The Earth Observation Data Centre and its task

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.

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.

The noise correction and image enhancements are done at the Data Processing Generation System (DPGS) at the National Remote Sensing Center (NRSC) satellite data production chain. Even after the noise correction algorithms, pixel noise is observed at the checkout by the Product Quality Control (PQC) system. These are detected manually at PQC and an alert is raised at the DPGS to recorrect the image. This process is time consuming due to the manual intervention and the application of noise correction algorithm on the entire image. This manuscript proposes a novel 2-stage, sliding window-based algorithm to automatically detect pixel noise in satellite images and localise the noise pixels so that the correction algorithms can be applied in a localised fashion. This local noise correction also restores the overall SNR of the image compared to global correction. This algorithm is realized and tested on data obtained from the Indian remote sensing (IRS) satellites like Cartosat-2S, Resourcesat-2/2A. The dataset is not open-sourced, and hence very minimal information is provided regarding the IRS data. However, we use Landsat-8 data to conduct a few analyses on the algorithm's performance. The role of patch size considered during the detection of pixel noise in satellite data is also analyzed.

The global change community need to use data sets that cross discipline boundaries and it is therefore critical that data management systems developed to support this community can handle heterogeneous data sets. Current systems focus on single data sets or groups of similar data sets and researchers must leam how to use a number of systems to identify, and interpret data from different sources. A new data management system designed to support the UK's Earth Observation Data Centre (EODC) is described. One of the main features of this system is its ability to handle many heterogeneous data sets within a single architectural framework. The system incorporates the emerging CEOS recommendations for earth observation catalogue systems and it is the intention to use the system as a platform for interoperability experiments.

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.

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

This paper describes a ‘segmentation’ algorithm, which has been designed for Synthetic-Aperture Radar (SAR) sea-ice images as part of the U.K. Earth Observation Data Centre (EODC) Science Team activity. The segmented sea-ice product consists of distinct, statistically-homogeneous regions of ice and water from which various atttributes can be determined, such as area, centroid position, mean intensity and standard deviation, shape, orientation and texture. Such attributes are important in providing a template from which it is possible to derive information such as ice type, concentration and dynamics, the position of the ice edge, and the characteristics of leads and polynyas. Although the method has been developed for processing ERS-1 data, it is demonstrated in this work using both simulations and SAR sea-ice data obtained from the Seasat campaign of 1978, as ERS-1 data were not yet available at the t ime of implementation.

Modelling species distributions with remote sensing data: bridging disciplinary perspectives

<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>

Chennai generates the largest per capita amount of waste in the country. The city has only two major open dump yards located at Perungudi & Kodungaiyur established in the year 1987 & 1985. Because of the emitted greenhouse gases, they are very hazardous for the nearby residents. The major greenhouse gases in the atmosphere are Carbon-dioxide (CO2) and Methane (CH4). Both of these gases are transparent, odourless, colourless, cannot be sensed by human beings, and are asphyxiant. The proposed device encompasses MG-811 CO2 gas sensor, TGS 2600 CH4 gas sensor, DTH-11 Temperature & humidity sensor, SIM900A GSM shield all interfaced with an Arduino UNO R3 device. The device will measure the amount of these two gases continuously and will feed the data to a web interface. The web interface uses National Remote Sensing Centre's Bhuvan Web Map Service as User Interface. This web interface is used to show the trends of change in concentration and can send immediate alerts to the authorities to take necessary action for the health and betterment of the people living in those areas.

This study implemented a coupled SWAT-FEM simulation model to evaluate the impacts of land use land cover and climate change scenarios (LCS) on the water resources of river catchments in Chennai, India. The land use land cover data were obtained by merging the source data from National Remote Sensing Centre (NRSC) and International Water Management Institute (IWMI). Climate change simulations were obtained from four global climate models (GCM), including GFDL Baseline Scenario (1981–2000), GFDL A1B Scenario (2081–2100), CCSM4 Baseline Scenario (1986–2005), and CCSM4 A1B Scenario (2081–2100). The LCS predicted temperature increases of 2.32 °C and 1.74 °C for GFDL and CCSM4, respectively, by the end of the century. The water use predictions suggested increases above 20% in the utilization of water by 2100, inferring the noticeable dynamics of inter-annual as well as inter-month variability in water resources in the river basins of Chennai soon. The study is novel through its implementation of a coupled modeling approach to improve the practicality of the SWAT-FEM model and to deliver useful projections of land and climate change impacts on hydrological responses. The results provide useful insights into how the variability in climate conditions alters the spatiotemporal water responses in catchment scales.

India has experienced rapid urban growth since the 1991 economic liberalisation, and this process has led to widespread conversion of agricultural land into built-up areas. Using data drawn from the Directorate of Economics and Statistics (Ministry of Agriculture & Farmers Welfare), Census of India reports, and land-cover classifications produced by the National Remote Sensing Centre up to 2022, this study maps the scale and geography of farmland loss across three decades. At the national level, land placed under non-agricultural uses increased from 18.3 million hectares in 1991-92 to 26.9 million hectares in 2021-22, whereas net sown area shrank by roughly 1.8 million hectares even as population rose sharply. The most severe declines have occurred in Punjab, Haryana, Uttar Pradesh, Maharashtra, Tamil Nadu, and Karnataka, with peri-urban rings around Delhi-NCR, Mumbai, Bengaluru, Hyderabad, and Chennai showing the highest rates of built-up expansion. Economic reforms, real-estate speculation, loosely regulated Special Economic Zones, lax zoning enforcement, and large infrastructure projects have jointly driven this transformation. The resultant effects include slower growth in food-grain output, displacement of marginal farmers and landless labourers, degradation of groundwater recharge zones, and heightened food-security risks. Although several protective laws and policy documents have appeared over the years, implementation has remained weak. The paper argues that, without binding safeguards for high-quality agricultural land and a deliberate shift towards compact and vertical urban development, India will continue to lose its productive soil base irreversibly.  

The concerns of water availability have been increasing due to population explosion, rapid industrialization, and other anthropogenic activities. This urges for an accurate estimation of streamflow at the river basin scale, which is typically carried out by the application of rainfall-runoff models. Though numerous models exist in hydrology literature, their efficiency over the mountainous catchments is generally observed to be poor, mostly due to the lack of high-quality data over such regions. This study presents an application of the widely used soil and water assessment tool (SWAT)-based hydrological modelling using high-resolution geospatial inputs over the Tehri reservoir catchment (India) located in the lower Himalayan region. The Resourcesat-2 Linear Imaging Self-Scanning System (LISS)-IV imageries of land use/land cover (LULC) and the Cartosat-1 digital elevation model (DEM) are procured from National Remote Sensing Centre (NRSC), Indian Space Research Organization (ISRO), India. The LULC and DEM have a spatial resolution of 5.8 m and 2.5 m, respectively. SWAT model is applied for a duration of 12 years (2006-2017) with 2006, 2007-2013 and 2014-2017 as the warm-up, calibration and validation periods, respectively. The results reveal an excellent performance of the model in streamflow simulation. The efficacy measures viz., Nash–Sutcliffe efficiency (NSE) and Coefficient of determination (R2) are obtained to be 0.83 and 0.84, respectively. The information reported in this study will be helpful to the water resources engineers for hydrologic modelling over the mountainous catchments.

A network of 25 lightning detection sensors (LDS) has been established by the National Remote Sensing Centre (NRSC), the Indian Space Research Organization (ISRO). At present, sensors are located in the northeast, east coastal, central, and southern locations of India under this network. Geolocation of the lightning occurrences is estimated using the time of arrival algorithm. These observations are compared well with the Indian Institute of Tropical Meteorology (IITM) sensor network data. The NRSC-LDS data have been used to derive essential climate variables (ECVs) and to understand cloud occurrences to ground lightning from July 2019 to November 2020. We note that December–January reported the least number of cloud-to-ground (CG) flash occurrences (flash density ~ 0.003 km−2 day−1), while August–September were the months with the most number of CG flash occurrences (flash density ~ 0.33 km−2 day−1). We also note that during the period under the scrutiny, Chhattisgarh, Jharkhand, Odisha, Maharashtra, and Madhya Pradesh states recorded the most number of CG lightning flash occurrences.

Over the past years, efforts have been made by the NSDI to re-engineer the geospatial feature data sets and compile and serve their metadata as per ISO/OGC specifications. These are required for provision of interoperable geospatial data services in a service-oriented architecture and automated development of GIS applications. Identification, registration and maintenance of metadata of the feature data sets and related services from the national- and state-level partnering agencies in a central metadata registry, called the National Data Registry (NDR), are essential for improving data utilization. Registering spatial data sets and services may typically involve capturing their semantics (definition), making each data item persistently and uniquely identifiable by assigning unique identifiers and organizing/maintaining their schema and associated classification systems (code lists) in a set of standard registers. In line with these requirements, a proof-of-concept (PoC) of a National Data Registry (NDR) for the Indian NSDI has been implemented and demonstrated using data services from Survey of India, National Remote Sensing Centre and the Ministry of Railways’ train running information. The PoC successfully demonstrated the utility of a sample registry and technical architecture and led to the development of a strategy for selection of a suitable system integrator for a real-life NDR development for the NSDI.