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1.
B. K. Bhattacharya V. K. Dadhwal 《Journal of the Indian Society of Remote Sensing》2005,33(2):331-338
The retrieval of land (soil-vegetation complex) surface temperature (LST) was carried out over semi-arid mixed agriculture
landscape of Gujarat using thermal bands (channel 4 and 5) and ground emissivity from atmospherically corrected NDVI of NOAA
AVHRR LAC images. The atmospheric correction of Visible and NIR band reflectance was done using SMAC model. The LST computed
from split-window method and subsequently corrected with fractional vegetation cover were then compared with near synchronous
ground observations of soil and air temperatures made during 13–17 January and April, 1997 at five Land Surface Processes
Experiment (LASPEX) sites of Anand, Sanand, Derol, Arnej and Khandha covering 100 km x 100 km. The fractional vegetation cover
corrected LST at noon hrs. varied from 301.6 – 311.9K in January and from 315.8 – 325.6K in April. The LSTcorr were found to lie in the mid way between AT and ST during January. But in April, LST were found to be more close to ST which
may be due to relatively poor vegetation growth as indicated by lower NDVI values in April indicating more contribution to
LST from exposed soil surface. 相似文献
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P. C. Padhy R. K. Nayak V. K. Dadhwal M. Salim D. Mitra S. B. Chaudhury P. R. Rao K. H. Rao C.B. S. Dutt 《Journal of the Indian Society of Remote Sensing》2016,44(1):135-143
An empirical model is developed and used with remotely sensed predictors: sea surface temperature (SST) and chlorophyll-a concentration (Chl-a), to compute surface water partial pressure of carbon dioxide (pCO2w) and air-sea fluxes of CO2 in the Hooghly estuary and its adjacent coastal oceans. In situ observations used here were based on measurements carried out in this region during winter and summer periods in 2008. The estimated pCO2w compares well with the in situ observations at root mean square error ±18 μatm. In winter, estimated pCO2w ranges between 320 and 500 μatm with large values (>400 μatm) on the south-western and south-eastern flanks of the coastal domain and lower values (340–375 μatm) on the main-channel. In summer, it remained spatially uniform at 450 μatm. Extrapolation of the results over the study region based on the Moderate Imaging Specroradiometer (MODIS) measured SST and Chl-a suggests that the region is a strong source of atmospheric CO2 during the summer with net release of 0.095 Tg C year?1 (equivalent to mean flux of 90 molC m?2 year?1) and is a weak source during the winter with net release of 0.006 Tg C yr?1 (0.5 molC m?2 year?1) from the geographical extent of 6000 Km2 area. 相似文献
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Kiran Chand Thumaty Rakesh Fararoda Suresh Middinti Rajashekar Gopalakrishnan C. S. Jha V. K. Dadhwal 《Journal of the Indian Society of Remote Sensing》2016,44(1):31-39
Reliable and accurate estimates of tropical forest above ground biomass (AGB) are important to reduce uncertainties in carbon budgeting. In the present study we estimated AGB of central Indian deciduous forests of Madhya Pradesh (M.P.) state, India, using Advanced Land Observing Satellite – Phased Array type L-band Synthetic Aperture Radar (ALOS-PALSAR) L-band data of year 2010 in conjunction with field based AGB estimates using empirical models. Digital numbers of gridded 1?×?1° dual polarization (HH & HV) PALSAR mosaics for the study area were converted to normalized radar cross section (sigma naught - σ0). A total of 415 sampling plots (0.1 ha) data collected over the study area during 2009–10 was used in the present study. Plot-level AGB estimates using volume equations representative to the study area were computed using field inventory data. The plot-level AGB estimates were empirically modeled with the PALSAR backscatter information in HH, HV and their ratios from different forest types of the study area. The HV backscatter information showed better relation with field based AGB estimates with a coefficient of determination (R2) of 0.509 which was used to estimate spatial AGB of the study area. Results suggested a total AGB of 367.4 Mt for forests of M.P. state. Further, validation of the model was carried out using observed vs. predicted AGB estimates, which suggested a root mean square error (RMSE) of ±19.32 t/ha. The model reported robust and defensible relation for observed vs. predicted AGB values of the study area. 相似文献
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A. Senthil Kumar T. Radhika P.K. Saritha V. Keerthi R. N. Anjani M. Suresh Kumar K. S. Sekhar P. Satyanarayana M. S. Naga Sudha M. V. R. Sesha Sai V. K. Dadhwal 《Journal of the Indian Society of Remote Sensing》2014,42(4):701-709
The use of Local Area Coverage (LAC) data from Ocean Color Monitor (OCM) sensor of Oceansat-2 with its high radiometric resolution (12 bits/pixel) and 2-day repeat cycle for rapid monitoring of vegetation growth and estimating surface albedo for the Indian region is demonstrated in this study. For the vegetation monitoring, normalized difference vegetation index (NDVI) and vegetation fraction (VF) products were estimated by maximum value composite approach fortnightly and were resampled to 1 km. The surface albedo products were realized by converting narrow-band eight-band spectral reflectance OCM data to a) visible (300–700 nm) and b) broad band (300–3,000 nm) data. For validation, the derived products were compared with respective MODIS global products and found to be in good agreement. 相似文献
7.
In the past researchers have suggested hard classification approaches for pure pixel remote sensing data and to handle mixed
pixels soft classification approaches have been studied for land cover mapping. In this research work, while selecting fuzzy
c-means (FCM) as a base soft classifier entropy parameter has been added. For this research work Resourcesat-1 (IRS-P6) datasets
from AWIFS, LISSIII and LISS-IV sensors of same date have been used. AWIFS and LISS-III datasets have been used for classification
and LISS-III and LISS-IV data were used for reference data generation, respectively. Soft classified outputs from entropy
based FCM classifiers for AWIFS and LISS-III datasets have been evaluated using sub-pixel confusion uncertainty matrix (SCM).
It has been observed that output from FCM classifier has higher classification accuracy with higher uncertainty but entropy-based
classifier with optimum value of regularizing parameter generates classified output with minimum uncertainty. 相似文献
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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
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The study reports estimates of above ground phytomass carbon pools in Indian forests for 1992 and 2002 using two different methodologies. The first estimate was derived from remote sensing based forest area and crown density estimates, and growing stock data for 1992 and 2002 and the estimated pool size was in the range 2,626–3,071 Tg C (41 to 48 Mg C ha???1) and 2,660–3,180 Tg C (39 to 47 Mg C ha???1) for 1992 and 2002, respectively. The second methodology followed IPCC 2006 guidelines and using an initial 1992 pool of carbon, the carbon pool for 2002 was estimated to be in the range of 2,668–3,112 Tg C (39 to 46 Mg C ha???1), accounting for biomass increment and removals for the period concerned. The estimated total biomass increment was about 458 Tg over the period 1992–2002. Removals from forests include mainly timber and fuel wood, whereby the latter includes large uncertainty as reported extraction is lower than actual consumption. For the purpose of this study, the annual extraction values of 23 million m3 for timber and 126 million m3 for fuel wood were used. Out of the total area, 10 million ha are plantation forests with an average productivity (3.2 Mg ha???1 year???1) that is higher than natural forests, a correction of 408 Tg C for the 10 year period was incorporated in total estimated phytomass carbon pool of Indian forests. This results in an estimate for the net sink of 4 Tg C year???1. Both approaches indicate Indian forests to be sequestering carbon and both the estimates are in agreement with recent studies. A major uncertainty in Indian phytomass carbon pool dynamics is associated with trees outside forests and with soil organic carbon dynamics. Using recent remote-sensing based estimates of tree cover and growing stock outside forests, the estimated phytomass carbon pool for trees outside forests for the year 2002, is 934 Tg C with a national average tree carbon density of 4 Mg C ha???1 in non-forest area, in contrast to an average density of 43 Mg C ha???1 in forests. Future studies will have to consider dynamics in both trees outside forests and soil for total terrestrial carbon dynamics. 相似文献
10.
R. Suraj Reddy G. Rajashekar C. S. Jha V. K. Dadhwal Raphel Pelissier Pierre Couteron 《Journal of the Indian Society of Remote Sensing》2017,45(4):657-665
Assessment of above ground forest biomass (AGB) is essential in carbon modelling studies to provide mitigation strategies as demonstrated by reducing emissions from deforestation and forest degradation. Several researchers have demonstrated the use of remote sensing data in spatial AGB estimation, in terms of spectral and radar backscatter based approaches at a landscape scale with several known limitations. However, these methods lacked the predictive ability at high biomass ranges due to saturation. The current study addresses the problem of saturation at high biomass ranges using canopy textural metric from high resolution optical data. Fourier transform based textural ordination (FOTO) technique, which involves deriving radial spectrum information via 2D fast Fourier transform and ordination through principal component analysis was used for characterizing the textural properties of forest canopies. In the current study, plot level estimated AGB from 15 (1 ha) plots was used to relate with texture derived information from very high resolution datasets (viz., IKONOS and Cartosat-1). In addition to the estimation of high biomass ranges, one of the prime objective of the current study is to understand the effects of spatial resolution on deriving textural-AGB relationship from 2.5 m IRS Cartosat data (Cartosat-A, viewing angle = ?5°) to that of IKONOS imagery with near nadir view. Further, since texture is impacted by several illumination geometry issues, the effect of viewing geometry on the relationship was evaluated using Cartosat-F (Viewing angle = 26°) imagery. The results show that the FOTO method using stereo Cartosat (A and F) images at 2.5 m resolution are able to perform well in characterizing high AGB values since the texture-biomass relationship is only subjected to 18 % relative error to that of 15 % in case of IKONOS and could aid in reduction of uncertainty in AGB estimation at a large landscape levels. 相似文献