Aboveground Biomass Estimation and Time Series Analyses in Mongolian Grasslands Utilizing PlanetScope Imagery
Mongolia, situated in central Asia and bordered by Russia to the north and China to the south, experiences a semi-arid climate across most of its territory. Grasslands are pivotal in Mongolia’s agricultural sustainability and food security, facing rapid changes in the last two decades that underscor...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-02-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/16/5/869 |
| _version_ | 1797263959659642880 |
|---|---|
| author | Margad-Erdene Jargalsaikhan Dorj Ichikawa Masahiko Nagai Tuvshintogtokh Indree Vaibhav Katiyar Davaagerel Munkhtur Erdenebaatar Dashdondog |
| author_facet | Margad-Erdene Jargalsaikhan Dorj Ichikawa Masahiko Nagai Tuvshintogtokh Indree Vaibhav Katiyar Davaagerel Munkhtur Erdenebaatar Dashdondog |
| author_sort | Margad-Erdene Jargalsaikhan |
| collection | DOAJ |
| description | Mongolia, situated in central Asia and bordered by Russia to the north and China to the south, experiences a semi-arid climate across most of its territory. Grasslands are pivotal in Mongolia’s agricultural sustainability and food security, facing rapid changes in the last two decades that underscore the ongoing need for innovative approaches to assess vegetation conditions. This study aims to evaluate grassland biomass measurement and prediction through the analysis of high-resolution satellite data. By conducting a time series assessment of grazing-induced changes in vegetation dynamics at the long-term monitoring sites of the Botanic Garden and Research Institute, Mongolian Academy of Sciences, we seek to refine our understanding. The investigation covers biomass estimation across various Mongolian grassland landscapes, encompassing desert, steppe, and mountain regions. Spanning the grassland growing season from May 2020 to October 2023, the research leveraged diverse ground data types, including surface reflectance measurements, geographic coordinates for satellite data correction, and aboveground dry biomass. These components were instrumental in developing a biomass estimation model reliant on establishing correlations between the satellite-derived Normalized Difference Vegetation Index and biomass. The predicted biomass facilitated the time series map analysis and dynamic analysis. The PlanetScope surface reflectance correlates strongly at 0.97 with field measurements, indicating robust relations. Biomass and the Normalized Difference Vegetation Index show correlations of 0.82 for dry grassland, 0.80 for mountain grassland, and 0.65 for desert grassland, with a combined correlation coefficient of 0.62, revealing distinct characteristics across these grasslands. Time series dynamic analysis reveals rising biomass differences between grazed and ungrazed areas, suggesting potential grassland degradation. Variations in the slope coefficient of biomass differences among grassland types indicate differing degradation patterns, emphasizing the need for effective grazing management practices to sustain and conserve Mongolian grasslands. This highlights the potential of remote sensing in monitoring and managing grassland ecosystems. |
| first_indexed | 2024-04-25T00:21:18Z |
| format | Article |
| id | doaj.art-970ef0af436543e2a21f92c7a3d32523 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-25T00:21:18Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-970ef0af436543e2a21f92c7a3d325232024-03-12T16:54:17ZengMDPI AGRemote Sensing2072-42922024-02-0116586910.3390/rs16050869Aboveground Biomass Estimation and Time Series Analyses in Mongolian Grasslands Utilizing PlanetScope ImageryMargad-Erdene Jargalsaikhan0Dorj Ichikawa1Masahiko Nagai2Tuvshintogtokh Indree3Vaibhav Katiyar4Davaagerel Munkhtur5Erdenebaatar Dashdondog6Graduate School of Science and Technology for Innovation, Yamaguchi University, 2-16-1, Ube 755-8611, Yamaguchi, JapanNew Space Intelligence Inc., 329-22, Ube 755-0151, Yamaguchi, JapanGraduate School of Science and Technology for Innovation, Yamaguchi University, 2-16-1, Ube 755-8611, Yamaguchi, JapanBotanic Garden and Research Institute, Mongolian Academy of Sciences, Ulaanbaatar 13330, MongoliaGraduate School of Science and Technology for Innovation, Yamaguchi University, 2-16-1, Ube 755-8611, Yamaguchi, JapanBotanic Garden and Research Institute, Mongolian Academy of Sciences, Ulaanbaatar 13330, MongoliaDepartment of Physics, National University of Mongolia, Ulaanbaatar 14200, MongoliaMongolia, situated in central Asia and bordered by Russia to the north and China to the south, experiences a semi-arid climate across most of its territory. Grasslands are pivotal in Mongolia’s agricultural sustainability and food security, facing rapid changes in the last two decades that underscore the ongoing need for innovative approaches to assess vegetation conditions. This study aims to evaluate grassland biomass measurement and prediction through the analysis of high-resolution satellite data. By conducting a time series assessment of grazing-induced changes in vegetation dynamics at the long-term monitoring sites of the Botanic Garden and Research Institute, Mongolian Academy of Sciences, we seek to refine our understanding. The investigation covers biomass estimation across various Mongolian grassland landscapes, encompassing desert, steppe, and mountain regions. Spanning the grassland growing season from May 2020 to October 2023, the research leveraged diverse ground data types, including surface reflectance measurements, geographic coordinates for satellite data correction, and aboveground dry biomass. These components were instrumental in developing a biomass estimation model reliant on establishing correlations between the satellite-derived Normalized Difference Vegetation Index and biomass. The predicted biomass facilitated the time series map analysis and dynamic analysis. The PlanetScope surface reflectance correlates strongly at 0.97 with field measurements, indicating robust relations. Biomass and the Normalized Difference Vegetation Index show correlations of 0.82 for dry grassland, 0.80 for mountain grassland, and 0.65 for desert grassland, with a combined correlation coefficient of 0.62, revealing distinct characteristics across these grasslands. Time series dynamic analysis reveals rising biomass differences between grazed and ungrazed areas, suggesting potential grassland degradation. Variations in the slope coefficient of biomass differences among grassland types indicate differing degradation patterns, emphasizing the need for effective grazing management practices to sustain and conserve Mongolian grasslands. This highlights the potential of remote sensing in monitoring and managing grassland ecosystems.https://www.mdpi.com/2072-4292/16/5/869grasslandbiomassdegradationMongoliaPlanetScope imagerygeometric registration |
| spellingShingle | Margad-Erdene Jargalsaikhan Dorj Ichikawa Masahiko Nagai Tuvshintogtokh Indree Vaibhav Katiyar Davaagerel Munkhtur Erdenebaatar Dashdondog Aboveground Biomass Estimation and Time Series Analyses in Mongolian Grasslands Utilizing PlanetScope Imagery Remote Sensing grassland biomass degradation Mongolia PlanetScope imagery geometric registration |
| title | Aboveground Biomass Estimation and Time Series Analyses in Mongolian Grasslands Utilizing PlanetScope Imagery |
| title_full | Aboveground Biomass Estimation and Time Series Analyses in Mongolian Grasslands Utilizing PlanetScope Imagery |
| title_fullStr | Aboveground Biomass Estimation and Time Series Analyses in Mongolian Grasslands Utilizing PlanetScope Imagery |
| title_full_unstemmed | Aboveground Biomass Estimation and Time Series Analyses in Mongolian Grasslands Utilizing PlanetScope Imagery |
| title_short | Aboveground Biomass Estimation and Time Series Analyses in Mongolian Grasslands Utilizing PlanetScope Imagery |
| title_sort | aboveground biomass estimation and time series analyses in mongolian grasslands utilizing planetscope imagery |
| topic | grassland biomass degradation Mongolia PlanetScope imagery geometric registration |
| url | https://www.mdpi.com/2072-4292/16/5/869 |
| work_keys_str_mv | AT margaderdenejargalsaikhan abovegroundbiomassestimationandtimeseriesanalysesinmongoliangrasslandsutilizingplanetscopeimagery AT dorjichikawa abovegroundbiomassestimationandtimeseriesanalysesinmongoliangrasslandsutilizingplanetscopeimagery AT masahikonagai abovegroundbiomassestimationandtimeseriesanalysesinmongoliangrasslandsutilizingplanetscopeimagery AT tuvshintogtokhindree abovegroundbiomassestimationandtimeseriesanalysesinmongoliangrasslandsutilizingplanetscopeimagery AT vaibhavkatiyar abovegroundbiomassestimationandtimeseriesanalysesinmongoliangrasslandsutilizingplanetscopeimagery AT davaagerelmunkhtur abovegroundbiomassestimationandtimeseriesanalysesinmongoliangrasslandsutilizingplanetscopeimagery AT erdenebaatardashdondog abovegroundbiomassestimationandtimeseriesanalysesinmongoliangrasslandsutilizingplanetscopeimagery |