A review of vegetation indices

Bannari, A.; Morin, Denis; Bonn, F.; Huete, Alfredo

doi:10.1080/02757259509532298

Cited by 1,208 publications

(678 citation statements)

References 59 publications

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“…Table 3 lists 19 vegetation, soil, and water indices that were derived from the Landsat 5 TM surface reflectance data and used in thematic classification. This is a small representative subset of the most widely used indices [12,56]. Another 18 morphometric terrain metrics were derived from the DEM to quantify the effects of topography and hydrological processes [28].…”

Section: Palsarmentioning

confidence: 99%

Mapping the Dabus Wetlands, Ethiopia, Using Random Forest Classification of Landsat, PALSAR and Topographic Data

et al. 2017

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Abstract:The Dabus Wetland complex in the highlands of Ethiopia is within the headwaters of the Nile Basin and is home to significant ecological communities and rare or endangered species. Its many interrelated wetland types undergo seasonal and longer-term changes due to weather and climate variations as well as anthropogenic land use such as grazing and burning. Mapping and monitoring of these wetlands has not been previously undertaken due primarily to their relative isolation and lack of resources. This study investigated the potential of remote sensing based classification for mapping the primary vegetation groups in the Dabus Wetlands using a combination of dry and wet season data, including optical (Landsat spectral bands and derived vegetation and wetness indices), radar (ALOS PALSAR L-band backscatter), and elevation (SRTM derived DEM and other terrain metrics) as inputs to the non-parametric Random Forest (RF) classifier. Eight wetland types and three terrestrial/upland classes were mapped using field samples of observed plant community composition and structure groupings as reference information. Various tests to compare results using different RF input parameters and data types were conducted. A combination of multispectral optical, radar and topographic variables provided the best overall classification accuracy, 94.4% and 92.9% for the dry and wet season, respectively. Spectral and topographic data (radar data excluded) performed nearly as well, while accuracies using only radar and topographic data were 82-89%. Relatively homogeneous classes such as Papyrus Swamps, Forested Wetland, and Wet Meadow yielded the highest accuracies while spatially complex classes such as Emergent Marsh were more difficult to accurately classify. The methods and results presented in this paper can serve as a basis for development of long-term mapping and monitoring of these and other non-forested wetlands in Ethiopia and other similar environmental settings.

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Section: Palsarmentioning

confidence: 99%

Mapping the Dabus Wetlands, Ethiopia, Using Random Forest Classification of Landsat, PALSAR and Topographic Data

et al. 2017

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“…Compared to the corrected MOD09GA result with MCD43A1 Collection 5 (Figure 7b), the spread of the Collection 6 result was narrower, with higher R 2 and lower RMSD and MAE, demonstrating the improvement of MCD43A1 Collection 6 over Collection 5. Moreover, it is important to note that BRDF correction improves the precision of even of ratio-based vegetation indices (VI) [46], due to its mitigation of the different anisotropies of different wavelengths. …”

Section: Variability Of the Mcd43a1 Productmentioning

confidence: 99%

Assessment of MODIS BRDF/Albedo Model Parameters (MCD43A1 Collection 6) for Directional Reflectance Retrieval

et al. 2017

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Measurements of solar radiation reflected from Earth's surface are the basis for calculating albedo, vegetation indices, and other terrestrial attributes. However, the "bi-directional" geometry of illumination and viewing (i.e., the Bi-directional Reflectance Distribution Function (BRDF)) impacts reflectance and all variables derived or estimated based on these data. The recently released MODIS BRDF/Albedo Model Parameters (MCD43A1 Collection 6) dataset enables retrieval of directional reflectance at arbitrary solar and viewing angles, potentially increasing precision and comparability of data collected under different illumination and observation geometries. We quantified the ability of MCD43A1 Collection 6 for retrieving directional reflectance and compared the daily Collection 6 retrievals to those of MCD43A1 Collection 5, which are retrieved on an eight-day basis. Correcting MODIS-based estimates of surface reflectance from the illumination and viewing geometry of the Terra satellite (MOD09GA) to that of the MODIS Aqua (MYD09GA) overpass, as well as MCD43A4 Collection 6 and Landsat-5 TM images show that the BRDF correction of MCD43A1 Collection 6 results in greater consistency among datasets, with higher R 2 (0.63-0.955), regression slopes closer to unity (0.718-0.955), lower root mean squared difference (RMSD) (0.422-3.142), and lower mean absolute error (MAE) (0.282-1.735) compared to the Collection 5 data. Smaller levels of noise (observed as high-frequency variability within the time series) in MCD43A1 Collection 6 in comparison to Collection 5 corroborates the improvement of BRDF parameters time series. These results corroborates that the daily MCD43A1 Collection 6 product represents the anisotropy of surface features and results in more precise directional reflectance derivation at any solar and viewing geometry than did the previous Collection 5.

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“…Different vegetation indices have been developed and used for classification or biomass estimation (Anderson and Hanson 1992, Anderson et al 1993, Eastwood et al 1997. Bannari et al (1995) reviewed more than forty indices presented through the literature. However, not all vegetation indices are significantly correlated with biomass.…”

Section: Discussionmentioning

confidence: 99%

Above-Ground Biomass Estimation of Successional and Mature Forests Using TM Images in the Amazon Basin

Mausel

Brondı́zio

et al. 2002

Advances in Spatial Data Handling

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Above-ground biomass estimation of successional and mature forests in moist tropical regions is attracting increasing attention. Because of complex stand structure and abundant vegetation species, rarely has remote-sensing research been successfully conducted in biomass estimation for moist tropical areas. In this paper, two study areas in the Brazilian Amazon basin-Altamira and Bragantina-with different biophysical characteristics were selected. Atmospherically corrected Thematic Mapper (TM) images and field vegetation inventory data were used in the analysis, and different vegetation indices and texture measures were explored. Multiple regression models were developed through integration of image data (including TM bands, different vegetation indices, and texture measures) and vegetation inventory data. These models were used for biomass estimation in both selected study areas. This study concludes that neither TM spectral bands nor vegetation indices alone are sufficient to establish an effective model for biomass estimation, but multiple regression models that consist of spectral and textural signatures improve biomass estimation performance. The models developed are especially suitable for above-ground biomass estimation of dense vegetation areas.

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A review of vegetation indices

Cited by 1,208 publications

References 59 publications

Mapping the Dabus Wetlands, Ethiopia, Using Random Forest Classification of Landsat, PALSAR and Topographic Data

Mapping the Dabus Wetlands, Ethiopia, Using Random Forest Classification of Landsat, PALSAR and Topographic Data

Assessment of MODIS BRDF/Albedo Model Parameters (MCD43A1 Collection 6) for Directional Reflectance Retrieval

Above-Ground Biomass Estimation of Successional and Mature Forests Using TM Images in the Amazon Basin

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