On the Use of Sentinel-2 NDVI Time Series and Google Earth Engine to Detect Land-Use/Land-Cover Changes in Fire-Affected Areas
This study aims to assess the potential of Sentinel-2 NDVI time series and Google Earth Engine to detect small land-use/land-cover changes (at the pixel level) in fire-disturbed environs. To capture both slow and fast changes, the investigations focused on the analysis of trends in NDVI time series,...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/19/4723 |
| _version_ | 1797477178312491008 |
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| author | Rosa Lasaponara Nicodemo Abate Carmen Fattore Angelo Aromando Gianfranco Cardettini Marco Di Fonzo |
| author_facet | Rosa Lasaponara Nicodemo Abate Carmen Fattore Angelo Aromando Gianfranco Cardettini Marco Di Fonzo |
| author_sort | Rosa Lasaponara |
| collection | DOAJ |
| description | This study aims to assess the potential of Sentinel-2 NDVI time series and Google Earth Engine to detect small land-use/land-cover changes (at the pixel level) in fire-disturbed environs. To capture both slow and fast changes, the investigations focused on the analysis of trends in NDVI time series, selected because they are extensively used for the assessment of post-fire dynamics mainly linked to the monitoring of vegetation recovery and fire resilience. The area considered for this study is the central–southern part of the Italian peninsula, in particular the regions of (i) Campania, (ii) Basilicata, (iii) Calabria, (iv) Toscana, (v) Umbria, and (vi) Lazio. For each fire considered, the study covered the period from the year after the event to the present. The multi-temporal analysis was performed using two main data processing steps (i) linear regression to extract NDVI trends and enhance changes over time and (ii) random forest classification to capture and categorize the various changes. The analysis allowed us to identify changes occurred in the selected case study areas and to understand and evaluate the trend indicators that mark a change in land use/land cover. In particular, different types of changes were identified: (i) woodland felling, (ii) remaking of paths and roads, and (ii) transition from wooded area to cultivated field. The reliability of the changes identified was assessed and confirmed by the high multi-temporal resolution offered by Google Earth. Results of this comparison highlighted that the overall accuracy of the classification was higher than 0.86. |
| first_indexed | 2024-03-09T21:14:56Z |
| format | Article |
| id | doaj.art-5fab883f9d9b4fe987e9ae1a73db091d |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T21:14:56Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-5fab883f9d9b4fe987e9ae1a73db091d2023-11-23T21:37:30ZengMDPI AGRemote Sensing2072-42922022-09-011419472310.3390/rs14194723On the Use of Sentinel-2 NDVI Time Series and Google Earth Engine to Detect Land-Use/Land-Cover Changes in Fire-Affected AreasRosa Lasaponara0Nicodemo Abate1Carmen Fattore2Angelo Aromando3Gianfranco Cardettini4Marco Di Fonzo5Institute of Methodologies for Environmental Analysis (CNR—IMAA), National Research Council, C.da S. Loja, 85050 Tito Scalo, ItalyInstitute of Heritage Science (CNR—ISPC), National Research Council, C.da S. Loja, 85050 Tito Scalo, ItalyInstitute of Methodologies for Environmental Analysis (CNR—IMAA), National Research Council, C.da S. Loja, 85050 Tito Scalo, ItalyInstitute of Methodologies for Environmental Analysis (CNR—IMAA), National Research Council, C.da S. Loja, 85050 Tito Scalo, ItalyInstitute of Methodologies for Environmental Analysis (CNR—IMAA), National Research Council, C.da S. Loja, 85050 Tito Scalo, ItalyComando Carabinieri per la Tutela Forestale, Via Carducci No. 5, 00187 Roma, ItalyThis study aims to assess the potential of Sentinel-2 NDVI time series and Google Earth Engine to detect small land-use/land-cover changes (at the pixel level) in fire-disturbed environs. To capture both slow and fast changes, the investigations focused on the analysis of trends in NDVI time series, selected because they are extensively used for the assessment of post-fire dynamics mainly linked to the monitoring of vegetation recovery and fire resilience. The area considered for this study is the central–southern part of the Italian peninsula, in particular the regions of (i) Campania, (ii) Basilicata, (iii) Calabria, (iv) Toscana, (v) Umbria, and (vi) Lazio. For each fire considered, the study covered the period from the year after the event to the present. The multi-temporal analysis was performed using two main data processing steps (i) linear regression to extract NDVI trends and enhance changes over time and (ii) random forest classification to capture and categorize the various changes. The analysis allowed us to identify changes occurred in the selected case study areas and to understand and evaluate the trend indicators that mark a change in land use/land cover. In particular, different types of changes were identified: (i) woodland felling, (ii) remaking of paths and roads, and (ii) transition from wooded area to cultivated field. The reliability of the changes identified was assessed and confirmed by the high multi-temporal resolution offered by Google Earth. Results of this comparison highlighted that the overall accuracy of the classification was higher than 0.86.https://www.mdpi.com/2072-4292/14/19/4723wildfireland-use/land-cover changeSentinel-2random forestlinear regressionmachine learning |
| spellingShingle | Rosa Lasaponara Nicodemo Abate Carmen Fattore Angelo Aromando Gianfranco Cardettini Marco Di Fonzo On the Use of Sentinel-2 NDVI Time Series and Google Earth Engine to Detect Land-Use/Land-Cover Changes in Fire-Affected Areas Remote Sensing wildfire land-use/land-cover change Sentinel-2 random forest linear regression machine learning |
| title | On the Use of Sentinel-2 NDVI Time Series and Google Earth Engine to Detect Land-Use/Land-Cover Changes in Fire-Affected Areas |
| title_full | On the Use of Sentinel-2 NDVI Time Series and Google Earth Engine to Detect Land-Use/Land-Cover Changes in Fire-Affected Areas |
| title_fullStr | On the Use of Sentinel-2 NDVI Time Series and Google Earth Engine to Detect Land-Use/Land-Cover Changes in Fire-Affected Areas |
| title_full_unstemmed | On the Use of Sentinel-2 NDVI Time Series and Google Earth Engine to Detect Land-Use/Land-Cover Changes in Fire-Affected Areas |
| title_short | On the Use of Sentinel-2 NDVI Time Series and Google Earth Engine to Detect Land-Use/Land-Cover Changes in Fire-Affected Areas |
| title_sort | on the use of sentinel 2 ndvi time series and google earth engine to detect land use land cover changes in fire affected areas |
| topic | wildfire land-use/land-cover change Sentinel-2 random forest linear regression machine learning |
| url | https://www.mdpi.com/2072-4292/14/19/4723 |
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