Monitoring the Impact of Grazing on Rangeland Conservation Easements Using MODIS Vegetation Indices

Abstract

Monitoring the effects of grazing on rangelands is crucial for ensuring sustainable rangeland ecosystem function and maintaining its conservation values. Residual dry matter (RDM), the dry grass biomass left on the ground at the end of the grazing season, is a commonly used proxy for rangeland condition in Mediterranean climates. Moderate levels of RDM are correlated with soil stability, forage production, wildlife habitat, and diversity of native plants. Therefore RDM is widely monitored on rangeland conservation properties. Current ground-based methods for RDM monitoring are expensive, are labor intensive, and provide information in the fall, after the effects of grazing have already occurred. In this paper we present a costeffective, rapid, and robust methodology to monitor and predict RDM using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. We performed a time series analysis of three MODIS-based vegetation indices (VIs) measured over the period 2000-2012: Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR). We examined the correlation between the four VIs and fall RDM measured at The Nature Conservancy's Simon Newman Ranch in central California. We found strong and significant correlations between maximum VI values in late spring and RDM in the fall. Among the VIs, LAI values had the most significant correlation with fall RDM. MODIS-based multivariate models predicted up to 63% of fall RDM. Importantly, maximum and sum VIs values were significantly higher in management units with RDM levels in compliance with RDM conservation easement terms compared with units out of compliance. On the basis of these results, we propose a management model that uses time series analysis of MODIS VIs to predict forage quantities, manage stocking rates, and monitor rangeland easement compliance. This model can be used to improve monitoring of rangeland conservation by providing information on range conditions throughout the year.

Temporal variation in resource selection of African elephants follows long‐term variability in resource availability

“…Unlike NDVI, which mainly quantifies green vegetation biomass, FPAR measures the photosynthetic capacity of vegetation, which continues, to some degree, in dry vegetation as well (Butterfield and Malmstrom , Tsalyuk et al. ). Therefore, FPAR measures both green and dry biomass, and is strongly correlated with herbaceous vegetation (Tsalyuk et al.…”

“…, Tsalyuk et al. ). We acquired all MODIS data version V006 available for the years 2000–2014 from the online Data Pool, courtesy of the NASA Land Processes Distributed Active Archive Center (LP DAAC ).…”

“…) NDVI may be a poor predictor of vegetation structure of sparse dry savannas if based on single date or short‐term composite imagery alone (Tsalyuk et al. ). By using multiannual time series of four VIs we were able to create robust prediction models for each vegetation type with high variance explained and low error (Tsalyuk et al.…”

Remotely Sensed Water Limitation in Vegetation: Insights from an Experiment with Unmanned Aerial Vehicles (UAVs)

“…Enhanced Vegetation Index (EVI)) and vegetation products derived partly from vegetation indices derived from spectral products (e.g. Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR)) that make use of other bands in Landsat, Sentinel, and MODIS imagery [37,38], are adopted in regional and global applications [39][40][41]. These vegetation indices are more responsive than NDVI to variations in canopy structure, plant physiognomy, and canopy architecture.…”

Mapping Canada's Rangeland and Forage Resources using Earth Observation

“…Using multiple VIs derived from optical imagery has shown promise for monitoring rangeland condition (Hagen et al, 2012;Mundava et al, 2014;Tsalyuk et al, 2015;Smith et al, 2015) and…”