Hyperspectral remote sensing for estimating aboveground biomass and for exploring species richness patterns of grassland habitats

Dry grassland sites are amongst the most species-rich habitats of central Europe and it is necessary to design effective management schemes for monitoring of their biomass production. This study explored the potential of hyperspectral remote sensing for mapping aboveground biomass in grassland habitats along a dry-mesic gradient, independent of a specific type or phenological period. Statistical models were developed between biomass samples and spectral reflectance collected with a field spectroradiometer, and it was further investigated to what degree the calibrated biomass models could be scaled to Hyperion data. Furthermore, biomass prediction was used as a surrogate for productivity for grassland habitats and the relationship between biomass and plant species richness was explored. Grassland samples were collected at four time steps during the growing season to capture normally occurring variation due to canopy growth stage and management factors. The relationships were investigated between biomass and (1) existing broad- and narrowband vegetation indices, (2) narrowband normalized difference vegetation index (NDVI) type indices, and (3) multiple linear regression (MLR) with individual spectral bands. Best models were obtained from the MLR and narrowband NDVI-type indices. Spectral regions related to plant water content were identified as the best estimators of biomass. Models calibrated with narrowband NDVI indices were best for up-scaling the field-developed models to the Hyperion scene. Furthermore, promising results were obtained from linking biomass estimations from the Hyperion scene with plant species richness of grassland habitats. Overall, it is concluded that ratio-based NDVI-type indices are less prone to scaling errors and thus offer higher potential for mapping grassland biomass using hyperspectral data from space-borne sensors.     

Combining ground-based measurements and MODIS-based spectral vegetation indices to track biomass accumulation in post-fire chaparral

Multi-temporal satellite imagery can provide valuable information on the patterns of vegetation growth over large spatial extents and long time periods, but corresponding ground-referenced biomass information is often difficult to acquire, especially at an annual scale. In this study, we test the relationship between annual biomass estimated using shrub growth rings and metrics of seasonal growth derived from Moderate Resolution Imaging Spectroradiometer (MODIS) spectral vegetation indices (SVIs) for a small area of southern California chaparral to evaluate the potential for mapping biomass at larger spatial extents. These SVIs are related to the fraction of photosynthetically active radiation absorbed by the plant canopy, which varies throughout the growing season and is correlated with net primary productivity. The site had most recently burned in 2002, and annual biomass accumulation measurements were available from years 5 to 11 post-fire. We tested the metrics of seasonal growth using six SVIs: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), soil adjusted vegetation index (SAVI), normalized difference water index (NDWI), normalized difference infrared index 6 (NDII6), and vegetation atmospherically resistant index (VARI). Several of the seasonal growth metrics/SVI combinations exhibit a very strong relationship with annual biomass, and all SVIs show a strong relationship with annual biomass (R² for base value time series metric ranging from 0.45 to 0.89). Although additional research is required to determine which of these metrics and SVIs are the most promising over larger spatial extents, this approach shows potential for mapping early post-fire biomass accumulation in chaparral at regional scales.     

Suivi du développement végétal au cours de l'été 1984 dans le Sahel Malien

A ground data-collection programme was initiated to establish a calibration between the normalized difference vegetation index (NDVI) from the NOAA Advanced Very High Resolution Radiometer (AVHRR) and grassland biomass. Thirty sites were selected representing a range of Sahclian vegetation communities in the Gourma region of Mali and monitored during the 1984 growing season. The sites were 1?km square and located within larger areas of homogeneous terrain. The herbaceous and woody strata were sampled every fourteen days, and above-ground green biomass and rainfall data were collected. Ground and airborne radiometer data were recorded to facilitate interpretation of the satellite data, and aerial photographs were taken to provide estimates of tree and shrub density. AVHRR LAC and GAC data were acquired and a thermal cloud mask was applied to the data. NDVI values were extracted for the ground sites and correlation analysis performed. Low correlation coefficients were calculated for the ground measured green biomass and satellite NDVI (0,67). The correlation between the maximum NDVI and the total biomass produced during the season was 0,73. A value of 0,05 was determined as the NDVI associated with the minimum vegetation cover identifiable by the satellite (100 kg/ha). Explanation is given for the possible causes for such low correlations, including the very low biomass production associated with the 1984 drought conditions, atmospheric haze and dust and poor locational accuracy of the satellite data     

Quantifying livestock effects on bunchgrass vegetation with Landsat ETM+ data across a single growing season

Grassland systems provide important habitat for native biodiversity and forage for livestock, with livestock grazing playing an important role influencing sustainable ecosystem function. Traditional field techniques to monitor the effects of grazing on vegetation are costly and limited to small spatial scales. Remote sensing has the potential to provide quantitative and repeatable monitoring data across large spatial and temporal scales for more informed grazing management. To investigate the ability of vegetation metrics derived from remotely sensed imagery to detect the effect of cattle grazing on bunchgrass grassland vegetation across a growing season, we sampled 32 sites across four prescribed stocking rates on a section of Pacific Northwest bunchgrass prairie in northeastern Oregon. We collected vegetation data on vertical structure, biomass, and cover at three different time periods: June, August, and October 2012 to understand the potential to measure vegetation at different phenological stages across a growing season. We acquired remotely sensed Landsat Enhanced Thematic Mapper Plus (ETM+) data closest in date to three field sampling bouts. We correlated the field vegetation metrics to Landsat spectral bands, 14 commonly used vegetation indices, and the tasselled cap wetness, brightness, and greenness transformations. To increase the explanatory value of the satellite-derived data, full, stepwise, and best-subset multiple regression models were fit to each of the vegetation metrics at the three different times of the year. Predicted vegetation metrics were then mapped across the study area. Field-based results indicated that as the stocking rate increased, the mean vegetation amounts of vertical structure, cover, and biomass decreased. The multiple regression models using common vegetation indices had the ability to discern different levels of grazing across the study area, but different spectral indices proved to be the best predictors of vegetation metrics for differing phenological windows. Field measures of vegetation cover yielded the highest correlations to remotely sensed data across all sampling periods. Our results from this analysis can be used to improve grassland monitoring by providing multiple measures of vegetation amounts across a growing season that better align with land management decision making.     

Using earth observation-based dry season NDVI trends for assessment of changes in tree cover in the Sahel

The co-existence of trees and grasses is a defining feature of savannah ecosystems and landscapes. During recent decades, the combined effect of climate change and increased demographic pressure has led to complex vegetation changes in these ecosystems. A number of recent Earth observation (EO)-based studies reported positive changes in biological productivity in the Sahelian region in relation to increased precipitation, triggering an increased amount of herbaceous vegetation during the rainy season. However, this ‘greening of the Sahel’ may mask changes in the tree–grass composition, with a potential reduction in tree cover having important implications for the Sahelian population. Large-scale EO-based evaluation of changes in Sahelian tree cover is assessed by analysing long-term trends in dry season minimum normalized difference vegetation index (NDVI_min) derived from three different satellite sensors: Système Pour l’Observation de la Terre (SPOT)-VEGETATION (VGT), Terra Moderate Resolution Imaging Spectroradiometer (MODIS), and the Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) dataset. To evaluate the reliability of using NDVI_min as a proxy for tree cover in the Sahel, two factors that could potentially influence dry season NDVI_min estimates were analysed: the total biomass accumulated during the preceding growing season and the percentage of burned area observed during the dry season. Time series of dry season NDVI_min derived from low-resolution satellite time series were found to be uncorrelated to dry grass residues from the preceding growing season and to seasonal fire frequency and timing over most of the Sahel (88%), suggesting that NDVI_min can serve as a proxy for assessing changes in tree cover. Good agreement (R² = 0.79) between significant NDVI_min trends (p < 0.05) derived from VGT and MODIS was found. Significant positive trends in NDVI_min were registered by both MODIS and VGT dry season NDVI_min time series over the Western Sahel, whereas trends based on GIMMS data were negative for the greater part of the Sahel. EO-based trends were generally not confirmed at the local scale based on selected study cases, partly caused by a temporal mismatch between data sets (i.e. different periods of analysis). Analysis of desert area NDVI_min trends indicates less stable values for VGT and GIMMS data as compared with MODIS. This suggests that trends in dry season NDVI_min derived from VGT and GIMMS should be used with caution as an indicator for changes in tree cover, whereas the MODIS data stream shows a better potential for tree-cover change analysis in the Sahel.     

On the relationship between monthly mean and maximum-value composite normalized vegetation indices

Abstract

A relationship between the maximum-value composite and monthly mean normalized difference vegetation index (NDVI) is derived statistically using data over the U.S. Great Plains during 1986. The monthly mean NDVI is obtained using a simple nine-day compositing technique based on the specifics of the scan patterns of the NOAA-9 Advanced Very High Resolution Radiometer (AVHRR). The results indicate that these two quantities are closely related over grassland and forest during the growing season. It is suggested that in such areas a monthly mean NDVI can be roughly approximated by 80 per cent of the monthly maximum NDVI, the latter being a standard satellite data product. The derived relationship was validated using data for the growing season of 1987.     

A spatial regression procedure for evaluating the relationship between AVHRR-NDVI and climate in the northern Great Plains

The relationship between vegetation and climate in the grassland and cropland of the northern US Great Plains was investigated with Normalized Difference Vegetation Index (NDVI) (1989–1993) images derived from the Advanced Very High Resolution Radiometer (AVHRR), and climate data from automated weather stations. The relationship was quantified using a spatial regression technique that adjusts for spatial autocorrelation inherent in these data. Conventional regression techniques used frequently in previous studies are not adequate, because they are based on the assumption of independent observations. Six climate variables during the growing season; precipitation, potential evapotranspiration, daily maximum and minimum air temperature, soil temperature, solar irradiation were regressed on NDVI derived from a 10-km weather station buffer. The regression model identified precipitation and potential evapotranspiration as the most significant climatic variables, indicating that the water balance is the most important factor controlling vegetation condition at an annual timescale. The model indicates that 46% and 24% of variation in NDVI is accounted for by climate in grassland and cropland, respectively, indicating that grassland vegetation has a more pronounced response to climate variation than cropland. Other factors contributing to NDVI variation include environmental factors (soil, groundwater and terrain), human manipulation of crops, and sensor variation.     

A ground spectral model for estimating biomass at the peak of the growing season in Hulunbeier grassland,Inner Mongolia,China

To investigate the application of hyperspectral remote sensing to estimate grassland biomass at the peak of the growing season, hyperspectral data were measured with an analytical spectral device (ASD) Fieldspec3 spectroradiometer, and harvested aboveground net primary productivity (ANPP) was recorded simultaneously in Hulunbeier grassland, Inner Mongolia, China. Ground spectral models were developed to estimate ANPP from the normalized difference vegetation index (NDVI) measured in the field following the same method as that of the National Aeronautic and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS-NDVI). Regression analysis was used to assess the relationship between ANPP and NDVI. Based on coefficients of determination (R ²) and error analysis, we determined that each vegetation type and the entire study area had unique optimal regression models. A linear equation best fit the arid steppe data, an exponential equation was best suited to wetland vegetation and power equations were optimal for meadow steppe and sand vegetation. After considering all factors, an exponential model between ANPP and NDVI (ANPP = 20.1921e^3.2154(NDVI); standard error (SE) = 62.50 g m^–2, R ² = 0.7445, p < 0.001) was selected for the entire Hulunbeier grassland study area. Ground spectral models could become the foundation for yield estimation over large areas of Hulunbeier grassland.     

Estimating grassland biomass and leaf area index using ground and satellite data

We compared estimates of regional biomass and LAI for a tallgrass prairie site derived from ground data versus estimates derived from satellite data. Linear regression models were estimated to predict LAI and biomass from Landsat-TM data for imagery acquired on three dates spanning the growing season of 1987 using co-registered TM data and ground measurements of LAl and biomass collected at 27 grassland sites. Mapped terrain variables including burning treatment, land-use, and topographic position were included as indicator variables in the models to acccount for variance in biomass and LAI not captured in the TM data. Our results show important differences in the relationships between Kauth-Thomas greenness (from TM), LAI, biomass and the various terrain variables. In general, site-wide estimates of biomass and LAI derived from ground versus satellite-based data were comparable. However, substantial differences were observed in June. In a number of cases, the regression models exhibited significantly higher explained variance due to the incorporation of terrain variables, suggesting that for areas encompassing heterogeneous landcover the inclusion of categorical terrain data in calibration procedures is a useful technique.     

基于无人机影像可见光植被指数的若尔盖草地地上生物量估算研究

地上生物量是衡量草地长势及生态系统服务功能的重要参数,对于草地生态系统碳收支、资源可持续开发等研究具有重要意义。研究基于若尔盖高原典型样带的无人机可见光影像和地面实测样本,建立生物量与多种可见光植被指数的指数回归模型,对比不同植被指数模型的生物量估算精度的差异。结果表明:可见光植被指数能够有效区分草地和其他覆盖类型,生物量与植被指数具有较好的相关关系。但基于不同波段建立的植被指数对生物量的估算精度存在差异,其中利用红、绿波段建立的植被指数NGRDI模型对生物量具有最高的模拟精度(R~2=0.856)和预测精度(验证样本ABE=94g/m~2,RMSE=124g/m~2)。研究获取了高空间分辨率的草地地上生物量,相关成果可为若尔盖高原碳收支、卫星遥感产品真实性检验、生态模型、资源可持续利用等研究提供方法与数据支撑。     

Assessing the biomass dynamics of Andean bofedal and totora high-protein wetland grasses from NOAA/AVHRR

Biomass measurements of totora and bofedal Andean wetland grasses in the Bolivian Northern Altiplano were correlated over a growing season to vegetation indices derived from 1-km visible and near-infrared bands of the advanced very high resolution radiometer (AVHRR) instrument flown on the NOAA-14 polar-orbiting meteorological satellite. This article discusses the potential and limits of these indices for the assessment of the spatial and temporal variation of biomass and of the fraction of the photosynthetic active radiation absorbed by these herbaceous native forages growing in water-saturated environments. Bidirectional reflectance distribution function (BRDF) normalization was also investigated based on simple kernel-driven models. BRDF normalized difference vegetation index (NDVI) performed the best for both totorales and bofedales vegetation associations, followed by the uncorrected maximum-value composite NDVI. BRDF normalized NDVI was shown to be sensitive to the green leaf or photosynthetically active biomass.Estimation of biomass production after Kumar and Monteith (1982) was used to determine the efficiency of solar energy conversion into biomass (ε_b) for the main phenological periods, corresponding to the rainy and dry seasons. Two approaches were investigated for the biomass production estimation: the first one is based on monthly field biomass measurements; the second one is based on estimates from the regression computed previously using Roujean's BRDF normalized NDVI. The values found for these efficiencies for the rainy season agree with those of the literature for grasslands of temperate regions. For the dry season, more accurate information on totora and bofedal senescence and on animal consumption is required to get a reasonable efficiency value. This is not surprising, as other workers have reported biomass estimation with remotely sensed data to be most relevant to the growing season.     

Leaf and stand-level carbon uptake of a Mediterranean forest estimated using the satellite-derived reflectance indices EVI and PRI

Various aspects of global environmental change affect plant photosynthesis, the primary carbon input in ecosystems. Thus, accurate methods of measuring plant photosynthesis are important. Remotely sensed spectral indices can monitor in detail the green biomass of ecosystems, which provides a measure of potential photosynthetic capacity. In evergreen vegetation types, however, such as Mediterranean forests, the amount of green biomass changes little during the growing season and, therefore, changes in green biomass are not responsible for changes in photosynthetic rates in those forests. This study examined the net photosynthetic rates and the diametric increment of stems in a Mediterranean forest dominated by Quercus ilex using three spectral indices (normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and photochemical reflectance index (PRI)) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Average annual EVI accounted for 83% of the variability of the diametric increment of Q. ilex stems over a 10 year period. NDVI was marginally correlated with the diametric increment of stems. This study was the first to identify a significant correlation between net photosynthetic rates and radiation use efficiency at the leaf level using PRI derived from satellite data analysed at the ecosystem level. These results suggest that each spectral index provided different and complementary information about ecosystem carbon uptake in a Mediterranean Q. ilex forest.     

MODIS-based vegetation growth of temperate grassland and its correlation with meteorological factors in northern China

Vegetation dynamics, particularly vegetation growth, are often used as indicators of potential grassland degradation. Grassland vegetation growth can be monitored using remotely sensed data, which has rapid and broad coverage. Grassland ecosystems are an important component of the regional landscape. In this study, we developed an applicable method for monitoring grassland growth. The dynamic variation in the grassland was analysed using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The normalized difference vegetation index (NDVI) was calculated from 2001 to 2010 during the grassland growing season. To evaluate the grassland growth, the use of the growth index (GI) was proposed. According to the GI values, five growth grades were identified: worse, slightly worse, balanced, slightly better, and better. We explored the spatial-temporal variation of grassland growth and the relationship between grassland growth and meteorological factors (i.e. precipitation and temperature factors). Our results indicated that, compared with the multi-year average, the spatial-temporal variation of grassland growth was significantly different between 2001 and 2010. The vegetation growth was worse in 2009 compared with the multi-year average. A GI of ‘worse’ accounted for 66.73% of the area. The vegetation growth in 2003 was the best of the years between 2001 and 2010, and a better GI accounted for 58.08% of the area in 2003. The GI from 2004 to 2008 exhibited significant fluctuations. The correlation coefficient between the GI and precipitation or temperature indicated that meteorological factors likely affected the inter-annual variations in the grassland growth. The peak of the grassland growth season was positively correlated with the spatial patterns of precipitation and negatively correlated with those of temperature. Precipitation during the growing season was the main influence in the arid and semi-arid regions. Monitoring grassland growth using remote sensing can accurately reveal the grassland growth status at the macro-scale in a timely manner. This research proposes an effective method for monitoring grassland growth and provides a reference for the sustainable development of grassland ecosystems.     

Satellite and ground-based pasture production assessment in Niger: 1986-1988

Abstract

The standing crop of herbaceous biomass produced during the 2-4?month summer rainy season by the annual grasses in the Sahel zone provides an indication of resource availability for livestock for the following 9-month dry season. Combined use of NOAA advanced very high resolution radiometer (AVHRR) local area coverage (LAC) satellite data and biomass data, obtained through vegetation sampling of 25-100 km₂ areas, allowed the development of a method for biomass assessment in Niger. Vegetation sampling involved both visual estimates and clipped plots (double sampling). The relationship between time-integrated normalized difference vegetation index (NDVI) statistics derived from NOAA AVHRR LAC data (dependent variable) and total herbaceous biomass (independent variable) was obtained through regression analysis. An inverse prediction was used to estimate biomass from the satellite data. Biomass maps and statistics of the grasslands were produced for the end of each rainy season: 1986, 1987 and 1988. This information is being used for planning purposes by the pastoral resource managers of the Government of Niger.     

Modified light use efficiency model for assessment of carbon sequestration in grasslands of Kazakhstan: combining ground biomass data and remote-sensing

A modified light use efficiency (LUE) model was tested in the grasslands of central Kazakhstan in terms of its ability to characterize spatial patterns and interannual dynamics of net primary production (NPP) at a regional scale. In this model, the LUE of the grassland biome (?_n) was simulated from ground-based NPP measurements, absorbed photosynthetically active radiation (APAR) and meteorological observations using a new empirical approach. Using coarse-resolution satellite data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), monthly NPP was calculated from 1998 to 2008 over a large grassland region in Kazakhstan. The modelling results were verified against scaled up plot-level observations of grassland biomass and another available NPP data set derived from a field study in a similar grassland biome. The results indicated the reliability of productivity estimates produced by the model for regional monitoring of grassland NPP. The method for simulation of ?_n suggested in this study can be used in grassland regions where no carbon flux measurements are accessible.     

A new approach to predict normalized difference vegetation index using time-delay neural network in the arid and semi-arid grassland

ABSTRACT

Normalized difference vegetation index (NDVI) has been used to conduct important research on plant growth and vegetation productivity. In this paper, a new approach to predict NDVI based on precipitation in the grass-growing season for the arid and semi-arid grassland is proposed using time-delay neural network (TDNN). To intuitively know the ability of TDNN to learn the relationship between NDVI and precipitation and to predict NDVI, the performance of the TDNN model is compared with back propagation neural network (BPNN) trained with the same data. The results indicate that TDNN works well to predict precipitation. Moreover, the relationship between precipitation and NDVI can be predicted accurately by the proposed TDNN model. The results show the goodness-of-fit between the observed NDVI and predicted NDVI measured by the determination coefficient of R² being 0.999 from the TDNN model, with the mean absolute percentage error, mean absolute error, and root-mean-square error to be 0.23%, 0.20, and 0.19, respectively. The analysis shows that the proposed method can result in an accurate NDVI prediction. Thus, the algorithm is applied to predict the NDVI during the grass-growing season for the validation of the approach. This validation results suggest the potential application of this approach for reduction of overgrazing pressure and vegetation restoration in the arid and semi-arid grassland.     

Quantifying characteristic growth dynamics in a semi-arid grassland ecosystem by predicting short-term NDVI phenology from daily rainfall: a simple four parameter coupled-reservoir model

Predicting impacts on phenology of the magnitude and seasonal timing of rainfall pulses in water-limited grassland ecosystems concerns ecologists, climate scientists, hydrologists, and a variety of stakeholders. This report describes a simple, effective procedure to emulate the seasonal response of grassland biomass, represented by the satellite-based normalized difference vegetation index (NDVI), to daily rainfall. The application is a straightforward adaptation of a staged linear reservoir that simulates the pulse-like entry of rainwater into the soil and its redistribution as soil moisture, the uptake of water by plant roots, short-term biomass development, followed by the subsequent transpiration of water through foliage. The algorithm precludes the need for detailed, site specific information on soil moisture dynamics, plant species, and the local hydroclimate, while providing a direct link between discrete rainfall events and consequential biomass responses throughout the growing season. We applied the algorithm using rainfall data from the Central Plains Experimental Range to predict vegetation growth dynamics in the semi-arid shortgrass steppe of North America. The mean annual rainfall is 342 mm, which is strongly bifurcated into a dominantly ‘wet’ season, where during the three wettest months (May, June and July) the mean monthly rainfall is approximately 55 mm month^?1; and a ‘dry’ season, where during the three driest months (December, January and February), the mean monthly rainfall is approximately 7 mm month^?1. NDVI data from the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD13Q1 16 day, 250 m × 250 m product were used as a proxy for grassland phenology for the period-of-record 2000–2013. Allowing for temporal changes in basic parameters of the response function over the growing season, the predicted response of the model tracks the observed NDVI metric with correlation coefficients exceeding 0.92. A two-stage series reservoir is preferred, whereby the characteristic time for transfer of a rainfall event to the peak response of NDVI decreases from 24 days (early growing season) to 12 days (late growing season), while the efficiency of a given volume of rainfall to produce a correspondingly similar amount of aboveground biomass decreases by a factor of 40% from April to October. Behaviours of the characteristic time of greenup and loss of rainfall efficiency with progression of the growing season are consistent with physiological traits of cool-season C₃ grasses versus warm-season C₄ grasses, and with prior research suggesting that early season production by C₃ grasses is more responsive to a given amount of precipitation than mid-summer growth of C₄ shortgrasses. Our model explains >90% of seasonal biomass dynamics. We ascribe a systematic underprediction of observed early season greenup following drought years to a lagged or ‘legacy’ effect, as soil inorganic nitrogen, accumulated during drought, becomes available for future plant uptake.     

Biomass quantification of Andean wetland forages using ERS satellite SAR data for optimizing livestock management

Spatio-temporal information on the biomass of totora reeds and bofedal water-saturated Andean grasslands, which are a critical forage resource for smallholders in Bolivia's Altiplano, is needed to promote their protection and improve livestock management. Satellite radar data appear well adapted to map biomass and to monitor biomass changes in this environment for two reasons: (a) the C-band (5.3 GHz) radar data is particularly sensitive to vegetation biomass when the canopy is over an underlying water surface or a water-saturated soil; this is through the dominant scattering mechanisms involving vegetation-water surface interaction; (b) the cloud cover during the growing period which corresponds to the rainy season. This paper assesses the potential of ERS satellite radar data for retrieving biomass information, which is spatially highly variable owing to the numerous small, nonuniform areas of totora harvesting and bofedal grazing. Ground data, including vegetation humid and dry biomass, were collected over 18 months during satellite descending passes at 12 sites located between the Eastern Cordillera and Titicaca Lake, representing three vegetation units: shoreline and inland totoras, and Puna bofedales.ERS-SAR data were analysed as a function of plant biomass at homogeneous totora and bofedal areas. Because of the small size of these areas (typically 20×30 m), the SAR data need to be processed using an advanced multitemporal filter which improves radiometric resolution without significant reduction of the spatial resolution. The radar backscattering coefficient (σ° in dB) measured by ERS was found to be sensitive at both per site and per vegetation unit levels to humid and dry biomass of totora reeds and bofedal grasslands. The sensitivity of the signal to biomass variation is high for dry biomass ranges less than 1 kg/m² for totora, and less than 2 kg/m² for bofedal. The corresponding biomass maps provided by inversion of SAR data are valuable information for livestock management for three critical periods: after the calving season (October-November), when animal pressure is most significant; toward the end of the rainy season (March-April), as an indicator of coming trends to promote the adoption of measures aimed at preventing shortages during the winter season; in the middle of the winter dry season (June-July), to adjust animal charge.     

Attribution of divergent northern vegetation growth responses to lengthening non-frozen seasons using satellite optical-NIR and microwave remote sensing

The non-frozen (NF) season duration strongly influences the northern carbon cycle where frozen (FR) temperatures are a major constraint to biological processes. The landscape freeze-thaw (FT) signal from satellite microwave remote sensing provides a surrogate measure of FR temperature constraints to ecosystem productivity, trace gas exchange, and surface water mobility. We analysed a new global satellite data record of daily landscape FT dynamics derived from temporal classification of overlapping SMMR and SSM/I 37 GHz frequency brightness temperatures (T_b). The FT record was used to quantify regional patterns, annual variability, and trends in the NF season over northern (≥45°N) vegetated land areas. The ecological significance of these changes was evaluated against satellite normalized difference vegetation index (NDVI) anomalies, estimated moisture and temperature constraints to productivity determined from meteorological reanalysis, and atmospheric CO₂ records. The FT record shows a lengthening (2.4 days decade^?1; p < 0.005) mean annual NF season trend (1979–2010) for the high northern latitudes that is 26% larger than the Northern Hemisphere trend. The NDVI summer growth response to these changes is spatially complex and coincides with local dominance of cold temperature or moisture constraints to productivity. Longer NF seasons are predominantly enhancing productivity in cold temperature-constrained areas, whereas these effects are reduced or reversed in more moisture-constrained areas. Longer NF seasons also increase the atmospheric CO₂ seasonal amplitude by enhancing both regional carbon uptake and emissions. We find that cold temperature constraints to northern growing seasons are relaxing, whereas potential benefits for productivity and carbon sink activity are becoming more dependent on the terrestrial water balance and supply of plant-available moisture needed to meet additional water use demands under a warming climate.     

A study of the seasonal dynamics of grassland growth rates in Inner Mongolia based on AVHRR data and a light‐use efficiency model

Growth rate data for different pastures could provide important reference data for developing rotation grazing plans, for hay production, and for forage replenishment. Based on AVHRR NDVI data and a light‐use efficiency (LUE) model, we estimated absorbed photosynthetically active radiation and LUE (ε) by integrating air and soil temperature, precipitation and total solar radiation time series data from 1986 to 1999, and calculated the absolute growth rate (AGR) and cumulative absolute growth rate (CAGR) of aboveground biomass in each growing season in China's Inner Mongolia region. AGR and CAGR estimated by the LUE model were validated using monthly growth data obtained for the vegetation in desert steppe, typical steppe, and meadow steppe ecosystems from 1986 to 1995. The LUE model provided sufficiently good simulation accuracy that its use should permit improved livestock feed management in the study area. From 1986 to 1999, average CAGR of steppe vegetation during the growing season increased quickly in June and July, reached a maximum in July and August, and declined in September. In 1999, AGR reflected the pattern of seasonal vegetation dynamics during the growing season.