Kharif rice acreage estimation in Orissa using NOAA-AVHRR data

Rice-acreage estimation of Orissa state was carried out using single-date NOAA-AVHRR data. Selection of optimum date of data acquisition for this purpose was studied using data of six acquisition dates viz. October, 3, 12, 21, 29, November 7 and 26, 1989. Comparative performance of MXL classification of two NOAA bands (Band-1: 0.58–0.68 μm and Band-2: 0.73–1.10 μm) and Normalised Difference Vegetation Index (NDVI) derived from these two-band data was examined. Acreage thus estimated was compared against Bureau of Economics and Statistics (BES) estimate of the same year. The acreage estimation obtained by two band classification was closer to BES estimate than that based on NDVI. Data acquired in the month of October have given better estimate for state level rice acreage than that acquired in the month of November.     

Wheat acreage estimation for Haryana using satellite digital data

This paper summarizes the procedures adopted and results obtained since 1985–86 for wheat inventory for Haryana using satellite digital data (MSS: 1985–86 to 1987–88, LISS-I: 1988–89 onwards). The approach followed is based on sample segments (10 × 10 km during 1985–86 to 1988–89, 7.5 × 7.5 km during 1989–90) and 10 percent sampling fraction and stratified sample design. There has been consistent improvement in accuracy over the years as judged from lower biases when compared with Bureau of Economics and Statistics (BES) acreage estimates and higher precision. In 1989–90, the state-level estimate achieved an accuracy goal of 90 percent at 90 percent confidence interval. A number of studies which have been carried out to study effect of choice of sensor, acquisition date, stratification approach, classification procedure on wheat inventory are also mentioned.     

Wheat crop production estimation using satellite data

This paper reports acreage, yield and production forecasting of wheat crop using remote sensing and agrometeorological data for the 1998–99 rabi season. Wheat crop identification and discrimination using Indian Remote Sensing (IRS) ID LISS III satellite data was carried out by supervised maximum likelihood classification. Three types of wheat crop viz. wheat-1 (high vigour-normal sown), wheat-2 (moderate vigour-late sown) and wheat-3 (low vigour-very late sown) have been identified and discriminated from each other. Before final classification of satellite data spectral separability between classes were evaluated. For yield prediction of wheat crop spectral vegetation indices (RVI and NDVI), agrometeorological parameters (ET_max and TD) and historical crop yield (actual yield) trend analysis based linear and multiple linear regression models were developed. The estimated wheat crop area was 75928.0 ha. for the year 1998–99, which sowed ?2.59% underestimation with land record commissioners estimates. The yield prediction through vegetation index based and vegetation index with agrometeorological indices based models were 1753 kg/ha and 1754 kg/ha, respectively and have shown relative deviation of 0.17% and 0.22%, the production estimates from above models when compared with observed production show relative deviation of ?2.4% and ?2.3% underestimations, respectively.     

Accuracy assessment in cotton acreage estimation using Indian remote sensing satellite data

The accuracy of cotton crop classification using satellite data has been assessed with respect to a detailed land cover map prepared by field survey. The effect of spatial resolution on classification accuracy was studied using LISS-I (spatial resolution 72.6 m) and LISS-II data (spatial resolution 36.25 m) of the Indian remote sensing satellite IRS-1B. The performances of the maximum likelihood and the minimum distance to mean as classifiers have also been assessed. LISS-II data have been found to give a higher classification accuracy. The estimate of cotton acreage using LISS-II data was closer to that obtained from the base map. The maximum likelihood classifier (MXL) and the minimum distance to mean (MDM) classifier performed equally well.     

Undulation and anomaly estimation using Geos-3 altimeter data without precise satellite orbits

The paper describes results obtained from the processing of 53 Geos-3 arcs of altimeter data obtained during the first weeks after the launch of the satellite in April, 1975. The measurement from the satellite to the ocean surface was used to obtain an approximate geoid undulation which was contaminated by long wavelength errors caused primarily by altimeter bias and orbit error. This long wavelength error was reduced by fitting with a low degree polynomial the raw undulation data to the undulations implied by the GEM 7 potential coefficients, in an adjustment process that included conditions on tracks that cross. The root mean square crossover discrepancy before this adjustment was ±12.4 meters while after the adjustment it was ±0.9 m. These adjusted undulations were used to construct a geoid map in the Geos-3 calibration area using a least squares filter to remove remaining noise in the undulations. Comparing these undulations to ones computed from potential coefficients and terrestrial gravity data indicates a mean difference of 0.25 m and a root mean square difference of ±1.92 m. The adjusted undulations were also used to estimate several 5^o, 2^o, and 1^o anomalies using the method of least squares collocation. The resulting predictions agreed well with known values although the 1^o x 1^o anomalies could not be considered as reliably determined.     

Tea acreage estimation and condition assessment using satellite data in nilgiri district,tamilnadu

In the present study an attempt has been made to estimate acreage and condition of tea plantations by using satellite based digital remotely sensed data in visible, near infra-red and middle infra-red spectral regions, in the Nilgiri district of Tamilnadu state. Landsat MSS and TM data, acquired on Dec. 26, 1990 were used in the analysis, Different spectral band combinations, Landsat MSS (1234), TM (1234), TM (2345) and TM (123457) were used for identification of tea plantations. District-boundary-overlaying approach with complete enumeration of digital data was used for estimation of tea acreages. Condition assessment of tea plantations is based on the Greenness Index. Use of Landsat MSS data resulted in an underestimation of area under tea whereas the acreages estimated by using TM spectral band combinations 1234 and 2345 compared closely with the estimates of Department of Horticulture (DOH). The distribution pattern of various condition classes of tea plantations compared well with the prevailing ground conditions as observed during post-classification field survey in September 1992 in the district.     

District-level mustard crop estimation for rajasthan: improving precision by in-season digital stratification

The acreage and yield of mustard crop in Rajasthan shows year to year variation. In the present study CAPE, analysis by incorporating digital stratification with current season data and comparison of coefficient variation (CV) at district level using conventional stratification with previous season data was undertaken. The stratification approach using current year’s data for mustard acreage estimation was adopted during 1994-95 and 1995-96 crop seasons and regional CV of less than 2 per cent was attained. A comparison of CV at district level for the years 1994-95 and 1995-96 with those obtained in previous two seasons (1992-93, 1993-94) indicated considerable improvement in precision (lower CV) is 7 out of 11 study districts. Mustard acreage estimate for Bharatpur (1995-96) had CV of 10.1 percent when conventional approach (past year data) for stratification was used. However, with the use of current year data for stratification CV reduced to 4.4 per cent The study suggests that use of in-season data for stratification improves precision for acreage estimation of crops like mustard which has high year to year variation in area.     

Long-term soil moisture content estimation using satellite and climate data in agricultural area of Mongolia

The purpose of this study is to estimate long-term SMC and find its relation with soil moisture (SM) of climate station in different depths and NDVI for the growing season. The study area is located in agricultural regions in the North of Mongolia. The Pearson’s correlation methodology was used in this study. We used MODIS and SPOT satellite data and 14 years data for precipitation, temperature and SMC of 38 climate stations. The estimated SMC from this methodology were compared with SM from climate data and NDVI. The estimated SMC was compared with SM of climate stations at a 10-cm depth (r² = 0.58) and at a 50-cm depth (r² = 0.38), respectively. From the analysis, it can be seen that the previous month’s SMC affects vegetation growth of the following month, especially from May to August. The methodology can be an advantageous indicator for taking further environmental analysis in the region.     

Rice-acreage estimation for Orrisa using remotely sensed data

Rice is one of the most important foodgrains grown in India. Attempts have been made to estimate kharif rice acreage of Orissa state since 1986 using digital remote sensing data from Landsat MSS/TM and/or IRS-1A. Accuracies of the estimates obtained have been evaluated against BES (Bureau of Economics and Statistics) estimate. This paper describes the methodology adopted for rice acreage estimation of Orissa state, the results obtained for three years, i.e. 1986–87, 1988–89 and 1989–90, and their accuracy.     

Growing stock estimation through stratified sampling on satellite remote sensing data

Aerial photographs are being extensively used for forest surveys i.e. forest cover type mapping, assessment of growing stock, estimation of area, vegetation studies, etc. Satellite remote sensing technology offers new possibilities and scope for achieving some of the above applications with higher accuracy and reliability. The assessment of growing stock through subjective stratification has also become possible with increase in spatial and spectral resolution. In the present study, the LANDSAT TM FCC (1966) has been visually interpreted on the basis of tonal characteristics. Stratified random sampling method has then used for determination of number of sample units for collection of ground Inventory data. Using regression equations, the volume per hectare of individual forest cover types were calculated. Satellite remote sensing data has been used for initial stratification, distribution of sample plots and calculation of area under various forest cover types. Estimate has been made for available commecial and non-commercial growing stocks in the study area.     

Web service for biodiversity estimation using remote sensing data

Abstract

This paper presents a technique for the assessment and mapping of land biodiversity by using remote sensing data. The proposed approach uses a fuzzy model that encapsulates different ecological factors influencing biodiversity. We implemented our approach as a web service for the Pre-Black Sea region of the Ukraine.     

Using pan-sharpened high resolution satellite data to improve impervious surfaces estimation

Impervious surface is an important environmental and socio-economic indicator for numerous urban studies. While a large number of researches have been conducted to estimate the area and distribution of impervious surface from satellite data, the accuracy for impervious surface estimation (ISE) is insufficient due to high diversity of urban land cover types. This study evaluated the use of panchromatic (PAN) data in very high resolution satellite image for improving the accuracy of ISE by various pan-sharpening approaches, with a further comprehensive analysis of its scale effects. Three benchmark pan-sharpening approaches, Gram-Schmidt (GS), PANSHARP and principal component analysis (PCA) were applied to WorldView-2 in three spots of Hong Kong. The on-screen digitization were carried out based on Google Map and the results were viewed as referenced impervious surfaces. The referenced impervious surfaces and the ISE results were then re-scaled to various spatial resolutions to obtain the percentage of impervious surfaces. The correlation coefficient (CC) and root mean square error (RMSE) were adopted as the quantitative indicator to assess the accuracy. The accuracy differences between three research areas were further illustrated by the average local variance (ALV) which was used for landscape pattern analysis. The experimental results suggested that 1) three research regions have various landscape patterns; 2) ISE accuracy extracted from pan-sharpened data was better than ISE from original multispectral (MS) data; and 3) this improvement has a noticeable scale effects with various resolutions. The improvement was reduced slightly as the resolution became coarser.     

Service-oriented infrastructure for flood mapping using optical and SAR satellite data

In this paper, we present the service-oriented infrastructure within the Wide Area Grid project that was carried out within the Working Group on Information Systems and Services of the Committee on Earth Observation Satellites. The developed infrastructure integrates services and computational resources of several regional and national Grid systems: Ukrainian Academician Grid (with satellite data processing Grid segment, UASpaceGrid) and Grid system at the Center on Earth Observation and Digital Earth of Chinese Academy of Sciences. The study focuses on integrating geo-information services on flood mapping provided by Ukrainian and Chinese entities to benefit from information acquired from multiple sources. We also describe services for workflow automation and management in Grid environment and provide an example of workflow automation for generating flood maps from optical and synthetic-aperture radar satellite imagery. We also discuss issues of enabling trust for the infrastructure using certificates and reputation-based model. Applications of utilizing the developed infrastructure for operational flood mapping in Ukraine and China are given as well.     

Rapeseed-mustard acreage estimation using IRS LISS-II data

The present paper describes the remote sensing-based acreage estimation of rapeseed-mustard crop in Mehsana and Banaskantha districts of Gujarat, using four-band data and Maximum Likelihood classification. IRS LISS-II data of November 25, 1989 has been used to estimate the acreage of rapeseed-mustard. It is found that the data of November 25 is useful in discriminating rapeseedmustard from other rabi crops. Talukawise acreage estimation has also been done for three talukas of Mehsana and two talukas of Banaskantha district.     

Winter wheat biomass estimation using high temporal and spatial resolution satellite data combined with a light use efficiency model

Winter wheat biomass was estimated using HJ CCD and MODIS data, combined with a radiation use efficiency model. Results were validated with ground measurement data. Winter wheat biomass estimated with HJ CCD data correlated well with observed biomass in different experiments (coefficients of determination R² of 0.507, 0.556 and 0.499; n?=?48). In addition, R² values between MODIS estimated and observed biomass are 0.420, 0.502 and 0.633. Even if we downscaled biomass estimated using HJ CCD data to MODIS pixel size (9?×?9 HJ CCD pixels to approximate that MODIS pixel), R² values between estimated and observed biomass were still higher than those from MODIS. We conclude that estimation with remote sensing data, such as the HJ CCD data with high spatial resolution and shorter revisit cycle, can show more detail in spatial pattern and improve the application of remote sensing on a local scale. There is also potential for applying the approach to many other studies, including agricultural production estimation, crop growth monitoring and agricultural ecosystem carbon cycle studies.     

Improving performance of GPS satellite DCB estimation for regional GPS networks using long-term stability

Compensation for differential code bias (DCB) is necessary because it is the major source of errors in total electron content (TEC) measurements. The DCB estimation performance is degraded when only the regional GPS network is used. Because DCB estimation is highly correlated with ionospheric modeling, this degradation is particularly evident for measurements concentrated in an area of high TEC concentration. This study proposes a DCB estimation method that uses the long-term stability of the DCB to improve the estimation performance of the regional GPS network. We estimate satellite DCBs by assuming their constancy over seven months. This extended period increases the number of measurements used in DCB estimation and changes the local time distribution of collected measurements. As a result, the unbalanced distribution of specific ionospheric conditions disappears. Tests are performed using both global and regional networks, and the estimation performance is evaluated based on the position error and pseudorange residuals. First, the difference between the global and regional networks when using the conventional method is analyzed. Second, proposed methods are applied to regional networks. The proposed method can improve the DCB estimation performance, and the results are similar to those obtained using one-day global network data.     

Regional scale evapotranspiration estimation using satellite derived albedo and surface temperature

In the studies reteted to surface energy balance, satellite data provides important inputs for estimating regional surface albedo and evapotranspiration. The paper describes the use of satellite data in determining the surface emissivity over heterogeneous a’reas by taking Normalized Difference Vegetation Index (NDVI) as modulating parameter at pixel resolution. The estimated emissivity values have been used to find the surface temperature at the pixel scale. Landsat-TM-visible, NIR, TIR bands data and some ground meteorological data have been used in an energy balance model for estimating surface albedo and evapotranspiration. The ET values derived from the model are in good agreement with the values obtained with. ‘CENTURY MODEL’ and ground observations over the area, suggesting the possible use of this approach fot regional scale studies on evapotranspiration.     

Testing estimation of water surface in Italian rice district from MODIS satellite data

Recent changes in rice crop management within Northern Italy rice district led to a reduction of seeding in flooding condition, which may have an impact on reservoir water management and on the animal and plant communities that depend on the flooded paddies. Therefore, monitoring and quantifying the spatial and temporal variability of water presence in paddy fields is becoming important. In this study we present a method to estimate dynamics of presence of standing water (i.e. fraction of flooded area) in rice fields using MODIS data. First, we produced high resolution water presence maps from Landsat by thresholding the Normalised Difference Flood Index (NDFI) made: we made it by comparing five Landsat 8 images with field-obtained information about rice field status and water presence. Using these data we developed an empirical model to estimate the flooding fraction of each MODIS cell. Finally we validated the MODIS-based flooding maps with both Landsat and ground information. Results showed a good predictability of water surface from Landsat (OA = 92%) and a robust usability of MODIS data to predict water fraction (R² = 0.73, EF = 0.57, RMSE = 0.13 at 1 × 1 km resolution). Analysis showed that the predictive ability of the model decreases with the greening up of rice, so we used NDVI to automatically discriminate estimations for inaccurate cells in order to provide the water maps with a reliability flag. Results demonstrate that it is possible to monitor water dynamics in rice paddies using moderate resolution multispectral satellite data. The achievement is a proof of concept for the analysis of MODIS archives to investigate irrigation dynamics in the last 15 years to retrieve information for ecological and hydrological studies.