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.     

Identification of central Kenyan Rift Valley Fever virus vector habitats with landsat TM and evaluation of their flooding status with airborne imaging radar

Rift Valley Fever (RVF) is a mosquito-borne virus that affects livestock and humans in Africa. Landsat Thematic Mapper (TM) data are shown to be effective in identifying dambos, intermittently flooded areas that are potential mosquito breeding sites, in an area north of Nairobi, Kenya. Positive results were obtained from a limited test of flood detection in dambos with airborne high resolution L, C, and X band multipolarization synthetic aperture radar (SAR) imagery. L and C bands were effective in detecting flooded dambos, but LHH was by far the best channel for discrimination (p < 0.01) between flooded and nonflooded sites in both sedge and short grass environments. This study demonstrates the feasibility of a combined passive and active remote sensing program for monitoring the location and condition of RVF vector habitats, thus making future control of the disease more promising.     

Use of SAR satellites for mapping zonation of vegetation communities in the Amazon floodplain

Radarsat and JERS-1 imagery were used for mapping zonation of vegetation communities in the Amazon floodplain. Imagery analysis indicates that at periods of minimum water level the backscattering values of both C and L bands are the lowest and as the water level rises, so do the backscattering values. JERS-1 imagery exhibits a larger dynamic range of backscattering in response to the ground cover for the two extremes of water level (10?dB) compared to Radarsat imagery. The backscattering differences from different ground cover allowed the use of a region-based classification that produced seasonal maps with accuracies higher than 95% for vegetated areas of the floodplain. These seasonal maps were used to estimate the spatial distribution and time of inundation and the vegetation cover of the floodplain. It was possible to determine that semi-aquatic vegetation, tree-like aquatic plants, and shrub-like trees colonize regions flooded for at least 300?days?year^?1. Secondary colonizers, such as tall well-developed floodplain forest, cover regions flooded for approximately 150?days?year^?1, and floodplain climax forest colonize regions inundated for approximately 60?days?year^?1.     

Inundated wetland dynamics over boreal regions from remote sensing: the use of Topex‐Poseidon dual‐frequency radar altimeter observations

This study presents a first attempt to estimate the extent and seasonality of northern wetlands using radar altimeter satellite observations. The sensitivity of the Topex‐Poseidon dual‐frequency radar altimeter to detect inundation is investigated and compared with passive and active microwave satellite measurements along with a land surface climatology database. The C band backscatter altimeter signal clearly tracks passive microwave emissivity observations of wetlands and is able to detect small flooded areas. Because of the nadir incidence angle, the radar altimeter also shows more capability to detect wetlands than the C band scatterometer. Monthly flooded areas are calculated by estimating flooded pixel fractional coverage using the altimeter C band backscatter magnitude and a linear mixing model with dual‐frequency altimeter backscatter difference, C–Ku, to account for vegetation effects. Because of the Topex‐Poseidon satellite spatial coverage, the results are given only from 40° N to 66° N. This region nevertheless represents more than 30% of world's inundated surfaces during the summer. A direct validation of the inundated extent is unfortunately impossible on a large scale, due to the scarcity of quantitative observations. As a consequence, the results are evaluated by comparison with other existing estimates. Radar altimetry estimates, comprising natural wetlands and river/lakes, indicate a maximum inundated area of 1.86×10⁶ km² for July 1993 and 1994 as compared with 1.31×10⁶ km² from passive microwave technique and ～2.10×10⁶ km² from climatology dataset. The wetland seasonal variability derived from the altimeter and passive microwave techniques agrees well. These promising results could soon be applied to the ENVISAT dual‐frequency radar altimeter that will provide a better survey of global inundated surfaces thanks to its much more complete spatial coverage.     

Radar detection of flooding beneath the forest canopy: a review

Abstract

Synthetic aperture radar remote sensing is a promising tool for detection of flooding on forested floodplains. The bright appearance of flooded forests on radar images results from double-bounce reflections between smooth water surfaces and tree trunks or branches. Enhanced back scattering at L-band has been shown to occur in a wide variety of forest types, including cypress-tupelo swamps, temperate bottomland hardwoods, spruce bogs, mangroves and tropical floodplain forests. Lack of enhancement is a function of both stand density and branching structure. According to models and measurements, the magnitude of the enhancement is about 3 to 10 dB. Steep incidence angles (20^°-30^°) are optimal for detection of flooding, since some forest types exhibit bright returns only at steeper angles. P-band should prove useful for floodwater mapping in dense stands, and multifrequency polarimetric analysis should allow flooded forests to be distinguished from marshes.     

The contribution of ALOS/PALSAR-1 multi-temporal data to map permanently and temporarily flooded coastal wetlands

ABSTRACT

Although regional wetland mapping studies have mostly relied on optical sensors, synthetic aperture radar (SAR) sensors are being increasingly applied. The aim of this study is to analyse the ability of the Phased Array type L-band Synthetic Aperture Radar on board of the Advanced Land Observing Satellite (ALOS/PALSAR-1) data to identify, delineate and monitor wetlands, and to evaluate the importance of scene selection in a highly unpredictable wetland. Three SAR scene sets (Year A, Year B and Inter-annual) were built for this purpose, considering the intra-annual and inter-annual hydrologic variability and the phenologic variability of the studied coastal wetland. Seven land cover types were defined, including three permanently flooded wetland classes, three temporarily flooded wetland classes and one non-wetland class. An object-based unsupervised classification approach was applied on each multi-temporal set. The obtained clusters were characterized by a temporal signature and assigned to the seven land cover types using a decision tree with user-defined thresholds. The accuracy assessment of each product was performed using a set of 258 data sites, including field collected data and data retrieved from Landsat 8 Operational Land Imager (OLI) imagery acquired during the dates of the field campaign. The Year B set showed the best accuracy (83.4% overall, 75% Kappa coefficient (κ)) and the lowest omission and commission mean errors (16.6% and 16.1% respectively). The classes that were best differentiated are permanently flooded wetlands (PFW) and non-wetlands (NW) in all sets.     

Characterization of terrestrial water dynamics in the Congo Basin using GRACE and satellite radar altimetry

The Congo Basin is the world's third largest in size (~ 3.7 million km²), and second only to the Amazon River in discharge (~ 40,200 m³ s^− 1 annual average). However, the hydrological dynamics of seasonally flooded wetlands and floodplains remains poorly quantified. Here, we separate the Congo wetland into four 3° × 3° regions, and use remote sensing measurements (i.e., GRACE, satellite radar altimeter, GPCP, JERS-1, SRTM, and MODIS) to estimate the amounts of water filling and draining from the Congo wetland, and to determine the source of the water. We find that the amount of water annually filling and draining the Congo wetlands is 111 km³, which is about one-third the size of the water volumes found on the mainstem Amazon floodplain. Based on amplitude comparisons among the water volume changes and timing comparisons among their fluxes, we conclude that the local upland runoff is the main source of the Congo wetland water, not the fluvial process of river-floodplain water exchange as in the Amazon. Our hydraulic analysis using altimeter measurements also supports our conclusion by demonstrating that water surface elevations in the wetlands are consistently higher than the adjacent river water levels. Our research highlights differences in the hydrology and hydrodynamics between the Congo wetland and the mainstem Amazon floodplain.     

Analysis of L-band SAR backscatter and coherence for delineation of land-use/land-cover

In this study, we investigated the potential improvement of land-use/land-cover (LU/LC) classification using multidate backscatter intensity as well as interferometric coherence images derived from Advanced Land Observing Satellite phased array L-band synthetic aperture radar data. Four interferometric synthetic aperture radar data pairs in horizontal–horizontal polarizations were processed to obtain backscatter intensity and coherence images. From the analysis of these images, it was observed that backscatter values alone are not sufficient to separate certain LU/LC classes, e.g. forest and mining areas, due to similarities in the associated scattering mechanisms producing similar backscatter values. However, the temporal coherence values from these LU/LC features were found to be distinctly different. Supervised classifications using maximum-likelihood distance were performed with various combinations of data (three-date backscatter intensity and two-date backscatter intensity with corresponding coherence data) to generate LU/LC maps of the study area. The comparison of classification accuracies obtained for different combinations of data indicates that the classification accuracy is improved by adding coherence information to the backscatter intensity data compared to using the multidate backscatter intensity data alone. Thus, the analysis of backscatter intensity along with coherence is a better alternative than using backscatter intensity alone to improve the accuracy in LU/LC classification.     

Assessment of C-band synthetic aperture radar data for mapping and monitoring Coastal Plain forested wetlands in the Mid-Atlantic Region,U.S.A.

Multi-temporal C-band SAR data (C-HH and C-VV), collected by ERS-2 and ENVISAT satellite systems, are compared with field observations of hydrology (i.e., inundation and soil moisture) and National Wetland Inventory maps (U.S. Fish and Wildlife Service) of a large forested wetland complex adjacent to the Patuxent and Middle Patuxent Rivers, tributaries of the Chesapeake Bay. Multi-temporal C-band SAR data were shown to be capable of mapping forested wetlands and monitoring hydroperiod (i.e., temporal fluctuations in inundation and soil moisture) at the study site, and the discrimination of wetland from upland was improved with 10 m digital elevation data. Principal component analysis was used to summarize the multi-temporal SAR data sets and to isolate the dominant temporal trend in inundation and soil moisture (i.e., relative hydroperiod). Significant positive, linear correlations were found between the first principal component and percent area flooded and soil moisture. The correlation (r²) between the first principal component (PC1) of multi-temporal C-HH SAR data and average soil moisture was 0.88 (p = < .0001) during the leaf-off season and 0.87 (p = < .0001) during the leaf-on season, while the correlation between PC1 and average percent area inundated was 0.82 (p = < .0001) and 0.47 (p = .0016) during the leaf-off and leaf-on seasons, respectively. When compared to field data, the SAR forested wetland maps identified areas that were flooded for 25% of the time with 63–96% agreement and areas flooded for 5% of the time with 44–89% agreement, depending on polarization and time of year. The results are encouraging and justify further studies to attempt to quantify the relative SAR-derived hydroperiod classes in terms of physical variables and also to test the application of SAR data to more diverse landscapes at a broader scale. The present evidence suggests that the SAR data will significantly improve routine wooded wetland mapping.     

电波传播对L波段地球同步轨道圆迹SAR重轨干涉性能的影响

不同于常规星载SAR,L波段地球同步轨道圆迹SAR(GEOCSAR)轨道高度高、合成孔径时间长、观测面积大,对流层折射率和电离层电子含量的时空变化将严重影响GEOCSAR重轨干涉性能。基于大气介质时空变化及GEOCSAR干涉信号的特点,从对流层和电离层相位延迟误差、电离层水平变化引起的图像方位向偏移、对流层折射率周日变化和电离层时空随机起伏引起的方位向聚焦性能下降、以及法拉第旋转效应几个方面分析了对流层和电离层对GEOCSAR重轨干涉相干系数、干涉相位的影响,并进行了仿真实验验证。分析以及实验结果表明,对流层和电离层均会对GEOCSAR干涉性能造成不可忽略的影响,其中电离层的周日时间变化和电离层起伏会导致图像散焦,进而严重降低干涉相干性。     

Estimating forest structure in wetlands using multitemporal SAR

Data from 202 forest plots on the Roanoke River floodplain, North Carolina were used to assess the capabilities of multitemporal radar imagery for estimating biophysical characteristics of forested wetlands. The research was designed to determine the potential for using widely available data from the current set of satellite-borne synthetic aperture radar (SAR) sensors to study forests over broad geographic areas and complex environmental gradients. The SAR data set included 11 Radarsat scenes, 2 ERS-1 images, and 1 JERS-1 scene. Empirical analyses were stratified by flood status such that sites were compared only if they exhibited common flooding characteristics. In general, the results indicate that forest properties are more accurately estimated using data from flooded areas, probably because variations in surface conditions are minimized where there is a continuous surface of standing water. Estimations yielded root mean square errors (RMSEs) for validation data around 10 m²/ha for basal area (BA), and less than 3 m for canopy height. The r² values generally exceeded .65 for BA, with the best predictions coming from sample sites for which both nonflooded and flooded SAR scenes were available. The addition of early spring normalized difference vegetation index (NDVI) values from Landsat Thematic Mapper (Landsat TM) improved model predictions for BA in forests where BA levels were <55 m²/ha. Further analyses indicated a very limited sensitivity of the individual SAR scenes to differences in forest composition, although soil properties in nonflooded areas exerted a weak but nevertheless important influence on backscatter.     

Influence of incidence angle on detecting flooded forests using C-HH synthetic aperture radar data

Hydrology is the single most important abiotic factor in the formation and functioning of a wetland. Many limitations still exist to accurately characterizing wetland hydrology over large spatial extents, especially in forested wetlands. Imaging radar has emerged as a viable tool for wetland flood mapping, although the limitations of radar data remain uncertain. The influence of incidence angle on the ability to detect flooding in different forest types was examined using C-HH Radarsat-1 data (23.5°, 27.5°, 33.5°, 39.0°, 43.5°, and 47.0°) during the leaf-off and leaf-on seasons. The ability to detect flooding under leaf-on conditions varied much more according to incidence angle while forest type (open canopy tupelo-cypress, tupelo-cypress, and bottomland hardwood) had a greater effect during the leaf-off season. When all forest types were considered together, backscatter generally decreased with increasing incidence angle under all conditions (2.45 dB between 23.5° and 47.0° flooded, leaf-off; 2.28 dB between 23.5° and 47.0° not flooded, leaf-off; 0.62 between 23.5° and 43.5° flooded, leaf-on; 1.73 dB between 23.5° and 43.5° not flooded, leaf-on; slope was not constant between incidence angles), but the distinction between flooded and non-flooded areas did not decline sharply with incidence angle. Differentiation of flooded and non-flooded forests was similar during the leaf-off and leaf-on seasons. The ability to detect inundation under forest canopies was less than expected at smaller incidence angles and greater than expected at larger incidence angles, based on the results of previous studies. Use of a wider range of incidence angles during the entire year increases the temporal resolution of imagery which may, in turn, enhance mapping of inundation beneath forest canopies.     

Correlation Analysis between L-band SAR and ForestStem Volume in Northeast China

Because Synthetic Aperture Radar(SAR)can penetrate into forest canopy and interact with the primary stem volume contents of the trees (trunk and branch),SAR data are widely used for forest stem volume estimation.This paper investigated the correlation between SAR data and forest stem volume in Xunke,Heilongjiang using the stand-wise forest inventory data in 2003 and ALOS PALSAR data for five dates in 2007.The influences of season and polarizations on the relationship between stem volume and SAR data were studied by analyzing the scatterplots;that was followed by interpretation of the mechanisms primarily based on a forest radar backscattering model-water cloud model.The results showed that the relationship between HV polarization backscatter and stem volume is better than HH polarization,and SAR data in summer dry conditions are more correlated to stem volume than the data acquired in other conditions.The interferometric coherence with 46-day temporal baseline is negatively correlated to the stem volume.The correlation coefficients from winter coherence are higher than those from summer coherence and backscatter.The study results suggest using the interferometric coherence in winter as the best choice for forest stem volume estimation with L-band SAR data.     

Monitoring global land surface using Nimbus-7 37 GHz data Theory and examples

Abstract

Radiative transfer models for 37 GHz brightness temperatures are developed under simplifying assumptions and the implications of the models are illustrated and discussed using the Nimbus-7 scanning multichannel microwave radiometer (SMMR) observations of the Earth's surface from January 1979 to December 1985. The difference of vertically and horizontally polarized brightness temperatures (ΔT) decreases with increasing vegetation density (that is, as the canopy water content per unit ground area increases) and this effect of vegetation density on ΔT is illustrated by the time series of ΔT over areas with varied vegetation density and phenology. Furthermore, the ΔT decreases as the surface roughness increases, and thus mountainous areas within sparsely vegetated areas (like the Sahara) have the appearance of more densely vegetated areas. Also, the ΔT values increase when the soil gets wet or flooded, and this effect is illustrated by correlating the temporal variations of ΔT with the river height for an area within the Amazon Basin. As examples of a more quantitative analysis of the ΔT data, empirical relations are developed between the integrated-average of the 84 months of ΔT data and the annual rainfall, primary productivity and actual evaporation. An analytical relation between primary productivity and ΔT is also established from physically based models under simplifying assumptions. Colour-coded maps of primary productivity and actual evaporation based upon the SMMR data are displayed and discussed in relation to other observations. It is concluded that to realize the full potential of the ΔTdata it would be necessary to calibrate and validate this data against ground observations and develop more realistic radiative transfer models in order to gain a better physical understanding.     

Feasibility of multi-temporal interferometric SAR data for stand-level estimation of boreal forest stem volume

The feasibility of interferometric SAR (INSAR) coherence observations for stem volume (biomass) retrieval is investigated by applying coherence data determined from 14 ERS-1 and ERS-2 C-band SAR image pairs. The image set covers a single forested test area in Finland, and both summer (snow-free) and winter conditions are represented. The data set enabled (a) the study of stem volume retrieval performance under varying conditions, (b) the analysis of the seasonal behavior of interferometric coherence, and (c) the determination of the accuracy characteristics of empirical (nonlinear) coherence modeling. Additionally, a new technique to estimate forest stem volume from INSAR data was developed based on constrained iterative inversion of the applied empirical model. The results indicate that the usability of winter images with snow-covered terrain is superior to that of images obtained under summer conditions. The applied empirical model appears to be adequate for describing the stand-wise coherence of boreal forest. Hence, a practical stem volume estimation method can be established based on it. The highest correlation coefficient between the estimated stem volume and the ground truth stem volume showed values as high as r=0.9 and a relative RMSE level of 48% was obtained, respectively.     

Methodology to detect long-term trends in groundwater by monitoring changes in vegetation distribution

Multidate remotely sensed images, covering a period of 34 years (1967–2001), have been used to detect long-term trends in groundwater resources around Lake Ngami in the distal reaches of Botswana's Okavango Delta. The detection methodology is based on monitoring changes in the vegetation distribution of the studied area. The changes consist of sustained shifts from perennial wetland to an intermittently flooded dryland and a significant increase in woody species adapted to low water table conditions (e.g. Acacia mellifera and Acacia erioloba).     

Space-based detection of wetlands' surface water level changes from L-band SAR interferometry

Interferometric processing of JERS-1 L-band Synthetic Aperture Radar (SAR) data acquired over south Florida during 1993-1996 reveals detectable surface changes in the Everglades wetlands. Although our study is limited to south Florida it has implication for other large-scale wetlands, because south Florida wetlands have diverse vegetation types and both managed and natural flow environments. Our analysis reveals that interferometric coherence level is sensitive to wetland vegetation type and to the interferogram time span. Interferograms with time spans less than six months maintain phase observations for all wetland types, allowing characterization of water level changes in different wetland environments. The most noticeable changes occur between the managed and the natural flow wetlands. In the managed wetlands, fringes are organized, follow patterns related to some of the managed water control structures and have high fringe-rate. In the natural flow areas, fringes are irregular and have a low fringe-rate. The high fringe rate in managed areas reflects dynamic water topography caused by high flow rate due to gate operation. Although this organized fringe pattern is not characteristic of most large-scale wetlands, the high level of water level change enables accurate estimation of the wetland InSAR technique, which lies in the range of 5-10 cm. The irregular and low rate fringe pattern in the natural flow area reflects uninterrupted flow that diffuses water efficiently and evenly. Most of the interferograms in the natural flow area show an elongated fringe located along the transitional zone between salt- and fresh-water wetlands, reflecting water level changes due to ocean tides.     

A mathematical analysis of the motion coherence theory

In motion perception, there are a number of important phenomena involving coherence. Examples include motion capture and motion cooperativity. We propose a theoretical model, called the motion coherence theory, that gives a possible explanation for these effects [1,2]. In this framework, the aperture problem can also be thought of as a problem of coherence and given a similar explanation. We propose the concept of a velocity field defined everywhere in the image, even where there is no explicit motion information available. Theough a cost function, the model imposes smoothness on the velocity field in a more general way than in previous theories. In this paper, we provide a detailed theoretical analysis of the motion coherence theory. We discuss its relations with previous theories and show that some of them are approximations to it. A second paper [3] provides extensions for temporal coherence and comparisons to psychophysical phenomena. The theory applies to both short-range and long-range motion. It places them in the same computational framework and provides a way to define interactions between the two processes.     

Multi-resolution and temporal characterization of land-use classes in a Mediterranean wetland with land-cover fractions

Four different methods for analysing land-use and land-cover fractions at multiple scales, namely composite operator, t-test, Dutilleul’s modified t-test and ternary diagrams of physical models for process pathways, were applied to sets of multi-resolution images in order to evaluate the usefulness of coarse-resolution satellite data (e.g. the Moderate Resolution Imaging Spectroradiometer; MODIS) in obtaining similar results to those obtainable with moderate-resolution satellite data (e.g. Landsat). A spectral-mixture model based on three endmembers (soil, vegetation and water) was used to determine the land-cover fractions of the main land-use classes of a wetland in southeast Spain. The land-use map was produced by applying the unsupervised k-means classification method to the moderate-resolution image. Spatial and temporal changes in the mixture fractions at multiple resolutions and their corresponding land-cover fraction maps were assessed. Three different t-tests (paired-samples, independent-samples and Dutilleul’s modified t-tests) were used to evaluate the effects of pixel aggregation on land-cover fractions and land-use maps in terms of surface-area estimations. Ternary plots of land-use classes characterized by land-cover fractions were used to visualize environmental processes pathways describing temporal changes in the landscape. The results obtained with moderate- and coarse-resolution data were not significantly different from each other. Land-use and land-cover surface-area estimations were not significantly different between Landsat moderate-resolution (30 m) and Landsat resampled coarse-resolution (300 m) data. Spatial autocorrelation had an important effect when comparing Landsat moderate-resolution (30 m) with MODIS coarse-resolution (250 m) data. In order to minimize these effects Dutilleul’s modified t-test was applied for the comparison of Landsat with MODIS image data. However, this test did not reveal significant differences between both datasets, whereas with the ordinary t-test, significant differences were found, which suggest the existence of a bias by spatial autocorrelation that must be taken into account for up-scaling or down-scaling of remote-sensing data. The results suggest the possibility of using coarse-resolution images (MODIS) to characterize environmental changes with a similar accuracy to those of moderate-resolution images (Landsat), as long as potential spatial autocorrelation effects are taken into account. This finding indicates that a substantial reduction in the costs of conducting wetland management and monitoring tasks can be achieved by using free or low-cost coarse-resolution satellite images.     

基于ALOS PALSAR数据时相特征的土地覆盖识别

合成孔径雷达遥感具备全天时、全天候的观测能力,是多时相数据获取的有效保证。以福建省漳浦县为研究区,利用ALOS PALSAR双极化数据开展土地覆盖识别研究。首先基于多时相的强度数据构建时相稳定性指数,基于重复轨道干涉数据的相位信息计算相干性,以此分析和描述该地区典型地物的雷达数据时相特征。然后以典型地物的时相特征为基础,构建决策树分类器,进行土地覆盖识别。最后以实地考察数据、ALOS AVNIR\|2影像和Google Earth影像为参考,进行分类结果的精度评价,总体精度达到81.43%,比利用不同时期的后向散射强度图像为输入波段的最大似然法的分类精度(总体精度为63.06%)高出很多。结果表明:在分类中有效融合时相信息,可以充分提高地物的可分性。