TY - GEN ID - AU - TI - ENVI Help ER - TY - THES ID - abera2005 AU - Abera, Berhe Gebreselassie TI - Application of Remote sensing and Spatial Data Integration Modeling to Predicitive Mapping of Apatite-Mineralized zones in the Bikilal Layered Gabbro Complex, Western Ethiopia PR - ITC T2 - International Institute for Geo-Information Science and Earth Observation CY - Enschede, The Netherlands PY - 2005 VL - Master ER - TY - JOUR ID - ahamed2011 AU - Ahamed, T. AU - Tian, L. AU - Zhang, Y. AU - Ting, K. C. TI - A review of remote sensing methods for biomass feedstock production UR - ://WOS:000292849200002 DO - 10.1016/j.biombioe.2011.02.028 T2 - Biomass & Bioenergy PY - 2011 DA - Jul SN - 0961-9534 VL - 35 IS - 7 SP - 2455-2469 N1 - ISI Document Delivery No.: 793UK Times Cited: 0 Cited Reference Count: 141 Ahamed, T. Tian, L. Zhang, Y. Ting, K. C. Pergamon-elsevier science ltd Oxford AB - Monitoring and maximization of bioenergy yield from biomass feedstock has recently become a critically important goal for researchers. Remote sensing represents a potential method to monitor and estimate biomass so as to increase biomass feedstock production from energy crops. This paper reviews the biophysical properties of biomass and remote sensing methods for monitoring energy crops for site-specific management. While several research studies have addressed the agronomic dimensions of this approach, more research is required on perennial energy crops in order to maximize the yield of biomass feedstock. Assessment of established methods could lead to a new strategy to monitor energy crops for the adoption of site-specific management in biomass feedstock production. In this article, satellite, aerial and ground-based remote sensing's were reviewed and focused on the spatial and temporal resolutions of imagery to adopt for site-specific management. We have concluded that the biomass yield prediction, the ground-based sensing is the most suitable to establish the calibration model and reference for aerial and satellite remote sensing. The aerial and satellite remote sensing are required for wide converge of planning and policy implementations of biomass feedstock production systems. (C) 2011 Elsevier Ltd. All rights reserved. KW - Perennial energy crops KW - Site-specific management KW - Vegetative indices KW - Leaf area index KW - Satellite imagery KW - Remote sensing KW - tropical forest biomass KW - landsat tm data KW - unmanned aerial vehicle KW - leaf-area index KW - aboveground biomass KW - radar backscatter KW - vegetation KW - index KW - water-stress KW - biophysical properties KW - image segmentation ER - TY - JOUR ID - aplin2006 AU - Aplin, P. TI - On scales and dynamics in observing the environment UR - http://dx.doi.org/10.1080/01431160500396477 DO - 10.1080/01431160500396477 T2 - International Journal of Remote Sensing PY - 2006 DA - 2006/06/01 SN - 0143-1161 VL - 27 IS - 11 SP - 2123-2140 AB - Natural and anthropogenic processes at the Earth's surface operate at a range of spatial and temporal scales. Different scales of observation are required to match the spatial scales of the processes under observation. At the same time, the temporal sampling rate of the observing systems must be reconciled with the dynamics of the processes observed. Bringing together these issues requires insight, innovation and, inevitably, compromise. This paper reviews spatial and temporal considerations in remote sensing and introduces the papers in this Special Issue on ?Scales and Dynamics in Observing the Environment?. The review comprises three main sections. The first section focuses on spatial variability in remote sensing, while the second section focuses on temporal variability in remote sensing. The third section links these two issues, focusing on the interplay of space and time in remote sensing. The review is primarily theoretical, explaining spatial and temporal properties of remote sensing and remotely sensed phenomena. Where appropriate, however, practical examples are included to demonstrate how remote sensing is used in environmental applications. Following the review, the papers included in the Special Issue are introduced, outlining their significance in the context of ?Scales and Dynamics in Observing the Environment?. ER - TY - JOUR ID - bastiaanssen2000 AU - Bastiaanssen, Wim G. M. AU - Molden, David J. AU - Makin, Ian W. TI - Remote sensing for irrigated agriculture: examples from research and possible applications UR - http://www.sciencedirect.com/science/article/pii/S0378377400000809 DO - 10.1016/s0378-3774(00)00080-9 T2 - Agricultural Water Management PY - 2000 SN - 0378-3774 VL - 46 IS - 2 SP - 137-155 AB - Researchers in various international programs have studied the potential use of remotely sensed data to obtain accurate information on land surface processes and conditions. These studies have demonstrated that quantitative assessment of the soil-vegetation-atmosphere-transfer processes can lead to a better understanding of the relationships between crop growth and water management. Information on land surface can now be obtained at a wide range of spatial (5–5000 m) and temporal resolutions (0.5–24 days). However, even though considerable progress has been made over the past 20 years in research applications, remotely sensed data remain underutilized by practicing water resource managers. This paper seeks to bridge the gap between researchers and practitioners, first, by illustrating where research tools and techniques have practical applications and, second, by identifying real problems that remote sensing could solve, albeit with additional research and development. As freshwater becomes an increasingly scarce resource, all opportunities to better manage water uses, particularly in irrigated agriculture, must be taken. An important challenge in the field of water resources is to utilize the timely, objective and accurate information provided by remote sensing. KW - Remote sensing KW - Irrigated farming KW - Land management KW - Water resources management KW - Crop yield KW - Water use efficiency KW - Water rights ER - TY - ABST ID - bausch1993 AU - Bausch, Walter C. TI - Soil background effects on reflectance-based crop coefficients for corn DO - 10.1016/0034-4257(93)90096-G T2 - Remote Sensing of Environment PY - 1993 VL - 46 SP - 213-222 ER - TY - JOUR ID - chen1996 AU - Chen, J.M. TI - Evaluation of vegetation indices and a modified simple ratio for boreal applications T2 - Canadian Journal of Remote Sensing PY - 1996 VL - 22 IS - 3 SP - 229-242 AB - A Modified Simple Ratio (MSR) Is proposed for retrieving biophysical parameters of boreal forests using remote sensing data. This vegetation index is formulated based on an evaluation of several two-band vegetation indices, including the Normalized Difference Vegetation Index (NDVI), Simple Ratio (SR), Soil Adjusted Vegetation Indices (SAVI, SAVI1, SAVI2), Weighted Difference Vegetation Index (WDVI), Global Environment Monitoring Index (GEMI), Non-Linear Index (NLI), and Renormalized Difference Vegetation Index (RDVI). MSR is an improved version of RDVI for the purpose of linearizing their relationships with biophysical parameters. All indices were obtained from Landsat-5 TM band 3 (visible) and band 4 (near infrared) images after atmospheric corrections (except for GEMI) and were correlated with ground-based measurements made in 20 Jack Pine (Pinus banksiana) and Black Spruce (Picea mariana) stands during the BOREAS field experiment in 1994. The measurements include Leaf Area Index (LAI) and the Fraction of Photosynthetically Active Radiation (FPAR) absorbed by the forest canopies. Among these vegetation indices, SR, MSR, and NDVI were found to be best correlated with LAI and FPAR in both spring and summer. All other indices performed poorly. Both NDVI and MSR can be expressed as a function of SR. Measurement errors in remote sensing data often occur due to changes in solar zenith angle, subpixel contamination of clouds, or dissimilar surface features and the variation in the local topography and other environmental factors. These errors generally cause simultaneous increases or decreases in the red and near infrared reflectances, and their effects can be greatly reduced by taking the ratio. All other indices involving mathematical operations other than ratioing could retain the errors or even amplify them. The major problem in using the vegetation indices obtained from red and near infrared bands is the small sensitivity to the overstorey vegetation conditions. Although many of the vegetation indices such as SAVI, SAVI1, and SAVI2 are developed to minimize the effect of the background on retrieving the vegetation information, they also reduce their sensitivity to the changes in the overstorey conditions. KW - Boreal forests KW - FPAR KW - LAI KW - Vegetation index ER - TY - JOUR ID - cibula1992 AU - Cibula, W. G. AU - Zetka, E. F. AU - Rickman, D. L. TI - Response of thematic mapper bands to plant water stress UR - http://dx.doi.org/10.1080/01431169208904236 DO - 10.1080/01431169208904236 PR - Taylor & Francis T2 - International Journal of Remote Sensing PY - 1992 DA - 1992-07-01 SN - 0143-1161 VL - 13 IS - 10 SP - 1869-1880 AB - Abstract Changes in leaf reflectance as water content decreases have been hypothesized to occur in the 1 55-1.75 and 2.08-2.35 ?m wavelength regions. To evaluate this hypothesis, studies were conducted on ryegrass (Lolium muitiflorum Lam.) and oats (Avena saliva L.), which were grown in a controlled, outdoor situation. Both fully-watered control beds and water-stressed beds were periodically examined with a spectroradiometer calibrated against a reflectance reference of polytetrafluoroethylene. The observed changes correspond to those predicted by stochastic leaf models employed by other investigators (leaf reflection increases in the l.55-l.75?m region as leaf water content decreases). Although the percentage changes in TM bands 1-3 are nearly as great as those found in TM bands 5 and 7, the absolute values of reflectance change are much lower. We believe that these patterns are probably characteristic of a broad range of vegetation types. In terms of phenomena detection, these patterns should be considered in any practical remote sensing sensor scenario. ER - TY - CPAPER ID - clarke2001 AU - Clarke, T. R. AU - Moran, M. S. AU - Barnes, E. M. AU - Pinter, P. J., Jr. AU - Qi, J. TI - Planar domain indices: a method for measuring a quality of a single component in two-component pixels DO - 10.1109/igarss.2001.976818 T2 - Geoscience and Remote Sensing Symposium, 2001. IGARSS '01. IEEE 2001 International PY - 2001 VL - 3 SP - 1279-1281 AB - A method is presented that reduces the difficulty of measuring a particular quality of one component in a multi-component element. The planar domain index design requires two measurements: that of a signal sensitive to the desired quality of the target component and another signal sensitive to the component's weight or relative proportion to the whole. The quality signal and component weight signal form the two dimensions of a plane, and the maximum and minimum possible values for each signal define the boundaries of a domain within this plane. The position of a coordinate pair within the domain can then be correlated to the quality being measured, independent of the component's proportion to the whole. Examples given involve mixed vegetation and soil targets, with vegetation indices used to measure the component weight. Quality signals of the example applications include canopy minus air temperature as a measure of evapotranspiration, a normalized difference of near infrared and far red wavelengths as a measure of chlorophyll content, and differential synthetic aperture radar (SAR) for measuring near-surface soil moisture KW - agriculture, geophysical signal processing, geophysical techniques, image processing, terrain mapping, vegetation mapping, canopy, geophysical measurement technique, land surface, planar domain index, planar domain indices, quality, remote sensing, single ER - TY - JOUR ID - crist1984 AU - Crist, Eric P. AU - Cicone, Richard C. TI - A Physically-Based Transformation of Thematic Mapper Data---The TM Tasseled Cap DO - 10.1109/tgrs.1984.350619 T2 - Geoscience and Remote Sensing, IEEE Transactions on PY - 1984 SN - 0196-2892 VL - GE-22 IS - 3 SP - 256-263 AB - In an extension of previous simulation studies, a transformation of actual TM data in the six reflective bands is described which achieves three objectives: a fundamental view of TM data structures is presented, the vast majority of data variability is concentrated in a few (three) features, and the defined features can be directly associated with physical scene characteristics. The underlying TM data structure, based on three TM scenes as well as simulated data, is described, as are the general spectral characteristics of agricultural crops and other scene classes in the transformed data space. ER - TY - JOUR ID - dang2011 AU - Dang, Y. P. AU - Pringle, M. J. AU - Schmidt, M. AU - Dalal, R. C. AU - Apan, A. TI - Identifying the spatial variability of soil constraints using multi-year remote sensing UR - ://WOS:000293989100008 DO - 10.1016/j.fcr.2011.05.021 T2 - Field Crops Research PY - 2011 DA - Sep SN - 0378-4290 VL - 123 IS - 3 SP - 248-258 N1 - ISI Document Delivery No.: 808OU Times Cited: 1 Cited Reference Count: 42 Dang, Y. P. Pringle, M. J. Schmidt, M. Dalal, R. C. Apan, A. Grains Research & Development Corporation We wish to thank Peter and Jim Russell for providing their yield data and access to their farm. We thank the Grains Research & Development Corporation for partial funding of this study. Thanks are due to Dr. Jeremy Whish for doing the APSIM simulations and to Drs. Brett Robinson and Robert Denham for comments on draft versions. Elsevier science bv Amsterdam AB - In north-eastern Australia, soil attributes such as salinity, sodicity, acidity, and phytotoxic concentrations of chloride constrain the growth of crops. It is difficult to delineate constrained areas using conventional sampling methods. Alternatively, where crops fail, over multiple years, to pass a certain yield threshold, we might infer the presence of a soil constraint. For a wheat-growing farm over 10-year period we used remote sensing to obtain a large volume of surrogate yield data, by calibrating an archive of Normalised Difference Vegetation Index (NDVI) to an archive of (limited) ground-based observations. The model used was a generalised additive model that related wheat yield as a non-linear function of NDVI and a linear function of post-anthesis rainfall. Field locations where predicted yield consistently failed to reach the 75th percentile in a given year, over a number of years, we regarded as limited by at least one unknown soil constraint. Soil samples averaged for the constrained locations showed, compared with the unconstrained locations, relatively high concentrations of subsoil chloride, and, in the topsoil, relatively high exchangeable sodium percentage, and unused nitrate nitrogen. On-farm experiments suggested that, for constrained areas, preclusion of monoammonium phosphate (MAP) fertiliser application, coupled to gypsum amelioration, could potentially benefit the farm by A$32/ha/year (MAP) and A$207/ha/3 years (gypsum). Crown Copyright (c) 2011 Published by Elsevier B.V. All rights reserved. KW - Soil constraints KW - Crop yield KW - Precision agriculture KW - NDVI KW - Whole-farm KW - economics KW - crop yield KW - wheat yield KW - subsoil constraints KW - grain production KW - australia KW - vegetation KW - satellite KW - ndvi KW - management KW - salinity ER - TY - JOUR ID - dorigo2007 AU - Dorigo, W. A. AU - Zurita-Milla, R. AU - de Wit, A. J. W. AU - Brazile, J. AU - Singh, R. AU - Schaepman, M. E. TI - A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling UR - ://WOS:000246320700008 DO - 10.1016/j.jag.2006.05.003 T2 - International Journal of Applied Earth Observation and Geoinformation PY - 2007 DA - May SN - 0303-2434 VL - 9 IS - 2 SP - 165-193 N1 - ISI Document Delivery No.: 165QP Times Cited: 39 Cited Reference Count: 181 Dorigo, W. A. Zurita-Milla, R. de Wit, A. J. W. Brazile, J. Singh, R. Schaepman, M. E. Conference on Advances in Airborne Electromagnetics and Remote Sensing of Agro-Ecosystems 2004 Wageningen, NETHERLANDS Elsevier science bv Amsterdam AB - During the last 50 years, the management of agroecosystems has been undergoing major changes to meet the growing demand for food, timber, fibre and fuel. As a result of this intensified use, the ecological status of many agroecosystems has been severely deteriorated. Modeling the behavior of agroecosystems is, therefore, of great help since it allows the definition of management strategies that maximize (crop) production while minimizing the environmental impacts. Remote sensing can support such modeling by offering information on the spatial and temporal variation of important canopy state variables which would be very difficult to obtain otherwise. In this paper, we present an overview of different methods that can be used to derive biophysical and biochemical canopy state variables from optical remote sensing data in the VNIR-SWIR regions. The overview is based on an extensive literature review where both statistical-empirical and physically based methods are discussed. Subsequently, the prevailing techniques of assimilating remote sensing data into agroecosystem models are outlined. The increasing complexity of data assimilation methods and of models describing agroecosystem functioning has significantly increased computational demands. For this reason, we include a short section on the potential of parallel processing to deal with the complex and computationally intensive algorithms described in the preceding sections. The studied literature reveals that many valuable techniques have been developed both for the retrieval of canopy state variables from reflective remote sensing data as for assimilating the retrieved variables in agroecosystem models. However, for agroecosystem modeling and remote sensing data assimilation to be commonly employed on a global operational basis, emphasis will have to be put on bridging the mismatch between data availability and accuracy on one hand, and model and user requirements on the other. This could be achieved by integrating imagery with different spatial, temporal, spectral, and angular resolutions, and the fusion of optical data with data of different origin, such as LIDAR and radar/microwave. (c) 2006 Elsevier B.V. All rights reserved. KW - data assimilation KW - agroecosystem modeling KW - vegetation indices KW - canopy KW - reflectance modeling KW - biophysical variables KW - biochemical variables KW - parallel processing KW - radiative-transfer models KW - leaf-area index KW - hyperspectral vegetation KW - indexes KW - hydrologic data assimilation KW - multiple linear-regression KW - canopy chlorophyll density KW - ensemble kalman filter KW - bidirectional KW - reflectance KW - soil-moisture KW - crop models ER - TY - CONF ID - escadafal1994 AU - Escadafal, R. AU - Belghith, A. AU - Ben Moussa, H. TI - Indices spectraux pour la dégradation des milieux naturels en Tunisie aride T2 - 6ème Symp. Int. Mesures Physiques et Signatures en Télédétection, ISPRS-CNES CY - Val d’lsère, France PY - 1994 DA - 0000-00-00 SP - 253–259 ER - TY - JOUR ID - ferencz2004 AU - Ferencz, C. AU - Bognar, P. AU - Lichtenberger, J. AU - Hamar, D. AU - Tarscai, G. AU - Timar, G. AU - Molnar, G. AU - Pasztor, S. AU - Steinbach, P. AU - Szekely, B. AU - Ferencz, O. E. AU - Ferencz-Arkos, I. TI - Crop yield estimation by satellite remote sensing UR - ://WOS:000224024000004 DO - 10.1080/01431160410001698870 T2 - International Journal of Remote Sensing PY - 2004 DA - Oct SN - 0143-1161 VL - 25 IS - 20 SP - 4113-4149 N1 - ISI Document Delivery No.: 856DL Times Cited: 8 Cited Reference Count: 53 Ferencz, C Bognar, P Lichtenberger, J Hamar, D Tarscai, G Timar, G Molnar, G Pasztor, S Steinbach, P Szekely, B Ferencz, OE Ferencz-Arkos, I Taylor & francis ltd Abingdon AB - Two methods for estimating the yield of different crops in Hungary from satellite remote sensing data are presented. The steps of preprocessing the remote sensing data (for geometric, radiometric, atmospheric and cloud scattering correction) are described. In the first method developed for field level estimation, reference crop fields were selected by using Landsat Thematic Mapper (TM) data for classification. A new vegetation index (General Yield Unified Reference Index (GYURI)) was deduced using a fitted double-Gaussian curve to the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data during the vegetation period. The correlation between GYURI and the field level yield data for corn for three years was R(2)=0.75. The county-average yield data showed higher correlation (R(2)=0.93). A significant distortion from the model gave information of the possible stress of the field. The second method presented uses only NOAA AVHRR and officially reported county-level yield data. The county-level yield data and the deduced vegetation index, GYURRI, were investigated for eight different crops for eight years. The obtained correlation was high (R(2)=84.6-87.2). The developed robust method proved to be stable and accurate for operational use for county-, region- and country-level yield estimation. The method is simple and inexpensive for application in developing countries, too. KW - high-resolution radiometer KW - near-infrared channels KW - noaa-avhrr KW - cloud KW - detection KW - postlaunch calibration KW - spectral reflectance KW - winter-wheat KW - sensed data KW - leaf-area KW - landsat ER - TY - JOUR ID - friedl1994 AU - Friedl, M. A. AU - Schimel, D. S. AU - Michaelsen, J. AU - Davis, F. W. AU - Walker, H. TI - Estimating grassland biomass and leaf area index using ground and satellite data UR - http://dx.doi.org/10.1080/01431169408954174 DO - 10.1080/01431169408954174 T2 - International Journal of Remote Sensing PY - 1994 DA - 1994/05/10 SN - 0143-1161 VL - 15 IS - 7 SP - 1401-1420 AB - Abstract We compared estimates of regional biomass and LAI for a tallgrass prairie site derived from ground data versus estimates derived from satellite data. Linear regression models were estimated to predict LAI and biomass from Landsat-TM data for imagery acquired on three dates spanning the growing season of 1987 using co-registered TM data and ground measurements of LAl and biomass collected at 27 grassland sites. Mapped terrain variables including burning treatment, land-use, and topographic position were included as indicator variables in the models to acccount for variance in biomass and LAI not captured in the TM data. Our results show important differences in the relationships between Kauth-Thomas greenness (from TM), LAI, biomass and the various terrain variables. In general, site-wide estimates of biomass and LAI derived from ground versus satellite-based data were comparable. However, substantial differences were observed in June. In a number of cases, the regression models exhibited significantly higher explained variance due to the incorporation of terrain variables, suggesting that for areas encompassing heterogeneous landcover the inclusion of categorical terrain data in calibration procedures is a useful technique. ER - TY - JOUR ID - gao1996 AU - Gao, Bo-cai TI - NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space UR - http://www.sciencedirect.com/science/article/pii/S0034425796000673 DO - 10.1016/s0034-4257(96)00067-3 T2 - Remote Sensing of Environment PY - 1996 SN - 0034-4257 VL - 58 IS - 3 SP - 257-266 ER - TY - JOUR ID - garson2003 AU - Garson, D. Caraux AU - Lacaze, B. TI - Monitoring Leaf Area Index of Mediterranean oak woodlands: Comparison of remotely-sensed estimates with simulations from an ecological process-based model UR - http://dx.doi.org/10.1080/0143116021000024267 DO - 10.1080/0143116021000024267 T2 - International Journal of Remote Sensing PY - 2003 DA - 2003/01/01 SN - 0143-1161 VL - 24 IS - 17 SP - 3441-3456 AB - Annual vegetation abundance mapping was carried out within the DeMon II European project over a period of 12 years (1984-1996). The project relied on advanced satellite-based methods for spatial and temporal monitoring of Mediterranean oak woodlands by means of a series of Landsat Thematic Mapper (TM) satellite data. A standardized approach developed previously focuses on the Languedoc site, Hautes Garrigues, a typical sensitive Mediterranean region, but now recovering after centuries of grazing and agricultural activities. After geometric and radiometric rectification of nine full Landsat TM scenes with a refined correction in a smaller area of 75 km 2 75 km, a GIS database was created containing satellite data, thematic maps of vegetation, geological maps, climatic data and field measurements. An empirical relation between radiometric ground truth measurements and satellite derived Normalized Difference Vegetation Index (NDVI) allows us to derive Leaf Area Index (LAI). An ecological process-based model (Forest BGC) has been adapted to simulate ecosystem processes in a satisfying way at a local scale. Consistent results were obtained from remote sensing data and from simulations at a local scale, suggesting the possible use of remote-sensing data to monitor vegetation abundance changes at a regional scale. Without considering human disturbances, it can be noted that not much variation of LAI induced by natural factors can be detected over the considered 12-year period. ER - TY - GEN ID - gitelson2004 AU - Gitelson, Anatoly A. TI - Wide Dynamic Range Vegetation Index for Remote Quantification of Biophysical Characteristics of Vegetation UR - http://www.sciencedirect.com/science/article/pii/S0176161704705726 DO - 10.1078/0176-1617-01176 T2 - Journal of Plant Physiology PY - 2004 SN - 0176-1617 VL - 161 IS - 2 SP - 165-173 AB - Summary The Normalized Difference Vegetation Index (NDVI) is widely used for monitoring, analyzing, and mapping temporal and spatial distributions of physiological and biophysical characteristics of vegetation. It is well documented that the NDVI approaches saturation asymptotically under conditions of moderate-to-high aboveground biomass. While reflectance in the red region (ρred) exhibits a nearly flat response once the leaf area index (LAI) exceeds 2, the near infrared (NIR) reflectance (ρNIR) continue to respond significantly to changes in moderate-to-high vegetation density (LAI from 2 to 6) in crops. However, this higher sensitivity of the ρNIR has little effect on NDVI values once the ρNIR exceeds 30 %. In this paper a simple modification of the NDVI was proposed. The Wide Dynamic Range Vegetation Index, WDRVI = (a * ρNIR-ρred)/(a * ρNIR+ρred), where the weighting coefficient a has a value of 0.1–0.2, increases correlation with vegetation fraction by linearizing the relationship for typical wheat, soybean, and maize canopies. The sensitivity of the WDRVI to moderate-to-high LAI (between 2 and 6) was at least three times greater than that of the NDVI. By enhancing the dynamic range while using the same bands as the NDVI, the WDRVI enables a more robust characterization of crop physiological and phenological characteristics. Although this index needs further evaluation, the linear relationship with vegetation fraction and much higher sensitivity to change in LAI will be especially valuable for precision agriculture and monitoring vegetation status under conditions of moderate-to-high density. It is anticipated that the new index will complement the NDVI and other vegetation indices that are based on the red and NIR spectral bands. KW - leaf area index KW - reflectance KW - remote estimation KW - vegetation fraction KW - vegetation index ER - TY - JOUR ID - glenn2010 AU - Glenn, E. P. AU - Nagler, P. L. AU - Huete, A. R. TI - Vegetation Index Methods for Estimating Evapotranspiration by Remote Sensing UR - ://WOS:000284318100001 DO - 10.1007/s10712-010-9102-2 T2 - Surveys in Geophysics PY - 2010 DA - Dec SN - 0169-3298 VL - 31 IS - 6 SP - 531-555 N1 - ISI Document Delivery No.: 681LY Times Cited: 7 Cited Reference Count: 112 Glenn, Edward P. Nagler, Pamela L. Huete, Alfredo R. Springer Dordrecht AB - Evapotranspiration (ET) is the largest term after precipitation in terrestrial water budgets. Accurate estimates of ET are needed for numerous agricultural and natural resource management tasks and to project changes in hydrological cycles due to potential climate change. We explore recent methods that combine vegetation indices (VI) from satellites with ground measurements of actual ET (ET(a)) and meteorological data to project ET(a) over a wide range of biome types and scales of measurement, from local to global estimates. The majority of these use time-series imagery from the Moderate Resolution Imaging Spectrometer on the Terra satellite to project ET over seasons and years. The review explores the theoretical basis for the methods, the types of ancillary data needed, and their accuracy and limitations. Coefficients of determination between modeled ET(a) and measured ET(a) are in the range of 0.45-0.95, and root mean square errors are in the range of 10-30% of mean ET(a) values across biomes, similar to methods that use thermal infrared bands to estimate ET(a) and within the range of accuracy of the ground measurements by which they are calibrated or validated. The advent of frequent-return satellites such as Terra and planed replacement platforms, and the increasing number of moisture and carbon flux tower sites over the globe, have made these methods feasible. Examples of operational algorithms for ET in agricultural and natural ecosystems are presented. The goal of the review is to enable potential end-users from different disciplines to adapt these methods to new applications that require spatially-distributed ET estimates. KW - NDVI KW - Enhanced Vegetation Index KW - Fluxnet KW - MODIS KW - Remote sensing KW - land-surface evaporation KW - basal crop coefficients KW - colorado river delta KW - modis satellite data KW - canopy reflectance KW - potential evapotranspiration KW - riparian evapotranspiration KW - meteorological data KW - eddy covariance KW - energy-balance ER - TY - JOUR ID - gonz_lez-sanpedro2008 AU - González-Sanpedro, MC AU - Le Toan, T AU - Moreno, J AU - Kergoat, L AU - Rubio, E TI - Seasonal variations of leaf area index of agricultural fields retrieved from Landsat data T2 - Remote Sensing of Environment PY - 2008 VL - 112 SP - 810-824 ER - TY - THES ID - hecker2003 AU - Hecker, Jeanna Hyde TI - Investigation of the Relationship between Chlorophyll Concentration and High Spectral Resolution Data of Phragmites australis in Heavy Metal Contaminated Sites T2 - International Institute for Geo-Information Science and Earth Observation CY - Enschede, The Neatherlands PY - 2003 VL - Master ER - TY - GEN ID - huete1988 AU - Huete, A. R. TI - A soil-adjusted vegetation index (SAVI) UR - http://www.sciencedirect.com/science/article/pii/003442578890106X DO - 10.1016/0034-4257(88)90106-x T2 - Remote Sensing of Environment PY - 1988 SN - 0034-4257 VL - 25 IS - 3 SP - 295-309 AB - A transformation technique is presented to minimize soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. Graphically, the transformation involves a shifting of the origin of reflectance spectra plotted in NIR-red wavelength space to account for first-order soil-vegetation interactions and differential red and NIR flux extinction through vegetated canopies. For cotton (Gossypium hirsutum L. var DPI-70) and range grass (Eragrosticslehmanniana Nees) canopies, underlain with different soil backgrounds, the transformation nearly eliminated soil-induced variations in vegetation indices. A physical basis for the soil-adjusted vegetation index (SAVI) is subsequently presented. The SAVI was found to be an important step toward the establishment of simple °lobal” that can describe dynamic soil-vegetation systems from remotely sensed data ER - TY - GEN ID - huete1992 AU - Huete, A. R. AU - Hua, G. AU - Qi, J. AU - Chehbouni, A. AU - van Leeuwen, W. J. D. TI - Normalization of multidirectional red and NIR reflectances with the SAVI UR - http://www.sciencedirect.com/science/article/pii/003442579290074T DO - 10.1016/0034-4257(92)90074-t T2 - Remote Sensing of Environment PY - 1992 SN - 0034-4257 VL - 41 IS - 2–3 SP - 143-154 AB - Directional reflectance measurements were made over a semidesert gramma (Bouteloua spp.) grassland at various times of the growing season. Azimuthal strings of view angle measurements from + 40° to − 40° were made for various solar zenith angles and soil moisture conditions. The sensitivity of the normalized difference vegetation index (NDVI) and the soil-adjusted vegetation index (SAVI) to these bidirectional measurements was assessed for purposes of improving remote temporal monitoring of vegetation activity. The NDVI response from the grassland canopy was strongly anisotropic about nadir view angles while the SAVI response was symmetric about nadir. This occurred for all sun angles, soil moisture condition, and grass densities. This enabled variations in SAVI-view angle response to be minimized with a cosine function. It is expected that this study will aid in improving the characterization of vegetation temporal activity from Landsat TM, SPOT, AVHRR, and the Earth Observing System MODIS sensor. ER - TY - JOUR ID - huete1988-2 AU - Huete, A. R. AU - Jackson, R. D. TI - Soil and atmosphere influences on the spectra of partial canopies UR - http://www.sciencedirect.com/science/article/pii/0034425788900430 DO - 10.1016/0034-4257(88)90043-0 T2 - Remote Sensing of Environment PY - 1988 SN - 0034-4257 VL - 25 IS - 1 SP - 89-105 AB - An atmospheric radiant transfer model was used to compare ground-measured radiances over partially vegetated canopies with their simulated responses at the top of a clear (100 km meteorological range) and a turbid (10 km) atmosphere. Radiance measurements in the first four bands of the Thematic Mapper were taken over incomplete cotton (Gossypium hirsutum L.) and Lehmann lovegrass (Eragrostis lehmanniana Nees) canopies with different soil backgrounds separately inserted underneath. Atmospheric influences on the spectra of partial canopies were found to be significantly dependent on the “brightness” of the underlying soil. The change in canopy red and near-infrared radiance between the ground and the top of the atmosphere was such that an increase, decrease, or no change could be observed, depending on the magnitude of the canopy substrate contribution. Both increasing soil “brightness” and atmospheric turbidity lowered the ratio (RVI) and normalized difference vegetation index values (NDVI). Consequently, atmospheric-induced RVI and NDVI degradation were greatest over canopies with darker soils and were not detectable over canopies with light-colored soils. In contrast, soil and atmospheric effects on the perpendicular vegetation index were independent with atmosphere degradation being similar across all soil backgrounds. Soil influences on vegetation indices from partial canopies were found to be of similar magnitude to those attributed to the atmosphere for the range of soil and atmosphere conditions examined here. ER - TY - JOUR ID - huete1997 AU - Huete, A. R. AU - Liu, H. Q. AU - Batchily, K. AU - van Leeuwen, W. TI - A comparison of vegetation indices over a global set of TM images for EOS-MODIS UR - http://www.sciencedirect.com/science/article/pii/S0034425796001125 DO - 10.1016/s0034-4257(96)00112-5 T2 - Remote Sensing of Environment PY - 1997 SN - 0034-4257 VL - 59 IS - 3 SP - 440-451 AB - A set of Landsat Thematic Mapper images representing a wide range of vegetation conditions from the NASA Landsat Pathfinder, global land cover test site (GLCTS) initiative were processed to simulate the Moderate Resolution Imaging Spectroradiometer (MODIS), global vegetation index imagery at 250 m pixel size resolution. The sites included boreal forest, temperate coniferous forest, temperate deciduous forest, tropical rainforest, grassland, savanna, and desert biomes. Differences and similarities in sensitivity to vegetation conditions were compared among various spectral vegetation indices (VIs). All VIs showed a qualitative relationship to variations in vegetation. However, there were significant differences among the VIs over desert, grassland, and forested biomes. The normalized difference vegetation index (NDVI) was sensitive to and responded primarily to the highly absorbing red reflectance band, while other indices such (is the soil and atmosphere resistant vegetation index (SARVI) were more responsive to variations in the near-infrared (NIR) band. As a result, we found the NDVI to mimic red reflectances and saturate over the forested sites while the SARVI, by contrast, did not saturate and followed variations in NIR refleetances. In the arid and semiarid biomes, the NDVI was much more sensitive to canopy background variations than the SARVI. Maximum differences among vegetation index behavior occurred over the evergreen needleleaf forest sites relative to the deciduous broadleaf forests and drier, grassland, and shrub sites. These differences appear to be useful in complementing the NDVI for improved monitoring of vegetation, with the NDVI sensitive to fraction of absorbed photosynthetic active radiation and the SARVI more sensitive to structural canopy parameters such as leaf area index and leaf morphology. ER - TY - JOUR ID - hunt_jr1989 AU - Hunt Jr, E. Raymond AU - Rock, Barrett N. TI - Detection of changes in leaf water content using Near- and Middle-Infrared reflectances UR - http://www.sciencedirect.com/science/article/pii/0034425789900461 DO - 10.1016/0034-4257(89)90046-1 T2 - Remote Sensing of Environment PY - 1989 SN - 0034-4257 VL - 30 IS - 1 SP - 43-54 AB - Detection of plant water stress by remote sensing has been proposed using indices of Near-Infrared (NIR, 0.7–1.3 µm) and Middle-Infrared (MIR, 1.3–2.5 µm) wavelengths. The first objective of this study was to test the ability of the Leaf Water Content Index (LWCI) to determine leaf Relative Water content (RWC) of different species with different leaf morphologies. The second objective was to determine how the Moisture Stress Index (MSI; MIR / NIR) varies with RWC and the Equivalent Water Thickness (EWT). Reflectance factors at 0.82 µm and 1.6 µm were measured on leaves of Quercus agrifolia (sclerophyllous leaves), Liquidambar styraciflua (hardwood deciduous tree leaves), Picea rubens and Picea pungens (conifer needles), and Glycine max (herbaceous dicot leaves) as they dried on a laboratory bench. RWC and EWT were measured concurrently with the reflectance measurements. The results showed that LWCI was equal to RWC for the species tested. However, the results of a sensitivity analysis indicated the reflectances at 1.6 µm for two different RWC must be known for accurate prediction of unknown RWC; thus the LWCI is impractical for field applications. MSI was linearly correlated to RWCwith each species having a different regression equation and to log10 EWT with data of all species falling on the same regression line. Because EWT is correlated with leaf area index, MSI should also be correlated with leaf area index. Assuming that the linear regression equation of MSI to EWT can be applied to canopies, then the minimum significant change of RWC that can be detected is 52%. For most plants, the natural variation in RWC from water stress is only about 20%, so that we conclude that indices derived from NIR and MIR reflectances cannot be used to remotely-sense water stress. ER - TY - GEN ID - hunt_jr1987 AU - Hunt Jr, Raymond E. AU - Rock, Barrett N. AU - Nobel, Park S. TI - Measurement of leaf relative water content by infrared reflectance UR - http://www.sciencedirect.com/science/article/pii/0034425787900940 DO - 10.1016/0034-4257(87)90094-0 T2 - Remote Sensing of Environment PY - 1987 SN - 0034-4257 VL - 22 IS - 3 SP - 429-435 AB - From basic considerations and Beer's law, a leaf water content index incorporating reflectances of wavelengths from 0.76 to 0.90 μm and from 1.55 to 1.75 μm (Landsat Thematic Mapper Bands TM4 and TM5, respectively) was developed that relates leaf reflectance to leaf relative water content. For the leaf succulent, Agave deserti, the leaf water content index was not significantly different from the relative water content for either individual leaves or an entire plant. Also, the relative water contents of intact plants of Encelia farinosa and Hilaria rigida in the field were estimated by the leaf water content index; variations in the proportion of living to dead leaf area could cause large errors in the estimate of relative water content. Thus, the leaf water content index may be able to estimate average relative water content of canopies when TM4 and TM5 are measured at a known relative water content and fraction of dead leaf material. ER - TY - GEN ID - hunt_jr_2011 AU - Hunt Jr., E. Raymond AU - Daughtry, C. S. T. AU - Eitel, Jan U. H. AU - Long, D. S. TI - Remote Sensing Leaf Chlorophyll Content Using a Visible Band Index T2 - Agronomy Journal PY - 2011 VL - 103 SP - 1090-1099 AB - Leaf chlorophyll content is an important variable for agricultural remote sensing because of its close relationship to leaf nitrogen content. We propose the triangular greenness index (TGI), which calculates the area of a triangle with three vertices: ('r, Rr), ('g, Rg), and ('b, Rb), where ' is the wavelength (nm) and R is the reflectance for bands in red (r), green (g), and blue (b) wavelengths. TGI was correlated with chlorophyll content using a variety of leaf and plot reflectance data. Generally, indices using the chlorophyll red-edge (710-730 nm) had higher correlations with chlorophyll content compared to TGI. However, with broad bands, correlations between TGI and chlorophyll content were equal or higher than other indices for corn and wheat. Simulations using the Scattering by Arbitrarily Inclined Leaves canopy model indicate an interaction among TGI, leaf area index (LAI) and soil type at low crop LAI, whereas at high crop LAI, TGI was only affected by leaf chlorophyll content. TGI will enable the use of low-cost sensors, including digital cameras, for nitrogen management by remote sensing. ER - TY - JOUR ID - h_ttich2009 AU - Hüttich, Christian AU - Gessner, Ursula AU - Herold, Martin AU - Strohbach, Ben AU - Schmidt, Michael AU - Keil, Manfred AU - Dech, Stefan TI - On the Suitability of MODIS Time Series Metrics to Map Vegetation Types in Dry Savanna Ecosystems: A Case Study in the Kalahari of NE Namibia UR - http://www.mdpi.com/2072-4292/1/4/620/ T2 - Remote Sensing PY - 2009 SN - 2072-4292 VL - 1 IS - 4 SP - 620-643 ER - TY - JOUR ID - jackson1991 AU - Jackson, Ray D. AU - Huete, Alfredo R. TI - Interpreting vegetation indices T2 - Preventive Veterinary Medicine PY - 1991 VL - 11 IS - 3-4 SP - 185-200 ER - TY - CONF ID - jacobsen1995 AU - Jacobsen, Anne AU - Broge, Niels H. AU - Hansen, Birger U. TI - Monitoring wheat fields and grasslands using spectral reflectance data T2 - International Symposium on Spectral Sensing Research (ISSSR) CY - Melbourne, Australia PY - 1995 DA - Nov. 26 to Dec. 1, 1995 ER - TY - GEN ID - karnieli2001 AU - Karnieli, A. AU - Kaufman, Y. J. AU - Remer, L. AU - Wald, A. TI - AFRI - aerosol free vegetation index UR - http://www.ingentaconnect.com/content/els/00344257/2001/00000077/00000001/art00190 http://dx.doi.org/10.1016/S0034-4257(01)00190-0 DO - 10.1016/s0034-4257(01)00190-0 T2 - Remote Sensing of Environment PY - 2001 VL - 77 IS - 1 SP - 10-21 AB -

Aircraft measurements using a field spectrometer over variety of ground surfaces in Israel reveals that under clear sky conditions, the shortwave infrared (SWIR) spectral bands around 1.6 and 2.1 mum are highly correlated with the visible - blue, green, and red - spectral bands. Empirical linear relationships, such as rho0.469=0.25rho2.1; rho0.555=0.33rho2.1; rho0.645=0.5rho2.1; and rho0.645=0.66rho1.6, were found to be statistically significant and consistent with previous findings. Based on the above relationships, a modified vegetation index (VI) is proposed and named Aerosol Free Vegetation Index (AFRI). Two versions of this VI are formulated as: AFRI1.6=(rhoNIR-0.66rho1.6)/(rhoNIR+0.66rho1.6) and AFRI2.1=(rhoNIR-0.5rho2.1)/(rhoNIR+0.5rho2.1). It is shown that under clear sky conditions, the AFRIs (and especially AFRI2.1) closely resemble the Normalized Difference Vegetation Index (NDVI) and their values are almost identical. The advantage of the derived AFRIs, based on the ability of the SWIR bands, is to penetrate the atmospheric column even when aerosols such as smoke or sulfates exist. Consequently, these indices have a major application in assessing vegetation in the presence of smoke, anthropogenic pollution, or volcanic plumes. This was demonstrated by applying the AFRI for a biomass burned forest in Brazil. Limited success of these indices is expected in case of dust due to presence of larger particles that are of similar size to the wavelength and therefore not transparent at 2.1 mum. The AFRIs can be implemented to data from any sensor that has the SWIR bands. Currently, the most commonly used of such instruments are the Landsat - Thematic Mapper (TM) and Advanced Thematic Mapper Plus (ETM+), Moderate Resolution Imaging Spectrometer (MODIS), Advanced Spaceborne Thermal Emission And Reflection (ASTER), and Japanese Earth Resources Satellite - Optical System (JERS-OPS). Although the AFRI2.1 was demonstrated to perform better than the AFRI1.6, the latter still can be used for the same application in conjunction with instruments that have onboard only the 1.6-mum band, such as Systeme Probatoire d'Observation del la Terre (SPOT4) - VEGETATION, Indian Remote Sensing (IRS-1C/D), and Resource-21.

ER - TY - ABST ID - kaufman1992 AU - Kaufman, Y. J.;Tanre, D. TI - Atmospherically resistant vegetation index (ARVI) for EOS-MODIS DO - 10.1109/36.134076 T2 - Geoscience and Remote Sensing, IEEE Transactions on PY - 1992 SN - 0196-2892 VL - 30 IS - 2 SP - 261-270 KW - ecology KW - remote sensing KW - 0.47 micron KW - 0.66 micron KW - 0.865 micron KW - EOS-HIRIS KW - EOS-MODIS KW - Earth Observing System KW - atmospherically resistant vegetation index KW - blue channel KW - dynamic range KW - near-IR channel KW - normalized difference vegetation index KW - radiance KW - radi ER - TY - JOUR ID - kauth1976 AU - Kauth, R. J. and Thomas, G. S. TI - The tasselled cap - a graphic description of the spectraltemporal development of agricultural crops as seen by Landsat PR - Purdue University, West Lafayette, Indiana T2 - Procs. Symposium on Machine Processing of Remotely Sensed Data PY - 1976 SP - 41-51 ER - TY - CPAPER ID - key2002 AU - Key, C.H. AU - N. Benson AU - D. Ohlen AU - S. Howard AU - R. McKinley AU - Zhu Z. TI - The normalized burn ratio and relationships to burn severity: ecology, remote sensing and implementation PR - J.D. Greer, ed. Rapid Delivery of Remote Sensing Products T2 - Proceedings of the Ninth Forest Service Remote Sensing Applications Conference. American Society for Photogrammetry and Remote Sensing, Bethesda, MD CY - San Diego, CA PY - 2002 DA - 8-12 April, 2002 ER - TY - JOUR ID - kooistra2004 AU - Kooistra, L. AU - Salas, E. A. L. AU - Clevers, J. G. P. W. AU - Wehrens, R. AU - Leuven, R. S. E. W. AU - Nienhuis, P. H. AU - Buydens, L. M. C. TI - Exploring field vegetation reflectance as an indicator of soil contamination in river floodplains UR - http://www.sciencedirect.com/science/article/pii/S0269749103002665 DO - 10.1016/s0269-7491(03)00266-5 T2 - Environmental Pollution PY - 2004 SN - 0269-7491 VL - 127 IS - 2 SP - 281-290 AB - This study investigated the relation between vegetation reflectance and elevated concentrations of the metals Ni, Cd, Cu, Zn and Pb in river floodplain soils. High-resolution vegetation reflectance spectra in the visible to near-infrared (400–1350 nm) were obtained using a field radiometer. The relations were evaluated using simple linear regression in combination with two spectral vegetation indices: the Difference Vegetation Index (DVI) and the Red-Edge Position (REP). In addition, a multivariate regression approach using partial least squares (PLS) regression was adopted. The three methods achieved comparable results. The best R2 values for the relation between metals concentrations and vegetation reflectance were obtained for grass vegetation and ranged from 0.50 to 0.73. Herbaceous species displayed a larger deviation from the established relationships, resulting in lower R2 values and larger cross-validation errors. The results corroborate the potential of hyperspectral remote sensing to contribute to the survey of elevated metal concentrations in floodplain soils under grassland using the spectral response of the vegetation as an indicator. Additional constraints will, however, have to be taken into account, as results are resolution- and location-dependent. KW - Heavy metals KW - Vegetation reflectance KW - Remote sensing KW - Multivariate statistics KW - River sediment ER - TY - JOUR ID - lymburner2000 AU - Lymburner, L. AU - Beggs, PJ. AU - Jacobson, CR. TI - Estimation of canopy-average surface-specific leaf area using Landsat TM data T2 - Photogrammetric Engineering and Remote Sensing PY - 2000 SN - 0099-1112 VL - 66 IS - 2 SP - 183-191 AB - Specific leaf area (SLA) is an important ecological variable because of its links with plant ecophysiology and leaf biochemistry. Variations in SLA are associated with variations in leaf optical properties, and these changes in leaf optical properties have been found to result in changes in canopy reflectance. This paper utilizes these changes to explore the potential of estimating SLA using Landsat TM data. Fourteen sites with varying vegetation were sampled on the Lambert Peninsula in Ku-ring-gai Chase National Park to the north of Sydney, Australia. A sampling strategy that facilitated the calculation of canopy-average surface SLA (SLA(CS)) was developed. The relationship between SLA(CS), reflectance in Landsat TM bands, and a number of vegetation indices, were explored using univariate regression. The observed relationships between SLA(CS) and canopy reflectance are also discussed in terms of trends observed in a pre-existing leaf optical properties dataset (LOPEX 93). Field data indicate that there is a strong correlation between SLA(CS) and red, near-infrared, and the second midinfrared bands of Landsat TM data. A strong correlation between SLA(CS) and the following vegetation indices: Soil and Atmosphere Resistant Vegetation Index (SARVI2), Normalized Difference Vegetation Index (NDVI), and Ratio Vegetation Index (RVI), suggests that these vegetation indices could be used to estimate SLA(CS) using Landsat TM data. ER - TY - JOUR ID - malthus1993 AU - Malthus, Tim J. AU - Andrieu, Bruno AU - Danson, F. Mark AU - Jaggard, Keith W. AU - Steven, Michael D. TI - Candidate high spectral resolution infrared indices for crop cover UR - http://www.sciencedirect.com/science/article/pii/003442579390095F DO - 10.1016/0034-4257(93)90095-f T2 - Remote Sensing of Environment PY - 1993 SN - 0034-4257 VL - 46 IS - 2 SP - 204-212 ER - TY - JOUR ID - metternicht2003 AU - Metternicht, G. TI - Vegetation indices derived from high-resolution airborne videography for precision crop management T2 - International Journal of Remote Sensing PY - 2003 SN - 0143-1161 VL - 24 IS - 14 SP - 2855-2877 ER - TY - JOUR ID - moran1997 AU - Moran, M. S. AU - Inoue, Y. AU - Barnes, E. M. TI - Opportunities and limitations for image-based remote sensing in precision crop management UR - http://www.sciencedirect.com/science/article/pii/S003442579700045X DO - 10.1016/s0034-4257(97)00045-x T2 - Remote Sensing of Environment PY - 1997 SN - 0034-4257 VL - 61 IS - 3 SP - 319-346 AB - This review addresses the potential of image-based remote sensing to provide spatially and temporally distributed information for precision crop management (PCM). PCM is an agricultural management system designed to target crop and soil inputs according to within, field requirements to optimize profitability and protect the environment. Progress in. PCM has been hampered by a lack of timely, distributed information on crop and soil conditions. Based on a review of the information requirements of PCM, eight areas were identified in which image-based remote sensing technology could provide information that is currently lacking or inadequate. Recommendations were made for applications with potential for near-term implementation with available remote sensing technology and instrumentation. We found that both aircraft- and satellite-based re-trote sensing could provide valuable information for PCM applications. Images from aircraft-based sensors have a unique role for monitoring seasonally variable crop/soil conditions and for time specific and time-critical crop management; current satellitebased sensors have limited, but important, applications; and upcoming commercial Earth observation satellites may provide the resolution, timeliness, and high quality required for many PCM operations. The current limitations for image-based remote sensing applications are mainly due to sensor attributes, such as restricted spectral range, coarse spatial resolution, slow turnaround time, and inadequate repeat coverage. According to experts in PCM, the potential market for remote sensing products in PCM is good. Future work should be focused on assimilating remotely sensed infonna- tion into existing decision support systems (DSS), and conducting economic and technical analysis of remote sensing applications with season-long pilot projects. ER - TY - JOUR ID - nagler2000 AU - Nagler, P. L AU - Daughtry, C. S. T. AU - Goward, S. N. TI - Plant Litter and Soil Reflectance DO - 10.1016/s0034-4257(99)00082-6 T2 - Remote Sensing of Environment PY - 2000 SN - 0034-4257 VL - 71 IS - 2 SP - 207-215 AB - The presence of plant litter on the soil surface influences the flow of nutrients, carbon, water, and energy in terrestrial ecosystems. Quantifying plant litter cover is important for interpreting vegetated landscapes and for evaluating the effectiveness of conservation tillage practices. Current methods of measuring litter cover are subjective, requiring considerable visual judgment. Reliable and objective methods are needed. The spectral reflectance (0.4–2.5 μm) of wet and dry soils (six types) and plant litters (2 crops, 14 forest, and 2 grasses) of different ages were measured. Discrimination of plant litters from the soils was ambiguous in the visible and near-infrared (0.4–1.1μm) wavelength region. An absorption feature associated with cellulose and lignin was observed at 2.1 μm in the spectra of dry plant litter, which was not present in the spectra of soils. A new spectral variable, cellulose absorption index (CAI), was defined using the relative depth of the reflectance spectra at 2.1 μm. CAI of dry litter was significantly greater than CAI of soils. CAI generally decreased with age of the litter. Water absorption dominated the spectral properties of both soils and plant litter and significantly reduced the CAI of the plant litters. Nevertheless, the CAI of wet litter was significantly greater then CAI of wet soil. This study provides a new methodology to discriminate plant litter from soils by differences in spectral reflectance produced by their physical and chemical attributes. This remote sensing method should improve quantification of plant litter cover and thus improve estimates of phytomass production, surface energy balance, and the effectiveness of soil conservation practices. Plant litter reflectance is a verifiable component in vegetative landscapes and should be labeled and modeled separately from soils in landscape studies. ER - TY - JOUR ID - pinder1999 AU - Pinder, J. E. AU - McLeod, K. W. TI - Indications of relative drought stress in longleaf pine from Thematic Mapper data T2 - Photogrammetric Engineering and Remote Sensing PY - 1999 VL - 65 SP - 495–501 ER - TY - JOUR ID - rock1986 AU - Rock, B. N. AU - Vogelmann, J. E. AU - Williams, D. L. AU - Vogehnann, A. F. AU - Hoshizaki, T. TI - Remote detection of forest damage T2 - Bioscience PY - 1986 VL - 36 SP - 439-445 ER - TY - JOUR ID - rondeaux1996 AU - Rondeaux, Geneviève AU - Steven, Michael AU - Baret, Frédéric TI - Optimization of soil-adjusted vegetation indices UR - http://www.sciencedirect.com/science/article/pii/0034425795001867 DO - 10.1016/0034-4257(95)00186-7 T2 - Remote Sensing of Environment PY - 1996 SN - 0034-4257 VL - 55 IS - 2 SP - 95-107 AB - The sensitivity of the normalized difference vegetation index (NDVI) to soil background and atmospheric effects has generated an increasing interest in the development of new indices, such as the soil-adjusted vegetation index (SAVI), transformed soil-adjusted vegetation index (TSAVI), atmospherically resistant vegetation index (AR VI), global environment monitoring index (GEMI), modified soil-adjusted vegetation index (MSAVI), which are less sensitive to these external influences. These indices are theoretically more reliable than NDVI, although they are not yet widely used with satellite data. This article focuses on testing and comparing the sensitivity of NDVI, SAVI, TSAVI, MSAVI and GEMI to soil background effects. Indices are simulated with the SAIL model for a large range of soil reflectances, including sand, clay, and dark peat, with additional variations induced by moisture and roughness. The general formulation of the SAVI family of indices with the form VI = (NIR - R) / (NIR + R + X) is also reexamined. The value of the parameter X is critical in the minimization of soil effects. A value of X = 0.16 is found as the optimized value. Index performances are compared by means of an analysis of variance. ER - TY - JOUR ID - schlerf2005 AU - Schlerf, M. AU - Atzberger, C. AU - Hill, J. TI - Remote sensing of forest biophysical variables using HyMap imaging spectrometer data T2 - Remote Sensing of Environment PY - 2005 VL - 95 SP - 177−194 ER - TY - JOUR ID - schmidt2001 AU - Schmidt, H. AU - Karnieli, A. TI - Sensitivity of vegetation indices to substrate brightness in hyper-arid environment: The Makhtesh Ramon Crater (Israel) case study UR - http://dx.doi.org/10.1080/01431160110063779 DO - 10.1080/01431160110063779 T2 - International Journal of Remote Sensing PY - 2001 DA - 2001/01/01 SN - 0143-1161 VL - 22 IS - 17 SP - 3503-3520 AB - The influence of soil background on most vegetation indices (VIs) derived from remotely sensed imagery is a well known phenomenon, and has generated interest in the development of indices that would be less sensitive to this influence. Several such indices have been developed thus far. This paper focuses on testing and comparing the sensitivity of seven intensively used, Landsat Thematic Mapper (TM) derived, VIs (NDVI, SAVI, MSAVI, PVI, WDVI, SAVI 2 and TSAVI) to bare surface variation with almost no vegetation signal. The study was conducted on a terrain composed of a high variety of bare surface materials of which basalt and gypsum are two extremely dark and bright substrates respectively. It was found that SAVI and MSAVI respond to bare surface material very similarly. Such close similarity was also found between PVI and WDVI, and between SAVI 2 and TSAVI. NDVI tends to be overestimated on dark surfaces, while SAVI, PVI and TSAVI show more sensitivity to bright surfaces. Comparison between DeltaVI (the difference between pairs of VIs) and the brightness of the different surface materials showed a high correlation in each case, which underlines the fact that the response of different VIs to bare surface variation is mainly related to the surface brightness. ER - TY - JOUR ID - thenkabail2002 AU - Thenkabail, P. S. AU - Smith, R. B. AU - De Pauw, E. TI - Evaluation of narrowband and broadband vegetation indices for determining optimal hyperspectral wavebands for agricultural crop characterization T2 - Photogrammetric Engineering And Remote Sensing PY - 2002 VL - 68 IS - 6 SP - 607-621 ER - TY - JOUR ID - underwood2003 AU - Underwood, Emma AU - Ustin, Susan AU - DiPietro, Deanne TI - Mapping nonnative plants using hyperspectral imagery UR - http://www.sciencedirect.com/science/article/pii/S0034425703000968 DO - 10.1016/s0034-4257(03)00096-8 T2 - Remote Sensing of Environment PY - 2003 SN - 0034-4257 VL - 86 IS - 2 SP - 150-161 AB - Nonnative plant species are causing enormous ecological and environmental impacts from local to global scale. Remote sensing images have had mixed success in providing spatial information on land cover characteristics to land managers that increase effective management of invasions into native habitats. However, there has been limited evaluation of the use of hyperspectral data and processing techniques for mapping specific invasive species based on their spectral characteristics. This research evaluated three different methods of processing hyperspectral imagery: minimum noise fraction (MNF), continuum removal, and band ratio indices for mapping iceplant (Carpobrotus edulis) and jubata grass (Cortaderia jubata) in California's coastal habitat. Validation with field sampling data showed high mapping accuracies for all methods for identifying presence or absence of iceplant (97%), with the MNF procedure producing the highest accuracy (55%) when the classes were divided into four different densities of iceplant. KW - AVIRIS KW - Hyperspectral KW - Invasive plants KW - Nonnative plants KW - Iceplant KW - Carpobrotus edulis KW - Jubata grass KW - Cortaderia jubata KW - Mapping ER - TY - JOUR ID - vogelmann1993 AU - Vogelmann, J.E. AU - Rock, B.N. AU - Moss, D.M. TI - Red Edge Spectral Measurements from Sugar Maple Leaves UR - http://dx.doi.org/10.1080/01431169308953986 DO - 10.1080/01431169308953986 PR - Taylor & Francis T2 - International Journal of Remote Sensing PY - 1993 SN - 0143-1161 VL - 14 IS - 8 SP - 1563-1575 AB - Abstract Many sugar maple stands in the northeastern United States experienced extensive insect damage during the 1988 growing season. Chlorophyll data and high spectral resolution spectrometer laboratory reflectance data were acquired for multiple collections of single detached sugar maple leaves variously affected by the insect over the 1988 growing season. Reflectance data indicated consistent and diagnostic differences in the red edge portion (680-750 nm) of the spectrum among the various samples and populations of leaves. These included differences in the red edge inflection point (REIP), a ratio of reflectance at 740-720 nm (RE3/RE2), and a ratio of first derivative values at 715-705 nm (D715/D705), All three red edge parameters were highly correlated with variation in total chlorophyll content. Other spectral measures, including the Normalized Difference Vegetation Index (NDVI) and the Simple Vegetation Index Ratio (VI), also varied among populations and over the growing season, but did not correlate well with total chlorophyll content. Leaf stacking studies on light and dark backgrounds indicated REIP, RE3/RE2 and D715/D705 to be much less influenced by differences in green leaf biomass and background condition than either NDVI or VI. ER - TY - JOUR ID - wang2007 AU - Wang, Fu-min AU - Huang, Jing-feng AU - Tang, Yan-lin AU - Wang, Xiu-zhen TI - New Vegetation Index and Its Application in Estimating Leaf Area Index of Rice UR - http://www.sciencedirect.com/science/article/pii/S1672630807600274 DO - 10.1016/s1672-6308(07)60027-4 T2 - Rice Science PY - 2007 SN - 1672-6308 VL - 14 IS - 3 SP - 195-203 AB - Leaf area index (LAI) is an important characteristic of land surface vegetation system, and is also a key parameter for the models of global water balancing and carbon circulation. By using the reflectance values of Landsat-5 blue, green and red channels simulated from rice reflectance spectrum, the sensitivities of the bands to LAI were analyzed, and the response and capability to estimate LAI of various NDVIs (normalized difference vegetation indices), which were established by substituting the red band of general NDVI with all possible combinations of red, green and blue bands, were assessed. Finally, the conclusion was tested by rice data at different conditions. The sensitivities of red, green and blue bands to LAI were different under various conditions. When LAI was less than 3, red and blue bands were more sensitive to LAI. Though green band in the circumstances was less sensitive to LAI than red and blue bands, it was sensitive to LAI in a wider range. When the vegetation indices were constituted by all kinds of combinations of red, green and blue bands, the premise for making the sensitivity of these vegetation indices to LAI be meaningful was that the value of one of the combinations was greater than 0.024, i.e. visible reflectance (VIS)>0.024. Otherwise, the vegetation indices would be saturated, resulting in lower estimation accuracy of LAI. Comparison on the capabilities of the vegetation indices derived from all kinds of combinations of red, green and blue bands to LAI estimation showed that GNDVI (Green NDVI) and GBNDVI (Green-Blue NDVI) had the best relations with LAI. The capabilities of GNDVI and GBNDVI to LAI estimation were tested under different circumstances, and the same result was acquired. It suggested that GNDVI and GBNDVI performed better to predict LAI than the conventional NDVI. KW - vegetation index KW - rice KW - leaf area index KW - reflectance spectrum KW - remote sensing ER - TY - JOUR ID - wilson2002 AU - Wilson, Emily Hoffhine AU - Sader, Steven A. TI - Detection of forest harvest type using multiple dates of Landsat TM imagery UR - http://www.sciencedirect.com/science/article/pii/S0034425701003182 DO - 10.1016/s0034-4257(01)00318-2 T2 - Remote Sensing of Environment PY - 2002 SN - 0034-4257 VL - 80 IS - 3 SP - 385-396 AB - A simple and relatively accurate technique for classifying time-series Landsat Thematic Mapper (TM) imagery to detect levels of forest harvest is the topic of this research. The accuracy of multidate classification of the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI) were compared and the effect of the number of years (1–3, 3–4, 5–6 years) between image acquisition on forest change accuracy was evaluated. When Landsat image acquisitions were only 1–3 years apart, forest clearcuts were detected with producer's accuracy ranging from 79% to 96% using the RGB-NDMI classification method. Partial harvests were detected with lower producer's accuracy (55–80%) accuracy. The accuracy of both clearcut and partial harvests decreased as time between image acquisition increased. In all classification trials, the RGB-NDMI method produced significantly higher accuracies, compared to the RGB-NDVI. These results are interesting because the less common NDMI (using the reflected middle infrared band) outperformed the more popular NDVI. In northern Maine, industrial forest landowners have shifted from clearcutting to partial harvest systems in recent years. The RGB-NDMI change detection classification applied to Landsat TM imagery collected every 2–3 years appears to be a promising technique for monitoring forest harvesting and other disturbances that do not remove the entire overstory canopy. ER - TY - JOUR ID - xie2007 AU - Xie, H. AU - Tian, Y. Q. AU - Granillo, J. A. AU - Keller, G. R. TI - Suitable remote sensing method and data for mapping and measuring active crop fields UR - ://WOS:000244093200023 DO - 10.1080/01431160600702673 T2 - International Journal of Remote Sensing PY - 2007 DA - Jan SN - 0143-1161 VL - 28 IS - 1-2 SP - 395-411 N1 - ISI Document Delivery No.: 134OR Times Cited: 9 Cited Reference Count: 22 Xie, H. Tian, Y. Q. Granillo, J. A. Keller, G. R. Taylor & francis ltd Abingdon AB - The objective of the study was to examine suitable remote sensing methods and data for mapping and measuring the acreages of active crop lands in order to improve irrigation management. We compared classification results from a supervised classification method and a method using normalized difference vegetation index (NDVI) with additional pre-classification processing. IKONOS and Landsat Enhanced Thematic Mapper Plus (ETM+) images were tested to see if high spatial resolution remote sensing data would have significant advantages in distinguishing between active and fallow lands. The classification achieved an overall accuracy of 93.63%. The results showed that the supervised classification did not have a clear advantage over the simple method using NDVI at the level of distinguishing between active crops and fallow lands. The result suggests that using ETM + instead of IKONOS high spatial resolution imageries is appropriate because of the high cost of IKONOS imageries and image heterogeneity of agricultural fields. It was shown that pre-processing with a mask to exclude the non-agricultural objects blended with agricultural fields is critical. KW - soil heat-flux KW - vegetation indexes KW - net-radiation KW - satellite KW - management KW - imagery ER - TY - GEN ID - zarco-tejada_p_j_miller_j_r_1999 AU - Zarco-Tejada P.J. AU - Miller J.R. TI - Land Cover Mapping at BOREAS using red edge spectral parameters from CASI imagery T2 - J. Geophys. Res. Atmospheres. PY - 1999 VL - 104 ER - TY - JOUR ID - zarco-tejada_p_j_sepulcre-cant_2007 AU - Zarco-Tejada P.J. AU - Sepulcre-Cantó, G. TI - REMOTE SENSING OF VEGETATION BIOPHYSICAL PARAMETERS FOR DETECTING STRESS CONDITION AND LAND COVER CHANGES T2 - Estudios de la Zona No Saturada del Suelo PY - 2007 VL - 8 ER - TY - ELEC ID - idb AU - Henrich, V. AU - Krauss, G. AU - Götze, C. AU - Sandow, C. TI - The IndexDatabase UR - https://www.indexdatabase.de/ CY - Bonn PY - 2011 DA - 2011 ER -