6 datasets found
  1. z

    Pakistan 30m land use land cover and carbon storage dataset (1990-2020)

    • zenodo.org
    • data.niaid.nih.gov
    tiff, zip
    Updated Oct 23, 2024
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    Waleed Mirza; Waleed Mirza (2024). Pakistan 30m land use land cover and carbon storage dataset (1990-2020) [Dataset]. http://doi.org/10.1016/j.eiar.2023.107396
    Explore at:
    tiff, zipAvailable download formats
    Dataset updated
    Oct 23, 2024
    Dataset provided by
    Elsevier
    Authors
    Waleed Mirza; Waleed Mirza
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Pakistan
    Description

    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

  2. S

    Dataset of China-Pakistan Economic Corridor permafrost distribution in 2016

    • scidb.cn
    Updated Oct 11, 2018
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    艾鸣浩; 张耀南; 康建芳; 冯克庭; 田德宇 (2018). Dataset of China-Pakistan Economic Corridor permafrost distribution in 2016 [Dataset]. http://doi.org/10.11922/sciencedb.662
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Oct 11, 2018
    Dataset provided by
    Science Data Bank
    Authors
    艾鸣浩; 张耀南; 康建芳; 冯克庭; 田德宇
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    China, Pakistan
    Description

    Passing through the Pamirs and Karakoram Mountain System, the China-Pakistan Economic Corridor has widely developed various types of geological disasters caused by freeze-thaw cycles in permafrost at altitudes above 4,000 meters. The study on distribution and mapping of permafrost is the basis for solving the practical engineering problems in the Corridor, and it is of great importance to the water resources utilization, ecological security and border defence construction. The spatial scope of the study is approximately in 23°47′ N ~ 40°55′ N, 60°20′ E ~ 80°16′ E, including Kashgar in Xinjiang, Kizilsu Kirghiz Autonomous Prefecture and Pakistan area. The data of the permafrost distribution in the Corridor (format: Tiff, spatial resolution: 1 km) is acquired on the basis of TTOP Model, which is conducted with the data on surface temperature for MODIS in 2016, glacial cataloging data for the Pamirs of China in 2009, glacier cataloging for Pakistan in 2003-2004 and World Soil Database for 2008 (HWSD v1.2). Coefficient of determination as a statistical method are used to analyze and evaluate the quality of the data and existed literature are used to verify the data result. This dataset can be served as a fundamental survey material of the permafrost changes in the Corridor, providing basic data support for the research on frost heaving and thaw in the construction of the region. Besides, the dataset could be analyzed with climate, hydrology, and other data to reveal the quantitative relation in hydrology-soil-atmosphere-ecology in the Corridor. With the climate change in this region, the dataset is expected to intensify the scientific understanding of the ecological environment and sustainable development in the region.

  3. Annual correlation of NDVI of vegetation classes with precipitation...

    • plos.figshare.com
    xls
    Updated Jun 13, 2023
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    Jamal Hassan Ougahi; Mark E. J. Cutler; Simon J. Cook (2023). Annual correlation of NDVI of vegetation classes with precipitation (precip), maximum temperature (tmax) and minimum temperature (tmin) over the UJRB. [Dataset]. http://doi.org/10.1371/journal.pone.0271991.t003
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 13, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Jamal Hassan Ougahi; Mark E. J. Cutler; Simon J. Cook
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Annual correlation of NDVI of vegetation classes with precipitation (precip), maximum temperature (tmax) and minimum temperature (tmin) over the UJRB.

  4. T

    Average annual vegetation coverage of China Pakistan Economic Corridor and...

    • casearthpoles.tpdc.ac.cn
    • tpdc.ac.cn
    • +1more
    zip
    Updated Jun 20, 2022
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    Haijun QIU (2022). Average annual vegetation coverage of China Pakistan Economic Corridor and Tianshan Mountains (2000-2018) [Dataset]. http://doi.org/10.11888/Terre.tpdc.272412
    Explore at:
    zipAvailable download formats
    Dataset updated
    Jun 20, 2022
    Dataset provided by
    TPDC
    Authors
    Haijun QIU
    Area covered
    Description

    This data uses a large number of MODIS remote sensing images to analyze and calculate the surface vegetation coverage of the Qinghai Tibet Plateau from 2000 to 2018 based on the Google Earth engine platform. Vegetation index (NDVI) is an important index for monitoring ground vegetation. The 6th edition data of Terra moderate resolution imaging spectrometer (MODIS) vegetation index level 3 product (mod13q1) are generated every 16 days with a spatial resolution of 250 meters. The annual average NDVI index calculated based on GEE platform can reflect the long-term change trend of vegetation coverage from 2000 to 2018. Meanwhile, the multi-year average NDVI index from 2000 to 2018 reflects the spatial distribution of the Qinghai Tibet Plateau. The spatial-temporal change monitoring of vegetation index (NDVI) is an indispensable basic information and key parameter for environmental change research and sustainable development planning, which is helpful to understand the changes and impacts of some ecological factors (temperature, precipitation) under the background of climate change.

  5. Annual Mann-Kendall trend statistics calculated from spatially averaged data...

    • plos.figshare.com
    xls
    Updated Jun 16, 2023
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    Jamal Hassan Ougahi; Mark E. J. Cutler; Simon J. Cook (2023). Annual Mann-Kendall trend statistics calculated from spatially averaged data of NDVI, precipitation, maximum temperature (Tmax) and minimum temperature (Tmin) for each vegetation type during 1982 to 2015. [Dataset]. http://doi.org/10.1371/journal.pone.0271991.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 16, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Jamal Hassan Ougahi; Mark E. J. Cutler; Simon J. Cook
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Annual Mann-Kendall trend statistics calculated from spatially averaged data of NDVI, precipitation, maximum temperature (Tmax) and minimum temperature (Tmin) for each vegetation type during 1982 to 2015.

  6. Seasonal Mann-Kendall trend statistics calculated from spatially averaged...

    • plos.figshare.com
    xls
    Updated Jun 13, 2023
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    Jamal Hassan Ougahi; Mark E. J. Cutler; Simon J. Cook (2023). Seasonal Mann-Kendall trend statistics calculated from spatially averaged data of precipitation, maximum temperature (Tmax) and minimum temperature (Tmin) over the whole UJRB and river flow in four quarters (Q1, Q2, Q3 and Q4) during 1982 to 2015. [Dataset]. http://doi.org/10.1371/journal.pone.0271991.t004
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 13, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Jamal Hassan Ougahi; Mark E. J. Cutler; Simon J. Cook
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Seasonal Mann-Kendall trend statistics calculated from spatially averaged data of precipitation, maximum temperature (Tmax) and minimum temperature (Tmin) over the whole UJRB and river flow in four quarters (Q1, Q2, Q3 and Q4) during 1982 to 2015.

  7. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Waleed Mirza; Waleed Mirza (2024). Pakistan 30m land use land cover and carbon storage dataset (1990-2020) [Dataset]. http://doi.org/10.1016/j.eiar.2023.107396

Pakistan 30m land use land cover and carbon storage dataset (1990-2020)

Explore at:
31 scholarly articles cite this dataset (View in Google Scholar)
tiff, zipAvailable download formats
Dataset updated
Oct 23, 2024
Dataset provided by
Elsevier
Authors
Waleed Mirza; Waleed Mirza
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Area covered
Pakistan
Description

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

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