Unveiling fractional vegetation cover dynamics: a spatiotemporal analysis using MODIS NDVI and machine learning
Understanding the dynamics of Fractional Vegetation Cover (FVC) is crucial for effective environmental monitoring and management, especially in regions like Pakistan that are sensitive to climate change. This study employs an innovative approach using MODIS NDVI data and the Pixel Dichotomy Model (P...
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier B.V.
2024
|
| Online Access: | http://psasir.upm.edu.my/id/eprint/114302/ http://psasir.upm.edu.my/id/eprint/114302/1/114302.pdf |
| _version_ | 1848866453310144512 |
|---|---|
| author | Anees, Shoaib Ahmad Mehmood, Kaleem Rehman, Akhtar Rehman, Nazir Ur Muhammad, Sultan Shahzad, Fahad Hussain, Khadim Luo, Mi Alarfaj, Abdullah A. Alharbi, Sulaiman Ali Khan, Waseem Razzaq |
| author_facet | Anees, Shoaib Ahmad Mehmood, Kaleem Rehman, Akhtar Rehman, Nazir Ur Muhammad, Sultan Shahzad, Fahad Hussain, Khadim Luo, Mi Alarfaj, Abdullah A. Alharbi, Sulaiman Ali Khan, Waseem Razzaq |
| author_sort | Anees, Shoaib Ahmad |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Understanding the dynamics of Fractional Vegetation Cover (FVC) is crucial for effective environmental monitoring and management, especially in regions like Pakistan that are sensitive to climate change. This study employs an innovative approach using MODIS NDVI data and the Pixel Dichotomy Model (PDM) to analyze the spatiotemporal dynamics of FVC across Pakistan from 2003 to 2020. Our findings reveal an overall increasing trend in FVC, with the highest value recorded in 2017 (0.37) and the lowest in 2004 (0.26). The Hurst exponent analysis (R/S ratio = 0.718) indicates a degree of long-term memory in the FVC time series. Rainfall was found to positively correlate with FVC (r = 0.6), while Land Surface Temperature (LST) and the Compounded Night Light Index (CNLI) exhibited negative correlations (r = −0.59 and r = −0.43, respectively). The Random Forest regression model highlighted CNLI as the most influential predictor (importance = 62.4%), emphasizing the need to consider human-induced factors in environmental management. These results provide critical insights for sustainable land management and contribute to understanding vegetation-climate interactions in arid and semi-arid environments." |
| first_indexed | 2025-11-15T14:20:50Z |
| format | Article |
| id | upm-114302 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:20:50Z |
| publishDate | 2024 |
| publisher | Elsevier B.V. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1143022025-03-10T01:23:46Z http://psasir.upm.edu.my/id/eprint/114302/ Unveiling fractional vegetation cover dynamics: a spatiotemporal analysis using MODIS NDVI and machine learning Anees, Shoaib Ahmad Mehmood, Kaleem Rehman, Akhtar Rehman, Nazir Ur Muhammad, Sultan Shahzad, Fahad Hussain, Khadim Luo, Mi Alarfaj, Abdullah A. Alharbi, Sulaiman Ali Khan, Waseem Razzaq Understanding the dynamics of Fractional Vegetation Cover (FVC) is crucial for effective environmental monitoring and management, especially in regions like Pakistan that are sensitive to climate change. This study employs an innovative approach using MODIS NDVI data and the Pixel Dichotomy Model (PDM) to analyze the spatiotemporal dynamics of FVC across Pakistan from 2003 to 2020. Our findings reveal an overall increasing trend in FVC, with the highest value recorded in 2017 (0.37) and the lowest in 2004 (0.26). The Hurst exponent analysis (R/S ratio = 0.718) indicates a degree of long-term memory in the FVC time series. Rainfall was found to positively correlate with FVC (r = 0.6), while Land Surface Temperature (LST) and the Compounded Night Light Index (CNLI) exhibited negative correlations (r = −0.59 and r = −0.43, respectively). The Random Forest regression model highlighted CNLI as the most influential predictor (importance = 62.4%), emphasizing the need to consider human-induced factors in environmental management. These results provide critical insights for sustainable land management and contribute to understanding vegetation-climate interactions in arid and semi-arid environments." Elsevier B.V. 2024 Article PeerReviewed text en cc_by_4 http://psasir.upm.edu.my/id/eprint/114302/1/114302.pdf Anees, Shoaib Ahmad and Mehmood, Kaleem and Rehman, Akhtar and Rehman, Nazir Ur and Muhammad, Sultan and Shahzad, Fahad and Hussain, Khadim and Luo, Mi and Alarfaj, Abdullah A. and Alharbi, Sulaiman Ali and Khan, Waseem Razzaq (2024) Unveiling fractional vegetation cover dynamics: a spatiotemporal analysis using MODIS NDVI and machine learning. Environmental and Sustainability Indicators, 24. art. no. 100485. pp. 1-19. ISSN 2665-9727; eISSN: 2665-9727 https://linkinghub.elsevier.com/retrieve/pii/S2665972724001533 10.1016/j.indic.2024.100485 |
| spellingShingle | Anees, Shoaib Ahmad Mehmood, Kaleem Rehman, Akhtar Rehman, Nazir Ur Muhammad, Sultan Shahzad, Fahad Hussain, Khadim Luo, Mi Alarfaj, Abdullah A. Alharbi, Sulaiman Ali Khan, Waseem Razzaq Unveiling fractional vegetation cover dynamics: a spatiotemporal analysis using MODIS NDVI and machine learning |
| title | Unveiling fractional vegetation cover dynamics: a spatiotemporal analysis using MODIS NDVI and machine learning |
| title_full | Unveiling fractional vegetation cover dynamics: a spatiotemporal analysis using MODIS NDVI and machine learning |
| title_fullStr | Unveiling fractional vegetation cover dynamics: a spatiotemporal analysis using MODIS NDVI and machine learning |
| title_full_unstemmed | Unveiling fractional vegetation cover dynamics: a spatiotemporal analysis using MODIS NDVI and machine learning |
| title_short | Unveiling fractional vegetation cover dynamics: a spatiotemporal analysis using MODIS NDVI and machine learning |
| title_sort | unveiling fractional vegetation cover dynamics: a spatiotemporal analysis using modis ndvi and machine learning |
| url | http://psasir.upm.edu.my/id/eprint/114302/ http://psasir.upm.edu.my/id/eprint/114302/ http://psasir.upm.edu.my/id/eprint/114302/ http://psasir.upm.edu.my/id/eprint/114302/1/114302.pdf |