Mapping the effects of water stress on Sphagnum: Preliminary observations using airborne remote sensing

Remote sensing of near-surface hydrological conditions within northern peatlands has the potential to provide important large-scale hydrological information regarding ecological and carbon-balance processes occurring within such systems. This article details how field knowledge of the spectral properties of Sphagnum spp., airborne remote sensing data and a range of image analysis approaches, may be combined to provide a suitable proxy for near-surface wetness. Co-incident field and airborne remote sensing data were acquired in May and September 2002 over an important UK raised bog (Cors Fochno). A combination of laboratory-tested NIR and SWIR water-based and biophysical spectral reflectance indices were applied to field and airborne reflectance spectra of Sphagnum pulchrum to elucidate changes in near-surface moisture conditions. Field results showed significant correlations between water-based indices (moisture stress index (MSI) and floating water band indices (fWBI₉₈₀ and fWBI₁₂₀₀₎)) and measures of both near-surface volumetric moisture content (VMC) and water-table position. Spectral indices formulated from the NIR (fWBI₉₈₀ and fWBI₁₂₀₀) proved to be the most useful for indicating near-surface wetness across the widest range of moisture conditions because of their ability to penetrate deeper into the Sphagnum canopy. Correlations between a biophysical index based upon chlorophyll content and both hydrological measures were not significant, possibly due to relatively high levels of surface wetness at the field site in both May and September. S. pulchrum lawns were successfully located and mapped from airborne imagery using the mixed tuned match filtering (MTMF) algorithm. Importantly, MSI derived from airborne data was significantly correlated with both field moisture and the water-table position. Relationships between measures of near-surface wetness and the MSI for naturally heterogeneous canopies were, however, found to be weaker for airborne imagery than for associated field data. This is likely to be a result of the formulation of the MSI itself and the possible preferential detection of “wetter” pixels within the imagery. This effectively reduced the ability of MSI to detect subtle changes in near-surface wetness under high moisture conditions, but would not impede the use of the index under drier conditions. Results from the field data suggest that indices formulated from the NIR may be more suitable for detailed estimations of near-surface and surface wetness at the landscape-scale although reliable hyperspectral data are required to test fully the performance of such indices. The relative merits of using such an approach to determine near-surface hydrological conditions across entire peatland complexes are also discussed.     

Bryophyte cover estimation in a boreal black spruce forest using airborne lidar and multispectral sensors

Bryophytes are the dominant ground cover vegetation layer in many boreal forests and in some of these forests the net primary production of bryophytes exceeds the overstory. Therefore it is necessary to quantify their spatial coverage and species composition in boreal forests to improve boreal forest carbon budget estimates. We present results from a small exploratory test using airborne lidar and multispectral remote sensing data to estimate the percentage of ground cover for mosses in a boreal black spruce forest in Manitoba, Canada. Multiple linear regression was used to fit models that combined spectral reflectance data from CASI and indices computed from the SLICER canopy height profile. Three models explained 63-79% of the measured variation of feathermoss cover while three models explained 69-92% of the measured variation of sphagnum cover. Root mean square errors ranged from 3-15% when predicting feathermoss, sphagnum, and total moss ground cover. The results from this case study warrant further testing for a wider range of boreal forest types and geographic regions.     

Remote detection of Trichodesmium blooms in optically complex coastal waters: Examples with MODIS full-spectral data

Remote detection of the Trichodesmium spp. cyanobacteria blooms on the west Florida shelf (WFS) has been problematic due to optical complexity caused by sediment resuspension, coastal runoff, and bottom interference. By combining MODIS data measured by the ocean bands and land bands, an approach was developed to identify surface mats of Trichodesmium on the WFS. The approach first identifies possible bloom patches in MODIS FAI (floating algae index) 250 m resolution imagery derived from the Rayleigh-corrected reflectance at 667, 859, and 1240 nm. Then, spectral analysis examines the unique reflectance characteristics of Trichodesmium at 469, 488, 531, 551, and 555 nm due to specific optical properties (absorption, backscattering, and fluorescence) of the unusual pigments in Trichodesmium. These spectral characteristics (i.e., high-low-high-low-high reflectance at 469-488-531-551-555 nm, respectively) differentiate Trichodesmium mats unambiguously from other features observed in the FAI imagery, such as Sargassum spp. Tests in other coastal locations show that the approach is robust and applicable to other optically complex waters. Results shown here can help study Trichodesmium bloom dynamics (e.g., initiation and bloom formation) and may also help design future sensors to better detect and quantify Trichodesmium, an important N₂ fixer in the global oceans.     

Assessing structural effects on PRI for stress detection in conifer forests

The retrieval of indicators of vegetation stress from remote sensing imagery is an important issue for the accurate assessment of forest decline. The Photochemical Reflectance Index (PRI) has been demonstrated as a physiological index sensitive to the epoxidation state of the xanthophyll cycle pigments and to photosynthetic efficiency, serving as a proxy for short-term changes in photosynthetic activity, stress condition, and pigment absorption, but highly affected by illumination conditions, viewing geometry and canopy structure. In this study, a diurnal airborne campaign was conducted over Pinus sylvestris and Pinus nigra forest areas with the Airborne Hyperspectral Scanner (AHS) to evaluate the effects of canopy structure on PRI when used as an indicator of stress in a conifer forest. The AHS airborne sensor was flown at two times (8:00 GMT and 12:00 GMT) over forest areas under varying field-measured stress levels, acquiring 2 m spatial resolution imagery in 80 spectral bands in the 0.43-12.5 μm spectral range. Five formulations of PRI (based on R₅₃₁ as a xanthophyll-sensitive spectral band) were calculated using different reference wavelengths, such as PRI₅₇₀ (reference band R_REF = R₅₇₀), and the PRI modifications PRI_m1 (R_REF = R₅₁₂), PRI_m2 (R_REF = R₆₀₀), PRI_m3 (R_REF = R₆₇₀), and PRI_m4 (R_REF = R₅₇₀, R₆₇₀), along with other structural indices such as NDVI, SR, OSAVI, MSAVI and MTVI2. In addition, thermal bands were used for the retrieval of the land surface temperature. A radiative transfer modeling method was conducted using the LIBERTY and INFORM models to assess the structural effects on the PRI formulations proposed, studying the sensitivity of PRI_m indices to detect stress levels while minimizing the effects caused by the conifer architecture. The PRI indices were related to stomatal conductance, xanthophyll epoxidation state (EPS) and crown temperature. The modeling analysis showed that the coefficient of variation (CV) for PRI was 50%, whereas the CV for PRIm1 (band R512 as a reference) was only 20%. Simulation and experimental results demonstrated that PRI_m1 (R_REF = R₅₁₂) was less sensitive than PRI (R_REF = R₅₇₀) to changes in Leaf Area Index (LAI) and tree densities. PRI₅₁₂ was demonstrated to be sensitive to EPS at both leaf (r² = 0.59) and canopy level (r² = 0.40), yielding superior performance than PRI₅₇₀ (r² = 0.21) at the canopy level. In addition, PRI₅₁₂ was significantly related to water stress indicators such as stomatal conductance (Gs; r² = 0.45) and water potential (Ψ; r² = 0.48), yielding better results than PRI₅₇₀ (Gs, r² = 0.21; Ψ, r² = 0.21) due to the structural effects found on the PRI₅₇₀ index at the canopy level.     

Remote sensing chlorophyll a of optically complex waters (rias Baixas, NW Spain): Application of a regionally specific chlorophyll a algorithm for MERIS full resolution data during an upwelling cycle

This study takes advantage of a regionally specific algorithm and the characteristics of Medium Resolution Imaging Spectrometer (MERIS) in order to deliver more accurate, detailed chlorophyll a (chla) maps of optically complex coastal waters during an upwelling cycle. MERIS full resolution chla concentrations and in situ data were obtained on the Galician (NW Spain) shelf and in three adjacent rias (embayments), sites of extensive mussel culture that experience frequent harmful algal events. Regionally focused algorithms (Regional neural network for rias Baixas or NNRB) for the retrieval of chla in the Galician rias optically complex waters were tested in comparison to sea-truth data. The one that showed the best performance was applied to a series of six MERIS (FR) images during a summer upwelling cycle to test its performance. The best performance parameters were given for the NN trained with high-quality data using the most abundant cluster found in the rias after the application of fuzzy c-mean clustering techniques (FCM). July 2008 was characterized by three periods of different meteorological and oceanographic states. The main changes in chla concentration and distribution were clearly captured in the images. After a period of strong upwelling favorable winds a high biomass algal event was recorded in the study area. However, MERIS missed the high chlorophyll upwelled water that was detected below surface in the ria de Vigo by the chla profiles, proving the necessity of in situ observations. Relatively high biomass “patches” were mapped in detail inside the rias. There was a significant variation in the timing and the extent of the maximum chla areas. The maps confirmed that the complex spatial structure of the phytoplankton distribution in the rias Baixas is affected by the surface currents and winds on the adjacent continental shelf. This study showed that a regionally specific algorithm for an ocean color sensor with the characteristics of MERIS in combination with in situ data can be of great help in chla monitoring, detection and study of high biomass algal events in an area affected by coastal upwelling such as the rias Baixas.     

Predicting riparian evapotranspiration from MODIS vegetation indices and meteorological data

A vegetation index (VI) model for predicting evapotranspiration (ET) from data from the Moderate Resolution Imaging Spectrometer (MODIS) on the EOS-1 Terra satellite and ground meteorological data was developed for riparian vegetation along the Middle Rio Grande River in New Mexico. Ground ET measurements obtained from eddy covariance towers at four riparian sites were correlated with MODIS VIs, MODIS land surface temperatures (LSTs), and ground micrometeorological data over four years. Sites included two saltcedar (Tamarix ramosissima) and two Rio Grande cottonwood (Populus deltoides ssp. Wislizennii) dominated stands. The Enhanced Vegetation Index (EVI) was more closely correlated (r=0.76) with ET than the Normalized Difference Vegetation Index (NDVI; r=0.68) for ET data combined over sites and species. Air temperature (T_a) measured over the canopy from towers was the meteorological variable that was most closely correlated with ET (r=0.82). MODIS LST data at 1- and 5-km resolutions were too coarse to accurately measure the radiant surface temperature within the narrow riparian corridor; hence, energy balance methods for estimating ET using MODIS LSTs were not successful. On the other hand, a multivariate regression equation for predicting ET from EVI and T_a had an r²=0.82 across sites, species, and years. The equation was similar to VI-ET models developed for crop species. The finding that ET predictions did not require species-specific equations is significant, inasmuch as these are mixed vegetation zones that cannot be easily mapped at the species level.     

Broadband, red-edge information from satellites improves early stress detection in a New Mexico conifer woodland

Multiple plant stresses can affect the health, esthetic condition, and timber harvest value of conifer forests. To monitor spatial and temporal dynamic forest stress conditions, timely, accurate, and cost-effective information is needed that could be provided by remote sensing. Recently, satellite imagery has become available via the RapidEye satellite constellation to provide spectral information in five broad bands, including the red-edge region (690-730 nm) of the electromagnetic spectrum. We tested the hypothesis that broadband, red-edge satellite information improves early detection of stress (as manifest by shifts in foliar chlorophyll a + b) in a woodland ecosystem relative to other more commonly utilized band combinations of red, green, blue, and near infrared band reflectance spectra. We analyzed a temporally dense time series of 22 RapidEye scenes of a piñon-juniper woodland in central New Mexico acquired before and after stress was induced by girdling. We found that the Normalized Difference Red-Edge index (NDRE) allowed stress to be detected 13 days after girdling — between and 16 days earlier than broadband spectral indices such as the Normalized Difference Vegetation Index (NDVI) and Green NDVI traditionally used for satellite based forest health monitoring. We conclude that red-edge information has the potential to considerably improve forest stress monitoring from satellites and warrants further investigation in other forested ecosystems.     

Neural network estimation of chlorophyll a from MERIS full resolution data for the coastal waters of Galician rias (NW Spain)

In typical Case 2 waters, accurate remote sensing retrieval of chlorophyll a (chla) is still a challenging task. In this study, focusing on the Galician rias (ΝW Spain), algorithms based on neural network (NN) techniques were developed for the retrieval of chla concentration in optically complex waters, using Medium Resolution Imaging Spectrometer (MERIS) data. There is considerable interest in the accurate estimation of chla for the Galician rias, because of the economic and social importance of the extensive culture of mussels, and the high frequency of harmful algal events. Fifteen MERIS full resolution (FR) cloud-free images paired with in situ chla data (for 2002-2004 and 2006-2008) were used for the development and validation of the NN. The scope of NN was established from the clusters obtained using fuzzy c-mean (FCM) clustering techniques applied to the satellite-derived data. Three different NNs were developed: one including the whole data set, and two others using only points belonging to one of the clusters. The input data for these latter two NNs was chosen depending on the quality level, defined on the basis of quality flags given to each data set. The fitting results were fairly good and proved the capability of the tool to predict chla concentrations in the study area. The best prediction was given for the NN trained with high-quality data using the most abundant cluster data set. The performance parameters in the validation set of this NN were R² = 0.86, mean percentage error (MPE) = − 0.14, root mean square error (RMSE) = 0.75 mg m^− 3, and relative RMSE = 66%. The NN developed in this study detected accurately the peaks of chla, in both training and validation sets. The performance of the Case-2-Regional (C2R) algorithm, routinely used for MERIS data, was also tested and compared with our best performing NN and the sea-truthing data. Results showed that this NN outperformed the C2R, giving much higher R² and lower RMSE values.This study showed that the combination of in situ data and NN technology improved the retrieval of chla in Case 2 waters, and could be used to obtain more accurate chla maps. A local-based algorithm for the chla retrieval from an ocean colour sensor with the characteristics of MERIS would be a great support in the quantitative monitoring and study of harmful algal events in the coastal waters of the Rias Baixas. The limitations and possible improvements of the developed chla algorithms are also discussed.     

Application of rhodamine B thiolactone to fluorescence imaging of Hg in Arabidopsis thaliana

Exposure to mercury causes severe damage to plants, animals and even humans. Concern over mercury toxicity has encouraged the development of efficient, sensitive, and selective methods for the in vivo detection of mercury. Although a variety of studies have been published describing fluorescence imaging of mercury in animal cells and tissues, no in vivo monitoring has been developed for plant systems until now. In this paper, we report the semi-quantitative fluorescence imaging of Hg²⁺ ions in a common model plant Arabidopsis thaliana (A. thaliana), with rhodamine B thiolactone (RBS) as a novel Hg²⁺ probe. The experimental results show that RBS is plant cell wall and cell membrane permeable, and the probe responds selectively to Hg²⁺ ions instead of the other species in plant systems. Real-time monitoring of Hg²⁺ absorption in roots of A. thaliana by RBS shows that saturation of Hg²⁺ uptake could occur in a short period of 3 days at most. The transportation and accumulation of Hg²⁺ ions in roots of A. thaliana have also been studied, revealing that most of Hg²⁺ ions reside in root cap and meristematic zone, and only a small amount of Hg²⁺ ions can reach the maturation zone. This indicates that the interaction of Hg²⁺ ions with any Hg²⁺-philic species including proteins in these regions may be responsible for plant poisoning and even death.     

Red tide detection and tracing using MODIS fluorescence data: A regional example in SW Florida coastal waters

Near real-time data from the MODIS satellite sensor was used to detect and trace a harmful algal bloom (HAB), or red tide, in SW Florida coastal waters from October to December 2004. MODIS fluorescence line height (FLH in W m^− 2 μm^− 1 sr^− 1) data showed the highest correlation with near-concurrent in situ chlorophyll-a concentration (Chl in mg m^− 3). For Chl ranging between 0.4 to 4 mg m^− 3 the ratio between MODIS FLH and in situ Chl is about 0.1 W m^− 2 μm^− 1 sr^− 1 per mg m^− 3 chlorophyll (Chl = 1.255 (FLH × 10)^0.86, r = 0.92, n = 77). In contrast, the band-ratio chlorophyll product of either MODIS or SeaWiFS in this complex coastal environment provided false information. Errors in the satellite Chl data can be both negative and positive (3-15 times higher than in situ Chl) and these data are often inconsistent either spatially or temporally, due to interferences of other water constituents. The red tide that formed from November to December 2004 off SW Florida was revealed by MODIS FLH imagery, and was confirmed by field sampling to contain medium (10⁴ to 10⁵ cells L^− 1) to high (> 10⁵ cells L^− 1) concentrations of the toxic dinoflagellate Karenia brevis. The FLH imagery also showed that the bloom started in mid-October south of Charlotte Harbor, and that it developed and moved to the south and southwest in the subsequent weeks. Despite some artifacts in the data and uncertainty caused by factors such as unknown fluorescence efficiency, our results show that the MODIS FLH data provide an unprecedented tool for research and managers to study and monitor algal blooms in coastal environments.     

Application of hyperspectral vegetation indices to detect variations in high leaf area index temperate shrub thicket canopies

Accurate measurement of leaf area index (LAI), an important characteristic of plant canopies directly linked to primary production, is essential for monitoring changes in ecosystem C stocks and other ecosystem level fluxes. Direct measurement of LAI is labor intensive, impractical at large scales and does not capture seasonal or annual variations in canopy biomass. The need to monitor canopy related fluxes across landscapes makes remote sensing an attractive technique for estimating LAI. Many vegetation indices, such as Normalized Difference Vegetation Index (NDVI), tend to saturate at LAI levels > 4 although tropical and temperate forested ecosystems often exceed that threshold. Using two monospecific shrub thickets as model systems, we evaluated the potential of a variety of algorithms specifically developed to improve accuracy of LAI estimates in canopies where LAI exceeds saturation levels for other indices. We also tested the potential of indices developed to detect variations in canopy chlorophyll to estimate LAI because of the direct relationship between total canopy chlorophyll content and LAI. Indices were evaluated based on data from direct (litterfall) and indirect measurements (LAI-2000) of LAI. Relationships between results of direct and indirect ground-sampling techniques were also evaluated. For these two canopies, the indices that showed the highest potential to accurately differentiate LAI values > 4 were derivative indices based on red-edge spectral reflectance. Algorithms intended to improve accuracy at high LAI values in agricultural systems were insensitive when LAI exceeded 4 and offered little or no improvement over NDVI. Furthermore, indirect ground-sampling techniques often used to evaluate the potential of vegetation indices also saturate when LAI exceeds 4. Comparisons between hyperspectral vegetation indices and a saturated LAI value from indirect measurement may overestimate accuracy and sensitivity of some vegetation indices in high LAI communities. We recommend verification of indirect measurements of LAI with direct destructive sampling or litterfall collection, particularly in canopies with high LAI.     

Comparison of two hyperspectral imaging and two laser-induced fluorescence instruments for the detection of zinc stress and chlorophyll concentration in bahia grass (Paspalum notatum Flugge.)

Bahia grass (Paspalum notatum Flugge.) plants were grown in silica sand and irrigated daily with one of five levels of Zn (0, 0.5, 25, 50, or 100 mg l⁻¹) to determine the effects of the heavy metal on the growth and development of plant canopies. Healthy and stressed plants were measured with two hyperspectral imagers, laser-induced fluorescence spectroscopy (LIFS), and laser-induced fluorescence imaging (LIFI) systems in order to determine if the four handheld remote sensing instruments were equally capable of detecting plant stress and measuring canopy chlorophyll levels in bahia grass. Symptoms of bahia grass plants grown at deficient (0 mg l⁻¹) or toxic (25, 50, or 100 mg l⁻¹) concentrations of Zn were dominated by leaf chlorosis and plant stunting. Leaf fresh weight, leaf dry weight, CO₂ assimilation, total chlorophyll, and leaf thickness followed (+) quadratic models in which control plants (0.5 mg l⁻¹ Zn) exhibited higher responses than plants grown at either deficient or toxic levels of Zn. Normalized difference vegetation index [NDVI=(NIR−Red)/(NIR+Red)] and ratio vegetation index [RVI=R₇₅₀/R₇₀₀, in which R denotes reflectance] values were calculated for calibrated digital images from both hyperspectral imagers. The NDVI and RVI values from both hyperspectral imagers were fit best by (+) quadratic models when treatments were constrained between 0 and 100 mg l⁻¹ Zn, but were fit best by linear regression models with (−) slopes when treatments were constrained between 0.5 and 100 mg l⁻¹ Zn. Furthermore, both NDVI and RVI algorithms were effective in predicting the concentrations of chlorophyll in canopies of bahia grass grown at the various levels of Zn. In contrast, red/far-red (R/FR) fluorescence ratios estimated from leaf fluorescence values measured with the LIFS and LIFI instruments were fit best by (−) quadratic models when treatments were constrained between 0 and 100 mg l⁻¹ Zn, but were fit best by linear regression models with (+) slopes when treatments were constrained between 0.5 and 100 mg l⁻¹ Zn. A series of regression analyses were conducted among plant biometric, biochemical, and leaf anatomical parameters (treated as independent variables) and the remote sensing algorithms, NDVI, RVI, blue/green (BL/GR), and R/FR (treated as dependant variables). In general, residuals were significantly higher for NDVI and RVI models compared to the BL/GR and R/FR models indicating that the NDVI and RVI algorithms were able to measure total chlorophyll and plant biomass more accurately than the BL/GR and R/FR algorithms. However, unique capabilities of LIFS and LIFI instruments continue to argue for the development of laser-induced fluorescence remote sensing technologies.     

A class-based approach to characterizing and mapping the uncertainty of the MODIS ocean chlorophyll product

Global chlorophyll products derived from NASA's ocean color satellite programs have a nominal uncertainty of ± 35%. This metric has been hard to assess, in part because the data sets for evaluating performance do not reflect the true distribution of chlorophyll in the global ocean. A new technique is introduced that characterizes the chlorophyll uncertainty associated with distinct optical water types, and shows that for much of the open ocean the relative error is under 35%. This technique is based on a fuzzy classification of remote sensing reflectance into eight optical water types for which error statistics have been calculated. The error statistics are based on a data set of coincident MODIS Aqua satellite radiances and in situ chlorophyll measurements. The chlorophyll uncertainty is then mapped dynamically based on fuzzy memberships to the optical water types. The uncertainty maps are thus a separate, companion product to the standard MODIS chlorophyll product.     

Automatic moment segmentation and peak detection analysis of heart sound pattern via short-time modified Hilbert transform

This paper proposes a novel automatic method for the moment segmentation and peak detection analysis of heart sound (HS) pattern, with special attention to the characteristics of the envelopes of HS and considering the properties of the Hilbert transform (HT). The moment segmentation and peak location are accomplished in two steps. First, by applying the Viola integral waveform method in the time domain, the envelope (E_T) of the HS signal is obtained with an emphasis on the first heart sound (S1) and the second heart sound (S2). Then, based on the characteristics of the E_T and the properties of the HT of the convex and concave functions, a novel method, the short-time modified Hilbert transform (STMHT), is proposed to automatically locate the moment segmentation and peak points for the HS by the zero crossing points of the STMHT. A fast algorithm for calculating the STMHT of E_T can be expressed by multiplying the E_T by an equivalent window (W_E). According to the range of heart beats and based on the numerical experiments and the important parameters of the STMHT, a moving window width of N = 1 s is validated for locating the moment segmentation and peak points for HS. The proposed moment segmentation and peak location procedure method is validated by sounds from Michigan HS database and sounds from clinical heart diseases, such as a ventricular septal defect (VSD), an aortic septal defect (ASD), Tetralogy of Fallot (TOF), rheumatic heart disease (RHD), and so on. As a result, for the sounds where S2 can be separated from S1, the average accuracies achieved for the peak of S1 (AP₁), the peak of S2 (AP₂), the moment segmentation points from S1 to S2 (AT₁₂) and the cardiac cycle (ACC) are 98.53%, 98.31% and 98.36% and 97.37%, respectively. For the sounds where S1 cannot be separated from S2, the average accuracies achieved for the peak of S1 and S2 (AP₁₂) and the cardiac cycle ACC are 100% and 96.69%.     

Extending separation logic with fixpoints and postponed substitution

We are interested in separation-logic-based static analysis of programs that use shared mutable data structures. In this paper, we introduce backward and forward analysis for a separation logic called BI^μν${\mathit{BI}}^{\mu \nu }$, an extension of separation logic [Ishtiaq and O’Hearn, BI as an assertion language for mutable data structures, in: POPL’01, 2001, pp. 14–26], to which we add fixpoint connectives and postponed substitution. This allows us to express recursive definitions within the logic as well as the axiomatic semantics of while statements.     

Electrochemical genosensor based on modified octadecanethiol self-assembled monolayer for Escherichia coli detection

An electrochemical genosensor based on 1-fluoro-2-nitro-4-azidobenzene (FNAB) modified octadecanethiol (ODT) self-assembled monolayer (SAM) has been fabricated for Escherichia coli detection. The results of electrochemical response measurements investigated using methylene blue (MB) as a redox indicator reveal that this nucleic acid sensor has 60 s of response time, high sensitivity (0.5 × 10⁻¹⁸ M) and linearity as 0.5 × 10⁻¹⁸-1 × 10⁻⁶ M. The sensor has been found to be stable for about four months and can be used about ten times. It is shown that water borne pathogens like Klebsiella pneumonia, Salmonella typhimurium and other gram-negative bacterial samples has no significant effects in the response of this sensor.     

Estimating phytoplankton biomass in coastal waters of Alaska using airborne remote sensing

Empirical airborne remote-sensing relationships were examined to estimate chlorophyll a concentration in the first optical depth (chl_FOD) of coastal waters of Afgonak/Kodiak Islands during July-August 2002. Band-ratio and spectral-curvature models were tested using satellite remote-sensing reflectance (R_rs(λ)) measurements. Additional shipboard and airborne R_rs(λ) data were also analysed to evaluate consistency of proposed chl_FOD-R_rs(λ) relationships. Validation of chlorophyll algorithms was performed using data collected in the northern-part of the Gulf of Alaska and Bering Sea during 1996, 2002, and 2003 cruises. Likewise, oceanographic conditions during the surveys were typified to interpret variability of chl_FOD fields. The SeaWiFS band-ratio algorithm OC2d was the most sensitive R_rs combination (R_rs(509)/R_rs(553)) to detect chl_FOD variability. Conversely, OC2a (R_rs(412)/R_rs(553)) had the lowest performance to derive chl_FOD values. No valid statistical regressions were established for spectral-curvature relationships in the blue spectrum (< 500 nm). Fertile waters (> 5 mg m^− 3) were preferentially located over shallow banks (∼50 m) and at the entrance of the bays. The approach used in this study to derive chl_FOD values could be universal for Alaskan coastal waters. However, chl_FOD-R_rs(λ) relationships must be calibrated locally for a given season.     

Prediction of species specific forest inventory attributes using a nonparametric semi-individual tree crown approach based on fused airborne laser scanning and multispectral data

While forest inventories based on airborne laser scanning data (ALS) using the area based approach (ABA) have reached operational status, methods using the individual tree crown approach (ITC) have basically remained a research issue. One of the main obstacles for operational applications of ITC is biased results often experienced due to segmentation errors. In this article, we propose a new method, called “semi-ITC” that overcomes the main problems related to ITC by imputing ground truth data within crown segments from the nearest neighboring segment. This may be none, one, or several trees. The distances between segments were derived based on a set of explanatory variables using two nonparametric methods, i.e., most similar neighbor inference (MSN) and random forest (RF). RF favored the imputation of common observations in the data set which resulted in significant biases. Main conclusions are therefore based on MSN. The explanatory variables were calculated by means of small footprint ALS and multispectral data. When testing with empirical data the new method compared favorably to the well-known ABA. Another advantage of the new method over the ABA is that it allowed for the modeling of rare tree species. The results of predicting timber volume with the semi-ITC method were unbiased and the root mean squared error (RMSE) on plot level was smaller than the standard deviation of the observed response variables. The relative RMSEs after cross validation using semi-ITC for total volume and volume of the individual species pine, spruce, birch, and aspen on plot level were 17, 38, 40, 101, and 222%, respectively. Due to the unbiasedness of the estimation, this study is a showcase for how to use crown segments resulting from ITC algorithms in a forest inventory context.     

Mapping nonnative plants using hyperspectral imagery

Nonnative plant species are causing enormous ecological and environmental impacts from local to global scale. Remote sensing images have had mixed success in providing spatial information on land cover characteristics to land managers that increase effective management of invasions into native habitats. However, there has been limited evaluation of the use of hyperspectral data and processing techniques for mapping specific invasive species based on their spectral characteristics. This research evaluated three different methods of processing hyperspectral imagery: minimum noise fraction (MNF), continuum removal, and band ratio indices for mapping iceplant (Carpobrotus edulis) and jubata grass (Cortaderia jubata) in California's coastal habitat. Validation with field sampling data showed high mapping accuracies for all methods for identifying presence or absence of iceplant (97%), with the MNF procedure producing the highest accuracy (55%) when the classes were divided into four different densities of iceplant.