Normalized Difference Vegetation Index Method Research Articles

Large-scale leaf area index inversion algorithms from high-resolution airborne imagery

Large-scale leaf area index (LAI) inversion algorithms were developed to determine the LAI of a forest located in Gatineau Park, Canada, using high-resolution colour and colour infrared (CIR) digital airborne imagery. The algorithms are parameter-independent and developed based on the principles of optical field instruments for gap fraction measurements. Cloud-free colour and CIR images were acquired on 21 August 2007 with 35 and 60 cm nominal ground pixel size, respectively. Normalized Difference Vegetation Index (NDVI), maximum likelihood and object-oriented classifications, and principal component analysis (PCA) methods were applied to calculate the mono-directional gap fraction. Subsequently, LAI was derived from inversion and compared with ground measurements made in 54 plots of 20 by 20 m using hemispherical photography between 10 and 20 August 2007. There was high inter-correlation (the Pearson correlation coefficient, R > 0.5, p < 0.01) among LAI values inverted using the classifications and PCA methods, but neither were highly correlated with LAI inverted from the NDVI method. LAI inverted from the NDVI-based gap fraction significantly correlated with ground-measured LAI (R = 0.63, root mean square error (RMSE) = 0.52), while LAI inverted from the classification and PCA-derived gap fraction showed poor correlation with ground-measured LAI. Consequently, the NDVI method was used to invert LAI for the whole study area and produce a 20‐m resolution LAI map.

Land Surface Temperature Retrieval by Using ATCOR3_T and Normalized Difference Vegetation Index Methods in Penang Island

Problem statement: Many studies have been devoted to establish the methodology for retrieval of LST from single channel algorithm by utilizing Landsat satellite images. However, this method required many atmospheric parameters to retrieve LST. Thus, it is very difficult to acquire the complete information for atmospheric parameters. Approach: While for ATCOR3_T, this method can be used to retrieve LST without any historical atmospheric parameters like transmittance and mean-atmospheric temperature. In this study, the retrieval of LST value from ATCOR3_T method was compared with the NDVI method, in order to calculate the average percentage of error. Results: The obtained result showed that the LST value retrieved from ATCOR3_T can be used to generate the LST map in equatorial region, such as Penang Island. The average percentage of error for 40 points of LST gives the percentage of 5.13%. Conclusion/Recommendations: Both of these methods can be used to estimate LST value in the study area. In addition, the retrieval of LST value from multi-temporal satellite images is useful to study urban heat island effects in Penang Island for future study.

Improved land surface emissivities over agricultural areas using ASTER NDVI

Land surface emissivity retrieval over agricultural regions is important for energy balance estimations, land cover assessment and other related environmental studies. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) produces images of sufficient spatial resolution (from 15 m to 90 m) to be of use in agricultural studies, in which fields of crops are too small to be well-resolved by low resolution sensors. The ASTER project generates land surface emissivity images as a Standard Product (AST05) using the Temperature/Emissivity Separation (TES) algorithm. However, the TES algorithm is prone to scaling errors in estimating emissivities for surfaces with low spectral contrast if the atmospheric correction is inaccurate. This paper shows a comparison between the land surface emissivity estimated with the TES algorithm and from a simple approach using the Normalized Difference Vegetation Index (NDVI) for five ASTER images (28 June 2000, 15 August 2000, 31 August 2000, 28 April 2001 and 02 August 2001) of the agricultural area of Barrax (Albacete, Spain). The results indicate that differences are < 1% for ASTER band 13 (10.7 μm) and < 1.5% for band 14 (11.3 μm), but > 2% for bands 10 (8.3 μm), 11 (8.6 μm) and 12 (9.1 μm). The emissivities for the five ASTER bands were tested against in situ measurements carried out with the CIMEL CE 312-2 field radiometer, the NDVI method giving root mean square errors (RMSE) < 0.005 over vegetated areas and RMSE < 0.015 over bare soil, and the TES algorithm giving RMSE ∼ 0.01 for vegetated areas but RMSE > 0.03 over bare soil. The errors and inconsistencies for ASTER bands 13 and 14 are within those anticipated for TES, but the greater errors for bands 10–12 suggest the presence of problems related to atmospheric compensation and model assumptions about soil spectra. The NDVI method uses visible/near-infrared data co-acquired with the thermal images to estimate vegetation cover and, hence, provides an independent constraint on emissivity. The success of this approach suggests that it may be useful for daytime images of agricultural or other heavily vegetated areas, in which the TES algorithm has occasional failures.

Separating surface emissivity and temperature using two-channel spectral indices and emissivity composites and comparison with a vegetation fraction method

The temperature-independent thermal infrared spectral indices (TISI) method is employed for the separation of land surface temperature (LST) and emissivity from surface radiances (atmospherically corrected satellite data). The daytime reflected solar irradiance and the surface emission at ∼3.8 μm have comparable magnitudes. Using surface radiances and a combination of day–night 2-channel TISI ratios, the ∼3.8 μm reflectivity is derived. For implementing the TISI method, coefficients for NOAA 9–16 AVHRR channels are obtained. A numerical analysis with simulated surface radiances shows that for most surface types (showing nearly Lambertian behavior) the achievable accuracy is ∼0.005 for emissivity (AVHRR channel-5) and ∼1.5 K for LST. Data from the European Centre for Medium-Range Weather Forecasts (ECMWF) is used for calculation of atmospheric attenuation. Comparisons are made over a part of central Europe on two different dates (seasons). Clouds pose a major problem to surface observations; hence, monthly emissivity composites are derived. Additionally, using TISI-based monthly composites of emissivities, a normalized difference vegetation index (NDVI)-based method is tuned to the particular study area and the results are intercompared. Once the coefficients are known, the NDVI method is easily implemented but holds well only for vegetated areas. The error of the NDVI-based emissivities (with respect to the TISI results) ranges between −0.038 and 0.032, but for vegetated areas the peak of the error-histogram is at ∼0.002. The algorithm for retrieving emissivity via TISI was validated with synthetic data. Due to the different spatial scales of satellite and surface measurements and the lack of homogeneous areas, which are representative for low-resolution pixels and ground measurements, ground-validation is a daunting task. However, for operational products ground-truth validation is necessary. Therefore, also an approach to identify suitable validation sites for meteorological satellite products in Europe is described.

A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM+ imagery

Plant foliage density expressed as leaf area index (LAI) is used in many ecological, meteorological, and agronomic models, and as a means of quantifying crop spatial variability for precision farming. LAI retrieval using spectral vegetation indices (SVI) from optical remotely sensed data usually requires site-specific calibration values from the surface or the use of within-scene image information without surface calibrations to invert radiative transfer models. An evaluation of LAI retrieval methods was conducted using (1) empirical methods employing the normalized difference vegetation index (NDVI) and a new SVI that uses green wavelength reflectance, (2) a scaled NDVI approach that uses no calibration measurements, and (3) a hybrid approach that uses a neural network (NN) and a radiative transfer model without site-specific calibration measurements. While research has shown that under a variety of conditions NDVI is not optimal for LAI retrieval, its continued use for remote sensing applications and in analysis seeking to develop improved parameter retrieval algorithms based on NDVI suggests its value as a “benchmark” or standard against which other methods can be compared. Landsat-7 ETM+ data for July 1 and July 8 from the Soil Moisture EXperiment 2002 (SMEX02) field campaign in the Walnut Creek watershed south of Ames, IA, were used for the analysis. Sun photometer data collected from a site within the watershed were used to atmospherically correct the imagery to surface reflectance. LAI validation measurements of corn and soybeans were collected close to the dates of the Landsat-7 overpasses. Comparable results were obtained with the empirical SVI methods and the scaled SVI method within each date. The hybrid method, although promising, did not account for as much of the variability as the SVI methods. Higher atmospheric optical depths for July 8 leading to surface reflectance errors are believed to have resulted in overall poorer performance for this date. Use of SVIs employing green wavelengths, improved method for the definition of image minimum and maximum clusters used by the scaled NDVI method, and further development of a soil reflectance index used by the hybrid NN approach are warranted. More importantly, the results demonstrate that reasonable LAI estimates are possible using optical remote sensing methods without in situ, site-specific calibration measurements.

Assessing satellite‐derived start‐of‐season measures in the conterminous USA

AbstractNational Oceanic and Atmospheric Administration (NOAA)‐series satellites, carrying advanced very high‐resolution radiometer (AVHRR) sensors, have allowed moderate resolution (1 km) measurements of the normalized difference vegetation index (NDVI) to be collected from the Earth's land surfaces for over 20 years. Across the conterminous USA, a readily accessible and decade‐long data set is now available to study many aspects of vegetation activity in this region. One feature, the onset of deciduous plant growth at the start of the spring season (SOS) is of special interest, as it appears to be crucial for accurate computation of several important biospheric processes, and a sensitive measure of the impacts of global change.In this study, satellite‐derived SOS dates produced by the delayed moving average (DMA) and seasonal midpoint NDVI (SMN) methods, and modelled surface phenology (spring indices, SI) were compared at widespread deciduous forest and mixed woodland sites during 1990–93 and 1995–99, and these three measures were also matched to native species bud‐break data collected at the Harvard Forest (Massachusetts) over the same time period. The results show that both SOS methods are doing a modestly accurate job of tracking the general pattern of surface phenology, but highlight the temporal limitations of biweekly satellite data. Specifically, at deciduous forest sites: (1) SMN SOS dates are close in time to SI first bloom dates (average bias of +0.74 days), whereas DMA SOS dates are considerably earlier (average bias of −41.24 days) and also systematically earlier in late spring than in early spring; (2) SMN SOS tracks overall yearly trends in deciduous forests somewhat better than DMA SOS, but with larger average error (MAEs 8.64 days and 7.37 days respectively); and (3) error in both SOS techniques varies considerably by year. Copyright © 2002 Royal Meteorological Society.

Impacts of vegetation dynamics on the identification of land-cover change in a biologically complex community in North Carolina, USA

A land-cover (LC) change detection experiment was performed in the biologically complex landscape of the Neuse River Basin (NRB), North Carolina using Landsat 5 and 7 imagery collected in May of 1993 and 2000. Methods included pixel-wise Normalized Difference Vegetation Index (NDVI) and Multiband Image Difference (MID) techniques. The NDVI method utilized non-normalized (raw) imagery data, while the MID method required normalized imagery. Image normalization techniques included both automatic scattergram-controlled regression (ASCR) and localized relative radiometric normalization (LRRN) techniques. Change/no-change thresholds for each method were optimized using calibration curves developed from reference data and a series of method-specific binary change masks. Cover class-specific thresholds were derived for each of the four methods using a previously developed NRB-LC classification (1998–1999) to support data stratification. An independent set of accuracy assessment points was selected using a disproportionate stratified sampling strategy to support the development of error matrices. Area-weighted conditional probability accuracy statistics were calculated based on the areal extent of change and no change for each cover class. All methods tested exhibited acceptable accuracies, ranging between 80% and 91%. However, change omission errors for woody cover types were unacceptably high, with values ranging between 60% and 79%. Overall commission errors in the change category were also high (42–51%) and strongly affected by the agriculture class. There were no significant differences in the Kappa coefficient between the NDVI, MID ASCR, and LRRN normalization methods. The MID non-normalized method was inferior to both the NDVI and MID ASCR methods. Stratification by major LC type had no effect on overall accuracies, regardless of method.

Satellite-based mapping of Canadian boreal forest fires: Evaluation and comparison of algorithms

This paper evaluates annual fire maps that were produced from NOAA-14/AVHRR imagery using an algorithm described in a companion paper (Li et al., International Journal of Remote Sensing, 21, 3057-3069, 2000 (this issue)). Burned area masks covering the Canadian boreal forest were created by compositing the daily maps of fire hot spots over the summer and by examining Normalized Difference Vegetation Index (NDVI) changes after burning. Both masks were compared with fire polygons derived by Canadian fire agencies through aerial surveillance. It was found that the majority of fire events were captured by the satellite-based techniques, but burnt area was generally underestimated. The burn boundary formed by the fire pixels detected by satellite were in good agreement with the polygons boundaries within which, however, there were some fires missed by the satellite. The presence of clouds and low sampling frequency of satellite observation are the two major causes for the underestimation. While this problem is alleviated by taking advantage of NDVI changes, a simple combination of a hot spot technique with a NDVI method is not an ideal solution due to the introduction of new sources of uncertainty. In addition, the performance of the algorithm used in the International Geosphere-Biosphere Programme (IGBP) Data and Information System (IGBPDIS) for global fire detection was evaluated by comparing its results with ours and with the fire agency reports. It was found that the IGBP-DIS algorithm is capable of detecting the majority of fires over the boreal forest, but also includes many false fires over old burned scars created by fires taking place in previous years. A step-by-step comparison between the two algorithms revealed the causes of the problem and recommendations are made to rectify them.