Satellite data, Empirical Orthogonal Functions, and the 1997-1998 El Niño off California

The features of the 1997-1998 El Niño event were analyzed by Empirical Orthogonal Functions (EOF) statistical methods applied to the remotely sensed sea surface temperature anomalies (SSTA) measured by AVHRR radiometers; anomalies of water circulation derived from sea surface height anomalies (SSHA) measured by TOPEX/Poseidon radar altimeter; and meteorological information (air temperature, upwelling index, and wind stress curl). EOF statistics demonstrated the features of an El Niño event during the second half of 1997 and the first half of 1998, with sea level elevated along the coast and with SSHA gradients, indicating a retarding of both the equatorward California Current and the alongshore poleward Southern California Countercurrent. The positive SST anomaly developed first in the Southern California Bight and then in the zone of upwelling to the north of Point Conception. The anomalies of upwelling index and the wind stress curl pattern also changed during the El Niño event, but these changes occurred later than hydrographic variations and were too weak to explain the observed changes in SSTA and SSHA. We conclude that off central and southern California oceanic teleconnection (i.e., the consequences of propagation northward of coastally trapped downwelling waves) was responsible for the 1997-1998 El Niño event.     

Response of Vietnam coastal upwelling to the 1997-1998 ENSO event observed by multisensor data

In this paper, we examine the behavior of the Vietnam coastal upwelling during the 1997-1998 El Niño-Southern Oscillation (ENSO) event. The baseline is 4 years of National Oceanic and Atmospheric Administration (NOAA) satellite Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature (SST) data taken from 1997 to 2000. Comparison of upwelling images to simultaneous ERS-2 (European Remote Sensing Satellite) wind fields indicates that the summer monsoon winds constitute a major generation forcing. During the 1997 El Niño, the monsoon winds enhanced the upwelling and induced the upwelling center to move southward. During the 1998 La Niña, the monsoon winds weakened the upwelling. In contrast with the tropical Pacific, in the study area, La Niña implies a warm event and El Niño a cold event. We use empirical orthogonal function (EOF) methods to analyze the spatial and temporal variance of the upwelling. The three principal modes account for 37%, 15%, and 8% of the total variance, respectively. The first EOF modes reveal that the SST variance in the north and south subregions underwent a positive-negative sign switch in summer 1997. The second EOF modes represent the monthly evolution in normal years. The third modes seem to be sensitive to the 1998 La Niña event. Simultaneous TOPEX/POSEIDON and ERS-2 altimeter data provide further evidence for our analysis. Comparison with California coastal upwelling and mid-Atlantic Bight (MAB) coastal upwelling indicates that the Vietnam coastal upwelling is the most intensive one.     

Characteristics of atmospheric divergence and convergence in the Indian Ocean inferred from scatterometer winds

Large-scale surface atmospheric convergence and divergence patterns in the Indian Ocean are mapped using high-spatial resolution, merged scatterometer wind vectors during 1991-2000. The convergence zone evolves to north of 15°S as a result of convection promoted by warm (> 28 °C) equatorial sea surface temperature (SST), and it exhibits strong intensity during boreal summer and winter. A divergence zone evolves to the south of 15°S as a result of subdued convection caused by colder SST (< 24 °C) that reduces outgoing long-wave radiation; it exhibits enhanced intensity in the eastern Indian Ocean during boreal winter. The interannual variability shows that the divergence in the eastern Indian Ocean lags its western counterpart by 5-7 months. The convergence in the eastern Indian Ocean is stronger than its western counterpart during boreal summer. Relationship between Southern Oscillation Index and spatially averaged convergence time series indicate that the latter weakened during strong El Niño years 1994 and 1997. Spatially averaged divergence time series show a near-contemporaneous relationship with all-India rainfall, with a temporal lag of 1∼2 months.     

Annual and interannual (ENSO) variability of spatial scaling properties of a vegetation index (NDVI) in Amazonia

The space-time variability of the Normalized Difference Vegetation Index (NDVI) over the Amazon River basin is quantified through the bi-dimensional Fourier spectrum, and moment-scaling analysis of monthly imagery at 8 km resolution, for the period July 1981-November 2002. Monthly NDVI fields exhibit power law Fourier spectra, E(k)=ck^−β, with k denoting the wavenumber, c the prefactor, and β the scaling exponent. Fourier spectra exhibit two scaling regimes separated at approximately 29 km, above which NDVI exhibit long-range spatial correlations (0<β<2), and below which NDVI behaves like white noise in space (β?0). Series of monthly values of c(t) and β(t) exhibit high negative correlation (−0.88, P>0.99), which suggest their linkages in power laws, but also that E_t(k)=c(t)k^−β(t), with t the time index. Results show a significant negative simultaneous correlation (−0.82, P>0.95) between monthly series of average precipitation over the Amazon, 〈P(t)〉, and scaling exponents, β(t); and high positive lagged correlation (0.63, P>0.95), between 〈P(t)〉 and 〈NDVI(t+3)〉. Parameters also reflect the hydrological seasonal cycle over Amazonia: during the wet season (November-March), β(t) ranges between 0.9 and 1.15, while during the dry season (May-September), β(t)?1.30. These results reflect the more (less) coherent spatial effect of the dry (wet) season over Amazonia, which translates into longer (shorter)-range spatial correlations of the NDVI field, as witnessed by higher (lower) values of β(t). At interannual timescales, both phases of ENSO reflect on both parameters, as β(t) is higher during El Niño than during La Niña, due to the more coherent effects of El Niño-related dryness, whereas NDVI spatial variability is enhanced during La Niña, due to positive rainfall anomalies. Results from the moment-scale analysis indicate the existence of multi-scaling in the spatial variability of NDVI fields. Departures from single scaling exhibit also annual and interannual variability, which consistently reflect the effects from both phases of ENSO. Furthermore, departures from single scaling are independent of the order moment, q, as the PDF of departures scaled by the mean collapse to a unique distribution. These results point out that ideas of spatial scaling constitute a promising framework to synthesize important hydro-ecological processes of Amazonia.     

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.     

Forecasting binary longitudinal data by a functional PC-ARIMA model

In order to forecast time evolution of a binary response variable from a related continuous time series a functional logit model is proposed. The estimation of this model from discrete time observations of the predictor is solved by using functional principal component analysis and ARIMA modelling of the associated discrete time series of principal components. The proposed model is applied to forecast the risk of drought from El Niño phenomenon.     

Observing the coupling effect between warm pool and “rain pool” in the Pacific Ocean

Traditionally, the tropical zone is known as the “heat reservoir” of the ocean and the “firebox” of the atmosphere. The western equatorial Pacific has been identified as both the warmest portion of the heat reservoir, named “warm pool” (WP), and the hottest portion of the firebox where a huge amount of precipitation-induced latent-heat release is accumulated. The latter mirrors a fact that the western tropical Pacific is also the wettest area on the globe, termed “rain pool” (RP), where the maximum annual precipitation is observed. The accumulation of continuous satellite data has reached a point that decade-long simultaneous observations of many important geophysical parameters have become available in recent years. One such example is the availability of a concurrent dataset of sea surface temperature, oceanic precipitation, and sea surface wind field for 1993-2002 derived from NOAA/AVHRR (Advanced Very High Resolution Radiometer), TOPEX/TMR (TOPEX Microwave Radiometer), and ERS-1,2/QuikSCAT, respectively. In the present study, this dataset is used to demonstrate and investigate the coupling and covarying effects of the Pacific WP and RP, leading to a number of interesting findings on their structural similarity, locational shift, phase lag, and evolutional coherency in association with the development of and the vacillation between El Niño and La Niña events.     

Neural network estimation of LAI, fAPAR, fCover and LAI×Cab, from top of canopy MERIS reflectance data: Principles and validation

A neural network is developed to operationally estimate biophysical variables over land surfaces from the observations of the ENVISAT-MERIS instrument: the leaf area index (LAI), the fraction of absorbed photosynthetically active radiation (fAPAR), the fraction of vegetation cover (fCover), and the canopy chlorophyll content (LAI×C_ab). The neural network requires as input the geometry of observation and the top of canopy reflectances, corrected from the atmospheric effects, in eleven spectral bands. It is trained on a reflectance database made of radiative transfer model simulations. The principles underlying the generation of the database and the design of the network are first presented. The estimated variables are then compared to other existing products, LAI- and fAPAR-MODIS and MGVI-MERIS, and validated against ground measurements performed in the framework of the VALERI project. Results show remarkable consistency of the temporal dynamics between the several products with however some differences in the range of variation. When compared to actual VALERI ground measurements, the proposed algorithm shows the best performances for LAI (RMSE = 0.47) and fAPAR (RMSE = 0.09).     

Mapping canopy damage from understory fires in Amazon forests using annual time series of Landsat and MODIS data

Understory fires in Amazon forests alter forest structure, species composition, and the likelihood of future disturbance. The annual extent of fire-damaged forest in Amazonia remains uncertain due to difficulties in separating burning from other types of forest damage in satellite data. We developed a new approach, the Burn Damage and Recovery (BDR) algorithm, to identify fire-related canopy damages using spatial and spectral information from multi-year time series of satellite data. The BDR approach identifies understory fires in intact and logged Amazon forests based on the reduction and recovery of live canopy cover in the years following fire damages and the size and shape of individual understory burn scars. The BDR algorithm was applied to time series of Landsat (1997-2004) and MODIS (2000-2005) data covering one Landsat scene (path/row 226/068) in southern Amazonia and the results were compared to field observations, image-derived burn scars, and independent data on selective logging and deforestation. Landsat resolution was essential for detection of burn scars < 50 ha, yet these small burns contributed only 12% of all burned forest detected during 1997-2002. MODIS data were suitable for mapping medium (50-500 ha) and large (> 500 ha) burn scars that accounted for the majority of all fire-damaged forests in this study. Therefore, moderate resolution satellite data may be suitable to provide estimates of the extent of fire-damaged Amazon forest at a regional scale. In the study region, Landsat-based understory fire damages in 1999 (1508 km²) were an order of magnitude higher than during the 1997-1998 El Niño event (124 km² and 39 km², respectively), suggesting a different link between climate and understory fires than previously reported for other Amazon regions. The results in this study illustrate the potential to address critical questions concerning climate and fire risk in Amazon forests by applying the BDR algorithm over larger areas and longer image time series.     

Interannual variability in water storage over 2003-2008 in the Amazon Basin from GRACE space gravimetry, in situ river level and precipitation data

We investigate the interannual variability over 2003-2008 of different hydrological parameters in the Amazon river basin: (1) vertically-integrated water storage from the GRACE space gravimetry mission, (2) surface water level of the Amazon River and its tributaries from in situ gauge stations, and (3) precipitation. We analyze the spatio-temporal evolution of total water storage from GRACE and in situ river level along the Amazon River and its main tributaries and note significant differences between the various parts of the basin. We also perform an Empirical Orthogonal Decomposition of total water storage, river level and precipitation over the whole basin. We find that the 2003-2008 period, is characterized by two major hydrological events: a temporary drought in late 2005 that affected the western and central parts of the basin and very wet conditions peaking in mid-2006, in the eastern, northern and southern regions of the basin. Derivative of basin-average water storage from GRACE is shown to be highly correlated with the Southern Oscillation Index (a proxy of ENSO — El Niño-Southern Oscillation), confirming that the spatio-temporal change in hydrology of the Amazon basin is at least partly driven by the ENSO phenomenon, as noticed in previous studies.     

Remote estimation of chl-a concentration in turbid productive waters — Return to a simple two-band NIR-red model?

Today the water quality of many inland and coastal waters is compromised by cultural eutrophication in consequence of increased human agricultural and industrial activities. Remote sensing is widely applied to monitor the trophic state of these waters. This study investigates the performance of near infrared-red models for the remote estimation of chlorophyll-a concentrations in turbid productive waters and evaluates several near infrared-red models developed within the last 34 years. Three models were calibrated for a dataset with chlorophyll-a concentrations from 0 to 100 mg m⁻³ and validated for independent and statistically different datasets with chlorophyll-a concentrations from 0 to 100 mg m⁻³ and 0 to 25 mg m⁻³ for the spectral bands of the MEdium Resolution Imaging Spectrometer (MERIS) and MODerate resolution Imaging Spectroradiometer (MODIS). The MERIS two-band model estimated chlorophyll-a concentrations slightly more accurately than the more complex models, with mean absolute errors of 2.3 mg m⁻³ for chlorophyll-a concentrations from 0 to 100 mg m⁻³ and 1.2 mg m⁻³ for chlorophyll-a concentrations from 0 to 25 mg m⁻³. Comparable results from several near infrared-red models with different levels of complexity, calibrated for inland and coastal waters around the world, indicate a high potential for the development of a simple universally applicable near infrared-red algorithm.     

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.     

Seasonal-to-decadal modes of global sea level variability derived from merged altimeter data

Motivated by the first availability of more than 16 years (1992-2009) of merged altimeter data from up to four concurrent satellite missions with TOPEX/Poseidon, ERS-1 and 2, ENVISAT, Jason-1, and Geosat Follow-On, the global sea level is reexamined with special emphasis on its distinctive scales of variability. The global sea level variability is dominated by eleven statistically significant and geographically robust principal modes at seasonal-to-decadal timescales. The identified modes can be divided into four regimes, i.e., the seasonal regime with two principal components at 3 and 6 months, the annual regime with a single 12-month component of global dominance, the interannual regime with seven principal components at 1.55, 1.74, 1.94, 2.34, 3.07, 4.20, and 5.40 years, as well as the decadal regime with a 9.28-year subdecadal component. The frequency dispersion of the identified modes is found to be at ∼ 0% level for the annual regime, at ∼ 5% level for the seasonal and interannual regimes, and at ∼ 10% level for the decadal regime. In the space domain, the locations of principal modes are found to be geographically correlated. The seasonal regime is characterized by a small number of localized amplitude highs in regional seas. The annual regime is associated with major western boundary currents of the Gulf Stream and Kuroshio. For the interannual regime, mode active zones are observed in the western Pacific warm pool (WPWP), El Niño, and western Indian Ocean regions, among which the WPWP has a well-defined two-core structure, and the northern core coincides with the most energetic zone of the decadal regime. Another interesting finding is that east-west (zonal) and equatorial-extratropical (meridional) teleconnections are evident for the major signals in a rather symmetric and systematic pattern. Joint spatiotemporal analysis reveals that multi-modality at a given location and multi-regionality for a given mode are fundamental features in global sea level variability.     

Monitoring canopy biophysical and biochemical parameters in ecosystem scale using satellite hyperspectral imagery: An application on a Phlomis fruticosa Mediterranean ecosystem using multiangular CHRIS/PROBA observations

This study focuses on the potential of satellite hyperspectral imagery to monitor vegetation biophysical and biochemical characteristics through narrow-band indices and different viewing angles. Hyperspectral images of the CHRIS/PROBA sensor in imaging mode 1 (5 observation angles, 62 bands, 410-1005 nm) were acquired throughout a two-year period for a Mediterranean ecosystem fully covered by the semi-deciduous shrub Phlomis fruticosa. During each acquisition, coincident ecophysiological field measurements were conducted. Leaf area index (LAI), leaf biochemical content (chlorophyll a, chlorophyll b, carotenoids) and leaf water potential were measured. The hyperspectral images were corrected for coherent noises, cloud and atmosphere, in order to produce ground reflectance images. The reflectance spectrum of each image was used to calculate a variety of vegetation indices (VIs) that are already published in relevant literature. Additionally, all combinations of the 62 bands were used in order to calculate Normalized Difference Spectral Indices (NDSI_(x,y)) and Simple Subtraction Indices (SSI_(x,y)). The above indices along with raw reflectance and reflectance derivatives were examined for linear relationship with the ground-measured variables and the strongest relationships were determined. It is concluded that higher observation angles are better for the extraction of biochemical indices. The first derivative of the reflectance spectra proved to be very useful in the prediction of all measured variables. In many cases, complex and improved spectral indices that are proposed in the literature do not seem to be more accurate than simple NDSIs such as NDVI. Even traditional broadband NDVI is proved to be adequate in LAI prediction, while green bands seem also very useful. However, in biochemical estimation narrow bands are necessary. Indices that incorporate red, blue and IR bands, such as PSRI, SIPI and mNDVI presented good performance in chlorophyll estimation, while CRI did not show any relevance to carotenoids and WI was poorly correlated to water potential. Moreover, analyses indicated that it is very important to use a near red-edge band (701 nm) for effective chlorophyll index design. SSIs that incorporate 701 nm with 511 or 605 nm showed best performance in chlorophyll determination. For carotenoid estimation, a band on the edge of carotenoid absorption (511 nm) combined with a red band performed best, while a normalized index of two water absorption bands (945, 971 nm) proved to be an effective water index. Finally, the attempt to investigate stress conditions through pigment ratios resulted in the use of the band centred at 701 nm.     

Simultaneous measurements of plant structure and chlorophyll content in broadleaf saplings with a terrestrial laser scanner

Plant structure and chlorophyll content strongly affect rates of photosynthesis. Rapid, objective, and repeatable methods are needed to measure these vegetative parameters to advance our understanding and modeling of plant ecophysiological processes. Terrestrial laser scanners (TLS) can be used to measure structural and potentially chemical properties of objects by quantifying the x,y,z coordinates and intensity of laser light, respectively, returned from an object's surface. The objective of this study was to determine the potential usefulness of TLS with a green (532 nm) laser to simultaneously measure the spatial distribution of chlorophyll a and b content (Chl_ab), leaf area (LA), and leaf angle (LAN). The TLS measurements were obtained from saplings of two tree species (Quercus macrocarpa and Acer saccharum) and from an angle-adjustable cardboard surface. The green laser return intensity value was strongly correlated with wet-chemically determined Chl_ab (r² = 0.77). Strong agreement was shown between measured and TLS-derived LA (r² = 0.95, intercept = − 1.43, slope = 0.97). The TLS derived LANs of both species followed a plagiophile LAN distribution, and the measured angles of the cardboard surface allowed us to quantify that these LAN values were strongly correlated with TLS derived angles (r² = 1.0, intercept and slope = 0.98). Our results show that terrestrial laser scanners are feasible for simultaneous measurement of LA, LAN, and Chl_ab in simple canopies of small broadleaved plants. Further research is needed in more complex and larger canopies.     

Constructing vertex-disjoint paths in (n, k)-star graphs

This work describes a novel routing algorithm for constructing a container of width n − 1 between a pair of vertices in an (n, k)-star graph with connectivity n − 1. Since Lin et al. [T.C. Lin, D.R. Duh, H.C. Cheng, Wide diameter of (n, k)-star networks, in: Proceedings of the International Conference on Computing, Communications and Control Technologies, vol. 5, 2004, pp. 160-165] already calculated the wide diameters in (n, n − 1)-star and (n, 1)-star graphs, this study only considers an (n, k)-star with 2 ? k ? n − 2. The length of the longest container among all constructed containers serves as the upper bound of the wide diameter of an (n, k)-star graph. The lower bound of the wide diameter of an (n, k)-star graph with 2 ? k ? ⌊n/2⌋ and the lower bound of the wide diameter of a regular graph with a connectivity of 2 or above are also computed. Measurement results indicate that the wide diameter of an (n, k)-star graph is its diameter plus 2 for 2 ? k ? ⌊n/2⌋, or its diameter plus a value between 1 and 2 for ⌊n/2⌋ + 1 ? k ? n − 2.     

Estimation of cyanobacterial pigments in a freshwater lake using OCM satellite data

Cyanobacteria represent a major harmful algal group in fresh to brackish water environments. Lac des Allemands, a freshwater lake of 49 km² southwest of New Orleans, Louisiana on the upper end of the Barataria Estuary, provides a natural laboratory for remote characterization of cyanobacterial blooms because of their seasonal occurrence. The Oceansat-1 satellite Ocean Colour Monitor (OCM) provides measurements similar to SeaWiFS but with higher spatial resolution, and this work is the first attempt to use OCM measurements to quantify cyanobacterial pigments. The satellite signal was first vicariously calibrated using SeaWiFS as a reference, and then corrected to remove the atmospheric effects using a customized atmospheric correction procedure. Then, empirical inversion algorithms were developed to convert the OCM remote sensing reflectance (R_rs) at bands 4 and 5 (centered at 510.6 and 556.4 nm, respectively) to concentrations of phycocyanin (PC), the primary cyanobacterial pigment. A holistic approach was used to minimize the influence of other optically active constituents on the PC algorithm. Similarly, empirical algorithms to estimate chlorophyll a (Chl a) concentrations were developed using OCM bands 5 and 6 (centered at 556.4 and 669 nm, respectively). The best PC algorithm (R² = 0.7450, p < 0.0001, n = 72) yielded a root mean square error (RMSE) of 36.92 μg/L with a relative RMSE of 10.27% (PC from 2.75 to 363.50 μg/L, n = 48). The best algorithm for Chl a (R² = 0.7510, p < 0.0001, n = 72) produced an RMSE of 31.19 μg/L with a relative RMSE of 16.56% (Chl a from 9.46 to 212.76 μg/L, n = 48). While more field data are required to further validate the long-term performance of these algorithms, currently they represent the best protocol for establishing a long time-series of cyanobacterial blooms in the Lac des Allemands using OCM data.     

Imaging chlorophyll fluorescence with an airborne narrow-band multispectral camera for vegetation stress detection

Progress in assessing the feasibility for imaging fluorescence using the O₂-A band with 1 nm full-width half-maximum (FWHM) bands centered at 757.5 and 760.5 nm is reported in this paper. Multispectral airborne data was acquired at 150 m above ground level in the thermal, visible and near infrared regions yielding imagery at 15 cm spatial resolution. Simultaneous field experiments conducted in olive, peach, and orange orchards (water stress trials), and an olive orchard (variety trial) enabled the detected variability in fluorescence emission to be examined as function of stress status. In a parallel modelling activity the coupled leaf-canopy reflectance-fluorescence model, FluorMOD, was used to assess fluorescence retrieval capability by the in-filling method, as well as by fluorescence indices from the published literature. Fluorescence retrievals using the in-filling method, the derivative index D702/D680 and reflectance indices R690/R630, R761-R757, and R761/R757 yielded the best results in the simulation study, while demonstrating insensitivity to leaf area index (LAI) variation. The fluorescence in-filling method, derivative index D702/D680, and R761-R757 were the indices least affected by chlorophyll a + b (Cab) variation. On the other hand, other published indices for fluorescence detection at leaf and canopy levels exhibited high sensitivity to variations in Cab and LAI, and therefore were considered less suitable for in-field fluorescence detection. The fluorescence signal extraction from airborne imagery using the in-filling method was validated through comparisons with field-measured steady-state fluorescence (Fs) using the PAM-2100 and GFS-3000 instruments, confirming simulation predictions. The water stress experiments conducted on olive and peach orchards demonstrated the feasibility of chlorophyll fluorescence (F) extraction at the tree level from the airborne imagery, yielding determination coefficients r² = 0.57 (olive), and r² = 0.54 (peach). Consistent results were obtained between airborne F and ground truth assimilation (A) measured in the olive variety field experiment under no water stress levels, yielding r² = 0.71.     

Regional aboveground live carbon losses due to drought-induced tree dieback in piñon-juniper ecosystems

Recent large-scale dieback of piñon-juniper (P-J) woodlands and forests across the western US occurred as a result of multi-year drought and subsequent insect and disease outbreaks. P-J vegetation is spatially extensive, thus large-scale mortality events such as the one that has occurred over the past several years could significantly alter regional carbon (C) budgets. Our objective was to use a remote sensing technique coupled with field-based data to estimate changes in aboveground live C stocks across a 4100 km² region of Colorado caused by P-J tree mortality. We hypothesized that dieback would amplify the phenological dynamics of P-J vegetation, and these variations would be related to drought-induced losses of live P-J aboveground biomass (AGB) that are discernible using time-series remote sensing vegetation data. Here, we assess live P-J AGB loss using dry season fractional photosynthetic vegetation cover (PV) derived from multi-year Landsat images. Our results showed a strong linear positive relationship between the maximum decline in PV and field-measured losses of live P-J AGB during the period 2000-05 (r² = 0.64, p = 0.002). These results were then used to map AGB losses throughout the study region. Mean live aboveground C loss (± sd) was 10.0 (± 3.4) Mg C ha^− 1. Total aboveground live P-J C loss was 4.6 Tg C, which was approximately 39 times higher than the concurrent C loss attributed to wildfire and management treatments within or near to the national forests of the study region. Our results suggest that spatially extensive mortality events such as the one observed in P-J woodlands across the western US in the past decade may significantly alter the ecosystem C balance for decades to come. Remote sensing techniques to monitor changes in aboveground C stocks, such as the one developed in our study, may support regional and global C monitoring in the future.