Data-Driven Spatial Analysis of Reforestation in Pakistan: Identifying Optimal Locations for Sustainable Forest Growth and Climate Change Mitigation
As the world’s second-largest carbon storehouse after oceans, forests mitigate global warming and its associated climate change challenges. Hence, forest conservation efforts such as reforestation are crucial in maintaining this vital ecological service. Current approaches to large-scale...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10772469/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849220087474552832 |
|---|---|
| author | Amna Faisal Rafia Mumtaz Muhammad Deedahwar Mazhar Qureshi Muhammad Ali Tahir N. Z. Jhanjhi Mehedi Masud Mohammad Shorfuzzaman |
| author_facet | Amna Faisal Rafia Mumtaz Muhammad Deedahwar Mazhar Qureshi Muhammad Ali Tahir N. Z. Jhanjhi Mehedi Masud Mohammad Shorfuzzaman |
| author_sort | Amna Faisal |
| collection | DOAJ |
| description | As the world’s second-largest carbon storehouse after oceans, forests mitigate global warming and its associated climate change challenges. Hence, forest conservation efforts such as reforestation are crucial in maintaining this vital ecological service. Current approaches to large-scale reforestation include considering factors like soil quality, climate, topography, and land-use history to narrow areas for reforestation. However, these methods often struggle to efficiently identify suitable land across vast and diverse landscapes, leading to suboptimal outcomes. Despite the promise of modern techniques like Geographic Information Systems (GIS), remote sensing, and spatial modeling for optimizing reforestation success and ecological benefits, their large-scale implementation for selecting suitable land remains limited. This paper proposes a novel technique using unsupervised machine learning and remotely sensed satellite data to assess reforestation potential across vast and diverse landscapes. The approach utilizes K-means clustering and Gaussian Mixture Models to group regions based on their indexes. Normalized Difference Water Index (NDWI) is used to measure water availability; Laterite, Biotite, and Gossan indexes are used for mineral content, and the Normalized Difference Built-up Index (NDBI) and Urbanization Index (UI) are used to measure urbanization of a region. By analyzing the variation in vegetation within these clusters, the model identifies areas with high potential for future forestation. Our proposed model achieved an accuracy of 92% compared to a validation data set obtained from the Conservator of Forests for the Rawalpindi district. The presented approach can potentially optimize reforestation efforts and maximize their impact on climate change mitigation, leading to benefits such as increased carbon sequestration in countries like Pakistan with limited forest cover. |
| format | Article |
| id | doaj-art-830d531b0e1444a68cb92f4e4d5ef923 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-830d531b0e1444a68cb92f4e4d5ef9232024-12-20T00:00:54ZengIEEEIEEE Access2169-35362024-01-011219037519038810.1109/ACCESS.2024.351056510772469Data-Driven Spatial Analysis of Reforestation in Pakistan: Identifying Optimal Locations for Sustainable Forest Growth and Climate Change MitigationAmna Faisal0https://orcid.org/0009-0007-2123-1149Rafia Mumtaz1https://orcid.org/0000-0002-0966-3957Muhammad Deedahwar Mazhar Qureshi2Muhammad Ali Tahir3https://orcid.org/0000-0002-2335-2776N. Z. Jhanjhi4https://orcid.org/0000-0001-8116-4733Mehedi Masud5https://orcid.org/0000-0001-6019-7245Mohammad Shorfuzzaman6https://orcid.org/0000-0002-8050-8431School of Computer Science, Taylor’s University, Subang Jaya, Selangor, MalaysiaSchool of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Punjab, PakistanSchool of Business Technology, Retail, and Supply Chain, TU Dublin, Dublin 2, IrelandSchool of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Punjab, PakistanSchool of Computer Science, Taylor’s University, Subang Jaya, Selangor, MalaysiaDepartment of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi ArabiaDepartment of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi ArabiaAs the world’s second-largest carbon storehouse after oceans, forests mitigate global warming and its associated climate change challenges. Hence, forest conservation efforts such as reforestation are crucial in maintaining this vital ecological service. Current approaches to large-scale reforestation include considering factors like soil quality, climate, topography, and land-use history to narrow areas for reforestation. However, these methods often struggle to efficiently identify suitable land across vast and diverse landscapes, leading to suboptimal outcomes. Despite the promise of modern techniques like Geographic Information Systems (GIS), remote sensing, and spatial modeling for optimizing reforestation success and ecological benefits, their large-scale implementation for selecting suitable land remains limited. This paper proposes a novel technique using unsupervised machine learning and remotely sensed satellite data to assess reforestation potential across vast and diverse landscapes. The approach utilizes K-means clustering and Gaussian Mixture Models to group regions based on their indexes. Normalized Difference Water Index (NDWI) is used to measure water availability; Laterite, Biotite, and Gossan indexes are used for mineral content, and the Normalized Difference Built-up Index (NDBI) and Urbanization Index (UI) are used to measure urbanization of a region. By analyzing the variation in vegetation within these clusters, the model identifies areas with high potential for future forestation. Our proposed model achieved an accuracy of 92% compared to a validation data set obtained from the Conservator of Forests for the Rawalpindi district. The presented approach can potentially optimize reforestation efforts and maximize their impact on climate change mitigation, leading to benefits such as increased carbon sequestration in countries like Pakistan with limited forest cover.https://ieeexplore.ieee.org/document/10772469/Remote sensingunsupervised learningsentinel-2reforestationclimate change mitigation |
| spellingShingle | Amna Faisal Rafia Mumtaz Muhammad Deedahwar Mazhar Qureshi Muhammad Ali Tahir N. Z. Jhanjhi Mehedi Masud Mohammad Shorfuzzaman Data-Driven Spatial Analysis of Reforestation in Pakistan: Identifying Optimal Locations for Sustainable Forest Growth and Climate Change Mitigation IEEE Access Remote sensing unsupervised learning sentinel-2 reforestation climate change mitigation |
| title | Data-Driven Spatial Analysis of Reforestation in Pakistan: Identifying Optimal Locations for Sustainable Forest Growth and Climate Change Mitigation |
| title_full | Data-Driven Spatial Analysis of Reforestation in Pakistan: Identifying Optimal Locations for Sustainable Forest Growth and Climate Change Mitigation |
| title_fullStr | Data-Driven Spatial Analysis of Reforestation in Pakistan: Identifying Optimal Locations for Sustainable Forest Growth and Climate Change Mitigation |
| title_full_unstemmed | Data-Driven Spatial Analysis of Reforestation in Pakistan: Identifying Optimal Locations for Sustainable Forest Growth and Climate Change Mitigation |
| title_short | Data-Driven Spatial Analysis of Reforestation in Pakistan: Identifying Optimal Locations for Sustainable Forest Growth and Climate Change Mitigation |
| title_sort | data driven spatial analysis of reforestation in pakistan identifying optimal locations for sustainable forest growth and climate change mitigation |
| topic | Remote sensing unsupervised learning sentinel-2 reforestation climate change mitigation |
| url | https://ieeexplore.ieee.org/document/10772469/ |
| work_keys_str_mv | AT amnafaisal datadrivenspatialanalysisofreforestationinpakistanidentifyingoptimallocationsforsustainableforestgrowthandclimatechangemitigation AT rafiamumtaz datadrivenspatialanalysisofreforestationinpakistanidentifyingoptimallocationsforsustainableforestgrowthandclimatechangemitigation AT muhammaddeedahwarmazharqureshi datadrivenspatialanalysisofreforestationinpakistanidentifyingoptimallocationsforsustainableforestgrowthandclimatechangemitigation AT muhammadalitahir datadrivenspatialanalysisofreforestationinpakistanidentifyingoptimallocationsforsustainableforestgrowthandclimatechangemitigation AT nzjhanjhi datadrivenspatialanalysisofreforestationinpakistanidentifyingoptimallocationsforsustainableforestgrowthandclimatechangemitigation AT mehedimasud datadrivenspatialanalysisofreforestationinpakistanidentifyingoptimallocationsforsustainableforestgrowthandclimatechangemitigation AT mohammadshorfuzzaman datadrivenspatialanalysisofreforestationinpakistanidentifyingoptimallocationsforsustainableforestgrowthandclimatechangemitigation |