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 - 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 - 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 - zarco-tejada2001 AU - Zarco-Tejada, P. J. AU - Miller, J. R. AU - Noland, T. L. AU - Mohammed, G. H. AU - Sampson, P. H. TI - Scaling-up and model inversion methods with narrow-band optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data T2 - IEEE Transactions on Geoscience and Remote Sensing PY - 2001 VL - 39 SP - 1491−1507 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 -