Estimate of net primary productivity of aquatic vegetation of the Amazon floodplain using Radarsat and JERS‐1

Synthetic aperture radar images, combined with field measurements, were used to estimate net primary productivity (NPP) of aquatic vegetation in the lower Amazon. Input data for a NPP model are (i) the total biomass of aquatic vegetation, determined by radar imagery and field measurements and (ii) the area occupied by aquatic vegetation, determined from radar imagery. After correction for monthly biomass losses, the NPP of one growth cycle of aquatic vegetation was calculated in the image domain. The total net primary productivity of Hymenachne amplexicaules, the dominant aquatic vegetation in the area, was on average 19×10¹¹ g C yr^?1 for the entire area. Spatially, lower values of produced organic carbon (<900 g C m^?2 yr^?1) are confined to regions where the plants developed only in the beginning of the rising phase of the hydrological cycle. In general, values are higher (>5000 g C m^?2 yr^?1) in areas closer to the Amazon River where the availability and influence of nutrient‐rich water is greater.     

Characterizing post‐fire vegetation recovery of California chaparral using TM/ETM+ time‐series data

A time series of normalized difference vegetation index (NDVI) data derived from 11 TM/ETM+ images was used to examine the recovery characteristics of chaparral vegetation in a small watershed near Santa Barbara, California following a fire event in 1985. The NDVI recovery trajectory was compared to a generalized recovery trajectory of leaf area index (LAI) for the same region, which was established using a chronosequence approach and TM/ETM+ data. Post‐fire NDVI recovery trajectories were derived for the entire catchment and for individual vegetation types. Post‐fire NDVI spatial patterns on each image date were compared to the pre‐fire pattern to determine the extent to which the pre‐fire pattern was re‐established, and the rate of this recovery. Results indicated that the post‐fire recovery trajectory for the catchment area average NDVI was similar to the previously established regional LAI trajectory based on a chronosequence approach. The NDVI recovery was disrupted by drought stress and attained pre‐fire levels approximately 10 years after the fire. Individual vegetation types did not exhibit different rates of recovery and the recovery trajectories were only distinguished by the maximum post‐fire NDVI observed after 10 years. The post‐fire NDVI spatial pattern also showed a systematic return to pre‐fire conditions, but exhibited a more substantial disruption due to drought stress than was the case for the average NDVI recovery trajectory.     

Reflectance of floodplain waterbodies using EO‐1 Hyperion data from high and receding flood periods of the Amazon River

The potential of Hyperion images acquired on September 2001 (receding flood period of the Amazon River) and June 2005 (high flood) was investigated for reflectance characterization of selected Amazon floodplain waterbodies using a linear spectral mixture model. The results show the ability of Hyperion to measure adequately the major variation in water reflectance spectral features in response to the annual flood pulse of the Amazon River. Mixture model fraction values were correlated with measured inorganic suspended solids (ISS) but not with chlorophyll (Chl) in the high flood period. Inspection of the fractions across the two images revealed variation in water composition. Small changes in ISS‐ and Chl‐bearing water fractions between the images indicated relatively stable spectral conditions for low (Tapajós River and Lake Juruparipucu) and high (Amazon River) turbidity waterbodies. Large changes indicated reflectance variation in some lakes when the water receded due to algal blooms (Lake Curumu) and sediment resuspension in shallow regions (Lake Aritapera). Although not all water constituents were modelled adequately for quantification purposes, spectral mixture modelling is still an interesting approach for spectral–temporal reflectance characterization of Amazonian floodplains with hyperspectral data.     

Examining pine spectral separability using hyperspectral data from an airborne sensor: An extension of field‐based results

Three southern USA forestry species, loblolly pine (Pinus taeda), Virginia pine (Pinus virginiana), and shortleaf pine (Pinus echinata), were previously shown to be spectrally separable (83% accuracy) using data from a full‐range spectroradiometer (400–2500 nm) acquired above tree canopies. This study focused on whether these same species are also separable using hyperspectral data acquired using the airborne visible/infrared imaging spectrometer (AVIRIS). Stepwise discriminant techniques were used to reduce data dimensionality to a maximum of 10 spectral bands, followed by discriminant techniques to measure separability. Discriminatory variables were largely located in the visible and near‐infrared regions of the spectrum. Cross‐validation accuracies ranged from 65% (1 pixel radiance data) to as high as 85% (3×3 pixel radiance data), indicating that these species have strong potential to be classified accurately using hyperspectral data from air‐ or space‐borne sensors.     

The recognition of road network from high‐resolution satellite remotely sensed data using image morphological characteristics

With the development of remote sensors and satellite technologies, high‐resolution satellite data such as IKONOS images have been available recently. By these new high‐resolution satellite data, remote sensing technologies can be successfully applied to more application areas such as extracting road network from high‐resolution satellite images. This paper proposes a newly developed approach to extract a road network from high‐resolution satellite images. The approach is based on the binary and greyscale mathematical morphology and a line segment match method. First, the outline of road network is detected based on the grey morphological characteristics. Then, the basic road network is detected by the line segment match method. Next, the detected basic road network is processed based on the knowledge about the roads and binary mathematical morphological methods. Finally, visual analysis and three indicators are used to evaluate the accuracy of the extracted road networks. The results of the accuracy evaluation demonstrate that the developed road network extraction approach can provide both good visual effect and high positional accuracy.     

Evaluation of AVHRR PAL and GIMMS 10‐day composite NDVI time series products using SPOT‐4 vegetation data for the African continent

Global 8 km resolution AVHRR (advanced very high resolution radiometer) NDVI (normalized difference vegetation index) 10‐day composite data sets have been used for numerous local to global scale vegetation time series studies during recent years. AVHRR Pathfinder (PAL) NDVI was available from 1981 until 2001, and the new AVHRR GIMMS NDVI was available from 1981 to the present time. A number of aspects potentially introduce noise in the NDVI data set due to the AVHRR sensor design and data processing. NDVI from SPOT‐4 VGT data is considered an improvement over AVHRR, and for this reason it is important to examine how and if the differences in sensor design and processing influence continental scale NDVI composite products. In this study, the quality of these AVHRR NDVI time series are evaluated by the continental scale 1 km resolution SPOT‐4 vegetation (VGT) 10‐day composite (S10) NDVI data. Three years of AVHRR PAL (1998–2000) and seven years of GIMMS (1998–2004) have been compared to 8 km resampled SPOT‐4 VGT (1998–2004) data. The dynamic range of SPOT‐4 VGT NDVI tends to be higher than the AVHRR PAL NDVI, whereas there is an exact match between AVHRR GIMMS NDVI and SPOT‐4 VGT NDVI. Ortho‐regression analysis on annually integrated values of AVHRR PAL/GIMMS and SPOT‐4 VGT on a continental scale reveals high correlations amongst the AVHRR and the SPOT data set, with lowest RMSE (root mean square error) on the GIMMS/SPOT‐4 VGT compared to the PAL/SPOT‐4 VGT.

Analyses on decade data likewise show that a linear relation exists between Spot‐4 VGT NDVI and the two AVHRR composite products; GIMMS explaining most of the Spot‐4 VGT NDVI variance compared to PAL. These results show that the AVHRR GIMMS NDVI is more consistent with Spot‐4 VGT NDVI compared to AVHRR PAL versus Spot‐4 VGT NDVI (in terms of RMSE and dynamic range) and can therefore be considered the more accurate long time AVHRR data record. Analyses performed on monthly maximum composites and decade composite data, however, reveal intra‐annual variations in the correlation between SPOT‐4 VGT and the two AVHRR data sets, which are attributed to different cloud masking algorithms. The SPOT‐4 VGT cloud‐screening algorithm is insufficient, thereby suppressing the rainy season NDVI.