Spatiotemporal fusion of multi-source remote sensing data for estimating aboveground biomass of grassland
Accurate estimation of aboveground biomass of grasslands is key to sustainable grassland utilization. However, most satellites cannot provide high temporal and spatial resolution data. Patterns of grassland dynamics associated with variability in climate conditions across spatiotemporal scales are y...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-02-01
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| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X23000341 |
| _version_ | 1797942046987649024 |
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| author | Yajun Zhou Tingxi Liu Okke Batelaan Limin Duan Yixuan Wang Xia Li Mingyang Li |
| author_facet | Yajun Zhou Tingxi Liu Okke Batelaan Limin Duan Yixuan Wang Xia Li Mingyang Li |
| author_sort | Yajun Zhou |
| collection | DOAJ |
| description | Accurate estimation of aboveground biomass of grasslands is key to sustainable grassland utilization. However, most satellites cannot provide high temporal and spatial resolution data. Patterns of grassland dynamics associated with variability in climate conditions across spatiotemporal scales are yet to be adequately quantified. A spatiotemporal fusion model offers the opportunity to combine the resolution advantages of different remote sensing data to achieve a high frequency and high precision monitoring of vegetation. We test a flexible spatiotemporal data fusion (FSDAF) methodology to generate synthetic normalized difference vegetation index (NDVI) data from Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat data sets. The methodology is tested for the semi-arid grassland of the Xilin River Basin, China. Based on NDVI data fusion and field measured aboveground biomass an aboveground biomass estimation model is established for the watershed. Exploring the temporal and spatial changes of biomass and its relationship with environmental factors. The results show that: (1) The FSDAF model performs well (R2 = 0.75) and has clear textural features. (2) The established Support Vector Machine Aboveground Biomass model not only ensured the accuracy of estimation (R2 = 0.78, RMSE = 15.43 g/m2), but also generated spatiotemporal maps of biomass with higher spatial (30 m) and temporal resolution (8 days). (3) The grassland aboveground biomass in this area decreases from southeast to northwest, and the grassland biomass reaches its peak at the end of July. The average biomass of different grasslands decreases in the order of meadow grassland > typical grassland > desert grassland. (4) Aboveground biomass increased linearly with increasing water content, organic carbon and total nitrogen, and was most sensitive to soil water content. During the early growing and rapid growing period, aboveground biomass is mainly affected by both air temperature and precipitation, while the effects of temperature and human activities gradually dominate in the middle and late growing periods. This study helps to improve the spatial and temporal resolution of dynamic monitoring of grassland biomass, and provides a scientific basis for grassland protection and management in arid and semi-arid regions. |
| first_indexed | 2024-04-10T20:02:33Z |
| format | Article |
| id | doaj.art-70a62830671e48a383426b5dce0ed95d |
| institution | Directory Open Access Journal |
| issn | 1470-160X |
| language | English |
| last_indexed | 2024-04-10T20:02:33Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj.art-70a62830671e48a383426b5dce0ed95d2023-01-27T04:19:43ZengElsevierEcological Indicators1470-160X2023-02-01146109892Spatiotemporal fusion of multi-source remote sensing data for estimating aboveground biomass of grasslandYajun Zhou0Tingxi Liu1Okke Batelaan2Limin Duan3Yixuan Wang4Xia Li5Mingyang Li6The College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China; Inner Mongolia Water Resource Protection and Utilization Key Laboratory, 010018 Hohhot, China; Autonomous Region Collaborative Innovation Center for Integrated Management of Water Resources and Water Environment in the Inner Mongolia Reaches of the Yellow River, Hohhot 010018, ChinaThe College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China; Inner Mongolia Water Resource Protection and Utilization Key Laboratory, 010018 Hohhot, China; Autonomous Region Collaborative Innovation Center for Integrated Management of Water Resources and Water Environment in the Inner Mongolia Reaches of the Yellow River, Hohhot 010018, China; Corresponding author.College of Science & Engineering, National Centre for Groundwater Research and Training, Flinders University, Adelaide, South Australia, AustraliaThe College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China; Inner Mongolia Water Resource Protection and Utilization Key Laboratory, 010018 Hohhot, China; Autonomous Region Collaborative Innovation Center for Integrated Management of Water Resources and Water Environment in the Inner Mongolia Reaches of the Yellow River, Hohhot 010018, ChinaThe College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China; Inner Mongolia Water Resource Protection and Utilization Key Laboratory, 010018 Hohhot, China; Autonomous Region Collaborative Innovation Center for Integrated Management of Water Resources and Water Environment in the Inner Mongolia Reaches of the Yellow River, Hohhot 010018, ChinaThe College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China; Inner Mongolia Water Resource Protection and Utilization Key Laboratory, 010018 Hohhot, China; Autonomous Region Collaborative Innovation Center for Integrated Management of Water Resources and Water Environment in the Inner Mongolia Reaches of the Yellow River, Hohhot 010018, ChinaThe College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China; Inner Mongolia Water Resource Protection and Utilization Key Laboratory, 010018 Hohhot, China; Autonomous Region Collaborative Innovation Center for Integrated Management of Water Resources and Water Environment in the Inner Mongolia Reaches of the Yellow River, Hohhot 010018, ChinaAccurate estimation of aboveground biomass of grasslands is key to sustainable grassland utilization. However, most satellites cannot provide high temporal and spatial resolution data. Patterns of grassland dynamics associated with variability in climate conditions across spatiotemporal scales are yet to be adequately quantified. A spatiotemporal fusion model offers the opportunity to combine the resolution advantages of different remote sensing data to achieve a high frequency and high precision monitoring of vegetation. We test a flexible spatiotemporal data fusion (FSDAF) methodology to generate synthetic normalized difference vegetation index (NDVI) data from Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat data sets. The methodology is tested for the semi-arid grassland of the Xilin River Basin, China. Based on NDVI data fusion and field measured aboveground biomass an aboveground biomass estimation model is established for the watershed. Exploring the temporal and spatial changes of biomass and its relationship with environmental factors. The results show that: (1) The FSDAF model performs well (R2 = 0.75) and has clear textural features. (2) The established Support Vector Machine Aboveground Biomass model not only ensured the accuracy of estimation (R2 = 0.78, RMSE = 15.43 g/m2), but also generated spatiotemporal maps of biomass with higher spatial (30 m) and temporal resolution (8 days). (3) The grassland aboveground biomass in this area decreases from southeast to northwest, and the grassland biomass reaches its peak at the end of July. The average biomass of different grasslands decreases in the order of meadow grassland > typical grassland > desert grassland. (4) Aboveground biomass increased linearly with increasing water content, organic carbon and total nitrogen, and was most sensitive to soil water content. During the early growing and rapid growing period, aboveground biomass is mainly affected by both air temperature and precipitation, while the effects of temperature and human activities gradually dominate in the middle and late growing periods. This study helps to improve the spatial and temporal resolution of dynamic monitoring of grassland biomass, and provides a scientific basis for grassland protection and management in arid and semi-arid regions.http://www.sciencedirect.com/science/article/pii/S1470160X23000341BiomassSpatiotemporal data fusionSupport Vector MachineXilin River Basin |
| spellingShingle | Yajun Zhou Tingxi Liu Okke Batelaan Limin Duan Yixuan Wang Xia Li Mingyang Li Spatiotemporal fusion of multi-source remote sensing data for estimating aboveground biomass of grassland Ecological Indicators Biomass Spatiotemporal data fusion Support Vector Machine Xilin River Basin |
| title | Spatiotemporal fusion of multi-source remote sensing data for estimating aboveground biomass of grassland |
| title_full | Spatiotemporal fusion of multi-source remote sensing data for estimating aboveground biomass of grassland |
| title_fullStr | Spatiotemporal fusion of multi-source remote sensing data for estimating aboveground biomass of grassland |
| title_full_unstemmed | Spatiotemporal fusion of multi-source remote sensing data for estimating aboveground biomass of grassland |
| title_short | Spatiotemporal fusion of multi-source remote sensing data for estimating aboveground biomass of grassland |
| title_sort | spatiotemporal fusion of multi source remote sensing data for estimating aboveground biomass of grassland |
| topic | Biomass Spatiotemporal data fusion Support Vector Machine Xilin River Basin |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X23000341 |
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