Vegetation canopy water content (CWC) is an important parameter for monitoring natural and agricultural ecosystems.
Previous studies focused on the observation of annual or monthly variations in CWC but lacked temporal details to study
vegetation physiological activities within a diurnal cycle. This study provides an evaluation of detecting vegetation
diurnal water stress using airborne data acquired with the MASTER instrument. Concurrent with the morning and
afternoon acquisitions of MASTER data, an extensive field campaign was conducted over almond and pistachio orchards
in southern San Joaquin Valley of California to collect CWC measurements. Statistical analysis of the field
measurements indicated a significant decrease of CWC from morning to afternoon. Field measured CWC was linearly
correlated to the normalized difference infrared index (NDII) calculated with atmospherically corrected MASTER
reflectance data using either FLAASH or empirical line (EL). Our regression analysis demonstrated that both
atmospheric corrections led to a root mean square error (RMSE) of approximately 0.035 kg/m2 for the estimation of
CWC (R2=0.42 for FLAASH images and R2=0.45 for EL images). Remote detection of the subtle decline in CWC awaits
an improved prediction of CWC. Diurnal CWC maps revealed the spatial patterns of vegetation water status in response
to variations in irrigation treatment.
Canopy water content is an important variable for forestry and agriculture management. This study was aimed at
building calibration models to estimate vegetation canopy (VC) equivalent water thickness (EWT) from high temporal
resolution and large areal coverage MODIS images. The models were developed for a semi-arid area in Arizona
(SMEX04) and the best one was applied to MODIS images covering a forest area in Southern Indiana. EWT derived
from hyperspectral data in the process of atmospheric correction was used for calibrating MODIS spectral indices.
Tested in this study were four vegetation indices: Normalized Difference Water Index (NDWI), Shortwave Infrared
Water Stress Index (SIWSI), Normalized Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI),
which were designed based on either water (NDWI and SIWSI) or chlorophyll absorptions (NDVI and EVI). Validating
these indices on field measured EWT for the SMEX04 site resulted in R2 correlations of 0.7547, 0.7509, 0.7299 and
0.7547, respectively. According to regression equations, however, EWT estimated using NDWI and SIWSI shows a
slope more close to 1 than those using NDVI and EVI when validated with ground measured EWT, thus showing a better
prediction ability than the two chlorophyll indices. The SIWSI-EWT model was chosen to apply to a time series of
MODIS images covering the Southern Indiana areas and the relationship of EWT derived from these images to
precipitation was examined.
Remote sensing is being applied with increasing success in the evaluation and management of coral ecosystems. We demonstrate a successful application of hyperspectral image analysis of the benthic composition in Kaneohe Bay, Hawaii using data acquired from NASA's Airborne Visible Infrared Imaging Spectrometer. We employ a multi-level approach, combining a semi-analytical inversion model with linear spectral unmixing, to extract information on the coral, algae and sand composition of each pixel. The unmixing model is based on the spectral characteristics of the dominant species and substrate types in Kaneohe Bay, and uses an optimization routine to mathematically invert the relationship of how each component spectrally interacts and mixes. The functional result is the ability to quantitatively classify individual pixel composition according to the percent contribution from each of three main reef components. Output compares favorably with available field measurements and habitat information for Kaneohe Bay, and the overall analysis illustrates the capacity to simultaneously derive information on water properties, bathymetry and habitat composition from hyperspectral remote sensing data. Further, the resulting spatial analysis capacity contributes an improved capability for monitoring coral ecosystems and an important basis for resource management decisions.
Methods to accurately estimate the biophysical and biochemical properties of vegetation are a major research objective of remote sensing. We assess the capability of the MODIS satellite sensor to measure canopy water content and evaluate its relationship to ecosystem exchange (NEE) for an evergreen forest canopy. A time-series of three vegetation indexes were derived from MODIS data, the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), and the Normalized Difference Infrared Index (NDII), which were compared to physically based estimates of equivalent water thickness (EWT) from the airborne AVIRIS hyperspectral instrument over a temperate conifer forest in southwestern Washington. After cross-calibration of the imagery, water indexes derived from MODIS showed good agreement with AVIRIS EWT, while the NDVI was insensitive to water content variation. Three years of NEE data from eddy covariance measurements at the Wind River AmeriFlux tower were compared with the time series of MODIS indexes, which show seasonal water content has similar trajectory with NEE. In contrast, the MODIS NDVI time series did not yield a good relationship with NEE. This study demonstrates the potential to use MODIS water indexes for spatial and temporal NEE estimation at regional and global scales in appropriate ecosystems.
Invasive plant species are causing severe environmental and ecological impacts. This study utilized airborne hyperspectral image data and digital image processing techniques to map one of the most aggressive weeds, kudzu (Pueraria montana), in western Georgia. Minimum Noise Fraction (MNF) transform followed by Spectral Angle Mapper (SAM) produced the best map results among several other procedures. Validation with field data show that this procedure delivered user's accuracy of 83.02% for kudzu-invaded plots and 95.90% for non-invaded plots, with Producer's accuracy of 73.26% and 82.47%, respectively. Further analysis using a GIS-based CART analysis indicates the importance of elevation in limiting the spatial distribution of kudzu.
Data from small and large footprint lidar systems were used to derive basic forest attributes from old-growth Douglas fir/western hemlock dominated stands at the Gifford Pinchot National Forest in the Pacific Northwest of United Sates. The derived forest attributes include canopy height and canopy closure. The crown depth estimates were made from the large footprint dataset. The study provides the unique opportunity to compare basic forest attributes derived from small and large footprint lidar systems, and also demonstrates the significance of complimentary analysis of data from different lidar systems in providing expanded information on forest structure. Results of the analysis showed a high degree of agreement between the canopy height estimates from both lidar systems
In recent years, the impact of aquatic invasive species on biodiversity has become a major global concern. In the
Sacramento-San Joaquin Delta region in the Central Valley of California, USA, dense infestations of the invasive
aquatic emergent weed, water hyacinth (Eichhornia crassipes) interfere with ecosystem functioning. This silent invader
constantly encroaches into waterways, eventually making them unusable by people and uninhabitable to aquatic fauna.
Quantifying and mapping invasive plant species in aquatic ecosystems is important for efficient management and
implementation of mitigation measures. This paper evaluates the ability of hyperspectral imagery, acquired using the
HyMap sensor, for mapping water hyacinth in the Sacramento-San Joaquin Delta region. Classification was performed
on sixty-four flightlines acquired over the study site using a decision tree which incorporated Spectral Angle Mapper
(SAM) algorithm, absorption feature parameters in the spectral region between 0.4 and 2.5μm, and spectral
endmembers. The total image dataset was 130GB. Spectral signatures of other emergent aquatic species like pennywort
(Hydrocotyle ranunculoides) and water primrose (Ludwigiapeploides) showed close similarity with the water hyacinth
spectrum, however, the decision tree successfully discriminated water hyacinth from other emergent aquatic vegetation
species. The classification algorithm showed high accuracy (κ value = 0.8) in discriminating water hyacinth.
Precision agriculture requires high spectral and spatial resolution imagery for advanced analyses of crop and soil
conditions to increase environmental protection and producers' sustainability. GIS models that anticipate crop responses
to nutrients, water, and pesticides require high spatial detail to generate application prescription maps. While the added
precision of geo-spatial interpolation to field scouting generates improved zone maps and are an improvement over
field-wide applications, it is limited in detail due to expense, and lacks the high precision required for pixel level
applications. Multi-spectral imagery gives the spatial detail required, but broad band indexes are not sensitive to many
variables in the crop and soil environment. Hyperspectral imagery provides both the spatial detail of airborne imagery
and spectral resolution for spectroscopic and narrow band analysis techniques developed over recent decades in the laboratory that will advance precise determination of water and bio-physical properties of crops and soils.
For several years, we have conducted remote sensing investigations to improve cotton production through field
spectrometer measurements, and plant and soil samples in commercial fields and crop trials. We have developed
spectral analyses techniques for plant and soil conditions through determination of crop water status, effectiveness of
pre-harvest defoliant applications, and soil characterizations. We present the most promising of these spectroscopic
absorption and narrow band index techniques, and their application to airborne hyperspectral imagery in mapping the
variability in crops and soils.
We assessed the capability of AVIRIS and MODIS to estimate canopy water content. Hyperspectral water retrievals with AVIRIS data, EWT, were compared to in situ leaf water content and LAI measurements at a semi-arid site in southeastern Arizona. Retrievals of EWT showed good correlation with field canopy water content measurements. Statistical analysis also suggested that EWT was significant among seven different vegetation communities. Four MODIS indexes derived from band ratios using the reflectance product and were compared to retrievals of EWT with AVIRIS at both the semi-arid site and a temperate conifer forest. Good statistical agreements were found between AVIRIS EWT and all four MODIS indexes at the semi-arid site in savanna shrub communities. Slightly poorer correlations were found at the forest site where water indexes had better correlation to AVIRIS EWT than vegetation indexes. Temporal patterns of the four indexes in all semi-arid vegetation communities except creosote bush and agriculture show distinct seasonal variation and responded to precipitation at the savanna site. Three years of net ecosystem exchange (NEE) data from eddy covariance measurements at the forest site were compared to the time series of MODIS indexes. MODIS water indexes showed similar seasonal patterns to NEE that were strongest during the period of net carbon sequestration. In contrast, the time series of MODIS vegetation indexes did not yield a good relationship to NEE.
The objective of the PEEIR (Pacific Estuarine Ecosystem Indicator Research Consortium) program is to develop new indicators for assessing wetland health or condition. As part of PEEIR program we are investigating the use of imaging spectrometry to map and characterize marsh vegetation of several estuarine systems in California. We obtained airborne Advanced Visible Infrared Imaging Spectrometer (AVIRIS) data, an instrument which measures a detailed reflectance spectrum (400-2500nm) for each pixel, over paired tidal marshes, having either a history of exposure to pollution or no known exposure. AVIRIS image data was analyzed based on comparison to field measurements and reflectance changes measured in hydroponic experiments. We report leaf and canopy reflectance measurements of several common plant species of Pacific coast salt marshes exposed to different concentrations of heavy metals (Cd, V) and crude oil contaminants. Species exhibited differential sensitivities to specific contaminants, however in general, Salicornia virginica, the most salt tolerant species and the dominant species in these wetlands (70-90% cover) was most sensitive to metal and petroleum contaminants. Field measurements of canopy reflectance, biomass and vegetation structure were acquired across GPS-located transects at each field site. The AVIRIS data were calibrated to surface reflectance using the FLAASH radiative transfer code and geometrically registered to coordinates using the 1m USGS digital orthophoto quads. AVIRIS results show spatial patterns of plant stress indicators (e.g., reduced chlorophyll and water contents) are consistent with known patterns of contamination in these tidal wetlands.
In water limited environments, the density and water content of plant canopies are highly correlated to available soil moisture. Specific absorption bands for liquid water are identifiable and the variation in their depths can be related to canopy water content using high spectral resolution (hyperspectral) imagery. The spectral absorption feature centered at approximately 980 nm has been widely utilized for estimating equivalent water thickness, a measure of the volume of canopy water if it is equally distributed over the area of the pixel. Although it is affected by canopy structure, it is highly correlated with plant water content, and is independent of reflectance changes due to photosynthetic pigments. This study relates the depth of the 980 nm water band absorption, measured by the continuum removal (CR) technique, to crop water stress, and compares these results to other vegetation and plant stress indicators, NDVI and NDWI.
Remote sensing technologies with high spatial and spectral resolution show a great deal of promise in addressing critical environmental monitoring issues, but the ability to analyze and interpret the data lags behind the technology. Robust analytical methods are required before the wealth of data available through remote sensing can be applied to a wide range of environmental problems for which remote detection is the best method. In this study we compare the classification effectiveness of two relatively new techniques on data consisting of leaf-level reflectance from plants that have been exposed to varying levels of heavy metal toxicity. If these methodologies work well on leaf-level data, then there is some hope that they will also work well on data from airborne and space-borne platforms. The classification methods compared were support vector machine classification of exposed and non-exposed plants based on the reflectance data, and partial east squares compression of the reflectance data followed by classification using logistic discrimination (PLS/LD). PLS/LD was performed in two ways. We used the continuous concentration data as the response during compression, and then used the binary response required during logistic discrimination. We also used a binary response during compression followed by logistic discrimination. The statistics we used to compare the effectiveness of the methodologies was the leave-one-out cross validation estimate of the prediction error.
Direct gradient analysis, and other canonical community ordination techniques, have been most commonly used by plant ecologists and others attempting to analyse complex multivariate datasets. These multivariate statistical techniques can be applied to a variety of spectral analyses. Particularly useful is the ability to test significance of environmental variables based upon Monte Carlo permutations, allowing for a step-wise model of variance to be built. This technique has been now applied to hyperspectral remotely sensed data, within the overall context of ESA DAISEX-99 experiment. An extensive field campaign in La-Mancha (Spain) was carried out, simultaneously with the overflight of two airborne imaging spectrometers (DAIS, HYMAP) and other sensors (POLDER, LEANDRE).We use in this work data from the 128-channels HYMAP imaging spectrometer jointly with the ground truth data. Direct gradient analysis of the imagery spectra indicated an overall statistical significance when a model based upon three variables was used. Leaf moisture, LAI, and total chlorophyll were the most highly correlated variables, and all demonstrated statistically significant p-values. Hyperspectral remote sensing data requires new techniques to analyse the increasingly complex data. Application of ordination techniques, although not commonly applied within the remote sensing data processing, show good perspectives for more in depth analysis of the whole DAISEX-99 dataset.
We present a hierarchical classification technique that discriminates broad categories of surface materials in terms of ground true features, such as water, vegetation, and soils from spectral information. Subsequently, we further discriminate these materials and extract finer ground features, like chemistries, peculiar to each. The interaction at various scales of the 3D spatial and the spectral domains is decomposed by using wavelet tools to address scale dependencies in the spatial domain, a robust spectral unmixing technique, called Hierarchical Foreground Background Analysis (HFBA) along the spectral axis. HFBA sequentially derives a series of weighting vectors discriminating features at different levels of detection: (1) constituent materials, (2) types within constituents, and (3) chemistries peculiar to each type. Our goal is two-fold. First, we present the combination of HFBA and wavelets as a supervised classification technique validating the categories imposed by the supervised classification, and manifesting clusters which can refine the classification at different scales. Second, we identify spectral redundancies between hyperspectral and multispectral information, studying mixture at different spatial/spectral resolutions and assess whether targeted features may be extracted as efficiently from multispectral data as they could be from hyperspectral data. Results on AVIRIS and simulated MODIS data illustrate the robustness and effectivity of the technique.
Improved vegetation maps are required for fire management and biodiversity assessment, from critical inputs for hydrological and biogeochemical models and represent a means for scaling-up point measurements. At scales greater than 10 meters, vegetation communities are typically mixed consisting of leaves, branches, exposed soil and shadows. To map mixed vegetation, many researchers employ spectral mixture analysis (SMA). In most SMA applications, a single set of spectra consisting of green vegetation, soil, non- photosynthetic vegetation and shade are used to 'unmix' images. However, because most scenes contain more than four components, this simple approach leads to fraction errors and may fail to differentiate many vegetation types. In this work, we apply a new approach called multiple endmember spectral mixture analysis, in which the number and types of endmembers vary per-pixel. Using this approach, hundreds of unique models are generated that account for community specific differences in plant chemistry, physical attributes and phenology. Additionally, we describe a new strategy for developing and organizing regionally specific spectral libraries. We present result from a study in the Santa Monica Mountains using AVIRIS data, in which we map grassland and chaparral communities, mapping species dominance in some cases to a high degree of accuracy.
Guillaume Perry, Joel Stearn, Vern Vanderbilt, Susan Ustin, Martha Diaz Barrios, Leslie Morrissey, Gerald Livingston, Francois-Marie Breon, Sophie Bouffies, Marc Leroy, Maurice Herman, Jean-Yves Balois
Representing the areal extent of circumpolar wetlands is a critical step to quantifying the emission of methane, an important greenhouse gas. Present estimates of the areal extent of these wetlands differ nearly seven fold, implying large uncertainties exist in the prediction of circumpolar methane emission rates. Our objective is to use multi- directional and polarization measurement provided by the French POLDER sensor to improve this estimate. The results show that wetlands can be detected, classified and their area quantified using the unique, highly polarized angular signature of the sunglint measured over their water surfaces.
An analysis, based on the inversion of a simple non-linear model of the ground reflectance, was conducted on several AVIRIS scenes. The scenes were acquired during the MAC EUROPE 91 campaign on the 5th and 22nd of July, over two test sites (Black Forest and Freiburg). The model consists in a linear mixing of the soil reflectance and a green vegetation reflectance described with a Kubelka-Munk formula containing the chlorophyll and water specific absorption coefficients. Its inversion provides a Green Vegetation Fraction of the pixel and two parameters related respectively to chlorophyll and water. The model can then be used to evaluate the magnitude of the 1.7 micrometers absorption feature which is thought to be a signature of the vegetation biochemical components. The spatial and temporal variability of this feature over the scenes is commented.17
The capability to predict the response of ecosystems to change relies on our ability to understand and model the effective functioning of biotic processes at large scales and the transport functions of the atmospheric/hydrospheric processes. To successfully evaluate changes in ecological processes at the required spatial and temporal scales remote sensing technology and ecosystem theory must be considered jointly. A review of developments in remote sensing analysis using high spectral resolution sensors has led to the selection of a potential set ofparameters to be used in ecosystem models. These parameters quantify the light interception properties that scale from leaf to landscape. Spectral mixture analysis forms a framework for the systematic separation of both vegetative and non-vegetative components at sub-pixel spatial resolution. The spectral concentrations of the vegetative components defined by the spectral mixture analysis are then used to drive canopy radiative transfer models from which the ecosystem parameters are inferred. 1.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.