Spatial Resolution Hyperspectral Imagery Research Articles

Balloon imagery verification of remotely sensed Phragmites australis expansion in an urban estuary of New Jersey, USA

The invasion of the exotic common reed ( Phragmites australis) is increasingly displacing local native species from northeastern coastal estuaries. This study evaluates the accuracy of a remote sensing technique to map the distribution of common reed, monitor the rate of invasion and determine areas of natural resistance to invasion. The current invasion footprint of Phragmites in the Hackensack Meadowlands District in Northern New Jersey was determined using high spectral and spatial resolution hyperspectral imagery. A tethered balloon-based imaging device with limited coverage area was used to assess the accuracy of the hyperspectral imagery classification. The accuracy assessment based on true color balloon images revealed that the hyperspectral classification technique from images covering hundreds of hectares was 90% accurate in separating the dominant common reed-invaded areas from the native vegetation. Furthermore, linear spectral un-mixing techniques for sub-pixel classification revealed that for mixed areas where Phragmites covered 75% or more of a pixel, the classification was correct 96% of the time. The accuracy dropped to 52% for pixels that contained 25% or less of Phragmites cover, and was only 4% for pixels where invasive and native species cover was the same (50–50%).

A first assessment of the use of high spatial resolution hyperspectral imagery in discriminating among animal species, and between animals and their surroundings

This paper presents the results of a first evaluation of the feasibility of using data from a high spatial resolution hyperspectral sensor to discriminate among animal species, and between animals and their surroundings. The evaluation was performed by simulating 1000 pixels produced by a shortwave infrared (1051–1789 nm) hyperspectral sensor with a pixel size of either 2 or 4 m, and a spectral resolution of 11.5 nm. The simulated pixels consisted of five classes: pure vegetation, and mixtures of four animal species (cattle, horses, sheep, and pigs) with vegetation. The data were processed by converting from reflectance to absorption, taking the first difference of the data, and then using stepwise discriminant analysis to create discriminant functions based on 250 of the 1000 pixels. These discriminant functions were then used to assign the remaining 750 pixels to classes. The results indicate that the spectra of the four animal species are quite different from each other, and that these differences include variations in the presence and strength of absorptions in the shortwave infrared portion of the spectrum. Overall accuracies were 90% based on the 2 m data and 61% based on the 4 m data. User's and producer's accuracies for individual classes based on the 2 m data ranged from 72 to 100%, but were lower for the 4 m data (40–90%). These results suggest that further study of the use of high spatial resolution hyperspectral imagery in locating and identifying animals is merited.

Mapping Downy Brome (Bromus tectorum) Using Multidate AVIRIS Data

Invasive plants impose threats to both natural and managed ecosystems. Downy brome is among the most aggressive invasive weeds that has infested the shrub-steppe ecoregion of eastern Washington. Hyperspectral remote sensing has potential for early detection and for monitoring the spread of downy brome—information that is essential for developing effective management strategies. Two airborne hyperspectral Advanced Visible Infrared Imaging Spectrometer (AVIRIS) images (electromagnetic spectrum ranging from 400 to 2,500 nm) were acquired at a nominal 4-m ground resolution over a study area in south-central Washington on July 27, 2000 and May 5, 2003. We used a mixture-tuned matched filtering (MTMF) algorithm to classify downy brome and predict its percent cover in each dataset plus a merged multiseasonal dataset using the transformed bands from a minimum noise fraction (MNF) output. The correlation coefficient was 0.79, calculated for the multidate MTMF predicted downy brome abundance, compared to 0.41 and 0.51 derived from the July 2000 and May 2003 data, respectively. Although this study used high spatial resolution (∼3 to 4 m) hyperspectral imagery, this result shows that data acquired in different seasons is more effective for detection of downy brome invasion, compared to single-date datasets. These results support expanded use of multitemporal data for weed mapping to capitalize on spectral differences between seasons for weeds, in this case downy brome, and the surrounding environment.

Detection of estuarine and tidal river hydromorphology using hyper-spectral and LiDAR data: Forth estuary, Scotland

High spatial resolution hyper-spectral imagery (CASI) and light detection and ranging (LiDAR) imagery acquired for the tidal River Carron and Forth estuary, Scotland, were used in conjunction with field surveys to assess the feasibility of monitoring hydromorphology and human alterations with satellite and airborne remote sensing data. The study was undertaken in the context of the European Union Water Framework Directive (WFD) that requires member states to monitor hydromorphological elements as a component of the ecological status of rivers, estuaries and shorelines. Visual assessment and automated classifications of the imagery were compared with field survey data for an estuarine reach comprising saline waters, mudflats, a tidal reach of a tributary river and an urban/industrialised shoreline. The morphology of the estuary and inflowing tidal waters together with most artificial features of interest could be clearly seen in the CASI imagery at 1 m spatial resolution. Supervised classification of the imagery produced an overall accuracy value of 72%. Downgrading the imagery to simulate the spatial resolution of 4 m IKONOS satellite data surprisingly improved the accuracy to 74%. Simulation of 10 m SPOT imagery resulted in an image where many artificial features of interest such as roads, pipelines and jetties were rendered invisible. Adding LiDAR data as an additional data set aided manual and automated identification of features and visualisation of the hydromorphology of the rivers and estuaries in the study area. Shadows cast from tall objects were a feature of the winter imagery and reduced automated classification accuracy. Overall, the study demonstrates that high spatial resolution remotely sensed digital imagery has the potential to be a useful tool for panoptic mapping of the geomorphology and human impact on tidal rivers and estuaries. In the context of the WFD, remote sensing provides a potential way forward for monitoring the physical status of rivers and estuaries at the national scale. The possibilities and constraints, in light of the findings of this study, are discussed.

Early Detection of Douglas-Fir Beetle Infestation with Subcanopy Resolution Hyperspectral Imagery

Abstract Early detection of insect or pathogen infestations in forests would be useful to forest managers who want to make decisions that minimize timber losses. Typical methods of forest reconnaissance to detect infestations have included analysis of multispectral imagery. Multispectral imagery, however, often lacks the sensitivity to detect subtle changes in tree canopy reflectance because of physiologic stress from insects or pathogens. Most hyperspectral imaging has the sensitivity to detect subtle changes in canopy reflectance but lacks high spatial resolution to identify affected trees. Our study examined the use of subcanopy spatial resolution hyperspectral imagery for differentiating Douglas-fir trees attacked by the Douglas-fir beetle. Comparison of the accuracies of step-wise discriminant analysis and classification and regression tree analysis (CART) revealed that CART provided the best separability among tree health classes (93% overall) because of CART's ability to use different band combinations for each class. Predictive accuracy of the CART method was estimated through cross-validation of the dataset using a jackknife resampling technique. Overall classification accuracy was promising (69%), as was classification among healthy and attacked, but still living, trees (50–70%). The results of our study provide support that hyperspectral imagery might be used for detecting and mapping tree stress in Douglas-fir stands. Although the rapid progress of beetle infestation somewhat limited the ability to differentiate among tree stress classes, which might limit the utility of this approach for fast moving infestations, the results were well beyond what might be expected from alternative detection methods. Slower moving infestations would benefit from the use of hyperspectral imagery because a lower percentage of infested trees would be asymtomatic. West. J. Appl. For. 18(3):202–206.

High spatial resolution hyperspectral mapping of in-stream habitats, depths, and woody debris in mountain streams

This article evaluates the potential of 1-m resolution, 128-band hyperspectral imagery for mapping in-stream habitats, depths, and woody debris in third- to fifth-order streams in the northern Yellowstone region. Maximum likelihood supervised classification using principal component images provided overall classification accuracies for in-stream habitats (glides, riffles, pools, and eddy drop zones) ranging from 69% for third-order streams to 86% for fifth-order streams. This scale dependency of classification accuracy was probably driven by the greater proportion of transitional boundary areas in the smaller streams. Multiple regressions of measured depths ( y) versus principal component scores ( x 1, x 2,…, x n ) generated R 2 values ranging from 67% for high-gradient riffles to 99% for glides in a fifth-order reach. R 2 values were lower in third-order reaches, ranging from 28% for runs and glides to 94% for pools. The less accurate depth estimates obtained for smaller streams probably resulted from the relative increase in the number of mixed pixels, where a wide range of depths and surface turbulence occurred within a single pixel. Matched filter (MF) mapping of woody debris generated overall accuracies of 83% in the fifth-order Lamar River. Accuracy figures for the in-stream habitat and wood mapping may have been misleadingly low because the fine-resolution imagery captured fine-scale variations not mapped by field teams, which in turn generated false “misclassifications” when the image and field maps were compared. The use of high spatial resolution hyperspectral (HSRH) imagery for stream mapping is limited by the need for clear water to measure depth, by any tree cover obscuring the stream, and by the limited availability of airborne hyperspectral sensors. Nonetheless, the high accuracies achieved in northern Yellowstone streams indicate that HSRH imagery can be a powerful tool for watershed-wide mapping, monitoring, and modeling of streams.

The use of earth observation techniques to improve catchment-scale pollution predictions

Remote sensing can potentially provide information useful in improving pollution transport modelling in agricultural catchments. Realisation of this potential will depend on the availability of the raw data, development of information extraction techniques, and the impact of the assimilation of the derived information into models. High spatial resolution hyperspectral imagery of a farm near Hereford, UK is analysed. A technique is described to automatically identify the soil and vegetation endmembers within a field, enabling vegetation fractional cover estimation. The aerially-acquired laser altimetry is used to produce digital elevation models of the site. At the subfield scale the hypothesis that higher resolution topography will make a substantial difference to contaminant transport is tested using the AGricultural Non-Point Source (AGNPS) model. Slope aspect and direction information are extracted from the topography at different resolutions to study the effects on soil erosion, deposition, runoff and nutrient losses. Field-scale models are often used to model drainage water, nitrate and runoff/sediment loss, but the demanding input data requirements make scaling up to catchment level difficult. By determining the input range of spatial variables gathered from EO data, and comparing the response of models to the range of variation measured, the critical model inputs can be identified. Response surfaces to variation in these inputs constrain uncertainty in model predictions and are presented. Although optical earth observation analysis can provide fractional vegetation cover, cloud cover and semi-random weather patterns can hinder data acquisition in Northern Europe. A Spring and Autumn cloud cover analysis is carried out over seven UK sites close to agricultural districts, using historic satellite image metadata, climate modelling and historic ground weather observations. Results are assessed in terms of probability of acquisition probability and implications for future earth observation missions.

Use of logistic regression for validation of maps of the spatial distribution of vegetation species derived from high spatial resolution hyperspectral remotely sensed data

Logistic regression was used for validation of image-derived maps depicting large woody debris and three Populus spp. in a riparian area in Yellowstone National Park, USA. High spatial resolution hyperspectral imagery was used for data input. The logistic regression model related presence/absence field data for large woody debris and the Populus spp. to the continuous measurement scale output from matched filter image analysis. The agreement between the image analysis and field data was excellent, as measured by Model χ 2, residual deviance, and Hosmer and Lemeshow's goodness of fit for the logistic regression. Kappa analysis for all possible thresholds imposed on the image output, and receiver operating characteristic curves, also indicated excellent goodness of fit for the image analysis output. The potential for use of logistic regression for both validation of hyperspectral image classification and calibration of results is discussed.

Mapping of stream microhabitats with high spatial resolution hyperspectral imagery

Maximum likelihood supervised classifications with 1-m 128 band hyperspectral data accurately map in-stream habitats in the Lamar River, Wyoming with producer's accuracies of 91% for pools, 87% for glides, 76% for riffles, and 85% for eddy drop zones. Coarser resolution 5-m hyperspectral data and 1-m simulated multiband imagery yield lower accuracies that are unacceptable for inventory and analysis. Both high spatial resolution and hyperspectral coverage are therefore necessary to map microhabitats in the study area. In many instances, the high spatial resolution hyperspectral (HSRH) imagery appears to map the stream habitats with greater accuracy than our ground-based surveys, thus challenging classical approaches used for accuracy assessment in remote sensing.

Area-based tests for association between spatial patterns

Edge effects pervade natural systems, and the processes that determine spatial heterogeneity (e.g. physical, geochemical, biological, ecological factors) occur on diverse spatial scales. Hence, tests for association between spatial patterns should be unbiased by edge effects and be based on null spatial models that incorporate the spatial heterogeneity characteristic of real-world systems. This paper develops probabilistic pattern association tests that are appropriate when edge effects are present, polygon size is heterogeneous, and the number of polygons varies from one classification to another. The tests are based on the amount of overlap between polygons in each of two partitions. Unweighted and area-weighted versions of the statistics are developed and verified using scenarios representing both polygon overlap and avoidance at different spatial scales and for different distributions of polygon sizes. These statistics were applied to Soda Butte Creek, Wyoming, to determine whether stream microhabitats, such as riffles, pools and glides, can be identified remotely using high spatial resolution hyperspectral imagery. These new “spatially explicit” techniques provide information and insights that cannot be obtained from the spectral information alone.

Mapping of stream microhabitats with high spatial resolution hyperspectral imagery

This paper presents a brief review of hyperspectral imagery and its analysis from a perspective based in Geographic Information Science. The ten original research priorities of the University Consortium for Geographic Information Science from 1996 and the five emerging themes from 2000/2001 are used as a framework to examine different aspects of management, analysis and use of hyperspectral imagery. A GIScience perspective identifies a series of issues that can develop the utility of hyperspectral imagery and gives an agenda for research that integrates hyperspectral imagery into Geographic Information Science to the benefit of both GIScience and hyperspectral remote sensing.