Evaluation of RADARSAT Standard Beam data for identification of potato and rice crops in India

The Canadian satellite RADARSAT launched in November 1995 acquires C-band HH polarisation Synthetic Aperture Radar (SAR) data in various incident angles and spatial resolutions. In this study, the Standard Beam S7 SAR data with 45°–49° incidence angle has been used to discriminate rice and potato crops grown in the Gangetic plains of West Bengal state. Four-date data acquired in the 24-day repeat cycle between January 2 and March 15, 1997 was used to study the temporal backscatter characteristics of these crops in relation to the growth stages. Two, three and four-date data were used to classify the crops. The results show that the backscatter was the lowest during puddling of rice fields and increased as the crop growth progressed. The backscatter during this period changed from −18 dB to −8 dB. This temporal behaviour was similar to that observed in case of ERS-SAR data. The classification accuracy of rice areas was 94% using four-date data. Two-date data, one corresponding to pre-field preparation and the other corresponding to transplantation stage, resulted in 92% accuracy. The last observation is of particular interest as one may estimate the crop area as early as within 20–30 days of transplantation. Such an early estimate is not feasible using optical remote sensing data or ERS-SAR data. The backscatter of potato crop varied from −9 dB to −6 dB during the growth phase and showed large variations during early vegetative stage. Two-date data, one acquired during 40–45 days of planting and another at maturing stage, resulted in 93% classification accuracy for potato. All other combinations of two-date data resulted in less than 90% classification accuracy for potato.     

Land Cover/Land use Mapping Using Different Wavelet Packet Transforms for LISS IV Madurai Imagery

Texture in high resolution satellite images requires substantial improvement in the conventional segmentation algorithms. The use of wavelet packet transforms for texture analysis and image classification of high spatial resolution LISS IV imagery provide more details about the urban areas. This paper analyses the performance of a combination of Wavelet Packet Statistical Features (WPSFs) and Wavelet Packet Co-occurrence Features (WPCFs) for the classification of LISS IV images. The classification accuracy per pixel is improved in this paper by varying the window size. Four indices—user’s accuracy, producer’s accuracy, overall accuracy and kappa co-efficient are used to assess the accuracy of the classified data. Experimental results show that a multi-band and multi-level wavelet packet approach can be used to drastically increase the classification accuracy.     

Evaluation of the spatial changes in seagrass cover in the lagoons of Lakshadweep islands,India, using IRS LISS III satellite images

A study was conducted in Lakshadweep islands to determine the feasibility of using Indian Remote Sensing (IRS) satellites for detecting changes in the seagrass from other coastal features. IRS ID and IRS P6 LISS III having spatial resolution of 23.5 m with lower cost compared to all other contemporary satellites with the same spatial resolution have not been widely used for monitoring the changes in seagrass cover. In this context, the present study attempted to explore the effectiveness of LISS III data for mapping seagrasses and to inform the international community about the usefulness of these low-cost imageries for coastal resource monitoring. Supervised classification and change detection studies found a significant decrease in seagrass cover of 73.03 ha in the Lakshadweep group of islands. An overall accuracy of 67.5% was obtained for the change maps, and seagrass cover and its changes vary at different islands.     

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.     

Analysis of cropping pattern changes in Bathinda district,Punjab

A study was conducted in the Bathinda district of Punjab state for mapping the cropping pattern and crop rotation, monitoring long term changes in cropping pattern by using the satellite based remote sensing data along other spatial and non-spatial collateral data. Multi-date IRS LISS I and IRS WiFS sensor data have been used for this study. Cropping pattern maps and crop rotation maps were generated for the years 1988-89 and 1998-99. The present study has shown the increase of cropping intensity significantly, mainly due to increase in rice area. However, crop diversity has decreased mainly due to decline in the area under the minor crops like pearl millet, gram, rapeseed/ mustard. There is increase in area coverage of cotton-wheat and rice-wheat rotation, at the expense of the minor crops.     

Crop classification accuracy as influenced by training strategy,data transformation and spatial resolution of data

The possibility of improving classification accuracies using different training strategies and data transformations within the framework of a supervised maximum likelihood classification scheme was explored in this study. The effect of spatial resolution of data on the accuracy of classification was also studied Single-pixel training strategy resulted in improved classification accuracy over the block-training method. Data transformations gave no significant improvements in accuracy over untransformed data. There was a reduction in classification accuracy as resolution of data improved from 72 m (LISS I) to 36 m (LISS II) while other sensor characteristics remained same.     

Evaluation of RADARSAT ScanSAR synthetic aperture radar data for rice crop inventory and monitoring

Radarsat ScanSAR Narrow (SN2) data acquired on July 24 and August 17, 1997 were used to analyse the signature of rice crop in West Bengal, India. The analysis showed that the lowland practice of cultivation gives a distinct signature to rice due to the initial water background. The relatively stable backscatter from water bodies in temporal data enhanced the separability of rice fields from water using two date data. Around 94 per cent classification accuracy was achieved for rice crop using two date data. It was feasible to discriminate rice sub-classes based on their planting period like early and late crop. The analysis indicates the suitability of ScanSAR data for large area rice crop monitoring as it has a wide swath of 300 km.     

Comparison of MODIS vegetation continuous field — based forest density maps with IRS-LISS III derived maps

The study compared forest cover maps derived using coarse resolution vegetation continuous fields (MODIS VCF; 500m resolution) with the maps derived from medium resolution (24m; IRS LISS-III) data. The comparison of VCF, per cent tree cover product, for the years 2000 to 2004 with LISS III forest density class maps of 2001 and 2003 was carried out for two sites representing hilly (Uttarakhand) and undulating terrains (Madhya Pradesh). Slicing VCF to corresponding forest crown cover, i.e., 0–10%, 10–40%, 40–70% and >70% produced considerable difference in forest area estimates when compared to original LISS III derived crown cover area. The corresponding value range in VCF for 0–10% of actual forest cover were 0–31% and 0–25% in 2 sites respectively, and the respective limit was consistent at 1–20% when VCF range were sliced with respect to upscaled LISS III at 500m resolution. Similarly, all other class limits were also found through iterative process. These limits were similar, within a site, across five years. Spatial Kappa match between these two data indicated higher match in 40–70% class, and also in undulating site. When compared at same resolution, similar forest area cover estimated with weighted area upscaling gave closest match. The study is useful in knowing the usability and limits of VCF product, and utility of spatial Kappa.     

Inter sensor comparison between RESOURCESAT LISS III, LISS IV and AWiFS with reference to coastal landuse/landcover studies

Coastal zone assumes importance due to high productivity of ecosystems, man-made developmental activities, natural hazards and dynamic nature of the coast. As costal ecosystems are unique and fragile, understanding the impact of developmental activities on the sustainability of the coastal zone is very important. Remote sensing, because of repetitive and synoptic nature is an ideal tool for studying this. Time series data analyses for monitoring coastal zone require different type of sensors. Present study deals with atmospheric correction of satellite data, reflectance, selection of coastal features like, mudflat, mangroves, vegetated dune, coastal water, etc. and their inter-comparison using different sensor data of RESOURCESAT sensors. Reflectance values give better separateability for various coastal features in comparison to DN values. LISS IV can be used in place of LISS III or merged (LISS III + PAN) for long-term coastal zone studies.     

Geostatistical and visualization analysis of crop suitability for diversification in sub-mountain area of Punjab,North-West India

This study presents a Geographic Information System (GIS)-based geostatistical and visualization analysis of crop suitability in two blocks of sub-mountain area of Punjab under diversification programme. It combines the limitation approach of land capability classification, productivity potential evaluation procedure and crop suitability evaluation framework of FAO. Two blocks from the sub mountain Siwalik region of Punjab viz., Mahalpur and Garhshankar were selected. This study evaluates the capabilities of the study area for traditional crops like wheat, paddy and maize, and recently introduced crops like sugarcane, sunflower, pea, rapeseed-mustard, potatoes and kinnow for agricultural diversification. The suitability of the crops has been worked out at the village level. About 35–40 per cent of total area mostly in Siwallik hills is not fit for growing any type of crop. Sandy texture, uneven topography, moderately steep slopes and excessive drainage are responsible for unsuitability of this area. The GIS based suitability analysis for traditional crops as well as for new crops, under diversification of agriculture has been undertaken. The geostatistical analysis points towards suitability of relatively large areas for new crops like sunflower, potato, pea (green) and sugarcane. Forty three and 14 per cent of total area has been found highly suitable and suitable respectively for growing green pea - a cash crop. Thirty three per cent of total area is suitable for growing kinnow fruit. The success of diversification programme is subject to logical government policy in terms of providing cold storage, food processing facility and marketing infrastructure.     

Multiresolution and Multispectral Data Fusion Using Discrete Wavelet Transform with IRS Images: Cartosat-1, IRS LISS III and LISS IV

Image fusion techniques integrate complimentary information from multiple image sensor data such that the new images are more suitable for the purpose of human visual perception and computer based processing tasks for extraction of detail information. As an important part of image fusion algorithms, pixel-level image fusion can combine spectral information of coarse resolution imagery with finer spatial resolution imagery. Ideally, the method used to merge data sets with high-spatial and highspectral resolution should not distort the spectral characteristics of the high-spectral resolution data. This paper describes the Discrete Wavelet Transform (DWT) algorithm for the fusion of two images using different spectral transform methods and nearest neighbor resampling techniques. This research paper investigates the performance of fused image with high spatial resolution Cartosat-1(PAN) with LISS IV and Cartosat-1(PAN) sensor images with the LISS III sensor image of Indian Remote Sensing satellites. The visual and statistical analysis of fused images has shown that the DWT method outperforms in terms of Geometric, Radiometric, and Spectral fidelity.     

Accuracy of mustard and wheat inventory at village-level using liss-II digital data in pali district, Rajasthan

A study was carried out to estimate the accuracy of crop discrimination and area inventory for wheat and mustard using IRS LISS-II digital data of two acquisition dates D1 (Dec. 28, 1994) and D2 (Feb. 10, 1995) over a test site (1413 ha) comprising of two villages in Pali district, Rajasthan, The D1 and D2 were optimal acquisitions for mustard and wheat respectively with deviations in aereage estimates of less than five per cent in comparison to field survey. The percent correctly classified pixels ter training site for optimal dates of each crop ranged between 85 and 86 per cent and they were much lower for other dates. These results with lower accuracies than reported earlier for sites with single dominant crop are indicative of accuracies for discrimination and area inventory fot sites having two crops and also sensitivity to acquisition period.     

Analysis of temporal sar and optical data for rice mapping

This study investigates the potential of multi-temporal signature analysis of satellite imagery to map rice area in South 24 Paraganas district of West Bengal. Two optical data (IRS ID LISS III) and three RADARSAT SAR data of different dates were acquired during 2001. Multi-temporal SAR backscatter signatures of different landcovers were incorporated into knowledge based decision rules and kharif landcover map was generated. Based on the spectral variation in signature, the optical data acquired during rabi (January) and summer (March) season were classified using supervised maximum likelihood classifier. A co-incidence matrix was generated using logical approach for a combined “rabi-summer” and “kharif-rabi-summer” landcover mapping. The major landcovers obtained in South 24 Paraganas using remote sensing data are rice, water, aquaculture ponds, homestead, mangrove, and urban area. The classification accuracy of rice area was 98.2% using SAR data. However, while generating combined “kharif-rabi-summer” landcovers, the classification accuracy of rice area was improved from 81.6% (optical data) to 96.6% (combined SAR-Optical). The primary aim of the study is to achieve better accuracy in classifying rice area using the synergy between the two kinds of remotely sensed data.     

Estimation of the Aerial Cover of Seagrasses of Lakshadweep Islands (India) Using Indian Remote Sensing Satellite (IRS P6 LISS IV)

Seagrasses ecosystems are fragile yet highly productive ecosystems of the world showing declining trend throughout the world due to natural and anthropogenic pressures. Effective conservation and management plan is thus required to protect these resources, to aid with conservation need mapping and monitoring of seagrasses using high resolution remote sensing data is very much required. Hence, the present study was made to record the seagrass aerial cover in the Lakshadweep islands using IRS P6 LISS IV satellite data. The suitability of LISS IV sensor for seagrass mapping was tested for the first time with an overall accuracy of 73.16%. The study found an area of 2590.2?ha of seagrasses in Lakshadweep islands with 1310.8?ha and 1279.4?ha dense and sparse seagrass cover respectively. The study recommends the use of LISS IV data for mapping the shallow water seagrasses, as mapping efficiency increases nearly 4 times more than the LISS III data, as the former (LISS IV) picks up the small patches of seagrasses and delineates the coral and reef vegetation patches from seagrass class.     

Age-based classification of arecanut crops: a case study of Channagiri,Karnataka, India

Arecanut is one of the predominant plantation crop grown in India. Yield of this crop depends upon age of the crop and there is no information on the spectral behaviour of arecanut crops across its ages. In this study popular supervised classification algorithms were utilized for age discrimination of arecanut crops using Hyperion imagery. Arecanut plantations selected for the study are located in Channagiri Taluk, Davanagere district of Karnataka state, India. Ground truth information collected involves: (i) GPS coordinates of selected plots, (ii) spectral reflectance of arecanut crops with age ranging from 1 to 50 years, using handheld spectroradiometer with 1 nm spectral resolution. These spectral measurements were made close in time to the acquisition of Hyperion imagery to build age-based spectral library. It is observed from the analysis that crops of ages below 3, 3–7, 8–15 and above 15 years were showing distinct spectral behaviour. Accordingly, crops age ranging from 1 to 50 were grouped into four classes. Classification of arecanut crops based on age groups was performed using methods like spectral angle mapper, support vector machine and minimum distance classifier, and were compared to find the most suitable method. Among the classification methods adopted, support vector machine with linear kernel function resulted in most accurate classification method with overall accuracy of 72% for within class seperability. Individual age group classification producer’s accuracy varied minimum of 12.5% for 3–7 years age group and maximum of 86.25% for above 15 years age group. It may be concluded that, not only age- based arecanut crop classification is possible, but also it is possible to develop age-based spectral library for plantation crop like arecanut.     

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.     

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.     

National Inventory of Rabi Pulses in India Using Remotely Sensed Data

A scheme called National Food Security Mission was launched by Government of India in 2007 for wheat, rice and pulses crops. At the request of Ministry of Agriculture for monitoring intensification of pulses a project called Pulses Intensification was taken up in Rabi season 2012–2013. Reliable statistics using advanced methods is very important for variety of pulse crops. Remotely sensed data can help in pre-harvest area estimation of pulse crops. Pulses in India are grown as partly scattered and partly contiguous crop. Growth in scattered areas and poor vegetation canopy of some of the pulse crops poses a challenge in its identification and discrimination using remotely sensed data. National Inventory of Rabi pulse crops in major growing regions of northern and southern parts of India was attempted. Multi-date AWiFS data and multi-date NDVI products of AWiFS of Rabi season 2014–2015 were used to study spectral-temporal behavior of pulse crops. Pulse crops accuracies of more than 95 % was observed in contiguous areas and 50–80.77 % in scattered regions. All India area estimate of Rabi pulses for the year 2014–2015 was 8963.327 ‘000 ha.     

利用Landsat TM和IRS P6 LISS III卫星影像及GIS对耕地和耕种模式的时空动态分析(英文)

The study of the spatial patterns and temporal changes of cropland is important to understand the underlying factors and the functional effects of the agricultural landscape. On the other hand, crop dynamics mapping is essential to know the overall agro-spatial diversity of the area. Therefore, this paper addressed a spatio-temporal analysis of cropland and cropping pattern change in the Bogra district of Bangladesh over the last 16 years (between 1988/89 and 2004/05). In this paper, crop mapping from multi-temporal and multi-sensor satellite images was described. Landsat TM and IRS P6 LISS III satellite images were used with GIS for spatial dynamics of cropland and cropping pattern change analysis. First, seasonal cropland maps were derived from object-based classification of satellite images, then two-date classified image differencing with GIS overlay technique and decision rules were applied. Cropping pattern change was analyzed in a spatial and quantitative way for the 16 years and for this, Integrated Land and Water Information System (ILWIS) and Land Change Modular (LCM) of IDRISI Andes were used. The results showed that in the area, mono crop cultivation was found in summer, but in winter, areas under different crop cultivation had changed dramatically. Change analysis showed that the changes mainly occurred in the north northwest and southwest of the areas, and during the time the highest change area was found under the rice-potato pattern.      

ANFIS Based Land Cover/Land Use Mapping of LISS IV Imagery Using Optimized Wavelet Packet Features

Digital image classification is the process of sorting all the pixels in an image into a finite number of individual classes. But, it is difficult to classify satellite images since they include both pure pixels and boundary pixels. The boundary pixels are ‘mixed’ pixels, representing an area occupied by more than one ground cover. That is, class boundaries represented by pixels, are not sharp but fuzzy. This paper discuses the application of Adaptive Neuro-Fuzzy inference system (ANFIS) for classification of remotely sensed images that contains mixed pixels. Decision making was performed in two stages: feature extraction using the Wavelet Packet Transforms (WPT) and the ANFIS trained with the back propagation gradient descent method in combination with the least squares method for classification. Genetic Algorithms (GA) based approach is analysed for the selection of a subset from the combination of Wavelet Packet Statistical Features (WPSF) and Wavelet Packet Co-occurrence (WPC) textural feature set, which are used to classify the LISS IV images. GA has been employed to reduce the complexity and increase the accuracy of classification. Four indices—user’s accuracy, producer’s accuracy, overall accuracy and kappa co-efficient are used to assess the accuracy of the classified data. Experiments show that the proposed approach produces better results compared to the results obtained when classical classifiers are used.