Satellite microwave detection of boreal forest recovery from the extreme 2004 wildfires in Alaska and Canada

The rate of vegetation recovery from boreal wildfire influences terrestrial carbon cycle processes and climate feedbacks by affecting the surface energy budget and land‐atmosphere carbon exchange. Previous forest recovery assessments using satellite optical‐infrared normalized difference vegetation index (NDVI) and tower CO₂ eddy covariance techniques indicate rapid vegetation recovery within 5–10 years, but these techniques are not directly sensitive to changes in vegetation biomass. Alternatively, the vegetation optical depth (VOD) parameter from satellite passive microwave remote sensing can detect changes in canopy biomass structure and may provide a useful metric of post‐fire vegetation response to inform regional recovery assessments. We analyzed a multi‐year (2003–2010) satellite VOD record from the NASA AMSR‐E (Advanced Microwave Scanning Radiometer for EOS) sensor to estimate forest recovery trajectories for 14 large boreal fires from 2004 in Alaska and Canada. The VOD record indicated initial post‐fire canopy biomass recovery within 3–7 years, lagging NDVI recovery by 1–5 years. The VOD lag was attributed to slower non‐photosynthetic (woody) and photosynthetic (foliar) canopy biomass recovery, relative to the faster canopy greenness response indicated from the NDVI. The duration of VOD recovery to pre‐burn conditions was also directly proportional (P < 0.01) to satellite (moderate resolution imaging spectroradiometer) estimated tree cover loss used as a metric of fire severity. Our results indicate that vegetation biomass recovery from boreal fire disturbance is generally slower than reported from previous assessments based solely on satellite optical‐infrared remote sensing, while the VOD parameter enables more comprehensive assessments of boreal forest recovery.     

盖度与冠层水深对沉水植物水盾草光谱特性的影响

遥感技术可应用于大尺度实时监测沉水植物的分布与生长状况。然而沉水植物的光谱特征受其冠层在水下深度的影响,从而影响湖泊和河流中沉水植物的遥感影像解译与信息提取。应用地物光谱仪,通过野外原位测定和室外控制试验,实测了沉水植物水盾草(Cabomba caroliniana)群落冠层在水下不同深度的反射光谱,分析了冠层水深对水盾草反射光谱的影响,并建立了基于光谱反射率和冠层水深的水盾草群落盖度反演模型。研究结果表明(1)不同盖度的水盾草群落光谱反射率的基本特征主要体现在绿光和近红外波段;(2)水盾草群落的光谱反射率与冠层水深基本呈负相关,相同盖度水盾草群落的光谱反射率随冠层水深的增加而减小,在近红外波段尤其明显;(3)水盾草群落冠层水深越小,其盖度与光谱反射率的相关性越强,且水盾草群落盖度越大,其光谱反射率与冠层水深的相关性越显著;(4)水盾草光谱反射率与盖度相关的最佳波段在692—898 nm,与冠层水深相关最佳的波段在710 nm和806 nm附近;(5)在710 nm和806 nm处建立的结合冠层水深的修正模型,无论是回归方程决定系数(R2),还是水盾草群落盖度的反演精度都明显高于仅用光谱反射率反演盖度的简单模型,因此可有效减除冠层水深对反演精度的影响。本研究的结果可为遥感监测沉水植物的分布和动态变化,以及沉水植物生物物理参量反演提供科学依据。     

Extension of the growing season due to delayed autumn over mid and high latitudes in North America during 1982–2006

Aim We intend to characterize and understand the spatial and temporal patterns of vegetation phenology shifts in North America during the period 1982–2006. Location North America. Methods A piecewise logistic model is used to extract phenological metrics from a time‐series data set of the normalized difference vegetation index (NDVI). An extensive comparison between satellite‐derived phenological metrics and ground‐based phenology observations for 14,179 records of 73 plant species at 802 sites across North America is made to evaluate the information about phenology shifts obtained in this study. Results The spatial pattern of vegetation phenology shows a strong dependence on latitude but a substantial variation along the longitudinal gradient. A delayed dormancy onset date (0.551 days year^?1, P= 0.013) and an extended growing season length (0.683 days year^?1, P= 0.011) are found over the mid and high latitudes in North America during 1982–2006, while no significant trends in greenup onset are observed. The delayed dormancy onset date and extended growing season length are mainly found in the shrubland biome. An extensive validation indicates a strong robustness of the satellite‐derived phenology information. Main conclusions It is the delayed dormancy onset date, rather than an advanced greenup onset date, that has contributed to the prolonged length of the growing season over the mid and high latitudes in North America during recent decades. Shrublands contribute the most to the delayed dormancy onset date and the extended growing season length. This shift of vegetation phenology implies that vegetation activity in North America has been altered by climatic change, which may further affect ecosystem structure and function in the continent.     

Variability and evolution of global land surface phenology over the past three decades (1982–2012)

Monitoring land surface phenology (LSP) is important for understanding both the responses and feedbacks of ecosystems to the climate system, and for representing these accurately in terrestrial biosphere models. Moreover, by shedding light on phenological trends at a variety of scales, LSP provides the potential to fill the gap between traditional phenological (field) observations and the large‐scale view of global models. In this study, we review and evaluate the variability and evolution of satellite‐derived growing season length (GSL) globally and over the past three decades. We used the longest continuous record of Normalized Difference Vegetation Index data available to date at global scale to derive LSP metrics consistently over all vegetated land areas and for the period 1982–2012. We tested GSL, start‐ and end‐of‐season metrics (SOS and EOS, respectively) for linear trends as well as for significant trend shifts over the study period. We evaluated trends using global environmental stratification information in place of commonly used land cover maps to avoid circular findings. Our results confirmed an average lengthening of the growing season globally during 1982–2012 – averaging 0.22–0.34 days yr^?1, but with spatially heterogeneous trends. About 13–19% of global land areas displayed significant GSL change, and over 30% of trends occurred in the boreal/alpine biome of the Northern Hemisphere, which showed diverging GSL evolution over the past three decades. Within this biome, the ‘Cold and Mesic’ environmental zone appeared as an LSP change hotspot. We also examined the relative contribution of SOS and EOS to the overall changes, finding that EOS trends were generally stronger and more prevalent than SOS trends. These findings constitute a step towards the identification of large‐scale phenological drivers of vegetated land surfaces, necessary for improving phenological representation in terrestrial biosphere models.     

Comparing canopy metrics derived from terrestrial and airborne laser scanning in a Douglas-fir dominated forest stand

Accurate estimates of vegetation structure are important for a large number of applications including ecological modeling and carbon budgets. Light detection and ranging (LiDAR) measures the three-dimensional structure of vegetation using laser beams. Most LiDAR applications today rely on airborne platforms for data acquisitions, which typically record between 1 and 5 “discrete” returns for each outgoing laser pulse. Although airborne LiDAR allows sampling of canopy characteristics at stand and landscape level scales, this method is largely insensitive to below canopy biomass, such as understorey and trunk volumes, as these elements are often occluded by the upper parts of the crown, especially in denser canopies. As a supplement to airborne laser scanning (ALS), a number of recent studies used terrestrial laser scanning (TLS) for the biomass estimation in spatially confined areas. One such instrument is the Echidna^® Validation Instrument (EVI), which is configured to fully digitize the returned energy of an emitted laser pulse to establish a complete profile of the observed vegetation elements. In this study we assess and compare a number of canopy metrics derived from airborne and TLS. Three different experiments were conducted using discrete return ALS data and discrete and full waveform observations derived from the EVI. Although considerable differences were found in the return distribution of both systems, ALS and TLS were both able to accurately determine canopy height (Δ height < 2.5 m) and the vertical distribution of foliage and leaf area (0.86 > r ² > 0.90, p < 0.01). When using more spatially explicit approaches for modeling the biomass and volume throughout the stands, the differences between ALS and TLS observations were more distinct; however, predictable patterns exist based on sensor position and configuration.     

Terrestrial gross primary production: Using NIRV to scale from site to globe

Terrestrial photosynthesis is the largest and one of the most uncertain fluxes in the global carbon cycle. We find that near‐infrared reflectance of vegetation (NIR_V), a remotely sensed measure of canopy structure, accurately predicts photosynthesis at FLUXNET validation sites at monthly to annual timescales (R² = 0.68), without the need for difficult to acquire information about environmental factors that constrain photosynthesis at short timescales. Scaling the relationship between gross primary production (GPP) and NIR_V from FLUXNET eddy covariance sites, we estimate global annual terrestrial photosynthesis to be 147 Pg C/year (95% credible interval 131–163 Pg C/year), which falls between bottom‐up GPP estimates and the top‐down global constraint on GPP from oxygen isotopes. NIR_V‐derived estimates of GPP are systematically higher than existing bottom‐up estimates, especially throughout the midlatitudes. Progress in improving estimated GPP from NIR_V can come from improved cloud screening in satellite data and increased resolution of vegetation characteristics, especially details about plant photosynthetic pathway.     

Site-level evaluation of satellite-based global terrestrial gross primary production and net primary production monitoring

Operational monitoring of global terrestrial gross primary production (GPP) and net primary production (NPP) is now underway using imagery from the satellite‐borne Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Evaluation of MODIS GPP and NPP products will require site‐level studies across a range of biomes, with close attention to numerous scaling issues that must be addressed to link ground measurements to the satellite‐based carbon flux estimates. Here, we report results of a study aimed at evaluating MODIS NPP/GPP products at six sites varying widely in climate, land use, and vegetation physiognomy. Comparisons were made for twenty‐five 1 km² cells at each site, with 8‐day averages for GPP and an annual value for NPP. The validation data layers were made with a combination of ground measurements, relatively high resolution satellite data (Landsat Enhanced Thematic Mapper Plus at ～30 m resolution), and process‐based modeling. There was strong seasonality in the MODIS GPP at all sites, and mean NPP ranged from 80 g C m^?2 yr^?1 at an arctic tundra site to 550 g C m^?2 yr^?1 at a temperate deciduous forest site. There was not a consistent over‐ or underprediction of NPP across sites relative to the validation estimates. The closest agreements in NPP and GPP were at the temperate deciduous forest, arctic tundra, and boreal forest sites. There was moderate underestimation in the MODIS products at the agricultural field site, and strong overestimation at the desert grassland and at the dry coniferous forest sites. Analyses of specific inputs to the MODIS NPP/GPP algorithm – notably the fraction of photosynthetically active radiation absorbed by the vegetation canopy, the maximum light use efficiency (LUE), and the climate data – revealed the causes of the over‐ and underestimates. Suggestions for algorithm improvement include selectively altering values for maximum LUE (based on observations at eddy covariance flux towers) and parameters regulating autotrophic respiration.     

基于HJ1B和ALOS/PALSAR数据的森林地上生物量遥感估算

森林地上生物量的精确估算能够减小碳储量估算的不确定性。为了探寻一种有效地提高森林生物量估算精度的方法,探讨了基于遥感物理模型和经验统计模型估算山地森林地上生物量的方法。首先,基于Li-Strahler几何光学模型和多元前向模式(MFM)进行模型模拟,结合查找表算法(LUT)从多光谱图像HJ1B估算贺兰山研究区的森林地上生物量。其次,采用统计方法建立了2种回归模型:(1)多光谱图像HJ1B进行混合像元分解(SMA),并与雷达图像ALOS/PALSAR进行图像融合建立生物量回归模型;(2)雷达图像ALOS/PALSAR后向散射系数和实测生物量建立了生物量回归模型。用实测数据对3种算法估算结果进行精度验证。研究结果表明:采用几何光学模型和MFM算法估算的森林地上生物量精度最好(决定系数R2=0.61,均方根误差RMSE=8.33 t/hm2,P0.001),其估算地上生物量与实测值一致性较好,估算生物量精度略优于SMA估算的精度(R2=0.60,RMSE=9.417 t/hm2);ALOS/PALSAR多元回归估算的精度最差(R2=0.39,RMSE=14.89 t/hm2)。由此可见,采用几何光学模型和混合像元分解SMA适合估算森林地上生物量,利用这2种方法进行森林地上生物量遥感监测研究具有一定的应用潜力。     

叶冠尺度野鸭湖湿地植物群落含水量的高光谱估算模型

利用高光谱遥感技术定量估测野鸭湖湿地植被含水量,对于监测和诊断野鸭湖湿地植被的生理状况及生长趋势具有重要意义,也能够为高光谱遥感影像在野鸭湖湿地植被含水量诊断中的实际应用提供理论依据和技术支持.采用Field Spec 3野外高光谱辐射仪,获取了野鸭湖典型湿地植被冠层和叶片的光谱,并测定了对应的含水量.以上述实测数据为基础,首先以芦苇为例初步探明了不同含水量水平下典型湿地植被冠层和叶片光谱反射率的响应模式,然后采用相关性及单变量线性与非线性拟合分析技术,从冠层和叶片两种层次,对不同尺度下的含水量与“三边”参数及高光谱植被指数进行了分析拟合,并采用交叉检验中的3K-CV方法对估算模型进行了测试和检验,确立了不同尺度下野鸭湖湿地植被含水量的定量监测模型.结果表明:(1)随着含水量水平的增加,芦苇冠层与叶片光谱在可见光波段(350-760 nm)和红外波段(760-2500 nm)的反射率均呈逐渐降低趋势.(2)不同尺度含水量与选取的光谱特征参数整体上相关性较强,与“三边”参数基本上都呈极显著相关,相关系数最大达到0.906;与高光谱指数全部呈极显著相关,相关系数最小为0.455,最大达到0.919,并通过选取不同尺度上相关性最佳的光谱特征参数,分别基于“三边”参数和高光谱植被指数构建了不同尺度下的含水量估算模型.其中,冠层尺度下,黄边面积(SDy)与SRWI( Simple Ratio Water Index)的估算效果最好,估算模型分别为y=-9.462x2 -2.671x+0.608和y=0.219e1.010x;叶片尺度下,红边面积(SDr)与WI( Water Index)的估算效果最好,估算模型分别为y=0.562x+0.376和y=2.028x2 -0.476x-1.009.通过3K-CV的交叉验证,不同尺度下的含水量估算模型均取得了较为理想的预测精度,预测精度的最小值为94.92％,最大值为97.06％,表明估测模型具有较高的可靠性与普适性.(3)高光谱植被指数与含水量拟合方程的拟合度相对高于“三边”参数与之建立方程的拟合度,说明多波段组合的光谱特征参数更适合含水量的判别.     

柽柳灌丛关键物候参数多种植被指数遥感提取的适用性——基于CO2通量观测和Sentinel-2数据

本研究以额济纳绿洲四道桥超级站为研究区,结合2018—2019年涡度通量、气象数据和2017—2020年Sentinel-2遥感影像,分析通量塔总初级生产力(GPP)与环境因子的关系,评估12种遥感植被指数对柽柳灌丛长势模拟和关键物候参数提取的适用性。采用7参数双逻辑斯蒂函数(DL-7)+全局模型函数(GMF)拟合GPP和各植被指数生长曲线,并逐年提取生长季始期(SOS)、生长季峰期(POS)和生长季末期(EOS)3种关键物候参数。结果表明: 有效积温(GDD)和土壤含水量是影响柽柳灌丛物候动态的主要环境因子。与2018年相比,2019年由于气温较低,SOS前的积温累积速率较慢,柽柳灌丛需要更长时间的热量积累来进入生长季,从而导致2019年SOS比2018年晚。在SOS与POS之间,2018和2019年水热条件相似,但2019年POS比2018年晚8 d,可能是2019年SOS较晚所致。POS以后,2019年较高的GDD和较低的土壤含水量使柽柳灌丛遭受水分胁迫,导致其生长季后期时间缩短。标准化的Sentinel-2植被指数与10:00—14:00 GPP均值的线性回归结果表明,宽波段植被指数中的增强型植被指数和窄波段植被指数中的叶绿素红边指数、倒红边叶绿素指数、红边归一化植被指数(NDVI705)能够较好地反映与柽柳灌丛GPP具有较高的一致性。柽柳灌丛SOS和EOS的遥感提取结果表明,Sentinel-2窄波段植被指数比宽波段植被指数的准确性更高,尤其是修正叶绿素吸收反射率指数提取SOS最准确,MERIS陆地叶绿素指数提取EOS最准确;Sentinel-2宽波段植被指数提取POS的准确性更高,尤其是两波段增强型植被指数和植被近红外反射率指数最准确。综合所有物候参数来看,NDVI705综合表现最佳。     

Spatial patterns of diatom diversity and community structure in ancient Lake Ohrid

For effective lakes’ management, high-frequent water quality data on a synoptic scale are essential. The aim of this study is to test the suitability of the latest generation of satellite sensors to provide information on lake water quality parameters for the five largest Italian subalpine lakes. In situ data of phytoplankton composition, chlorophyll-a (chl-a) concentration and water reflectance were used in synergy with satellite observations to map some algal blooms in 2016. Chl-a concentration maps were derived from satellite data by applying a bio-optical model to satellite data, previously corrected for atmospheric effects. Results were compared with in situ data, showing good agreement. The shape and magnitude of water reflectance from different satellite data were consistent. Output chl-a concentration maps, show the distribution within each lake during blooming events, suggesting a synoptic view is required for these events monitoring. Maps show the dynamic of bloom events with concentration increasing from 2 up to 7 mg m^?3 and dropping again to initial value in less than 20 days. Latest generation sensors were shown to be valuable tools for lakes monitoring, thanks to frequent, free of charge data availability over long time periods.     

Aboveground biomass of naturally regenerated and replanted semi-tropical shrublands derived from aerial imagery

Rapid assessment of plant size and population densities is important for estimating biomass over large areas, but it has often been limited by methods requiring intensive labor and resources. In this study, we demonstrate how shrub biomass can be estimated from fine-grained aerial photographs for a large area (23,000 ha) located in the Lower Rio Grande Valley, Texas, USA. Over the past 30 years, refuge land management has included the replanting of native shrubs to promote the restoration of wildlife habitat and carbon sequestration. To assess shrub regrowth, we developed a method to estimate individual shrub canopy areas from digital aerial imagery that was used to calculate biomass from allometric equations. The accuracy of the automated delineation of individual canopies was 79 % when compared to that of hand-digitized shrub canopies. When applied to photographs across the refuge, we found higher shrub densities for older naturally regenerated sites (174 individuals ha^?1) compared to those of younger replanted sites (156 individuals ha^?1). In contrast, naturally regenerated sites had less biomass (3.43 Mg ha^?1) than replanted sites (4.78 Mg ha^?1) indicating that shrubland restored for habitat conservation has the potential to sequester more carbon in a shorter period. There was an inverse relationship between aridity and aboveground shrub biomass for replanted sites in the drier west (p < 0.05). We found a difference in predicted biomass among shrub species in replanted sites that was also associated with climate (p < 0.05). We conclude that the canopy of individual shrubs detected from remote sensing can be used to estimate and monitor vegetation biomass over large areas across environmental gradients.     

Climate,Topography, and Canopy Chemistry Exert Hierarchical Control Over Soil N Cycling in a Neotropical Lowland Forest

Nutrient availability varies substantially across lowland tropical forests and constrains their responses to global change. However, interactions among regional, landscape, and local controls of nutrient availability are poorly understood. In that context, we explored the effects of rainfall, topography, and canopy chemistry on nitrogen (N) cycling across the Osa Peninsula (Costa Rica). We sampled soils from catenas in regions receiving 3000 versus 5000 mm y^?1 rainfall. In both regions, we sampled catenas starting on narrow, knife-edged ridges, and in the less humid region we compared catenas starting on rapidly eroding knife-edged ridges to catenas with ridges consisting of slowly eroding terraces. On the stable terraces, we sampled soils from 0.25 ha plots with either high or low mean canopy N. In all sites, we measured metrics of long- (soil δ¹⁵N) and short-term (net nitrification, net N mineralization, and KCl-extractable N) N availability. Mean soil δ¹⁵N was elevated in the less humid region (3.8 ± 0.16 vs. 3.1 ± 0.14‰; P = 0.003). Within that region, mean δ¹⁵N was enriched by approximately 1‰ on stable terraces (5.3 ± 0.14‰) relative to nearby knife-edged ridges (4.0 ± 0.24‰; P < 0.001). Short-term N metrics did not vary with rainfall or topography (P > 0.05). By contrast, short-term soil N metrics differed under canopies with high versus low canopy N, but soil δ¹⁵N did not. These results illustrate the role of climate and topography as dominant drivers of long-term N cycling in the region, as well as the potential for canopy characteristics, which are likely determined by species composition in this system, to impose small-scale heterogeneity within those broader constraints. Overall, our work suggests the utility of a hierarchical framework for understanding how diverse drivers of nutrient status interact across space and time in tropical forests.     

Precipitation impacts on vegetation spring phenology on the Tibetan Plateau

The ongoing changes in vegetation spring phenology in temperate/cold regions are widely attributed to temperature. However, in arid/semiarid ecosystems, the correlation between spring temperature and phenology is much less clear. We test the hypothesis that precipitation plays an important role in the temperature dependency of phenology in arid/semiarid regions. We therefore investigated the influence of preseason precipitation on satellite‐derived estimates of starting date of vegetation growing season (SOS) across the Tibetan Plateau (TP). We observed two clear patterns linking precipitation to SOS. First, SOS is more sensitive to interannual variations in preseason precipitation in more arid than in wetter areas. Spatially, an increase in long‐term averaged preseason precipitation of 10 mm corresponds to a decrease in the precipitation sensitivity of SOS by about 0.01 day mm^?1. Second, SOS is more sensitive to variations in preseason temperature in wetter than in dryer areas of the plateau. A spatial increase in precipitation of 10 mm corresponds to an increase in temperature sensitivity of SOS of 0.25 day °C^?1 (0.25 day SOS advance per 1 °C temperature increase). Those two patterns indicate both direct and indirect impacts of precipitation on SOS on TP. This study suggests a balance between maximizing benefit from the limiting climatic resource and minimizing the risk imposed by other factors. In wetter areas, the lower risk of drought allows greater temperature sensitivity of SOS to maximize the thermal benefit, which is further supported by the weaker interannual partial correlation between growing degree days and preseason precipitation. In more arid areas, maximizing the benefit of water requires greater sensitivity of SOS to precipitation, with reduced sensitivity to temperature. This study highlights the impacts of precipitation on SOS in a large cold and arid/semiarid region and suggests that influences of water should be included in SOS module of terrestrial ecosystem models for drylands.     

Estimating Carbon Flux Phenology with Satellite-Derived Land Surface Phenology and Climate Drivers for Different Biomes: A Synthesis of AmeriFlux Observations

Carbon Flux Phenology (CFP) can affect the interannual variation in Net Ecosystem Exchange (NEE) of carbon between terrestrial ecosystems and the atmosphere. In this study, we proposed a methodology to estimate CFP metrics with satellite-derived Land Surface Phenology (LSP) metrics and climate drivers for 4 biomes (i.e., deciduous broadleaf forest, evergreen needleleaf forest, grasslands and croplands), using 159 site-years of NEE and climate data from 32 AmeriFlux sites and MODIS vegetation index time-series data. LSP metrics combined with optimal climate drivers can explain the variability in Start of Carbon Uptake (SCU) by more than 70% and End of Carbon Uptake (ECU) by more than 60%. The Root Mean Square Error (RMSE) of the estimations was within 8.5 days for both SCU and ECU. The estimation performance for this methodology was primarily dependent on the optimal combination of the LSP retrieval methods, the explanatory climate drivers, the biome types, and the specific CFP metric. This methodology has a potential for allowing extrapolation of CFP metrics for biomes with a distinct and detectable seasonal cycle over large areas, based on synoptic multi-temporal optical satellite data and climate data.     

Evaluating Efficacy of Landsat-Derived Environmental Covariates for Predicting Malaria Distribution in Rural Villages of Vhembe District,South Africa

Malaria in South Africa is still a problem despite existing efforts to eradicate the disease. In the Vhembe District Municipality, malaria prevalence is still high, with a mean incidence rate of 328.2 per 100,0000 persons/year. This study aimed at evaluating environmental covariates, such as vegetation moisture and vegetation greenness, associated with malaria vector distribution for better predictability towards rapid and efficient disease management and control. The 2005 malaria incidence data combined with Landsat 5 ETM were used in this study. A total of nine remotely sensed covariates were derived, while pseudo-absences in the ratio of 1:2 (presence/absence) were generated at buffer distances of 0.5–20 km from known presence locations. A stepwise logistic regression model was applied to analyse the spatial distribution of malaria in the area. A buffer distance of 10 km yielded the highest classification accuracy of 82% at a threshold of 0.9. This model was significant (ρ < 0.05) and yielded a deviance (D²) of 36%. The significantly positive relationship (ρ < 0.05) between the soil-adjusted vegetation index and malaria distribution at all buffer distances suggests that malaria vector (Anopheles arabiensis) prefer productive and greener vegetation. The significant negative relationship between water/moisture index (a₁ index) and malaria distribution in buffer distances of 0.5, 10, and 20 km suggest that malaria distribution increases with a decrease in shortwave reflectance signal. The study has shown that suitable habitats of malaria vectors are generally found within a radius of 10 km in semi-arid environments, and this insight can be useful to aid efforts aimed at putting in place evidence-based preventative measures against malaria infections. Furthermore, this result is important in understanding malaria dynamics under the current climate and environmental changes. The study has also demonstrated the use of Landsat data and the ability to extract environmental conditions which favour the distribution of malaria vector (An. arabiensis) such as the canopy moisture content in vegetation, which serves as a surrogate for rainfall.     

Soil organic carbon and nitrogen stocks in an age-sequence of poplar stands planted on marginal agricultural land in Northeast China

The capacity of a ground-based canopy sensor to detect stress-related parameters of cotton (Grossypium hirsutum) was investigated in a split-plot field experiment for two consecutive growing seasons in central Greece. Three levels of irrigation (22, 31 and 40 cm water) were the whole-plot factor and three rates of fertilizer (60, 110 or 160 kg N ha^?1) were the split-plot factor with three replications. Irrigation level was the major factor that explained variations in leaf isotopic composition (δ¹⁵N and δ¹³C) within growing seasons and cotton yield at harvest. The rate of fertilizer application did not have a significant effect on cotton yield because there was sufficient residual soil N to meet crop needs. Canopy NDVI was highly correlated to yield when cotton response to differential irrigation was detected. The obtained correlations between canopy reflectance and stress-related parameters (leaf N, δ¹⁵N and δ¹³C) and the stability of the relationship between NDVI and yield over two consecutive seasons indicated that ground-based remote sensing can be used to assess the level of water stress that occurred during the growing season. The application of this technology for in-field monitoring of water stress may prove valuable in semiarid regions where water is often the most limiting factor in crop production.     

The effects of water availability on root growth and morphology in an Amazon rainforest

This study examined how root growth and morphology were affected by variation in soil moisture at four Amazon rainforest sites with contrasting vegetation and soil types. Mean annual site root mass, length and surface area growth ranged between 3–7 t ha^?1, 2–4 km m^?2 and 8–12 m² m^?2 respectively. Mean site specific root length and surface area varied between 8–10 km kg^?1 and 24–34 m² kg^?1. Growth of root mass, length and surface area was lower when soil water was depleted (P?<?0.001) while specific root length and surface area showed the opposite pattern (P?<?0.001). These results indicate that changes in root length and surface area per unit mass, and pulses in root growth to exploit transient periods of high soil water availability may be important means for trees in this ecosystem to increase nutrient and water uptake under seasonal and longer-term drought conditions.     

A comparative study of satellite and ground-based phenology

Long time series of ground-based plant phenology, as well as more than two decades of satellite-derived phenological metrics, are currently available to assess the impacts of climate variability and trends on terrestrial vegetation. Traditional plant phenology provides very accurate information on individual plant species, but with limited spatial coverage. Satellite phenology allows monitoring of terrestrial vegetation on a global scale and provides an integrative view at the landscape level. Linking the strengths of both methodologies has high potential value for climate impact studies. We compared a multispecies index from ground-observed spring phases with two types (maximum slope and threshold approach) of satellite-derived start-of-season (SOS) metrics. We focus on Switzerland from 1982 to 2001 and show that temporal and spatial variability of the multispecies index correspond well with the satellite-derived metrics. All phenological metrics correlate with temperature anomalies as expected. The slope approach proved to deviate strongly from the temporal development of the ground observations as well as from the threshold-defined SOS satellite measure. The slope spring indicator is considered to indicate a different stage in vegetation development and is therefore less suited as a SOS parameter for comparative studies in relation to ground-observed phenology. Satellite-derived metrics are, however, very susceptible to snow cover, and it is suggested that this snow cover should be better accounted for by the use of newer satellite sensors.     

Soil microbial community structure in European forests in relation to forest type and atmospheric nitrogen deposition

The objective of the present study was to evaluate the combined effect of vegetation and N deposition on microbial community composition in forest soils. For this, microbial biomass and community structure were assessed by ester linked fatty acid methyl ester (EL-FAME) analyses for 12 European forest sites representing different forest types (coniferous/deciduous) and differing in annual N loads (2?C40 kg?N?ha^?1). Microbial community composition was affected by vegetation as indicated by a higher proportion of the marker for arbuscular mycorrhiza (AM) fungi??16:1 11???in deciduous forest soils (1.2%?C5.7% of total EL-FAMEs) compared to acidic coniferous forest soils (0.5%?C1.6%). The two pine forest sites investigated showed the highest proportion of fungi (up to 28% of total EL-FAMEs) and the lowest proportions of Gram-negative and Gram-positive bacteria of all study sites. Nitrogen deposition rates were highly correlated with the ratios of cyclopropyl fatty acids to their precursors (r?=?0.82; P?<?0.01) and of bacteria to fungi (r?=?0.71; P?<?0.05). The two sites with the highest N deposition (??32.3 kg?N?ha^?1a^?1) were depleted in the marker fatty acids for AM fungi and other fungi. Our findings suggest that vegetation has a pronounced effect on microbial community structure, but this effect is masked by high N inputs (>30 kg?N?ha^?1a^?1).