Pixel and region-oriented classification of Sentinel-2 imagery to assess LULC dynamics and their climate impact in Nowshera, Pakistan
Land use and land cover (LULC) changes are important for gaining a perspective on environmental dynamics and the impact on climate, urbanization, and resources. To ensure that it is safe to monitor the changes over time and to adopt the right forceful changes in our area, remote sensing is one of th...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2025-01-01
|
| Series: | Open Geosciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/geo-2022-0745 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832570438377537536 |
|---|---|
| author | Farnaz Nuthammachot Narissara Shabbir Rabia Iqbal Benazeer |
| author_facet | Farnaz Nuthammachot Narissara Shabbir Rabia Iqbal Benazeer |
| author_sort | Farnaz |
| collection | DOAJ |
| description | Land use and land cover (LULC) changes are important for gaining a perspective on environmental dynamics and the impact on climate, urbanization, and resources. To ensure that it is safe to monitor the changes over time and to adopt the right forceful changes in our area, remote sensing is one of the ways to monitor the local and regional level land use, land cover patterns, and landscape changes. This study investigates the temporal LULC changes in the Nowshera region of Pakistan for the years 2016–2023 using pixel and region-oriented classification methods. As a first step, freely available high-resolution multispectral data of Sentinel-2 satellite are acquired, which serves as input dataset for both pixel and region-oriented classifiers. The accuracy assessment scores confirm that for the classified data of the year 2016, the region-oriented technique demonstrated higher overall classification accuracy (89.6%) over pixel-based classification (80.77%). Moreover, for the dataset of the year, the region-oriented method achieved a higher overall Kappa hat score (0.88) as compared to the pixel-based method (0.71). Similarly, for the classified data of the year 2023, the region-oriented method achieved higher scores for both the overall accuracy and Kappa hat (93.6 and 0.92%) over the pixel-based method (77.18 and 0.66%). The study states that for the assessment of LULC changes in Nowshera, the region-oriented image analysis provides a higher level of classification accuracy than the pixel-based approach. These results illustrate that this tool is particularly effective in monitoring detailed land cover transformations, thereby enhancing the quality of environmental management. Furthermore, the regression analysis reveals a substantial correlation between LULC changes and alterations in temperature and precipitation, and this result suggests the necessity of the development of specific climate adaptation programs. |
| format | Article |
| id | doaj-art-7780a568c1504cdb8c85b3e56fd800d1 |
| institution | Kabale University |
| issn | 2391-5447 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Geosciences |
| spelling | doaj-art-7780a568c1504cdb8c85b3e56fd800d12025-02-02T15:45:33ZengDe GruyterOpen Geosciences2391-54472025-01-0117111106890210.1515/geo-2022-0745Pixel and region-oriented classification of Sentinel-2 imagery to assess LULC dynamics and their climate impact in Nowshera, PakistanFarnaz0Nuthammachot Narissara1Shabbir Rabia2Iqbal Benazeer3Faculty of Environmental Management, Prince of Songkla University, Hat Yai, Songkhla, ThailandFaculty of Environmental Management, Prince of Songkla University, Hat Yai, Songkhla, ThailandDepartment of Environmental Sciences, Fatima Jinnah Women University, Rawalpindi, PakistanNational Centre of Excellence in Geology, University of Peshawar, 25130Peshawar, PakistanLand use and land cover (LULC) changes are important for gaining a perspective on environmental dynamics and the impact on climate, urbanization, and resources. To ensure that it is safe to monitor the changes over time and to adopt the right forceful changes in our area, remote sensing is one of the ways to monitor the local and regional level land use, land cover patterns, and landscape changes. This study investigates the temporal LULC changes in the Nowshera region of Pakistan for the years 2016–2023 using pixel and region-oriented classification methods. As a first step, freely available high-resolution multispectral data of Sentinel-2 satellite are acquired, which serves as input dataset for both pixel and region-oriented classifiers. The accuracy assessment scores confirm that for the classified data of the year 2016, the region-oriented technique demonstrated higher overall classification accuracy (89.6%) over pixel-based classification (80.77%). Moreover, for the dataset of the year, the region-oriented method achieved a higher overall Kappa hat score (0.88) as compared to the pixel-based method (0.71). Similarly, for the classified data of the year 2023, the region-oriented method achieved higher scores for both the overall accuracy and Kappa hat (93.6 and 0.92%) over the pixel-based method (77.18 and 0.66%). The study states that for the assessment of LULC changes in Nowshera, the region-oriented image analysis provides a higher level of classification accuracy than the pixel-based approach. These results illustrate that this tool is particularly effective in monitoring detailed land cover transformations, thereby enhancing the quality of environmental management. Furthermore, the regression analysis reveals a substantial correlation between LULC changes and alterations in temperature and precipitation, and this result suggests the necessity of the development of specific climate adaptation programs.https://doi.org/10.1515/geo-2022-0745image classificationclimate variability impactrandom forest algorithm and support vector machineenvironmental monitoringgeographic information systemurbanizationagriculture land use changeland cover transformationsremote sensing analysisregression analysis |
| spellingShingle | Farnaz Nuthammachot Narissara Shabbir Rabia Iqbal Benazeer Pixel and region-oriented classification of Sentinel-2 imagery to assess LULC dynamics and their climate impact in Nowshera, Pakistan Open Geosciences image classification climate variability impact random forest algorithm and support vector machine environmental monitoring geographic information system urbanization agriculture land use change land cover transformations remote sensing analysis regression analysis |
| title | Pixel and region-oriented classification of Sentinel-2 imagery to assess LULC dynamics and their climate impact in Nowshera, Pakistan |
| title_full | Pixel and region-oriented classification of Sentinel-2 imagery to assess LULC dynamics and their climate impact in Nowshera, Pakistan |
| title_fullStr | Pixel and region-oriented classification of Sentinel-2 imagery to assess LULC dynamics and their climate impact in Nowshera, Pakistan |
| title_full_unstemmed | Pixel and region-oriented classification of Sentinel-2 imagery to assess LULC dynamics and their climate impact in Nowshera, Pakistan |
| title_short | Pixel and region-oriented classification of Sentinel-2 imagery to assess LULC dynamics and their climate impact in Nowshera, Pakistan |
| title_sort | pixel and region oriented classification of sentinel 2 imagery to assess lulc dynamics and their climate impact in nowshera pakistan |
| topic | image classification climate variability impact random forest algorithm and support vector machine environmental monitoring geographic information system urbanization agriculture land use change land cover transformations remote sensing analysis regression analysis |
| url | https://doi.org/10.1515/geo-2022-0745 |
| work_keys_str_mv | AT farnaz pixelandregionorientedclassificationofsentinel2imagerytoassesslulcdynamicsandtheirclimateimpactinnowsherapakistan AT nuthammachotnarissara pixelandregionorientedclassificationofsentinel2imagerytoassesslulcdynamicsandtheirclimateimpactinnowsherapakistan AT shabbirrabia pixelandregionorientedclassificationofsentinel2imagerytoassesslulcdynamicsandtheirclimateimpactinnowsherapakistan AT iqbalbenazeer pixelandregionorientedclassificationofsentinel2imagerytoassesslulcdynamicsandtheirclimateimpactinnowsherapakistan |