Airborne Imaging Spectroscopy Data Research Articles

Cast Shadow Detection to Quantify the Aerosol Optical Thickness for Atmospheric Correction of High Spatial Resolution Optical Imagery

The atmospheric correction of optical remote sensing data requires the determination of aerosol and gas optical properties. A method is presented which allows the detection of the aerosol scattering effects from optical remote sensing data at spatial sampling intervals below 5 m in cloud-free situations from cast shadow pixels. The derived aerosol optical thickness distribution is used for improved atmospheric compensation. In a first step, a novel spectral cast shadow detection algorithm determines the shadow areas using spectral indices. Evaluation of the cast shadow masks shows an overall classification accuracy on an 80% level. Using the such derived shadow map, the ATCOR atmospheric compensation method is iteratively applied on the shadow areas in order to find the optimum aerosol amount. The aerosol optical thickness is found by analyzing the physical atmospheric correction of fully shaded pixels in comparison to directly illuminated areas. The shadow based aerosol optical thickness estimation method (SHAOT) is tested on airborne imaging spectroscopy data as well as on photogrammetric data. The accuracy of the reflectance values from atmospheric correction using the such derived aerosol optical thickness could be improved from 3–4% to a level of better than 2% in reflectance for the investigated test cases.

Sensitivity of Common Vegetation Indices to the Canopy Structure of Field Crops

Leaf inclination angle distribution is an important canopy structure characteristic which directly impacts the fraction of the intercepted solar radiation. Together with the leaf area index (LAI) it determines the structure and fractional cover of a homogeneous crop canopy. Unfortunately, this key canopy parameter has usually been ignored when applying common multispectral vegetation indices to the mapping of LAI, although its impact is known from model simulations. Therefore, we measured leaf angles and determined their distribution (quantified using the leaf mean tilt angle, MTA) for six crop species with different structures growing on 162 plots with a broad range of LAI (1.1–5.0) and leaf chlorophyll content (26–94 μg cm−2). Next, we calculated six vegetation indices widely used for LAI monitoring—the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the two band enhanced vegetation index (EVI2), the modified triangular vegetation index (MTVI2), the optimized soil adjusted vegetation index (OSAVI) and the wide dynamic range vegetation index (WDRVI)—from airborne imaging spectroscopy data. We calculated the Spearman’s correlation coefficient R s , a non-parametric statistic chosen because of the non-normal distribution of canopy parameters. All studied indices depended on the LAI ( 0.50 ≤ R s ≤ 0.71 ) , but the dependence on the MTA was of similar magnitude ( − 0.83 ≤ R s ≤ − 0.53 ) with EVI, EVI2, OSAVI and MTVI2 depending more strongly on MTA than on LAI. All studied indices were good proxies ( 0.78 ≤ R s ≤ 0.88 ) for vegetation fractional cover (Fcover) which, for homogeneous crop canopies, is a nonlinear function of LAI and MTA. EVI2 and MTVI2 were the most strongly correlated with Fcover, although the difference to the other studied indices was small. This first study involving a large range of crop structures confirms the results from canopy reflectance simulations and emphasizes the necessity of leaf angle information for the successful mapping of LAI with Earth observation data.

Field and Airborne Spectroscopy Cross Validation—Some Considerations

Field spectroscopy is increasingly used in various fields of science: either as a research tool in its own right or in support of airborne- or space-based optical instruments for calibration or validation purposes. Yet, while the use of the instruments appears deceptively simple, the processes of light and surface interactions are complex to be measured in full and are further complicated by the multidimensionality of the measurement process. This study exemplifies the cross validation of in situ point spectroscopy and airborne imaging spectroscopy data across all processing stages within the spectroscopy information hierarchy using data from an experiment focused on vegetation. In support of this endeavor, this study compiles the fundamentals of spectroscopy, the challenges inherent to field and airborne spectroscopy, and the best practices proposed by the field spectroscopy community. This combination of theory and case study shall enable the reader to develop an understanding of 1) some of the commonly involved sources of errors and uncertainties, 2) the techniques to collect high-quality spectra under natural illumination conditions, and 3) the importance of appropriate metadata collection to increase the long-term usability and value of spectral data.

In situ data supporting remote sensing estimation of spruce forest parameters at the ecosystem station Bílý Kříž

Remote sensing offers an effective way of mapping vegetation parameters in a spatially continuous manner, at larger spatial scales and repeatedly in time compared to traditional in situ mapping approaches that are typically accurate, but limited to a few distributed location and few repetitions. In case of forest ecosystems, remote sensing allow to assess quantitative parameters or indicators related to forest health status such as leaf area index, leaf pigment content, chlorophyll fluorescence, etc. Development, calibration and validation of remote sensing-based methods, however, still rely on supportive in situ data. The aim of this contribution is to introduce the individual in situ components in the framework for the retrieval of forest quantitative parameters from airborne imaging spectroscopy data. All measurements were acquired during an extensive in situ/flight campaign that took place at the Norway spruce dominated study site Bílý Kříž (Moravian-Silesian Beskydy Mts., Czech Republic) during August 2016. In addition to airborne remote sensing data acquisition, the in situ activities included terrestrial laser scanning for tree 3D modelling, measurements of needle biochemical and optical properties, leaf area index measurements and spectral measurements of various natural and artificial surfaces. Leaf pigments varied between 25.2 and 49.1 µg cm-2 for chlorophyll a+b content, 4.9 – 10.6 µg cm-2 for carotenoid content depending on needle age and its adaptation to sun illumination, whereas ratio between the two pigments was stable around 4.6 – 5. 3. Specific leaf area of spruce needles varied between 49.3 and 105.8 cm2 g-1, being the highest for the shade adapted needles of the current year. Leaf area index of spruce stands of various age and densities varied between 5.3 and 9. 3.

Creating Multi-Temporal Composites of Airborne Imaging Spectroscopy Data in Support of Digital Soil Mapping

An increasing demand for full spatio-temporal coverage of soil information drives the growing use of soil spectroscopy. Soil spectroscopy application performed under laboratory conditions or in-field studies in semi-arid areas have shown promising results. However, when acquiring data in temperate zones, limitations by vegetation-free coverage, variation in soil moisture and management are driving coherent spatio-temporal data collection. This study explores the use of multi-temporal imaging spectroscopy data to increase the total mapping area of bare soils in a heterogeneous agricultural landscape. Spectrally and spatially high-resolution data from the Airborne Prism Experiment (APEX) were collected in September 2013, April 2014 and April 2015. Bare soils in all acquisitions were identified. To eliminate short-term differences in soil moisture and soil surface roughness, the empirical line method was used to calibrate the reflectance values of the singular images (2013 and 2015) towards the singular image with most bare soil pixels (2014). Difference indicators show that the calibration was successful (decrease in root mean square difference and angle difference, increase in R2 and gain and offset close to one and zero). Finally, the multi-temporal composite image contained more than double the amount of bare soil pixels as compared to a singular acquisition. Summary statistics show that reflectance values of the multi-temporal composite approximate the single image data of 2014 (mean and standard deviation of 2014: 24.2 ± 8.9 vs. 24.0 ± 9.5 for the multi-temporal composite of 2013, 2014 and 2015). This indicates that global differences in soil moisture and land management have been corrected for. As a result, an improved spatial representation of soil parameters can be retrieved from the composite data. Spatial distribution of the correction factors and analysis of the spatial variability of all images, however, indicate that non-linear, short-term differences like variation in soil moisture and land management largely influence the result of the multi-temporal composite. Quantification and attribution of those factors will be required in the future to allow correcting for them.

How to predict plant functional types using imaging spectroscopy: linking vegetation community traits, plant functional types and spectral response

Summary The comparable and integrated nature of plant functional types and advances in high‐spectral‐resolution remote sensing techniques (i.e. imaging spectroscopy) make their combination highly interesting for spatially continuous and repeatable large‐scale ecosystem monitoring. Depending on physical environment and stress, plants invest in covarying biochemical and structural traits, influencing spectral characteristics of vegetation. These traits are assumed to bear a more direct causal relationship to plant functional types than to plant life/growth forms. However, the connection between a vegetation community's functional and spectral response remains to be established. We assessed the correlation structure between (i) biochemical and structural vegetation traits (biomass, dry matter content, nitrogen content, neutral detergent fibre content), (ii) plant life/growth forms and (iii) seven plant functional types of two categories (strategy types, indicator values) collected in heterogeneous alpine grassland. We then used airborne imaging spectroscopy data from the same area to model and predict plant life/growth forms and plant functional types at the vegetation community level using partial least squares regression and validated our models based on an independent data set. We found high correlations between many of the biochemical and structural vegetation traits, plant life/growth forms and plant functional types tested. Using airborne imaging spectroscopy data, we successfully modelled and predicted most plant life/growth forms (R2 max. = 0·56) and all plant functional types (R2 max. = 0·62). However, model performance for plant life/growth forms decreased substantially during external validation and overall model consistency was low (average change in R2 = 72%), while plant functional type models were much more consistent (average change in R2 = 20%). Based on our findings, we developed a conceptual framework using the theory and methodology of vegetation ecology and imaging spectroscopy to link the vegetation community's functional to its spectral signature. Our results encourage the use of plant functional types in imaging spectroscopy in order to aid the large‐scale monitoring of ecosystems, which is particularly important given the increased availability of airborne data and the prospective launches of spaceborne instruments in the near future.

Measuring landscape-scale spread and persistence of an invaded submerged plant community from airborne remote sensing.

Processes of spread and patterns of persistence of invasive species affect species and communities in the new environment. Predicting future rates of spread is of great interest for timely management decisions, but this depends on models that rely on understanding the processes of invasion and historic observations of spread and persistence. Unfortunately, the rates of spread and patterns of persistence are difficult to model or directly observe, especially when multiple rates of spread and diverse persistence patterns may be co-occurring over the geographic distribution of the invaded ecosystem. Remote sensing systematically acquires data over large areas at fine spatial and spectral resolutions over multiple time periods that can be used to quantify spread processes and persistence patterns. We used airborne imaging spectroscopy data acquired once a year for 5years from 2004 to 2008 to map an invaded submerged aquatic vegetation (SAV) community across 2220km2 of waterways in the Sacramento-San Joaquin River Delta, California, USA, and measured its spread rate and its persistence. Submerged aquatic vegetation covered 13-23km2 of the waterways (6-11%) every year. Yearly new growth accounted for 40-60% of the SAV area, ~50% of which survived to following year. Spread rates were overall negative and persistence decreased with time. From this dataset, we were able to identify both radial and saltatorial spread of the invaded SAV in the entire extent of the Delta over time. With both decreasing spread rate and persistence, it is possible that over time the invasion of this SAV community could decrease its ecological impact. A landscape-scale approach allows measurements of all invasion fronts and the spatial anisotropies associated with spread processes and persistence patterns, without spatial interpolation, at locations both proximate and distant to the focus of invasion at multiple points in time.

EVALUATION OF VARIOUS SPECTRAL INPUTS FOR ESTIMATION OF FOREST BIOCHEMICAL AND STRUCTURAL PROPERTIES FROM AIRBORNE IMAGING SPECTROSCOPY DATA

Abstract. In this study we evaluated various spectral inputs for retrieval of forest chlorophyll content (Cab) and leaf area index (LAI) from high spectral and spatial resolution airborne imaging spectroscopy data collected for two forest study sites in the Czech Republic (beech forest at Štítná nad Vláří and spruce forest at Bílý Kříž). The retrieval algorithm was based on a machine learning method – support vector regression (SVR). Performance of the four spectral inputs used to train SVR was evaluated: a) all available hyperspectral bands, b) continuum removal (CR) 645 – 710 nm, c) CR 705 – 780 nm, and d) CR 680 – 800 nm. Spectral inputs and corresponding SVR models were first assessed at the level of spectral databases simulated by combined leaf-canopy radiative transfer models PROSPECT and DART. At this stage, SVR models using all spectral inputs provided good performance (RMSE for Cab < 10 μg cm−2 and for LAI < 1.5), with consistently better performance for beech over spruce site. Since application of trained SVRs on airborne hyperspectral images of the spruce site produced unacceptably overestimated values, only the beech site results were analysed. The best performance for the Cab estimation was found for CR bands in range of 645 – 710 nm, whereas CR bands in range of 680 – 800 nm were the most suitable for LAI retrieval. The CR transformation reduced the across-track bidirectional reflectance effect present in airborne images due to large sensor field of view.

EVALUATION OF VARIOUS SPECTRAL INPUTS FOR ESTIMATION OF FOREST BIOCHEMICAL AND STRUCTURAL PROPERTIES FROM AIRBORNE IMAGING SPECTROSCOPY DATA

In this study we evaluated various spectral inputs for retrieval of forest chlorophyll content (Cab) and leaf area index (LAI) from high spectral and spatial resolution airborne imaging spectroscopy data collected for two forest study sites in the Czech Republic (beech forest at Štítná nad Vláří and spruce forest at Bílý Kříž). The retrieval algorithm was based on a machine learning method – support vector regression (SVR). Performance of the four spectral inputs used to train SVR was evaluated: a) all available hyperspectral bands, b) continuum removal (CR) 645 – 710 nm, c) CR 705 – 780 nm, and d) CR 680 – 800 nm. Spectral inputs and corresponding SVR models were first assessed at the level of spectral databases simulated by combined leaf-canopy radiative transfer models PROSPECT and DART. At this stage, SVR models using all spectral inputs provided good performance (RMSE for Cab < 10 μg cm<sup>−2</sup> and for LAI < 1.5), with consistently better performance for beech over spruce site. Since application of trained SVRs on airborne hyperspectral images of the spruce site produced unacceptably overestimated values, only the beech site results were analysed. The best performance for the Cab estimation was found for CR bands in range of 645 – 710 nm, whereas CR bands in range of 680 – 800 nm were the most suitable for LAI retrieval. The CR transformation reduced the across-track bidirectional reflectance effect present in airborne images due to large sensor field of view.

Mapping tree species diversity of a tropical montane forest by unsupervised clustering of airborne imaging spectroscopy data

With the ongoing global biodiversity loss, approaches to measuring and monitoring biodiversity are necessary for effective conservation planning, especially in tropical forests. Remote sensing has much potential for biodiversity mapping, and high spatial resolution imaging spectroscopy (IS) allows for direct prediction of tree species diversity based on spectral reflectance. The objective of this study was to test an approach for mapping tree species alpha diversity that takes advantage of an unsupervised object-based clustering. Tree species diversity of a tropical montane forest in the Taita Hills, Kenya, was mapped based on spectral variation of high spatial resolution IS data.Airborne IS data and species data from 31 field plots were collected in the study area. Species diversity measures were obtained from the IS data by clustering spectrally similar image segments representing tree crowns. In order to do this, the image was segmented to objects that represented tree crowns. Three measures of species diversity were calculated based on the field data and on the clustering results, and the relationships were statistically analyzed.According to the results, the approach succeeded well in revealing tree species diversity patterns. Especially, tree species richness was well predicted (RMSE=3 species; r2=0.50) directly based on the clustering results. The optimal number of clusters was found to be close to the estimated number of tree species in the forest. Minimum tree size was an important determinant of the relationships, because only part of the trees are visible to the airborne sensor in the multi-layered closed canopy forest.In general, the object-based approach proved to be a viable alternative to a pixel-based clustering. The approach takes advantage of the capability of IS to detect spectral differences among tree crowns, but without the need for spectral training data, which is expensive to collect. With further development, the approach could be applied also for estimating beta diversity.

Termite mounds alter the spatial distribution of African savanna tree species

AbstractAimTermite mounds form small islands of enhanced water and soil nutrient availability on otherwise dry and nutrient‐poor hill crests, which can have important impacts on the plant community. However, the way in which termite mounds alter the spatial distribution of particular tree species across broad savanna landscapes is poorly understood. We aimed to understand the nature and extent of the relationship between termite mounds and key woody savanna species at landscape scales through the use of airborne remote sensing.LocationKruger National Park, South Africa.MethodsWe mapped 9894 termite mounds and 666,679 savanna trees from 15 species across two landscapes with contrasting rainfall regimes using airborne imaging spectroscopy and LiDAR data. We then examined changes in tree species densities and community composition with respect to distance from termite mounds.ResultsIn both landscapes, termite mounds reduced overall tree densities over distances up to 10 m from mound centres. However, the effect of termite mounds on tree density differed among species, with some species, typically associated with lowland and riparian habitats, showing increased density near termite mounds. Indeed, changes in overall tree community composition revealed that termite mounds harbour tree communities similar to lowland communities, with this similarity decreasing with increased distance from the nearest mound. Termite effects were more pronounced in the savanna landscape receiving higher annual rainfall, whereas a greater percentage of the landscape was affected in the drier landscape due to higher mound densities.Main conclusionsTermite mounds mediate the spatial distribution of tree species in savanna landscapes, increasing the abundance of tree species typically associated with lowland habitats. This contributes to the spatial heterogeneity of savanna vegetation within landscapes and the maintenance of savanna biodiversity.

Operational Tree Species Mapping in a Diverse Tropical Forest with Airborne Imaging Spectroscopy.

Remote identification and mapping of canopy tree species can contribute valuable information towards our understanding of ecosystem biodiversity and function over large spatial scales. However, the extreme challenges posed by highly diverse, closed-canopy tropical forests have prevented automated remote species mapping of non-flowering tree crowns in these ecosystems. We set out to identify individuals of three focal canopy tree species amongst a diverse background of tree and liana species on Barro Colorado Island, Panama, using airborne imaging spectroscopy data. First, we compared two leading single-class classification methods—binary support vector machine (SVM) and biased SVM—for their performance in identifying pixels of a single focal species. From this comparison we determined that biased SVM was more precise and created a multi-species classification model by combining the three biased SVM models. This model was applied to the imagery to identify pixels belonging to the three focal species and the prediction results were then processed to create a map of focal species crown objects. Crown-level cross-validation of the training data indicated that the multi-species classification model had pixel-level producer’s accuracies of 94–97% for the three focal species, and field validation of the predicted crown objects indicated that these had user’s accuracies of 94–100%. Our results demonstrate the ability of high spatial and spectral resolution remote sensing to accurately detect non-flowering crowns of focal species within a diverse tropical forest. We attribute the success of our model to recent classification and mapping techniques adapted to species detection in diverse closed-canopy forests, which can pave the way for remote species mapping in a wider variety of ecosystems.

Foraging ecology of three sympatric ungulate species - Behavioural and resource maps indicate differences between chamois, ibex and red deer.

BackgroundThe spatial distribution of forage resources is a major driver of animal movement patterns. Understanding where animals forage is important for the conservation of multi-species communities, since interspecific competition can emerge if different species use the same depletable resources. However, determining forage resources in a spatially continuous fashion in alpine grasslands at high spatial resolution was challenging up to now, because terrain heterogeneity causes vegetation characteristics to vary at small spatial scales, and methods for detection of behavioural phases in animal movement patterns were not widely available. We delineated areas coupled to the foraging behaviour of three sympatric ungulate species (chamois, ibex, red deer) using Time Local Convex Hull (T-LoCoH), a non-parametric utilisation distribution method incorporating spatial and temporal autocorrelation structure of GPS data. We used resource maps of plant biomass and plant nitrogen content derived from high-resolution airborne imaging spectroscopy data, and multinomial logistic regression to compare the foraging areas of the three ungulate species.ResultsWe found significant differences in plant biomass and plant nitrogen content between the core foraging areas of chamois, ibex and red deer. Core foraging areas of chamois were characterised by low plant biomass and low to medium plant nitrogen content. Core foraging areas of ibex were, in contrast, characterised by high plant nitrogen content, but varied in plant biomass, and core foraging areas of red deer had high plant biomass, but varied in plant nitrogen content.ConclusionsPrevious studies carried out in the same study area found no difference in forage consumed by chamois, ibex and red deer. Methodologically, those studies were based on micro-histological analysis of plant fragments identifying them to plant family or functional type level. However, vegetation properties such as productivity (biomass) or plant nutrient content can vary within vegetation communities, especially in highly heterogeneous landscapes. Thus, the combination of high spatial resolution resource maps with a utilisation distribution method allowing to generate behavioural maps (T-LoCoH) provides new insights into the foraging ecology of the three sympatric species, important for their conservation and to monitor expected future changes.Electronic supplementary materialThe online version of this article (doi:10.1186/s40462-015-0033-x) contains supplementary material, which is available to authorized users.

Retrieving crop leaf tilt angle from imaging spectroscopy data

Leaf angle distribution (LAD) is an important vegetation parameter quantifying the structure of the canopy. Together with leaf area index, LAD is often assumed to fully describe the structure of herbaceous plant canopies such as field crops. To this date, only a few papers have addressed the direct estimation of LAD from near-nadir imaging spectroscopy data. Using simulations with the PROSAIL vegetation reflectance model and measurements of six field crops, we propose and evaluate two methods of determining leaf mean tilt angle (MTA), the central moment of LAD, from reflectance data in blue, red and near infrared wavebands. First, we noted that when canopy reflectance in blue (479nm) is plotted against that in red (663nm), the PROSAIL-simulated canopies with different MTAs occupied different areas in the plot. Similarly, the six crop species in the measured dataset were separated in the red–blue reflectance plane. To retrieve MTA from spectral reflectance measurements, we created rotated coordinate axes in the red–blue plane and aligned one of the axes with the largest change in MTA. The new coordinate, which was highly and near-linearly correlated with MTA (R2=0.97), was subsequently used to estimate MTA from airborne imaging spectroscopy data. The second MTA retrieval algorithm was based on earlier findings of a high correlation (R2=0.78) between MTA and reflectance at 748nm. Using a lookup table created with PROSAIL, we inverted MTA from measured crop reflectance at this far red edge wavelength. We evaluated the two MTA retrieval methods using photographically measured species-specific MTA. For the red–blue method, MTA was estimated with a root mean square difference (RMSD) of 18.7° between field measured and spectroscopically estimated values. Retrieval from crop reflectance at 748nm provided a RMSD of 11.4°. To demonstrate the generality of our methods, analyze their limitations, and explain the differences between the two retrieval algorithms, we give an analytical interpretation of the observed correlations using the spectral invariants theory.

Spectroscopic remote sensing of plant stress at leaf and canopy levels using the chlorophyll 680 nm absorption feature with continuum removal

This paper explores the use of spectral feature analysis to detect plant stress in visible/near infrared wavelengths. A time series of close range leaf and canopy reflectance data of two plant species grown in hydrocarbon-contaminated soil was acquired with a portable spectrometer. The ProSpecTIR-VS airborne imaging spectrometer was used to obtain far range hyperspectral remote sensing data over the field experiment. Parameters describing the chlorophyll 680nm absorption feature (depth, width, and area) were derived using continuum removal applied to the spectra. A new index, the Plant Stress Detection Index (PSDI), was calculated using continuum-removed values near the chlorophyll feature centre (680nm) and on the green-edge (560 and 575nm). Chlorophyll feature’s depth, width and area, the PSDI and a narrow-band normalised difference vegetation index were evaluated for their ability to detect stressed plants. The objective was to analyse how the parameters/indices were affected by increasing degrees of plant stress and to examine their utility as plant stress indicators at the remote sensing level (e.g. airborne sensor). For leaf data, PSDI and the chlorophyll feature area revealed the highest percentage (67–70%) of stressed plants. The PSDI also proved to be the best constraint for detecting the stress in hydrocarbon-impacted plants with field canopy spectra and airborne imaging spectroscopy data. This was particularly true using thresholds based on the ASD canopy data and considering the combination of higher percentage of stressed plants detected (across the thresholds) and fewer false-positives.

New ways to extract archaeological information from hyperspectral pixels

Airborne remote sensing for archaeology is the discipline that encompasses the study of archaeological remains using data collected from an airborne platform by means of digital or film-based aerial photography, airborne laser scanning, hyperspectral imaging etc. So far, airborne hyperspectral scanning or – more accurately – airborne imaging spectroscopy (AIS) has occupied only a very small niche in the field of archaeological remote sensing: besides reasons of cost, the common archaeologically-insufficient ground-sampling distance can be considered the main limiting factor. Moreover, the technical processing of these data sets with a high level of potential redundancy needs specialized software. Typically, calculation of band ratios and a principal component analysis are applied. As a result, the few practical applications of archaeological AIS have not been entirely convincing so far. The aim of this paper is to present new approaches for analysing archaeological AIS data. The imagery under study has a ground-sampling distance of 40 cm and covers the Roman town of Carnuntum (Austria). Using two algorithms embedded in a specifically developed MATLAB® toolbox, it will be shown how the extracted archaeological information can be enhanced from high-resolution hyperspectral images. A comparison with simultaneously acquired vertical photographs will indicate the specific advantages of high-resolution AIS data and the gain one can obtain when exploiting its potential using any of the newly presented methods.

Modelling plant species distribution in alpine grasslands using airborne imaging spectroscopy.

Remote sensing using airborne imaging spectroscopy (AIS) is known to retrieve fundamental optical properties of ecosystems. However, the value of these properties for predicting plant species distribution remains unclear. Here, we assess whether such data can add value to topographic variables for predicting plant distributions in French and Swiss alpine grasslands. We fitted statistical models with high spectral and spatial resolution reflectance data and tested four optical indices sensitive to leaf chlorophyll content, leaf water content and leaf area index. We found moderate added-value of AIS data for predicting alpine plant species distribution. Contrary to expectations, differences between species distribution models (SDMs) were not linked to their local abundance or phylogenetic/functional similarity. Moreover, spectral signatures of species were found to be partly site-specific. We discuss current limits of AIS-based SDMs, highlighting issues of scale and informational content of AIS data.

Burned Area Detection and Burn Severity Assessment of a Heathland Fire in Belgium Using Airborne Imaging Spectroscopy (APEX)

Uncontrolled, large fires are a major threat to the biodiversity of protected heath landscapes. The severity of the fire is an important factor influencing vegetation recovery. We used airborne imaging spectroscopy data from the Airborne Prism Experiment (APEX) sensor to: (1) investigate which spectral regions and spectral indices perform best in discriminating burned from unburned areas; and (2) assess the burn severity of a recent fire in the Kalmthoutse Heide, a heathland area in Belgium. A separability index was used to estimate the effectiveness of individual bands and spectral indices to discriminate between burned and unburned land. For the burn severity analysis, a modified version of the Geometrically structured Composite Burn Index (GeoCBI) was developed for the field data collection. The field data were collected in four different vegetation types: Calluna vulgaris-dominated heath (dry heath), Erica tetralix-dominated heath (wet heath), Molinia caerulea (grass-encroached heath), and coniferous woodland. Discrimination between burned and unburned areas differed among vegetation types. For the pooled dataset, bands in the near infrared (NIR) spectral region demonstrated the highest discriminatory power, followed by short wave infrared (SWIR) bands. Visible wavelengths performed considerably poorer. The Normalized Burn Ratio (NBR) outperformed the other spectral indices and the individual spectral bands in discriminating between burned and unburned areas. For the burn severity assessment, all spectral bands and indices showed low correlations with the field data GeoCBI, when data of all pre-fire vegetation types were pooled (R2 maximum 0.41). Analysis per vegetation type, however, revealed considerably higher correlations (R2 up to 0.78). The Mid Infrared Burn Index (MIRBI) had the highest correlations for Molinia and Erica (R2 = 0.78 and 0.42, respectively). In Calluna stands, the Char Soil Index (CSI) achieved the highest correlations, with R2 = 0.65. In Pinus stands, the Normalized Difference Vegetation Index (NDVI) and the red wavelength both had correlations of R2 = 0.64. The results of this study highlight the superior performance of the NBR to discriminate between burned and unburned areas, and the disparate performance of spectral indices to assess burn severity among vegetation types. Consequently, in heathlands, one must consider a stratification per vegetation type to produce more reliable burn severity maps.

Detecting diurnal and seasonal variation in canopy water content of nut tree orchards from airborne imaging spectroscopy data using continuous wavelet analysis

Continuous wavelet analysis (CWA) has recently been applied to leaf-level spectroscopic data for quantifying foliar chemistry, but it is unclear how well or whether CWA can be applied to imaging spectroscopy data under the conditions of higher noise level and more complicating factors. This study evaluates the application of CWA to airborne imaging spectroscopy data for predicting diurnal and seasonal variation in canopy water content (CWC) for nut tree orchards. We collected CWC measurements and concurrent imagery from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) instrument twice a day (morning and afternoon) in spring and fall of 2011 in California, USA. Several robust wavelet features were determined and compared to four water-sensitive spectral indices, three existing in the literature and one optimized in this study, for the assessment of predictive performance. Results showed that the best prediction using CWA (R2=0.84 and root mean square error (RMSE)=0.027kg/m2) was produced by a combination of three wavelet features and it was considerably better than those by the existing water indices. While the best wavelet feature (1100nm, scale 6) characterized the water absorption in the near-infrared region, the optimized index ND850,720 used a red edge band at 720nm instead of a direct water absorption band. A bootstrap sampling of the validation data set indicated that ND850,720 predicted CWC significantly worse (p<0.0001) and exhibited greater sensitivity to seasonality. Both CWA and ND850,720 revealed statistically significant diurnal declines of CWC in two different seasons in the context of a substantial seasonal decline, but the former detected greater declines in diurnal CWC. Our results demonstrated the feasibility of applying CWA to airborne imaging spectroscopy data for CWC mapping and its superiority to spectral indices for improved prediction of CWC and understanding of spectral–chemical relations.

Photographic measurement of leaf angles in field crops

Leaf angle distribution (LAD) is one of the most important parameters used to describe the structure of horizontally homogeneous vegetation canopies such as field crops. LAD affects how incident photosynthetically active radiation is distributed on plant leaves, thus directly affecting plant productivity. Knowledge of LAD is also required for retrieval of other important biophysical variables from measurements of canopy radiation transmittance or spectral reflectance. Unfortunately, its determination is laborious and measured data is rarely available. In this study, we applied a recently developed method for determination of the inclination angles using leveled digital photography to the leaves of six cool-temperate crops: faba bean, narrow-leafed lupin, turnip rape, wheat, barley, and oat. The method, previously applied only to small and flat leaves of broadleaved trees and bushes, was extended to be applicable to the narrow and curved leaves of cereals. A reasonable match was found between the leaf angles determined by photographic measurements and the mean leaf tilt angles (MTA) measured using a LAI-2000 plant canopy analyzer for five out of the six species (R2=0.92). The error caused by assuming a spherical LAD, when calculating LAI from canopy transmittance measurements, varied between 0 and 1.5 LAI units, depending on species. Finally, we analyzed the correlation between photographically determined species-specific LADs and airborne imaging spectroscopy data acquired for the same species in a similar growth stage. The highest correlation between spectral reflectance factor and leaf mean tilt angle was found at a wavelength of 748nm (R=0.80). The high correlation between MTA and this red edge waveband can be useful for MTA determination from imaging spectroscopy.