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231.
Sheshakumar K. Goroshi R. P. Singh S. Panigrahy J. S. Parihar 《Journal of the Indian Society of Remote Sensing》2011,39(3):315-321
This paper highlights the spatial and temporal variability of atmospheric columnar methane (CH4) concentration over India and its correlation with the terrestrial vegetation dynamics. SCanning IMaging Absorption spectrometer
for Atmospheric CHartographY (SCIAMACHY) on board ENVIronmental SATellite (ENVISAT) data product (0.5° × 0.5°) was used to
analyze the atmospheric CH4 concentration. Satellite Pour l'Observation de la Terre (SPOT)-VEGETATION sensor’s Normalized Difference Vegetation Index
(NDVI) product, aggregated at 0.5° × 0.5° grid level for the same period (2004 and 2005), was used to correlate the with CH4 concentration. Analysis showed mean monthly CH4 concentration during the Kharif season varied from 1,704 parts per billion volume (ppbv) to 1,780 ppbv with the lowest value in May and the highest value
in September. Correspondingly, mean NDVI varied from 0.28 (May) to 0.53 (September). Analysis of correlation between CH4 concentration and NDVI values over India showed positive correlation (r = 0.76; n = 6) in Kharif season. Further analysis using land cover information showed characteristic low correlation in natural vegetation region
and high correlation in agricultural area. Grids, particularly falling in the Indo-Gangetic Plains showed positive correlation.
This could be attributed to the rice crop which is grown as a predominant crop during this period. The CH4 concentration pattern matched well with growth pattern of rice with the highest concentration coinciding with the peak growth
period of crop in the September. Characteristically low correlation was observed (r = 0.1; n = 6) in deserts of Rajasthan and forested Himalayan ecosystem. Thus, the paper emphasizes the synergistic use of different
satellite based data in understanding the variability of atmospheric CH4 concentration in relation to vegetation. 相似文献
232.
Water-removed spectra increase the retrieval accuracy when estimating savanna grass nitrogen and phosphorus concentrations 总被引:2,自引:0,他引:2
Abel Ramoelo Andrew K. SkidmoreMartin Schlerf Renaud MathieuIgnas M.A. Heitkönig 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(4):408-417
Information about the distribution of grass foliar nitrogen (N) and phosphorus (P) is important for understanding rangeland vitality and for facilitating the effective management of wildlife and livestock. Water absorption effects in the near-infrared (NIR) and shortwave-infrared (SWIR) regions pose a challenge for nutrient estimation using remote sensing. The aim of this study was to test the utility of water-removed (WR) spectra in combination with partial least-squares regression (PLSR) and stepwise multiple linear regression (SMLR) to estimate foliar N and P, compared to spectral transformation techniques such as first derivative, continuum removal and log-transformed (Log(1/R)) spectra. The study was based on a greenhouse experiment with a savanna grass species (Digitaria eriantha). Spectral measurements were made using a spectrometer. The D. eriantha was cut, dried and chemically analyzed for foliar N and P concentrations. WR spectra were determined by calculating the residual from the modelled leaf water spectra using a nonlinear spectral matching technique and observed leaf spectra. Results indicated that the WR spectra yielded a higher N retrieval accuracy than a traditional first derivative transformation (R2=0.84, RMSE = 0.28) compared to R2=0.59, RMSE = 0.45 for PLSR. Similar trends were observed for SMLR. The highest P retrieval accuracy was derived from WR spectra using SMLR (R2=0.64, RMSE = 0.067), while the traditional first derivative and continuum removal resulted in lower accuracy. Only when using PLSR did the first derivative result in a higher P retrieval accuracy (R2=0.47, RMSE = 0.07) than the WR spectra (R2=0.43, RMSE = 0.070). It was concluded that the water removal technique is a promising technique to minimize the perturbing effect of foliar water content when estimating grass nutrient concentrations. 相似文献
233.
Sandip R. Oza R. K. K. Singh N. K. Vyas B. S. Gohil Abhijit Sarkar 《Journal of the Indian Society of Remote Sensing》2011,39(2):147-152
The identification of sea-ice has frequently been cited as one of the most important tasks for deriving the sea-ice parameters
and to avoid erroneous retrieval of wind vector over sea-ice infested oceans using space-borne scatterometer data. Discrimination
between sea-ice and ocean is ambiguous under the high wind and/or thin/scattered ice conditions. The pre-launch technique
developed for Oceansat-2, utilizes the dual-polarized QuikSCAT scatterometer data by using the spatio-temporal coherence properties
of sea ice in addition to backscatter coefficient and the Active Polarization Ratio. Results were compared with the operational
sea-ice products from National Snow and Ice Data Center. The threshold API value of −0.025 was found optimum for sea-ice and
ocean discrimination. The overall sea-ice identification accuracy achieved was of the order of 95 per cent, ranging from 92.5%
(during December in Southern Hemisphere) to 98% (during March in Northern Hemisphere). The applicability of the algorithm
for both the Arctic as well as Antarctic makes it suitable for its operational use with the Oceansat-2 scatterometer data. 相似文献
234.
Sushma Panigrahy Shibendu Shankar Ray K. R. Manjunath P. S. Pandey S. K. Sharma Anil Sood Manoj Yadav P. C. Gupta N. Kundu Jai Singh Parihar 《Journal of the Indian Society of Remote Sensing》2011,39(3):355-364
Cropping system study is not only useful to understand the overall sustainability of agricultural system, but also it helps
in generating many important parameters which are useful in climate change impact assessment. Considering its importance,
Space Applications Centre, took up a project for mapping and characterizing major cropping systems of Indo-Gangetic Plains
of India. The study area included the five states of Indo-Gangetic Plains (IGP) of India, i.e. Punjab, Haryana, Uttar Pradesh,
Bihar and West Bengal. There were two aspects of the study. The first aspect included state and district level cropping system
mapping using multi-date remote sensing (IRS-AWiFS and Radarsat ScanSAR) data. The second part was to characterize the cropping
system using moderate spatial resolution multi-date remote sensing data (SPOT VGT NDVI) and ground survey. The remote sensing
data was used to compute three cropping system performance indices (Multiple Cropping Index, Area Diversity Index and Cultivated
Land Utilization Index). Ground survey was conducted using questionnaires filled up by 1,000 farmers selected from 103 villages
based on the cropping systems map. Apart from ground survey, soil and water sampling and quality analysis were carried out
to understand the effect of different cropping systems and their management practices. The results showed that, rice-wheat
was the major cropping system of the IGP, followed by Rice-Fallow-Fallow and Maize-Wheat. Other major cropping systems of
IGP included Sugarcane based, Pearl millet-Wheat, Rice-Fallow-Rice, Cotton-Wheat. The ground survey could identify 77 cropping
systems, out of which 38 are rice-based systems. Out of these 77 cropping systems, there were 5 single crop systems, occupying
6.5% coverage (of all cropping system area), 56 double crop systems with 72.7% coverage, and 16 triple crop systems with 20.8%
coverage. The cropping system performance analysis showed that the crop diversity was found to be highest in Haryana, while
the cropping intensity was highest in Punjab state. 相似文献
235.
C. Jeganathan N.A.S. Hamm S. Mukherjee P.M. Atkinson P.L.N. Raju V.K. Dadhwal 《International Journal of Applied Earth Observation and Geoinformation》2011
Fine spatial resolution (e.g., <300 m) thermal data are needed regularly to characterise the temporal pattern of surface moisture status, water stress, and to forecast agriculture drought and famine. However, current optical sensors do not provide frequent thermal data at a fine spatial resolution. The TsHARP model provides a possibility to generate fine spatial resolution thermal data from coarse spatial resolution (≥1 km) data on the basis of an anticipated inverse linear relationship between the normalised difference vegetation index (NDVI) at fine spatial resolution and land surface temperature at coarse spatial resolution. The current study utilised the TsHARP model over a mixed agricultural landscape in the northern part of India. Five variants of the model were analysed, including the original model, for their efficiency. Those five variants were the global model (original); the resolution-adjusted global model; the piecewise regression model; the stratified model; and the local model. The models were first evaluated using Advanced Space-borne Thermal Emission Reflection Radiometer (ASTER) thermal data (90 m) aggregated to the following spatial resolutions: 180 m, 270 m, 450 m, 630 m, 810 m and 990 m. Although sharpening was undertaken for spatial resolutions from 990 m to 90 m, root mean square error (RMSE) of <2 K could, on average, be achieved only for 990–270 m in the ASTER data. The RMSE of the sharpened images at 270 m, using ASTER data, from the global, resolution-adjusted global, piecewise regression, stratification and local models were 1.91, 1.89, 1.96, 1.91, 1.70 K, respectively. The global model, resolution-adjusted global model and local model yielded higher accuracy, and were applied to sharpen MODIS thermal data (1 km) to the target spatial resolutions. Aggregated ASTER thermal data were considered as a reference at the respective target spatial resolutions to assess the prediction results from MODIS data. The RMSE of the predicted sharpened image from MODIS using the global, resolution-adjusted global and local models at 250 m were 3.08, 2.92 and 1.98 K, respectively. The local model consistently led to more accurate sharpened predictions by comparison to other variants. 相似文献
236.
Dhaval Vyas N.S.R. Krishnayya K.R. Manjunath S.S. Ray Sushma Panigrahy 《International Journal of Applied Earth Observation and Geoinformation》2011
There is an urgent necessity to monitor changes in the natural surface features of earth. Compared to broadband multispectral data, hyperspectral data provides a better option with high spectral resolution. Classification of vegetation with the use of hyperspectral remote sensing generates a classical problem of high dimensional inputs. Complexity gets compounded as we move from airborne hyperspectral to Spaceborne technology. It is unclear how different classification algorithms will perform on a complex scene of tropical forests collected by spaceborne hyperspectral sensor. The present study was carried out to evaluate the performance of three different classifiers (Artificial Neural Network, Spectral Angle Mapper, Support Vector Machine) over highly diverse tropical forest vegetation utilizing hyperspectral (EO-1) data. Appropriate band selection was done by Stepwise Discriminant Analysis. The Stepwise Discriminant Analysis resulted in identifying 22 best bands to discriminate the eight identified tropical vegetation classes. Maximum numbers of bands came from SWIR region. ANN classifier gave highest OAA values of 81% with the help of 22 selected bands from SDA. The image classified with the help SVM showed OAA of 71%, whereas the SAM showed the lowest OAA of 66%. All the three classifiers were also tested to check their efficiency in classifying spectra coming from 165 processed bands. SVM showed highest OAA of 80%. Classified subset images coming from ANN (from 22 bands) and SVM (from 165 bands) are quite similar in showing the distribution of eight vegetation classes. Both the images appeared close to the actual distribution of vegetation seen in the study area. OAA levels obtained in this study by ANN and SVM classifiers identify the suitability of these classifiers for tropical vegetation discrimination. 相似文献
237.
Gerard W. Hazeu Arnold K. Bregt Allard J.W. de Wit Jan G.P.W. Clevers 《International Journal of Applied Earth Observation and Geoinformation》2011
Land cover and land use are important information sources for environmental issues. One of the most important changes at the Earth's surface concerns land cover and land use. Knowledge about the location and type of these changes is essential for environmental modeling and management. Remote sensing data in combination with additional spatial data are recognized as an important source of information to detect these land cover and land use changes. 相似文献
238.
This letter describes the extension of signal subspace processing (SSP) to the arena of anomaly detection. In particular, we develop an SSP-based, local anomaly detector that exploits the rich information available in the multiple bands of a hyperspectral (HS) image. This SSP approach is based on signal processing considerations, and its entire formulation reduces to a straightforward (and intuitively pleasing) geometric and algebraic development. We extend the basic SSP concepts to the HS anomaly detection problem, develop an SSP HS anomaly detector, and evaluate this algorithm using multiple HS data files. 相似文献
239.
S. Anbazhagan T. K. Biswal T. Roy K. N. Kusuma 《Journal of the Indian Society of Remote Sensing》2006,34(4):331-341
The geology of northwestern part of Indian peninsula is considered to be important due to complete preservation of rocks from Archaean to Upper Proterozoic. Further, these rocks have served as ideal host of varieties of economic minerals. The present work is an attempt to study the structurally deformed granulitic terrain in parts of Gujarat and Rajasthan in light of remote sensing. The study area falls under Sirohi, Banas Kantha and Sabar Kantha districts of Rajasthan and Gujarat. Remote sensing technique is utilized for the understanding of structural geology and deciphering the shear pattern. The methods adopted in this study include generation of False Color Composite (FCC) of satellite data, interpretation of lineaments from FCC and study the drainage pattern, structural basin delineation, profiling, and field mapping. It is observed that the area has undergone extensive deformation. There are two major sets of lineaments interpreted in the granulitic terrain such as WNW-ESE and NE-SW directions. Majority of the WNW-ESE lineaments are brittle in nature and N-S, NE-SW trending lineaments are ductile in nature. Overall the study area bifurcated into seven structural basins comprises of basic granulites, calc granulites and pelitic granulites. 相似文献
240.
Remotely sensed estimation of forest canopy density: A comparison of the performance of four methods
Chudamani Joshi Jan De Leeuw Andrew K. Skidmore Iris C. van Duren Henk van Oosten 《International Journal of Applied Earth Observation and Geoinformation》2006
In recent years, a number of alternative methods have been proposed to predict forest canopy density from remotely sensed data. To date, however, it remains difficult to decide which method to use, since their relative performance has never been evaluated. In this study the performance of: (1) an artificial neural network, (2) a multiple linear regression, (3) the forest canopy density mapper and (4) a maximum likelihood classification method was compared for prediction of forest canopy density using a Landsat ETM+ image. Comparison of confusion matrices revealed that the regression model performed significantly worse than the three other methods. These results were based on a z-test for comparison of weighted kappa statistics, which is an appropriate statistic for analysis of ranked categories. About 89% of the variance of the observed canopy density was explained by the artificial neural networks, which outperformed the other three methods in this respect. Moreover, the artificial neural networks gave an unbiased prediction, while other methods systematically under or over predicted forest canopy density. The choice of biased method could have a high impact on canopy density inventories. 相似文献