Kuwait's arid desert landscape, geological formations, and extreme climate conditions make it a potential site for establishing a terrestrial Mars analog, as this research presents a new GIS-based methodology. The Analog Conjunctive Method (ACM) was specifically developed to identify a suitable location in Kuwait to hold a terrestrial Mars analog using a geographic information system (GIS) and remote sensing techniques. Analogs play a crucial role in simulating different Martian conditions, supporting astronaut training, testing various exploration technologies, and doing different types of scientific research on these environments. The ACM method integrates GIS and remote sensing techniques to evaluate the study area, resulting in potential sites for analog. The analysis employs two stages to finalize the best location. In stage one, the newly developed ACM is applied; it systematically eliminates unstable areas while allowing minimal flexibility for real-world environmental adjustment, particularly in regions with natural wind barriers. ACM is used to process the buffers created for the seven criteria (urban areas and farms, coastal areas, streets, airports, oil fields, natural reserves, and country borders) in QGIS to exclude unsuitable areas. Stage two screens the stage one map locations using different data (STRM, Copernicus sentinel-2, and field visits) to polish the selection based on other criteria (water bodies, dust rate, vegetation cover, and topography). The result shows nine locations in Jal Al-Zor as potential analog sites where a random location is selected for a 3D model creation to visualize the analog. Java Mission-planning and Analysis for Remote Sensing (JMARS) software was used to identify similarities between specific areas, such as the Jal Al-Zor escarpment and Huwaimllyah sand dunes in the Kuwait desert, and comparable terrains on Mars. The research concluded that Jal Al-Zor holds substantial potential as a terrestrial Mars analog site due to its geological and topographical similarities to Martian landscapes. This makes it an ideal location for crew training, Mars equipment testing, and further research in Mars analog studies, providing valuable insights for future planetary exploration.

This dataset contains an explanation of data analysis for creating a flood vulnerability map of Samarinda Seberang District. The dataset contains sub-criteria for each flood parameter and its score value. In addition, this dataset contains the weight value of each parameter, flood vulnerability level and its coloring, and the results of calculating the area of each vulnerability level.

Dataset

This dataset was created by Mohammad Shahwan (MSH)

Contents

Dataset

This dataset was created by Mohammad Shahwan (MSH)

Contents

Urbanization-led land cover change impacts terrestrial carbon storage capacity: A high-resolution remote sensing-based nation-wide assessment in Pakistan (1990–2020) -

This dataset provides high-resolution, nationwide land use/land cover (LULC) and terrestrial carbon stock maps of Pakistan for four epochs: 1990, 2000, 2010, and 2020. Developed using multi-sensor satellite imagery and advanced classification techniques in Google Earth Engine (GEE), the dataset presents a comprehensive analysis of land cover changes driven by urbanization and their impacts on carbon storage capacity over 30 years.

The LULC data includes nine distinct classes, covering key land cover types such as forest cover, agriculture, rangeland, wetlands, barren lands, water bodies, built-up areas, and snow/ice. Classification was performed using a hybrid machine learning approach, and the accuracy of the land cover maps was validated using a stratified random sampling approach.

The carbon stock maps were derived using the InVEST model, which estimated carbon storage in four major carbon pools (above-ground biomass, below-ground biomass, soil organic carbon, and dead organic matter) based on the LULC maps. The results showed a significant decline in carbon storage due to rapid urban expansion, particularly in major cities like Karachi and Lahore, where substantial forest and agricultural lands were converted into urban areas. The study estimates that Pakistan lost approximately -5% of its carbon storage capacity over this period, with urban areas growing by over ~1040%.

This dataset is a valuable resource for researchers, policymakers, and environmental managers, providing crucial insights into the long-term impacts of urbanization on land cover and carbon sequestration. It is expected to support future land management strategies, urban planning, and climate change mitigation efforts. The high temporal and spatial resolution of the dataset makes it ideal for monitoring land cover dynamics and assessing ecosystem services over time.

This dataset is aslo available as Google Earth Engine application. For more details check:

> Github Project repository: https://github.com/waleedgeo/lulc_pk
> Paper DOI: https://doi.org/10.1016/j.eiar.2023.107396
> Paper PDF: https://waleedgeo.com/papers/waleed2024_paklulc.pdf

If you find this work useful, please consider citing it as

Waleed, M., Sajjad, M., & Shazil, M. S. (2024). Urbanization-led land cover change impacts terrestrial carbon storage capacity: A high-resolution remote sensing-based nation-wide assessment in Pakistan (1990–2020). Environmental Impact Assessment Review, 105, 107396.

Contributors:
Mirza Waleed (email) (Linkedin)
Muhammad Sajjad (email) (Linkedin)
Muhammad Shareef Shazil

To check other work, please check:
My Webpage & Google Scholar

Dataset

This dataset was created by Mohammad Shahwan (MSH)

Contents

Dataset

This dataset was created by Muhammad Irfan

Contents

Dataset

This dataset was created by Mohammad Shahwan (MSH)

Contents

As scientific technology and space science progress, remote sensing has emerged as an innovative solution to ease the challenges of the COVID-19 pandemic. To examine the research characteristics and growth trends in using remote sensing for monitoring and managing the COVID-19 research, a bibliometric analysis was conducted on the scientific documents appearing in the Scopus database. A total of 1,509 documents on this study topic were indexed between 2020 and 2022, covering 165 countries, 577 journals, 5239 institutions, and 8,616 authors. The studies related to remote sensing and COVID-19 have a significant increase of 30% with 464 articles. The United States (429 articles, 28.42% of the global output), China (295 articles, 19.54% of the global output), and the United Kingdom (174 articles, 11.53%) appeared as the top three most contributions to the literature related to remote sensing and COVID-19 research. Sustainability, Science of the Total Environment, and International Journal of Environmental Research and Public Health were the three most productive journals in this research field. The utmost predominant themes were COVID-19, remote sensing, spatial analysis, coronavirus, lockdown, and air pollution. The expansion of these topics appears to be associated with cross-sectional research on remote sensing, evidence-based tools, satellite mapping, and geographic information systems (GIS). Global pandemic risks will be monitored and managed much more effectively in the coming years with the use of remote sensing technology.

Introduction

To address the debris-cover glacier the on field work sometime difficult and not accessible therefore we use satellite, image through remote sense technique. Remote sensing techniques are used for mapping debris covered glacier, but these techniques are failed when applied to glaciers covered by debris because of similar spectral response from supra glacial debris and peril glacial. Moreover, few researchers focused on debris covered glacier through onscreen manual digitization. But the accuracy of manual digitalization of debris-covered glaciers varies, conversely, based on image resolution and personal expertise resulting in different mappings for the same glacier. To address this challenge, we use machine learning algorithms to classify glaciers, debris covered glacier and non-glaciated areas from remote sensed data.

Conclusion

In this research work, I am going to investigate three supervised ML classifiers for automatic classification of the glaciers, debris covered glaciers and non-glaciated areas from multi-temporal Sentinel-2 imagary using texture, topographic through remotely sensed data, the ML algorithm will use in this research are Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest (RF).

GIS_data_and_jupyter_python_notebook.zip: Data for Modeling SDS via Random Forest Models. Contains a ArcGIS Pro project with example data collected at Marston Farm (Boone, IA) and cropped Planet scope 4-band imagery of the area for 2016, 2017 and 2018.Preview for jupyter notebook: preview of a jupyter (Python 3) notebook that demonstrates the use of Random forest classifier using the GIS data.

Data was used for the study "Unveiling Leptospirosis Hotspots with Earth Observation and AI".The study embarks on the spatiotemporal analysis of leptospirosis hotspot areas in Selangor using secondary data from 2011 to 2019. Point shape files were plotted based on the coordinates of case's possible source of infection. Cases were aggregated according to respective subdistrict polygon areas. Monthly Hotspot analysis was initially conducted using the Getis Ord Gi* in ArcGIS Pro software. Satellite data for monthly rainfall and LST was retrieved from the NASA Geovanni EarthData website. Monthly values (2-11-2019) for every subdistrict were extracted using ArcGIS Pro software.Data contains monthly data for 55 subdistricts in Selangor (not individually labelled) from 2011 to 2019 - (5 columns and 5940 rows)leptospirosis hotspot (H) (Yes[1] or No[0].Precipitation (P) - monthly values in millimetresLand Surface Temperature (T) - monthly values in degrees Celsius (oC)The code snippets used for machine learning data analysis are also available. Codes include three algorithms used:LGBM, 2. Random Forest, and 3. SVM

This is the one page write up proposing my Term Project in GISWR 2016 class (CEE 6440).

Changes taking place along the shoreline are attributed to several physical and dynamical processes. Sindh Coast of Pakistan particularly Karachi is continuously under the process of morphological changes due to anthropogenic activities and natural processes. This study examined spatiotemporal morphological changes over five decades (1972–2020) along Karachi Coast bordering Arabian Sea using SRS and GIS techniques. This study reveals major morphological changes along the shoreline of Karachi in the vicinity of Karachi Port, Clifton Beach, Bundal Island and Port Qasim, which have been calculated in the form of erosion and accretion using erase tool of ArcGIS software. Quantitative analysis shows highest accretion of >15.50 km2, at the Eastern End of Clifton Beach due to human intervention. Natural processes caused major erosion (10.95 km2) and accretion (19.50 km2) during 1972–2020 in the vicinity of Bundal Island. During 2011–2020, significant change (2.40 km2) is observed opposite to Port Qasim causing damage to mangrove vegetation of 1.40 km2 calculated using NDVI. Highest rate of erosion (0.31 km2year−1) and highest rate of accretion (0.70 km2year−1) are found in Bundal Island during 1972–1987 and 1987–1999, respectively. Maximum increase in the length of shoreline (6.78 km) is depicted at Eastern End of Clifton Beech between 1999–2010.