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Rangeland fine fuel biomass is a key factor in determining fire spread and intensity, while the accuracy of biomass estimation is limited due to inherent heterogeneity in rangeland ecosystems. In this study, high spatial resolution (0.23 m) images were used to classify fuel types and predict rangeland fine fuel biomass in west Texas based on the random forest algorithm. Two biomass models, including one with the fuel type, original spectral bands, and vegetation indices as explanatory variables, and the other that contained a combination of the fuel type, original spectral bands, vegetation, and texture indices as explanatory variables, were assessed. Furthermore, the biomass models were also examined by upscaling the remote sensing images from high to medium (30 m) spatial resolution with the spectral curves derived from Landsat images. The fuel type map had an accuracy of more than 95%, and herbaceous fuel types were kept for estimating fine fuel biomass. The results showed that around 76% and 80% of biomass variances were explained by models without texture indices and with texture indices, respectively. The fuel type and the normalized difference vegetation index (NDVI) were two significant input variables influencing fine fuel biomass for both models and adding texture indices contributed to the improvement of model accuracy. An upscaling analysis for biomass estimation using medium spatial resolution imagery showed that approximately 60% of the variance in biomass was explained by the model. The addition of fractional vegetation cover improved the model performance by explaining an additional 5% of the variance in biomass estimation. These findings indicate that high spatial resolution images have the potential to effectively estimate rangeland fuel types and fine fuel biomass, which can be helpful for mapping the spatial distribution of fine fuels to aid in monitoring and fire management on rangelands. [ABSTRACT FROM AUTHOR]

Interacting effects of climate change and livestock grazing on semi-arid grassland ecosystems have not been well studied, especially on a long-term basis. This paper analyzes changes in plant community composition in relation to grazing intensity and climate change based on repeated monitoring along long-term grazing intensity gradients in three Mongolian ecological zones over 20 yr. We synthesized our findings into state-and-transition models of vegetation change, contributing to our understanding of ecological dynamics in relation to management and environmental change, and to the development of tools for resilience-based rangeland management. In the mountain steppe (MS), community composition was driven largely by climate, and transitions from one community to another were associated with climate change or combined climate and grazing effects. The MS experienced the largest number of long-term transitions (14 of 15 plots) over 20 yr. In the steppe (ST), grazing intensity was the strongest influence on community composition, but transitions between communities from the early 1990s to 2013 were most strongly correlated with climate change. Ten of the 15 ST plots transitioned to other communities over 20 yr. Community composition in the desert steppe (DS) was unrelated to either grazing intensity or climate change and only six of 15 plots transitioned permanently over 20 yr. The MS appears most vulnerable to climate-induced community change, as others have suggested. Some degraded ST communities are resilient to climate change, while ST communities on drier sites are vulnerable to grazing-induced community changes. Our findings illustrate the utility of state-and-transition models as a means to synthesize and depict plant community dynamics in relation to climate and management factors. These models identify communities that may be growing rarer or more common under the combined effects of climate change and grazing, and highlight species and communities that may be useful conservation targets or indicators of climate- or grazing-induced change. [ABSTRACT FROM AUTHOR]