Creation and environmental applications of 15-year daily inundation and vegetation maps for Siberia by integrating satellite and meteorological datasets
Abstract As a result of climate change, the pan-Arctic region has seen greater temperature increases than other geographical regions on the Earth’s surface. This has led to substantial changes in terrestrial ecosystems and the hydrological cycle, which have affected the distribution of vegetation an...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2024-02-01
|
| Series: | Progress in Earth and Planetary Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40645-024-00614-1 |
| _version_ | 1827325787809775616 |
|---|---|
| author | Hiroki Mizuochi Taiga Sasagawa Akihiko Ito Yoshihiro Iijima Hotaek Park Hirohiko Nagano Kazuhito Ichii Tetsuya Hiyama |
| author_facet | Hiroki Mizuochi Taiga Sasagawa Akihiko Ito Yoshihiro Iijima Hotaek Park Hirohiko Nagano Kazuhito Ichii Tetsuya Hiyama |
| author_sort | Hiroki Mizuochi |
| collection | DOAJ |
| description | Abstract As a result of climate change, the pan-Arctic region has seen greater temperature increases than other geographical regions on the Earth’s surface. This has led to substantial changes in terrestrial ecosystems and the hydrological cycle, which have affected the distribution of vegetation and the patterns of water flow and accumulation. Various remote sensing techniques, including optical and microwave satellite observations, are useful for monitoring these terrestrial water and vegetation dynamics. In the present study, satellite and reanalysis datasets were used to produce water and vegetation maps with a high temporal resolution (daily) and moderate spatial resolution (500 m) at a continental scale over Siberia in the period 2003–2017. The multiple data sources were integrated by pixel-based machine learning (random forest), which generated a normalized difference water index (NDWI), normalized difference vegetation index (NDVI), and water fraction without any gaps, even for areas where optical data were missing (e.g., cloud cover). For the convenience of users handling the data, an aggregated product is provided, formatted using a 0.1° grid in latitude/longitude projection. When validated using the original optical images, the NDWI and NDVI images showed small systematic biases, with a root mean squared error of approximately 0.1 over the study area. The product was used for both time-series trend analysis of the indices from 2003 to 2017 and phenological feature extraction based on seasonal NDVI patterns. The former analysis was used to identify areas where the NDVI is decreasing and the NDWI is increasing, and hotspots where the NDWI at lakesides and coastal regions is decreasing. The latter analysis, which employed double-sigmoid fitting to assess changes in five phenological parameters (i.e., start and end of spring and fall, and peak NDVI values) at two larch forest sites, highlighted a tendency for recent lengthening of the growing period. Further applications, including model integration and contribution to land cover mapping, will be developed in the future. |
| first_indexed | 2024-03-07T14:35:23Z |
| format | Article |
| id | doaj.art-e6adff5447f14b02bc5d02dd4120bd41 |
| institution | Directory Open Access Journal |
| issn | 2197-4284 |
| language | English |
| last_indexed | 2024-03-07T14:35:23Z |
| publishDate | 2024-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Progress in Earth and Planetary Science |
| spelling | doaj.art-e6adff5447f14b02bc5d02dd4120bd412024-03-05T20:42:30ZengSpringerOpenProgress in Earth and Planetary Science2197-42842024-02-0111112310.1186/s40645-024-00614-1Creation and environmental applications of 15-year daily inundation and vegetation maps for Siberia by integrating satellite and meteorological datasetsHiroki Mizuochi0Taiga Sasagawa1Akihiko Ito2Yoshihiro Iijima3Hotaek Park4Hirohiko Nagano5Kazuhito Ichii6Tetsuya Hiyama7Geological Survey of Japan, National Institute of Advanced Industrial Science and TechnologyGraduate School of Science and Technology, University of TsukubaGraduate School of Life and Agricultural Sciences, the University of TokyoDepartment of Geography, Tokyo Metropolitan UniversityInstitute for Space-Earth Environmental Research (ISEE), Nagoya UniversityInstitute of Science and Technology, Niigata UniversityCenter for Environmental Remote Sensing (CEReS), Chiba UniversityInstitute for Space-Earth Environmental Research (ISEE), Nagoya UniversityAbstract As a result of climate change, the pan-Arctic region has seen greater temperature increases than other geographical regions on the Earth’s surface. This has led to substantial changes in terrestrial ecosystems and the hydrological cycle, which have affected the distribution of vegetation and the patterns of water flow and accumulation. Various remote sensing techniques, including optical and microwave satellite observations, are useful for monitoring these terrestrial water and vegetation dynamics. In the present study, satellite and reanalysis datasets were used to produce water and vegetation maps with a high temporal resolution (daily) and moderate spatial resolution (500 m) at a continental scale over Siberia in the period 2003–2017. The multiple data sources were integrated by pixel-based machine learning (random forest), which generated a normalized difference water index (NDWI), normalized difference vegetation index (NDVI), and water fraction without any gaps, even for areas where optical data were missing (e.g., cloud cover). For the convenience of users handling the data, an aggregated product is provided, formatted using a 0.1° grid in latitude/longitude projection. When validated using the original optical images, the NDWI and NDVI images showed small systematic biases, with a root mean squared error of approximately 0.1 over the study area. The product was used for both time-series trend analysis of the indices from 2003 to 2017 and phenological feature extraction based on seasonal NDVI patterns. The former analysis was used to identify areas where the NDVI is decreasing and the NDWI is increasing, and hotspots where the NDWI at lakesides and coastal regions is decreasing. The latter analysis, which employed double-sigmoid fitting to assess changes in five phenological parameters (i.e., start and end of spring and fall, and peak NDVI values) at two larch forest sites, highlighted a tendency for recent lengthening of the growing period. Further applications, including model integration and contribution to land cover mapping, will be developed in the future.https://doi.org/10.1186/s40645-024-00614-1Continental-scale water and vegetation mapsSatellite dataPan-Arctic regionData fusionMachine learningTrend analysis |
| spellingShingle | Hiroki Mizuochi Taiga Sasagawa Akihiko Ito Yoshihiro Iijima Hotaek Park Hirohiko Nagano Kazuhito Ichii Tetsuya Hiyama Creation and environmental applications of 15-year daily inundation and vegetation maps for Siberia by integrating satellite and meteorological datasets Progress in Earth and Planetary Science Continental-scale water and vegetation maps Satellite data Pan-Arctic region Data fusion Machine learning Trend analysis |
| title | Creation and environmental applications of 15-year daily inundation and vegetation maps for Siberia by integrating satellite and meteorological datasets |
| title_full | Creation and environmental applications of 15-year daily inundation and vegetation maps for Siberia by integrating satellite and meteorological datasets |
| title_fullStr | Creation and environmental applications of 15-year daily inundation and vegetation maps for Siberia by integrating satellite and meteorological datasets |
| title_full_unstemmed | Creation and environmental applications of 15-year daily inundation and vegetation maps for Siberia by integrating satellite and meteorological datasets |
| title_short | Creation and environmental applications of 15-year daily inundation and vegetation maps for Siberia by integrating satellite and meteorological datasets |
| title_sort | creation and environmental applications of 15 year daily inundation and vegetation maps for siberia by integrating satellite and meteorological datasets |
| topic | Continental-scale water and vegetation maps Satellite data Pan-Arctic region Data fusion Machine learning Trend analysis |
| url | https://doi.org/10.1186/s40645-024-00614-1 |
| work_keys_str_mv | AT hirokimizuochi creationandenvironmentalapplicationsof15yeardailyinundationandvegetationmapsforsiberiabyintegratingsatelliteandmeteorologicaldatasets AT taigasasagawa creationandenvironmentalapplicationsof15yeardailyinundationandvegetationmapsforsiberiabyintegratingsatelliteandmeteorologicaldatasets AT akihikoito creationandenvironmentalapplicationsof15yeardailyinundationandvegetationmapsforsiberiabyintegratingsatelliteandmeteorologicaldatasets AT yoshihiroiijima creationandenvironmentalapplicationsof15yeardailyinundationandvegetationmapsforsiberiabyintegratingsatelliteandmeteorologicaldatasets AT hotaekpark creationandenvironmentalapplicationsof15yeardailyinundationandvegetationmapsforsiberiabyintegratingsatelliteandmeteorologicaldatasets AT hirohikonagano creationandenvironmentalapplicationsof15yeardailyinundationandvegetationmapsforsiberiabyintegratingsatelliteandmeteorologicaldatasets AT kazuhitoichii creationandenvironmentalapplicationsof15yeardailyinundationandvegetationmapsforsiberiabyintegratingsatelliteandmeteorologicaldatasets AT tetsuyahiyama creationandenvironmentalapplicationsof15yeardailyinundationandvegetationmapsforsiberiabyintegratingsatelliteandmeteorologicaldatasets |