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The purpose of this study is to evaluate the potential of the DMC satellite data as suitable alternative to Landsat‐7 ETM+ satellite data sets in view of the aging conditions, the failure of the Scan Line Corrector (SLC) and resulting scan line anomaly within the Landsat‐7 ETM+ data sets.

The DMC and Landsat‐7 ETM+ satellite data were compared by obtaining quantitative parameters such as; spatial, geometric, spectral, radiometric properties from coincident date images of the two satellite sensors, while inter‐relationship between DMC and Landsat‐7 ETM+ satellite data were established by deriving sensor inter‐calibration from linear regression equation.

The result shows that the performances of UK‐DMC match well with Landsat‐7 ETM+ and the accuracy of the UK‐DMC with respect to spatial, geometric properties. The frequency of DN distribution in each waveband for the two sensors and a pair‐wise relationship between the DN of selected targets was established using linear regression equation with coefficient of determination (R²) values that range from 0.92 to 0.95. These are adequate for data integration of the UK‐DMC and Landsat‐7 ETM+ sensors, and indicate that UK‐DMC can be used as a replacement for Landsat‐7 ETM+ and can provide a suitable platform for multi‐temporal and multi‐sensor approach that is required for the study of environmental dynamics.

The challenge in this study is that information on DMC sensor calibration and radiometric parameters such as time‐dependent change in the data derived from pre‐flight measurements, in‐flight calibration and ground‐based calibration data were not available at the time of this study. Therefore, absolute radiometric correction of converting the digital number (DN) recorded by the sensor to spectral radiance detected by the sensor using sensor‐specific calibration parameters was not possible. The suitable alternative is to use spectrally invariant targets for relative radiometric correction of DN to DN pair wise pixel technique and selecting similar targets on the images.

This study shows that a suitable platform for multi‐temporal and multi‐sensor approach that is required for the study of environmental dynamics can be provided.

The issue of climate change was mentioned in this manuscript because satellite sensors that were used previously for climate change (multi‐disciplinary approach) does not have the temporal dynamics of daily coverage (temporal) and spatial resolution like the DMC satellites while there is usually a trade‐off between temporal and spatial resolution. The DMC satellites have got the exceptional capability of daily temporal and medium spatial resolution that can be suitable for monitoring climate change. That is why in this study investigation was carried out on the unique properties of the DMC satellites by making comparative assessments Landsat‐7 ETM+.

The originality/value of this paper lies on the fact that; for the first time the DMC satellite data are being compared with Landsat‐7 ETM+ because of similar characteristics in terms of wavebands (near infrared, red and green) and spatial resolution (Landsat‐7 ETM+: 30 m, DMC: 32 m).

This study aims to quantify the influence of urbanization on housing prices at the district-based level, while also investigating the heterogeneous impacts across different quantiles of housing prices.

The study uses remote-sensed spectral images from the Landsat 7 ETM+ satellite to measure urbanization, replacing prior reliance solely on urban population metrics. Subsequently, the two-step system generalized method of moments is used to evaluate how urbanization influences district-based housing prices through three spectrometries: Urban Index (UI), Normalized Difference Built-up Index (NDBI) and Built-Up Index (BUI). Finally, this study examines the heterogeneous impacts across various housing price quantiles through Dynamic Panel Quantile Regression with non-additive fixed effects under Markov Chain Monte Carlo simulation.

The study demonstrates that urbanization leads to an increase in regional housing prices. However, these impact magnitudes vary across housing price quantiles. Specifically, the impact exhibits an inverse V-shaped curve, with urbanization exerting a more pronounced influence on the 60th percentile of housing prices, while its effect on the 10th and 90th percentiles is comparatively weaker.

This study uses a novel method of remote sensing to measure urbanization and investigates its effects on housing prices. Furthermore, it provides an empirical application of non-additive fixed effect quantile regression for analyzing heterogeneity.

In a tropical country like Malaysia, forest fire is a very common natural and man‐made disaster that prevails in the whole South East Asian region throughout the year. Recently, the haze problem in Malaysia has created a lot of awareness among the government and eco‐tourism sectors. Therefore, detection of the hotspot is very important to delineate the forest fire susceptibility mapping. In this study, remote sensing and geographical information systems (GIS) have been used to evaluate forest fire susceptibility at Sungai Karang and Raja Muda Musa Forest Reserve, Selangor, Malaysia. Frequency ratio model has been applied for the delineation of forest fire mapping for the study area.

Forest fire locations were identified in the study area from historical hotspots data from year 2000 to 2005 using AVHRR NOAA 12 and NOAA 16 satellite images. Various other supported data such as soil map, topographic data, and agro climate were collected and created using GIS. These data were constructed into a spatial database using GIS. The factors that influence fire occurrence, such as fuel type and Normalized Differential Vegetation Index (NDVI), were extracted from classified Landsat‐7 ETM imagery. Slope and aspect of topography were calculated from topographic database. Soil type was extracted from soil database and dry month code from agroclimate data. Forest fire susceptibility was analyzed using the forest fire occurrence factors by likelihood ratio method.

A new statistical method has been applied for the forest fire susceptibility mapping. The results of the analysis were verified using forest fire location data with the help of a newly written programming code. The validation results show satisfactory agreement between the susceptibility map and the existing data on forest fire location. The GIS was used to analyze the vast amount efficiently, and statistical programs were used to maintain the specificity and accuracy. The result can be used for early warning, fire suppression resources planning and allocation.

All data used in this study are original. The forest fire susceptibility mapping has been done in this study area for the first time. A new program has been coded to cross‐verify the susceptibility map. The results were also verified with field data and other supporting weather data.

The purpose of this paper is to develop user friendly software with the minimum error and maximum performance in a form of remote sensing satellites evaluation software for estimation of weights and ranks of the remote sensing satellite plans, to decrease risk of management decisions.

The analytic hierarchy process (AHP) as a comprehensive framework for strategic decision making is used to arrive at the weights of criteria and sub‐criteria of remote sensing satellites. The Ms‐Access software is written to compute the ranks of the remote sensing satellite plans based on the relative weights of inputs and then, the outputs from AHP are shown as a numerical graph and generates the Ms‐Access database.

One of the main objectives of this paper is an attempt to access this skill that compare several remote sensing satellite plans on quantity and quality point of view by several effective criteria such as mass, power consumption and cost of satellites, in addition to the remote sensing subsystem, communication subsystem, telemetry, tracking and control subsystem, attitude determination control subsystem and their own sub‐criteria.

It is hard in just one paper, to gather lots of information about remote sensing satellite systems, use a new methodology that is unknown for aerospace engineering, and talk about an innovative software.

This paper provides helpful evaluating software which has a data bank that it is very useful and impartial advice for space strategy's managing organization to compare several plans.

This study provides low cost, time‐saving, and high‐performance remote sensing satellite evaluation software and gives valuable information and guidelines which help management decisions of aerospace organization.

The purpose of the paper is to critically evaluate the concept of ecosystem conservation in the areas where human and ecosystem interactions are really complex as – in one of the biodiversity hotspots – the Himalayas in India.

The research results reported in this paper are the outcome of empirical studies performed for last 12 years in the Himalayas, combined with a review of available literature, discussions with stakeholders and experimental‐design in the field conditions.

The paper identifies that conservation and management of the ecosystems/landscapes and the development of the local economy are interwoven. It evaluates the landscape management and development in economic and ecological perspectives and concurrently develops the theoretical model to understand the change in human behavior under shifting political, socio‐economic and environmental conditions.

Through the development of a theoretical agent model this study would help the design and development of sound methodological approaches in providing feasible solutions for complex human‐ecosystems interactions for sustainable landscape development in the fragile Himalayan environment.

This paper is original and shows that the development of a model framework and of appropriate models for natural resource management in the Himalayan region are still very limited and the paper is an initiative in this endeavor.

Deepor Beel is one of the Ramsar Site and a wetland of great biodiversity, situated in the south-western part of Guwahati, Assam. With urban development at its forefront city of Guwahati, Deepor Beel is under constant threat. The study aims to calculate the lake water volume from the water surface area and the underwater terrain data using a triangulated irregular network (TIN) volume model.

The lake water surface boundaries for each year were combined with field-observed water level data to generate a description of the underwater terrain. Time series LANDSAT images of 2001, 2011 and 2019 were used to extract the modified normalized difference water index (MNDWI) in GIS domain.

The MNDWI was 0.462 in 2001 which reduced to 0.240 in 2019. This shows that the lake water storage capacity shrank in the last 2 decades. This leads to a major problem, i.e. the storage capacity of the lake has been declining gradually from 20.95 million m³ in 2001 to 16.73 million m³ in 2011 and further declined to 15.35 million m³ in 2019. The fast decline in lake water volume is a serious concern in the age of rapid urbanization of big cities like Guwahati.

None of the studies have been done previously to analyze the decline in the volume of Deepor Beel lake. Therefore, this study will provide useful insights in the water resource management and the conservation of Deepor Beel lake.

This paper aims to analyse the changes in the extent of the green space in the city of Srinagar, a unique urban Himalayan destination, and examine whether the difference in the green space has a significant effect on the destination business performance measured in terms of loss of ecological attractiveness.

This study was carried out in two phases in the study area. In phase I, the changes in the extent of the green space area were analysed using Landsat TM and Sentinel Images for classification. The study has used the period from 2001 to 2018 to understand changes in the green space. The Post-Classification Comparison technique was used to investigate the variation in the green space zones in the city of Srinagar. In phase II, the paper evaluated the impact of change in the green space on the destination business performance. The data was collected from the tour operation companies through a questionnaire survey, and the impact path was examined using structural equation modelling.

Results reveal that the green space in the city of Srinagar has decreased over the past 18 years, and the decreasing green space has a significant effect on the destination business performance.

Identifying the impact of decreasing green space on the destination business performance of the study area under investigation is essential for tourism development both in terms of new product development and resource preservation. Developing a measurement scale showing the impact of decreasing green space on destination business performance could offer destination managers a means of identifying the essence of the green space in the destination regions. These findings add to the growing literature on the attributes of tourism destinations, providing scholars with new insights into the role of green space in destination performance. The current study offers evidence of the impact of decreasing green space on the destination's performance. This provides a new perspective for future studies on visitor satisfaction as a potential mediator of the relationship between reducing greenspace and destination business performance. The main limitation of this study is that the researchers have only analysed the impact of decreasing green space on the destination business performance in terms of its ecological competitiveness. Other destinations business performance verticals, such as hotels, restaurants and grocery stores were not considered by this study and can be taken up for future investigation.

This study provides empirical insights that can have significant implications for researchers, policymakers, destination management organizations, academia and practitioners and further enrich the existing literature by establishing an empirical argument in the context of urban destinations positioned with a fragile Himalayan ecosystem.

This study aims to assist the urban administrators in improving the green space ecosystem in the region, which can help attain the sustainability of the city environment and assist in economic regeneration in urban settings.

This research aims to monitor vegetation indices to assess drought in paddy rice fields in Mazandaran, Iran, and propose the best index to predict rice yield.

A three-step methodology is applied. First, the paddy rice fields are mapped by using three satellite-based datasets, namely SRTM DEM, Landsat8 TOA and MYD11A2. Second, the maps of indices are extracted using MODIS. And finally, the trend of indices over rice-growing seasons is extracted and compared with the rice yield data.

Rice paddies maps and vegetation indices maps are provided. Vegetation Health Index (VHI) combining average Temperature Condition Index (TCI) and minimum Vegetation Condition Index (VCI), and also VHI combining TCI_min and VCI_min are found to be the most proper indices to predict rice yield.

The results serve as a guideline for policy-makers and practitioners in the agro-food industry to (1) support sustainable agriculture and food safety in terms of rice production; (2) help balance the supply and demand sides of the rice market and move towards SDG2; (3) use yield prediction in the rice supply chain management, pricing and trade flows management; and (4) assess drought risk in index-based insurances.

This study, as one of the first research assessing and mapping vegetation indices for rice paddies in northern Iran, particularly contributes to (1) extracting the map of paddy rice fields in Mazandaran Province by using satellite-based data on cloud-computing technology in the Google Earth Engine platform; (2) providing the map of VCI and TCI for the period 2010–2019 based on MODIS data and (3) specifying the best index to describe rice yield through proposing different calculation methods for VHI.

The urban heat island is an unintended consequence of humans building upon rural and native landscapes. We hypothesized that variations in vegetation and land use patterns across an urbanizing regional landscape would produce a temperature distribution that was spatially heterogeneous and correlated with the social characteristics of urban neighborhoods. Using biophysical and social data scaled to conform to US census geography, we found that affluent whites were more likely to live in vegetated and less climatically stressed neighborhoods than low-income Latinos in Phoenix, Arizona. Affluent neighborhoods had cooler summer temperatures that reduced exposure to outdoor heat-related health risks, especially during a heat wave period. In addition to being warmer, poorer neighborhoods lacked critical resources in their physical and social environments to help them cope with extreme heat. Increased average temperatures due to climate change are expected to exacerbate the impacts of urban heat islands.

This paper aims to give an oversight of what is being done by researchers in GIS and remote sensing (field) to explore minerals. The main objective of this review is to explore how GIS and remote sensing have been beneficial in identifying mineral deposits for easier and cost-effective mining.

The approach of this research used Web of Science to generate a database of published articles on the application of GIS and remote sensing techniques for mineral exploration. The literature was further digested, noting the main findings, adopted method, illustration and research scales.

When applied alone, each technique seems effective, but it is important to know that combining different methods is more effective in identifying ore deposits.

This paper also examined and provided possible solutions to both current and future perspective issues relating to the application of GIS and remote sensing to mineral exploration. The authors believe that the conclusions and recommendations drawn from case studies and literature review will be of great importance to geoscientists and policymakers.