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This study investigates the utility of an off-the-shelf, consumer-grade unmanned aerial vehicle (UAV) for invasive species mapping in a lacustrine fringe environment. Specifically, this work sought to determine whether such a UAV would be capable of creating accurate maps of the extent of patches of an invasive plant, yellow flag iris (Iris pseudacorusL.), more efficiently than could be accomplished by a traditional field survey, which is often considered in the literature to provide the most accurate maps. The study was conducted at two lakes in the central interior of British Columbia. The UAV used in this study was a DJI Phantom 3 Professional that can acquire images using the built-in 12.4 MP digital camera. This UAV was selected because it is representative of the type of aerial platform that is easily accessible to invasive plant managers in terms of cost, ease of use, and lack of legal restrictions. Three methods of mapping the yellow flag iris were compared: (1) field survey, (2) manual interpretation of the raw UAV-acquired imagery and the orthoimage created from these data, and (3) pixel-based classification of the orthoimage created from the UAV imagery using a random forest classifier. The results revealed that, at both lakes considered, manual interpretation of the UAV-acquired imagery produced the most accurate maps of yellow flag iris infestation, with a false-positive and false-negative classification rate of less than 1%. [ABSTRACT FROM PUBLISHER]

Little is known about how satellite imagery can be used to describe burn severity in tundra landscapes. The Anaktuvuk River Fire (ARF) in 2007 burned over 1000 km2 of tundra on the North Slope of Alaska, creating a mosaic of small (1 m2) to large (>100 m2) patches that differed in burn severity. The ARF scar provided us with an ideal landscape to determine if a single-date spectral vegetation index can be used once vegetation recovery began and to independently determine how pixel size influences burn severity assessment. We determine and explore the sensitivity of several commonly used vegetation indices to variation in burn severity across the ARF scar and the influence of pixel size on the assessment and classification of tundra burn severity. We conducted field surveys of spectral reflectance at the peak of the first growing season post-fire (extended assessment period) at 18 field sites that ranged from high to low burn severity. In comparing single-date indices, we found that the two-band enhanced vegetation index (EVI2) was highly correlated with normalized burn ratio (NBR) and better distinguished among three burn severity classes than both the NBR and the normalized difference vegetation index (NDVI). We also show clear evidence that shortwave infrared (SWIR) reflectivity does not vary as a function of burn severity. By comparing a Quickbird scene (2.4 m pixels) to simulated 30 and 250 m pixel scenes, we are able to confirm that while the moderate spatial resolution of the Landsat Thematic Mapper (TM) sensor (30 m) is sufficient for mapping tundra burn severity, the coarser resolution of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor (250 m) is not well matched to the fine scale of spatial heterogeneity in the ARF burn scar. [ABSTRACT FROM AUTHOR]

The Shuttle Radar Topography Mission (SRTM) provides for the first time a near-global high-resolution digital elevation model (DEM) with great advantages of homogeneous quality and free availability. The last 10 years or so have seen rapid advances in the data processing and applications of SRTM DEM. From the perspective of SRTM, we present in this article a brief overview of the principles, datasets, void filling and accuracy of SRTM DEM first. Special emphasis is on its application advances in various research fields including, but not limited to, geology, geomorphology, water resources and hydrology, glaciology, evaluation of natural hazards and vegetation surveys. Problems that arose from the applications and the future research interests are also addressed. We hope this study will greatly facilitate the ease of use of SRTM DEM in extensive fields. [ABSTRACT FROM AUTHOR]

Long-term changes in the Normalized Difference Vegetation Index (NDVI) have been evaluated in several studies but results have not been conclusive due to differences in data processing as well as the length and time of the analysed period. In this research a newly developed 1 km Advanced Very High Resolution Radiometer (AVHRR) satellite data record for the period 1985-2006 was used to rigorously evaluate NDVI trends over Canada. Furthermore, climate and land cover change as potential causes of observed trends were evaluated in eight sample regions. The AVHRR record was generated using improved geolocation, cloud screening, correction for sun-sensor viewing geometry, atmospheric correction, and compositing. Results from both AVHRR and Landsat revealed an increasing NDVI trend over northern regions where comparison was possible. Overall, 22% of the vegetated area in Canada was found to have a positive NDVI trend based on the Mann-Kendal test at the 95% confidence level. Of these, 40% were in northern ecozones. The mean absolute difference of NDVI measurements between AVHRR and Landsat data was <7%. When compared with results from other studies, similar trends were found over northern areas, while in southern regions the results were less consistent. Local assessment of potential causes of trends in each region revealed a stronger influence of climate in the north compared to the south. Southern regions with strong positive trends appeared to be most influenced by land cover change. [ABSTRACT FROM AUTHOR]

This paper aims to evaluate the effectiveness of using Landsat ETM+ data to identify the extent and severity of locust damage. Two cloud-free Landsat ETM+ images of the study area, taken before and after peak locust plague, were compared to determine the extent and severity of the 2002 locust plague according to the decrease of the Normalized Difference Vegetation Index (NDVI) derived from the two images. The results showed that the locust plague could be classified into heavy, moderate and light damage degrees based on the NDVI value decrease calculated by each pixel, which further evaluated its accuracy by extensive ground survey data. Locust plague can be identified with 98% and 92% accuracy for determining geographic extent and severity respectively using Landsat ETM+ data. [ABSTRACT FROM AUTHOR]

The relevance vector machine (RVM), a Bayesian extension of the support vector machine (SVM), has considerable potential for the analysis of remotely sensed data. Here, the RVM is introduced and used to derive a multi-class classification of land cover with an accuracy of 91.25%, a level comparable to that achieved by a suite of popular image classifiers including the SVM. Critically, however, the output of the RVM includes an estimate of the posterior probability of class membership. This output may be used to illustrate the uncertainty of the class allocations on a per-case basis and help to identify possible routes to further enhance classification accuracy. [ABSTRACT FROM AUTHOR]

The influence of the lack of atmospheric correction of the optical images used to calculate land surface emissivity (LSE) was assessed. When thermal emissivity is determined by the vegetation cover method (VCM), information from the solar spectrum is required to calculate the vegetation cover fraction. The atmospheric correction was obtained in this study by using a combination of the dark dense vegetation (DDV) method and the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) code. The methodology was applied to a Landsat Thematic Mapper (TM) image of Tomelloso, Spain. We determined that the emissivity between 10 and 12 µm only increases by 0.4% (which represents a systematic error of approximately +0.2 K) when atmospherically corrected reflectances are used in relation to non-corrected Tomelloso scenes. Nevertheless, other test areas could yield larger differences. [ABSTRACT FROM AUTHOR]

We have designed a system that predicts species richness in the mixed wood boreal forest of Canada. The system is based on a simple multivariate linear model that uses four landscape characteristics as independent variables: canopy species type, distance from the nearest ridgeline, time since the last fire and canopy stem density. The model is shown to provide statistically significant estimates of richness when using observed independent variables. We developed models for estimating the four landscape characteristics from geospatial data consisting of remotely sensed imagery and a digital elevation model. We ran the model at the stand scale and the pixel scale and found that stand scale predictions were be more accurate that pixel scale predictions. We produced a map of vegetation species richness for Prince Alberta National Park in central Saskatchewan Canada that is consistent with our expectations. We also estimated the uncertainty in the four landscape characteristic estimates and developed a methodology for propagating this uncertainty through the system to produce estimates of uncertainty in the pixel-based richness predictions. While the uncertainty is significant, the estimation and management of uncertainty in a mapping system of this type represents an innovation. [ABSTRACT FROM AUTHOR]

This study examined a new vegetation index, based on the universal pattern decomposition method (VIUPD). The universal pattern decomposition method (UPDM) allows for sensor-independent spectral analysis. Each pixel is expressed as the linear sum of standard spectral patterns for water, vegetation and soil, with supplementary patterns included when necessary. Pattern decomposition coefficients for each pixel contain almost all the sensor-derived information, while having the benefit of sensor independence. The VIUPD is expressed as a linear sum of the pattern decomposition coefficients; thus, the VIUPD is a sensor-independent index. Here, the VIUPD was used to examine vegetation amounts and degree of terrestrial vegetation vigor; VIUPD results were compared with results by the normalized difference vegetation index (NDVI), an enhanced vegetation index (EVI) and a conventional vegetation index based on pattern decomposition (VIPD). The results showed that the VIUPD reflects vegetation and vegetation activity more sensitively than the NDVI and EVI. [ABSTRACT FROM AUTHOR]

High resolution infrared digital camera imagery was used to determine the extent of the invasion of hill slopes by Pteronia incana , an unpalatable dwarf shrub. On the basis of a priori field surveys, the imagery was classified into different degrees of the invasion and other surface cover types using Idrisi32 GIS. The imagery was noted to have limited constraints, as evidenced by the high level of classification accuracy. A range of vegetation indices was examined to identify one that best characterized the spatial distribution of the shrub and its degree of invasion. Distinct spectral separability of the invader from other surface cover types was achieved by means of the perpendicular vegetation index (PVI), as opposed to the ratio based vegetation indices (NDVI, SAVI, and MSAVI). Depending on the local geographical characteristics, the PVI could be particularly suited for the identification of perennial shrubs with characteristics similar to P. incana , which are usually interspersed with considerable bare zones. [ABSTRACT FROM AUTHOR]

This paper is a presentation of the practical application of the algorithms developed in Part I of this series. It is based on the assessment of tree changes with respect to topography in a part of the Adulam Nature Reserve, Israel, between 1945 and 1996. The study is an input to ongoing ecological studies to determine the rate and pattern of tree invasion of plant species in the Adulam Nature Reserve. Datasets available for this study are images of the project site for the years 1945, 1956, 1965, 1967, 1980, 1984 and 1996. For the change detection process, a post classification image differencing method was adopted. In order to enhance the change detection and analysis processes as well as minimize errors, image differencing was applied to grid images generated from image classification products. Since ground data for the accuracy assessment of classification results were unavailable, a method of fuzzy classification and accuracy assessment of classification was employed. Results of this study show a significant general increase in percentage cover of trees in the study area between 1945 and 1996. A peak percentage range of tree cover (35–36%) was observed between 1980 and 1984. Also, a statistically significant interaction between topography and rate of change of tree cover was observed. [ABSTRACT FROM AUTHOR]

The estimation of transpiration fluxes through wide vegetated land surfaces is of great importance for the proper planning and management of environmental resources, particularly in areas where water is a main limiting factor during at least part of the growing cycle. While remotely sensed techniques cannot directly measure these fluxes, they can provide useful information on vegetation variables such as Leaf Area Index (LAI), which are functionally related to the mentioned processes. The aims of the present work were: (a) to illustrate the use of multi-temporal LAI profiles derived from National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) Normalized Difference Vegetation Index (NDVI) data as input for a biogeochemical model (Forest-BGC) which simulates the main processes of forest vegetation (transpiration and photosynthesis); and (b) to analyse the sensitivity of the calibrated model to its main driving variables (meteorological data and NDVI-derived LAI profiles) in order to assess their relative importance for operational transpiration monitoring. In particular, the model was applied to two oak stands in the Tuscany Region (central Italy), which are representative of Mediterranean forests and for which a calibration phase had already been performed. Simulations were carried out for a 15-year period (1986-2000) using as inputs daily meteorological data and NDVI-derived monthly LAI profiles. The sensitivity of the model to both input types was then assessed through other model runs with fixed values of the two variables. The results of these experiments indicated that the remotely sensed LAI estimates are the main determinant of simulated transpirations, especially during the Mediterranean arid season (summer) when water resources are the primary limiting factor for vegetation development. [ABSTRACT FROM AUTHOR]

This paper outlines the strategies available for estimating the biophysical properties of crop canopies from remotely sensed data. Spectral reflectance and biophysical data were obtained over 132 plots of sugar beet ( Beta vulgaris L.) and in the first part of the paper the strength of the relationships between vegetation indices (VI) and leaf area index (LAI) are examined. In the second part, an approach is tested in which a canopy reflectance model is used to generate simulated spectra for a wide range of biophysical conditions and these data are used to train an artificial neural network (ANN). The advantage of the second approach is that a priori knowledge of the measurement conditions including soil reflectance, canopy architecture and solar position can be included explicitly in the modelling. The results show that the estimation of sugar beet LAI using a trained neural network is more reliable than the use of VI and has the potential to replace the use of VI for operational applications. The use of a priori data on the variation in soil spectral reflectance gave rise to a small increase in LAI estimation accuracy. [ABSTRACT FROM AUTHOR]

A key issue when generating a land cover map from remotely sensed data is the selection of the minimum mapping unit (MMU) to be employed, which determines the extent of detail contained in the map. This study analyses the effects of MMU in land cover spatial configuration and composition, by using simulated landscape thematic patterns generated by the Modified Random Clusters method. This approach allows a detailed control of the different factors influencing landscape metrics behaviour, as well as taking into account a wide range of land cover pattern possibilities. Land cover classes that are sparse and fragmented can be considerably misrepresented in the final map when increasing MMU, while the classes that occupy a large percentage of map area tend to become more dominant. Mean Patch Size and Number of Patches are very poor indicators of pattern fragmentation in this context. In contrast, Landscape Division (LD) and related indices (Splitting Index and Effective Mesh Size) are clearly suitable for comparing the fragmentation of landscape data with different MMUs. We suggest that the Mean Shape Index, the most sensitive to MMU of those considered in this study, should not be used in further landscape studies if land cover data with different MMU or patch size frequency distribution are to be compared. In contrast, the Area Weighted Mean Shape Index presents a very robust behaviour, which advocates the use of this index for the quantification of the overall irregularity of patch shapes in landscape spatial patterns. The results presented allow quantifying the biases resulting from selecting a certain MMU when generating a land cover dataset. In general, a bigger MMU implies underestimating landscape diversity and fragmentation, as well as over-estimating animal population dispersal success. Guidelines are provided for the proper use and comparison of spatial pattern indices measured in maps with different MMUs. [ABSTRACT FROM AUTHOR]

Poor water management coupled with inadequate drainage in the irrigated commands of the arid and semi-arid regions leads to waterlogging and subsequent salinization and alkalization. Space-borne multispectral data have been found to be extremely useful in deriving information on salt-affected soils and waterlogged areas. The study, reported here, aims at delineating and monitoring the extent of waterlogged areas and vegetation cover in part of the Indira Gandhi Canal command area, Rajasthan State, Western India, during the period 1975 to 1995 using Landsat Multi-Spectral Scanner (MSS) and Thematic Mapper (TM), and the Indian Remote Sensing Satellite (IRS-1A) Linear Imaging Self-scanning Sensor (LISS-I) data. Vegetation condition was assessed by generating the Normalized Difference Vegetation Index (NDVI ) from multispectral digital data whereas waterlogged areas were delineated through a systematic visual interpretation approach. An objective comparison of the temporal NDVI images has revealed two striking phenomena: (i) the areas which were non-vegetated during 1975 were found supporting more and more vegetation especially crops in successive years due to introduction of canal irrigation; and (ii) the palaeochannel (old river course) which supported very good crops during 1975 gradually became waterlogged during the 20 year period thereby resulting in the extinction of croplands therein. The detailed methodology and observations made are reported in the article. [ABSTRACT FROM AUTHOR]

We analysed the POLDER-ADEOS level 3 dataset (coefficients of a bi-directional reflectance distribution (BRDF) model fitted to reflectances) to interpret the spatial and temporal patterns of BRDF for the Australian continent in terms of landscape attributes. Significant noise was identified and systematically removed by transparent and mechanistically sound filtering and interpolation. We investigated the hypotheses that the spatial variation of the Roujean model coefficients over the continent is greater than the temporal variation over the eight month time series and that vegetation structure has more influence on the BRDF than does landform. We found systematic, interpretable relationships between the Roujean model coefficients and structural attributes for both woody and grassy vegetation. For woody vegetation, the model coefficients were closely related to the spacing to height ratio of vegetation elements. The Roujean model coefficients were generalized to ten types by an unsupervised classification which produced coherent spatial patterns. While these could be related to vegetation types, they indicated that there were other factors operating. Finally, the magnitude of the BRDF normalization of AVHRR to nadir viewing and fixed solar zenith angle was investigated for the continent under summer and winter illumination conditions. The magnitude of normalization was small during summer, but was as large as 100% at the winter solstice. [ABSTRACT FROM AUTHOR]

Cover of vegetation understorey and overstorey was determined from aerial photography at 1:25 000 and 1:40 000 scales by a grid sampling technique. Models were developed relating values of aerial cover to field cover as determined by intensive field measurement. The influence of photo-scale, photo colour, the angle of the image, shadow, the hiatus between aerial and field sampling, crown width, crown height, proportion of dead trees, drought prior to aerial sampling, land type, previously cleared vegetation and incline on explanatory models was also examined. The only variables that could be clearly interpreted as influencing the models were vegetation height, photo-scale and land type. Only the latter two variables are useful for predictive models. The smaller the scale of photography the greater the exaggeration of the aerial image of tree crowns. This probable result of photo graininess would be most significant when tree crowns are small, an inverse surrogate of tree height. Two-phase models were developed for predicting basal area and biomass from aerial cover. In most instances models were successful for predicting overstorey and understorey cover and for predicting total basal area and biomass. The technique offers a powerful and cost-effective method of assessing vegetation change over long time periods in a way that no other technique can duplicate. [ABSTRACT FROM AUTHOR]

The spatio-temporal distribution of vegetation is a fundamental component of the urban environment that can be quantified using multispectral imagery. However, spectral heterogeneity at scales comparable to sensor resolution limits the utility of conventional hard classification methods with multispectral reflectance data in urban areas. Spectral mixture models may provide a physically based solution to the problem of spectral heterogeneity. The objective of this study is to examine the applicability of linear spectral mixture models to the estimation of urban vegetation abundance using Landsat Thematic Mapper (TM) data. The inherent dimensionality of TM imagery of the New York City area suggests that urban reflectance measurements may be described by linear mixing between high albedo, low albedo and vegetative endmembers. A three-component linear mixing model provides stable, consistent estimates of vegetation fraction for both constrained and unconstrained inversions of three different endmember ensembles. Quantitative validation using vegetation abundance measurements derived from high-resolution (2 m) aerial photography shows agreement to within fractional abundances of 0.1 for vegetation fractions greater than 0.2. In contrast to the Normalised Difference Vegetation Index (NDVI), vegetation fraction estimates provide a physically based measure of areal vegetation abundance that may be more easily translated to constraints on physical quantities such as vegetative biomass and evapotranspiration. [ABSTRACT FROM AUTHOR]

Inversion of biomass for sunflower fields using radar backscattering data has been carried out with neural network algorithms. An electromagnetic model is used to generate the scattering coefficients for training and testing of the net. The model is validated with experimental data obtained from the Montespertoli test site during the Remote Sensing Campaign Mac-Europe 91. The inversion results show that the neural network is capable of performing the retrieval with good accuracy. By optimizing the structural complexity of the net, a better inversion result is obtained. [ABSTRACT FROM AUTHOR]

A quantitative approach has been made for the estimation of biophysical parameters of a vegetation canopy by the inversion of a vegetation canopy reflectance model. Model inversion has been done using a non-linear optimization scheme against directional reflectance data over the canopy. A quasi-Newton algorithm has been employed that searches the minimum of a function iteratively using the functional values only. The technique provides a reasonably good estimation of the biophysical parameters. A study has been conducted to quantify the error related to the estimation of biophysical parameters of vegetation with simulated satellite data corrected with improper values of atmospheric aerosol and water vapour contents. In the visible, atmospheric correction of satellite data with improper values of atmospheric aerosol content results in a modification of the amplitude and angular pattern of the directional reflectance for both low-density and high-density vegetation canopies. However, in the near-infrared, the atmospheric correction of data with improper values of aerosol and water vapour contents changes the amplitude of directional reflectance, but, no significant changes in angular pattern are noticed. This study indicates that parameter estimation can be significantly influenced by using improper values of both aerosol and water vapour contents during data correction in the visible and near-infrared regions of the solar spectrum. The estimation accuracy is higher for a low-density canopy than for a dense vegetation canopy. Retrievals of all the surface parameters are not equally affected by such improper atmospheric correction of data. Particularly, estimations of soil reflectance and leaf area index are significantly influenced by such improper correction for a high-density vegetation canopy. However, the accuracy of the retrieved parameter values is higher in the near-infrared than in the visible for both high-density and low-density canopies. [ABSTRACT FROM AUTHOR]

The objective of this work was to document the utility of phenological data derived from satellite sensors by comparing them with modelled phenology. Surface phenological model outputs (first leaf and first bloom dates) were correlated positively with satellite sensor-derived start of season (SOS) dates for 1991-1995 across the eastern United States. The correlation was highest for forest (r 0.62 for deciduous trees and 0.64 for mixed woodland) and tall grass (r 0.46) and lowest for short grass (r 0.37). The average correlation over all land cover types was 0.61. Average SOS dates were consistently earlier than Spring Index dates across all land cover types. This finding and limited native tree phenology data suggest that the SOS technique detects understorey green-up in the forest rather than overstorey species. The biweekly temporal resolution of the satellite sensor data placed an upper limit on prediction accuracy; thus, year-to-year variations at individual sites were typically small. Nevertheless, the correct biweek SOS could be identified from the surface models 61% of the time, and 1 biweek 96% of the time. Further temporal refinement of the satellite sensor measurements is necessary in order to connect them with surface phenology adequately and to develop links among 'green wave' components in selected biomes. [ABSTRACT FROM AUTHOR]