Pixel Noise Localization Algorithm for Indian Satellite Data Quality Control: A Novel Approach

Instance difficulty-based noise correction for crowdsourcing

Worker similarity-based noise correction for crowdsourcing

Land Use and Land Cover (LULC) dynamics have a major impact on environmental sustainability, re-source management, and urban development. Effective decision-making depends on correct forecasting of these changes. This research predicts LULC changes with the Random Forest (RF) machine learning tech-nique using satellite-derived data from the National Remote Sensing Centre (NRSC) for the years from 2005 to 2023. The dataset includes various LULC categories such as built-up land, agricultural lands, plan-tation/orchards, forests, wetlands, grasslands, wastelands, and water bodies. This study focuses on an area of Tamilnadu, India, covered by NRSC’s LULC datasets from 2005 to 2023. The Random Forest model was first trained on data for the years from 2005 to 2017 to predict LULC for the next consecutive years 2018–2023, with validated against actual LULC values for Tamilnadu, India from the year 2018 to 2023, achieving high accuracy with a correlation coefficient (R > 0.97 in later years) and decreasing Mean Abso-lute Error. Based on the complete historical dataset from 2005 to 2023, the trained model is then applied to predict LULC changes for the years from 2024 to 2028. The results indicate a significant increase in urban development of 0.001 hectare annually and a consistent decrease in double/triple cropping areas of ap-proximately 1.5 hectare between 2024 and 2028, stable but slightly declining forest cover, and water body spread oscillations. The steady increase in built-up land underscores the importance of controlled urban expansion and the decline in double/triple cropping areas calls for policies that support sustainable farm-ing practices. With forest cover slightly declining, policymakers should strengthen conservation initia-tives, afforestation efforts, and enforce stricter land-use regulations to prevent degradation. The oscillating spread of water bodies highlights the need for improved watershed management strategies.

A draft Standard International Classification (SIC) of land use has been proposed and adopted by the Conference of European Statisticians. The classification scheme has been critically assessed for its suitability for application to different forms of land use survey in the United Kingdom. and also for its compatibility with land cover categories derived from data acquired by remote sensing techniques. At the most general level there is a high degree of agreement with the categories proposed in the USGS classification for use with remote sensor data. In the context of British requirements, the most suitable portion of the SIC appears to be in the classification of' forest and other wooded land'. An alternative classification of 'agricultural land' is offered which is better suited to the needs of the UK agricultural census. The SIC treatment of 'natural terrain' is criticised for being out of step with current ecological practice. especially in the treatment of 'wetlands'. It is suggested that the SIC should be open to modification in the light of its application in the next few years, especially with the likelihood of more widespread adoption for land cover mapping of high resolution satellite data. 1. Background Arising originally out of a meeting on land use statistics held in Geneva in March 1980 under the auspices of the Economic Commission for Europe Con­ ference of European Statisticians, an amended version of the draft Standard International classification (SIC) of land use was approved at the 31st plenary session of the Conference of European Statisticians in June, 1983, [2] and Table 1. In the United Kingdom, at about the same time, the Land Applications Working Group (LAWG) of the National Remote Sensing Centre was deliberat­ ing on the matter of land use classification, with particular reference to identify­ ing a classification suitable for use with remotely sensed images, from aircraft or satellite, as data sources. Robert Wright is a graduate of Glasgow University (B.Sc. Honours in Geography), the International Institute for Aerospace Survey and Earth Sciences (ITC) in the Netherlands (B.Sc. in Photogram­ metric Engineering) and the University of Michigan (M.Sc. in Remote Sensing). He is currently a lecturer in the Geography department at Aberdeen University, where he is responsible for teaching and research activity in the field of environmental remote sensing. He is a member of the Land Applications Working Group of the U.K. National Remote Sensing Centre, Royal Aircraft Establishment, Farnborough, England.

The launch of the first Indian Remote Sensing Satellite in 1988 ushered in a new era of utilization of satellite data and technology in natural resources monitoring, coastal area management, disaster mitigation, weather and climate change studies. India with its very long coast line, areal extent, resources and population would have been a huge challenge to the coastal researchers and administrators if not for the Indian Remote Sensing satellite data that provided the much needed temporal, thematic information for effective mapping, monitoring and management of the coastal areas. The country’s large and diverse coastal regions are economically important and ecologically rich but also extremely vulnerable and highly prone to natural and anthropogenic hazards. Through the last three decades, the Indian remote sensing programme has contributed immensely to our understanding of the coast and ocean processes, coastal ecosystems, habitats, marine pollution and the vulnerability of the coast to natural hazards. IRS-1C with its improved spatial resolutions, additional spectral band, improved repetitively ushered in a new era of research specifically in the management of coastal areas and resources. Availability of spatial data at various resolutions, low cost, advances in technology resulted in new applications for management coastal areas. IRS-1C also can be aptly termed as the precursor for thematic satellite series such as OCEANSAT, RESOURCESAT and CARTOSAT, thereby setting the environs for thematic research in coastal areas. Integrated utilization of remote sensing data, numerical models, GIS and GPS has paved the way for the development of operation systems for management of coastal areas, thereby realizing the vision of Dr. Vikram Sarabhai, the father of Indian Space Programme, of building a self-reliant and indigenous Space Programme for the betterment of quality of life of the common people.

To satisfy a user’s request for spaceborne remote sensing data a series of elements in the data chain must come together. The complete data chain has three major elements; orbiting platforms (to collect the data), data reception facilities and data processing facilities. The UK has a wide range of remote sensing users and, to support them, it has a strong presence in all areas of the data chain.This paper will describe the less well known, but equally important area of data processing and dissemination. The two main UK facilities are the National Remote Sensing Centre (NRSC) and the Earth Observation Data Centre (EODC).

Land use is an important factor in planning and managing land resources. Increasing pressure due to population and human resources in the world’s resources to meet growing needs contributes to significant land reform in various land uses. Remote sensing and GIS (geographic information system) techniques have been used to study land use change and land cover on the Tuticorin coast in Tamil Nadu. This study examines land use and land cover (LULC) changes from 2001 to 2017 for the coast of Tuticorin. The main objective of this study was to assess changes under the NRSC (National Remote Sensing Center) classification using Landsat ETM + and OLI images using visual interpretation with the help of image interpretation keys. The digitized land use and land cover features are categorized as aquaculture, built-up land, water bodies, cropland, fallow land, forest, a forest plantation, industrial area/mining, mangrove/swamp area, plantation, salt-affected land, saltpan, sandy areas, land with scrub, land without scrub, and waterlogged area. Apparently the whole study from 2001 to 2017 found that built-up land (+20.44 sq. km) and industrial/mining activities (+5.78 sq. km) were increased and cropland (−8.04 sq. km) and plantation (−7.62 sq. km) were decreased. The ground truth verification of the LULC features performed is made with an effective assessment of the changes. This study shows a significant environmental impact in the study area. In addition, it is crucial to strongly monitor the land use/land cover changes to maintain sustainable growth and in-depth coastal management requirements that can be taken to protect human health and property.

<p>Groundwater is said to be depleting at an alarming rate, and is stated as a major concern for agriculturally driven countries like India. Therefore, understanding the dynamics of water system of the country is prerequisite for assuring its sustainability. According to the GRACE (Gravity Recovery and Climate Experiment) satellite data, the declining TWS (terrestrial water storage) trends are apparent in north and south of India during 2003-2016, while the Narmada river basin which is situated in the central west of the country, shows apparent increase of TWS. In this study, part of the Narmada river basin was chosen as the study site. The major occupation in the basin is agriculture, and hence, water is, in principle, consumed for irrigation. Between 2003 and 2016, the two dams (Indira Sagar dam (2005) and Omkareshwar dam (2008)) were constructed, and the resulting canal system was considered to highly influence water resources availability in the area. To understand the possible effects of the canal system on groundwater level behaviour, we chose the Maheshwar block as the study domain because of its simple canal system layout and single basaltic aquifer setting. The groundwater levels were analysed based on two situations, i.e., before and after canal construction. For the analysis, two distinct seasons, i.e., dry pre-monsoon and rainy monsoon seasons were also taken into account. In the block, the first canal was constructed by 2010, and second by 2013. Based on the extent of each Canal Command Area (CCA), the block was divided into two zones, Zone A (CCA under 1<sup>st</sup> canal) and Zone B (CCA under 2<sup>nd</sup> canal). Among the wells studied, five were located within Zone A. After the canal construction, on an average, about 2 m rise was observed in these well water levels, that is, about 2.45 m in pre-monsoon while 1.62 m in monsoon seasons, respectively. Similar analysis was performed for wells not located in CCA, and it was found that no recognizable change of the groundwater levels was observed. The changes in the land use land cover (LULC) pattern were studied using Landsat 5, Landsat 7 ETM+ and Landsat 8 OLI/TIRS imageries in the block. All the LULC maps were cross-checked with maps from National Remote Sensing Centre (NRSC), India, and these were consistent between each other. The expansion of the agricultural area was studied through 2003-2016. The cultivated area increased from about 8% before the operation of the canal to about 27% after operation in Zone A, whereas the increase was smaller in Zone B, that is, from 2% to around 11%. Based on the NDVI (Normalized Difference Vegetation Index) obtained through Landsat images from different seasons, we also observed that cropping patterns have changed from fallow/single cropping to double/triple cropping after the introduction of canal system in both zones. Based on observations, available amount of water and groundwater storage have increased after canal operation compared with before the operation, and this may at least partly explain the reason why TWS has increased in this area.</p>

IR Earth horizon sensors are used for attitude determination of satellites. Simulation of IR horizon and ground calibration are major problems for testing sensors used in low altitude orbits due to geometrical constraints. A large earth simulator and a test facility with a 3500 mm. diameter disc have been developed for the calibration and the qualification of different types of IR earth sensors used on-board Indian Remote Sensing (IRS) satellites. The unique design of the test facility enables the simulation of the satellite altitudes, roll and pitch angles, IR radiation from space and Earth, gradients due to seasonal changes in radiance and thermo-vacuum environments of the sensor under test. The sensor performance can be tested either in open air conditions or under clean vacuum conditions at different temperatures of the sensor. The gradient of the Earth's IR radiation which changes with latitudes and seasons, and the Sun and the Moon interference can also be simulated. A computer based data acquisition system along with the instrumentation is used for controlling and monitoring vacuum, linear and rotary motions, temperature distribution on the earth disc and also for monitoring and recording of the sensor outputs. This paper describes the methodology behind various simulations and the testing involved. The constructional details of the large earth simulator and the alignment of the sensor with respect to the earth simulator are explained. A few of the actual sensor test results obtained are also included.

This paper presents a survey on various novel defect and noise detection and correction algorithms used in CMOS image sensors. This class of sensors is extremely relevant in number of applications in the areas of gaming, security, medical, automotive, high-end camera market, etc. This survey outlines the algorithms and the hardware implementation of novel defect and noise detection and correction schemes to detect and correct defective pixels, and discusses their performance and advantages in terms of their applications. Experimental results on various images illustrate the capabilities of the studied approaches.

This study reports the glacier changes of Chandra–Bhaga basin, northwest Himalaya, India, from 1980 to 2010. Satellite remote-sensing data from the Landsat Multispectral Scanner (MSS) and Thematic Mapper (TM), the Linear Imaging Self Scanning Sensor (LISS) and Advanced Wide Field Sensor (AWiFS) of the Indian Remote Sensing (IRS) series, and the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) were used to study the changes in glacier parameters such as glacier area, length, snout elevation, and the impact of glacier topographical parameters (glacier slope, aspect, and altitude range) on the glacier changes. It was found that the total glaciated area had shrunk to 368.2 km2 in 2010 from 377.6 km2 in 1980, a loss of 2.5%. The average position of glacier terminuses retreated by 465.5 ± 169.1 m from 1980 to 2010 with an average rate of 15.5 ± 5.6 m year−1. The decadal scale analysis showed that the average rate of retreat had increased the most in the recent decade. A moraine-dammed lake located in the study region was found to have expanded in area from (0.65 ± 0.01) km2 in 1980 to (1.26 ± 0.03) km2 in 2010. Glaciers with steep slope and less altitude range have lost more area than the glaciers having gentle slope and greater altitude range.

An efficient nonparametric, hierarchical, symbolic agglomerative clustering procedure based on the mutual nearest neighbourhood concept is proposed for classifying remotely sensed multispectral data. The procedure utilized a data reduction technique and an innovative symbolic concept to minimize the memory and computational time requirements. A new non-metric similarity measure and a novel method of formulation of composite symbolic objects are proposed to enrich the performance of the algorithm. A Mean Difference Index (MDI) concept for identifying the optimal number of classes was used. Experiments were conducted on IRS (Indian Remote Sensing) satellite data to authenticate the efficacy of the procedure.

In the paper the idea of the system of production flow and quality control and its data model is presented. There are distinguished the functional moduls of preparing technological processes’ variants, of production scheduling, and of quality control. The solution is dedicated to the enterprises characterized by a make-to-order production approach, which have to react flexibly to changing production conditions and the backlog of orders.

Identification of homogeneous regions in a satellite image is essentially the clustering of pixels in intensity space. Importantly remote sensing image like satellite images contain varieties of land cover types. Some of the covers are significantly large areas whereas some are relatively smaller regions. Therefore, automatically detecting such wide varying areas is a challenging task. Hence, this fact motivated us to propose an improved clustering technique viz. Ensemble based Differential Evolution for Fuzzy Clustering (EDEFC). For this purpose, very recently developed three variants of differential evolution (DE) are used in order to perform the clustering with different set of solutions. As a result, better clustering solution yields from the ensemble of DEs by exhaustive exploration of search space. The proposed EDEFC technique is applied on two numeric remote sensing datasets and Indian Remote Sensing (IRS) satellite image of Kolkata. The results of the EDEFC are shown quantitatively and visually by comparing with eight other clustering techniques. Moreover, the statistical significance test has also been performed in order to judge the superiority of EDEFC.

Coastal ecosystem is complex, sensitive and is subject to the influences of nature as well as human interferences. In recent years the prawn farming has increased significantly in the coastal areas of Andhra Pradesh, India, due to great demand in the export market at the coast of agricultural areas and mangrove forests which are essential for the coastal ecological balance. Hence, a study was carried out with the objective of mapping and monitoring the area under prawn farming and mangroves using multitemporal remotely sensed data along Krishna delta of Guntur and Krishna districts of Andhra Pradesh. Remotely sensed data from Landsat‐MSS pertaining to 1973, 1985 and 1990 years and Indian Remote Sensing Satellite‐1A (IRS‐1A) of 1992 are used for the study. The methodology adopted is digital analysis of multitemporal data. The study revealed that during 1985 to 1992 period the area under prawn farming increased significantly. Initially prawn cultivation started on a smaller scale on saline waste lands along the coast. Later due to commercialization of prawn culture, its cultivation has been extended into agricultural lands and mangrove forest.