Using vegetation indices derived from conventional digital cameras as selection criteria for wheat breeding in water-limited environments

The ability to assess green biomass is of particular interest in a number of wheat breeding environments. However, the measurement of this and similar traits is either tedious and time-consuming or requires the use of expensive, sophisticated equipment, such as field-based spectroradiometers to measure vegetation indices (VIs). Here, conventional digital cameras are proposed as affordable and easy-to-use tools for gathering field data in wheat breeding programmes. Using appropriate software, a large set of images can be automatically processed to calculate a number of VIs, based on the performance of simple colour operations on each picture. The purpose of this study was to identify a set of picture-derived vegetation indices (picVIs) and to evaluate their performance in durum wheat trials growing under rainfed and supplementary irrigation conditions. Here, zenithal pictures of each plot were obtained roughly 2 weeks after anthesis, and the picVIs that were calculated were compared with the normalised difference vegetation index (NDVI), an index derived from spectroradiometrical measurements, and with the grain yield (GY) from the same plots. The picVIs that performed best were the Hue, CIE-Lab a* and CIE-Luv u* components of the average colour of each picture, the relative green area (GA) and the 'greener area', similar to GA but excluding the more yellowish-green pixels. Our results showed a high correlation between all these picVIs and the NDVI. Moreover, in rainfed conditions, each picVI provided an estimation of GY similar to or slightly better than that provided by the NDVI. However, in irrigated conditions during anthesis, neither these picVIs nor the NDVI provided a good estimation of GY, apparently because of the saturation of the VI response in conditions of complete soil cover and high plant density.     

1981—2018年新疆草地归一化植被指数时空特征及其对气候变化的响应

在新疆开展长时间序列的草地监测,分析草地生长的时空变化特征,有利于草地环境压力分析和草地生态健康预测。以NOAA-AVHRR NDVI为数据源,采用最大值合成、一元回归分析与相关性分析,分别在年际尺度和多个空间尺度(全疆、南北疆与各地区及其11种草地类型)上探讨了1981—2018年新疆草地归一化植被指数(NDVI)时空特征及其对气温、降水的响应。结果表明：(1)1981—2018年,新疆草地NDVI多年均值0.326,变化范围0.259—0.386,具有轻微年际波动特征;(2)北疆、南疆草地NDVI均表现为轻微增加趋势;全疆占草地总面积41%的区域NDVI呈显著增加趋势,9%为显著减少区域,北疆草地NDVI显著增加的面积是南疆的1.7倍;(3)由于垂直地带性及区域差异,新疆草地NDVI由山区向盆地的荒漠降低;北疆草地NDVI是南疆1.4倍,总体上北疆各地区草地NDVI高于南疆各地区;(4)草地类型植被NDVI对降水的显著响应高于气温,其中温性荒漠类、温性荒漠草原类与温性草原类草地NDVI对降水变化的响应明显高于其余草地类型,降水对草地NDVI的影响更为显著,表明降水引起的地表水分变化...     

Non-invasive monitoring of photosynthetic activity and water content in forest lichens by spectral reflectance data and RGB colors from photographs

There is a need for non-invasive monitoring of temporal and spatial variation in hydration and photosynthetic activity of red-listed poikilohydric autotrophs. Here, we simultaneously recorded kinetics in RGB-colors (photos), reflectance spectra, water content, maximal (F_V/F_M), and effective quantum yield of PSII (Φ_PSII) during desiccation in foliose lichens differing in cortical characteristics and photobionts. The spectral absorbance peaks of chlorophyll a, phycocyanin, and phycoerythrin were clearly displayed at high hydration levels. Brightness and total RGB colors of the lichens strongly increased during desiccation. The normalized difference vegetation index (NDVI) efficiently estimated hydration level and Φ_PSII – a proxy for lichen photosynthesis – in all species, including threatened old forest lichens. Color and reflectance indices based on green wavelengths gave good estimates of water content in cephalo- and chlorolichens, but not in cyanolichens with a wider range of photosynthetic pigments. Due to species-specific characteristics, species-wise calibration is essential for non-invasive assessments of lichen functioning.     

Edge effects on vegetation and soils in a Virginia old-field

We investigated the effect of proximity to forest edge on plant community structure and ecosystem properties during succession, using field measurements of leaf area index (LAI), species composition, and soil carbon. Data were collected along four transects within a 14-year-old temperate successional field in north-central Virginia over the 2000 growing season. Additionally, the normalized difference vegetation index (NDVI) was calculated from LANDSAT 7 satellite data at a resolution of 30 m for the entire field. Results showed that relative frequencies of trees increased with proximity to forest edge suggesting a more advanced stage of succession in areas close to the adjacent secondary forest. Significant negative relationships were observed between distance from forest edge and both peak season LAI and NDVI. LAI and NDVI values within 60 m of the forest edge, however, were not significantly different from those values in the adjacent mature secondary forest, suggesting that some community level properties may take relatively short periods of time to reach undisturbed states. The presence of several key plant species, particularly Celastrus scandens (climbing bittersweet), exhibited a strong control on the spatial variability of LAI and potentially the aboveground net primary production. Soil carbon levels did not show a significant increase at sites close to the adjacent secondary forest (relative to an adjacent crop field), as seen with LAI and NDVI, suggesting no recovery of soil carbon in these systems after 14 years. This study points to the complexity of factors that influence spatial patterns of succession in old-fields and suggests that invasive species may play an important role in successional pathways and carbon cycling.     

Univariate analysis of tsetse habitat in the common fly belt of Southern Africa using climate and remotely sensed vegetation data

Abstract Tsetse are vectors of trypanosomes that cause diseases both in humans and livestock. Traditional tsetse surveys, using sampling methods such as Epsilon traps and black screen fly rounds, are often logistically difficult, costly and time-consuming. The distribution of tsetse, as revealed by such survey methods, is strongly influenced by environmental conditions, such as climate and vegetation cover, which may be readily mapped using satellite data. These data may be used to make predictions of the probable distribution of tsetse in unsurveyed areas by determining the environmental characteristics of areas of tsetse presence and absence in surveyed areas. The same methods may also be used to characterize differences between tsetse species and subspecies. In this paper we analyse the distribution of Glossina morsitans centralis, Glossina morsitans morsitans and Glossina pallidipes in southern Africa with respect to single environmental variables. For G.m.centralis the best predictions were made using the average NDVI (75% correct predictions; range > 0.37) and the average of the maximum temperature (70% correct predictions; 27.0–29.2°C). For G.m.morsitans the best prediction was given by the maximum of the minimum temperature (84% correct predictions; range > 18.8°C), and for G.pallidipes , also by the maximum of the minimum temperature (86% correct predictions; range > 19.6 °C). The following paper compares a range of multivariate techniques for making predictions about the distribution of these species in the same region.     

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.     

Determining the growing season of land vegetation on the basis of plant phenology and satellite data in Northern China

The objectives of this study are to explore the relationships between plant phenology and satellite-sensor-derived measures of greenness, and to advance a new procedure for determining the growing season of land vegetation at the regional scale. Three phenological stations were selected as sample sites to represent different climatic zones and vegetation types in northern China. The mixed data set consists of occurrence dates of all observed phenophases for 50–70 kinds of trees and shrubs from 1983 to 1988. Using these data, we calculated the cumulative frequency of phenophases in every 5-day period (pentad) throughout each year, and also drew the cumulative frequency distribution curve for all station-years, in order to reveal the typical seasonal characteristics of these plant communities. The growing season was set as the time interval between 5% and 95% of the phenological cumulative frequency. Average lengths of the growing season varied between 188 days in the northern, to 259 days in the southern part of the research region. The beginning and end dates of the surface growing season were then applied each year as time thresholds, to determine the corresponding 10-day peak greenness values from normalized difference vegetation index curves for 8-km² pixels overlying the phenological stations. Our results show that, at the beginning of the growing season, the largest average greenness value occurs in the southern part, then in the northern, and finally the middle part of the research region. In contrast, at the end of the growing season, the largest average greenness value is measured in the northern part, next in the middle and lastly the southern part of the research region. In future studies, these derived NDVI thresholds can be applied to determine the growing season of similar plant communities at other sites, which lack surface phenological data. Received: 29 November 1999 / Revised: 14 March 2000 / Accepted: 15 March 2000     

定居放牧方式下归一化植被指数（NDVI）的空间变化特征

利用遥感数据,对内蒙古锡林河流域定居放牧方式下归一化植被指数（ＮＤＶＩ）的空间变化特征进行了分析,结果表明：１．定居放牧方式下,ＮＤＶＩ随定居点距离的变化格局经历了３个阶段。第一阶段,草场处于原生阶段,ＮＤＶＩ不随距离变化;第二阶段,定居点附近开始局部退化,ＮＤＶＩ随距离增加而增大;第三阶段,退化区域扩大,ＤＮＶＩ不随距离变化。２．在草场局部退化阶段,ＮＤＶＩ随距离的变化呈对数函数规律,定居点的放     

Interannual Variability in Terrestrial Net Primary Production: Exploration of Trends and Controls on Regional to Global Scales

Climate and biophysical regulation of terrestrial plant production and interannual responses to anomalous events were investigated using the NASA Ames model version of CASA (Carnegie–Ames–Stanford Approach) in a transient simulation mode. This ecosystem model has been calibrated for simulations driven by satellite vegetation index data from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) over the mid-1980s. Relatively large net source fluxes of carbon were estimated from terrestrial vegetation about 6 months to 1 year following El Niño events of 1983 and 1987, whereas the years 1984 and 1988 showed a drop in net primary production (NPP) of 1–2 Pg (10¹⁵ g) C from their respective previous years. Zonal discrimination of model results implies that the northern hemisphere low latitudes could account for almost the entire 2 Pg C decrease in global terrestrial NPP predicted from 1983 to 1984. Model estimates further suggest that from 1985 to 1988, the northern middle-latitude zone (between 30° and 60°N) was the principal region driving progressive increases in NPP, mainly by an expanded growing season moving toward the zonal latitude extremes. Comparative regional analysis of model controls on NPP reveals that although Normalized Difference Vegetation Index “greenness” can alone account for 30%–90% of the variation in NPP interannual anomalies, temperature or radiation loading can have a fairly significant 1-year lag effect on annual NPP at middle- to high-latitude zones, whereas rainfall amount and temperature drying effects may carry over with at least a 2-year lag time to influence NPP in semiarid tropical zones.     

Are responses of herbivores to environmental variability spatially consistent in alpine ecosystems?

Animal responses to global climate variation might be spatially inconsistent. This may arise from spatial variation in factors limiting populations' growth or from differences in the links between global climate patterns and ecologically relevant local climate variation. For example, the North Atlantic Oscillation (NAO) has a spatially consistent relation to temperature, but inconsistent spatial relation to snow depth in Scandinavia. Furthermore, there are multiple mechanistic ways by which climate may limit animal populations, involving both direct effects through thermoregulation and indirect pathways through trophic interactions. It is conceptually appealing to directly model the predicted mechanistic links. This includes the use of climate variables mimicking such interactions, for example, to use growing degree days (GDD) as a proxy for plant growth rather than average monthly temperature. Using a unique database of autumn body mass of 83331 domestic lambs from the period 1992–2007 in four alpine ranges in Norway, we demonstrate the utility of hierarchical, mechanistic path models fitted using a Bayesian approach to analyse explicitly predicted relationships among environmental variables and between lamb body mass and the environmental variables. We found large spatial variation in strength of responses of autumn lamb body mass to the NAO, to a proxy for plant growth in spring (the Normalized Difference Vegetation Index, NDVI) and effects even differed in direction to local summer climate. Average local temperature outperformed GDD as a predictor of the NDVI, whereas the NAO index in two areas outperformed local weather variables as a predictor of lamb body mass, despite the weaker mechanistic link. Our study highlights that spatial variation in strength of herbivore responses may arise from several processes. Furthermore, mechanistically more appealing measures do not always increase predictive power due to scale of measurement and since global measures may provide more relevant “weather packages” for larger scales.