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20 October 2023 Integrating NDVI data from different remote sensing satellite platforms to produce consistent serial forest productivity index (FPI) for Australian national carbon accounting model
Tingbao Xu
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Proceedings Volume 12727, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV; 127270G (2023) https://doi.org/10.1117/12.2684032
Event: SPIE Remote Sensing, 2023, Amsterdam, Netherlands
Abstract
We have to employ remote sensing data, such as Normalized Difference Vegetation Index (NDVI), from different satellite systems/programs for our multi-decade monitoring programs on ground. Calibration is essential to integrate different sourced NDVI data. NDVI is an essential layer for generating the Australian national Forest Productivity Index (FPI), which is a key component of the Australian Full Carbon Accounting Model (FullCAM). FullCAM is used to calculate Australia’s greenhouse gas emissions from the land sector. This study/project developed a correlation algorithm by using statistical regression analysis of long-term average monthly NDVI derived from a random points network over Australia, which aimed to calibrate 1km monthly MODIS/NDVI grids into AVHRR/NDVI-orientated NDVI layers to produce consistent Australian national Forest Productivity Index (FPI) products without rewriting the complex FPI model. The calibrated NDVI layers have been used to (1) accurately reproduce national FPI from 2001 to 2019. The national 1km monthly and annual FPI grids calculated from calibrated NDVI well replicated the national spatial pattern and conserved spatial details locally compared to those produced using AVHRR/NDVI; (2) generate consistent FPI products since 2020. This study provides a practically useful reference to other similar NDVI applications.
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Tingbao Xu "Integrating NDVI data from different remote sensing satellite platforms to produce consistent serial forest productivity index (FPI) for Australian national carbon accounting model", Proc. SPIE 12727, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV, 127270G (20 October 2023); https://doi.org/10.1117/12.2684032
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KEYWORDS
Data modeling
Atmospheric modeling
Carbon
Remote sensing
Calibration
Data integration
Satellites
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