The JERS Amazon Multi-season Mapping Study (JAMMS): Observation strategies and data characteristics

The Japanese Earth Resources Satellite (JERS-1) Amazon Multiseason Mapping Study (JAMMS), part of the Global Rain Forest Mapping (GRFM) project led by the National Space Development Agency of Japan (NASDA), had an ambitious agenda to map the entire Amazon river floodplain (and surrounding areas) twice at high resolution. The observation strategy carried out by NASDA for the JAMMS project and the other elements of the GRFM project (1995-1997) constituted the first time that a spaceborne Synthetic Aperture Radar (SAR) successfully implemented a continental scale, coordinated seasonal mapping campaign. This observation strategy, chosen around the flooding cycle of the major river systems, was designed to provide the first high-resolution measurement of inundation extent by the Amazon river and its tributaries. In order for the scientific community at large to be able to exploit this dataset, the characteristics of the data (resolution, radiometric and geometric calibration, coverage, and ability to be mosaicked) must be well understood. We find that the quantization of the Alaska SAR Facility (ASF) imagery impacts the range of backscatter values that may be observed, in contrast to the NASDA processed imagery. The noise equivalent σ 0 is -15 dB at worst, but improves to about -20 dB at the centre of the swath. The resolution of the ASF imagery is slightly worse than that processed by NASDA. The initial geolocation accuracy of the ASF processed imagery is quite poor, but may be improved during the mosaicking process. The relative radiometric calibration of the data may be improved to about 0.2 dB by comparing the calibration of overlapping imagery, and through a careful analysis of cross-track trends in the data.     

Geocoded digital videography for validation of land cover mapping in the Amazon basin

Four Validation Overflights for Amazon Mosaics (VOAM) aerial video surveys have been carried out in the Brazilian Amazon to provide ground verification for mapping of wetland cover with the Global Rain Forest Mapping (GRFM) Project JERS-1 (Japanese Earth Remote Sensing Satellite) mosaics of the Amazon basin. Surveys in 1995 and 1996, acquired with handheld analog camcorders from small aircraft, were timed to imaging of the GRFM low- and high-water mosaics, and limited to within 600 km of Manaus. For the 1997 and 1999 flights, digital camcorder systems were installed in the Bandeirante survey plane operated by Brazil's National Institute for Space Research. The VOAM97 and VOAM99 surveys circumscribed the Brazilian Amazon, documenting ground conditions at resolutions on the order of 1 m (wide-angle format) and 10 cm (zoom format) for wetlands, forests, savannas, and human-impacted areas. Global Positioning System (GPS) information encoded on the video audio track was extracted by mosaicking software that automatically generates geocoded digital mosaics from video clips. On the 1999 survey, a laser altimeter recorded profiles of terrain and vegetation canopy heights. A validation dataset was compiled from the videography for a portion of the GRFM mosaics extending 6° by 4° in longitude and latitude, using randomly selected points along flight lines. Other applications of the VOAM videography include acquisition of ground control points for image geolocation, creation of a high-resolution geocoded mosaic of a forest study area, forest biomass estimation, and rapid assessment of fire damage. Geocoded digital videography provides a cost-effective means of compiling high-resolution validation datasets for land cover mapping in remote, cloud-covered regions.     

Geo-referencing of continental-scale JERS-1 SAR mosaics based on matching homologous features with a digital elevation model: theory and practice

An effective method for a posteriori ortho-rectification of continental-scale synthetic aperture radar (SAR) mosaics using a digital elevation model (DEM) has been developed. The method is based on homologous feature matching between the DEM and a simulated SAR image. The simulated image is derived from the radar-viewing geometry, topographic information and contextual information provided by the Shuttle Radar Topography Mission (SRTM), shorelines and water bodies database (SWBD) and GeoCover Landsat mosaics. Two large L-band SAR mosaics (the global boreal forest mapping (GBFM) Siberia mosaic and the global rain forest mapping (GRFM) Africa mosaic), assembled from the Japanese Earth Resources Satellite-1 (JERS-1) data, were accurately geo-referenced and ortho-rectified. The GRFM Africa mosaic was also radiometrically corrected for topographic effects. The accurate co-registration with the DEM allows for improved classification methods based on the combination of SAR backscatter with terrain features. Comparison of the revised GBFM and GRFM mosaics with a forthcoming set of continental-scale mosaics assembled from the Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) data will offer a unique possibility for change detection studies over the Tropical and Boreal forest zones with a temporal spacing of some 10 years.     

Vegetation mapping using JERS-1 SAR mosaic for northern Australia

The Japanese Earth Resources Satellite (JERS-1) Synthetic Aperture Radar (SAR) L-band HH polarization data over northern Australia were acquired in 1996 as part of the Global Rainforest Mapping (GRFM) experiment by the National Space Development Agency of Japan (NASDA). The data were mosaiced by NASDA and re-sampled to a resolution of 100 m 2 100 m per pixel. As the mosaiced data is a preliminary product, the problem of calibration, including the removal of effects of look angle variations within each scene and the relative calibration among multiple scenes due to different acquisition dates, has not been dealt with sufficiently, which greatly degrades the quality of the product. It is understood that precise calibration in mosaiced data is of necessity in order to retrieve accurate geophysical parameters. Nevertheless, the preliminary mosaiced data is used in the paper to investigate the potential of using SAR data for vegetation mapping in a regional or global scale. Pixel-based mapping using SAR data has shown limited success due to speckle. However, area analysis techniques allow the computation of reliable statistical and textural measures, thus providing a base for pursuing radar classification. In this paper, an image is first segmented using a Gaussian Markov random field model, so that area analysis can be pursued. An unsupervised classification method is developed in which as many as 12 statistical and textural measures are used to group segments with similar textural measures into the same class in a fashion of maximum likelihood. For a pre-given number of 50 as the final number of classes in the unsupervised classification iteration, the average B- distance among these 50 classes is 1.96 when all 12 textural measures are used to compute the distance.     

Mapping land cover types in the Amazon Basin using 1 km JERS-1 mosaic

During the Global Rain Forest Mapping (GRFM) project, the JERS-1 SAR (Synthetic Aperture Radar) satellite acquired wall-to-wall image coverage of the humid tropical forests of the world. The rationale for the project was to demonstrate the application of spaceborne L-band radar in tropical forest studies. In particular, the use of orbital radar data for mapping land cover types, estimating the area of floodplains, and monitoring deforestation and forest regeneration were of primary importance. In this paper we examine the information content of the JERS-1 SAR data for mapping land cover types in the Amazon basin. More than 1500 high-resolution (12.5 m pixel spacing) images acquired during the low flood period of the Amazon river were resampled to 100 m resolution and mosaicked into a seamless image of about 8 million km², including the entire Amazon basin. This image was used in a classifier to generate a 1 km resolution land cover map. The inputs to the classifier were 1 km resolution mean backscatter and seven first-order texture measures derived from the 100 m data by using a 10 x 10 independent sampling window. The classification approach included two interdependent stages. First, a supervised maximum a posteriori Baysian approach classified the mean backscatter image into five general cover categories: terra firme forest (including secondary forest), savanna, inundated vegetation, open deforested areas and open water. A hierarchical decision rule based on texture measures was then applied to attempt further discrimination of known subcategories of vegetation types based on taxonomic information and woody biomass levels. True distributions of the general categories were identified from the RADAMBRASIL project vegetation maps and several field studies. Training and validation test sites were chosen from the JERS-1 image by consulting the RADAM vegetation maps. After several iterations and combining land cover types, 14 vegetation classes were successfully separated at the 1 km scale. The accuracy of the classification methodology was estimated to be 78% when using the validation sites. The results were also verified by comparison with the RADAM- and AVHRR-based 1 km resolution land cover maps.     

Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States

Since January 2008, the U.S. Department of Interior / U.S. Geological Survey have been providing free terrain-corrected (Level 1T) Landsat Enhanced Thematic Mapper Plus (ETM+) data via the Internet, currently for acquisitions with less than 40% cloud cover. With this rich dataset, temporally composited, mosaics of the conterminous United States (CONUS) were generated on a monthly, seasonal, and annual basis using 6521 ETM+ acquisitions from December 2007 to November 2008. The composited mosaics are designed to provide consistent Landsat data that can be used to derive land cover and geo-physical and bio-physical products for detailed regional assessments of land-cover dynamics and to study Earth system functioning. The data layers in the composited mosaics are defined at 30 m and include top of atmosphere (TOA) reflectance, TOA brightness temperature, TOA normalized difference vegetation index (NDVI), the date each composited pixel was acquired on, per-band radiometric saturation status, cloud mask values, and the number of acquisitions considered in the compositing period. Reduced spatial resolution browse imagery, and top of atmosphere 30 m reflectance time series extracted from the monthly composites, capture the expected land surface phenological change, and illustrate the potential of the composited mosaic data for terrestrial monitoring at high spatial resolution. The composited mosaics are available in 501 tiles of 5000 × 5000 30 m pixels in the Albers equal area projection and are downloadable at http://landsat.usgs.gov/WELD.php. The research described in this paper demonstrates the potential of Landsat data processing to provide a consistent, long-term, large-area, data record.     

The suitability of decadal image data sets for mapping tropical forest cover change in the Democratic Republic of Congo: implications for the global land survey

Landsat remote sensing of the central African humid tropics is confounded by persistent cloud cover and, since 2003, missing data due to the Landsat‐7 Enhanced Thematic Mapper Plus (ETM+) scan line corrector (SLC) malfunction. To quantify these limitations and their effects on contemporary forest cover and change characterization, a comparison was made of multiple Landsat‐7 image mosaics generated for a six Landsat path/row study site in central Africa for 2000 and 2005. Epoch 2000 mosaics were generated by compositing (i) two to three Landsat acquisitions per path/row, (ii) using the best single GeoCover 2000 acquisition for each path/row. Epoch 2005 composites were generated by compositing SLC‐off data using (iii) five to seven acquisitions per path/row, (iv) three acquisitions per path/row. Eighty per cent of pixels were of suitable quality for change detection between (ii) and (iv), emulating that which is possible with current GeoCover and planned Global Land Survey (GLS) inputs. In a more data intensive change detection analysis using mosaics (i) and (iii), 96% of pixels had suitable quality. Compositing more acquisitions per path/row for the study area systematically reduced the percentage of SLC‐off gaps and, when more than three acquisitions were composited, reduced the percentage of pixels with high likelihood of cloud, haze or shadow. The results indicate that additional input imagery to augment both the Geocover and GLS data may be required to enable forest cover and change analyses for regions of the humid tropics.     

Long-term ERS/ENVISAT deformation time-series generation at full spatial resolution via the extended SBAS technique

We extend the small baseline subset (SBAS) differential synthetic aperture radar (SAR) interferometry (DInSAR) approach to allow the generation of deformation time-series by processing, at the full spatial resolution scale, long sequences of European Remote Sensing (ERS-1 and ERS-2) and Environmental Satellite (ENVISAT) SAR data acquired with the same illumination geometry. In particular, we avoid the generation of ERS/ENVISAT cross-interferograms, which are severely affected by noise phenomena due to the carrier frequency separation of the two SAR systems, and we focus on single-platform interferograms only (i.e. ERS/ERS and ENVISAT/ENVISAT interferograms) that are properly combined by applying the singular value decomposition (SVD)-based SBAS approach. Moreover, we exploit the Doppler centroid variations of the post-2000 acquisitions of the ERS-2 sensor and the carrier frequency difference between the ERS-1/2 and the ENVISAT systems, in order to maximize the number of investigated SAR pixels and to improve their geocoding. The presented results, achieved on two data sets relevant to the Napoli Bay area and to the Murge region, both located in southern Italy, confirm the effectiveness of the extended SBAS technique and demonstrate the relevance of deformation analysis carried out at the scale of single buildings or human-made structures with more than 15 years of ERS and ENVISAT acquisitions.     

Floodplain water storage in the Negro River basin estimated from microwave remote sensing of inundation area and water levels

The objective of this study is to determine spatio-temporal variations of water volume over inundated areas located in large river basins using combined observations from the Synthetic Aperture Radar (SAR) onboard the Japanese Earth Resources Satellite (JERS-1), the Topex/Poseidon (T/P) altimetry satellite, and in-situ hydrographic stations. Ultimately, the goal is to quantify the role of floodplains for partitioning water and sediment fluxes over the great fluvial basins of the world. SAR images are used to identify the type of surface (open water, inundated areas, forest) and, hence, the areas covered with water. Both radar altimetry data and in-situ hydrographic measurements yield water level time series. The basin of the Negro River, the tributary which carries the largest discharge to the Amazon River, was selected as a test site. By combining area estimates derived from radar images classification with changes in water level, variations of water volume (focusing on a seasonal cycle) have been obtained. The absence of relationship between water volume and inundated area, reflecting the diverse and widely dispersed floodplains of the basin, is one of the main result of this study.     

Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels

This paper presents and analyzes in detail an efficient search method based on evolutionary algorithms (EA) assisted by local Gaussian random field metamodels (GRFM). It is created for the use in optimization problems with one (or many) computationally expensive evaluation function(s). The role of GRFM is to predict objective function values for new candidate solutions by exploiting information recorded during previous evaluations. Moreover, GRFM are able to provide estimates of the confidence of their predictions. Predictions and their confidence intervals predicted by GRFM are used by the metamodel assisted EA. It selects the promising members in each generation and carries out exact, costly evaluations only for them. The extensive use of the uncertainty information of predictions for screening the candidate solutions makes it possible to significantly reduce the computational cost of singleand multiobjective EA. This is adequately demonstrated in this paper by means of mathematical test cases and a multipoint airfoil design in aerodynamics.     

Patterns of irrigated rice growth and malaria vector breeding in Mali using multi-temporal ERS-2 synthetic aperture radar

We explored the use of the European Remote Sensing Satellite 2 Synthetic Aperture Radar (ERS-2 SAR) to trace the development of rice plants in an irrigated area near Niono, Mali and relate that to the density of anopheline mosquitoes, especially An. gambiae. This is important because such mosquitoes are the major vectors of malaria in sub-Saharan Africa, and their development is often coupled to the cycle of rice development. We collected larval samples, mapped rice fields using GPS and recorded rice growth stages simultaneously with eight ERS-2 SAR acquisitions. We were able to discriminate among rice growth stages using ERS-2 SAR backscatter data, especially among the early stages of rice growth, which produce the largest numbers of larvae. We could also distinguish between basins that produced high and low numbers of anophelines within the stage of peak production. After the peak, larval numbers dropped as rice plants grew taller and thicker, reducing the amount of light reaching the water surface. ERS-2 SAR backscatter increased concomitantly. Our data support the belief that ERS-2 SAR data may be helpful for mapping the spatial patterns of rice growth, distinguishing different agricultural practices, and monitoring the abundance of vectors in nearby villages.     

Ground deformation of Long Valley caldera and Mono Basin,eastern California,mapped by satellite radar interferometry

We have illustrated the key results of the Differential SAR Interferometry (DInSAR) analysis focused on the ground deformation of Long Valley caldera and Mono Basin, eastern California. In particular, we have applied the DInSAR algorithm referred to as Small BAseline Subset (SBAS) approach and processed 21 SAR images, spanning the time interval from 1992 to 2000, acquired from descending arbits by the ERS‐1 and ERS‐2 sensors of the European Space Agency (ESA). The deformation affecting the resurgent dome of Long Valley caldera has been highlighted as well as the previously unreported subsidence of the Pahoa island, located in Mono Lake.     

Water level dynamics of Amazon wetlands at the watershed scale by satellite altimetry

In this study we used satellite altimetry to characterize the time and space variations in water stored in or circulating through rivers, floodplains, wetlands and lakes in the major sub-basins of the Amazon basin. Using a specific methodology to rigorously select original three-dimensional (3D) data from an Environmental Satellite (ENVISAT) mission, water level time series were calculated at the crossing path of the satellite tracks with the water bodies. We took advantage of the continuous sampling of the water level along the satellite track segments that cross the watershed to analyse both spatial and temporal relationships between: (i) the river and its floodplain and (ii) different basins. This work evidences in particular the existence of water leaking between the Negro and Solimões basins at the high water stage. It highlights that the phenomenon of a secondary flood peak occurring in the water level series in the Solimões basin at rising water, known as repiquete, is caused by the rain equatorial regime of the northern upstream tributaries of the Solimões River, but is disconnected from the same phenomenon occurring within the Rio Negro basin.     

Observation of hydrological features with Nimbus-7 37 GHz data,applied to South America

Abstract

The microwave polarization difference temperature (MPDT) prepared from 37 GHz Scanning Multifrequency Microwave Radiometer (SMMR) data from the Nimbus-7 satellite allows a unique vision of hydrological features, especially of humid areas. From the series of monthly images from 1979 to 1985 the major hydrological features of four major river basins of South America are briefly examined. These include rivers of the Amazon, La Plata, Orinoco and Sao Francisco basins, wetlands in the La Plata and Amazon basins and floods in the La Plata basin. Many hydrological features appear much as they do on conventional maps. An interesting perspective can be derived from the ways in which they differ from conventional maps.     

Dual-season mapping of wetland inundation and vegetation for the central Amazon basin

Wetland extent was mapped for the central Amazon region, using mosaicked L-band synthetic aperture radar (SAR) imagery acquired by the Japanese Earth Resources Satellite-1. For the wetland portion of the 18×8° study area, dual-season radar mosaics were used to map inundation extent and vegetation under both low-water and high-water conditions at 100-m resolution, producing the first high-resolution wetlands map for the region. Thematic accuracy of the mapping was assessed using high-resolution digital videography acquired during two aerial surveys of the Brazilian Amazon. A polygon-based segmentation and clustering was used to delineate wetland extent with an accuracy of 95%. A pixel-based classifier was used to map wetland vegetation and flooding state based on backscattering coefficients of two-season class combinations. Producer's accuracy for flooded and nonflooded forest classes ranged from 78% to 91%, with lower accuracy (63-65%) for flooded herbaceous vegetation. Seventeen percent of the study quadrat was occupied by wetlands, which were 96% inundated at high water and 26% inundated at low water. Flooded forest constituted nearly 70% of the entire wetland area at high water, but there are large regional variations in the proportions of wetland habitats. The SAR-based mapping provides a basis for improved estimates of the contribution of wetlands to biogeochemical and hydrological processes in the Amazon basin, a key question in the Large-Scale Biosphere-Atmosphere Experiment in Amazônia.     

Temporally smoothed and gap‐filled MODIS land products for carbon modelling: application of the fPAR product

TIMESAT software is used to produce a temporally and spatially Gap‐Filled and Smoothed (GFS) version of the MODIS (Moderate Resolution Imaging Spectro‐radiometer) fPAR (fraction of absorbed photosynthetically active radiation) product (MOD15). We apply this new ?PAR product within two commonly used carbon and vegetation productivity models, CASA (Carnegie‐Ames‐Stanford Approach) and the MODIS GPP (Gross Primary Production) algorithm (MOD17). The GFS product removes noise present within the original MOD15 fPAR dataset, yet is comparable to the linearly interpolated UMT (University of Montana) fPAR used in the MOD17 algorithm. However, the GSF data provides a realistic fPAR time‐series in relation to magnitude and seasonality associated with radiation in regions where persistent cloud cover is an issue. It is available for North America and the northern part of South America covering the Amazon basin for the MODIS acquisition period (2000–2005).     

Mapping tropical coastal vegetation using JERS-1 and ERS-1 radar data with a decision tree classifier

The objective of this paper is to investigate the complementarity of JERS-1 and ERS-1 data for mapping coastal tropical regions. We use a decision tree classifier to classify a coastal region of Gabon and describe the feature contribution using the decision tree diagram. The JERS-1 Global Rain Forest Mapping (GRFM) and ERS-1 Central Africa Mosaic Project (CAMP) datasets are used. The result is a land cover map of the west coast of Gabon. The analysis explicitly shows the complementary characteristics of the L- and C-band Synthetic Aperture Radar (SAR) instruments. We demonstrate the usefulness of combined use of L- and C-band data for large area mapping of coastal regions, especially in flooded areas for discrimination of high and low mangroves as well as grasses and tree swamps. The overall classification accuracy increases by 18% over single band classification.     

Influence of buried topography on surface drainage pattern

Abstract

An automated system has been developed for mosaicking spaceborne synthetic aperture radar (SAR) imagery. The system is capable of producing multiframe mosaics for large-scale mapping by combining images in both the along-track direction and adjacent cross-track swaths from ascending and descending passes. The system requires no operator interaction and is capable of achieving high registration accuracy. The output product is a geocoded mosaic on a standard map grid such as UTM or polar stereographic. The procedure described in detail in this paper consists essentially of remapping the individual image frames into these standard grids, frame-to-frame image registration and radiometric smoothing of the seams. These procedures are directly applicable to both the Magellan Venus Mapper and a scanning SAR design such as Radarsat, Eos SAR in addition to merging image frames from traditional SAR systems such as SEASAT and SIR-B. With minor modifications, it may also be applied to spaceborne optical sensor data to generate large-scale mosaics efficiently and with a high degree of accuracy. The system has been tested with SEASAT, SIR-B and Landsat TM data. Examples presented in this paper include a 38-frame mosaic of the Yukon River basin in central Alaska, a 33-frame mosaic of southern California and a three-frame terrain-corrected geocoded mosaic of the Wind River basin in Wyoming.     

Modelling and testing spatially distributed sediment budgets to relate erosion processes to sediment yields

Identifying the erosion processes contributing to increased basin fine sediment yield is important for reducing downstream impacts on aquatic ecosystems. However, erosion rates are spatially variable, and much eroded sediment is stored within river basins and not delivered downstream. A spatially distributed sediment budget model is described that assesses the primary sources (hillslope soil erosion, gully and riverbank erosion) and sinks (floodplain and reservoir deposition) of fine sediment for each link in a river network. The model performance is evaluated in a 17,000-km² basin in south-east Australia using measured suspended sediment yields from eight catchments within the basin, each 100–700 km² in area. Spatial variations within the basin in yield and area-specific yield were reliably predicted. Observed yields and area-specific yields varied by 17-fold and 15-fold respectively between the catchments, while predictions were generally within a factor of 2 of observations. Model efficiency at predicting variations in area-specific yield was good outside forested areas (0.58), and performance was weakly sensitive to parameter values. Yields from forested areas were under-predicted, and reducing the predicted influence of riparian vegetation on bank erosion improved model performance in those areas. The model provided more accurate and higher resolution predictions than catchment area interpolation of measured yields from neighbouring river basins. The model is suitable for guiding the targeting of remediation measures within river basins to reduce downstream sediment yields.     

Modelling sustainable international tourism demand to the Brazilian Amazon

The Amazon rainforest is one of the world's greatest natural wonders and holds great importance and significance for the world's environmental balance. Around 60% of the Amazon rainforest is located in the Brazilian territory. The two biggest states of the Amazon region are Amazonas (the upper Amazon) and Pará (the lower Amazon), which together account for around 73% of the Brazilian Legal Amazon, and are the only states that are serviced by international airports in Brazil's north region. The purpose of this paper is to model and forecast sustainable international tourism demand for the states of Amazonas, Pará, and the aggregate of the two states. By sustainable tourism is meant a distinctive type of tourism that has relatively low environmental and cultural impacts. Economic progress brought about by illegal wood extraction and commercial agriculture has destroyed large areas of the Amazon rainforest. The sustainable tourism industry has the potential to contribute to the economic development of the Amazon region without destroying the rainforest. The paper presents unit root tests for monthly and annual data, estimates alternative time series models and conditional volatility models of the shocks to international tourist arrivals, and provides forecasts for 2006 and 2007.