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.     

Results on potential use of simulated IRS-1C WiFS data for crop monitoring

Some of the basic requirements for cropping system analysis are updated information on crops grown, their phenological behaviour, method and duration of establishment and harvest, inter and intra crop variability, sequential cropping patterns. The next generation Indian Remote Sensing Satellite with high repeat cycle opens new possibility of crop surveys to derive such information. In this study, an attempt has been made to analyse cropping system at district level using simulated IRS-1C Wide Field Sensor (WiFS) data. Data acquired for nineteen dates during 1992–93 season for Bardhaman district, West Bengal has been used. It was feasible to derive accurate information on cropping pattern, crop rotation, crop duration, progress of harvest, crop growth profiles and annual crop acreage using multidate data. It was observed that even a seven to eight day interval of data acquisition during critical growth periods significantly affected classification and identification accuracy.     

Wheat crop classification using multidate IRS LISS-I data

Multi temporal dat acquired at different growth stages increases the dimensionality information content and have advantage over single date data for crop classification. Attempt was made to select suitable single date and combination of multidate data for wheat crop classification in Nalanda district of Bihar state where pulses and other crops are also grown in rabi season. Amongst the single date data February data was found to be better for wheat classification in comparison to November. January, March and April data. Combination of first two principal components each derived from IRS LISS-I four band data acquired in January and February was found to be the best set. Wheat classification accuracy achieved was 94.54 percent.     

Utilization of Resourcesat-1 data for improved crop discrimination

Subsequent to the launch of the state-of-art third generation Indian Remote Sensing satellite, Resourcesat-1, studies have been conducted to understand the capabilities of the on-board sensors for crop discrimination. The paper discusses the unique capabilities of the AWiFS, LISS-III and LISS-IV sensors in terms of their dimensionality, radiometry and spatial resolutions for crop discrimination and monitoring. The studies have indicated better crop discriminability especially using the short wave infrared data in 1.55–1.70 μm data among the spectrally confusing land cover classes, attributed to the relative differences of water contents. 10-bit radiometry of AWiFS data in four bands has been observed to be a better discriminant. Intrafield variability was very well captured by the LISS-IV data revealing the potential of data for applications like precision farming. The studies have revealed that potential of Resourcesat-1 data becoming the workhorse for several agricultural applications.     

Delineation of surface waterlogged areas in parts of bihar using IRS-1C LISS-III data

Pre-monsoon and post-monsoon surface waterlogged areas were delineated using satellite remote sensing data for Muzaffarpur, Vaishali and Saran districts of North Bihar. Digital data of IRS-1C LISS-III sensor acquired on December 7, 1998 and April 6, 1999 were analyzed using digital image processing software-ERDAS Imagine 8.3.1. The surface waterlogged areas were delineated using modeling technique which is the most advanced and accurate method. Using the modeling technique, a pixel is classified as water if the digital number (DN) value of its Near Infra Red (NIR) band is less than the DN value of the Red band and the Green band, and the Normalized Difference Water Index (NDWI) is greater than or equal to 0.32. The pre-monsoon surface waterlogged areas are found to be 14.02, 23.61 and 9.61 km² while the post-monsoon surface waterlogged areas are found to be 231.83, 118.19 and 176.06 km² for Muzaffarpur, Vaishali and Saran districts, respectively. Also, land use/land cover maps were prepared.     

Burnt area mapping of Bandipur National Park,India using IRS 1C/1D LISS III data

The Bandipur National Park situated in the Western Ghats of Karnataka State, is one of the biodiversity hotspots of the world. During recent years, this park has witnessed repeated fires, affecting considerable areas under vegetation. The temporal satellite data from 1997 to 2006 have been analyzed to map the burnt areas using Remote Sensing (RS) and Geographic Information System (GIS) techniques. The vegetation cover is moist deciduous, dry deciduous, scrub forests and teak plantation. Information on extent of the burnt areas and the type of vegetation affected were derived forest range-wise. The fire prone regions have been identified by integrating vegetation type/density, road and settlement network and past history of forest fire occurrence, by assigning subjective weightage according to their fire-inducing capability or their sensitivity to fire. Comparison between each temporal dataset in terms of the extent of burnt area was also carried out to interpret fire incidence pattern. Three categories of fire risk regions such as Low, Moderate and High fire intensity zones were identified and it was found that almost 40% of the study area falls under low risk zone. An evaluation of the existing fire management systems and the implication of fire prevention programmes has been discussed, besides an assessment of causal factors for fire incidence in the park.     

Selection of band combination for IRS data

Selection of band combination for generating a colour composite image using IRS data is discussed from statistical considerations. Most suitable three band combination turns out to be bands 1, 3 and 4. It is also shown that intrinsic dimensionality of IRS data is approximately two.     

Evaluation of simulated IRS-1C WiFS data for concurrent monitoring of command areas at disaggregated level

Data of Wide Field Sensor (WiFS) to go onboard Indian Remote Sensing Satellite, IRS-1C, in December 1995, is simulated mainly from IRS IB LISS I data of Bhadra command area, Karnataka (India) during 1993–94 summer season, to evaluate its capability in concurrent monitoring of irrigated crops at disaggregated level Crop area, crop-growth profiles of homogeneous crops like paddy, as obtained from both simulated WiFS data and LISS I data are very close for almost all the distributary commands of Bhadra project Though non-paddy-crop groups could also be classified satisfactorily, the Workability with small-extent-individual crops like groundnut, garden and sugarcane is found to be less due to coarse resolution of WiFS data and hence the individual crops could not be separated out. This study proves the potential of WiFS in concurrent monitoring of fairly-large-extent irrigated crops at distributary level. The basic feasibility of WiFS had been established in an earlier work at broad level and this study demonstrates the feasibility of information extraction at distributary command level from WiFS data.     

Multi temporal ERS-1 SAR data for identification of rice crop

Microwave sensors having all-weather capabilities provide an opportunity to monitor rice grown in monsoon season. An attempt has been made to identify rice crop using multitemporal ERS-1 SAR data in C-band (5.3 GHz). Data acquired on August 15 (D1), September 19 (D2), October 24 (D3) and November 28 (D4) 1993 were taken. Combinations of data acquired on different dates were used for identification of rice crop. Single-date IRS-1B LISS II data in visible and NIR bands acquired on October 23, 1993 was also used for comparison of estimated rice area. Analysis of the results has shown that a combination of SAR data acquired at the tillering (August), booting (September) and heading (October) stages of rice crop enabled identification and area estimation of rice crop grown under lowland conditions. Single-date SAR data acquired in the month of October was found to be better for identification of rice compared to other dates.     

Study on the utility of IRS-P6 AWiFS SWIR band for crop discrimination and classification

This present study was conducted to find out the usefulness of SWIR (Short Wave Infra Red) band data in AWiFS (Advanced Wide Field Sensor) sensor of Resourcesat 1, for the discrimination of different Rabi season crops (rabi rice, groundnut and vegetables) and other vegetations of the undivided Cuttack district of Orissa state. Four dates multi-spectral AWiFS data during the period from 10 December 2003 to 2 May 2004 were used. The analysis was carried out using various multivariate statistics and classification approaches. Principal Component Analysis (PCA) and separability measures were used for selection of best bands for crop discrimination. The analysis showed that, for discrimination of the crops in the study area, NIR was found to be the best band, followed by SWIR and Red. The results of the supervised MXL classification showed that inclusion of SWIR band increased the overall accuracy and kappa coefficient. The ‘Three Band Ratio’ index, which incorporated Red, NIR and SWIR bands, showed improved discrimination in the multi-date dataset classification, compared to other SWIR based indices.     

Area estimation of arecanut plantations in Sirsi Taluk using IRS data

Extent and distribution pattern of arecanut plantations in Sirsi Taluk, Uttara Kannada District, Kamataka have been studied using IRS, LISS II data. The plantations are found to exhibit perfect zonality, distinct structure and contrasting tonal characteristics and thereby enable their differentiation from other land use/land cover categories encountered in the area. The study not only established the utility of IRS data in acreage estimation of this unique category of land use having significant economic relevance in the area but also in assessing the scope for planning development of spices in these plantations.     

Mapping of hydrogeomorphological features in varaha river basin using IRS data

Hydrogeomorphological mapping in Varaha River Basin (VRB) was carried out to develop relationship between groundwater condition and geomophology of the area. The VRB is located in the north coastal region of Andhra Pradesh is characterised by variability in precipitation, temperature, vegetation, infiltration and run-off. Realising the significance of remote sensing in natural resource management and development, IRS 1A data was used in the study. It is inferred that various hydrogeomorphological units identified in the study area, are the outcome of different geomorphic processes that have been operating in the area and reveal which close relationship between groundwater condition and geology and geomorphol-ogy of the area. Areas covered by buried channels have shallow aquifers of good quality wa-ter with excellent yield. Lineaments and fractures may prove to be potential zones for ground-water development.     

Evaluation and integration of ERS-1-SAR and optical sensor data (TM and IRS) for geological investigations

The C-band imaging radar of ERS-1, due to its high sensitivity to terrain surface features, holds tremendous potential in topographic terrain mapping for various applications. This is being examined for geological applications, mainly structural and lithological mapping in a mineral belt of Bihar and Orissa, India. The high image contrast that facilitates structural interpretation and highlights topography on the SAR images, reflects the high sensitivity of the ERS-1-SAR to change in terrain slope in the study area. Extensive lineaments, fold structure and major lithological contacts are easily mappable from the SAR imagery. Many of the lineaments, lithological contacts and fold pattern are mapped equally from optical data (Landsat-TM and IRS-1B FCC). The close association of fold pattern and mineral deposits in the region has necessitated the study of those structures carefully from various remote sensing data products. Synergism between SAR and TM provided useful results regarding structure and lithology of the region. The advantage of SAR in highlighting topography and detecting lineaments are affected to a great extent by the speckle noise and low pixel resolution. The present study shows that future geologic interpretation demands high spatial resolution and efficient data processing technique which reduces the speckle noise more significantly.     

Mapping of Lateritic Soils of Midlands in Kerala Using Resourcesat-1 LISS-III and SRTM DEM Data

Lateritic soils of Mathamangalam, Kannur District, located in midlands of Kerala, were morphologically studied, characterized, classified and mapped at 1:50,000 scale using remote sensing techniques. The terrain of the study area being hilly and covered with perennial vegetation, soil-landscape model was applied. For this purpose physiographic information was inferred from SRTM DEM, Resourcesat-1 LISS-III satellite image and topographical maps. The interpreted units were validated in the field and characterized through soil-site examination, soil profile study and soil analysis. The study indicated that the lateritic soils of midlands of Kerala vary in physical, chemical and morphological properties in relation to micro-relief. Soils developed on moderately steeply sloping side slopes (15–30% slope) are deep, moderately well drained with gravelly clay textured, where as the soils developed on moderately slopping side slope (10–15% slope) are very deep and well drained. The soils of valleys are very deep, moderately well drained with fine texture. Very gently sloping (1–3%) laterite plateau tops have extremely shallow soils associated with rock outcrops. These soils mainly belong to Order Ultisols followed by Inceptisols and Entisols. These were further grouped up to Family and Series level by tentatively establishing seven soil series. This study helps in understanding the behaviour of lateritic soils of midlands of Kerala, which can be useful in generation of interpretative maps and in optimizing the land use.     

Interpretation and analysis of multispectral data for aricultural crop cover types-role of crop spectral response and crop sectral signature

Airborne multispectral data obtained over mono and multiple cropping systems of small farming agriculture was studied for two cropping seasons for a possible development of crop spectral signatures and to utilize such signatures for interpretation of multispectral data and for assessing agricultural potentials of a region. In multiple cropping system, the unique crop spectral response exhibited by crop species at specific growth stages facilitated interpretation and analysis of multispectral data with the knowledge of crop phenology. For resolving spectral confusion between crop species due to growtn stages of different crop species, temporal data were observed to be useful. Development and use of crop spectral sigrature for interpretation and analysis multispectral data related to mono cropping system were found to be relatively less complex and offer great promise because of minimum spectral confusion.     

Comparative performance of per-pixel classifiers using ers-1 sar data for classification of rice crop

Three-date ERS-1 SAR data acquired on August 24, September 28 and November 2, 1995, was used to classify rice crop in a predominant rice growing region of West Bengal. India, Artificial neural network, maximum likelihood, decision rute and K-Means clustering classifiers were used. Classification accuracy was evaluated from the error matrix of same set of training and validating pixels. Rice classification accuracy improved significantly using neural network classifier. The decision rule based classifier performed equally good for most of the sites, indicating the feasibility of deriving a common rule based algorithm for large area application. Law aecuracy was observed for maximum likelihood classifier.     

Mapping crop phenology using NDVI time-series derived from HJ-1 A/B data

With the availability of high frequent satellite data, crop phenology could be accurately mapped using time-series remote sensing data. Vegetation index time-series data derived from AVHRR, MODIS, and SPOT-VEGETATION images usually have coarse spatial resolution. Mapping crop phenology parameters using higher spatial resolution images (e.g., Landsat TM-like) is unprecedented. Recently launched HJ-1 A/B CCD sensors boarded on China Environment Satellite provided a feasible and ideal data source for the construction of high spatio-temporal resolution vegetation index time-series. This paper presented a comprehensive method to construct NDVI time-series dataset derived from HJ-1 A/B CCD and demonstrated its application in cropland areas. The procedures of time-series data construction included image preprocessing, signal filtering, and interpolation for daily NDVI images then the NDVI time-series could present a smooth and complete phenological cycle. To demonstrate its application, TIMESAT program was employed to extract phenology parameters of crop lands located in Guanzhong Plain, China. The small-scale test showed that the crop season start/end derived from HJ-1 A/B NDVI time-series was comparable with local agro-metrological observation. The methodology for reconstructing time-series remote sensing data had been proved feasible, though forgoing researches will improve this a lot in mapping crop phenology. Last but not least, further studies should be focused on field-data collection, smoothing method and phenology definitions using time-series remote sensing data.     

Estimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data

In the present study, Sentinel-1A Synthetic Aperture Radar analysis of time series data at C-band was carried out to estimate the winter wheat crop growth parameters. Five different date images were acquired during January 2015–April 2015 at different growth stages from tillering to ripening in Varanasi district, India. The winter wheat crop parameters, i.e. leaf area index, vegetation water content (VWC), fresh biomass (FB), dry biomass (DB) and plant height (PH) were estimated using random forest regression (RFR), support vector regression (SVR), artificial neural network regression (ANNR) and linear regression (LR) algorithms. The Ground Range Detected products of Interferometric Wide (IW) Swath were used at VV polarization. The three different subplots of 1 m² area were taken for the measurement of crop parameters at every growth stage. In total, 73 samples were taken as the training data-sets and 39 samples were taken as testing data-sets. The highest sensitivity (adj. R^2?=?0.95579) of backscattering with VWC was found using RFR algorithm, whereas the lowest sensitivity (adj. R^2?=?0.66201) was found for the PH using LR algorithm. Overall results indicate more accurate estimation of winter wheat parameters by the RFR algorithm followed by SVR, ANNR and LR algorithms.     

Assessing the suitability of data from Sentinel-1A and 2A for crop classification

Sentinel-1A C-SAR and Sentinel-2A MultiSpectral Instrument (MSI) provide data applicable to the remote identification of crop type. In this study, six crop types (beans, beetroot, grass, maize, potato, and winter wheat) were identified using five C-SAR images and one MSI image acquired during the 2016 growing season. To assess the potential for accurate crop classification with existing supervised learning models, the four different approaches namely kernel-based extreme learning machine (KELM), multilayer feedforward neural networks, random forests, and support vector machine were compared. Algorithm hyperparameters were tuned using Bayesian optimization. Overall, KELM yielded the highest performance, achieving an overall classification accuracy of 96.8%. Evaluation of the sensitivity of classification models and relative importance of data types using data-based sensitivity analysis showed that the set of VV polarization data acquired on 24 July (Sentinel-1A) and band 4 data (Sentinel-2A) had the greatest potential for use in crop classification.