Authors: Tuo Feng*, University of Washington, Daniel Vogt, University of Washington, Monika Moskal, University of Washington, Stephan Gmur, Polaris Applied Sciences, Kristiina Vogt, University of Washington, Korena Mafune, University of Washington
Topics: Remote Sensing
Keywords: NPP, Landsat, Biome-BGC, Stand age, TSRI, LiDAR, Climate, InTEC, Olympic Peninsula
Session Type: Paper
Presentation File: No File Uploaded
Understanding the trend and variation of total Ecosystem Net Primary Productivity (NPP) is critical for simulating and predicting changes in the terrestrial carbon cycle across different spatial and temporal scales. In this study, four different models were used to estimate mean annual NPP between 1986 to 2017 in two watersheds in the Olympic Peninsula of Washington state. The spatial-temporal changes in NPP were correlated with climate factors, topography and stand age. Spatially, annual NPP was estimated to be lower in the northwestern watershed #2 vs that found in the southeastern watershed #1. This was probably due to the differences in forest structure, species composition and incident solar radiation. In the two watersheds, the models suggested a general trend of NPP levels increasing with time and two potential Turning Points were detected in 1997 and 2007 respectively.These study results predicted NPP levels being highly correlated to climate factors and three of the models were very sensitive to changes in annual mean temperatures. Topographic effect on NPP was analyzed using Topographic Solar Radiation Index (TSRI) acquired from airborne LiDAR data. Changes in NPP estimates using LiDAR-based TSRI resulted in significant R2 values of 0.59 (deciduous forests), 0.38 (evergreen forests) and 0.64 (mixed forest) respectively. Finally, three empirical functions were developed based on InTEC (Integrated Terrestrial Ecosystem Carbon) that described the relationship between stand age and NPP levels in terms of different forest types. Our study provides valuable information to assess the potential drivers of changes in carbon sequestration rates under dynamic climates.
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