Derivation of pan-Arctic soil decomposition rate constant,heterotrophic respiration and NEE using AMSR-E and MODIS data

An approach was developed for regional assessment and monitoring of land-atmosphere carbon dioxide (CO₂) exchange, soil heterotrophic respiration (R _h), and vegetation productivity of Arctic tundra using global satellite remote sensing at optical and microwave wavelengths. C- and X-band brightness temperatures were used from the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) to extract surface wetness and temperature, and MODerate Resolution Imaging Spectroradiometer (MODIS) data were used to derive land cover, Leaf Area Index (LAI), and Net Primary Production (NPP) information. Calibration and validation activities involve comparisons between satellite remote sensing and tundra CO₂ eddy flux towers, and hydroecological process model simulations. Analyses of spatial and temporal anomalies and environmental drivers of land-atmosphere net CO₂ exchange at weekly and annual time steps were conducted. Surface soil moisture and temperature, as detected from satellite remote-sensing observations, were found to be major drivers for spatial and temporal patterns of tundra net ecosystem CO₂ exchange and photosynthetic and respiration processes. Satellite microwave measurements are capable of capturing seasonal variations and regional patterns in tundra soil heterotrophic respiration and CO₂ exchange, while the ability to extract spatial patterns at the scale of surface heterogeneity is limited by the coarse spatial scale of the satellite remote-sensing footprint. The microwave-derived surface temperature and soil moisture were used to estimate net ecosystem carbon exchange (NEE) at the boreal-Arctic region. These were validated using flux tower sites data. Existing satellite-based measurements of vegetation structure (i.e. LAI) and productivity (i.e. Gross Primary Production (GPP) and NPP) from the Aqua/Terra MODIS with the AMSR-E-derived land-surface temperature and soil moisture were used and integrated. Spatially explicit estimates of NEE for the pan-Arctic region at daily, weekly and annual intervals were derived. Comparative analysis of satellite data-derived NEE with measurements from CO₂ eddy flux tower sites and the BIOME-BGC model were carried out and good agreement was found. The comparative analysis is statistically significant with high regression (i.e. R ²?=?0.965), especially in the R _h calculation and the overall NEE regression is 0.478. The results also indicate that the carbon cycle response to climate change is nonlinear and is strongly coupled to Arctic surface hydrology.     

Spectral indices for remote sensing of phytomass,deciduous shrubs,and productivity in Alaskan Arctic tundra

The relationships among in situ spectral indices, phytomass, plant functional types, and productivity were determined through field observations of moist acidic tundra (MAT), moist non-acidic tundra (MNT), heath tundra (HT), and sedge-shrub tundra (SST) in the Arctic coastal tundra, Alaska, USA. The two-band enhanced vegetation index (EVI2) was found more useful for estimating vascular plant green phytomass, leaf carbon and nitrogen, leaf carbon and nitrogen turnover, and vascular plant net primary productivity (NPP) without root production than the normalized difference vegetation index (NDVI). Deciduous shrub green phytomass was strongly correlated with deciduous shrub index (DSI), defined as EVI2 × (R_blue + R_green – R_red)/(R_blue + R_green + R_red) (with a coefficient of determination (R²) of 0.63). R_blue, R_green, and R_red denote the blue, green, and red bands, respectively. This is because R_blue and R_green values were higher than the R_red values for green leaves, deciduous shrub stems, lichens, and rocks compared with other ecosystem components, and EVI2 values of lichens and rocks were very low. The vascular plant NPP without root production was estimated with an R² of 0.67 using DSI and EVI2. Our results offer empirical evidence that a new spectral index predicts the distribution of deciduous shrub and plant production, which influences the interactions between tundra ecosystems and the atmosphere.     

Separating grassland and shrub vegetation by multidate pixel‐adaptive spectral mixture analysis

Monitoring and assessment of land degradation and the processes driving it require effective change indicators at appropriate scales and spatial extent. In this context, the decomposition of Mediterranean rangeland vegetation into woody and herbaceous fractions is of great significance. This study demonstrates that a stratification of vegetation into woody and herbaceous components is possible with two satellite images of moderate spatial and spectral resolution. We used a pixel‐adaptive spectral mixture analysis to derive subpixel‐level vegetation abundances from satellite imagery representing two specific phenological stages of Mediterranean rangeland vegetation. The transferability of endmember models is often a problem of multidate spectral mixture analysis because of uneven spectral dimensionality within and among datasets. In our approach, the dimensionality of the mixture model was determined automatically, based on error calculations. This method enables the transfer of the mixture model to multiple scenes and allows for quantitative comparison of vegetation abundances. The results show that the woody vegetation fraction corresponds well with field data (R ² = 0.76–0.91) and vegetation cover mapped from a very high resolution satellite image. The herbaceous vegetation fraction displays a good correlation compared to field mapped cover but still implies a moderate level of uncertainty (R ² = 0.52–0.76). The approach pursued in this research may be valuable for the characterization of rangeland plant communities and for the derivation of vegetation‐related indicators useful for the monitoring and assessment of degradation.     

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.     

Real-time monitoring of vegetation biomass with NOAA-AVHRR in Etosha National Park,Namibia, for fire risk assessment

Estimates of biomass production are important in a wildlife reserve such as Etosha National Park, Namibia, for assessment of fire risk and subsequent selection of sites for controlled burning. We present methodology for using locally acquired NOAA-AVHRR images to make estimates of biomass in near-to-realtime. To this end, techniques for rapid measurement of the biomass of herbaceous and woody vegetation were developed using a rising disc pasture meter and individual plant dimensions. A field sampling methodology is presented to make biomass estimates which were compatible with the scale of AVHRR spatial resolution and sufficiently close to the time of satellite overpasses to enable correlation with the NDVI from single images. Initial results show high correlations of biomass with NDVI for individual vegetation cover classes, which appear to be temporally stable. There seem to be different regression equations for the different savanna vegetation types although more field observations are needed to confirm this. The results were exploited to illustrate the potential application of this work for fire management. The combination of rapid field methods and real time image acquisition developed in this work provides a sound basis for biomass monitoring at local level.     

Mapping live fuel moisture with MODIS data: A multiple regression approach

Live fuel moisture (LFM) is an important factor for ascertaining fire risk in shrublands located in Mediterranean climate regions. We examined empirical relationships between LFM and numerous vegetation indices calculated from MODIS composite data for two southern California shrub functional types, chaparral (evergreen) and coastal sage scrub (CSS, drought-deciduous). These relationships were assessed during the annual March–September dry down period for both individual sites, and sites pooled by functional type. The visible atmospherically resistant index (VARI) consistently had the strongest relationships for individual site regressions. An independent method of accuracy assessment, cross validation, was used to determine model robustness for pooled site regressions. Regression models were developed with n ? 1 datasets and tested on the dataset that was withheld. Additional variables were included in the regression models to account for site-specific and interannual differences in vegetation amount and condition. This allowed a single equation to be used for a given functional type. Multiple linear regression models based on pooled sites had slightly lower adjusted R² values compared with simple linear regression models for individual sites. The best regression models for chaparral and CSS were inverted, and LFM was mapped across Los Angeles County, California (LAC). The methods used in this research show promise for monitoring LFM in chaparral and may be applicable to other Mediterranean shrubland communities.     

Mapping Arctic tundra vegetation types using digital SPOT/HRV-XS data A preliminary assessment

Abstract

Multispectral (XS) image data recorded by the High Resolution Visible (HRV) sensor aboard the SPOT-1 satellite are being evaluated for the mapping of Arctic tundra vegetation in the Arctic Foothill Province of Alaska. This research is part of a current ecosystems study that requires an efficient means for mapping vegetation types over large areas. Conventional spectral-based image classification techniques were applied to SPOT/HRV-XS data from a single date. The unique characteristics of the vegetation cover (mainly tussock tundra) and illumination conditions of the location necessitated a detailed examination of classification approaches that have generally been applied in mid-latitude studies. Preliminary results suggest that areal estimates of Arctic tundra vegetation types can be made accurately (±2·5 per cent per category), but maps generated by classifying spectral features of SPOT/HRV-XS data alone arc unsuitably accurate (56 per cent). This is partly due to the high occurrence of relatively small vegetation parcels, determined by measuring the characteristic lengths of vegetation parcels from a ‘ground reference’ map covering the same area as the SPOT/HRV-XS subscene.     

Evaluation of MODIS and NOAA AVHRR vegetation indices with in situ measurements in a semi‐arid environment

Much effort has been made in recent years to improve the spectral and spatial resolution of satellite sensors to develop improved vegetation indices reflecting surface conditions. In this study satellite vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Very High Resolution Radiometer (AVHRR) are evaluated against two years of in situ measurements of vegetation indices in Senegal. The in situ measurements are obtained using four masts equipped with self‐registrating multispectral radiometers designed for the same wavelengths as the satellite sensor channels. In situ measurements of the MODIS Normalized Difference Vegetation Index (NDVI) and AVHRR NDVI are equally sensitive to vegetation; however, the MODIS NDVI is consistently higher than the AVHRR NDVI. The MODIS Enhanced Vegetation Index (EVI) proved more sensitive to dense vegetation than both AVHRR NDVI and MODIS NDVI. EVI and NDVI based on the MODIS 16‐day constrained view angle maximum value composite (CV‐MVC) product captured the seasonal dynamics of the field observations satisfactorily but a standard 16‐day MVC product estimated from the daily MODIS surface reflectance data without view angle constraints yielded higher correlations between the satellite indices and field measurements (R ² values ranging from 0.74 to 0.98). The standard MVC regressions furthermore approach a 1?:?1 line with in situ measured values compared to the CV‐MVC regressions. The 16‐day MVC AVHRR data did not satisfactorily reflect the variation in the in situ data. Seasonal variation in the in situ measurements is captured reasonably with R ² values of 0.75 in 2001 and 0.64 in 2002, but the dynamic range of the AVHRR satellite data is very low—about a third to a half of the values from in situ measurements. Consequently the in situ vegetation indices were emulated much better by the MODIS indices than by the AVHRR NDVI.     

Relaxation vegetation index in non-linear modelling of ground plant cover by satellite remote-sensing data

Abstract

In order to obtain a model equation for the calculation of percentage plant cover by multi-spectral radiances remotely-sensed by satellites, a regression procedure is used to connect space remote-sensing data to ground plant cover measurement. A traditional linear regression model using the normalized difference vegetation index (NDVI) is examined by remote-sensing data of the SPOT satellite and ground measurement of LCTA project for a test site at Hohenfels. Germany. A relaxation vegetation index (RVI) is proposed in a non-linear regression modelling to replace the NDVI in linear regression modelling to get a better calculation of percentage plant cover. The definition of the RVI is

where X _i is raw remote-sensing data in channel i. Using the RVI, the correlation coefficient between calculated and observed percentage plant cover for a test scene in 1989 reaches 0·9 while for the NDVI it is only 0·7; the coefficient of multiple determination R ² reaches 0·8 for the RVI while it is only 0·5 for the NDVI. Numerical testing shows that the ability of using the RVI to predict percentage plant cover by space remote-sensing data for the same scene or the scene in other years is much stronger than the NDVI.     

Using very high spatial resolution multispectral satellite sensor imagery to monitor refugee camps

Detailed geographic information is a key factor in decision making processes during refugee relief operations. The forthcoming commercial very high spatial resolution (VHSR) satellite sensors will be capable of acquiring multispectral (MS) images at spatial resolutions of 1m (panchromatic) and 4m (multispectral) of refugee camps and their environment. This work demonstrates how refugee camp environment, area and population can be analysed using a VHSR MS satellite sensor image from the Russian KVR-1000 sensor. This image, with a spatial resolution of 3.3m, was used to study Thailand's Site 2 refugee camps, which were established to accommodate Khmer refugees on the Thai-Kampuchean border. At the time of image acquisition, the total population of Site 2's five refugee camps was close to 143000. The VHSR MS image was found to be suitable for mapping the refugee camp environment and area. A statistically significant linear relationship between camp area and population was determined. Accordingly, the study suggests that VHSR MS images in general may be useful for refugee camp planning and management and points toward the utilization of forthcoming commercial VHSR MS satellite sensor images in humanitarian relief operations.     

Predicting the spectral information of future land cover using machine learning

Application of machine learning models to study land-cover change is typically restricted to the change detection of categorical, i.e. classified, land-cover data. In this study, our aim is to extend the utility of such models to predict the spectral band information of satellite images. A Random Forests (RF)-based machine learning model is trained using topographic and historical climatic variables as inputs to predict the spectral band values of high-resolution satellite imagery across two large sites in the western United States, New Mexico (10,570 km²), and Washington (9400 km²). The model output is used to obtain a true colour photorealistic image and an image showing the normalized difference vegetation index values. We then use the trained model to explore what the land cover might look like for a climate change scenario during the 2061–2080 period. The RF model achieves high validation accuracy for both sites during the training phase, with the coefficient of determination (R²) = 0.79 for New Mexico site and R² = 0.73 for Washington site. For the climate change scenario, prominent land-cover changes are characterized by an increase in the vegetation cover at the New Mexico site and a decrease in the perennial snow cover at the Washington site. Our results suggest that direct prediction of spectral band information is highly beneficial due to the ability it provides for deriving ecologically relevant products, which can be used to analyse land-cover change scenarios from multiple perspectives.     

Do soil-adjusted or standard vegetation indices better predict above ground biomass of semi-arid,saline rangelands in North-East Iran?

ABSTRACT

Satellite remote sensing has greatly facilitated the assessment of aboveground biomass in rangelands. Soil-adjusted vegetation indices have been developed to provide better predictions of aboveground biomass, especially for dryland regions. Semi-arid rangelands often complicate a remote sensing based assessment of aboveground biomass due to bright reflecting soils combined with sparse vegetation cover. We aim at evaluating whether soil-adjusted vegetation indices perform better than standard, i.e. unadjusted, vegetation indices in predicting dry aboveground biomass of a saline and semi-arid rangeland in NE-Iran. 672 biomass plots of 2 × 2 m were gathered and aggregated into 13 sites. Generalized Linear Regression Models (GLM) were compared for six different vegetation indices, three standard and three soil-adjusted vegetation indices. Vegetation indices were calculated from the MODIS MCD43A4 product. Model comparison was done using Akaike Information Criterion (AICc), Akaike weights and pseudo R². Model fits for dry biomass showed that transformed NDVI and NDVI fitted best with R² = 0.47 and R² = 0.33, respectively. The optimized soil-adjusted vegetation index (OSAVI) behaved similar to NDVI but less precise. The soil-adjusted vegetation index (SAVI), the modified soil-adjusted vegetation index (MSAVI2) and the enhanced vegetation index (EVI) performed worse than a null model. Hence, soil-adjusted indices based on the soil-line concept performed worse than a simple square root transformation of the NDVI. However, more studies that compare MODIS based vegetation indices for rangeland biomass estimation are required to support our findings. We suggest applying a similar model comparison approach as performed in this study instead of relying on single vegetation indices in order to find optimal relationships with aboveground biomass estimation in rangelands.     

Mapping of continuous floristic gradients in grasslands using hyperspectral imagery

Transitions between plant species assemblages are often continuous with the form of the transition dependent on the ‘slope’ of environmental gradients and on the style of self-organization in vegetation. Image segmentation can present misleading or even erroneous results if applied to continuous spatial changes in vegetation. Even methods that allow for multiple-class memberships of pixels presuppose the existence of ideal types of species assemblages that constitute mixtures—an assumption that does not fit the case of continua where any section of a gradient is as ‘pure’ as any other section like in modulations of grassland species composition.Thus, we attempted to spatially model floristic gradients in Bavarian meadows by extrapolating axes of an unconstrained ordination of species data. The models were based on high-resolution hyperspectral airborne imagery. We further modelled the distribution of plant functional response types (Ellenberg indicator values) and the cover values of selected species. The models were made with partial least squares (PLS) regression analyses. The realistic utility of the regression models was evaluated by full leave-one-out cross-validation.The modelled floristic gradients showed a considerable agreement with ground-based observations of floristic gradients (R²=0.71 and 0.66 for the first two axes of ordination). Apart from mapping the most important continuous floristic differences, we mapped gradients in the appearance of plant functional response groups as represented by averaged Ellenberg indicator values for soil pH (R²=0.76), water supply (R²=0.66) and nutrient supply (R²=0.75), while models for the cover of single species were weak.Compared to many other vegetation attributes, plant species composition is difficult to detect with remote sensing techniques. This is partly caused by a lack of compatibility between methods of vegetation ecology and remote sensing. We believe that the present study has the potential to increase compatibility as neither spectral nor vegetation information gets lost by a classifying step.     

Model development and comparison to predict softwood and hardwood per cent cover using high and medium spatial resolution imagery

The recent availability of high spatial resolution multispectral scanners provides an opportunity to adapt existing methods and test models to derive spatially explicit forest type and per cent cover information at the Landsat pixel level. A regression modelling methodology was applied for scaling‐up high resolution (IKONOS) to medium spatial resolution satellite imagery (Landsat) to predict softwood and hardwood forest type and density (per cent cover) in a northern Maine study area. Regression relationships (63 different models) were developed and compared. The model variables included vegetation indices and several date (season) combinations of Landsat Enhanced Thematic Mapper Plus (ETM+) imagery (August, September, October and May).

A model incorporating all variables from four dates of Landsat ETM+ imagery produced the highest coefficient of variation in predicting both softwood (0.655) and hardwood cover (0.66). The addition of vegetation indices with the six ETM+ reflected bands did not significantly improve or detract from the regression relationships for any of the multi‐date or single date models examined. A two‐date combination of October and May variables provided an acceptable (and arguably more cost‐effective) model as the adjusted R ² value was 0.645 for softwood and 0.649 for hardwood. A significant result was that all single‐date models produced inferior results with a sharp drop in adjusted R ², compared with the multi‐date seasonal models. This research has demonstrated that the regression models including multi‐date variables produce good results and can provide spatially explicit forest type and stand structure data that has been difficult or infeasible to obtain from medium spatial resolution imagery using traditional classification methods.     

Daily MODIS products for analyzing early season vegetation dynamics across the North Slope of Alaska

Monitoring the growth and distribution of Arctic tundra vegetation is important for understanding changes in early growing season conditions in Arctic ecosystems in response to a warming climate. The primary objective of this study is to examine the utility of computed Daily Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) products relative to 16-day maximum value composite (MVC) datasets for observing early season green-up dynamics of Arctic tundra vegetation across the North Slope of Alaska. Greening in the Arctic typically occurs shortly after snowmelt and can potentially be captured by using satellite observations that are available on a daily basis. Daily MODIS Snow Cover products were employed to retrieve dates of complete snowmelt (DOCS) for 2003-2005 for pixels that were cloud free at the time of complete snowmelt. Given the sparseness of cloud-free observations in both space and time, early season NDVI trajectories for cloud-free pixels were derived using daily MODIS data based on two approaches: a chronosequence (temporally continuous but aspatial) and a pixel trajectory (temporally discontinuous but spatial explicit) approach.On average during the three-year period, 12.5% of the North Slope region was cloud free at the time of complete snowmelt and a majority of these cloud-free pixels (65%) were associated with the Coastal Plain province. In contrast, the Foothills region was relatively less cloudy from the time following complete snowmelt until peak greenness (56%) than the Coastal Plain province (61%). As a result, vegetation communities that lie mostly in the Foothills province such as shrub tundra and moist acidic tundra classes had more cloud-free observations available to characterize NDVI trajectories using the pixel trajectory approach. Complete snowmelt in the North Slope generally occurred between day of year (DOY) 140 and 170 over the three years with areas covered by the shrub tundra vegetation community (Foothills province) experiencing snowmelt first in all three years with mean DOCS ranging from DOY 148 in 2004 to DOY 158 in 2003. For approximately two weeks following complete snowmelt (Phase I, a period of rapid NDVI increase), the Daily NDVI derived trajectories were substantially different from the MVC NDVI trajectories. Early season integrated NDVI (ESINDVI) values computed for Phase I were 7% higher using the Daily NDVI approaches relative to those derived from the MVC MODIS data for the North Slope region. Following this initial period, until peak greenness (Phase 2, a period of gradual NDVI increase), the Daily and MVC trajectories were similar in shape and magnitude. This study demonstrates the utility of the Daily MODIS Snow product for assessing cloud cover and snowmelt patterns and Daily MODIS NDVI data for observing and detecting sharp and rapid changes in early season vegetation phenology as seen during Phase I.     

NDVI from low altitude aircraft and composited NOAA AVHRR data for scaling Arctic ecosystem fluxes

The relationship between normalized difference vegetation index (NDVI) patterns obtained from high spatial resolution aircraft and low spatial resolution satellite data (Advanced Very High Resolution Radiometer (AVHRR)) was investigated with the intent of using multilevel data to scale carbon flux models in Arctic tundra ecosystems. Despite variable illumination conditions during the aircraft missions and maximum value compositing of the AVHRR data, the difference between 3?km average aircraft and AVHRR NDVI values was generally constant along each flight transect. However, the magnitude of the offset differed between flight dates and small lakes had a greater effect on area averaged aircraft NDVI values than on the satellite values. A cloud index was calculated using incident solar radiation measured by the aircraft and this index was used to identify periods when the aircraft NDVI values may have been biased by cloud cover. Removal of NDVI values based on a cloud index threshold did not appear to be justified given the marginal improvement in the relationship between the two NDVI datasets. If the systematic difference between AVHRR and aircraft NDVI values can be determined, then the scaling of carbon flux models based on the NDVI should be a viable approach in Arctic ecosystems.     

Understanding burn severity sensing in Arctic tundra: exploring vegetation indices,suboptimal assessment timing and the impact of increasing pixel size

Little is known about how satellite imagery can be used to describe burn severity in tundra landscapes. The Anaktuvuk River Fire (ARF) in 2007 burned over 1000 km² of tundra on the North Slope of Alaska, creating a mosaic of small (1 m²) to large (>100 m²) patches that differed in burn severity. The ARF scar provided us with an ideal landscape to determine if a single-date spectral vegetation index can be used once vegetation recovery began and to independently determine how pixel size influences burn severity assessment. We determine and explore the sensitivity of several commonly used vegetation indices to variation in burn severity across the ARF scar and the influence of pixel size on the assessment and classification of tundra burn severity. We conducted field surveys of spectral reflectance at the peak of the first growing season post-fire (extended assessment period) at 18 field sites that ranged from high to low burn severity. In comparing single-date indices, we found that the two-band enhanced vegetation index (EVI2) was highly correlated with normalized burn ratio (NBR) and better distinguished among three burn severity classes than both the NBR and the normalized difference vegetation index (NDVI). We also show clear evidence that shortwave infrared (SWIR) reflectivity does not vary as a function of burn severity. By comparing a Quickbird scene (2.4 m pixels) to simulated 30 and 250 m pixel scenes, we are able to confirm that while the moderate spatial resolution of the Landsat Thematic Mapper (TM) sensor (30 m) is sufficient for mapping tundra burn severity, the coarser resolution of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor (250 m) is not well matched to the fine scale of spatial heterogeneity in the ARF burn scar.     

Estimation of the fractional canopy cover of pecan orchards using Landsat 5 satellite data,aerial imagery,and orchard floor photographs

Pecan orchards are the largest agricultural water consumer in the lower part of the Mesilla Valley, NM, USA. Knowledge of fractional canopy (FC) cover allows better crop water use assessment and orchard management. FC can be estimated from vegetation indices (VIs), such as the normalized difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI), the simple ratio (SR), and the triangular vegetation index (TVI), using satellite imagery. The main objective of this research is to develop an approach to determine the FC from a simultaneous imagery campaign consisting of aerial imagery, orchard floor photographs, and satellite images. All the required data were collected based on satellite overpass times at three different times during the initial part of the growing season to enhance the quality of data and reduce errors. The data were processed using the software package Environment for Visualizing Images (ENVI^® 4.6.1; ITT Research Systems Inc.). The orchard floor digital photographs were used as a ground truth data set that gave a good correlation to the aerial photography. The aerial images were then used to determine the relationship between the FC and the VIs using these ‘corrected FCs’. The results showed significant correlation between NDVI and FC (R ² = 0.80; p < 0.0001). Likewise, the calculated SR not only showed good correlation to the FCs but also verified the calculated NDVI. The results indicated that the methodology of this research can be applied to other tree crops as an aid in estimating the FC.     

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.