Humans have grazed on the Qinghai–Tibetan Plateau (QTP) for many thousands of years. In recent decades, the intensity of grazing has increased and several new management strategies have been put into place to address the resulting changes in rangeland condition. Effective management of grazing activities in this region requires understanding the impact of livestock grazing across the diverse array of alpine grassland ecosystems present in the QTP, but recent studies have identified a number of critical uncertainties in the ecological science that underlies these management principles. To address these uncertainties, we carried out a synthesis analysis of the effect of livestock grazing on 26 indicators of ecosystem structure and function based on 61 studies from 88 independent research sites within the QTP. Our synthesis results indicate that livestock grazing exerts complex controls on ecosystem structure and function, which vary according to local landscape characteristics. We found that grazing contributes to greater plant species diversity (Shannon–Wiener index, Simpson dominance index, and Pielou evenness index significantly increased 0.18, 0.05, and 0.03, respectively, due to grazing), but decreased aboveground biomass (47.15%), soil organic carbon (12.41%), soil total nitrogen (12.75%), and microbial biomass carbon (9.42%). Further, ecosystem function is controlled by interactions between grazing and other landscape characteristics such as elevation and mean annual temperature. The management regime currently in place in the QTP, which involves complete exclusion of grazing in some areas, can have variable effects on grassland health. Therefore, the complexity of these responses is an indication that livestock and grassland management may benefit from a more nuanced management regime than is currently utilized in the QTP.

Maps of forest biomass are important tools for managing natural resources and reporting terrestrial carbon stocks. Using the San Juan National Forest in Southwest Colorado as a case study, we evaluate regional biomass maps created using physical variables, spectral vegetation indices, and image textural analysis on Landsat TM imagery. We investigate eight gray level co-occurrence matrix based texture measures (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation) on four window sizes (3 × 3, 5 × 5, 7 × 7, 9 × 9) at four offsets ([1,0], [1,1], [0,1], [1,−1]) on four Landsat TM bands (2, 3, 4, and 5). The map with the highest prediction quality was created using three texture metrics calculated from Landsat Band 2 on a 3 × 3 window and an offset of [0,1]: entropy, mean and correlation; and one physical variable: slope. The correlation of predicted versus observed biomass values for our texture-based biomass map is r = 0.86, the Root Mean Square Error is 45.6 Mg∙ha⁻¹, and the Coefficient of Variation of the Root Mean Square Error is 0.31. We find that models including image texture variables are more strongly correlated with biomass than models using only physical and spectral variables. Additionally, we suggest that the use of texture appears to better capture the magnitude and direction of biomass change following disturbance compared to spectral approaches. The biomass mapping methods we present here are widely applicable throughout the US, as they are based on publically available datasets and utilize relatively simple analytical routines.