Motivation,development and validation of a new spectral greenness index: A spectral dimension related to foliage projective cover |
| |
| Authors: | T Moffiet JD Armston K Mengersen |
| |
| Institution: | 1. Universities Space Research Association, Columbia, MD 21046, USA;2. Biospheric Science Laboratory (Code 618), NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA;1. Institute for Informatics and Automation Problems of the National Academy of Sciences of the Republic of Armenia, P. Sevak 1, Yerevan 0014, Armenia;2. Center for Ecological-Noosphere Studies of the National Academy of Sciences of the Republic of Armenia, Abovyan 68, Yerevan 0025, Armenia;3. Institute for Environmental Sciences, University of Geneva, 7 route de Drize, CH 1227 Carouge, GE, Switzerland;4. United Nations Environment Programme, Global Resource Information Database, 11 chemin des Anmones, CH 1219, Chtelaine, GE, Switzerland;5. Forel Institute, University of Geneva, 10 route de Suisse, CP 416, CH-1290 Versoix, Switzerland |
| |
| Abstract: | A method is presented for the development of a regional Landsat-5 Thematic Mapper (TM) and Landsat-7 Enhanced Thematic Mapper plus (ETM+) spectral greenness index, coherent with a six-dimensional index set, based on a single ETM+ spectral image of a reference landscape. The first three indices of the set are determined by a polar transformation of the first three principal components of the reference image and relate to scene brightness, percent foliage projective cover (FPC) and water related features. The remaining three principal components, of diminishing significance with respect to the reference image, complete the set.The reference landscape, a 2200 km2 area containing a mix of cattle pasture, native woodland and forest, is located near Injune in South East Queensland, Australia. The indices developed from the reference image were tested using TM spectral images from 19 regionally dispersed areas in Queensland, representative of dissimilar landscapes containing woody vegetation ranging from tall closed forest to low open woodland. Examples of image transformations and two-dimensional feature space plots are used to demonstrate image interpretations related to the first three indices. Coherent, sensible, interpretations of landscape features in images composed of the first three indices can be made in terms of brightness (red), foliage cover (green) and water (blue). A limited comparison is made with similar existing indices. The proposed greenness index was found to be very strongly related to FPC and insensitive to smoke. A novel Bayesian, bounded space, modelling method, was used to validate the greenness index as a good predictor of FPC. Airborne LiDAR (Light Detection and Ranging) estimates of FPC along transects of the 19 sites provided the training and validation data. Other spectral indices from the set were found to be useful as model covariates that could improve FPC predictions. They act to adjust the greenness/FPC relationship to suit different spectral backgrounds. The inclusion of an external meteorological covariate showed that further improvements to regional-scale predictions of FPC could be gained over those based on spectral indices alone. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
