Use of a super‐resolution method in interpretation of forests from multiple NOAA/AVHRR images

Super‐resolution methods take several images from the same location and fuse them into one, called a super‐resolution image. If the fusion is carried out correctly, it is possible to recognize details from the super‐resolution image which are not visible in the original images. In this study, a super‐resolution technique is applied to sequences of National Oceanic and Atmospheric Administration, Advanced Very High Resolution Radiometer (NOAA/AVHRR) scenes using a varying number of images and acquisition times, in order to determine whether it is possible to recognize forested and non‐forested areas from NOAA/AVHRR images more accurately using a super‐resolution method than with the original images. The overall proportion of forest and the proportion of forest with a growing stock volume of over 50 m³/ha were calculated for each pixel, and the results evaluated using landscape indices, r.m.s. error (RMSE) and a parameter showing how large a proportion of the estimates are closer to the ground truth in the original image sequence than in the corresponding super‐resolution image. The results showed the super‐resolution estimates to be better in all cases than those based on the original image material, but the improvement was marginal. Neither the number of images nor the image acquisition time had any obvious effect on the results.     

A comparison of NOAA–AVHRR fire data with three Landsat data sets in arid and semi-arid Australia

Burnt area data, derived from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) imagery, are validated in 11 regions of arid and semi-arid Australia, using three separate Landsat-derived burnt area data sets. Mapping accuracy of burnt extent is highly variable between areas and from year to year within the same area. Where there are corresponding patches in the AVHRR and Landsat data sets, the fit is good. However, the AVHRR data set misses some large patches. Overall, 63% of the Landsat burnt area is also mapped in the AVHRR data set, but this varies from 0% to 89% at different sites. In total, 81% of the AVHRR burnt area data are matched in the Landsat data set, but range from 0% to 94%. The lower match rates (<50%) are generally when little area has burnt (0–500 km²), with figures generally better in the more northerly sites. Results of regressions analysis based on 10 km?×?10 km cells are also variable, with R ² values ranging from 0.37 (n?=?116) to 0.94 (n?=?85). For the Tanami Desert scene, R ² varies from 0.41 to 0.61 (n?=?368) over three separate years. Combining the data results in an R ² of 0.60 (n?=?1315) (or 0.56 with the intercept set to 0). The slopes of the regressions indicate that mapping the burnt area from AVHRR imagery underestimates the ‘true’ extent of burning for all scenes and years. Differences in mapping accuracy between low and high fire years are examined, as well as the influence of soil, vegetation, land use and tenure on mapping accuracy. Issues which are relevant to mapping fire in arid and semi-arid environments and discontinuous fuels are highlighted.     

Evaluation of MODIS and NOAA AVHRR vegetation indices with in situ measurements in a semi‐arid environment

Much effort has been made in recent years to improve the spectral and spatial resolution of satellite sensors to develop improved vegetation indices reflecting surface conditions. In this study satellite vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Very High Resolution Radiometer (AVHRR) are evaluated against two years of in situ measurements of vegetation indices in Senegal. The in situ measurements are obtained using four masts equipped with self‐registrating multispectral radiometers designed for the same wavelengths as the satellite sensor channels. In situ measurements of the MODIS Normalized Difference Vegetation Index (NDVI) and AVHRR NDVI are equally sensitive to vegetation; however, the MODIS NDVI is consistently higher than the AVHRR NDVI. The MODIS Enhanced Vegetation Index (EVI) proved more sensitive to dense vegetation than both AVHRR NDVI and MODIS NDVI. EVI and NDVI based on the MODIS 16‐day constrained view angle maximum value composite (CV‐MVC) product captured the seasonal dynamics of the field observations satisfactorily but a standard 16‐day MVC product estimated from the daily MODIS surface reflectance data without view angle constraints yielded higher correlations between the satellite indices and field measurements (R ² values ranging from 0.74 to 0.98). The standard MVC regressions furthermore approach a 1?:?1 line with in situ measured values compared to the CV‐MVC regressions. The 16‐day MVC AVHRR data did not satisfactorily reflect the variation in the in situ data. Seasonal variation in the in situ measurements is captured reasonably with R ² values of 0.75 in 2001 and 0.64 in 2002, but the dynamic range of the AVHRR satellite data is very low—about a third to a half of the values from in situ measurements. Consequently the in situ vegetation indices were emulated much better by the MODIS indices than by the AVHRR NDVI.