Thu, Aug 05, 2021:On Demand
Background/Question/Methods
Drylands cover approximately 40% of the Earth’s land surface. There are relatively few remotely sensed vegetation indices that are tailored to the challenges that come with observing arid and semi-arid rangelands from space. Low and heterogeneous vegetation cover, exposed soils and a mixture of woody and herbaceous cover present problems for discerning forage abundance and distribution. The novel vegetation index, Normalized Difference Phenometric Index (NDPI), was developed to overcome some of these challenges by taking advantage of the different phenology types typical in dryland landscapes while also minimizing the effects of soils. Shrubs and grasses in particular have different phenology and react to climate drivers differently. Here we test the ability of the NDPI to capture the fraction of grass and shrub cover at the Jornada Experimental Range in southwestern New Mexico and examine the ability of the NDPI to represent the spatial variability of precipitation impacts on grass productivity. MODIS/Terra surface reflectance 8-day composites with a spatial resolution of 500m were used to test the ability of NDPI to separate grass and shrub features at the Jornada. We calculated NDPI over 15 years, from 2005-2020, on Google Earth Engine. NDPI = (VIp2-VIp1)/(VIp2+VIp1) where VI is the MSAVI2 Vegetation Index and p1 and p2 refer to distinct phenological periods for grasses (dormancy and maximum greenness).
Results/Conclusions The spatial coverage of shrubs and grasses estimated from NDPI were compared to an existing high resolution shrub map. After validating the approach for separating grasses and shrubs on the landscape, the spatial patterns of grass productivity estimated with NDPI were evaluated against the spatial variability of precipitation over the same time period. Early results suggest that NDPI is effective at discriminating grasses from shrub features within the dryland landscape of the Jornada. NDPI was also able to capture considerable variability in the grass component greenness across the landscape. When evaluated with respect to locations that are known productive grasslands (reference pixels), NDPI can also be used to estimate grass productivity. Results show NDPI’s performance at estimating the spatial variability of grass productivity in dryland landscapes such as the Jornada. NDPI was originally developed to assist rangeland managers in predicting forage availability and to plan usage of the landscape. This index may also be useful for researchers who are studying vegetation community dynamics in arid and semi-arid landscapes.
Results/Conclusions The spatial coverage of shrubs and grasses estimated from NDPI were compared to an existing high resolution shrub map. After validating the approach for separating grasses and shrubs on the landscape, the spatial patterns of grass productivity estimated with NDPI were evaluated against the spatial variability of precipitation over the same time period. Early results suggest that NDPI is effective at discriminating grasses from shrub features within the dryland landscape of the Jornada. NDPI was also able to capture considerable variability in the grass component greenness across the landscape. When evaluated with respect to locations that are known productive grasslands (reference pixels), NDPI can also be used to estimate grass productivity. Results show NDPI’s performance at estimating the spatial variability of grass productivity in dryland landscapes such as the Jornada. NDPI was originally developed to assist rangeland managers in predicting forage availability and to plan usage of the landscape. This index may also be useful for researchers who are studying vegetation community dynamics in arid and semi-arid landscapes.