Temporal and spatial changes in a mountainous area of central Italy

Abstract. In this paper we present an application of aerial remote sensing to the analysis of spatial information in a mountainous area of central Italy by applying texture measures and landscape indices. Land cover data acquired in different time periods are used to calculate measures of landscape pattern and structure at pixel level (tone and texture variables) and patch‐level (landscape indices). Images of the patches from the 1950s, 1980s and 1990s have been derived from the tone‐texture classification of scanned black‐and‐white photographs. Multitemporal analysis of landscape indices has been performed to detect changes of landscape elements and related effects on vegetation dynamics due to the reduction of human impact.     

Environmental controls of NDVI and sheep production in the Tierra del Fuego steppe of Argentina

Abstract. We analysed vegetation dynamics in Tierra del Fuego steppes using Normalized Difference Vegetation Index (NDVI) data provided by advanced very high‐resolution radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration (NOAA) polar satellite. Our objective, at a regional scale, was to analyse the spatial variability of NDVI dynamics in relation to parent material and geographic location, representing the fertility and climate gradients respectively; at a local scale, it was to analyse the inter‐annual variability associated with climate and its relation with sheep production indices. The general pattern of NDVI dynamics was analysed with Principal Component Analysis. We found that the geographic location was more important than landscape type in explaining NDVI dynamics despite the fact that the variation in landscape type reflects a fertility gradient strongly associated with floristic composition and secondary productivity. Discriminant Analysis was performed to identify the variables that better distinguish geographic units. The Northern region (with the lowest precipitation and the highest temperatures) had lower NDVI values over the year. In the Central region, NDVI reached the highest value of the season, surpassing both other regions. The Southern region (the coldest and moistest) had its growth pattern displaced towards the summer. For the Central region we analysed 10 years of monthly NDVI data with PCA. We found that precipitation from August to December and winter temperature are the most important determinants of overall NDVI values. Lamb production was correlated with spring and early summer NDVI values. Sheep mortality is affected by low NDVI values in late summer and high annual amplitude. Satellite information allowed us to characterize the vegetation dynamics of three ecological areas across the Fuegian steppe.     

植被净第一性生产力研究回顾与发展趋势

植被的净第一性生产力(NPP)反映了植物每年通过光合作用所固定的碳总量,目前NPP研究是全球变化研究的重要内容之一。本文回顾国内外植被NPP研究的历史,对遥感和地理信息系统(GIS)在NPP研究中的应用做了分析,并对近期陆地植被NPP研究的特点及发展趋势作了总结。     

Forecasting sagebrush ecosystem components and greater sage-grouse habitat for 2050: Learning from past climate patterns and Landsat imagery to predict the future

Sagebrush (Artemisia spp.) ecosystems constitute the largest single North American shrub ecosystem and provide vital ecological, hydrological, biological, agricultural, and recreational ecosystem services. Disturbances have altered and reduced this ecosystem historically, but climate change may ultimately represent the greatest future risk. Improved ways to quantify, monitor, and predict climate-driven gradual change in this ecosystem is vital to its future management. We examined the annual change of Daymet precipitation (daily gridded climate data) and five remote sensing ecosystem sagebrush vegetation and soil components (bare ground, herbaceous, litter, sagebrush, and shrub) from 1984 to 2011 in southwestern Wyoming. Bare ground displayed an increasing trend in abundance over time, and herbaceous, litter, shrub, and sagebrush showed a decreasing trend. Total precipitation amounts show a downward trend during the same period. We established statistically significant correlations between each sagebrush component and historical precipitation records using a simple least squares linear regression. Using the historical relationship between sagebrush component abundance and precipitation in a linear model, we forecasted the abundance of the sagebrush components in 2050 using Intergovernmental Panel on Climate Change (IPCC) precipitation scenarios A1B and A2. Bare ground was the only component that increased under both future scenarios, with a net increase of 48.98 km² (1.1%) across the study area under the A1B scenario and 41.15 km² (0.9%) under the A2 scenario. The remaining components decreased under both future scenarios: litter had the highest net reductions with 49.82 km² (4.1%) under A1B and 50.8 km² (4.2%) under A2, and herbaceous had the smallest net reductions with 39.95 km² (3.8%) under A1B and 40.59 km² (3.3%) under A2. We applied the 2050 forecast sagebrush component values to contemporary (circa 2006) greater sage-grouse (Centrocercus urophasianus) habitat models to evaluate the effects of potential climate-induced habitat change. Under the 2050 IPCC A1B scenario, 11.6% of currently identified nesting habitat was lost, and 0.002% of new potential habitat was gained, with 4% of summer habitat lost and 0.039% gained. Our results demonstrate the successful ability of remote sensing based sagebrush components, when coupled with precipitation, to forecast future component response using IPCC precipitation scenarios. Our approach also enables future quantification of greater sage-grouse habitat under different precipitation scenarios, and provides additional capability to identify regional precipitation influence on sagebrush component response.     

Harmonization of the Land Cover Classification System (LCCS) with the General Habitat Categories (GHC) classification system

Monitoring land cover and habitat change is a key issue for conservation managers because of its potential negative impact on biodiversity. The Land Cover Classification System (LCCS) and the General Habitat Categories (GHC) System have been proposed by the remote sensing and ecological research community, respectively, for the classification of land covers and habitats across various scales. Linking the two systems can be a major step forward towards biodiversity monitoring using remote sensing. The translation between the two systems has proved to be challenging, largely because of differences in definitions and related difficulties in creating one-to-one relationships between the two systems. This paper proposes a system of rules for linking the two systems and additionally identifies requirements for site-specific contextual and environmental information to enable the translation. As an illustration, the LCCS classification of the Le Cesine protected area in Italy is used to show rules for translating the LCCS classes to GHCs. This study demonstrates the benefits of a translation system for biodiversity monitoring using remote sensing data but also shows that a successful translation is often depending on the degree of ecological knowledge of the habitats and its relationship with land cover and contextual information.     

Remote sensing of the xanthophyll cycle and chlorophyll fluorescence in sunflower leaves and canopies

Summary Sudden illumination of sunflower (Helianthus annuus L. cv. CGL 208) leaves and canopies led to excess absorbed PFD and induced apparent reflectance changes in the green, red and near-infrared detectable with a remote spectroradiometer. The green shift, centered near 531 nm, was caused by reflectance changes associated with the de-epoxidation of violaxanthin to zeaxanthin via antheraxanthin and with the chloroplast thylakoid pH gradient. The red (685 nm) and near-infrared (738 nm) signals were due to quenching of chlorophyll fluorescence. Remote sensing of shifts in these spectral regions provides non-destructive information on in situ photosynthetic performance and could lead to improved techniques for remote sensing of canopy photosynthesis.CIW Publication #1072     

区域防护林体系总体热效应的遥感分析

本文利用NOAA/AVHRR卫星资料分析了章古台地区不同生境的反射率及地表温度,并研究了防护林体系对辐射平衡和表面温度的影响。结果表明,固沙造林可使辐射平衡由占总辐射的46％提高到57％,白天地面有效辐射由占总辐射的35％减小到29％,表面温度日较差由13.0—14.0℃减小到2.0—3.5℃。     

Safety and feasibility of atrial fibrillation ablation using Amigo® system versus manual approach: A pilot study

Background

The Amigo^® Remote Catheter System is a relatively new robotic system for catheter navigation. This study compared feasibility and safety using Amigo (RCM) versus manual catheter manipulation (MCM) to treat paroxysmal atrial fibrillation (PAF). Contact force (CF) and force-time integral (FTI) values obtained during pulmonary vein isolation (PVI) ablation were compared.

Space remote sensing for spatial vegetation characterization

The study area, Madhav National Park (MP) represents northern tropical dry deciduous forest. The national park, due to its unique location (nearest to township), is under tremendous biotic pressure. In order to understand vegetation structure and dynamics, vegetation mapping at community level was considered important. Prolonged leafless period and background reflection due to open canopy poses challenge in interpretation of satellite data. The vegetation of Madhav National Park was mapped using Landsat TM data. The ground data collected from sample points were subjected to TWINSPAN analysis to cluster sample point data into six communities. The vegetation classification obtained by interpretation (visual and digital) of remote sensing data and TWINSPAN were compared to validate the vegetation classification at community level. The phytosociological data collected from sample points were analysed to characterize communities. The results indicate that structural variations in the communities modulate spectral signatures of vegetation and form basis to describe community structure subjectively and at spatial level.