Aboveground net primary production spatio-temporal changes in the bioclimates of Alborz mountains based on multi-sensor satellite data
Abstract Aboveground Net Primary Production (ANPP) is a key indicator for assessing the health of rangeland ecosystems. This study estimated ANPP in the central Alborz rangelands of Iran from 2000 to 2020 based on satellite data (MOD13Q1.061, Sentinel-2 L1C), ground-based measurements, along with me...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-08923-1 |
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| Summary: | Abstract Aboveground Net Primary Production (ANPP) is a key indicator for assessing the health of rangeland ecosystems. This study estimated ANPP in the central Alborz rangelands of Iran from 2000 to 2020 based on satellite data (MOD13Q1.061, Sentinel-2 L1C), ground-based measurements, along with meteorological data through the Carnegie Ames Stanford Approach model. ANPP trends were ascertained across various bioclimates and vegetation types. A total of 240 sampling sites were selected to measure ANPP using a systematic-random design. Sentinel-2 ANPP values ranged between 2.4 and 44.6 gC/m2 in the study area. The model evaluation, based on the coefficient of determination, indicated a strong relationship between Sentinel-2 derived ANPP and ground data (R² = 0.86, P < 0.01). A significant relationship was also observed between ANPP estimates from the Sentinel-2 and MODIS sensors (R² = 0.8, P < 0.01). The climatic conditions and type of vegetation have a significant impact on rangeland production. The highest annual average ANPP, estimated using MODIS, was 60.57 gC/m2, witnessed in the Psathyrostachys fragilis-Agropyron tauri vegetation type within a humid and cold climate. In contrast, the lowest ANPP, 39.07 gC/m2, was recorded for the Seidlitzia rosmarinus-Artemisia sieberi type in a hyper-arid and cold climate. Generally, the findings demonstrated that integrating modeling approaches with satellite imagery enables robust estimation and analysis of rangeland production dynamics across diverse bioclimates and vegetation types. |
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| ISSN: | 2045-2322 |