Predictions of aboveground net herbaceous production are from satellite-derived APAR are more sensitive to ecosiste than grazing management strategy in shortgrass steppe

Authors: Peirce, Erika; Kearney, Sean; SANTAMARIA, NIKOLAS - New College Of Florida; Augustine, David; Porensky, Lauren

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/23/2024
Publication Date: N/A
Citation: N/A

Interpretive Summary: This study set out to figure out how to accurately estimate the amount of herbaceous biomass grown in rangelands, which is difficult to observe directly. Herbaceous biomass is the total weight of all living plant material, such as grasses, forbs, and small shrubs, some of which are edible forage for livestock. We used a combination of satellite data and ground-based measurements to estimate herbaceous biomass. Satellites can tell us how much sunlight plants use, which is a good indicator of their growth rate. We used the normalized difference vegetation index (NDVI), a metric calculated from satellite images, to estimate this sunlight absorption. By combining this data with ground measurements of sunlight, we could calculate how much sunlight the plants in an area use for growth. Our main goal was to see if different ways of managing grazing animals would affect the model we used to detect herbaceous biomass using satellites in the shortgrass steppe in northeastern Colorado. We also wanted to compare two models for estimating herbaceous growth: one based on ground data (our empirical model) and another based on a more complicated computer model (the Rangeland Analysis Platform). Interestingly, when we modeled herbaceous biomass, the model was more sensitive to the type of vegetation present than by how the animals were being managed. We also found that the empirical method could predict herbaceous growth well in the shortgrass steppe area we studied. When it came to comparing the two methods, the empirical model method worked better overall than the one based on broader, continent-wide information. This suggests that satellites can provide a good estimate of how much herbaceous biomass is growing and that the types of plants in an area are important for using satellites to accurately predict how much herbaceous biomass is present.

Technical Abstract: The accurate estimation of aboveground net herbaceous production (ANHP) is crucial in rangeland management and monitoring. Remote and rural rangelands typically lack direct observation infrastructure, making satellite-derived methods essential. When ground data are available, a simple and effective way to estimate ANHP from satellites is to derive the empirical relationship between ANHP and plant-absorbed photosynthetically activate radiation (APAR), which can be estimated from the normalized difference vegetation index (NDVI). While there is some evidence that this relationship will differ across rangeland vegetation types, it is unclear whether this relationship will change across grazing management regimes. This study aimed to assess the impact of grazing management on the relationship between ground-observed ANHP and satellite-derived APAR, considering variations in plant communities across ecological sites in the shortgrass steppe of northeastern Colorado. Additionally, we compared satellite-predicted biomass production from the process-based Rangeland Analysis Platform (RAP) model to our empirical APAR-based model. We found that APAR could be used to predict ANHP in the shortgrass steppe, with the relationship being influenced by ecosite characteristics rather than grazing management practices. Specifically, we found steeper slopes for the APAR to ANHP relationship in ecosites with taller structured herbaceous vegetation, likely due to increased allocation of plant resources aboveground per unit of APAR for C3 mid-grasses compared to the C4 short-grasses that dominate the shorter structured ecosites. Moreover, we found that our locally-calibrated empirical model generally performed better than a continentally-calibrated process-based model, though the latter performed reasonably well for the dominant ecosite. Managers in the shortgrass steppe can use satellites to estimate herbaceous production even without detailed information on short-term grazing management practices. The results from our study underscore the importance of understanding plant community composition for enhancing the accuracy of remotely sensed predictions of ANHP.