Comparison of Gross Primary Productivity Derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia

Abstract

Gross primary production (GPP) plays an important role in the net ecosystem exchange of CO 2 between the atmosphere and terrestrial ecosystems. It is particularly important to monitor GPP in Southeast Asia because of increasing rates of tropical forest OPEN ACCESSRemote Sens. 2014, 6 2109 degradation and deforestation in the region in recent decades. The newly available, improved, third generation Normalized Difference Vegetation Index (NDVI3g) from the Global Inventory Modelling and Mapping Studies (GIMMS) group provides a long temporal dataset, from July 1981 to December 2011, for terrestrial carbon cycle and climate response research. However, GIMMS NDVI3g-based GPP estimates are not yet available. We applied the GLOPEM-CEVSA model, which integrates an ecosystem process model and a production efficiency model, to estimate GPP in Southeast Asia based on three independent results of the fraction of photosynthetically active radiation absorbed by vegetation (FPAR) from GIMMS NDVI3g (GPP NDVI3g ), GIMMS NDVI1g (GPP NDVI1g ), and the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2 FPAR product (GPP MOD15 ). The GPP results were validated using ground data from eddy flux towers located in different forest biomes, and comparisons were made among the three GPPs as well as the MOD17A2 GPP products (GPP MOD17 ). Based on validation with flux tower derived GPP estimates the results show that GPP NDVI3g is more accurate than GPP NDVI1g and is comparable in accuracy with GPP MOD15 . In addition, GPP NDVI3g and GPP MOD15 have good spatial-temporal consistency. Our results indicate that GIMMS NDVI3g is an effective dataset for regional GPP simulation in Southeast Asia, capable of accurately tracking the variation and trends in long-term terrestrial ecosystem GPP dynamics.

Does degradation from selective logging and illegal activities differently impact forest resources? A case study in Ghana

“…Previous studies showed the ability of GIMMS3g to capture productivity dynamics (Wang et al 2014, Zhu & Southworth 2013. Our result of NDVI increasing trends is in agreement with the increase in carbon storage observed for African intact tropical forests ), as well as with the increase in net primary production recorded at a global scale (Nemani et al 2003).…”

Future Climate Impact on the Desertification in the Dry Land Asia Using AVHRR GIMMS NDVI3g Data

“…It is a proxy for vegetation productivity of the terrestrial ecosystem [15,16]. The GIMMS (Global Inventory Modeling and Mapping Studies) 15-day composite NDVI3g dataset applied here has been shown to be more accurate than the GIMMS NDVI for monitoring vegetation activity and phonological change [17][18][19]. Its spatial resolution is 8 km.…”

Comparison of Different GPP Models in China Using MODIS Image and ChinaFLUX Data

“…Therefore, we suggest that the uncertainty of climatic factors has little impact on MOD17. However, FPAR, as a key forcing variable of remote sensing, has a large uncertainty owing to the data qualities, thus it may be an important reason for underestimated peak values of GPP_EC [31][32][33][34]. Additionally, FPAR is derived from MODIS NDVI, which is sensitive to canopy background variations and saturates in areas with dense tree canopy, especially in the growing season [35,36].…”