current ecological niche. Analytical Hierarchical Process (AHP) method involving Saaty’s pair-wise comparison was performed to rank the explanatory powers of each compared variable. Variables were suitably weighted using fuzzy factor standardization scheme to reflect their relative importance in defining species niche. An optimum indicator was then chosen for deriving a site suitability map of brown oak. This study establishes the role of aspect in the current distribution of the species along with known influence of altitude. Future niches of oak has been tracked in the projected climate change scenario of +1°C and +2°C rise in temperature and 20 mm in precipitation. The results show that on predicted +1°C and +2°C increase in temperature, present habitat of brown oak distribution may be reduced by 40 per cent and 76 per cent respectively.     

Crop Evapotranspiration Estimation for Wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.) Using Remote Sensing Data in Semi-Arid Region of Maharashtra

Penman–Monteith method adapted to satellite data was used for the estimation of wheat crop evapotranspiration during the entire growth period using satellite data together with ground meteorological measurements. The IRS-1D/IRS-P6 LISS-III sensor data at 23.5 m spatial resolution for path 096 and row 059 covering the study area were used to derive, albedo, normalized difference vegetation index, leaf area index and crop height and then to estimate wheat crop evapotranspiration referred to as actual evapotranspiration (ETact). The ETact varied from 0.86 to 3.41 mm/day during the crop growth period. These values are on an average 16.40 % lower than wheat crop potential evapotranspiration (ETc) estimated as product of reference crop evapotranspiration estimated by Penman–Monteith method and lysimetric crop coefficient (Kc). The deviation of ETact from ETc is significant, when both the values were compared with t test for paired two sample means. Though the observations on ETact were taken from well maintained unstressed experimental plot of 120 × 120 m size, there was significant deviation. This deviation could be attributed to, the satellite images representing the actual crop evapotranspiration as function crop canopy biophysical parameters, condition of the crop stand, climatic and soil conditions and the microclimate variation over area of one hectare. However, Penman–Monteith method represents a flat rate of specific growth stage of the crop.     

The polar form of the spherical harmonic spectrum: implications for filtering <Emphasis Type="BoldSmallCaps">grace</Emphasis> data

Representing the spherical harmonic spectrum of a field on the sphere in terms of its amplitude and phase is termed as its polar form. In this study, we look at how the amplitude and phase are affected by linear low-pass filtering. The impact of filtering on amplitude is well understood, but that on phase has not been studied previously. Here, we demonstrate that a certain class of filters only affect the amplitude of the spherical harmonic spectrum and not the phase, but the others affect both the amplitude and phase. Further, we also demonstrate that the filtered phase helps in ascertaining the efficacy of decorrelation filters used in the grace community.     

An iteratively reweighted least-squares approach to adaptive robust adjustment of parameters in linear regression models with autoregressive and <Emphasis Type="Italic">t</Emphasis>-distributed deviations

In this paper, we investigate a linear regression time series model of possibly outlier-afflicted observations and autocorrelated random deviations. This colored noise is represented by a covariance-stationary autoregressive (AR) process, in which the independent error components follow a scaled (Student’s) t-distribution. This error model allows for the stochastic modeling of multiple outliers and for an adaptive robust maximum likelihood (ML) estimation of the unknown regression and AR coefficients, the scale parameter, and the degree of freedom of the t-distribution. This approach is meant to be an extension of known estimators, which tend to focus only on the regression model, or on the AR error model, or on normally distributed errors. For the purpose of ML estimation, we derive an expectation conditional maximization either algorithm, which leads to an easy-to-implement version of iteratively reweighted least squares. The estimation performance of the algorithm is evaluated via Monte Carlo simulations for a Fourier as well as a spline model in connection with AR colored noise models of different orders and with three different sampling distributions generating the white noise components. We apply the algorithm to a vibration dataset recorded by a high-accuracy, single-axis accelerometer, focusing on the evaluation of the estimated AR colored noise model.     

Utilizing Geo-information Tools for Mapping Spatio-temporal Changes in Population of <Emphasis Type="Italic">Prosopis cineraria</Emphasis> (khejri) in Agroforestry System of Arid Western Rajasthan

The present study is an attempt to assess the spatial variability and the changes in the population of Prosopis cineraia (khejri) trees in the agroforestry systems of arid part of western Rajasthan. This tree is regarded as an important natural resource for the rural livelihood of desert dwellers because of its multifarious uses. The tree populations were mapped in a geographical information system using available information from aerial photographs, google earth images and IRS-LISS-III satellite images to compare their populations between the past and the very recent period. Mapping carried out in a part of Nagaur district in western part of Rajasthan, indicated increase in khejri trees in the region in 2013–2014 compared to 1960s. The number and tree density were higher under irrigated croplands than under rainfed. Increase in rainfall, multiple uses of the tree, societal and scientific support are the key reasons for the observed changes in their population.