Over the past decades, a continuous rise in global air temperatures resulted in significant changes in the global hydrological cycle. Regionally increased frequencies of extreme weather events and changes in the regional extent of drylands resulted in new areas at risk of desertification, a complex process driven by socio-economic and climate-related factors. Although desertification is not confined to drylands, they are the most vulnerable to land degradation processes. To investigate possible changes in climate patterns over the past 60 years, we couple the information obtained from the Koppen–Geiger (KG) climate classification and the FAO aridity index (AI), providing an overview of the most evident global changes in climate regimes from 1951–1980 to 1981–2010 and focussing on the modifications of the extent of drylands. KG and AI indicators have been computed on a 0.5° × 0.5° global grid using precipitation data from the Full Data Reanalysis (v6.0) of the Global Precipitation Climatology Centre, and mean temperature and potential evapotranspiration data from the Climate Research Unit of the University of East Anglia (CRUTSv3.20). Both KG and AI show that the arid areas globally increased between 1951–1980 and 1981–2010, but decreased on average in the Americas. North-Eastern Brazil, Southern Argentina, the Sahel, Zambia and Zimbabwe, the Mediterranean area, North-Eastern China and Sub-Himalayan India have been identified as areas with a significant increase of drylands extent. An analysis of the scientific literature gives evidence that most of the areas identified are effectively undergoing desertification, thus confirming the validity of AI and KG to highlight the areas under risk of desertification. We also discuss the global decrease of cold areas, the progressive change from continental to temperate climate in Central Europe, the shift from tundra to continental climate in Alaska, Canada and North-Eastern Russia and the widening of the tropical belt.

Towards identifying areas at climatological risk of desertification using the Köppen–Geiger classification and FAO aridity index

Grassland biomass is essential for maintaining grassland ecosystems. Moreover, biomass is an important characteristic of grassland. In this study, we combined field sampling with remote sensing data and calculated five vegetation indices (VIs). Using this combined information, we quantified a remote sensing estimation model and estimated biomass in a temperate grassland of northern China. We also explored the dynamic spatio-temporal variation of biomass from 2006 to 2012. Our results indicated that all VIs investigated in the study were strongly correlated with biomass (α < 0.01). The precision of the model for estimating biomass based on ground data and remote sensing was greater than 73%. Additionally, the results of our analysis indicated that the annual average biomass was 11.86 million tons and that the average yield was 604.5 kg/ha. The distribution of biomass exhibited substantial spatial heterogeneity, and the biomass decreased from the eastern portion of the study area to the western portion. The interannual biomass exhibited strong fluctuations during 2006–2012, with a coefficient of variation of 26.95%. The coefficient of variation of biomass differed among the grassland types. The highest coefficient of variation was found for the desert steppe, followed by the typical steppe and the meadow steppe.

Remote Sensing-Based Biomass Estimation and Its Spatio-Temporal Variations in Temperate Grassland, Northern China

The mountain systems of the Hindu Kush Himalaya (HKH) are changing rapidly due to climatic change, but an overlooked component is the subnival ecosystem (between the treeline and snow line), characterized by short-stature plants and seasonal snow. Basic information about subnival vegetation distribution and rates of ecosystem change are not known, yet such information is needed to understand relationships between subnival ecology and water/carbon cycles. We show that HKH subnival ecosystems cover five to 15 times the area of permanent glaciers and snow, highlighting their eco-hydrological importance. Using satellite data from the Landsat 5, 7 and 8 missions, we measured change in the spatial extent of subnival vegetation from 1993 to 2018. The Landsat surface reflectance-derived Normalized Difference Vegetation Index product was thresholded at 0.1 to indicate the presence/absence of vegetation. Using this product, the strength and direction of time-series trends in the green pixel fraction were measured within three regions of interest. We controlled for cloud cover, snow cover and evaluated the impact of sensor radiometric differences between Landsat 7 and Landsat 8. Using Google Earth Engine to expedite data processing tasks, we show that there has been a weakly positive increase in the extent of subnival vegetation since 1993. Strongest and most significant trends were found in the height region of 5,000-5,500 m a.s.l. across the HKH extent: R2  = .302, Kendall's τ = 0.424, p < .05, but this varied regionally, with height, and according to the sensors included in the time series. Positive trends at lower elevations occurred on steeper slopes whilst at higher elevations, flatter areas exhibited stronger trends. We validated our findings using online photographs. Subnival ecological changes have likely impacted HKH carbon and water cycles with impacts on millions of people living downstream, but the strength and direction of impacts of vegetation expansion remain unknown.

https://onlinelibrary.wiley.com/doi/epdf/10.1111/gcb.14919

Vegetation expansion in the subnival Hindu Kush Himalaya.

Fil: Gaitan, Juan J.. Instituto Nacional de Tecnologia Agropecuaria. Centro Regional Patagonia Norte. Estacion Experimental Agropecuaria San Carlos de Bariloche; Argentina

/pdf/vegetation-structure-is-as-important-as-climate-for-duf8uz89iy.pdf

Vegetation structure is as important as climate for explaining ecosystem function across Patagonian rangelands

https://opus.lib.uts.edu.au/bitstream/10453/114836/5/Manuscript_EI_20160611.pdf

Assessing the ability of MODIS EVI to estimate terrestrial ecosystem gross primary production of multiple land cover types

https://maestrelab.com/wp-content/uploads/2015/10/EcolInd2013.pdf

Evaluating the performance of multiple remote sensing indices to predict the spatial variability of ecosystem structure and functioning in Patagonian steppes

Percutaneous Radiofrequency Ablation to Occlude the Thoracic Duct: Preclinical Studies in Swine for a Potential Alternative to Embolization.

Abstract No. 43 Percutaneous radiofrequency ablation to occlude the thoracic duct: preclinical studies in swine for a potential alternative to embolization

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Evaluating the performance of multiple remote sensing indices to predict the spatial variability of ecosystem structure and functioning in Patagonian steppes

Percutaneous Radiofrequency Ablation to Occlude the Thoracic Duct: Preclinical Studies in Swine for a Potential Alternative to Embolization.

Abstract No. 43 Percutaneous radiofrequency ablation to occlude the thoracic duct: preclinical studies in swine for a potential alternative to embolization